Subjects -> MANUFACTURING AND TECHNOLOGY (Total: 363 journals)
    - CERAMICS, GLASS AND POTTERY (31 journals)
    - MACHINERY (34 journals)
    - MANUFACTURING AND TECHNOLOGY (223 journals)
    - METROLOGY AND STANDARDIZATION (6 journals)
    - PACKAGING (19 journals)
    - PAINTS AND PROTECTIVE COATINGS (4 journals)
    - PLASTICS (42 journals)
    - RUBBER (4 journals)

METROLOGY AND STANDARDIZATION (6 journals)

Showing 1 - 6 of 6 Journals sorted alphabetically
Aeolian Research     Hybrid Journal   (Followers: 7)
International Journal of Instrumentation Technology     Hybrid Journal   (Followers: 9)
Journal of Applied Meteorology and Climatology     Hybrid Journal   (Followers: 40)
Journal of Measurements in Engineering     Open Access   (Followers: 5)
Nanomanufacturing and Metrology     Hybrid Journal  
NCSLI Measure : The Journal of Measurement Science     Hybrid Journal  
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Journal of Measurements in Engineering
Number of Followers: 5  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2335-2124 - ISSN (Online) 2424-4635
Published by JVE International Homepage  [3 journals]
  • A precise localization algorithm for unmanned aerial vehicles integrating
           visual-internal odometry and cartographer

    • Authors: Xu; Jiaqi, Chen, Zhou, Chen, Jie, Zhou, Jingyan, Du, Xiaofei
      Pages: 13
      Abstract: Journal of Measurements in Engineering, (in Press).
      Jiaqi Xu, Zhou Chen, Jie Chen, Jingyan Zhou, Xiaofei Du
      Accurate positioning in space is an important foundation for ensuring the stability of autonomous flight and successful mapping and navigation of unmanned aerial vehicles (UAVs). At present, the SLAM algorithm based on the Cartographer algorithm for real-time positioning and mapping is widely used in fields such as robot navigation and autonomous driving. However, in the context of UAV applications, this algorithm has a high dependence on the amount of point cloud information in the surrounding environment, and cannot achieve precise positioning in open spaces with insufficient lighting and fewer feature points, resulting in significant mapping errors. In order to solve the problem of low positioning estimation accuracy in the Cartographer algorithm in above environment, this paper proposes a precise positioning algorithm for UAVs that integrates VIO and Cartographer. This algorithm can continuously output more accurate position estimation information during UAV flight, compensating for the problem of inaccurate position estimation caused by partial feature loss or coordinate system drift in point clouds. In addition, this algorithm ensures navigation obstacle avoidance in narrow spaces by improving positioning accuracy and mapping accuracy, making it more applicable in the field of UAVs. Finally, the effectiveness of the proposed positioning algorithm was verified through experimental analysis of the Cartographer dataset and practical testing of UAVs in real scenarios.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-03-02T00:00:00Z
      DOI: 10.21595/jme.2024.23726
      Issue No: Vol. 12, No. 2 (2024)
       
  • Lightweight small target detection based on aerial remote sensing images

    • Authors: Li; Muzi
      Pages: 15
      Abstract: Journal of Measurements in Engineering, (in Press).
      Muzi Li
      With the upgrading of aviation space technology, the amount of information contained in remote sensing images in the aviation is gradually increasing, and the detection technology based on small targets has developed. For lightweight small targets, pixels per unit area contain more information than large targets, and their area is too small, which is easily overlooked by conventional detection models. To enhance the attention of such algorithms, this study first introduces a Control Bus Attention Mechanism (CBAM) in the fifth generation You Only Look Once (YOLOv5) algorithm to increase the algorithm’s attention to small targets and generate optimization algorithms. Then convolutional neural network is used to mark feature pixels of the optimization algorithm, eliminate redundant information, and generate fusion algorithm, which is used to generate redundant information with high similarity when the optimization algorithm surveys pixel blocks. The novelty of this study lies in using CBAM to improve YOLOv5 algorithm. CBAM module can extract important features from images by adaptively learning the channel and spatial attention of feature maps. By weighting the channel and spatial attention of the feature map, the network can pay more attention to important features and suppress irrelevant background information. This attention mechanism can help the network better capture the characteristics of small targets and improve the accuracy and robustness of detection. Embedding CBAM module into YOLOv5 detection network can enhance the network's perception of small targets. CBAM module can improve the expressive ability and feature extraction ability of the network without increasing the complexity of the network. By introducing CBAM module, YOLOv5 can better capture the characteristics of small targets in aerial remote sensing images, and improve the detection accuracy and recall rate. Finally, the proposed fusion algorithm is used for experiments on the Tiny-Person dataset and compared with the fifth, sixth, and seventh generations of You Only Look Once. When the fusion algorithm tests the target, the classification accuracy of Sea-person is 39 %, the classification accuracy of Earth-person is 31 %, and the probability of being predicted as the background is 56 % and 67 %, respectively. And the overall accuracy of this algorithm is 0.987, which is the best among the four algorithms. The experimental results show that the fusion algorithm proposed in the study has precise positioning for lightweight small targets and can achieve good application results in aerial remote sensing images.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-02-23T00:00:00Z
      DOI: 10.21595/jme.2024.23609
      Issue No: Vol. 12, No. 2 (2024)
       
