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  Subjects -> HUMANITIES (Total: 975 journals)
    - ASIAN STUDIES (167 journals)
    - CLASSICAL STUDIES (142 journals)
    - DEMOGRAPHY AND POPULATION STUDIES (162 journals)
    - ETHNIC INTERESTS (166 journals)
    - GENEALOGY AND HERALDRY (9 journals)
    - HUMANITIES (301 journals)
    - NATIVE AMERICAN STUDIES (28 journals)

HUMANITIES (301 journals)                  1 2     

Showing 1 - 71 of 71 Journals sorted alphabetically
Aboriginal and Islander Health Worker Journal     Full-text available via subscription   (Followers: 15)
Aboriginal Child at School     Full-text available via subscription   (Followers: 6)
About Performance     Full-text available via subscription   (Followers: 12)
Access     Full-text available via subscription   (Followers: 26)
ACCESS: Critical Perspectives on Communication, Cultural & Policy Studies     Full-text available via subscription   (Followers: 12)
Acta Academica     Full-text available via subscription   (Followers: 6)
Acta Universitaria     Open Access   (Followers: 7)
Adeptus     Open Access   (Followers: 1)
Advocate: Newsletter of the National Tertiary Education Union     Full-text available via subscription   (Followers: 1)
Afghanistan     Hybrid Journal   (Followers: 1)
African and Black Diaspora: An International Journal     Hybrid Journal   (Followers: 19)
African Historical Review     Hybrid Journal   (Followers: 17)
AFRREV IJAH : An International Journal of Arts and Humanities     Open Access   (Followers: 5)
Agriculture and Human Values     Open Access   (Followers: 14)
Akademika : Journal of Southeast Asia Social Sciences and Humanities     Open Access   (Followers: 7)
Alterstice : Revue internationale de la recherche interculturelle     Open Access  
Altre Modernità     Open Access   (Followers: 3)
Amaltea. Revista de mitocrítica     Open Access   (Followers: 1)
American Imago     Full-text available via subscription   (Followers: 3)
American Journal of Humanities and Social Sciences     Open Access   (Followers: 12)
American Review of Canadian Studies     Hybrid Journal   (Followers: 7)
Anabases     Open Access   (Followers: 2)
Analyse & Kritik. Zeitschrift für Sozialtheorie     Hybrid Journal   (Followers: 1)
Angelaki: Journal of Theoretical Humanities     Hybrid Journal   (Followers: 19)
Anglo-Saxon England     Hybrid Journal   (Followers: 36)
Antik Tanulmányok     Full-text available via subscription  
Antipode     Hybrid Journal   (Followers: 57)
Anuario Americanista Europeo     Open Access  
Arbutus Review     Open Access   (Followers: 1)
Argumentation et analyse du discours     Open Access   (Followers: 7)
Ars & Humanitas     Open Access   (Followers: 14)
Artefact : Techniques, histoire et sciences humaines     Open Access  
Artes Humanae     Open Access  
Arts and Humanities in Higher Education     Hybrid Journal   (Followers: 37)
Asia Europe Journal     Hybrid Journal   (Followers: 5)
Australasian Journal of Popular Culture, The     Hybrid Journal   (Followers: 2)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Behemoth     Open Access   (Followers: 3)
Belin Lecture Series     Open Access   (Followers: 1)
Bereavement Care     Hybrid Journal   (Followers: 12)
Bulletin of the School of Oriental and African Studies     Hybrid Journal   (Followers: 21)
Cahiers de praxématique     Open Access   (Followers: 1)
Cankiri Karatekin University Journal of Faculty of Letters     Open Access  
Child Care     Full-text available via subscription   (Followers: 6)
Chronicle of Philanthropy     Full-text available via subscription   (Followers: 3)
Ciencias Sociales y Humanidades     Open Access   (Followers: 4)
Claroscuro     Open Access   (Followers: 1)
Coaching: An International Journal of Theory, Research and Practice     Hybrid Journal   (Followers: 10)
Cogent Arts & Humanities     Open Access   (Followers: 3)
Colloquia Humanistica     Open Access  
Communication and Critical/Cultural Studies     Hybrid Journal   (Followers: 29)
Comprehensive Therapy     Hybrid Journal   (Followers: 3)
Con Texte     Open Access  
Congenital Anomalies     Hybrid Journal   (Followers: 1)
Conjunctions. Transdisciplinary Journal of Cultural Participation     Open Access   (Followers: 4)
Conservation Science in Cultural Heritage     Open Access   (Followers: 10)
Creative Industries Journal     Hybrid Journal   (Followers: 8)
Critical Arts : South-North Cultural and Media Studies     Hybrid Journal   (Followers: 12)
Crossing the Border : International Journal of Interdisciplinary Studies     Open Access   (Followers: 6)
Cuadernos de historia de España     Open Access   (Followers: 3)
Cultural History     Hybrid Journal   (Followers: 27)
Cultural Studies     Hybrid Journal   (Followers: 60)
Culturas : Debates y Perspectivas de un Mundo en Cambio     Open Access   (Followers: 1)
Culture, Theory and Critique     Hybrid Journal   (Followers: 29)
Daedalus     Hybrid Journal   (Followers: 24)
Dandelion : Postgraduate Arts Journal & Research Network     Open Access   (Followers: 4)
Death Studies     Hybrid Journal   (Followers: 19)
Digital Humanities Quarterly     Open Access   (Followers: 55)
Digital Studies / Le champ numerique     Open Access  
Digitális Bölcsészet / Digital Humanities     Open Access  
Diogenes     Hybrid Journal   (Followers: 8)
Doct-Us Journal     Open Access  
Dokuz Eylül Üniversitesi Edebiyat Fakültesi Dergisi / Dokuz Eylül University Journal of Humanities     Open Access  
Dorsal : Revista de Estudios Foucaultianos     Open Access  
E+E : Estudios de Extensión en Humanidades     Open Access  
e-Hum : Revista das Áreas de Humanidade do Centro Universitário de Belo Horizonte     Open Access   (Followers: 2)
Early Modern Culture Online     Open Access   (Followers: 36)
Égypte - Monde arabe     Open Access   (Followers: 6)
Eighteenth-Century Fiction     Full-text available via subscription   (Followers: 17)
Éire-Ireland     Full-text available via subscription   (Followers: 7)
En-Claves del pensamiento     Open Access   (Followers: 1)
Enfoques     Open Access  
Ethiopian Journal of the Social Sciences and Humanities     Full-text available via subscription   (Followers: 8)
Études arméniennes contemporaines     Open Access   (Followers: 3)
Études canadiennes / Canadian Studies     Open Access   (Followers: 2)
Études de lettres     Open Access   (Followers: 3)
European Journal of Cultural Studies     Hybrid Journal   (Followers: 30)
European Journal of Social Theory     Hybrid Journal   (Followers: 21)
Expositions     Full-text available via subscription  
Fields: Journal of Huddersfield Student Research     Open Access  
Fronteras : Revista de Ciencias Sociales y Humanidades     Open Access   (Followers: 2)
Frontiers in Digital Humanities     Open Access   (Followers: 5)
Fudan Journal of the Humanities and Social Sciences     Hybrid Journal  
GAIA - Ecological Perspectives for Science and Society     Full-text available via subscription   (Followers: 2)
German Research     Hybrid Journal   (Followers: 1)
German Studies Review     Full-text available via subscription   (Followers: 28)
Germanic Review, The     Hybrid Journal   (Followers: 6)
Globalizations     Hybrid Journal   (Followers: 9)
Gruppendynamik und Organisationsberatung     Hybrid Journal   (Followers: 2)
Habitat International     Hybrid Journal   (Followers: 6)
Hacettepe Üniversitesi Edebiyat Fakültesi Dergisi     Open Access   (Followers: 2)
Harvard Journal of Asiatic Studies     Full-text available via subscription   (Followers: 13)
Heritage & Society     Hybrid Journal   (Followers: 16)
History of Humanities     Full-text available via subscription   (Followers: 8)
Hopscotch: A Cultural Review     Full-text available via subscription   (Followers: 1)
Human Affairs     Hybrid Journal   (Followers: 1)
Human and Ecological Risk Assessment: An International Journal     Hybrid Journal   (Followers: 4)
Human Nature     Hybrid Journal   (Followers: 21)
Human Performance     Hybrid Journal   (Followers: 9)
Human Remains and Violence : An Interdisciplinary Journal     Full-text available via subscription  
Human Studies     Hybrid Journal   (Followers: 9)
humanidades     Open Access  
Humanitaire     Open Access   (Followers: 2)
Humanities     Open Access   (Followers: 14)
Humanities Diliman : A Philippine Journal of Humanities     Open Access  
Hungarian Cultural Studies     Open Access  
Hungarian Studies     Full-text available via subscription  
Ibadan Journal of Humanistic Studies     Full-text available via subscription  
Inkanyiso : Journal of Humanities and Social Sciences     Open Access   (Followers: 1)
Insaniyat : Journal of Islam and Humanities     Open Access   (Followers: 1)
Inter Faculty     Open Access  
Interim : Interdisciplinary Journal     Open Access   (Followers: 4)
International Journal for History, Culture and Modernity     Open Access   (Followers: 9)
International Journal of Arab Culture, Management and Sustainable Development     Hybrid Journal   (Followers: 7)
International Journal of Cultural Studies     Hybrid Journal   (Followers: 31)
International Journal of Heritage Studies     Hybrid Journal   (Followers: 18)
International Journal of Humanities and Arts Computing     Hybrid Journal   (Followers: 13)
International Journal of Humanities and Cultural Studies     Open Access   (Followers: 11)
International Journal of Humanities of the Islamic Republic of Iran     Open Access   (Followers: 10)
International Journal of Humanity Studies     Open Access  
International Journal of Listening     Hybrid Journal   (Followers: 3)
International Journal of the Classical Tradition     Hybrid Journal   (Followers: 13)
Interventions : International Journal of Postcolonial Studies     Hybrid Journal   (Followers: 18)
ÍSTMICA. Revista de la Facultad de Filosofía y Letras     Open Access   (Followers: 1)
Jangwa Pana     Open Access  
Jewish Culture and History     Hybrid Journal   (Followers: 19)
Journal de la Société des Américanistes     Open Access  
Journal des africanistes     Open Access   (Followers: 1)
Journal des Economistes et des Etudes Humaines     Hybrid Journal  
Journal for Cultural Research     Hybrid Journal   (Followers: 12)
Journal for General Philosophy of Science     Hybrid Journal   (Followers: 7)
Journal for Learning Through the Arts     Open Access   (Followers: 7)
Journal for New Generation Sciences     Open Access   (Followers: 4)
Journal for Research into Freemasonry and Fraternalism     Hybrid Journal  
Journal for Semitics     Full-text available via subscription   (Followers: 8)
Journal Of Advances In Humanities     Open Access   (Followers: 3)
Journal of Aesthetics & Culture     Open Access   (Followers: 23)
Journal of African American Studies     Hybrid Journal   (Followers: 11)
Journal of African Cultural Studies     Hybrid Journal   (Followers: 5)
Journal of African Elections     Full-text available via subscription  
Journal of Arts & Communities     Hybrid Journal   (Followers: 6)
Journal of Arts and Humanities     Open Access   (Followers: 22)
Journal of Arts and Social Sciences     Open Access  
Journal of Bioethical Inquiry     Hybrid Journal   (Followers: 3)
Journal of Cultural Economy     Hybrid Journal   (Followers: 9)
Journal of Cultural Geography     Hybrid Journal   (Followers: 21)
Journal of Data Mining and Digital Humanities     Open Access   (Followers: 39)
Journal of Developing Societies     Hybrid Journal   (Followers: 1)
Journal of Family Theory & Review     Hybrid Journal   (Followers: 3)
Journal of Franco-Irish Studies     Open Access   (Followers: 1)
Journal of Happiness Studies     Hybrid Journal   (Followers: 28)
Journal of Interactive Humanities     Open Access   (Followers: 3)
Journal of Intercultural Communication Research     Hybrid Journal   (Followers: 15)
Journal of Intercultural Studies     Hybrid Journal   (Followers: 12)
Journal of Interdisciplinary History     Hybrid Journal   (Followers: 22)
Journal of Labor Research     Hybrid Journal   (Followers: 19)
Journal of Medical Humanities     Hybrid Journal   (Followers: 22)
Journal of Medieval and Early Modern Studies     Full-text available via subscription   (Followers: 37)
Journal of Modern Greek Studies     Full-text available via subscription   (Followers: 4)
Journal of Modern Jewish Studies     Hybrid Journal   (Followers: 14)
Journal of Open Humanities Data     Open Access   (Followers: 2)
Journal of Semantics     Hybrid Journal   (Followers: 15)
Journal of the Musical Arts in Africa     Hybrid Journal   (Followers: 1)
Journal of University of Babylon for Humanities     Open Access  
Journal of Visual Culture     Hybrid Journal   (Followers: 33)
Journal Sampurasun : Interdisciplinary Studies for Cultural Heritage     Open Access   (Followers: 1)
Jurisprudence     Hybrid Journal   (Followers: 18)
Jurnal Ilmu Sosial dan Humaniora     Open Access  
Jurnal Pendidikan Humaniora : Journal of Humanities Education     Open Access   (Followers: 1)
Jurnal Sosial Humaniora     Open Access   (Followers: 2)
L'Orientation scolaire et professionnelle     Open Access   (Followers: 1)
La lettre du Collège de France     Open Access   (Followers: 1)
Lagos Notes and Records     Full-text available via subscription  
Language and Intercultural Communication     Hybrid Journal   (Followers: 23)
Language Resources and Evaluation     Hybrid Journal   (Followers: 5)
Law and Humanities     Hybrid Journal   (Followers: 7)
Law, Culture and the Humanities     Hybrid Journal   (Followers: 12)
Le Portique     Open Access   (Followers: 1)
Leadership     Hybrid Journal   (Followers: 39)
Legal Ethics     Hybrid Journal   (Followers: 14)
Legon Journal of the Humanities     Full-text available via subscription  
Letras : Órgano de la Facultad de Letras y Ciencias Huamans     Open Access   (Followers: 1)
Literary and Linguistic Computing     Hybrid Journal   (Followers: 5)
Litnet Akademies : 'n Joernaal vir die Geesteswetenskappe, Natuurwetenskappe, Regte en Godsdienswetenskappe     Open Access  
Lwati : A Journal of Contemporary Research     Full-text available via subscription   (Followers: 2)
Marx-Engels Jahrbuch     Hybrid Journal  
Measurement     Hybrid Journal   (Followers: 4)
Medical Humanities     Hybrid Journal   (Followers: 21)
Medieval Encounters     Hybrid Journal   (Followers: 11)
Médiévales     Open Access   (Followers: 3)

        1 2     

Similar Journals
Journal Cover
Measurement
Journal Prestige (SJR): 0.733
Citation Impact (citeScore): 3
Number of Followers: 4  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0263-2241
Published by Elsevier Homepage  [3185 journals]
  • Quantitative evaluation of Bayer chromatic imaging on the accuracy of
           photogrammetric measurements
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Haibin Zhu, Xiaojuan Zhang, Jia Li, Lingfeng Chen, Xuejin Liu, Qinwei Ma, Shaopeng Ma Bayer chromatic cameras are increasingly being used in photogrammetry for high-accuracy deformation measurements. However, studies on the quantitative accuracy evaluation of different photogrammetric methods using Bayer chromatic images are lacking, as the influence mechanism of chromatic images on measurement accuracy is unclear. This work performed substantial tests to investigate the influence of chromatic images on the photogrammetric measurement accuracy. Firstly, the spatial resolution and signal-to-noise ratio (SNR) of chromatic images were quantitatively evaluated. The performance of Bayer chromatic images was found to be systematically worse than the monochromatic counterparts with the same imaging configurations. Different photogrammetric measurements were then carried out using monochromatic and chromatic images. The measurement accuracy from chromatic images was significantly lower than that from monochromatic images, except for the grid method with square-profile fringes. Therefore, it is recommended to use monochromatic cameras for carrying out deformation measurements, unless there is a requirement of colour identification.
       
