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Publisher: Hindawi   (Total: 338 journals)

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Showing 1 - 200 of 338 Journals sorted alphabetically
Abstract and Applied Analysis     Open Access   (Followers: 3, SJR: 0.343, CiteScore: 1)
Active and Passive Electronic Components     Open Access   (Followers: 7, SJR: 0.136, CiteScore: 0)
Advances in Acoustics and Vibration     Open Access   (Followers: 36, SJR: 0.147, CiteScore: 0)
Advances in Aerospace Engineering     Open Access   (Followers: 53)
Advances in Agriculture     Open Access   (Followers: 9)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Astronomy     Open Access   (Followers: 39, SJR: 0.257, CiteScore: 1)
Advances in Bioinformatics     Open Access   (Followers: 17, SJR: 0.565, CiteScore: 2)
Advances in Biology     Open Access   (Followers: 9)
Advances in Chemistry     Open Access   (Followers: 22)
Advances in Civil Engineering     Open Access   (Followers: 40, SJR: 0.539, CiteScore: 1)
Advances in Computer Engineering     Open Access   (Followers: 4)
Advances in Condensed Matter Physics     Open Access   (Followers: 10, SJR: 0.315, CiteScore: 1)
Advances in Decision Sciences     Open Access   (Followers: 3, SJR: 0.303, CiteScore: 1)
Advances in Electrical Engineering     Open Access   (Followers: 31)
Advances in Electronics     Open Access   (Followers: 70)
Advances in Emergency Medicine     Open Access   (Followers: 12)
Advances in Endocrinology     Open Access   (Followers: 5)
Advances in Environmental Chemistry     Open Access   (Followers: 7)
Advances in Epidemiology     Open Access   (Followers: 8)
Advances in Fuzzy Systems     Open Access   (Followers: 5, SJR: 0.161, CiteScore: 1)
Advances in Geology     Open Access   (Followers: 19)
Advances in Geriatrics     Open Access   (Followers: 5)
Advances in Hematology     Open Access   (Followers: 11, SJR: 0.661, CiteScore: 2)
Advances in Hepatology     Open Access   (Followers: 2)
Advances in High Energy Physics     Open Access   (Followers: 19, SJR: 0.866, CiteScore: 2)
Advances in Human-Computer Interaction     Open Access   (Followers: 20, SJR: 0.186, CiteScore: 1)
Advances in Materials Science and Engineering     Open Access   (Followers: 30, SJR: 0.315, CiteScore: 1)
Advances in Mathematical Physics     Open Access   (Followers: 4, SJR: 0.218, CiteScore: 1)
Advances in Medicine     Open Access   (Followers: 3)
Advances in Meteorology     Open Access   (Followers: 21, SJR: 0.48, CiteScore: 1)
Advances in Multimedia     Open Access   (Followers: 1, SJR: 0.173, CiteScore: 1)
Advances in Nonlinear Optics     Open Access   (Followers: 6)
Advances in Numerical Analysis     Open Access   (Followers: 5)
Advances in Nursing     Open Access   (Followers: 29)
Advances in Operations Research     Open Access   (Followers: 12, SJR: 0.205, CiteScore: 1)
Advances in Optical Technologies     Open Access   (Followers: 4, SJR: 0.214, CiteScore: 1)
Advances in Optics     Open Access   (Followers: 5)
Advances in OptoElectronics     Open Access   (Followers: 6, SJR: 0.141, CiteScore: 0)
Advances in Orthopedics     Open Access   (Followers: 8, SJR: 0.922, CiteScore: 2)
Advances in Pharmacological Sciences     Open Access   (Followers: 8, SJR: 0.591, CiteScore: 2)
Advances in Physical Chemistry     Open Access   (Followers: 10, SJR: 0.179, CiteScore: 1)
Advances in Power Electronics     Open Access   (Followers: 33, SJR: 0.184, CiteScore: 0)
Advances in Preventive Medicine     Open Access   (Followers: 6)
Advances in Public Health     Open Access   (Followers: 23)
Advances in Regenerative Medicine     Open Access   (Followers: 2)
Advances in Software Engineering     Open Access   (Followers: 10)
Advances in Statistics     Open Access   (Followers: 4)
Advances in Toxicology     Open Access   (Followers: 2)
Advances in Tribology     Open Access   (Followers: 12, SJR: 0.265, CiteScore: 1)
Advances in Urology     Open Access   (Followers: 9, SJR: 0.51, CiteScore: 1)
Advances in Virology     Open Access   (Followers: 7, SJR: 0.838, CiteScore: 2)
AIDS Research and Treatment     Open Access   (Followers: 3, SJR: 0.758, CiteScore: 2)
Analytical Cellular Pathology     Open Access   (Followers: 2, SJR: 0.886, CiteScore: 2)
Anatomy Research Intl.     Open Access   (Followers: 2)
Anemia     Open Access   (Followers: 5, SJR: 0.669, CiteScore: 2)
Anesthesiology Research and Practice     Open Access   (Followers: 14, SJR: 0.501, CiteScore: 1)
Applied and Environmental Soil Science     Open Access   (Followers: 17, SJR: 0.451, CiteScore: 1)
Applied Bionics and Biomechanics     Open Access   (Followers: 8, SJR: 0.288, CiteScore: 1)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 13)
Archaea     Open Access   (Followers: 3, SJR: 0.852, CiteScore: 2)
Arthritis     Open Access   (Followers: 5, SJR: 0.454, CiteScore: 1)
Autism Research and Treatment     Open Access   (Followers: 26)
Autoimmune Diseases     Open Access   (Followers: 4, SJR: 0.805, CiteScore: 2)
Behavioural Neurology     Open Access   (Followers: 9, SJR: 0.786, CiteScore: 2)
Biochemistry Research Intl.     Open Access   (Followers: 6, SJR: 0.437, CiteScore: 2)
Bioinorganic Chemistry and Applications     Open Access   (Followers: 10, SJR: 0.419, CiteScore: 2)
BioMed Research Intl.     Open Access   (Followers: 4, SJR: 0.935, CiteScore: 3)
Biotechnology Research Intl.     Open Access   (Followers: 1)
Bone Marrow Research     Open Access   (Followers: 2, SJR: 0.531, CiteScore: 1)
Canadian J. of Gastroenterology & Hepatology     Open Access   (Followers: 4, SJR: 0.867, CiteScore: 1)
Canadian J. of Infectious Diseases and Medical Microbiology     Open Access   (Followers: 5, SJR: 0.548, CiteScore: 1)
Canadian Respiratory J.     Open Access   (Followers: 1, SJR: 0.474, CiteScore: 1)
Cardiology Research and Practice     Open Access   (Followers: 8, SJR: 1.237, CiteScore: 4)
Case Reports in Anesthesiology     Open Access   (Followers: 10)
Case Reports in Cardiology     Open Access   (Followers: 3, SJR: 0.219, CiteScore: 0)
Case Reports in Critical Care     Open Access   (Followers: 8)
Case Reports in Dentistry     Open Access   (Followers: 5, SJR: 0.229, CiteScore: 0)
Case Reports in Dermatological Medicine     Open Access   (Followers: 2)
Case Reports in Emergency Medicine     Open Access   (Followers: 14)
Case Reports in Endocrinology     Open Access   (Followers: 1, SJR: 0.209, CiteScore: 1)
Case Reports in Gastrointestinal Medicine     Open Access   (Followers: 2)
Case Reports in Genetics     Open Access   (Followers: 1)
Case Reports in Hematology     Open Access   (Followers: 4)
Case Reports in Hepatology     Open Access   (Followers: 1)
Case Reports in Immunology     Open Access   (Followers: 4)
Case Reports in Infectious Diseases     Open Access   (Followers: 5)
Case Reports in Medicine     Open Access   (Followers: 2)
Case Reports in Nephrology     Open Access   (Followers: 4)
Case Reports in Neurological Medicine     Open Access   (Followers: 1)
Case Reports in Obstetrics and Gynecology     Open Access   (Followers: 10)
Case Reports in Oncological Medicine     Open Access   (Followers: 2, SJR: 0.204, CiteScore: 1)
Case Reports in Ophthalmological Medicine     Open Access   (Followers: 3)
Case Reports in Orthopedics     Open Access   (Followers: 5)
Case Reports in Otolaryngology     Open Access   (Followers: 6)
Case Reports in Pathology     Open Access   (Followers: 5)
Case Reports in Pediatrics     Open Access   (Followers: 6)
Case Reports in Psychiatry     Open Access   (Followers: 13)
Case Reports in Pulmonology     Open Access   (Followers: 3)
Case Reports in Radiology     Open Access   (Followers: 9)
Case Reports in Rheumatology     Open Access   (Followers: 6)
Case Reports in Surgery     Open Access   (Followers: 11)