  • Improving piano music signal recognition through enhanced frequency domain
           analysis

    • Authors: Gao; Hongjiao
      Pages: 11
      Abstract: Journal of Measurements in Engineering, (in Press).
      Hongjiao Gao
      Feature extraction is a crucial component in the analysis of piano music signals. This article introduced three methods for feature extraction based on frequency domain analysis, namely short-time Fourier transform (STFT), linear predictive cepstral coefficient (LPCC), and Mel-frequency cepstral coefficient (MFCC). An improvement was then made to the MFCC. The inverse MFCC (IMFCC) was combined with mid-frequency MFCC (MidMFCC). The Fisher criterion was used to select the 12-order parameters with the maximum Fisher ratio, which were combined into the F-MFCC feature for recognizing 88 single piano notes through a support vector machine. The results indicated that when compared with the STFT and LPCC, the MFCC exhibited superior performance in recognizing piano music signals, with an accuracy rate of 78.03 % and an F1 value of 85.92 %. Nevertheless, the proposed F-MFCC achieved a remarkable accuracy rate of 90.91 %, representing a substantial improvement by 12.88 % over the MFCC alone. These findings provide evidence for the effectiveness of the designed F-MFCC feature for piano music signal recognition as well as its potential application in practical music signal analysis.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-02-23T00:00:00Z
      DOI: 10.21595/jme.2024.23774
      Issue No: Vol. 12, No. 2 (2024)
       
  • Research on stress through photoelastic experiment and finite element
           method considering sliding wear

    • Authors: Yang; Tao, Wei, Yunpeng, Han, Jihao, Chen, Zhidong
      Pages: 8
      Abstract: Journal of Measurements in Engineering, (in Press).
      Tao Yang, Yunpeng Wei, Jihao Han, Zhidong Chen
      Sliding contact on the contact interface of friction pairs is a common type of contact. The sliding wear caused by sliding contact has an obvious influence on the stress in the contact area. In this study, the photoelastic experiment and finite element method are adopted to study variation laws of stress in the contact area. The results show that the stress in the contact region is very concentrated, and the contact half-width gradually ascends with the increase of sliding wear. The stress intensity in the contact region and von Mises stress at the contact centre decrease with the increase of wear depth. In the case of a wear depth of less than 0.3 mm, the stress intensity and the contact stress decrease rapidly with the growth of wear depth. When the wear depth exceeds 0.3 mm, the influence of wear depth on the stress intensity and contact stress is small. The results of this research clarify the effect of sliding wear on the stress in the contact area, and provide a reference for studying the contact issues.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-02-23T00:00:00Z
      DOI: 10.21595/jme.2024.23791
      Issue No: Vol. 12, No. 2 (2024)
       
  • Analysis and experimental research on the reliability of the connection
           between large-diameter bridge piles and caps