  • A robust two-stage transit-based evacuation model for large-scale disaster
           response
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Xuehong Gao, Moddassir Khan Nayeem, Ibrahim M. Hezam After a natural or man-made large-scale disaster occurs, it is a great danger to the residents who are living in the affected area. Evacuees in the (potential) impacted area need to be assembled at pick-up points and evacuated within the specified time by using vehicles that transport them to the safe shelters, potentially multiple times. It is necessary to consider this transit-based evacuation problem right after the occurrence of a large-scale disaster with different time windows caused by different radius to the disaster center point. As the pick-up points of assembling evacuees can greatly influence the evacuation process, it is crucial to identify the critical pick-up point locations to assemble evacuees. We decompose the problem into two stages: determination of pick-up point locations, vehicle routing and scheduling. In the first stage, the goal is to determine a set of pick-up points to assemble evacuees while minimizing the total walking time of evacuees from their locations to pick-up points. In the second stage, the aim is to allocate vehicles to safe shelters to evacuate evacuees from pick-up points to safer shelters to minimize the total transit-based evacuation time. The first-stage problem is formulated as an integer nonlinear programming model and the second-stage problem is modeled as a mixed-integer programming model. To better recognize the locations of pick-up points, a hybrid genetic algorithm (HGA) is developed. An interval/roundtrip-based routing and scheduling heuristic (IRRSH) algorithm is proposed to route and schedule the vehicles under time-window constraint. Finally, computational results are provided to demonstrate the validity and robustness of the proposed model.
       
  • Lung cancer detection from CT image using improved profuse clustering and
           deep learning instantaneously trained neural networks
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): P. Mohamed Shakeel, M.A. Burhanuddin, Mohamad Ishak Desa Automatic lung disease detection is a critical challenging task for researchers because of the noise signals getting included into creative signals amid the image capturing process which may corrupt the cancer image quality thusly bringing about the debased performance. So as to evade this, Lung cancer preprocessing has turned into an imperative stage with the key parts as edge detection, lung image resampling, lung image upgrade and image denoising for improving the nature of input image. Image Denoising is a critical pre-processing task preceding further preparing of the image like feature extraction, segmentation, surface examination, and so forth which elminates the noise whereas retaining the edges and additional complete features to the extent possible. This paper deals with improvement of the quality of lung image and diagnosis of lung cancer by reducing misclassification. The lung CT images are collected from Cancer imaging Archive (CIA) dataset, noise present in the images are eliminated by applying weighted mean histogram equalization approach which successfully removes noise from image, also enhancing the quality of the image, using improved profuse clustering technique (IPCT) for segmenting the affected region. Various spectral features are derived from the affected region. These are examined by applying deep learning instantaneously trained neural network for predicting lung cancer. Eventually, the system is examined by the efficiency of the system using MATLAB based simulation results. The system ensures that 98.42% of accuracy with minimum classification error 0.038.
       
  • Incipient rolling element bearing weak fault feature extraction based on
           adaptive second-order stochastic resonance incorporated by mode
           decomposition
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Changbo He, Pei Niu, Rui Yang, Chaoge Wang, Zhixiong Li, Hongkun Li Incipient bearing fault characteristic is extremely weak and interfered by strong noise, which makes the early fault warning work very difficult. Considering traditional characteristic extraction methods cannot identify the fault frequency effectively, a method is proposed in this paper based on the cooperation of complete ensemble EMD with adaptive noise (CEEMDAN) and improved adaptive underdamped stochastic resonance (AUSR). Specifically, the principles and shortcomings of classical mode decomposition methods EMD, EEMD and CEEMD are briefly introduced first. Aiming at these shortcomings, CEEMDAN is adopted to decompose target signal for the extraction of sensitive IMF. Then, a more general theoretical analysis of USR is conducted by taking damping factor into account. Furthermore, an AUSR method is proposed based on GA. Both the superiority of CEEMDAN compared with other mode decomposition methods and the effectiveness of proposed overall analysis scheme are demonstrated by different cases of simulation analysis. Subsequently, the proposed method is further applied on two cases of experimental signals for bearing weak fault characteristic frequency enhancement and extraction. The analyzed results show that the characteristic frequency can be significantly enhanced with the help of proposed method, which further demonstrates its effectiveness and superiority in engineering application.
       
  • Artificial neural network approach for locomotive maintenance by
           monitoring dielectric properties of engine lubricant
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Olcay Altıntaş, Murat Aksoy, Emin Ünal, Oğuzhan Akgöl, Muharrem Karaaslan In this paper, we proposed an approach for locomotive maintenance systems by observing engine lube oil. The mechanical particles in lube oil give information about locomotive engine system condition. The engine lubricant is monthly monitored by a spectral analyzer (SA) to detect engine system failure and routine maintenance time. However, this old fashioned technique has many disadvantages such as non-real time measuring, high cost and time consumption. A novel approach is proposed to eliminate these disadvantages. The new method determines the lubricant sample conditions with respect to electrical characteristics by using artificial neural network (ANN). The study focuses on a relationship between mechanical particles (in ppm) and dielectric characteristics of the lube oil samples. Therefore, ANN method is applied to observe linear relation between observed and predicted dielectric constant and loss factor values of the engine oil samples. The electrical characteristics of the samples are observed at four frequency points (2.40 GHz, 5.80 GHz, 7.40 GHz and 9.60 GHz). ANN studies are realized by using data at these frequency points. The regression (R) coefficients are obtained as 0.7239, 0.7951, 0.8513 and 0.7463 for dielectric constant and 0.7627, 0.7196, 0.8015 and 0.7334 for dielectric loss, respectively. Moreover, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are calculated and examined. The obtained results are very sufficient and this approach can be applied to a sensor device having low cost and real time working mechanism in the future.
       
  • A bearing data analysis based on kurtogram and deep learning sequence
           models
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Sandeep S. Udmale, Sanjay Kumar Singh, Sunil G. Bhirud The condition monitoring of rotating machinery has been widely accepted by the industrial system for intelligent fault diagnosis to achieve sustainability, high performance and provide safety to workers. Therefore, in recent years, artificial intelligence (AI) and signal processing (SP) methods are operated collectively for fault diagnosis. The complex and hybrid input feature set are constructed using SP methods for AI-based fault diagnosis. Thus, over the years the numbers of features in the feature space are increasing to represent the various faults as well as fault severities, and also, different feature selection techniques are operated on feature space to determine the ideal features. Consequently, it is a challenging task to design the dominant feature set for distinguishing the type of defects. Also, the requirement of a number of features is changing due to various working conditions of rotating machinery. Therefore, a new intelligent diagnosis method for fault classification build on the kurtogram and sequence models (SM) of deep learning is proposed in this paper. The kurtogram is a comprehensive tool for providing well-defined information about defects by organizing frequency domain kurtosis values in order at each level. Thus the SM analyzes the kurtogram as sequential data for fault diagnosis and hence helps to eliminate the feature selection exercise for identifying the dominant features from feature space. The proposed method has examined using two vibration datasets of bearing. The result demonstrates that the proposed method has a promising performance and achieves decent fault classification accuracy in comparison with other methods.Graphical abstractGraphical abstract for this article
       
  • Determination and accuracy analysis of individual tree crown parameters
           using UAV based imagery and OBIA techniques
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Huseyin Yurtseven, Mustafa Akgul, Suleyman Coban, Sercan Gulci In the process of producing information from images with very or ultra-high spatial resolution (VHR and UHR), the most accurate results are achieved by using object-based image analysis (OBIA) techniques. The most economical method to obtain UHR images is to use sensor systems that are integrated into unmanned aerial vehicles (UAV). In this study, which combines UHR-UAV-based images and OBIA-based analyzes, individual tree crown parameters were obtained, and the results were examined using various accuracy analysis techniques. For this purpose, the UAV data acquisition was performed at the altitude of 40 m above ground level, and a ground sample distance (GSD) of 1.28 cm was obtained. Photogrammetric processes were performed using the structure-from-motion (SfM) techniques, and orthomosaic and photogrammetric point cloud data were generated with 2.46 cm RMSE. OBIA-based techniques were applied to these data, and the individual tree heights, crown borders and related parameters were derived. For the accuracy analysis, actual tree heights were collected with terrestrial measurements. The reference tree crown borders were stereoscopically interpreted from UAV-based images. In this study, the accuracy of the tree crown borders and tree heights were tested over 31 parameters. Recommendations were presented by interpreting the ultimate accuracy values to determine the accuracy of the data obtained using OBIA techniques. As a result, OBIA techniques will increase the effectiveness and preciseness forest inventory applications, such as determination of the stand structural characteristics (canopy cover, canopy gaps, stand height etc.).Graphical abstractGraphical abstract for this article
       
  • In-Situ object calibration software (ISOCS) technique for 235U
           mass verification
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Z. Ahmed Measurement of nuclear materials by ISOCS technique an important and significant tool for IAEA activities verification. ISOCS technique able to measuring samples with different geometry. In this work, ISOCS technique was used to calculate the absolute efficiency of the Hyper Pure Germanium (HPGe) detector. The calculated absolute efficiency of the detector was used in combination with experimental work to determine U-235 mass in cylindrical NM samples. A good agreement was obtained when compared estimated and declared values of 235U mass content with difference in the ranged from −0.827% to 1.635%.
       
  • A general accuracy measure for quality of elliptic sections fitting
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Orhan Kurt, Ozan Arslan Least squares (LS) fitting, the most widespreadly used approach for ellipses, operates by minimizing the sum of squares of some error term measured at each data point. It is not an easy task to measure the accuracy of any fitting method in fitting elliptical sections since they rely on different error criteria. There is no unique criterion valid for any fitting method for quantifying for ellipse fitting. For this reason, there is a need for a general measure that can be used to compare the accuracy of fitted ellipses using different methods. In this work, an error measure is proposed which can be used both to measure the accuracy of any ellipse fitting method and to compare the accuracy of the ellipses fitted with different (i.e. algebraic or geometric) methods. This measure is generated from the widely known orthogonal least squares fitting (OLSF) method revising the computation scheme of initial values for the orthogonal contacting points for an ellipse in the study. This is a flexible error measure since it always computes orthogonal distance residuals between data points and the optimal ellipse and can then be used to compare the performance of different ellipse fitting methods. By computing this measure it is possible to obtain the precision of the ellipse parameters with respect to the orthogonal distance residuals. This measure is applied to the measurement of the outer section of a piston and results indicate the effectiveness of the criterion.
       
  • Influence of camber angle on tire tread behavior by an on-board
           strain-based system for intelligent tires
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): J. Yunta, D. Garcia-Pozuelo, V. Diaz, O. Olatunbosun Tires are a key sub-system of vehicles that have a big responsibility in comfort, fuel consumption and traffic safety. Nevertheless, current tires are just passive rubber elements which do not contribute actively to improve the driving experience or the vehicle safety. The lack of information that tires provide during driving is the main reason to develop an intelligent tire, which could provide useful information to other systems and become an active safety system. In this paper, an experimental tire strain-based system is used to measure tire tread deformation by means of strain gauges. Tests under different working conditions such as vertical load or slip angle considering a certain camber angle have been carried out using an indoor tire test rig. The results prove that the camber angle has a significant effect on strain signal, so it should be considered for tire working conditions estimation purposes.
       
  • AC impedance function of electrochemical working station as novel curing
           degree monitor method: A model curing system of epoxy/anhydride/DMP-30
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Yufeng Han, Zhi Wang, Song Zhao, Jixiao Wang This paper presents a novel method based on AC impedance for monitoring the curing process of epoxy resin. Effects of DMP-30 and temperature on the reaction rate and curing degree were investigated here. Experimental results showed that DMP-30 has significant influence on reaction rate of the curing system, and the resin system, containing 1.5 wt% DMP-30 can be cured completely in the shortest time at 80 °C. Impedance analysis showed that the curing degree increased obviously with increasing temperature, while the curing time got shortened sharply. DSC technique was also employed here to verify the effectivity of impedance analysis of curing degree, and a good agreement between AC impedance and DSC was observed, especially at higher temperature, demonstrating the great potential application of AC impedance in field of cure monitoring.
       
  • Studying thermal protection for mobile sensor operating in combustion
           environment
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Ilkka Korhonen, Jero Ahola So far, the size of industrial energy and recovery boilers has been increased continuously. This development has led to a situation where the middle parts of the large boilers are extremely difficult to reach with traditional measurement methods. Due to this, our research group has studied possibilities to develop a mobile sensor ball propagating freely also in the center parts of combustion chambers.The sensor ball will consist of sensor electronics with sensor elements and a protective cover or envelope. The duty of the envelope is to delay the heating of sensor electronics inside the ball. This article focuses on studying the thermal protection issues of the sensor ball and its operation times in flames.
       
  • Assessment of the effect of GNSS sampling rate on GNSS/INS relative
           accuracy on different time scales for precision measurements
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Quan Zhang, Xiaoji Niu, Chuang Shi GNSS/INS relative accuracy representing navigation error variation or instability on different time scales has been increasingly required in precision measurement. However, research on the effect of GNSS sampling rate on GNSS/INS relative accuracy is limited. We mainly focus on the assessment of the effect of GNSS sampling rate on GNSS/INS relative accuracy on different time scales through Allan variance, and propose to improve the relative accuracy by adjusting GNSS sampling rate and applying backward smoothing. The field test results show that the time-correlation of GNSS measurement noise caused by high-rate sampling should be dealt with; increasing the GNSS sampling rate can improve the short-term position accuracy in the real-time mode; the backward smoothing can mitigate the weakness of low-rate GNSS sampling in the post-processing mode. This work can provide guidance for the reasonable selection of GNSS sampling rates to meet the demands of GNSS/INS relative accuracy.
       
  • Experimental and numerical study on mechanical and cracking behaviors of
           flawed granite under triaxial compression
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Wei Yao, Yanyan Cai, Jin Yu, Jianfeng Zhou, Shiyu Liu, Bingxiong Tu The mechanical and cracking behaviors of rock mass are strongly affected by natural discontinuities and confining pressure. Uniaxial and triaxial compressive experiments were conducted on intact and single- and double-flawed granite specimens using MTS 815 electrohydraulic servo-controlled triaxial testing system to understand further the influences of confining pressure and fracture on rock strength and failure mode. Confining pressures of 5 MPa and 10 MPa were designed in this study. The effects of the confining pressure and the configuration of pre-existing flaws on the mechanical and cracking behaviors of flawed granite specimens were studied numerically using PFC. Experimental study results show that under the same confining pressure, the stress–strain curve of the intact granite specimen drops rapidly at the peak stress point, whereas the deformation with different numbers of flawed granite shows a stepwise softening phenomenon, which occurs when the confining pressure is relatively small. However, such phenomenon becomes progressively unobservable as the confining pressure increases. With the increase in confining pressure, the mechanical property of the granite gradually turns from brittle to ductile. Consequently, the cracking behaviors become progressive. Numerical simulation results show that the tensile fracture of flawed granite specimens is often observed under a low confining pressure, whereas the tensile–shear mixture fracture and the shear fracture of flawed granite specimens are often observed under moderate and high confining pressures, respectively. Under a constant confining pressure, the peak stress of flawed specimens decreases with the increase in the number of prefabricated flaws and the stress concentration becomes more serious, thereby decreasing the number and development degree of the micro-cracks outside the stress concentration region of the specimens.
       