Case Reports in Transplantation     Open Access  
Case Reports in Urology     Open Access   (Followers: 9)
Case Reports in Vascular Medicine     Open Access  
Case Reports in Veterinary Medicine     Open Access   (Followers: 6)
Child Development Research     Open Access   (Followers: 18, SJR: 0.144, CiteScore: 0)
Chinese J. of Engineering     Open Access   (Followers: 2, SJR: 0.114, CiteScore: 0)
Chinese J. of Mathematics     Open Access  
Cholesterol     Open Access   (Followers: 1, SJR: 0.424, CiteScore: 1)
Chromatography Research Intl.     Open Access   (Followers: 6)
Complexity     Hybrid Journal   (Followers: 6, SJR: 0.531, CiteScore: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2, SJR: 0.403, CiteScore: 1)
Computational Intelligence and Neuroscience     Open Access   (Followers: 11, SJR: 0.326, CiteScore: 1)
Contrast Media & Molecular Imaging     Open Access   (Followers: 3, SJR: 0.842, CiteScore: 3)
Critical Care Research and Practice     Open Access   (Followers: 10, SJR: 0.499, CiteScore: 1)
Current Gerontology and Geriatrics Research     Open Access   (Followers: 9, SJR: 0.512, CiteScore: 2)
Depression Research and Treatment     Open Access   (Followers: 14, SJR: 0.816, CiteScore: 2)
Dermatology Research and Practice     Open Access   (Followers: 3, SJR: 0.806, CiteScore: 2)
Diagnostic and Therapeutic Endoscopy     Open Access   (SJR: 0.201, CiteScore: 1)
Discrete Dynamics in Nature and Society     Open Access   (Followers: 5, SJR: 0.279, CiteScore: 1)
Disease Markers     Open Access   (Followers: 1, SJR: 0.9, CiteScore: 2)
Economics Research Intl.     Open Access   (Followers: 1)
Education Research Intl.     Open Access   (Followers: 19)
Emergency Medicine Intl.     Open Access   (Followers: 9, SJR: 0.298, CiteScore: 1)
Enzyme Research     Open Access   (Followers: 4, SJR: 0.653, CiteScore: 3)
Evidence-based Complementary and Alternative Medicine     Open Access   (Followers: 19, SJR: 0.683, CiteScore: 2)
Game Theory     Open Access   (Followers: 1)
Gastroenterology Research and Practice     Open Access   (Followers: 2, SJR: 0.768, CiteScore: 2)
Genetics Research Intl.     Open Access   (Followers: 1, SJR: 0.61, CiteScore: 2)
Geofluids     Open Access   (Followers: 4, SJR: 0.952, CiteScore: 2)
Hepatitis Research and Treatment     Open Access   (Followers: 6, SJR: 0.389, CiteScore: 2)
HPB Surgery     Open Access   (Followers: 6, SJR: 0.824, CiteScore: 2)
Infectious Diseases in Obstetrics and Gynecology     Open Access   (Followers: 5, SJR: 1.27, CiteScore: 2)
Interdisciplinary Perspectives on Infectious Diseases     Open Access   (Followers: 1, SJR: 0.627, CiteScore: 2)
Intl. J. of Aerospace Engineering     Open Access   (Followers: 74, SJR: 0.232, CiteScore: 1)
Intl. J. of Agronomy     Open Access   (Followers: 6, SJR: 0.311, CiteScore: 1)
Intl. J. of Alzheimer's Disease     Open Access   (Followers: 11, SJR: 0.787, CiteScore: 3)
Intl. J. of Analysis     Open Access  
Intl. J. of Analytical Chemistry     Open Access   (Followers: 21, SJR: 0.285, CiteScore: 1)
Intl. J. of Antennas and Propagation     Open Access   (Followers: 11, SJR: 0.233, CiteScore: 1)
Intl. J. of Atmospheric Sciences     Open Access   (Followers: 21)
Intl. J. of Biodiversity     Open Access   (Followers: 4)
Intl. J. of Biomaterials     Open Access   (Followers: 4, SJR: 0.511, CiteScore: 2)
Intl. J. of Biomedical Imaging     Open Access   (Followers: 3, SJR: 0.501, CiteScore: 2)
Intl. J. of Breast Cancer     Open Access   (Followers: 13, SJR: 1.025, CiteScore: 2)
Intl. J. of Cell Biology     Open Access   (Followers: 3, SJR: 1.887, CiteScore: 4)
Intl. J. of Chemical Engineering     Open Access   (Followers: 8, SJR: 0.327, CiteScore: 1)
Intl. J. of Chronic Diseases     Open Access   (Followers: 1)
Intl. J. of Combinatorics     Open Access   (Followers: 1)
Intl. J. of Computer Games Technology     Open Access   (Followers: 10, SJR: 0.287, CiteScore: 2)
Intl. J. of Corrosion     Open Access   (Followers: 10, SJR: 0.194, CiteScore: 1)
Intl. J. of Dentistry     Open Access   (Followers: 6, SJR: 0.649, CiteScore: 2)
Intl. J. of Differential Equations     Open Access   (Followers: 7, SJR: 0.191, CiteScore: 0)
Intl. J. of Digital Multimedia Broadcasting     Open Access   (Followers: 5, SJR: 0.296, CiteScore: 2)
Intl. J. of Electrochemistry     Open Access   (Followers: 8)
Intl. J. of Endocrinology     Open Access   (Followers: 4, SJR: 1.012, CiteScore: 3)
Intl. J. of Engineering Mathematics     Open Access   (Followers: 5)
Intl. J. of Food Science     Open Access   (Followers: 4, SJR: 0.44, CiteScore: 2)
Intl. J. of Forestry Research     Open Access   (Followers: 3, SJR: 0.373, CiteScore: 1)
Intl. J. of Genomics     Open Access   (Followers: 2, SJR: 0.868, CiteScore: 3)
Intl. J. of Geophysics     Open Access   (Followers: 4, SJR: 0.182, CiteScore: 1)
Intl. J. of Hepatology     Open Access   (Followers: 4, SJR: 0.874, CiteScore: 2)
Intl. J. of Hypertension     Open Access   (Followers: 6, SJR: 0.578, CiteScore: 1)
Intl. J. of Inflammation     Open Access   (SJR: 1.264, CiteScore: 3)
Intl. J. of Inorganic Chemistry     Open Access   (Followers: 3)
Intl. J. of Manufacturing Engineering     Open Access   (Followers: 2)
Intl. J. of Mathematics and Mathematical Sciences     Open Access   (Followers: 3, SJR: 0.177, CiteScore: 0)
Intl. J. of Medicinal Chemistry     Open Access   (Followers: 6, SJR: 0.31, CiteScore: 1)
Intl. J. of Metals     Open Access   (Followers: 4)
Intl. J. of Microbiology     Open Access   (Followers: 4, SJR: 0.662, CiteScore: 2)
Intl. J. of Microwave Science and Technology     Open Access   (Followers: 3, SJR: 0.136, CiteScore: 1)
Intl. J. of Navigation and Observation     Open Access   (Followers: 20, SJR: 0.267, CiteScore: 2)
Intl. J. of Nephrology     Open Access   (Followers: 1, SJR: 0.697, CiteScore: 1)
Intl. J. of Oceanography     Open Access   (Followers: 7)
Intl. J. of Optics     Open Access   (Followers: 7, SJR: 0.231, CiteScore: 1)
Intl. J. of Otolaryngology     Open Access   (Followers: 3)
Intl. J. of Partial Differential Equations     Open Access   (Followers: 2)
Intl. J. of Pediatrics     Open Access   (Followers: 6)
Intl. J. of Peptides     Open Access   (Followers: 4, SJR: 0.46, CiteScore: 1)
Intl. J. of Photoenergy     Open Access   (Followers: 2, SJR: 0.341, CiteScore: 1)
Intl. J. of Plant Genomics     Open Access   (Followers: 4, SJR: 0.583, CiteScore: 1)
Intl. J. of Polymer Science     Open Access   (Followers: 24, SJR: 0.298, CiteScore: 1)
Intl. J. of Population Research     Open Access   (Followers: 2)
Intl. J. of Quality, Statistics, and Reliability     Open Access   (Followers: 15)
Intl. J. of Reconfigurable Computing     Open Access   (SJR: 0.123, CiteScore: 1)
Intl. J. of Reproductive Medicine     Open Access   (Followers: 4)
Intl. J. of Rheumatology     Open Access   (Followers: 4, SJR: 0.645, CiteScore: 2)
Intl. J. of Rotating Machinery     Open Access   (Followers: 2, SJR: 0.193, CiteScore: 1)
Intl. J. of Spectroscopy     Open Access   (Followers: 7)
Intl. J. of Stochastic Analysis     Open Access   (Followers: 3, SJR: 0.279, CiteScore: 1)
Intl. J. of Surgical Oncology     Open Access   (Followers: 1, SJR: 0.573, CiteScore: 2)
Intl. J. of Telemedicine and Applications     Open Access   (Followers: 5, SJR: 0.403, CiteScore: 2)
Intl. J. of Vascular Medicine     Open Access   (SJR: 0.782, CiteScore: 2)
Intl. J. of Zoology     Open Access   (Followers: 2, SJR: 0.209, CiteScore: 1)
Intl. Scholarly Research Notices     Open Access   (Followers: 192)
ISRN Astronomy and Astrophysics     Open Access   (Followers: 7)
J. of Addiction     Open Access   (Followers: 12)
J. of Advanced Transportation     Hybrid Journal   (Followers: 13, SJR: 0.581, CiteScore: 1)
J. of Aerodynamics     Open Access   (Followers: 12)