    • Authors: Ni; Hongmei, Yin, Xupeng
      Pages: 11
      Abstract: Journal of Measurements in Engineering, (in Press).
      Hongmei Ni, Xupeng Yin
      This article investigated the construction conditions of the pile foundation in the Wuxing section of the “Shanghai Suzhou Huzhou” railway bridge project. To test the reliability of large diameter connectors, it has established a finite element model with ABAQUS software for numerical simulation. Based on on-site tests, the reliability of the connection between the pipe pile and the cover steel was studied. According to the simulation results, when the load is P= 900 kN, the displacements of A2 and A3 steel pipe piles are 55.8 mm and 60.1 mm, respectively. The load-displacement relationship shows a high-order curve distribution. According to the results of on-site experiments, the displacements are 77.9 mm and 60.2 mm, respectively. The load-displacement relationship is linear. The results for the simulation and on-site testing are consistent. This study provides a basis to the research on the reliability of the connection between large-diameter steel pipe piles.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-02-21T00:00:00Z
      DOI: 10.21595/jme.2024.23639
      Issue No: Vol. 12, No. 2 (2024)
       
  • Optimal path for automated pedestrian detection: image deblurring
           algorithm based on generative adversarial network

    • Authors: Dong; Xiujuan, Lan, Jianping
      Pages: 13
      Abstract: Journal of Measurements in Engineering, (in Press).
      Xiujuan Dong, Jianping Lan
      The pedestrian detection technology of automated driving is also facing some challenges. Aiming at the problem of specific target deblurring in the image, this research built a pedestrian detection deblurring model in view of Generative adversarial network and multi-scale convolution. First, it designs an image deblurring algorithm in view of Generative adversarial network. Then, on the basis of image deblurring, a pedestrian deblurring algorithm in view of multi-scale convolution is designed to focus on deblurring the pedestrians in the image. The outcomes showcase that the peak signal to noise ratio and structural similarity index of the image deblurring algorithm in view of the Generative adversarial network are the highest, which are 29.7 dB and 0.943 dB respectively, and the operation time is the shortest, which is 0.50 s. The pedestrian deblurring algorithm in view of multi-scale convolution has the highest peak signal-to-noise ratio (PSNR) and structural similarity indicators in the HIDE test set and GoPro dataset, with 29.4 dB and 0.925 dB, 40.45 dB and 0.992 dB, respectively. The resulting restored image is the clearest and possesses the best visual effect. The enlarged part of the face can reveal more detailed information, and it is the closest to a real clear image. The deblurring effect is not limited to the size of the pedestrians in the image. In summary, the model constructed in this study has good application effects in image deblurring and pedestrian detection, and has a certain promoting effect on the development of autonomous driving technology.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-02-21T00:00:00Z
      DOI: 10.21595/jme.2023.23765
      Issue No: Vol. 12, No. 2 (2024)
       
  • Multi-factor coupled thermal simulation of flat-panel digital PCR
           structure

    • Authors: Li; Qixuan, Qin, Xulei, Wang, Haoyu
      Pages: 12
      Abstract: Journal of Measurements in Engineering, (in Press).
      Qixuan Li, Xulei Qin, Haoyu Wang
      To achieve uniform reaction temperature in flat-plate digital polymerase chain reaction (dPCR), we propose a multi-factor coupled thermal simulation method for the structure of flat-plate digital PCR using finite element analysis. This will help us obtain the optimal method for the structure of flat-plate dPCR. Thermal simulations were conducted to analyze the effects of forced air cooling, thermoelectric cooler (TEC) arrangement spacing, and heat-conducting plate thickness on the temperature uniformity of the flat-plate dPCR. The resulting isothermal surfaces and velocity magnitude vectors were used to summarize the impact of each factor. The study found that maintaining a mechanical fan speed of 3000 revolutions per minute (RPM) ±10 % during the heating period resulted in a 29.3 % reduction in the standard deviation of the temperature on the surface of the heat-conducting plate. Additionally, when the TEC spacing was between 2 mm and 3.5 mm, the standard deviation of the temperature on the plate's surface decreased by 87.1 % to 93.4 %. When the thickness of the thermal plate ranges from 3.5 mm to 4.5 mm, the standard deviation of the temperature on the surface of the thermal plate varies by approximately 0.006. The experimental results, obtained by sampling and analyzing the temperature on the surface of the thermal plate, are consistent with the simulation results. This proves that the method is informative in determining the structural parameters of the dPCR to enhance temperature uniformity.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-02-21T00:00:00Z
      DOI: 10.21595/jme.2024.23599
      Issue No: Vol. 12, No. 2 (2024)
       