  • Determination of topological properties of thin samples by the van der
           Pauw method
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Krzysztof R. Szymański, Cezary J. Walczyk, Jan L. Cieśliński We solve the problem of determining basic topological properties of flat samples by performing measurements on their outer edge. The global maximum of four probe resistances shows a characteristic behaviour, which is dependent on the genus (i.e., the number of holes) of the domain. An extension of the van der Pauw method on domains having zero, one, or two holes is presented and discussed. A possibility of measuring topological properties of condensed matter is demonstrated. Experimental results for triply connected domains are presented and explained by continuous symmetry breaking caused by the presence of two holes. The results are consistent with the topological theorem of Hurwitz on the number of automorphisms of Riemann surfaces.Graphical abstractThe authors report the properties of flows that are realized by the van der Pauw method on singly, doubly, and triply connected domains while taking into account the property of symmetry breaking by increased multiconnectivity. The global maximum of four probe resistances shows the characteristic behaviour, which is dependent on the genus of the domain.Graphical abstract for this article
       
  • Measuring shape and motion of a high-speed object with designed features
           from motion blurred images
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Hance Zhou, Mingjun Chen, Liyan Zhang, Nan Ye, Cong Tao Vision-based geometry measurement plays a crucial role in many science and industrial areas. Plenty of researches devoted to measuring static objects, while few focused on motion blurred situations, which inevitably arise when the object being measured moves fast relative to the camera(s). Motion blur usually invalids the vision-based measurement algorithms designated for static objects. In this paper, we devote to accurate three dimensional (3D) reconstruction of moving objects from motion blurred stereo image pairs. A convolutional neural network (CNN) based method is first proposed to recognize the motion blurred visual targets. A motion blur model based on inner-frame path superposition imaging is then established. Finally, an optimization framework is set up to reconstruct the 3D target motion path during the camera exposure. Experiments are involved to demonstrate the validity and accuracy of the method.
       
  • Freshness Assessment of Broccoli using Electronic Nose
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Madeshwari Ezhilan, Noel Nesakumar, K. Jayanth Babu, C.S. Srinandan, John Bosco Balaguru Rayappan Broccoli is one of the nutrient-rich vegetables that can easily be affected by bacteria and consumption of bacterial contaminated fresh products leads to a number of food borne illnesses. A multi-dimensional approach has been adapted to evaluate the freshness of broccoli considering four different mutually supporting techniques namely electronic nose, bacterial culture test, head sampler method with Gas Chromatography-Mass Spectrometry (GC–MS) and Fourier Transform Infrared (FTIR) spectroscopy. Electronic nose output variations from 0.05 V to 1.5 V and the presence of Staphylococcus, Salmonella and Shigella in the order of Zero, 106 and 105–107 CFU mL−1 were observed for fresh, half and completely contaminated broccoli samples. GC–MS data revealed the presence of acetic acid, hexanoic acid, nonanol evolved from half and completely contaminated broccoli samples. In addition, Principle Component Analysis, Centroid-link, and Completely-link cluster analyses were used on electronic nose data in correlation with other techniques for decision making.Graphical abstractGraphical abstract for this article
       
  • Assessing a Binary Measurement System: A New Plan Using Targeted
           Verification with Conditional Sampling and Baseline Information
    • Abstract: Publication date: Available online 15 June 2019Source: MeasurementAuthor(s): Daniel E. Severn, Stefan H. Steiner, R. Jock MacKay We investigate efficient plans to assess the misclassification error rates of a binary measurement system used as an in-line inspection protocol. We assume that parts can be inspected repeatedly and that each part has its own (latent) misclassification rate. We propose a three-phase assessment plan. Phase I consists of data from recent inspection history. In Phase II, we select a sample of failed parts that we re-measure multiple times with the binary measurement system of interest. In Phase III, we verify a carefully selected subsample of the parts from Phase II with the aid of a binary gold standard measurement system. We show that the proposed plan is a substantial improvement over existing assessment plans in terms of cost and/or precision.
       
  • A structural model of direct measurement
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Alessandro Giordani, Luca Mari In the last few decades foundations of measurement have developed so as to account for both the role of modeling in measurement, in particular relating to the presence and the effects of measurement uncertainty, and the fact that any measurement is performed by using instruments that work on the basis of transduction effects and provide justified results only in so far as they are properly calibrated. This has triggered a new interest about the role of instruments in the models of measurement. The structure of the process has been variously studied in reference to the connection between measured properties and indications provided by instruments, and to the way in which intersubjective information on the measurand is acquired through instrument calibration. From such a background this paper proposes a comprehensive structural model of direct measurement, whose functional elements, empirical and informational, are presented with a bottom-up strategy as a set of interrelated modules. The result, shown to be a generalization of some of the models currently available in the literature of measurement science, highlights the key role of scales for measurement, clarifies the conceptual and operative relations between direct measurement and calibration, and identifies the principal sources of measurement uncertainty in the structural context of the process. This model is intended to interpret both physical and non-physical measurements, and as such it is a component of a “measurement across the sciences” research programme.
       
  • Tacho-less automatic rotational speed estimation (TARSE) for a mechanical
           system with gear pair under non-stationary conditions
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Mario Luis Ruiz Barrios, Fidel Ernesto Hernández Montero, Julio César Gómez Mancilla, Evelio Palomino Marín The condition monitoring of mechanical systems by vibration analysis becomes a problem whenever the rotational speed varies and is unknown. This work proposes an algorithm to automatically estimate the rotational speed from the vibration generated by a gear pair. The algorithm is based on the decomposition of the vibration signal into mono-component signals and the detection of amplitude modulations. Singular Spectrum Decomposition was used for decomposing the vibration signal into the mono-component signals, and a non-quadratic phase coupling analysis was applied in order to detect the amplitude modulations. The algorithm is validated by working with simulated and true signals resulting from the application of different speed profiles. Finally, two practical cases are presented: (1) the implementation of order tracking through the rotational angle estimated by the new algorithm; and (2) the identification of the first harmonic of gear-mesh frequency from a time-frequency representation of the vibration signal.
       
  • Residual displacement and residual strain measurement for steel frame
           under seismic load by integrated dot centroid tracking method
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Takuya Toyoshi The applicability of the image-based method for residual displacement and strain measurement in a structure which is damaged by seismic load was experimentally investigated. A specimen of steel frame was constructed and was subjected to seismic excitation in one direction. Residual displacement and strain field in a beam-to-column connection which suffered from seismic load was measured by Integrated Dot Centroid Tracking (IDCT) method which is one of the image-based methods. A statistical processing result showed the measurement was achieved with a smaller resolution than residual displacement under a minimum seismic load case. The residual displacement due to local tensile and shear force explained with the deformed triangular mesh which made by the centroids of dots. As a comparison with other measurement methods, an elongation between two centroids of dots on beam and column was compared using a displacement transducer installed inside the beam. The measurement result by the displacement transducer validated compared with a calculated displacement waveform using acceleration. As the number of measurements is increased, both measurement results showed a tendency to increase. Differences of residual displacement by two types measuring method was within 15–20%. The series of experiment in this study revealed the applicability of measurement for real structure using IDCT method.
       
  • An investigation of ramie fiber cross-section image analysis methodology
           based on edge-enhanced image fusion
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Zhengye Zhang, Binjie Xin, Na Deng, Wenyu Xing, Yang Chen Usually the quality of cross sectional fiber image is affected by the slicing and imaging device, some image processing techniques could be used to solve this problem instead of hardware operation. In this paper, a series of edge detection and weighted average image fusion algorithms are used to enhance the ramie fiber image to improve the quality of fiber cross-section image obtained by the traditional section and optical microscope. The cross-section characteristic parameters of ramie fiber could be extracted after the image pre-processing including threshold segmentation, median filtering and corrosion. The experimental results show that this method can be used to solve the identification problem of low contrast ramie fiber cross-section images caused by background blur, improve the accuracy of image recognition and analysis, it provides an effective algorithm basis for the digital measurement of ramie fibers.
       
  • Design of a pavement scanning system based on structured light of
           interference fringe
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Chu Chu, Hailu Yang, Linbing Wang In recent years, pavement scanning technology based on three-dimensional structured light has been widely used due to its multiple advantages. However, structured light of interference fringe was rarely applied in pavement scanning because of the low image quality when scanning surfaces with large sizes. The purpose of this study is to design a pavement scanning system based on structured light of interference fringe. The system aims to acquire pavement fringe images with high quality, which contain the three-dimensional information of the objects to measure. The basic principle of the scanning system is described in this paper. A fringe generator, which adopts a new fiber fixing method, is manufactured. It can emit fringe with higher spatial frequency. Then, the system parameters are determined. The influences of fringe spatial frequency f0 and incident angle ɸ on the quality of fringe images are analyzed, which verifies the rationality of the design. The scanning system can be used to scan the pavement distress and obtain the relevant fringe images.
       
  • Relationship among hand forces imparted on a viscoelastic hand-handle
           interface
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Yumeng Yao, Subhash Rakheja, Pierre Marcotte Design of a flexible thin-film hand sensor is presented for reliable measurements of the contact pressure/force distribution at a viscoelastic hand-handle interface, including the contact force developed by a gloved-hand grasping a tool handle. The static properties of the developed hand sensor were evaluated in terms of its drift, linearity, repeatability and hysteresis under global as well as local loads. The measured results revealed low hysteresis (
       
  • Experimental Studies on Electro-Discharge Machining of Inconel 825 Super
           Alloy using Cryogenically Treated Tool/Workpiece
    • Abstract: Publication date: Available online 10 June 2019Source: MeasurementAuthor(s): Rahul, Saurav Datta, Bibhuti Bhusan Biswal The present work studied effects of Cryogenic Treatment (CT) of tool/workpiece on machinability of Inconel 825 super alloys during Electro-Discharge Machining (EDM). The ease of machining was evaluated in terms of multiple performance features viz. Material Removal Rate (MRR), roughness average (Ra), Surface Crack Density (SCD) and White Layer Thickness (WLT) developed over machined surfaces. Considering three process parameters namely, peak discharge current (Ip), pulse-on duration (Ton) and duty factor (τ), EDM experiments were conducted using the following tool/work combinations: (i) Normal Tool and Normal Workpiece (NTNW), (ii) Cryogenically Treated Tool and Normal Workpiece (CTTNW), and (iii) Normal Tool and Cryogenically Treated Workpiece (NTCTW). Apart from investigating parametric effect on various machining responses, attempt was also made to carry out a comparative study on chemical composition, metallurgical aspects, residual stress and micro-indentation hardness of the machined specimens obtained by using NTNW, CTTNW and NTCTW, respectively. Effects of cooling rate (ramp-down) during cryogenic treatment cycle of the workpiece were also examined. Morphology of the machined surface was studied through scanning electron microscopy. Moreover, effects of cryogenic treatment of tool electrode were discussed from the viewpoint of shape retention capability, wear and extent of carbon deposition onto the bottom surface of the tool electrode.
       
  • Estimating the Friction Angle of Black Shale Core Specimens with
           Hybrid-ANN Approaches
    • Abstract: Publication date: Available online 10 June 2019Source: MeasurementAuthor(s): Zehui Shao, Danial Jahed Armaghani, Behnam Yazdani Bejarbaneh, M.A. Mu'azu, Edy Tonnizam Mohamad Shear strength parameters of rock play a significant role in designing stage of various geotechnical structures such as earth dams, embankments, foundations and tunnels. Direct determination of these parameters in laboratory is time consuming and expensive. Additionally, preparing core specimens with a good-quality is sometimes difficult, especially in weathered and highly fractured rocks. This paper presents an indirect determination of internal friction angle of shale rock specimens through two hybrid neural net based models that combine artificial neural net with genetic algorithm (GA-ANN) and imperialist competitive algorithm (ICA-ANN). In fact, GA and ICA were utilized to improve the efficiency of ANN predictive model via weights and biases adjustment. To achieve this aim, an extensive experimental program was designed, according to which a series of black shale specimens were tested with various laboratory tests such as p-wave velocity, Schmidt hammer, point load and triaxial compression. After establishing a proper database for the analysis, simple and multiple regression as well as hybrid intelligent models were developed to predict internal friction angle of the shale specimens. To compare the obtained results from the models, several performance statistical indices were computed. The results indicated that simple and multiple regression models are not good enough in predicting internal friction angle. Concluding remark is that the proposed intelligent models are superior in comparison with simple and multiple regression models. However, results of the developed GA-ANN model based on coefficient of determination values were obtained as 0.917 and 0.909 for training and testing datasets, respectively whereas these values were achieved as 0.960 and 0.956 for the ICA-ANN model which showed that ICA-ANN can provide higher performance capacity in estimating internal friction angle. In addition, results of other performance indices, i.e. variance account for and root mean square error confirmed that the hybrid ICA-ANN predictive model can be introduced as a new technique for predicting internal friction angle of shale rock specimens in practice.
       
  • Suppression of noises using fast independent component analysis (FICA) and
           signal saturation using fuzzy adaptive histogram equalization (FAHE) for
           intensive care unit false alarms
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): V. Ravindra Krishna Chandar, M. Thangamani In the medical field, fake alarms are classically described as alarms with no clinical or therapeutic effects. A variety of studies exist in the clinical literature regarding the alarms monitoring in Arterial Blood Pressure (ABP) Signal and intensive care medicine. In the proposed work measurement of each one of the ABP, signal values are carried out employing the Fast Independent Component Analysis (FICA), which detects areas affected with high-frequency noise. When the noises in the samples are eliminated, then the signal saturation values are decided with the help of the Fuzzy Wavelet Transform (FWT) technique. Then, the automated feature engineering was carried out utilizing the signal for ABP along with a processed signal, which has the count of the times of every monitored heartbeat acquired from the ABP signal. Subsequently, Kullback–Leibler divergence Kernel -Support Vector Machine (KLDK-SVM), Random Forest (RF), and SVM classifiers were trained so as to generate the classification models. The newly introduced scheme can be used to help the medical professional and specialists, letting them become more useful and are responsive to alarms as quickly as possible.
       
  • Framework proposal for high-resolution spectral image acquisition of
           effect-coatings
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Eva M. Valero, Miguel A. Martínez, Eric Kirchner, Ivo van der Lans, María García-Fernández, Timo Eckhard, Rafael Huertas Hyperspectral imaging of effect coated samples can be challenging, mainly because of the large differences in irradiance that stem from the orientation distribution of the metallic flakes contained in the coating, and from the lightness variations from one sample to another. Besides, high spatial resolution is needed to sample the details of the texture (sparkle) typical of these samples. In addition, focus search strategy and image registration are essential to achieve high quality data for further analysis. In this work, we propose and fully validate a capture framework for measuring spectral reflectance of effect-coated samples with high spatial resolution in 45/0 geometry, using an LCTF (Liquid Crystal Tunable Filter) coupled with a monochrome camera. The main features of the proposed framework are an optimized focus search method based on object movement, a very precise alignment for the images captured in different bands (image registration), achieving sub-pixel accuracy, and a dynamic procedure that uses several white reference surfaces in exposure time estimation to cope with very dark or highly reflective samples. The proposed capture device produces spectral reflectance values comparable to a conventional spectroradiometer using the same observation/illumination geometry, with the additional advantage of achieving a spatial resolution more than two times higher than the human visual system.Graphical abstractGraphical abstract for this article
       
  • Objectivity, realism, and psychometrics
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Trisha Nowland, Alissa Beath, Simon Boag The aim of this paper is raise and address questions regarding the status of objectivity for the generalized latent variable model (GLVM) in psychometric research, given the conceptual, logical and mathematical problems of circularity, conditional independence, and factor indeterminacy, respectively. The question of objectivity for the model is examined with respect to measurement and realist perspectives. Drawing on insights from measurement and systems dynamics literature, a proposal for a conceptual framework is presented, that integrates: i) inference from the best systematisation; and ii) axiomatic set theory. This conceptual framework, which addresses the whole of a research project, invites specification of the expected relations, conditions, and assumptions which are relevant to the implementation of the GLVM. While this does not eliminate the problems for the GLVM, it provides future researchers with maximal objective information in standardized form, supporting minimization of definitional and instrumental uncertainty, in psychological modelling practices.
       
  • On the signature of cup anemometers’ opto-electronic output signal:
           extraction based on Fourier analysis
    • Abstract: Publication date: Available online 8 June 2019Source: MeasurementAuthor(s): Alvaro Ramos-Cenzano, Mikel Ogueta-Gutierrez, Santiago Pindado The output signal of cup anemometers equipped with opto-electronic output signal generators (such as Vector Instruments A100 LK or Thies First Class Advanced) is analyzed to extract the signature of the anemometer. This signature is a unique information for each single anemometer whose output signal is a train of square-pulses. This signature reflects errors introduced within the fabrication process of the slotted wheels from the opto-electronic output signal generator. The signature of a cup anemometer can be used to filter the output signal, increasing the accuracy of the wind sensor.
       