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Complexity
Journal Prestige (SJR): 0.531
Citation Impact (citeScore): 2
Number of Followers: 6  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1076-2787 - ISSN (Online) 1099-0526
Published by Hindawi Homepage  [338 journals]
  • Vehicle Information Influence Degree Screening Method Based on GEP
           Optimized RBF Neural Network
    • Abstract: Due to the continuous progress in the field of vehicle hardware, the condition that a vehicle cannot load a complex algorithm no longer exists. At the same time, with the progress in the field of vehicle hardware, a number of studies have reported exponential growth in the actual operation. To solve the problem for a large number of data transmissions in an actual operation, wireless transmission is proposed for text information (including position information) on the basis of the principles of the maximum entropy probability and the neural network prediction model combined with the optimization of the Huffman encoding algorithm, from the exchange of data to the entire data extraction process. The test results showed that the text-type vehicle information based on a compressed algorithm to optimize the algorithm of data compression and transmission could effectively realize the data compression, achieve a higher compression rate and data transmission integrity, and after decompression guarantee no distortion. Therefore, it is important to improve the efficiency of vehicle information transmission, to ensure the integrity of information, to realize the vehicle monitoring and control, and to grasp the traffic situation in real time.
      PubDate: Sun, 14 Oct 2018 00:00:00 +000
       