  • Line segment detection algorithm in image extraction improvement study

    • Authors: Ren; Yuemei, Li, Lei
      Pages: 14
      Abstract: Journal of Measurements in Engineering, (in Press).
      Yuemei Ren, Lei Li
      In recent years, image processing technology has been developing and maturing, but due to the influence of many interfering factors in the acquisition process, there is a large amount of redundant information in the images obtained. The line segment detection algorithm in image extraction needs to be improved. This study utilizes computer technology to improve the line segment detection technology, and designs a line segment detection algorithm based on the linear detection improvement. Firstly, based on the basic principle of straight line detection algorithm, for the problems of line segment breakage and missing in straight line detection, RGB three-channel grayscale map is applied to detect line segments. Then the detected line segments are connected, merged and deleted. The test results show that the line segment detection algorithm improved based on straight line detection has the highest accuracy rate of 94.50 %, and the average processing time per image is also the lowest at 0.2 s. The algorithm runs faster at 0.25 s and has a higher F-value. It is able to detect the boundaries of a variety of rectangular targets, using the improved line segment detection algorithm has a wide range of applicability, lower error rate, and strong anti-interference ability. The improved line segment detection algorithm has a greater advantage in rectangular target extraction for document, text and book type images.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-02-29T00:00:00Z
      DOI: 10.21595/jme.2024.23856
      Issue No: Vol. 12, No. 1 (2024)
       
  • Comparison of optical 3D scanner and coordinate measurement system from
           the standpoint of macro-geometry measurement

    • Authors: Pätoprstý; Boris, Vozár, Marek, Hrušecký, Róbert, Buranský, Ivan
      Pages: 8
      Abstract: Journal of Measurements in Engineering, (in Press).
      Boris Pätoprstý, Marek Vozár, Róbert Hrušecký, Ivan Buranský
      The aim of the experiment described in the paper was to determine the possibility of using an optical 3D scanner to measure the macro-geometry of cutting tools. To verify this possibility, a precise component was measured, and the accuracy of optical 3D scanner was compared to a tactile coordinate measurement machine. A precise cemented carbide rod was used as a reference part and the measurement data were compared with the measurement result from the ZEISS Prismo coordinate measurement machine. The data obtained from the measurements were evaluated and compared. The experiment was carried out so that the use of an optical 3D scanner to measure cutting tools could be verified based on the desired requirements. Both dimensions and geometrical tolerancing – circularity were measured. The experiment has shown that an optical 3D scanner can achieve sufficient accuracy for the purpose of measuring macro-geometry of cutting tools.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-02-03T00:00:00Z
      DOI: 10.21595/jme.2023.23533
      Issue No: Vol. 12, No. 1 (2024)
       
  • Research on power equipment troubleshooting based on improved AlexNet
           neural network

    • Authors: Xu; Fangheng, Liu, Sha, Zhang, Wen
      Pages: 20
      Abstract: Journal of Measurements in Engineering, (in Press).
      Fangheng Xu, Sha Liu, Wen Zhang
      Power equipment is an important component of the whole power system, so that it is obvious that it is required to develop a correct method for accurate analysis of the infrared image features of the equipment in the field of detection and recognition. This study proposes a troubleshooting strategy for the power equipment based on the improved AlexNet neural network. Multi-scale images based on the Pan model are used to determine the equipment features, and to determine the shortcomings of AlexNet neural network, such as slower recognition speed and easy overfitting. After knowing these shortcomings, it would become possible to improve the specific recognition model performance by adding a pooling layer, modifying the activation function, replacing the LRN with BN layer, and optimizing the parameters of the improved WOA algorithm, and other measures. In the simulation experiments, this paper's algorithm was compared with AlexNet, YOLO v3, and Faster R-CNN algorithms in the lightning arrester fault detection, circuit breaker fault detection, mutual transformer fault detection, and insulator fault detection improved by an average of 5.47 %, 4.69 %, and 3.42 %, which showed that the algorithm had a better recognition effect.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-01-21T00:00:00Z
      DOI: 10.21595/jme.2023.23786
      Issue No: Vol. 12, No. 1 (2024)
       
  • A train F-TR lock anti-lifting detection method based on improved BP
           neural network