  • Research on the milling tool wear and life prediction by establishing an
           integrated predictive model
    • Abstract: Publication date: October 2019Source: Measurement, Volume 145Author(s): Yinfei Yang, Yuelong Guo, Zhiping Huang, Ni Chen, Liang Li, Yifan Jiang, Ning He As the tool wear increases, the surface quality of the workpiece will decrease, and even the workpiece will be scrapped. Therefore, in order to obtain a better machined workpiece quality, monitoring the tool wear is necessary. By monitoring the machining condition, the degree of the tool wear and the remaining useful life (RUL) can be obtained in time. This paper establishes an integrated prediction model based on trajectory similarity and support vector regression, which can predict the tool wear and life. The time domain and wavelet analysis are carried out. The relationship between the signal characteristic quantity and the tool wear is studied. Five eigenvectors are selected as the input vectors of the prediction model by studying the correlation between 45 characteristic quantities and the tool wear. The model training is carried out by using the PHM public data set. The relative errors of VB value prediction accuracy in the stable stage of the sample tool is above 88% and the prediction accuracy of the stable stage of Tool 1, 2, and 3 is 88.5%, 87.5%, and 90.5% respectively, by using this integrated prediction model, which is better than other four single algorithms.
       
  • Predicting the relative density and hardness of 3YPSZ/316L composites
           using adaptive neuro-fuzzy inference system and support vector regression
           models
    • Abstract: Publication date: Available online 6 June 2019Source: MeasurementAuthor(s): E. Jajarmi, S.A. Sajjadi, J. Mohebbi The purpose of this research was to evaluate the accuracy of two computational intelligence methods in the prediction of hardness and density of 3Y-PSZ/316L composites. For this reason, 3Y-PSZ/316L composites with different compositions were produced using spark plasma sintering (SPS) process. Hardness and density of the composites, as the mechanical and physical properties, were determined experimentally. To predict the values of the properties in the composites two computational intelligence methods including Support Vector Regression (SVR) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models were used for the first time. Moreover, the performance and precision of the models in the prediction of the properties were compared together. The ANFIS was developed and validated by randomly limited experimental data separated into two sections, training and testing. Configuration of the ANFIS was determined by Hybrid learning method. To have a comparison between the obtained results and the experimental values, some statistical parameters such as mean relative error (MRE %), coefficient of determination (R2) and root mean squared error (RMSE) were used. The accuracy of both models was demonstrated by the difference between the experimental data and the predicted values with mean relative error less than 1.487%. It showed that both models are powerful tools for prediction of relative density and hardness of 3Y-PSZ/316L composites. However, the lower RMSE and MRE% for ANFIS showed that it offers much superior performance compared with the SVR model.Graphical abstractGraphical abstract for this article
       
  • Intelligent Vibration Detection and Control System of Agricultural
           Machinery Engine
    • Abstract: Publication date: Available online 5 June 2019Source: MeasurementAuthor(s): Jin Xin, Chen Kaikang, Ji Jiangtao, Zhao Kaixuan, Du Xinwu, Ma Hao Vibration of agricultural machinery not only affects the quality and effect of the operation, but also reduces the service life of machinery. A vibration detection and control system based on intelligent sensor nodes was developed to detect and control the frame vibration caused by high-power engine in agricultural machinery. Each intelligent detection node can work independently or networking as Internet of Things system. The effect of the vibration isolation system can be assessed by installing the test nodes at the measuring points of the two-stage vibration isolation system. In this paper, intelligent sensor nodes were used to reduce the vibration of an agricultural machinery engine. According to the optimized stiffness of the double-layer vibration isolation system, a test platform system is designed. The intelligent sensor nodes were used to assess the vibration spectrum characteristics of engine under various working conditions, and acceleration values of upper and lower measuring points of stage 1 and stage 2 isolators. The relationship between acceleration transfer rate and engine speed was analyzed before and after double-layer vibration isolation was used, and the vibration isolation effect of double-layer vibration isolation system is verified. The results show that the double-layer vibration isolation system can play a significant role in changing the stiffness of the whole system and avoiding the occurrence of coupling resonance. The transmission rate of vibration acceleration decreases from 0.94 to 0.54 at the engine speed of 2700 r/min, with a decrease of 42.3%. The results show that the proposed intelligent detection node is able to detect the engine vibration in agricultural machinery, and the proposed double-layer vibration isolation system can effectively reduce the engine vibration.
       
  • A new voltage probe with improved performance at the 10 kHz – 500 kHz
           frequency range for field measurements in LV networks
    • Abstract: Publication date: Available online 5 June 2019Source: MeasurementAuthor(s): Igor Fernández, Mikel Alberro, Jon Montalbán, Amaia Arrinda, Itziar Angulo, David de la Vega Voltage measurements in the frequency range from 10 kHz to 500 kHz are used to quantify the level of the Narrow Band Power Line Communication transmitted signals, the noise and the non-intentional conducted emissions generated by the devices connected to the Low Voltage grid. Considering that the voltage levels within this frequency range are very small if compared to measurements below 2 kHz, measuring equipment of higher precision is needed, but existing standards do not currently cover this frequency band. In this paper, a voltage adapter with improved performance at the 10 kHz – 500 kHz frequency range for field measurements in LV networks is presented. Moreover, a measurement setup and methodology for the frequency-dependent characterization of this type of voltage adapters is described, which is used to demonstrate the outperformance of the designed probe with respect to four commercial devices.
       
  • Rotating-arm method for low speed calibration of hot-wire probes in water
           applications
    • Abstract: Publication date: Available online 4 June 2019Source: MeasurementAuthor(s): M. Özkan An instrument for calibration of hot-wire probes at very low velocities in water applications is introduced. The technique is based on moving a hot-wire probe by means of a rotating-arm device in still water. This simple in-situ calibration method is principally beneficial for the investigations of three-dimensional boundary layers which are encountered for instance over rotating disks. In addition to this proposed technique, an existing alternative method in which the calibration is performed against a known reference velocity over a flat surface of a rotating disk is conducted. The comparison of both methods show that the rotating-arm technique is superior to that existing alternative flat-surface calibration method.
       
  • Identifying Pneumonia in Chest X-Rays: A Deep Learning Approach
    • Abstract: Publication date: Available online 4 June 2019Source: MeasurementAuthor(s): Amit Kumar Jaiswal, Prayag Tiwari, Sachin Kumar, Deepak Gupta, Ashish Khanna, Joel J.P.C. Rodrigues The rich collection of annotated datasets piloted the robustness of deep learning techniques to effectuate the implementation of diverse medical imaging tasks. Over 15% of deaths include children under age five are caused by pneumonia globally. In this study, we describe our deep learning based approach for the identification and localization of pneumonia in Chest X-rays (CXRs) images. Researchers usually employ CXRs for the diagnostic imaging study. Several factors such as positioning of the patient and depth of inspiration can change the appearance of the chest X-ray, complicating interpretation further. Our identification model is based on Mask-RCNN, a deep neural network which incorporates global and local features for pixel-wise segmentation. Our approach achieves robustness through critical modifications of the training process and a novel post-processing step which merges bounding boxes from multiple models. The proposed identification model achieves better performances evaluated on chest radiograph dataset which depict potential pneumonia causes.
       
  • Evolution of a Capacitive Electromyography Contactless Biosensor: Design
           and Modelling Techniques
    • Abstract: Publication date: Available online 4 June 2019Source: MeasurementAuthor(s): Charn Loong Ng, Mamun Bin Ibne Reaz Musculoskeletal disorders (MSDs) and ergonomic issues have long-term impacts on the human body, affecting patient health and the world’s economy. To address these issues, electromyography (EMG) can provide detailed information of human muscular activity during the stages of diagnosis and recovery and for general monitoring. The conventional way to extract EMG signals from the human body requires a professional setup and complex post-signal processing and may cause side effects to the subject’s body. This paper focuses on two prominent areas: it provides an in-depth analysis of the EMG signal characteristics, and a detailed discussion of the research and development of the hardware for a contactless EMG biosensor. This study provides an extensive review and performance comparison on the capacitive EMG sensors developed by different researchers. It also presents guidelines and parameters for future researchers to comply with in developing a practical and rugged contactless EMG biosensor.
       
  • Measurement of local creep strain in the notch region using AC Potential
           Drop Technique
    • Abstract: Publication date: Available online 3 June 2019Source: MeasurementAuthor(s): C.M. Omprakash, A. Kumar, M. Kamaraj, B. Srivathsa, D.V.V. Satyanarayana In the present study, an attempt has been made to ascertain the capability of Alternating current potential drop (ACPD) technique to measure local creep strain in the notch region of specimens, which has an important technological significance in terms of monitoring creep damage in aeroengine components with intricate shapes. To this end creep strain measurements on both smooth and notched specimens of DSCM247 superalloy at 1050°C and 125MPa were carried out using linear variable differential transducers (LVDTs) as well as ACPD techniques and established that the creep strain measured by ACPD technique corresponds to the strain accumulated in the notch region. This was further substantiated by direct measurement of creep strain in the notch region by using non-contact laser gauging system.
       
  • Application of Improved SOM Network in Gene Data Cluster Analysis
    • Abstract: Publication date: Available online 1 June 2019Source: MeasurementAuthor(s): Feng Nan, Yang Li, XueYong Jia, LiYan Dong, YongJie Chen At present, cluster analysis has become a very good channel for analyzing gene expression data to obtain biological information. In recent years, many experts have used traditional clustering algorithms and new clustering algorithms to mine gene expression data. This article first introduces the preprocessing of gene expression data. Then, by using principal component analysis (PCA) to process the gene data, a small number of characteristic variables are extracted as new indicators, and the indicators are evaluated to achieve the purpose of dimensionality reduction. The dimension reduction index is applied to the dynamic self-organizing neural network(DSOM) neural network, and the victory neurons are selected by the minimum Euclidean distance. The characteristics of the genetic data are clustered through the DSOM network, and the gene types with similar characteristics are divided into one region. The results show that PCA and DSOM networks have a high accuracy rate for clustering of genetic data, and a clear division of boundaries.
       
  • Measurement of small rotation angle of flange joints by a novel flexure
           magnifying mechanism
    • Abstract: Publication date: Available online 31 May 2019Source: MeasurementAuthor(s): Xiaotao Zheng, Fen Wei, Haofeng Chen, Sujuan Guo, Fuzhen Xuan Bolted flange joints are indispensable components in process industries due to the good sealing, assemble and disassemble capacities. Generally, the flange rigidity characterized by the rotation angle is a key index to evaluate the sealing tightness of flange joints. However, the rotation angle of flange is usually too small (less than 1°) to monitor during the assemble and operation stages. Accordingly, a novel flexure magnifying mechanism is designed to measure the small rotation angle of flange joints under internal pressure and external bending moment. The magnification factor and calculation approach of the flexure amplification mechanism are deduced and verified by experimental data and finite element simulation. Results indicate that the proposed measuring apparatus has good performance to monitor the maximum rotation angle. It is of great interest that the measured location of the maximum rotation angle is in good agreement with that in the experiment, and the average error is 7.3%, which is acceptable for practical application. Additionally, the leakage rate at the top of flange joints slowly and almost linearly increases with the increment of external bending moment ascribing to the decrease the gasket stress near the top of flange joints.Graphical abstractGraphical abstract for this article
       
  • Modeling and optimization of machining parameters during grinding of flat
           glass using response surface methodology and probabilistic uncertainty
           analysis based on Monte Carlo simulation
    • Abstract: Publication date: Available online 31 May 2019Source: MeasurementAuthor(s): Sabri Öztürk, Mehmet Fatih Kahraman In this paper, the performance of diamond grinding wheels was investigated. The industrial diamond crystals with a size of 140/170 mesh were utilized. The microstructure of the grinding tool was observed using a Scanning Electron Microscope (SEM) and Energy Dispersive X-ray Analysis Device (EDX). The experiments were designed using Box–Behnken method and optimum grinding parameters for glass were analytically determined. Experimental studies were carried out on a surface grinding machine in a flat glass factory. Grinding characteristics were examined with respect to surface roughness. The effects of grinding parameter on output responses were studied using analysis of variance (ANOVA). Probabilistic uncertainty analysis depends on Monte Carlo simulation was applied. Moreover, after the experiments using the optimized cutting parameters, the microstructure of the grinding wheels was analyzed. From results, the established model and optimization method could be employed for predicting surface roughness and this work is reliable and suitable for solving the problems encountered in machining operations. The lifetime of Cu-based grinding discs can be increased by adding Zn and Fe to the matrix material.Graphical abstractGraphical abstract for this article
       
  • Multi-target Space Position Identification and Matching Algorithm in
           Multi-screen Intersection Measurement System Using Information Constraint
           Method
    • Abstract: Publication date: Available online 31 May 2019Source: MeasurementAuthor(s): Hanshan Li To improve the identification ability and accuracy of multi-target and multi-screen intersection measurement systems, this paper proposes a new identification method. This method uses the multi-screen spatio-temporal information constraint theory for multi-screen intersection measurement systems, and develops both recognition model and signal processing method using wavelet identification on a single detection screen. According to target signal identification algorithm and space geometric structure relationship, the spatio-temporal characteristics are analyzed when targets pass six photoelectric detection screens. Furthermore, the multi-target recognition and matching calculation model is structured based on the spatio-temporal information constraints of this multi-screen intersection measurement system. When the targets pass the screens, temporal and spatial information constraints affect both adjacent photoelectric detection screens. If the targets reach the same screen at the same time, they will not be detected at the same time. The proposed method solves the identification and matching problem of multi-screen intersection measurement systems.Graphical abstractGraphical abstract for this article
       
  • Multi-angle automotive fuse box detection and assembly method based on
           machine vision
    • Abstract: Publication date: Available online 31 May 2019Source: MeasurementAuthor(s): Weiqiang Wang, Yi Luo, Kun Yang, Chunxue Shang With the detection and assembly of automobile fuse box entering the automation stage, the traditional method has low detection accuracy, poor assembly efficiency and cannot automatically adjust the torque of the torque wrench according to the production needs. Therefore, it is necessary to propose a new method to improve the system's shortcomings and improve the system's application. This study proposes a multi-angle automotive fuse box detection and assembly method based on machine vision. Firstly, the HSV color extraction method and OCR character recognition method are used to obtain the fuse color and model on the fuse box, and the fuses of each model in the fuse box are quickly detected and recognized. Then, the upper surface image of the fuse box is captured by two cameras with different viewing angles. The coordinates and depth of field information generated by the difference in viewing angle are used to judge the position of the torque wrench, and the torque of the torque wrench is automatically adjusted. Finally, the prototype of the detection and assembly system is designed. The simulation results show that the detection accuracy, detection speed and reliability of the method reach a high level. The field test results of a certain type of fuse box on the prototype show that the recognition speed is about 0.085s/piece, and the fuse detection accuracy for 99.9%, the position recognition accuracy of the torque wrench is 100%.
       
  • Gear misalignment diagnosis using statistical features of vibration and
           airborne sound spectrums
    • Abstract: Publication date: Available online 31 May 2019Source: MeasurementAuthor(s): Muhammad Ali Khan, Muhammad Atayyab Shahid, Syed Adil Ahmed, Sohaib Zia Khan, Kamran Ahmed Khan, Syed Asad Ali, Muhammad Tariq Failure in gears, transmission shafts and drivetrains is very critical in machineries such as aircrafts and helicopters. Real time condition monitoring of these components, using predictive maintenance techniques is hence a proactive task. For effective power transmission and maximum service life, gears are required to remain in prefect alignment but this task is just beyond the bounds of possibility. These components are flexible, thus even if perfect alignment is achieved, random dynamic forces can cause shafts to bend causing gear misalignments. This paper investigates the change in energy levels and statistical parameters including Kurtosis and Skewness of gear mesh vibration and airborne sound signals when subjected to lateral and angular shaft misalignments. Novel regression models are proposed after validation that can be used to predict the degree and type of shaft misalignment, provided the relative change in signal RMS from an aligned condition to any misaligned condition is known.
       