  • Integrating Correlation-Based Feature Selection and Clustering for
           Improved Cardiovascular Disease Diagnosis
    • Abstract: Based on the growing problem of heart diseases, their efficient diagnosis is of great importance to the modern world. Statistical inference is the tool that most physicians use for diagnosis, though in many cases it does not appear powerful enough. Clustering of patient instances allows finding out groups for which statistical models can be built more efficiently. However, the performance of such an approach depends on the features used as clustering attributes. In this paper, the methodology that consists of combining unsupervised feature selection and grouping to improve the performance of statistical analysis is considered. We assume that the set of attributes used in clustering and statistical analysis phases should be different and not correlated. Thus, the method consisting of selecting reversed correlated features as attributes of cluster analysis is considered. The proposed methodology has been verified by experiments done on three real datasets of cardiovascular cases. The obtained effects have been evaluated regarding the number of detected dependencies between parameters. Experiment results showed the advantage of the presented approach compared to other feature selection methods and without using clustering to support statistical inference.
      PubDate: Sun, 14 Oct 2018 00:00:00 +000
       
  • A New Approach to Modeling and Controlling a Pneumatic Muscle
           Actuator-Driven Setup Using Back Propagation Neural Networks
    • Abstract: Pneumatic muscle actuators (PMAs) own excellent compliance and a high power-to-weight ratio and have been widely used in bionic robots and rehabilitated robots. However, the high nonlinear characteristics of PMAs due to inherent construction and pneumatic driving principle bring great challenges in applications acquired accurately modeling and controlling. To tackle the tricky problem, a single PMA mass setup is constructed, and a back propagation neural network (BPNN) is employed to identify the dynamics of the setup. An offline model is built up using sampled data, and online modifications are performed to further improve the quality of the model. An adaptive controller based on BPNN is designed using gradient descent information of the built-up model. Experiments of identifying the PMA setup using BPNN and position tracking by adaptive BPNN controller are performed, and results demonstrate the good capacity in accurate controlling of the PMA setup.
      PubDate: Sun, 14 Oct 2018 00:00:00 +000
       
  • RAMS Analysis of Train Air Braking System Based on GO-Bayes Method and Big
           Data Platform
    • Abstract: The RAMS (reliability, availability, maintainability, and security) of the air braking system is an important indicator to measure the safety performance of the system; it can reduce the life cycle cost (LCC) of the rail transit system. Existing safety analysis methods are limited to the level of relatively simple factual descriptions and statistical induction, failing to provide a comprehensive safety evaluation on the basis of system structure and accumulated data. In this paper, a new method of safety analysis is described for the failure mode of the air braking system, GO-Bayes. This method combines the structural modeling of the GO method with the probabilistic reasoning of Bayes methods, introduces the probability into the analysis process of GO, performs reliability analysis of the air braking system, and builds a big data platform for the air braking system to guide the system maintenance strategy. An automatic train air braking system is taken as an example to verify the usefulness and accuracy of the proposed method. Using ExtendSim software shows the feasibility of the method and its advantages in comparison with fault tree analysis.
      PubDate: Sun, 14 Oct 2018 00:00:00 +000
       
  • The Permeable Character of CSG Dams and Their Seepage Fields
    • Abstract: Current studies regarding the permeable properties, corrosion properties, and seepage characteristics of cemented sand and gravel (CSG) materials are based on laboratory tests. Thus, there is a lack of studies analyzing the permeable character of seepage fields based on monitoring data from real prototypes working under practical operating conditions. In this paper, on the basis of measured data from the Dahuaqiao cofferdam, we establish an inversion analysis method for unsteady seepage fields covering different time periods within a time sequence. The results indicate an effective dynamic change law for the material permeability coefficient and the real dynamic evolution characteristics of seepage fields. The permeability coefficient of CSG exhibits a “self-healing” phenomenon similar to concrete, with the seepage characteristics of a dam tending to become stable over time. Under the long-term action of water pressure, the seepage behavior of the dam body shows no obvious deterioration, suggesting that CSG can satisfy the required anticorrosion property expected of dam construction materials. Thus, abnormal CSG might serve as an effective antiseepage layer that can meet the running requirements of cofferdams. The results of this research can provide reference for further improvement in the CSG dam design theory.
      PubDate: Sun, 14 Oct 2018 00:00:00 +000
       
  • Complex Optimization and Simulation in Power Systems
    • PubDate: Sun, 14 Oct 2018 00:00:00 +000
       
  • A Novel Decision-Making Approach to Fund Investments Based on
           Multigranulation Rough Set
    • Abstract: Fund investment is a hot issue in today’s society. How to choose a project for investment is affected by many factors. In view of this problem, this paper starts from the granular computing point of view and combines the multigranulation rough set decision-making method to construct a fund investment decision information system; then, the fund investment decision information system is reduced under different thresholds, and the decision rules are extracted through reduction. And from the aspects of decision accuracy and rule accuracy, the rules are analyzed. Finally, decision rules are used to give the decision of the fund investment project. This study provides a new approach to fund management.
      PubDate: Sun, 14 Oct 2018 00:00:00 +000
       