    • Authors: Jiang; Jun
      Pages: 12
      Abstract: Journal of Measurements in Engineering, (in Press).
      Jun Jiang
      In the railway container yard, there are few mature intelligent lifting prevention solutions available for train flatbed loading and unloading operations due to the poor detection accuracy or speed of traditional detection methods. This paper designs a train Flatbed Twist Rail (F-TR) lock anti-lifting detection method based on an improved BP neural network. The system collects weight and laser distance measurement data from the four locks of the hoist, establishes a flatbed lifting detection model based on the BP neural network, and optimizes the model's performance by incorporating a momentum factor and adaptive learning rate during weight adjustment. In practical tests, this system demonstrates a high detection rate and fast detection speed, offering intelligent safety protection for automated rail mounted gantry in the railway container yard.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-01-08T00:00:00Z
      DOI: 10.21595/jme.2023.23638
      Issue No: Vol. 12, No. 1 (2024)
       
  • Sound radiation patterns of the Sarasvati Veena and their relation with
           the modal behavior of its top plate

    • Authors: Chauhan; Chandrashekhar, Singru, Pravin, Vathsan, Radhika
      Pages: 12
      Abstract: Journal of Measurements in Engineering, (in Press).
      Chandrashekhar Chauhan, Pravin Singru, Radhika Vathsan
      The large wooden resonator of the Sarasvati Veena amplifies and radiates the sound in almost all directions. The directional and spatial dependence of this radiation is studied in conjunction with the mode shapes of the top plate of the resonator. Sound radiation patterns are simulated theoretically using the nodal displacement data obtained from the numerical modal analysis of the resonator. The experimental analysis involves the manual plucking of the Veena string. The radiated sound is recorded by placing microphones around the resonator in circular arrays of different radii in the different planes. These combinations of arrays at different distances and planes provide a thorough knowledge of sound radiating out of the resonator. The intensities of different frequencies in the recorded spectral data as functions of direction and distance from the approximate center of the top plate of the resonator are studied. Experimentally measured patterns show the importance of the top plate over the body of the resonator. Theoretical and experimental radiation patterns for different harmonics of the plucked string are compared and a good match is observed. The behavior of the radiating sound in the different planes at different radial distances from the assumed center is discussed.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-01-04T00:00:00Z
      DOI: 10.21595/jme.2023.23505
      Issue No: Vol. 12, No. 1 (2024)
       
  • YOLOv3-MSSA based hot spot defect detection for photovoltaic power
           stations

    • Authors: Gu; Kaiming, Chen, Yong
      Pages: 16
      Abstract: Journal of Measurements in Engineering, (in Press).
      Kaiming Gu, Yong Chen
      With the continuous development of the energy industry, photovoltaic power generation is gradually becoming one of the main power generation methods. However, detecting hot spot defects in photovoltaic power stations is challenging. Therefore, enhancing detection efficiency using information technology has become a crucial aspect. The study presents a defect detection model for PV power stations using the YOLOv3 (You Only Look Once v3) algorithm. The model incorporates coordinate attention module (CAM) and self-attention module (SAM) to improve feature extraction in low-resolution conditions. The Multi objective Sparrow is employed to achieve multiple objectives. It is very contributing in the detection of low-resolution features. It shows that the research method can reduce the loss value to 0.009 after 400 iterations of the loss curve test. The precision-recall (P-R) curve generated by the research method only starts to drop sharply when the Recall value reaches 0.96. The number of parameters generated by the research method is 3.46×106. The detection accuracy of the research method reaches 98.86 % when there are five defective fault types. The results indicate that the proposed research method offers improved detection speed and higher accuracy in identifying hot spot defects in PV power stations. This technology provides valuable support for hot spot defect detection and presents new opportunities for the field.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-01-03T00:00:00Z
      DOI: 10.21595/jme.2023.23418
      Issue No: Vol. 12, No. 1 (2024)
       
  • An assembled hot wire anemometer design

    • Authors: Yao; Xingxing, Shen, Fanhao, Zheng, Yuan, Xiao, Ting
      Pages: 10
      Abstract: Journal of Measurements in Engineering, (in Press).
      Xingxing Yao, Fanhao Shen, Yuan Zheng, Ting Xiao
      The hot wire anemometer is a widely utilized device in laboratory settings for measuring air speed. This paper investigates the relationship between air speed and hot wire temperature across various air speed ranges, employing the theory of thermal equilibrium. We designed a measurement circuit and hot wire shape based on the hot wire anemometer principle, and validated the linear relationship between current and temperature at different air speeds within an adjustable air speed field. The measured current serves as a representative of air speed. Experimental validation of the designed hot wire anemometer demonstrates accurate measurement results that align with theoretical values across different air speed ranges. Finally, we determined the sensitivity of the anemometer in various measurement ranges, considering the instrument's uncertainty and measurement formula.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-01-03T00:00:00Z
      DOI: 10.21595/jme.2023.23637
      Issue No: Vol. 12, No. 1 (2024)
       