  • A new laboratory test method for tire-pavement noise
    • Abstract: Publication date: Available online 31 May 2019Source: MeasurementAuthor(s): Wanyan Ren, Sen Han, Tien Fang Fwa, Jiahao Zhang, Zhihao He Little previous research results have been reported from laboratory tire-pavement noise test methods, especially on small laboratory specimens. This research aims to introduce and explore the feasibility of a new test method used with laboratory specimens. The proposed method measured the tire-pavement noise when a rolling tire from a sloping track hit a horizontal slab specimen of a given pavement mixture. A high speed camera and a weighing sensor were utilized to identify the tire-pavement contact time and distance. The contact time was found to be 30.72ms. The exact start time of contact was determined by analyzing the recorded sound signal. Altogether, five data analysis methods were employed. The analysis results were evaluated using Sound Pressure Level (SPL) and 1/3 octave band spectrum. They were compared with the corresponding reference pavements from past research, and on site results. Finally, two methods were recommended. They may potentially characterize laboratory tire-pavement noise.Graphical abstractGraphical abstract for this article
       
  • Blind Source Separation of Composite Bearing Vibration Signals with
           Low-rank and Sparse Decomposition
    • Abstract: Publication date: Available online 31 May 2019Source: MeasurementAuthor(s): Guozheng Li, Gang Tang, Huaqing Wang, Yanan Wang Fault diagnosis is pivotal for health monitoring of rotating machinery. On practical engineering occasions, collected signals are usually from multi-sources. Moreover, the complex transmission path between multi-source and sensors further complicates the situation. So existing conventional methods for blind separation and detection could not meet the requirement in complex operating conditions. Inspired by the success of robust principal component analysis in sound signal processing, we proposed a novel strategy that explores the rank and sparsity features of signals for blind separation and detection of composite faults. The main methodological contribution of this paper is to consider the problem of bearing faults from the perspective of signal’s rank and sparsity, which are firstly explored in time-frequency domain. And their diversities are utilized to carry out further separation. Experiment shows that the proposed strategy can achieve target detection and multi-source separation, making it more effective to separate and detect bearing composite faults.
       
  • Surface analysis on revised hip implants with stem taper for wear and
           failure incidence evaluation: a first investigation
    • Abstract: Publication date: Available online 31 May 2019Source: MeasurementAuthor(s): Saverio Affatato, Alessandro Ruggiero BackgroundAs revision surgeries of total hip arthroplasty are going to increase, success or failure of revised implants is a fundamental aspect in the orthopaedic field. Today, slight is known about the success rate of revised total hip arthroplasty when the femoral head is substituted on a retained stem.Question: We wanted to investigate if the interposition of a stem taper adapter between the paired elements can influence this rate of success.MethodsFrom a total of more than 1000 revised implants, recorded during their follow up in a regional registry, the study was limited to the ones subjected to a surgical revision, where the head has been revised but the stem taper has not. Investigations on wear, roughness, and corrosion on the available explants were conducted, with the aim of providing visual and empirical response on the topic and to furnish to other researches useful data for comparison.ResultsOur results highlighted a higher incidence of second revisions when the femoral head is substituted without using an adapter. The topographic analysis refers that it is not evident a predominant roughness along any of the stems directions: no evidences were found that would define a recurrent rougher direction. This indicates a homogeneous worsening or corrosion of the stem taper along the circumference. Further, we found that the roughness values of the specimens analyzed not influence their surfaces and there is no a direct relation between the roughness of the bore heads and the stem tapers.
       
  • A Reinforcement Learning Unit Matching Recurrent Neural Network for the
           State Trend Prediction of Rolling Bearings
    • Abstract: Publication date: Available online 31 May 2019Source: MeasurementAuthor(s): Feng Li, Yong Chen, Jiaxu Wang, Xueming Zhou, Baoping Tang This paper proposes a novel neural network, called a reinforcement learning unit matching recurrent neural network (RLUMRNN), with the aim of resolving the problem that the generalization performance and nonlinear approximation ability of typical neural networks are not controllable, which is caused by the experience-based selection of the hidden layer number and hidden layer node number. In the proposed RLUMRNN, the monotone trend discriminator is constructed by using the least squares linear regression method for dividing the whole state degradation trend of rolling bearings into the following three kinds of monotonic trend units: ascending unit, descending unit and stationary unit. Moreover, by virtue of reinforcement learning, the recurrent neural network (RNN) with the hidden layer number and hidden layer node number fitted to a corresponding monotone trend unit is selected to enhance the generalization performance and nonlinear approximation ability of RLUMRNN. Additionally, three monotonic trend units and different hidden layer and node numbers are respectively used to represent the status and action of the Q value table, and a new reward function associated with the RNN’s output errors is constructed to clarify the purpose of reinforcement learning. This makes the RNN’s output errors smaller, which avoids the blind search of Agent (i.e., decision function) in the update of the Q value table and improves the convergence speed of RLUMRNN. By taking advantage of RLUMRNN in the generalization performance, nonlinear approximation ability and convergence speed, a new state trend prediction method for rolling bearings is proposed. In this prediction method, the moving average singular spectral entropy is first used as the state degradation feature of rolling bearings, and then the feature is input into RLUMRNN to accomplish the state trend prediction of rolling bearings. The examples of the state trend prediction for double-row roller bearings demonstrate the higher prediction accuracy and higher calculation efficiency of the proposed method.
       
  • Comparative analysis of MGEX products for post-processing multi-GNSS PPP
    • Abstract: Publication date: Available online 31 May 2019Source: MeasurementAuthor(s): Berkay Bahadur, Metin Nohutcu In recent years, the precise products generated by the International GNSS Service (IGS) as a part of the Multi-GNSS Experiment (MGEX) project have been increasingly used for multi-GNSS applications. Nowadays, six IGS Analysis Centers (ACs) have been providing GNSS products with different features. However, there is still neither a combined solution nor a standard accuracy definition for MGEX products, unlike the standard IGS products. For the GNSS techniques that are directly dependent on precise products, such as Precise Point Positioning (PPP), the quality of these products is a very crucial point in positioning performance. In this context, this study aims to investigate the impact of MGEX products provided by different IGS ACs on post-processing PPP performance in terms of accuracy, availability, and consistency. For this purpose, an experimental test was performed including all possible multi-GNSS combinations of GPS, GLONASS, Galileo, and BeiDou. 24-hour observation datasets collected at ten IGS stations during the 1-month period of May 1-31 were processed with twelve PPP modes using all available precise products. As a first step, an analysis of product availability was carried out for the related MGEX precise products within the test period to be able to assess the impact of the availability on the test results. PPPH software was used to perform the test and the results were statistically assessed as regards positioning error, RMS error and convergence time. The results indicate that the PPP performance may considerably differ depending on the precise products utilized in the PPP process. For the test period, PPP solutions utilizing the precise products generated by GFZ (GeoForschungsZentrum Potsdam) and WU (Wuhan University) agencies have relatively better positioning performance for nearly all processing modes compared to other solutions. The quality and availability of precise products are significant factors which lay behind the better PPP performance. On the other hand, while the integration of two or more systems significantly strength the PPP performance, GPS is still the dominant system for PPP and the solutions that do not include GPS constellation have very poor performance. The results also show that MGEX products have different impacts on the PPP performance as varying with the constellation involved in PPP solution and the geographical location of the station.
       
  • Design of a Parallel Plate Shearing Device for Visualization of
           Concentrated Suspensions
    • Abstract: Publication date: Available online 30 May 2019Source: MeasurementAuthor(s): Ahmad Shakeel, Paul J.M. van Kan, Claire Chassagne A modified version of the commercially available RheOptiCAD® was developed to visualize the microscopic structural changes occurring in concentrated suspensions, such as the break-up of flocs in clay suspensions, under shearing action. This is made possible by replacing the inverted microscope used in the traditional RheOptiCAD set-up by an upright modular microscope equipped with a CMOS camera and epi-illumination. Our device retains the following features of the previous version of RheOptiCAD®: [i] uniaxial translational motion of two parallel plates, [ii] three modes of shear straining, [iii] controlled thermal environment, [iv] vacuum joining of microscopy glass slides. The validation of the new design was done using a model system of un-flocculated and flocculated kaolin suspensions and concentrated natural mud suspension. The results showed that the constructed device is a promising tool for studying, from fundamental and industrial perspectives, the microstructural behaviour of complex suspended systems under controlled thermal and mechanical conditions.Graphical abstractGraphical abstract for this article
       
  • Measurement and prediction of vibration displacement in micro-milling of
           nickel-based superalloy
    • Abstract: Publication date: Available online 30 May 2019Source: MeasurementAuthor(s): Xiaohong Lu, Zhenyuan Jia, Xinxin Wang, Yubo Liu, Mingyang Liu, Yixuan Feng, Steven Y. Liang The relative vibration between the micro-milling cutter and workpiece influences the processing quality and tool life. To solve the difficult problem of measurement of vibration displacement between cutting tool and workpiece during micro-milling process, we designed a laser displacement sensor holder, which realizes the adjustment on multi degrees of freedom. A set of vibration displacement measurement system is developed, which utilizes laser displacement sensor to collect vibration signal during micro-milling process. The frequency of micro-milling force is obtained by using varying cutting parameters method. The relationship between cutting force amplitude, frequency and vibration displacement is ascertained by using neural network method to realize vibration displacement prediction under given cutting parameters. The research lays the foundation for vibration suppression of micro-milling, which can help improve surface quality and extend tool life.
       
  • Automatic Digital Modulation Classification using Extreme Learning Machine
           with Local Binary Pattern Histogram Features
    • Abstract: Publication date: Available online 30 May 2019Source: MeasurementAuthor(s): Ahmet Güner, Ömer Faruk Alçin, Abdulkadir Şengür Discrimination of the Local Binary Pattern (LBP) in the classification of different digital modulation types was investigated in this study. It has been shown that LBP can be used as a feature extraction method for AMC schemes. A new AMC scheme is proposed using Extreme Learning Machine (ELM) as a classifier, which has a faster learning process and better generalization performance than conventional machine learning methods. The study also investigated the stability of the proposed AMC scheme, which is affected by variation in the values of the roll-off factor, frequency and phase offset that can affect the stability and performance of the system. Through simulation, a classification accuracy of over 95% was achieved at low SNR levels such as -2 dB. It was also shown that the proposed AMC scheme is more successful under similar conditions when making comparisons to other studies.
       
  • Motor Current Signal Analysis Using Deep Neural Networks for Planetary
           Gear Fault Diagnosis
    • Abstract: Publication date: Available online 29 May 2019Source: MeasurementAuthor(s): Feng Li, Xinyu Pang, Zhaojian Yang Failures in planetary gearboxes can cause accidents, downtime, and high maintenance costs. Motor current signal analysis (MCSA) offers a non-intrusive method for detecting mechanical faults in rotating machinery. Recently, many intelligent diagnostic methods based on deep learning have been proposed, providing an effective way to quickly process a large amount of fault data and automatically produce accurate diagnostic results. However, most current intelligent methods for diagnosing faults are based on vibration signals. Owing to the characteristic differences between current signals and vibration signals, ideal diagnostic results cannot be obtained by directly using the spectrum signal as the sample. This study diagnosed faults in planetary gears by preprocessing current signals and using deep neural network algorithms. The effectiveness of this method is proven using experimental data, which contain substantial measurement signals covering different health conditions under different loading conditions. Furthermore, this method is compared with other methods to demonstrate its superiority.
       
  • Influence of contamination on measurement accuracy of the calorimetric air
           flow sensor
    • Abstract: Publication date: Available online 28 May 2019Source: MeasurementAuthor(s): Zhongsheng Sun, Yongxi Shen, Changrong Yuan, Xiaoning Li Calorimetric flow sensors are now widely applied in many industrial fields. Unfortunately, measurement accuracy is seriously affected when contaminants are deposited on the sensor chip. In this paper, the researchers propose simulation and experimental methods to evaluate the influences of contamination on the measurement accuracy of the calorimetric flow sensor. A natural contamination deposition experiment is proposed to present the contamination distribution on the sensor chip and quantitatively analyze the change in measurement values with the advance of contamination. Regarding contamination as a single shape, the 3D finite element model was built to study the influences of geometrical parameters of contamination, and to conclude the key influencing factor. In order to verify the experiment and simulation results, the micro-jetting experiment used wax droplets as contamination to further demonstrate the measurement variations of the sensor. The reason for the measurement deviations is explored through results obtained from these simulations and experiments.
       
  • Quality assessment and deviation analysis of three-dimensional geometrical
           characterization of a metal pipeline by pulse-echo ultrasonic and laser
           scanning techniques
    • Abstract: Publication date: Available online 28 May 2019Source: MeasurementAuthor(s): Bruno Silva Marció, Philipp Nienhaysen, Daniel Habor, Rodolfo C. C. Flesch Although ultrasonic-based techniques are the most widely used ones for the assessment of pipeline integrity they have some limitations, and alternative methods show potential to overcome them, such as laser scanning techniques. This paper presents the results of an experimental evaluation of pulse-echo ultrasonic and laser scanning techniques for pipeline inspection using a metal specimen which represents a damaged pipeline. Both techniques were able to detect all the defects on the inner surface of the specimen, but the defect geometry was important to define the accuracy of each technique. For almost all the evaluated defects, the differences between the reference and the three-dimensional representations created from the experimental data showed that the ultrasonic technique presented errors with magnitude around 0.2 mm, which is in general half the error observed for the laser scanning technique. However, ultrasonic technique demanded 8 hours for specimen inspection, while laser technique required only 10 minutes.
       
  • Five-point Cylindricity Error Separation Technique
    • Abstract: Publication date: Available online 28 May 2019Source: MeasurementAuthor(s): Wenwen Liu, JunSen Fu, Biao Wang, Shanlin Liu This study focuses on developing a new error separation technique for the in situ measurement and reconstruction of cylindrical profile. Based on a five-point system proposed, the least squares center vectors of the cross-sectional profile of the cylinder can are accurately extracted by the error separation model. The vectors are based on a spatial straight line that connects two least squares centers of the fixed cross-sectional profiles, which makes the double integration calculations avoided and the ability to resist interference improved. A cylindrical profile conforming to the mathematical definition can be reconstructed for cylindricity error evaluation. Theories, numerical validations and experiments verified that the spindle’s radial and tilt error motions as well as the probe carriage’s straightness deviations are all removed. This innovative technique solved a crucial problem regarding the accurate and high-precision reconstruction of a cylindrical profile in the in situ measurement.
       
  • Digital signal processing for self-vibration monitoring in grinding: A new
           approach based on the time-frequency analysis of vibration signals
    • Abstract: Publication date: Available online 28 May 2019Source: MeasurementAuthor(s): Rogério Thomazella, Wenderson Nascimento Lopes, Paulo Roberto Aguiar, Felipe Aparecido Alexandre, Arthur Alves Fiocchi, Eduardo Carlos Bianchi The occurrence of chatter can generate parts outside the dimensional and geometric tolerances or even cause irreversible damage, such as changes to the hardness and surface roughness of the ground part. The objective of this study is to propose a new vibration signal processing technique based on the short-time Fourier transform (STFT) and the ratio of power (ROP) statistic for the detection of chatter during the tangential surface grinding of AISI 1045 steel with different grinding wheels. Vibration signals were recorded at 2 MHz in the grinding tests. The Vickers hardness, roughness and metallography of the ground workpiece surfaces were also evaluated. A digital processing technique based on the STFT and ROP was applied to the vibration signals to extract the characteristics of the chatter. The results show that this technique can be used to characterize over time the spectral patterns of a frequency band related to chatter.
       
  • Engineering method of tire rolling resistance evaluation
    • Abstract: Publication date: Available online 28 May 2019Source: MeasurementAuthor(s): Jerzy Ejsmont, Wojciech Owczarzak Tire rolling resistance is one of the most difficult tire parameters to measure. The reason is that for modern tires the force of rolling resistance corresponds to 0.5 – 1% of tire load, thus measurements of very small forces must be performed in a heavily loaded system. This constitutes great problems, especially in road conditions. Laboratory measurements are easier to perform, as the environment may be better controlled, but roadwheel facilities based on outer drums, in general cannot be equipped with real road pavements. Typically they have steel drums or drums covered by replicas at best. This article describes a laboratory method of rolling resistance evaluation that may be used in preliminary assessment of road pavements (based on small pavement samples) and tires. The method is based on impact induced tire oscillations and gives good ranking of tires and road pavements related to the energy losses that control rolling resistance.
       