  • Interface Data Modeling to Detect and Diagnose Intersystem Faults for
           Designing and Integrating System of Systems
    • Abstract: In system of systems engineering, system integrators are in charge of compatible and reliable interfaces between subsystems. This study explains a systematic solution to identify and diagnose interface faults during designing and integrating systems of systems. Because the systems targeted in this study are real underwater vessels, we first have anatomized 188 interface data transferred between 22 subsystems of them. Based on this, two interface data models are proposed, which include data sets regarding messages and inner fields and transition and decision functions for them. Specifically, a structure model at the message level evaluates how inner fields belong to a message, and a logic model at the field level assesses how each field is interpreted and if the interpreted value is understandable. The software that supports the modeling is implemented using the following concepts: (1) a model-view-viewmodel pattern for overall software design and (2) a computer network for representing sequential properties of field interpretations. The proposed modeling and software facilitate diagnostic decisions by checking the consistency between interface protocols and obtained real data. As a practical use, the proposed work was applied to an underwater shipbuilding project. Within 10 interfaces, 14 fault cases were identified and diagnosed. They were gradually resolved during the system design and integration phases, which formed the basis of successful submarine construction.
      PubDate: Sun, 14 Oct 2018 00:00:00 +000
       
  • A Pythagorean Fuzzy Multigranulation Probabilistic Model for Mine
           Ventilator Fault Diagnosis
    • Abstract: In coal mining industry, the running state of mine ventilators plays an extremely significant role for the safe and reliable operation of various industrial productions. To guarantee the better reliability, safety, and economy of mine ventilators, in view of early detection and effective fault diagnosis of mechanical faults which could prevent unscheduled downtime and minimize maintenance fees, it is imperative to construct some viable mathematical models for mine ventilator fault diagnosis. In this article, we plan to establish a data-based mine ventilator fault diagnosis method to handle situations where engineers are absent or they are incapable of coming to a conclusion from multisource data. In the process of building the mine ventilator fault diagnosis model, considering that probabilistic rough sets (PRSs) could reduce the errors triggered by incompleteness, inconsistency, and inaccuracy without needing any additional assumptions and Pythagorean fuzzy multigranulation rough sets (PF MGRSs) over the two universes’ model could effectively handle data representation, fusion, and analysis issues, we generalize the existing PF MGRSs over the two universes’ model to the PRS setting, as well as to further establish a novel model named Pythagorean fuzzy multigranulation probabilistic rough sets (PF MG-PRSs) over two universes. In the granular computing paradigm, three types of PF MG-PRSs over two universes based on the risk attitude of engineers are proposed at first. Afterwards, several basic propositions of the newly proposed model are explored. Moreover, a PF multigranulation probabilistic model for mine ventilator fault diagnosis based on PF MG-PRSs over two universes is investigated. At last, a real-world case study of dealing with a mine ventilator fault diagnosis problem is given to illustrate the practicality of the presented model, and a validity test, a sensitivity analysis, and a comparison analysis are further explored to demonstrate the effectiveness of the presented model.
      PubDate: Sun, 14 Oct 2018 00:00:00 +000
       
  • Threshold Dynamics of an SIR Model with Nonlinear Incidence Rate and
           Age-Dependent Susceptibility
    • Abstract: We propose an SIR epidemic model with different susceptibilities and nonlinear incidence rate. First, we obtain the existence and uniqueness of the system and the regularity of the solution semiflow based on some assumptions for the parameters. Then, we calculate the basic reproduction number, which is the spectral radius of the next-generation operator. Second, we investigate the existence and local stability of the steady states. Finally, we construct suitable Lyapunov functionals to strictly prove the global stability of the system, which are determined by the basic reproduction number and some assumptions for the incidence rate.
      PubDate: Sun, 14 Oct 2018 00:00:00 +000
       
  • Networked Control System Design for Turbofan Aeroengines with Aging and
           Deterioration
    • Abstract: This paper is concerned with designing a networked controller for a mixed flow two-spool turbofan aeroengine with aging and deterioration. Firstly, the state-space representation of the aeroengine considering aging and deterioration is identified, by which the engine system with aging and deterioration is modeled as an uncertain linear system. Then based on this uncertain linear system, theoretical results from the networked control systems and the regional pole assignment are introduced to formulate the networked engine control design in the form of linear matrix inequalities (LMIs). By solving these LMIs simultaneously, a networked engine controller is obtained which guarantees both the robustness against delay/dropout and the satisfactory dynamic performance. Finally, the proposed method is applied to an aerothermodynamic component-level engine simulator to demonstrate its validity and applicability. The corresponding delay/dropout margin is also calculated, which provides reference for the future development of the distributed engine control system.
      PubDate: Thu, 11 Oct 2018 01:35:54 +000
       
  • Fault Diagnosis of Rolling Bearing Based on a Novel Adaptive High-Order
           Local Projection Denoising Method
    • Abstract: Rolling bearings are vital components in rotary machinery, and their operating condition affects the entire mechanical systems. As one of the most important denoising methods for nonlinear systems, local projection (LP) denoising method can be used to reduce noise effectively. Afterwards, high-order polynomials are utilized to estimate the centroid of the neighborhood to better preserve complete geometry of attractors; thus, high-order local projection (HLP) can improve noise reduction performance. This paper proposed an adaptive high-order local projection (AHLP) denoising method in the field of fault diagnosis of rolling bearings to deal with different kinds of vibration signals of faulty rolling bearings. Optimal orders can be selected corresponding to vibration signals of outer ring fault (ORF) and inner ring fault (IRF) rolling bearings, because they have different nonlinear geometric structures. The vibration signal model of faulty rolling bearing is adopted in numerical simulations, and the characteristic frequencies of simulated signals can be well extracted by the proposed method. Furthermore, two kinds of experimental data have been processed in application researches, and fault frequencies of ORF and IRF rolling bearings can be both clearly extracted by the proposed method. The theoretical derivation, numerical simulations, and application research can indicate that the proposed novel approach is promising in the field of fault diagnosis of rolling bearing.
      PubDate: Thu, 11 Oct 2018 00:00:00 +000
       