  • Laser cladding powder flow field detection system based on ISR
           optimization algorithm

    • Authors: Tong; Yuan, Wang, Hongbo, Jin, Zhaowen
      Pages: 15
      Abstract: Journal of Measurements in Engineering, (in Press).
      Yuan Tong, Hongbo Wang, Zhaowen Jin
      In coaxial powder feeding laser cladding, the morphology of the powder flow field is crucial for the forming quality. Therefore, this study utilizes high-speed imaging technology and an Image Super Resolution algorithm to create a laser cladding powder flow field detection system that is capable of detecting and tracking powder particles in the laser cladding environment. The experiment shows that the optimized algorithm has significant improvement in structural similarity indicators, with an improvement rate of nearly 11 %. For powder particle tracking, the distance accuracy of the optimized model is 1.5 lower than that of the unimproved model. In addition, by combining with the Kalman filtering algorithm, the tracking effect of powder particles has been further improved. This paper also found a relationship between powder transfer rate and powder utilization rate. In summary, the powder flow field analysis based on visual detection and image processing technology designed in this study can effectively reflect and predict the trend of changes in cladding quality.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-01-02T00:00:00Z
      DOI: 10.21595/jme.2023.23571
      Issue No: Vol. 12, No. 1 (2024)
       
  • Displacement analysis and numerical simulation of pile-anchor retaining
           structure in deep foundation pit

    • Authors: Yin; Xupeng, Ni, Hongmei
      Pages: 13
      Abstract: Journal of Measurements in Engineering, (in Press).
      Xupeng Yin, Hongmei Ni
      Foundation pit excavation can cause settlement and displacement of surrounding existing buildings and roads. In order to study the influence of soil unloading on the surrounding buildings during pit foundation excavation, the application of a pile-anchor retaining structure in a deep foundation pit was studied, with the deep foundation pit project of Anhui Bright Pearl Mall as the research subject. Through theoretical analysis, field measurements, and FLAC3D numerical simulations, the supporting structure was comprehensively analyzed. A comparison was made between the measured displacement data and the numerical simulation results of the supporting structure and the surrounding environment during the excavation process of the foundation pit. The results indicate that the model results, obtained through the use of the FLAC3D software for numerical simulations, generally align with the field data. This approach can more accurately reflect the evolutionary laws of soil pressure and deformation during the excavation of the foundation pit. The maximum displacement of the horizontal displacement monitoring point in this project's foundation pit is 25.96 mm, which is less than the monitoring alarm value of 30 mm. The horizontal displacement monitoring of the sidewall of the foundation pit is crucial among them. An analysis of the three major causes of numerical deviation provides valuable insights for the design of deep foundation pit supporting structures.
      Citation: Journal of Measurements in Engineering
      PubDate: 2024-01-02T00:00:00Z
      DOI: 10.21595/jme.2023.23635
      Issue No: Vol. 12, No. 1 (2024)
       
  • Static transmission error measurement of various gear-shaft systems by
           finite element analysis

    • Authors: Czakó; Alexander, Řehák, Kamil, Prokop, Aleš, Rekem, Jakub, Láštic, Daniel, Trochta, Miroslav
      Pages: 15
      Abstract: Journal of Measurements in Engineering, (in Press).
      Alexander Czakó, Kamil Řehák, Aleš Prokop, Jakub Rekem, Daniel Láštic, Miroslav Trochta
      Transmission error (TE) is a significant parameter related to gears vibration widely investigated by many authors using different approaches. However, in previous studies, spur and helical gears were mainly examined. There is a lack of studies addressed to double helical and herringbone gears and a comparison among several types of gearing with parallel axes. In this paper, spur, helical, double helical, and herringbone gears are analyzed in terms of static transmission error (STE), contact pressure and tooth root stress. Static contact analyses were conducted using the finite element method (FEM) which is often considered a tool for validating other methods and approaches. Moreover, three variants of boundary conditions of each gear type are introduced, including flexible shafts and the effect of a tip relief modification at sole gears, without shafts, was analyzed. In addition, a concept of a compact test rig intended for STE measurements at low loads was presented. The results have shown, among other things, significant influence of the shaft stiffness and boundary conditions on meshing characteristics.
      Citation: Journal of Measurements in Engineering
      PubDate: 2023-12-28T00:00:00Z
      DOI: 10.21595/jme.2023.23843
      Issue No: Vol. 12, No. 1 (2023)
       