  • Electrochemical investigation of a novel surfactant for sensitive
           detection of folic acid in pharmaceutical and biological samples by
           multivariate optimization
    • Abstract: Publication date: Available online 28 May 2019Source: MeasurementAuthor(s): Mahdi Mollaei Sadiany, Sayed Mehdi Ghoreishi, Asma Khoobi The present study is a report about of an electrochemical sensor for folic acid (FA) detection based on the adsorption of a cationic surfactant, n-dodecylpyridinium chloride (DPC), at the surface of a carbon paste electrode (CPE). The DPC performance compares with the cetyltrimethylammonium bromide (CTAB) to improve the electrochemical reaction and response of FA. It is notable for the first time, DPC is successfully utilized in electrochemical analysis. Determination of FA is performed by differential pulse voltammetry (DPV), cyclic voltammetry (CV) and chronocoulometry (CC) techniqques. Response surface methodology (RSM) and central composite rotatable design (CCRD) is chosen for optimization of effective variables in the voltammetric peak current of FA. Another innovation in this study is the utilization of cubic terms in the experimental design. Under the optimized conditions, the FA oxidation peak current was linear to the FA concentration in the range of 0.01 µM to 10.69 μM with the detection limit of 2.9 nM (3σ). After studying the influence of probable interferences, it was found that the offered method has excellent selectivity for FA. Finally, the offered procedure was successfully applied to the FA detection in tablet and urine samples with satisfactory results.
       
  • A Feature Extraction Method Based on Composite Multi-scale Permutation
           Entropy and Laplacian Score for Shearer Cutting State Recognition
    • Abstract: Publication date: Available online 28 May 2019Source: MeasurementAuthor(s): Lei Si, Zhongbin Wang, Chao Tan, Xinhua Liu In the field of coal mining, accurate cutting state recognition of shearer is the basis and premise for achieving automatic coal cutting. In this paper, a novel shearer cutting state recognition method is presented based on composite multi-scale permutation (CMPE), Laplacian score (LS) and fly optimization algorithm-based support vector machine classification (FOA-SVM). CMPE is proposed to overcome the shortcomings of MPE and can extract the hidden state characteristics from the vibration signals of shearer rocker arm. Some simulations are provided to select the appropriate parameter settings and prove the superiority of CMPE to MPE. In addition, LS algorithm is employed to sort the extracted features over different scales according to their importance and the sensitive feature combinations can be generated scientifically. The FOA-SVM classifier is constructed to achieve intelligent recognition of shearer cutting state. Finally, some experiments are presented and the comparison results indicated that the proposed method can realize the recognition of shearer cutting state with higher accuracy than the existing methods.
       
  • Improving the efficiency and recognizability of the spectroscopy method
           for measuring nanofluid stability
    • Abstract: Publication date: Available online 27 May 2019Source: MeasurementAuthor(s): Tun-Chien Teng, Tun-Ping Teng This study proposed a method to improve the efficiency and recognizability of the spectroscopy method for measuring nanofluid stability. The 0.2 wt.% and 0.4 wt.% carbon-based nanofluids (CBNFs) were used as samples. The volumes of three test types of samples in the cuvette were 2.5 mL (Case 1), 1.8 mL (Case 2), and 1.8 mL (Case 3), respectively. In addition, an opaque black tape covered the lower half of the original area spotted by the probe beam in Case 3. The spectrometer was used to measure absorbance at wavelengths of 400–800 nm at fixed time interval. The results indicate that reducing the distance between the area spotted by the probe beam and the liquid level can improve the efficiency and recognizability of CBNF stability detection. For the optimal condition, the time to detect CBNF settling reduces by 50% and recognizability improves by 34.9% in the design of Case 2.Graphical abstractGraphical abstract for this article
       
  • A Deep Convolutional Neural Networks Model for Intelligent Fault Diagnosis
           of a Gearbox under Different Operational Conditions
    • Abstract: Publication date: Available online 27 May 2019Source: MeasurementAuthor(s): Guangqi Qiu, Yingkui Gu, Quan Cai For intelligent fault diagnosis of a gearbox using deep convolutional neural networks (DCNNs), we performed a gearbox vibration experiment. To understand the effects of different operational conditions and a cumulative degradation of the operational process of the gearbox, we collected the vertical and horizontal vibration signals of five different degradation states under three different operational conditions (rotational speeds of 1000r/min, 1050r/min and 1090r/min). Using feature extraction methods, such as time-domain analysis, frequency-domain analysis, and wavelet packet decomposition, a feature space with 52 features was constructed. The dimensionality of the extracted features was reduced to 4 by principal component analysis (PCA), with contribution rates of 51.55%, 20.45%, 12.33%, and 5.99%, respectively. To verify the superiority of DCNNs, the performance was compared to a support vector machine (SVM) classifier, and the hyper-parameters of the SVM classifier were optimized using a grid search technique. Results show that the vertical vibration signals are correlated with the degradation of the gear, and the identification accuracy is increased by imposing a certain load. DCNNs have been shown to achieve a higher accuracy than the SVM classifier, indicating that DCNNs are a more suitable method for solving a multi-state fault identification problem. Additionally, by inputting the raw signals directly, the gearbox intelligent fault diagnosis, based on DCNNs, has also achieved a higher accuracy with a lower computational time cost.
       
  • Analytical modeling of tool health monitoring system using multiple sensor
           data fusion approach in hard machining
    • Abstract: Publication date: Available online 27 May 2019Source: MeasurementAuthor(s): Amarjit P. Kene, Sounak K. Choudhury In recent years, a lot of work has been carried out on advance materials, especially hard metals and alloys, which is a vital requirement in many manufacturing industries. Nowadays steel with hardness beyond 30 HRC is employed in aircraft, hydraulics, car part manufacturing areas, etc. In the machining process, as the hardness of work material increases, tool health prone to deteriorate. Therefore, for healthier machinability requirements in machining of hard metals, the online tool health monitoring systems (THMS) are being developed using different feedback techniques. This study mainly concentrates on the advancement of online THMS using multiple sensors. Force, tool vibration, and surface roughness signals were recorded while machining EN24 hardened steel using coated carbide insert on CNC lathe. A novel analytical model of sensor data fusion has been presented for better understanding of sensors and their interaction. A comparative assessment of sensor fusion approach has been investigated. Experimental findings have successfully shown that the results obtained using a fusion function (U), validates better confirmability over single sensor-based approach.
       
  • Label-free electrochemical detection of Cloxacillin antibiotic in milk
           samples based on Molecularly Imprinted Polymer and graphene oxide-gold
           nanocomposite
    • Abstract: Publication date: Available online 27 May 2019Source: MeasurementAuthor(s): Saeid Jafari, Mohammad Dehghani, Navid Nasirizadeh, Mohammad Hadi Baghersad, Mostafa Azimzadeh Detection of applied antibiotics in food productions could be the first step to avoid future health consequences. The aim of this work was to the synthesis of molecularly imprinted polymer (MIP) particles via non-covalent procedure and evaluation of efficiency MIP for the selective collection of Cloxacillin (CLO) from aqueous and biological samples and consequently quantification of separated CLO using an electrochemical nanosensor. The effect of operational parameters including pH, contact time and MIP dosage for optimization of CLO collection condition were studied. The CLO quantities were determined by a developed electrochemical nanosensor based on a screen-printed electrode modified with graphene oxide and gold nanoparticles. The results showed that the optimum conditions for removal of CLO (92%) were determined at pH = 8.5, with 89 min as contact time and 0.79 g MIP. The linear range was from 110 to 750 nM and the detection limit of the nanosensor was 36 nM. The performance of the MIP-based nanosensor for the spiked Cloxacillin detection in real milk samples showed the potential of the developed nanosensor to be considered in future real sample measurement analysis.Graphical abstractGraphical abstract for this article
       
  • Preface of the special issue, Psychometric Metrology
    • Abstract: Publication date: Available online 25 May 2019Source: MeasurementAuthor(s): Mark Wilson, William Fisher
       
  • Phase triangulation method with spatial modulation frequency optimization
    • Abstract: Publication date: Available online 21 May 2019Source: MeasurementAuthor(s): Sergey V. Dvoynishnikov, Vitaliy V. Rakhmanov, Ivan K. Kabardin, Vladimir G. Meledin The paper proposes the phase triangulation method with an optimization of spatial modulation frequency. The optimal frequency is calculated based on the analysis of the amplitude-frequency characteristics of the measuring optical path according to the data obtained during the formation of structured images encoding the binary Gray code. The proposed approach makes it possible to use the optimal frequency of optical source spatial modulation in the 3D geometry measurements using phase steps triangulation and binary Gray code approaches. As a result, the method provides the minimum level of measurement error, regardless to parameters of the used optical-electronic elements of the measuring system.
       
  • Transformation Temperatures of Shape Memory Alloy Based on
           Electromechanical Impedance Technique
    • Abstract: Publication date: Available online 21 May 2019Source: MeasurementAuthor(s): Maxsuel F. Cunha, José M.B. Sobrinho, Cícero R. Souto, Alexsandro J.V. dos Santos, Alexandre C. de Castro, Andreas Ries, Nathan L.D. Sarmento This paper presents the use of Electromechanical Impedance (EMI) technique as an alternative tool to measure phase transformation temperatures of Shape Memory Alloys (SMAs).Differential Scanning Calorimetry (DSC) is the most common tool used to identify the transformation temperatures of SMAs. Meanwhile this work proposes a method based on a different operating principle, named as EMIH (Electromechanical Impedance Heterostructure). Samples composed of Lead Zirconate Titanate (PZT) ceramics and Ni-Ti alloys were investigated. The proposed method is quantitatively evaluated by means of impedance magnitude and phase angle measurements with different fixed frequencies, besides sweep frequency analysis. These analyses are intended to observe the phase transformation process by means of an impedance-based technique under thermal cycling. PZT/Steel, PZT/Copper and PZT/Brass samples were also analyzed by EMI in order to compare results with PZT/SMA, and for validating purposes. Experimental phase transformation temperatures present errors between 4.9% and 35.2% when compared to DSC method.Graphical abstractGraphical abstract for this article
       
  • An Adaptive Distributed Approach for The Real-Time Vision-Based Navigation
           System
    • Abstract: Publication date: Available online 21 May 2019Source: MeasurementAuthor(s): Majdeddin Najafi, Zahra Nadealian, Shahabudddin Rahmanian, Vahid Ghafarinia Real time navigation has remained as a major challenge for guidance and control of robots in indoor applications. In this paper, an adaptive image processing method is suggested to determine the 3D position and velocity of moving objects by using a distributed camera array. In this method objects are detected and 2D localized by independently and asynchronously processing in each camera. Afterwards, the extracted location data of all cameras are fed into a distributed adaptive extended Kalman filter to calculate the 3D position of multiple targets. Improving some specifications such as accuracy, area coverage, robustness against camera noise and camera failure, sampling time and the total price of the system is the main design objectives. The presented data fusion technique enables real-time localization of moving objects in 3D space by using a low-cost camera array. An experimental setup was used to illustrate the performance of the suggested localization system.
       
  • Review on Jitter terminology and definitions
    • Abstract: Publication date: Available online 21 May 2019Source: MeasurementAuthor(s): E. Balestrieri, F. Picariello, S. Rapuano, I. Tudosa Jitter is a crucial parameter in digital and analog electronics and particularly in RF communication systems as it can seriously affect their proper performances. Unfortunately, several different approaches for defining jitter and methods for measuring it have been developed depending on the particular system and application. This can lead to misconceptions and setup dependent results. In the paper a classification scheme concerning jitter terminology is proposed to provide an overview of the different existing jitter terms that can help in the perspective of finding a way to carry out a harmonized standardization of the jitter measurements.
       
  • A Method of Steel Ball Surface Quality Inspection Based on Flexible
           Arrayed Eddy Current Sensor
    • Abstract: Publication date: Available online 21 May 2019Source: MeasurementAuthor(s): Huayu Zhang, Longyu Ma, Fengqin Xie In order to perform the rapid and efficient automatic on-line inspection of the surface quality of steel ball, a new spherical deployment method based on flexible array eddy current sensor (FAECS) is proposed. Full unfolding of the steel ball surface can be performed by combining the flexible array eddy current sensor with the one-dimensional orbital deployment. The inspection coil of array eddy current sensor is fabricated on flexible printed circuit board (FPCB), which is bent into a semicircle to fix on the top of one-dimensional track. When the steel ball rolls on the one-dimensional track, complete inspection of steel ball surface can be performed by the flexible array eddy current sensor. In this method, one-dimensional mechanical motion is used instead of the traditional two-dimensional deployment motion, which reduces the complexity and cost of deployment structure. Three typical defects include linear cracks (0.12 mm width × 0.5 mm depth), cross cracks (0.06 mm width × 0.9 mm depth) and arc cracks (0.05 mm width × 0.1 mm depth) are measured in the experiment to verify the effectiveness of the inspection system. Subsequently, same batch of steel balls are tested at speed of 4 pcs/s, results show that the inspection accuracy of surface defect of steel balls is 95.52%, and the false inspection rate is less than 0.7%, which proves this method efficiency.
       
  • Estimation of Angular Velocity and Acceleration with Kalman Filter, Based
           on Position Measurement Only
    • Abstract: Publication date: Available online 18 May 2019Source: MeasurementAuthor(s): Johnny Rodriguez-Maldonado This paper presents a method to obtain good synchronous and instantaneous estimates in position, velocity and acceleration; using a position measurement only. The proposed method shows better estimates in position, velocity and acceleration than other methods such as nonlinear tracking differentiator (TD), extended state estimation (ESO) and digital differentiator based on Taylor series (DDBTS). The proposed method allows for an increased accuracy of estimates by generating a feedback frequency obtained to the position measurement signals. The model and method proposed in this paper reduce the error when the measurement signal presents a change in frequency. The model update and adjusts simultaneously to changes in sample rates using a feedback frequency estimate. The proposed method was validated with the QNET DC Motor Control Trained (DCMCT). Since the method requires the measurement position only from an encoder, it eliminates the need for more sensors for velocity and acceleration, thus begin less costly.
       
  • Spatial force measurement using a rigid hexapod-based end-effector with
           structure-integrated force sensors in a hexapod machine tool
    • Abstract: Publication date: Available online 18 May 2019Source: MeasurementAuthor(s): C. Friedrich, B. Kauschinger, S. Ihlenfeldt In machine tools, in-process force measurement is required by many manufacturing applications, where a particular demand for spatial measurements in up to 6 degrees of freedom (DoF) is growing. Beside expensive commercial 6 DoF force/torque sensors or vague drive current evaluation, sensor integration as part of machine components or joints has been discussed for a long time. The approach presented here, integrates 6 cost-efficient commercial 1 DoF force sensors in hexapod structures and kinematics, that are particularly suitable for sensor integration due to the absence of friction, the presence of mainly longitudinal forces and the availability of 6 DoF. These sensors can be placed at different positions, whereby this article focuses on a rigid hexapod-based end-effector. As the end-effector is not an independent measuring system, but part of a machine, that moves dynamically through the workspace and carries workpieces or tools, a suitable measurement model is necessary that addresses all those influences. After a brief literature overview and introduction to the approach, this work presents the dynamic measurement model includingerror , aspects about optimal framework design and several steps of validation and evaluation of the new measuring system. These include application of static loads, workspace analysis, dynamic transfer behaviour, rigid body dynamics compensation and, finally, process force measurement during a milling process.
       
  • A novel method for measuring traffic load-induced settlement at different
           layers of embankment in highway
    • Abstract: Publication date: Available online 18 May 2019Source: MeasurementAuthor(s): Xiao Zheng, Yu-You Yang, Qian-Qing Zhang, Shi-qian Wu, Wei Cui A large number of high embankments have been constructed in highway engineering with the fast development of highway. The measurement and treatment of traffic loading-induced settlement is a research focus in recent years. This paper focuses on the measurement of the layered settlement, induced by the traffic loading, in the embankment of highway. A novel method to measure the traffic loading-induced settlement at different depths in the embankment is proposed. The practical measurement equipment, including a multipoint extensometer, rigid base, settlement detecting plate, protector tube and connecting rod, is developed. The field tests were carried out in the filling and surcharge period to verify the stability and accuracy of measurement equipment. The test results indicate that the developed equipment and the presented installation method can be capable of measuring traffic-induced settlement at different depths in the embankment of the operating highway.
       