  • Technoeconomic Distribution Network Planning Using Smart Grid Techniques
           with Evolutionary Self-Healing Network States
    • Abstract: The transition to a secure low-carbon system is raising a set of uncertainties when planning the path to a reliable decarbonised supply. The electricity sector is committing large investments in the transmission and distribution sector upon 2050 in order to ensure grid resilience. The cost and limited flexibility of traditional approaches to 11 kV network reinforcement threaten to constrain the uptake of low-carbon technologies. This paper investigates the suitability and cost-effectiveness of smart grid techniques along with traditional reinforcements for the 11 kV electricity distribution network, in order to analyse expected investments up to 2050 under different DECC demand scenarios. The evaluation of asset planning is based on an area of study in Milton Keynes (East Midlands, United Kingdom), being composed of six 11 kV primaries. To undertake this, the analysis used a revolutionary new model tool for electricity distribution network planning, called scenario investment model (SIM). Comprehensive comparisons of short- and long-term evolutionary investment planning strategies are presented. The work helps electricity network operators to visualise and design operational planning investments providing bottom-up decision support.
      PubDate: Wed, 10 Oct 2018 09:51:54 +000
       
  • Complexity and Project Management: A General Overview
    • Abstract: As projects have become more and more complex, there has been an increasing concern about the concept of project complexity. An understanding of project complexity and how it might be managed is of significant importance for project managers because of the differences associated with decision-making and goal attainment that are related to complexity. Complexity influences project planning and control; it can hinder the clear identification of goals and objectives, it can affect the selection of an appropriate project organization form, or it can even affect project outcomes. Identifying the different concepts associated to project complexity, its main factors and characteristics, the different types of project complexity, and the main project complexity models, can be of great support in assisting the global project management community. In this paper, we give a general overview of how complexity has been investigated by the project management community and propose several ideas to address this topic in the future.
      PubDate: Wed, 10 Oct 2018 06:19:45 +000
       
  • Behavior-Interior-Aware User Preference Analysis Based on Social Networks
    • Abstract: There is a growing trend recently in big data analysis that focuses on behavior interiors, which concern the semantic meanings (e.g., sentiment, controversy, and other state-dependent factors) in explaining the human behaviors from psychology, sociology, cognitive science, and so on, rather than the data per se as in the case of exterior dimensions. It is more intuitive and much easier to understand human behaviors with less redundancy in concept by exploring the behavior interior dimensions, compared with directly using behavior exteriors. However, they usually approach from a unidimensional perspective with a lack of a sense of interrelatedness. Thus, integrating multiple behavior dimensions together into some numerical measures to form a more comprehensive view for subsequent prediction processes becomes a pivotal issue. Moreover, these studies usually focus on the magnitude but neglect the associated temporal features. In this paper, we propose a behavior interior dimension-based neighborhood collaborative filtering method for the top- hashtag adoption frequency prediction that takes into account the interdependence in temporal dynamics. Our proposed approach couples the similarity in user preference and their impact propagation, by integrating the linear threshold model and the enhanced CF model based on behavior interiors. Experiments on Twitter demonstrate that the behavior-interior-aware CF models achieve better adoption prediction results than the state-of-the-art methods, and the joint consideration of similarity in user preference and their impact propagation results in a significant improvement than treating them separately.
      PubDate: Tue, 09 Oct 2018 07:23:19 +000
       
  • Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles
           for Text Classification
    • Abstract: The use of ensemble learning, deep learning, and effective document representation methods is currently some of the most common trends to improve the overall accuracy of a text classification/categorization system. Ensemble learning is an approach to raise the overall accuracy of a classification system by utilizing multiple classifiers. Deep learning-based methods provide better results in many applications when compared with the other conventional machine learning algorithms. Word embeddings enable representation of words learned from a corpus as vectors that provide a mapping of words with similar meaning to have similar representation. In this study, we use different document representations with the benefit of word embeddings and an ensemble of base classifiers for text classification. The ensemble of base classifiers includes traditional machine learning algorithms such as naïve Bayes, support vector machine, and random forest and a deep learning-based conventional network classifier. We analysed the classification accuracy of different document representations by employing an ensemble of classifiers on eight different datasets. Experimental results demonstrate that the usage of heterogeneous ensembles together with deep learning methods and word embeddings enhances the classification performance of texts.
      PubDate: Tue, 09 Oct 2018 07:19:30 +000
       
  • Complexity in the Acceptance of Sustainable Search Engines on the
           Internet: An Analysis of Unobserved Heterogeneity with FIMIX-PLS
    • Abstract: This paper analyses the complexity of user behaviour when facing the challenge of using sustainable applications, such as Internet search engines. This paper analyses an acceptance model using extended TAM (Technology Acceptance Model) with Trust as an added external variable. It was suggested that Trust indirectly influences the final Intention to Use with the perceptions of Utility and Ease of Use. To test the proposed model, a survey was carried out with users from different geographical areas of Spain (). The second aim of this study was to understand the complexity of marketing segmentation by separating the application users into different user groups. Users were grouped by their preference of favorite Internet search engine. Unobserved heterogeneity was studied using FIMIX-PLS, and three different user behaviours with search engines were identified. These corresponded to the number of inhabitants who live in the user area. In this way, the impact that the environment has on user choice, acceptance, and use of this type of sustainable applications was shown. The results were checked using PLS-SEM and showed that the model for the adoption of sustainable search engines is explanatory and predictive because confidence and acceptance for this TAM were validated. The conclusions are interesting for developers of environmentally sustainable and responsible applications which want to coincide with current trends to ensure that users prefer them.
      PubDate: Tue, 09 Oct 2018 06:56:08 +000
       
  • Broad Learning-Based Optimization and Prediction of Questionnaire Survey:
           Application to Mind Status of College Students
    • Abstract: The mind status of college students is important since it can reflect how the public opinion is going. Only with the accurate prediction, the corresponding actions can be conducted to prevent the situation from going worse. This paper focused on the data analysis using the recent developed broad learning method to obtain the learning model and then the prediction can be done. Firstly, the questionnaire related to the ideological state is designed. Secondly, the data are collected and classified using the typical questions and answers. Thirdly, for each pair of the question and the answer, the score is obtained and considered as data training of the system. Fourthly, the input and the output are selected according to the key questions and conclusions. Finally, the broad learning using flat network is employed for data analysis without deep structure. Tests show that the design using broad learning can efficiently deal with the regression problem and the learning network can be used for prediction.
      PubDate: Tue, 09 Oct 2018 00:00:00 +000
       