  • Test and application of movable steel barrier with grade SB light
           composite corrugated beam

    • Authors: Zhang; Aling, Bu, Qianmiao, Zhang, Wen, He, Guomeng, Deng, Yong
      Pages: 21
      Abstract: Journal of Measurements in Engineering, (in Press).
      Aling Zhang, Qianmiao Bu, Wen Zhang, Guomeng He, Yong Deng
      In this study, movable steel barrier with grade SB light composite corrugated beam is designed, which addresses the problems of the prior central partition belt portable guardrail in terms of easy mobility, local safety, easy construction, and other indications. This guardrail employs explicit algorithms to conduct a dynamic finite element simulation analysis and a real vehicle crash test, and verifies the guardrails' blocking, guiding, and buffering functions in accordance with the SB level collision conditions listed in the Standard for Safety Performance Evaluation of Highway Barriers (JTG B05-01-2013). According to the results, the safety performance of SB grade lightweight composite corrugated beam movable steel guardrail meets the requirements of the Standard for Safety Performance Evaluation of Highway Barriers (JTG B05-01-2013). In addition, the guardrail can be opened for 12 meters in 1 minute and returned to close in 2 minutes. The opening and restoration of the movable guardrail is superior to the previous central divider movable guardrail. This guardrail has been tried for some high-speed and its safety performance has been verified again in actual high-speed vehicle collisions.
      Citation: Journal of Measurements in Engineering
      PubDate: 2023-12-23T00:00:00Z
      DOI: 10.21595/jme.2023.23386
      Issue No: Vol. 12, No. 1 (2023)
       
  • Utilizing a knowledge-based training algorithm and time-domain extraction
           for pattern recognition in cylindrical features through vibration and
           sound signals

    • Authors: Dirhamsyah; M., Riza, Hammam, Rizal, M. Syamsu
      Pages: 12
      Abstract: Journal of Measurements in Engineering, (in Press).
      M. Dirhamsyah, Hammam Riza, M. Syamsu Rizal
      This study presents a new solution to address challenges encountered in additive manufacturing, specifically in the context of 3D printing, where failures can occur due to complications associated with the nozzle or filament. The proposed solution in this research involves using a time-domain feature extraction method that leverages sound and vibration patterns. By implementing sensors to capture these signals in a controlled and noise-free environment, and then utilizing a Multi-Layer Perceptron (MLP) model trained accurately to predict upcoming signals and vibrations, proactive anticipation of printing outcomes is facilitated, including potential failures. Simulation results obtained using MATLAB for the MLP showcase the effectiveness of this approach, demonstrating remarkably low error rates. Furthermore, through rigorous data validation, the proposed method's ability to accurately identify sound and vibration signals is confirmed. As a result, the likelihood of failures is significantly reduced, thereby preventing defects in the filament. The implications of this solution hold great promise in substantially enhancing the reliability and efficiency of additive manufacturing processes.
      Citation: Journal of Measurements in Engineering
      PubDate: 2023-12-14T00:00:00Z
      DOI: 10.21595/jme.2023.23452
      Issue No: Vol. 12, No. 1 (2023)
       
  • Application of AI intelligent vision detection technology using deep
           learning algorithm

    • Authors: Huang; Yan
      Pages: 16
      Abstract: Journal of Measurements in Engineering, (in Press).
      Yan Huang
      This study aims to design efficient and reliable artificial intelligence vision detection models to improve detection efficiency and accuracy. The study filters defect-free images by image preprocessing and region of interest detection techniques. AlexNet network is enhanced by introducing attention mechanism modules, deep separable convolutions, and more to effectively boost the network's feature extraction capacity. An area convolutional neural network is developed to rapidly identify and locate defects on steel plate surfaces, utilizing an enhanced AlexNet network for feature extraction. Results demonstrated that the algorithm attained an average detection rate of 98 % and can identify defects in a minimal time of only 0.0011 seconds. For the detection of six types of steel plate defects, the average accuracy of the optimized fast regional convolutional neural network reached more than 0.9, especially for the detection of small-size defects with excellent performance. This improved AlexNet network has a great advantage in F1 value. The conclusion of the study shows that the designed artificial intelligence vision detection model has high detection accuracy, speed, and performance stability in steel plate surface defect detection and has a wide range of application prospects.
      Citation: Journal of Measurements in Engineering
      PubDate: 2023-12-05T00:00:00Z
      DOI: 10.21595/jme.2023.23506
      Issue No: Vol. 12, No. 1 (2023)
       