  • Possibilities of Linking Results of Key and Supplementary Comparisons in
           Field of Electricity and Magnetism
    • Abstract: Publication date: Available online 18 May 2019Source: MeasurementAuthor(s): Oleh Velychko The linking procedures for Regional Metrology Organization (RMO) key comparisons and for RMO key and supplementary comparisons are described. It was proposed to check the consistency of the obtained linked results of all National Metrology Institute (NMI) participants. Linked results of RMO key and supplementary comparisons for some electrical quantities (inductance and capacitance) according to the described procedure were presented. Results for participants of presented key and supplementary comparisons are satisfied for chi-squared test and En numbers. Represented linked comparison results can be used also as the technical basis of confirming Calibration and Measurement Capabilities (CMCs) of NMIs.
       
  • A new fault diagnosis method for planetary gear based on image feature
           extraction and bag-of-words model
    • Abstract: Publication date: Available online 18 May 2019Source: MeasurementAuthor(s): Hao Zheng, Gang Cheng, Yong Li, Chang Liu Because the fault features of planetary gear are very weak, the feature extraction process of planetary gear fault under traditional thinking is becoming more and more complex, and the amount of extracted features is very small. To solve these problems, a new fault diagnosis method for planetary gear based on image feature extraction and bag-of-words (BoW) model is proposed, which does not need complex noises reduction and redundant features elimination. In order to extract a large number of features quickly, the FAST-Unoriented-SIFT feature extraction algorithm is proposed, which combines the advantages of FAST and SIFT algorithms, and ignores the orientations of features. After converting the original vibration signals to gray-scale images, the features are extracted directly by FAST-Unoriented-SIFT, and finally the BoW model is built on the extracted features to realize fault classification. The overall recognition rate reaches 98.67%. The experimental results certify the effectiveness of the proposed method.
       
  • Assessment of the Bacharach Hi Flow® Sampler Characteristics and
           Potential Failure Modes when Measuring Methane Emissions
    • Abstract: Publication date: Available online 18 May 2019Source: MeasurementAuthor(s): J.I. Connolly, R.A. Robinson, T.D. Gardiner The Bacharach Hi Flow® Sampler (BHFS) has been widely used to monitor methane leaks from industrial sources, however results have been challenged due to possible instrument performance issues. This study focused on improving the understanding of the BHFS performance by investigating its characteristics and potential failure modes. BHFS operation was split into three modes: catalytic oxidation (CO), thermal conductivity (TC) and a transition region. Good linear performance was observed in CO and TC modes (R2> 0.992), however, the calibration factor changed between experiments highlighting the importance of regular calibration. Measurements in the middle region were dominated by noise with poor linearity. Instrument failure due to high non-methane hydrocarbons occurred sometimes; a hypothesis to explain this has been established. We found the BHFS to be a suitable instrument for measuring methane emissions if operated correctly and with knowledge of its limitations. Some key operational guidelines are provided in the conclusions.
       
  • SVM based Measurement Method and Implementation of Gas-liquid Two-phase
           Flow for CMF
    • Abstract: Publication date: Available online 18 May 2019Source: MeasurementAuthor(s): Jing Yue, Ke-Jun Xu, Wen Liu, Jian-Guo Zhang, Zheng-Yu Fang, Lun Zhang, Hao-Ran Xu The lack of timely flowtube drive and large measurement error are two major problems for Coriolis mass flowmeter (CMF) to measure gas-liquid two-phase flow. To solve above problems, a dual-core CMF transmitter is developed with a DSP chip and an FPGA chip. The FPGA is used to realize a digital drive method for driving the flowtube to vibrate normally. The DSP is utilized to realize a zero-crossing detection algorithm for calculating the mass flowrate. The Support Vector Machine (SVM) method is adopted to establish the mass flow measurement error (MFME) model for on-line real-time correction. The experimental results show that the developed transmitter improves the update speed of the drive signal and drive efficiency, and reduces the execution time of the algorithm. The SVM method has higher modeling accuracy and good generalization performance, and can be effectively applied to on-line real-time correction of the MFMEs under the gas-liquid two-phase flow.
       
  • Digital image recognition based on Fractional-order-PCA-SVM coupling
           algorithm
    • Abstract: Publication date: Available online 18 May 2019Source: MeasurementAuthor(s): Lin Hu, Jian Cui This paper use the fractional differential mask operator to describe and deal with the highly self similar digital medical image, then extracting the main features of the these images with principal component analysis, and recognizing these images with SVM algorithm. To provide an efficient algorithm for medical image processing with high degree of self similarity. Based on the analysis of fractional differential theory, principal component analysis theory and support vector machine (SVM) algorithm, designing a Fractional-order-PCA-SVM coupling algorithm for digital image recognition. and taking the digital image in ORL face database as the empirical object, using PCA-NN coupling algorithm, SVM algorithm, PCA-SVM algorithm, Sobel-PCA-NN algorithm, coupling coupling Sobel-SVM coupling algorithm, Sobel-PCA-SVM algorithm, Fractional-order-PCA-NN algorithm and coupling coupling Fractional-order-SVM coupling algorithm as the comparison algorithm, creating four experiments with the same sample, regarding the speed of operation and the accuracy of recognition as the criteria of high evaluation, verifying the superiority of the Fractional-order-PCA-SVM coupling algorithm designed in this paper. The average run time of the Fractional-order-PCA-SVM coupling algorithm only 4.152s in the four experiments, although the speed of operation is not the highest, it also meets the demand of identification, The reason that the operation speed of the algorithm slightly lower than the three control algorithms is that the three algorithms are monotonic; The average accuracy rate of Fractional-order-PCA-SVM coupling algorithm is 99.2425% in four experiments, significantly better than the eight comparison algorithm; For digital medical image recognition the coupling algorithm designed in this paper is effective.
       
  • Application of a New EWT-Based Denoising Technique in Bearing Fault
           Diagnosis
    • Abstract: Publication date: Available online 18 May 2019Source: MeasurementAuthor(s): Saeed Nezamivand Chegini, Ahmad Bagheri, Farid Najafi The vibration signal analysis is a popular method for extracting sensitive fault features. The vibration signals are usually contaminated by noise, and therefore the extracted features cannot be providing sufficient information about the bearing faults. In this paper, a new technique is introduced for denoising the vibration signals and recognizing the bearing faults based on the empirical wavelet transform (EWT). Firstly, the vibration signals are decomposed by the EWT method into a set of functions called the empirical modes. Then, the noise-dominate modes have been denoised by an improved thresholding function that has been recently presented. Finally, the kurtosis parameter and the envelope spectrum of the denoised signal are used for early fault detection and diagnosing the fault type, respectively. The result of the simulated signal and different experimental datasets illustrate that the presented work is preferable for the empirical mode decomposition based denoising technique in the early fault detection.
       
  • Mutation based Self Regulating and Self Perception Particle Swarm
           Optimization for Efficient Object Tracking in a Video
    • Abstract: Publication date: Available online 17 May 2019Source: MeasurementAuthor(s): Prajna Parimita Dash, Dipti Patra Object tracking is a pre-eminent task in many computer vision application.This paper addresses a realistic object tracking problem, considering single as well as multiple objects with different challenging perspectives. In view of this, a Mutation-based Self Regulating and self-Perception Particle Swarm Optimization (MSRSP-PSO) algorithm is proposed in order to explore the effective sampling strategy as well as to estimate and localize the optimum position of the target object. In conjunction with the optimization technique, an effective appearance model is also used for the reinforcement of tracker to achieve better accuracy. The appearance model is constructed with the joint distribution of the Local Binary Pattern (LBP)/Local Contrast Measure (LCM) based texture description and the Ohta color features. In pursuance of validating the efficacy of the proposed tracking algorithm, performance of other state-of-arts such as, Particle Swarm Optimization (PSO) based tracking, Sequential Particle Swarm Optimization (SPSO) based tracking, and Adaptive Gaussian Particle Swarm Optimization (AGPSO) based tracking algorithms are compared with the proposed tracking algorithm. Both the quantitative and qualitative evaluation of the experiments, validate the effectiveness of the proposed technique.The performance measures include the convergence rate, center location error and the coverage test (PASCAL SCORE). Furthermore, the non-parametric statistical analysis using the Sign test and Wilcoxon signed rank test, is also figured out to validate the statistical superiority of the proposed MSRSP-PSO algorithm over others. The experimental results reveal that the proposed algorithm robustly track single as well as multiple similar objects with complex interaction.
       
  • Identification of Moving Loads Based on the Information Fusion of
           Weigh-In-Motion System and Multiple Camera Machine Vision
    • Abstract: Publication date: Available online 17 May 2019Source: MeasurementAuthor(s): Danhui Dan, Liangfu Ge, Xingfei Yan Accurately identifying moving loads is of significance for the health monitoring of bridges. However, since the existing identification techniques can only realize load identification in one direction or for part of bridges, it is still a challenge to simultaneously identify transverse and longitudinal loads on the full deck of bridge. This paper proposed an information-fusion-based method for the load identification to be applied to bridges of different lengths. In this method, the pavement-based weigh-in-motion system (WIMs) laid out at the beginning of the bridge is used to obtain the weight of vehicles captured by cameras. The videos of traffic flow acquired by multiple cameras arranged along the bridge are employed to calculate the vehicle’s trajectory and location. The weight and location data are matched when the vehicle in the video crosses the piezoelectric sensor of WIMs for the same time as the WIMs records a weight information. Further, since the vehicles are equivalent to concentrated loads, values and locations of all moving loads on the whole bridge are identified in real time. The reliability and accuracy of the proposed approach is verified by multi-view 3D simulation video data and the field data from a ramp bridge.
       
  • Analysis and elimination the vibration disturbance in all-fiber
           distributed polarization coupling measurement
    • Abstract: Publication date: Available online 16 May 2019Source: MeasurementAuthor(s): Guoqiang Wen, Hongxia Zhang, Yuyao Wang, Xinyu Shi, Dagong Jia, Tiegen Liu Michelson interferometer (MI) is used to measure distributed polarization coupling signal in high-birefringence polarization-maintaining fibers. Compared with space-light MI, the direct current (DC) components of interferograms of all-fiber MI fluctuate more intensely. Based on the analysis of the structure of all-fiber MI, the fluctuation is mainly induced by the disturbance of motorized delay line (MDL) in motion, as proven by the corresponding simulations and experiments. The signal processing method of group averages is proposed to eliminate the low-frequency vibration disturbance in the DC component of interferograms and prove such elimination by experiments. The frequency of vibration disturbance and interference principle maximum increases with increasing scanning speed of MDL. The initial position of MDL affects the frequency components and intensity. Additionally, the group average method can be used for other interferometers.
       
  • Optimization of machining parameters in turning nimonic-75 using machine
           vision and acoustic emission signals by taguchi technique
    • Abstract: Publication date: Available online 16 May 2019Source: MeasurementAuthor(s): Y.D. Chethan, H.V. Ravindra, Y.T. Krishnegowda To select the optimal conditions of cutting which potentially characterizes status of tool wear, acoustic emission (AE) and machine vision signals have been used and the same is presented in this paper. Nimonic 75 is turned using carbide insert with titanium coating and Taguchi’s L27 array has been adopted for parametric optimization. Speed of spindle, feed and cutting depth being varied during experimentation and the tabulated data in terms of AE and machine vision signals have been analyzed further. The parameters such as wear area, perimeter, AERMS and AECOUNT have been found responsive to tool wear and their relationship follows the time wear trend. Depth of cut is optimally constant in terms of the considered parameters whereas slight inexplicable variation in speed and feed has been observed and this inexplicit variation may be accounted for under noise, distortion and inconsistent illumination. At the obtained optimal cutting conditions machining was repeated and the results ascertained the correctness of the procedure adopted. In processing nimonic75 using coated carbide insert, the results presented in this paper in terms of optimal parameters, could be used in the manufacturing sector that potentially enhances quality. Of all the non-traditional measuring techniques, machine vision and AE have proved better in measuring response parameters to optimize machining.
       
  • Application of electrical conductivity method for adsorption of lead ions
           by rice husk ash
    • Abstract: Publication date: Available online 16 May 2019Source: MeasurementAuthor(s): Jung Uk Song, Jong-Sub Lee, Hyung-Koo Yoon The potential of hydrogen (pH) has limitations when analyzing the behaviors of contaminated fluids associated with pollutant concentrations. This study proposed electrical conductivity to assess the removal rate of contaminants while considering their concentration. A lead aqueous solution is used to induce heavy metal contamination, and Rice husk ash is used as an absorbent to stabilize the contaminated material. The absorbent concentrations increased with a constant lead concentration of 30 ppm, and the change in concentration over time is observed by the electrical conductivity and pH. The Atomic Absorption spectrometer (AA spectrometer) is also used to obtain the lead concentration as a reference value. The converted loss exhibited similar trends and however, the ability of pH to provide information related to concentration is limited. This study demonstrates that the selected method can be directly used to assess the purification of contaminated materials while considering changes in concentration.Graphical abstractGraphical abstract for this article
       
  • Performance-based fault detection approach for the dew point process
           through a fuzzy multi-label support vector machine
    • Abstract: Publication date: Available online 16 May 2019Source: MeasurementAuthor(s): P. Ghalyani, A.H. Mazinan Nowadays, there are many fault detection approaches, which are taken into consideration in two categories including the data-driven and the model-based fault detection approaches. One of the well-known data-based fault detection techniques is the support vector machine that is proved to be a powerful approach in the classification. This one has weakness in dealing with a variety of complicated data. To address this concern, based on the investigation presented, an integration of the two approaches including the fuzzy and the multi-label SVM is proposed. In a word, the performance-based approach can classify the noisy and the multi-label data. This one is carried out for the dew point process with the cooling cycle in the real-world plant data in correspondence with the simulated plant via the HYSYS software environment. The proposed performance-based approach is about 10% percent more accurate than the conventional multi-label SVM in identifying the faults of the process.Graphical abstractGraphical abstract for this article
       
  • Measurement of performance and emission distinctiveness of Aegle marmelos
           seed cake pyrolysis oil/diesel/TBHQ opus powered in a DI diesel engine
           using ANN and RSM
    • Abstract: Publication date: Available online 16 May 2019Source: MeasurementAuthor(s): P. Baranitharan, K. Ramesh, R. Sakthivel The present investigation focuses on Artificial neural network (ANN) and Response surface methodology (RSM) modelling of a CI (Compression ignition) engine powered by Aegle marmelos (AM) pyrolysis oil/diesel/Tert-butyl hydroxyl quinone antioxidant (TBHQ) blend as a test fuel to predict and optimize the engine behaviour. Bio-oil is derived from AM de-oiled seed cake in a fixed bed pyrolysis reactor at 600 °C under the heating rate of 30 °C/min. To obtain data for testing and training the suggested RSM and ANN models, a direct injection, single cylinder CI engine was fuelled with proposed test fuel 80% diesel + 20% AM bio-oil + 1000 ppm TBHQ (A20D80T). The A20D80T has been assessed for the combined effects of varying compression ratio (CR=16:1-17.5:1) and engine load (W=25%-100%) in variable compression ratio (VCR) diesel engine through experimental investigation and ANN prediction and RSM optimization techniques. Using the experimental data for training, an ANN replica was developed according to feed forward back propagation algorithm (FFBP). Multi-layer perception (MLP) network was used for non-linear mapping between the experimental and predicted values. Engine process parameters were accurately predicted by trained ANN. The optimal values of engine performance (brake specific fuel consumption (BSFC) = 0.33 kg/kWh and brake thermal efficiency (BTE) = 22.01%) and emission behaviour (carbon monoxide (CO) = 0.67%, hydro carbon (HC) = 244 ppm, carbon dioxide (CO2) = 8.33% and oxides of nitrogen (NOx) = 351 ppm) were obtained by RSM optimization. The compression ratio of 17.5:1 at peak load condition was found to be superior engine characteristics through experimental assessment and ANN, RSM models. In the predicted ANN model the mean absolute average error (MAAE) was 0.552% and optimized RSM model MAAE was 1.231%. The ANN and RSM models gave the average correlation coefficient (R) of 0.998 and average coefficient of a determination (R2) of 0.991 respectively. The experimental, ANN and RSM analysis results depict that A20D80T blend delivered the enhanced performance and better emission behaviours compared with neat diesel fuel (D).
       