  • Postmortem Analysis of Decayed Online Social Communities: Cascade Pattern
           Analysis and Prediction
    • Abstract: Recently, many online social networks, such as MySpace, Orkut, and Friendster, have faced inactivity decay of their members, which contributed to the collapse of these networks. The reasons, mechanics, and prevention mechanisms of such inactivity decay are not fully understood. In this work, we analyze decayed and alive subwebsites from the Stack Exchange platform. The analysis mainly focuses on the inactivity cascades that occur among the members of these communities. We provide measures to understand the decay process and statistical analysis to extract the patterns that accompany the inactivity decay. Additionally, we predict cascade size and cascade virality using machine learning. The results of this work include a statistically significant difference of the decay patterns between the decayed and the alive subwebsites. These patterns are mainly cascade size, cascade virality, cascade duration, and cascade similarity. Additionally, the contributed prediction framework showed satisfactorily prediction results compared to a baseline predictor. Supported by empirical evidence, the main findings of this work are (1) there are significantly different decay patterns in the alive and the decayed subwebsites of the Stack Exchange; (2) the cascade’s node degrees contribute more to the decay process than the cascade’s virality, which indicates that the expert members of the Stack Exchange subwebsites were mainly responsible for the activity or inactivity of the Stack Exchange subwebsites; (3) the Statistics subwebsite is going through decay dynamics that may lead to it becoming fully-decayed; (4) the decay process is not governed by only one network measure, it is better described using multiple measures; (5) decayed subwebsites were originally less resilient to inactivity decay, unlike the alive subwebsites; and (6) network’s structure in the early stages of its evolution dictates the activity/inactivity characteristics of the network.
      PubDate: Tue, 09 Oct 2018 00:00:00 +000
       
  • Permanence and Almost Periodic Solutions for -Species Nonautonomous
           Lotka-Volterra Competitive Systems with Delays and Impulsive Perturbations
           on Time Scales
    • Abstract: We investigate a class of nonautonomous -species Lotka-Volterra-type competitive systems with time delays and impulsive perturbations on time scales. By using comparison theorems of impulsive dynamic equations on time scales, we obtain sufficient conditions to guarantee the permanence of the system. Then based on the Massera-type theorem for impulsive dynamic equations on time scales, we establish existence and uniformly asymptotic stability of the unique positive almost periodic solution of the system. Finally, an example is employed to illustrate our main results.
      PubDate: Tue, 09 Oct 2018 00:00:00 +000
       
  • Delay-Dependent Stability in Uncalibrated Image-Based Dynamic Visual
           Servoing Robotic System
    • Abstract: This paper addresses the stability problem of uncalibrated image-based visual servoing robotic systems. Both the visual feedback delay and the uncalibrated visual parameters can be the sources of instability for visual servoing robotic systems. To eliminate the negative effects caused by kinematic uncertainties and delays, we propose an adaptive controller including the delay-affected Jacobian matrix and design an adaptive law accordingly. Besides, the delay-dependent stability conditions are provided to show the relationship between the system stability and the delayed time in order to obtain less conservative results. A Lyapunov-Krasovskii functional is constructed, and a rigorously mathematic proof is given. Finally, the simulation results are presented to show the effectiveness of the proposed control scheme.
      PubDate: Mon, 08 Oct 2018 00:00:00 +000
       
  • Green Supplier Selection for Process Industries Using Weighted Grey
           Incidence Decision Model
    • Abstract: Proper supplier selection to meet production demand is a major aspect of all manufacturing and process industries. Green supplier selection has been one of the most critical factors for environmental protection on account of increasing consumption levels and for sustainable development as well. This paper aims at developing an applicable methodology for green supplier selection for the process industry. In this study, both economic and environmental criteria are considered and a comprehensive weighted grey incidence decision approach for green supplier evaluation and selection in a process industry is proposed. First, an overall green supplier selection index system for process industries is considered; then a weighted grey incidence decision-making model with improved grey incidence coefficients and weighted degree of grey incidence is provided. Improved grey incidence coefficients are defined using transformation sequences of the initial data. To eliminate the ill effects from the use of equal weights, the maximum entropy method is used to determine the weights of the improved grey incidence coefficients. An application example is proposed with the data collected for the chemical processing industry, which provides acceptable results in determining the better supplier. In the end appendix, some theory regarding the weights for grey incidence coefficients is proposed. The empirical results indicate that the model is of great practical value for green supplier selection in the process industry.
      PubDate: Mon, 08 Oct 2018 00:00:00 +000
       
  • Application of the Variable Precision Rough Sets Model to Estimate the
           Outlier Probability of Each Element
    • Abstract: In a data mining process, outlier detection aims to use the high marginality of these elements to identify them by measuring their degree of deviation from representative patterns, thereby yielding relevant knowledge. Whereas rough sets (RS) theory has been applied to the field of knowledge discovery in databases (KDD) since its formulation in the 1980s; in recent years, outlier detection has been increasingly regarded as a KDD process with its own usefulness. The application of RS theory as a basis to characterise and detect outliers is a novel approach with great theoretical relevance and practical applicability. However, algorithms whose spatial and temporal complexity allows their application to realistic scenarios involving vast amounts of data and requiring very fast responses are difficult to develop. This study presents a theoretical framework based on a generalisation of RS theory, termed the variable precision rough sets model (VPRS), which allows the establishment of a stochastic approach to solving the problem of assessing whether a given element is an outlier within a specific universe of data. An algorithm derived from quasi-linearisation is developed based on this theoretical framework, thus enabling its application to large volumes of data. The experiments conducted demonstrate the feasibility of the proposed algorithm, whose usefulness is contextualised by comparison to different algorithms analysed in the literature.
      PubDate: Mon, 08 Oct 2018 00:00:00 +000
       
  • Advanced Methods to Analyse the Complexity of the Brain
    • PubDate: Mon, 08 Oct 2018 00:00:00 +000
       
  • A Comprehensive Algorithm for Evaluating Node Influences in Social
           Networks Based on Preference Analysis and Random Walk
    • Abstract: In the era of big data, social network has become an important reflection of human communications and interactions on the Internet. Identifying the influential spreaders in networks plays a crucial role in various areas, such as disease outbreak, virus propagation, and public opinion controlling. Based on the three basic centrality measures, a comprehensive algorithm named PARW-Rank for evaluating node influences has been proposed by applying preference relation analysis and random walk technique. For each basic measure, the preference relation between every node pair in a network is analyzed to construct the partial preference graph (PPG). Then, the comprehensive preference graph (CPG) is generated by combining the preference relations with respect to three basic measures. Finally, the ranking of nodes is determined by conducting random walk on the CPG. Furthermore, five public social networks are used for comparative analysis. The experimental results show that our PARW-Rank algorithm can achieve the higher precision and better stability than the existing methods with a single centrality measure.
      PubDate: Mon, 08 Oct 2018 00:00:00 +000
       
  • Error Estimates for the Heterogeneous Multiscale Finite Volume Method of
           Convection-Diffusion-Reaction Problem
    • Abstract: Based on the heterogeneous multiscale method, this paper presents a finite volume method to solve multiscale convection-diffusion-reaction problem. The paper constructs an algorithm of the optimal order convergence rate in -norm under periodic medias.
      PubDate: Mon, 08 Oct 2018 00:00:00 +000
       
  • A Novel Efficient Feature Dimensionality Reduction Method and Its
           Application in Engineering
    • Abstract: In the engineering field, excessive data dimensions affect the efficiency of machine learning and analysis of the relationships between data or features. To render feature dimensionality reduction more effective and faster, this paper proposes a new feature dimensionality reduction approach combining a sampling survey method with a heuristic intelligent optimization algorithm. Drawing on feature selection, this method builds a feature-scoring system and a reduced-dimension length-scoring system based on the sampling survey method. According to feature scores and reduced-dimension lengths, the method selects a number of features and reduced-dimension lengths that are ranked in the front with high scores. This feature dimensionality reduction method allows for in-depth optimal selection of features and reduced-dimension lengths with high scores using an improved heuristic intelligent optimization algorithm. To verify the effectiveness of the dimensionality reduction method, this paper applies it to road roughness time-domain estimation based on vehicle dynamic response and gene-selection research in bioengineering. Results in the first case show that the proposed method can improve the accuracy of road roughness time-domain estimation to above 0.99 and reduce measured data of the vehicle dynamic response, reducing the experimental workload significantly. Results in the second case show that the method can select a set of genes quickly and effectively with high disease recognition accuracy.
      PubDate: Mon, 08 Oct 2018 00:00:00 +000
       
  • Minor Prime Factorization for -D Polynomial Matrices over Arbitrary
           Coefficient Field
    • Abstract: In this paper, we investigate two classes of multivariate (-D) polynomial matrices whose coefficient field is arbitrary and the greatest common divisor of maximal order minors satisfy certain condition. Two tractable criterions are presented for the existence of minor prime factorization, which can be realized by programming and complexity computations. On the theory and application, we shall obtain some new and interesting results, giving some constructive computational methods for carrying out the minor prime factorization.
      PubDate: Mon, 08 Oct 2018 00:00:00 +000
       
  • Automatic Sleep Stage Classification Based on Convolutional Neural Network
           and Fine-Grained Segments
    • Abstract: Sleep stage classification plays an important role in the diagnosis of sleep-related diseases. However, traditional automatic sleep stage classification is quite challenging because of the complexity associated with the establishment of mathematical models and the extraction of handcrafted features. In addition, the rapid fluctuations between sleep stages often result in blurry feature extraction, which might lead to an inaccurate assessment of electroencephalography (EEG) sleep stages. Hence, we propose an automatic sleep stage classification method based on a convolutional neural network (CNN) combined with the fine-grained segment in multiscale entropy. First, we define every 30 seconds of the multichannel EEG signal as a segment. Then, we construct an input time series based on the fine-grained segments, which means that the posterior and current segments are reorganized as an input containing several segments and the size of the time series is decided based on the scale chosen depending on the fine-grained segments. Next, each segment in this series is individually put into the designed CNN and feature maps are obtained after two blocks of convolution and max-pooling as well as a full-connected operation. Finally, the results from the full-connected layer of each segment in the input time sequence are put into the softmax classifier together to get a single most likely sleep stage. On a public dataset called ISRUC-Sleep, the average accuracy of our proposed method is 92.2%. Moreover, it yields an accuracy of 90%, 86%, 93%, 97%, and 90% for stage W, stage N1, stage N2, stage N3, and stage REM, respectively. Comparative analysis of performance suggests that the proposed method is better, as opposed to that of several state-of-the-art ones. The sleep stage classification methods based on CNN and the fine-grained segments really improve a key step in the study of sleep disorders and expedite sleep research.
      PubDate: Mon, 08 Oct 2018 00:00:00 +000
       
  • The Credit Asset of Enterprise Accounts Receivable Pricing Model
    • Abstract: Based on the thinking of holism and reductionism, this paper creatively constructed the credit asset pricing model of enterprises’ accounts receivable, namely, the BEST pricing model, and it was demonstrated effectively. The model gave an overall evaluation on the default probability of buyer and environment, as well as buyer loss given default resulting from the factors including Seller (S), Buyer (B), and Environment (E). The model is also utilized with the optimal control management Technology (T) to maximize the intrinsic value of the credit asset. The paper put forward the Duration of accounts receivable aging, measurement method of dynamic free interest rate, and amended the KMV model to solve the default probability of accounts receivable of listed and nonlisted companies. To evaluate the credit asset risk, the following were selected: three effective financial indicators, seven nonfinancial index clusters, and sixty-three specific nonfinancial index variables of the buyer; one index and eight specific indicators of the seller; and one index and fourteen specific indicators of nonsystematic risk of the environment. Five appropriate hedge parameters are used to control the risk.
      PubDate: Mon, 08 Oct 2018 00:00:00 +000
       
 
 
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