  • Identifying centromere position of human chromosome images using contour
           and shape based analysis
    • Abstract: Publication date: Available online 16 May 2019Source: MeasurementAuthor(s): Nirmala Madian, K.B. Jayanthi, D. Somasundaram, S. Suresh The most significant information of the shape of any image/object is concentrated in curvature regions along the contour and objects boundaries rather than uniformly distributed contour. The points belonging to greater magnitude of curvature gives more meaningful information about the shape of an object. The sign of the curvature can be positive (convex) and negative (concave), the negative curvature information is most significant for segmentation. The contour and region based geometry gives a better visual representation of the shape of an object and helps in identifying the centromere position in chromosomes. Centromere of a chromosome is the constriction point which divides the chromosome into two sections or arms. The two arms are p arm (short arm) and q arm (long arm). The size of the arms are calculated with respect to the position of the centromere. The centromere is identified using boundary concavity method which helps in detecting the dominant points (centromere points) in chromosomes. The method uses the concave function and weighted shortest path calculation for centromere detection. SVM classifier is used for improving the accuracy in detecting the centromere of the chromosomes. As the classifier is binary classifier, it helps in recognizing the centromere and non-centromere regions in chromosomes. Comparative analysis is performed with two other methods (i) Medial Axis Transform (MAT) and (ii) Projection Vector. Boundary concavity proves to be efficient for straight, bent and severely bent chromosomes.
       
  • Design and Optimisation of Mutual Inductance based Pulsed Eddy Current
           Probe
    • Abstract: Publication date: Available online 15 May 2019Source: MeasurementAuthor(s): Ona Denis Ijike, Gui Yun Tian, Ruslee Sutthaweekul, Syed Mohsen Naqvi High lift-off inspection arising from thick insulation and requirement to protect probe from weld zones are challenges to pulsed eddy current (PEC) testing. In traditional PEC probe with fixed coil gap, weakening interaction between probe and test sample as lift-off increases reduces sensitivity. To analyse this influence, numerical simulation and experiments on design and optimisation of driver-pickup PEC probes are carried out. Results validate that both coil gap and lift-off have significant effects on resultant mutual inductance of driver-pickup PEC probes above test samples. In addition, the probe sensitivity is enhanced at a higher lift-off when coil gap exceeds a certain value. The increase in detection lift-off with coil gap could be optimised.
       
  • Investigation into the intergranular corrosion behaviour of electron beam
           welded Hastelloy C-276 sheet using laser displacement sensor
    • Abstract: Publication date: Available online 13 May 2019Source: MeasurementAuthor(s): Kalinga Simant Bal, Jyotsna Dutta Majumdar, Asimava Roy Choudhury The laser displacement sensor scan technique proposed in the present study helps to calculate the corrosion rate (CR), and subsequently, determine the susceptibility to intergranular corrosion, without preparing separate samples for the weld zone (WZ), adjacent heat affected zone (HAZ) and unaffected base metal (BM). Processing of the displacement data collected during laser displacement sensor scan of the electron beam welded Hastelloy C-276 sample before and after the corrosion test shows that the (CR)WZ> (CR)BM> (CR)HAZ. The (CR)WZ was found to higher due to the (a) lower concentration of Mo and Cr in the dendrite body (of WZ) and (b) higher non-uniformity in Mo concentration per unit length within the WZ. The weld thermal cycle did not cause sensitization of HAZ (i.e., the formation of Mo depleted regions), and hence, the intergranular attack was not evident along the grain boundaries of the HAZ and the (CR)HAZ was lower.Graphical abstractGraphical abstract for this article
       
  • Noise Comparison of Triple Frequency GNSS Carrier Phase, Doppler and
           Pseudorange Observables
    • Abstract: Publication date: Available online 13 May 2019Source: MeasurementAuthor(s): Gethin Wyn Roberts The relative noise of data from all GNSS is investigated, considering the various satellite constellations, the different frequencies and also the historical satellite systems such as the various GPS blocks. The noise of the Range Residual and Ionospheric Residual geometry free approaches are scrutinized, illustrating the expected measurement precisions from the different types of satellites, and their comparisons. The noise characteristics are also studied by varying the elevation mask angle, as these techniques measure the noise in the observables, which can increase by lowering the elevation mask angle.The results illustrate that the newer generations of satellites do have less noisy data, and that GLONASS and SBAS in particular have the noisiest observables, and Galileo and BeiDou have the least noisy.The techniques presented in this paper could provide a real time data quality check in order to choose which satellites to use for the positioning solution.Graphical abstractGraphical abstract for this article
       
  • A novel shearer cutting pattern recognition model with chaotic
           gravitational search optimization
    • Abstract: Publication date: Available online 13 May 2019Source: MeasurementAuthor(s): Yaping Jiang, Zhipeng Xu, Zeyin Zhang, Xinggao Liu The accurate recognition of the shearer cutting pattern is the focus in fully mechanized coal mining. Hence, a new cutting pattern recognition model based on the combination of Relevance Vector Machine (RVM) and Chaotic Gravitational Search Algorithm (CGSA) is proposed. Initially, the motor operation data, including voltage, current and motor speed, are collected as the detection signal and the RVM classifier based on Bayesian framework is chosen for pattern recognition. In order to optimize the parameters in RVM, which has a great influence on the performance of RVM, the optimization algorithm Gravitational Search Algorithm (GSA) is introduced. Finally, the basic GSA is modified into CGSA with the chaotic mapping for increasing the search diversity of the algorithm. The experimental study demonstrates the advantageous performance of the proposed model even without any feature extraction operations.Graphical abstractGraphical abstract for this article
       
  • Geological assessment for tunnel excavation under river with shallow
           overburden using surface site investigation data and electrical
           resistivity tomography
    • Abstract: Publication date: Available online 13 May 2019Source: MeasurementAuthor(s): Mohd Ashraf Mohamad Ismail, Taksiah A. Majid, Chin Ong Goh, Siao Phin Lim, Chee Ghuan Tan Tunnelling is a challenging task as many geotechnical uncertainties involved during construction, especially under a river crossing with shallow overburden. This study focussed on geological assessment for the New Austrian Tunneling Method excavation that crosses under Kerau River with shallow overburden. 3D filtered tunnel models using Boolean filter for rock mass weathering grade, rock quality designation, and Lugeon value are developed. Results predict that 53.3% of the tunnel excavation under the Kerau River crossing are highly weathered rocks (Grades III and IV), and 64.51% are in the moderately to highly fractured zone (0% to 75% RQD) along the roof of the tunnel granitic bedrock. The Lugeon value models show that 71.64% of the excavating rocks possess a Lugeon value of 5 to 50 (6–60 × 10−5 cm/s), which denotes considerably high permeability. 2D electrical resistivity tomography is used to verify the permeability results from the Lugeon test.
       
  • Monitoring of Friction Stir Welding based on Vision System coupled with
           Machine Learning Algorithm
    • Abstract: Publication date: Available online 13 May 2019Source: MeasurementAuthor(s): S. Sudhagar, M. Sakthivel, P. GaneshKumar The increase in utilization of FSW process demands online monitoring system for early detection and control of defects. This research attempts to develop a system for detection and classification of defective welds using weld surface image. Welding joints are produced at different welding condition by varying tool rotational speed, welding speed, tool shoulder diameter and pin diameter. The weld surfaces produced at different welding condition are captured using digital camera and processed to extract features. The features from weld surface image has been extracted using maximally stable extremal region algorithm and which is used as input for classification of weld joint. The Support Vector Machines is used for classification of weld using features from surface image. Support Vector Machines is trained with different kernel functions and found that linear and quadratic kernel function classify defect weld and good weld with accuracy of 95.8%.
       
  • Design and Application Research of the Multi-longitudinal mode laser
           Self-mixing vibration measurement system
    • Abstract: Publication date: Available online 10 May 2019Source: MeasurementAuthor(s): Yunkun Zhao, Junfeng Zhou, Chenchen Wang, Rong Xiang, Tiezhu Bi, Liang Lu The free spectral range of the resonant cavity is calculated to be 78.95GHz based on 1.90mm of the waveform separation period as a multi-longitudinal mode laser diode is employed as the light source of the self-mixing vibration measurement system. As such, the influence of the vibration amplitude of the loudspeaker to the self-mixing vibration signal waveform is further researched by means of the feasible theoretical analysis and experimental characterization. The primary experimental results demonstrate that the amplitude limitation ratio coefficient Al of this sensing system is required to design below 2.053×10-3 corresponding to an entire waveform separation period, of which is to prevent the self-mixing signal waveform from distorting effectively in the context of the integral order of the external cavity mode. Furthermore, the feedback levels of the multi-longitudinal mode laser self-mixing vibration system are studied in detail. It has been shown that the experimental result of the free spectral range fits well with theoretical value when this system is matched to the applicable amplitude limitation range 0
       
  • Model Based Design of a Stereo Vision System for Intelligent Deep-Sea
           Operations
    • Abstract: Publication date: Available online 10 May 2019Source: MeasurementAuthor(s): Tomasz Łuczyński, Piotr Łuczyński, Lukas Pehle, Manfred Wirsum, Andreas Birk A stereo vision system for deep-sea operations is presented. The system consists of cameras in pressure bottles, which are daisy-chained to a computer bottle. The system has substantial computation power for on-board stereo processing as well as for further computer vision methods to support autonomous intelligent functions, e.g., object recognition, navigation, mapping, inspection, and intervention. The model based design presented here includes two main aspects. First, a formalized approach to the component selection for the stereo set-up is introduced, i.e., given especially accuracy and baseline constraints as well as lens and imager options, an algorithmic analysis is provided. This approach is also of interest for the design of stereo systems in general. Second, the specific aspects of deep sea operations are addressed. This includes especially the validation and optimization of the pressure bottles for the cameras with a Finite Element Method (FEM). Experiments and results are presented, which include a validation of the stereo performance in air, robustness tests of the bottles in pressure tanks, and field trials of the complete system off the shore of Marseille on a commercial Remotely Operated Vehicle (ROV).
       
  • A Miniaturized Low-power Vector Impedance Analyser for Accurate
           Multi-parameter Measurement
    • Abstract: Publication date: Available online 9 May 2019Source: MeasurementAuthor(s): G. Luciani, R. Ramilli, A. Romani, M. Tartagni, P.A. Traverso, M. Crescentini Distributed measurements are important in many application fields, from environment to biomedicine. In both cases, the sensor nodes employed in the measurement network have to satisfy many requirements; among them, the most important ones are: i) low power consumption, ii) miniaturization, iii) adequate accuracy, and iv) capability of multi-parameter measurement. This paper presents a Vector Impedance Analyser architecture that satisfies these main requirements. The architecture is specifically devised to be interfaced with an array of impedimetric sensors for environmental applications, such as water distributed monitoring, or for mobile-Health/wearable biomedical devices. The proposed architecture is based on delta-sigma digital-to-analogue (D/A) conversion for the generation of the low-noise excitation and on band-pass delta-sigma analogue-to-digital (A/D) conversion for the low-power and high-accuracy acquisition of the impedimetric sensor response. The proposed combination of delta-sigma D/A and A/D conversion allows to i) implement many measurement cores in a single silicon chip with reduced dimensions, ii) achieve a fair accuracy/power trade-off, and iii) tune the operative frequency in real time so as to span the target portion of the frequency domain.A prototype of the VIA is implemented in a 3x6-cm PCB board to investigate the potentialities of the architecture. The low-noise analogue circuits of the architecture are implemented in an Application Specific Integrated Circuit (ASIC), while part of the digital circuits are implemented on a commercial microcontroller for better testability purposes. The prototype embeds four independent cores to allow real-time multi-parameter measurement. To prove the performance of the proposed VIA, the prototype is characterized in terms of noise (input-referred noise between 20 mΩ and of 70 mΩ in 10-Hz bandwidth, i.e. from 25 to 92 ppm of the full scale), accuracy (average uncertainty of 0.14% of the full scale for the magnitude and 0.72° for the argument, accounting for the limited accuracy of the reference instrument used for calibration), and power consumption (approximately 125 mW per-core including the power consumption of the microcontroller and the ancillary circuits used for power management and communication). The multi-parameter measurement capability is demonstrated by the realistic case-study of estimating the concentration of total dissolved solids in a potassium chloride (KCl) solution by means of direct concurrent measurements of conductivity and temperature.
       
  • An Investigation of the Mechanical Filtering Effect of Tactile CMM in the
           Measurement of Additively Manufactured Parts
    • Abstract: Publication date: Available online 2 May 2019Source: MeasurementAuthor(s): S. Lou, S.B. Brown, W. Sun, W. Zeng, X. Jiang, P.J. Scott The high level of surface roughness of additively manufactured parts post challenges to the applicability of different dimensional measurement techniques, including tactile, optical and X-ray computed tomography. Tactile measurement is traditionally considered to have the best accuracy and traceability; however, its measurement can be significantly influenced by the mechanical filtering effect. This work investigates the influence of the mechanical filtering effect on tactile measurements of additively manufactured parts. Both experimental and simulation work are utilised to reveal this effect. Particularly the numerical simulation based on the morphological method allows a single influence factor e.g. the stylus diameter to be investigated. The maximum measurement errors caused by the stylus mechanical filtering effect are determined by the convex hull points of the measurement profile, which is equivalent to using an infinitely large stylus. Coordinate measuring machine and X-ray computed tomography measurement results of an additively manufactured test part’s cylinder diameters are compared, along with the application of morphological method to “compensate” the coordinate measuring machine’s mechanical filtering effect.
       
  • Performance enhanced Ripplet transform based compression method for
           medical images
    • Abstract: Publication date: Available online 18 April 2019Source: MeasurementAuthor(s): J. Anitha, P. Eben Sophia, Le Hoang Son, Victor Hugo C. de Albuquerque Ripplet transforms provide efficient representation of images with all the spatial details. Since more details are extracted, the compression ratio is usually less. In this work, lossless prediction and lossy singular value decomposition of the ripplet coefficients are carried out to improve the performance of conventional ripplet transform based system. Initially, lossless prediction exploits the correlation between the image pixels and eases the process of transformation. Fast and computationally efficient randomized singular value decomposition technique is used to capture the essential information of the high frequency ripplet coefficients. The low frequency and the high frequency components are then encoded using entropy method. Experimental results show promising results for the proposed approached in terms of performance measure.
       
  • High Throughput Rapid Detection for SLM Manufactured Elements Using
           Ultrasonic Measurement
    • Abstract: Publication date: Available online 25 April 2019Source: MeasurementAuthor(s): Yu Liu, Xiongbing Li, Chao Chen, Yongfeng Song Both pore parameters and elastic properties are important for the elements manufactured by the Selective Laser Melting (SLM) technology; however, their measurements are usually destructive and time-consuming. Thus, the ultrasonic longitudinal and transverse wave velocities (dual-mode velocities) method is extended here; then it is combined with the longitudinal wave attenuation to develop an ultrasonic high throughput rapid detection for SLM manufactured elements. As the focal transducer is used, a multi-Gaussian beam model is introduced to correct the diffraction attenuation. Twenty-eight parameters can be obtained by a single ultrasonic experiment, including the pore parameters and their distributions, the two-point spatial correlation function, and the Young’s modulus. Ti-6Al-4V samples with different porosities manufactured by SLM technology are used to conduct the ultrasonic experiment. The experimental results agree well with the results of metallographic, tensile experiment and micro-CT results. This method provides a useful tool for nondestructively, rapidly and accurately evaluating the pore parameters and elastic properties of SLM manufactured elements.
       
 
 
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