  • Target detection algorithm based on super- resolution color remote sensing
           image reconstruction

    • Authors: Wang; Zhihong, Wang, Chaoying, Chen, Yonggang, Li, Jianxin
      Pages: 15
      Abstract: Journal of Measurements in Engineering, (in Press).
      Zhihong Wang, Chaoying Wang, Yonggang Chen, Jianxin Li
      An improved generative adversarial network model is adopted to improve the resolution of remote sensing images and the target detection algorithm for color remote sensing images. The main objective is to solve the problem of training super-resolution reconstruction algorithms and missing details in reconstructed images, aiming to achieve high-precision detection of medium and low-resolution color remote sensing targets. First, a lightweight image super-resolution reconstruction algorithm based on an improved generative adversarial network (GAN) is proposed. This algorithm combines the pixel attention mechanism and up-sampling method to restore image details. It further integrates edge-oriented convolution modules into traditional convolution to reduce model parameters and achieve better feature collection. Then, to further enhance the feature collection ability of the model, the YOLOv4 object detection algorithm is also improved. This is achieved by introducing the Focus structure into the backbone feature extraction network and integrating multi-layer separable convolutions to improve the feature extraction ability. The experimental results show that the improved target detection algorithm based on super resolution has a good detection effect on remote sensing image targets. It can effectively improve the detection accuracy of remote sensing images, and have a certain reference significance for the realization of small target detection in remote sensing images.
      Citation: Journal of Measurements in Engineering
      PubDate: 2023-11-18T00:00:00Z
      DOI: 10.21595/jme.2023.23510
      Issue No: Vol. 12, No. 1 (2023)
       
  • Prediction of comprehensive dynamic performance for probability screen
           based on AR model-box dimension

    • Authors: Chen; Qingtang, Huang, Yijian
      Pages: 10
      Abstract: Journal of Measurements in Engineering, (in Press).
      Qingtang Chen, Yijian Huang
      In order to evaluate the comprehensive dynamic performance of probability screen and select the appropriate working conditions, a dynamic model of probability screen vibration system is established. Then, the calculation method of the dynamic characteristic parameters, based on the time series Auto Regression (AR) model of vibration test, is used. The relationship among the comprehensive dynamic characteristics, the screening efficiency and the box dimension of probability screen vibration system is analyzed, and Least Square Support Vector Machine (LSSVM), Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are used to predict the screening efficiency with box dimension. The analysis result shows that the screening efficiency, the stability, the response rapidity and the comprehensive dynamic characteristic of the system are all related to the box dimension of time series. As for the complexity of probability screen vibration system, it affects the comprehensive dynamic performance, and ultimately touches the screening efficiency of the probability screen; The best working conditions for the system are selected by the curve between box dimension and the working condition parameter; Taking box dimension as the only input variable, the prediction accuracy of the screening efficiency is high by using LSSVM,GRNN and BPNN methods, the prediction results are stable and reliable, and the box dimension can be used as a single input variable to predict the screening efficiency, it has the advantages of fewer input parameters, high prediction efficiency, and high prediction accuracy, which has great potential for expanding application space and further research value.
      Citation: Journal of Measurements in Engineering
      PubDate: 2023-11-13T00:00:00Z
      DOI: 10.21595/jme.2023.23522
      Issue No: Vol. 11, No. 4 (2023)
       
  • Cross domain fault diagnosis method based on MLP-mixer network

    • Authors: Mao; Xiaodong
      Pages: 13
      Abstract: Journal of Measurements in Engineering, (in Press).
      Xiaodong Mao
      The quality of rolling bearings determines the safety of mechanical equipment operation, and bearings with more precise structures are prone to damage due to excessive operation. Therefore, cross domain fault diagnosis of bearings has become a research hotspot. To better improve the accuracy of bearing cross domain fault diagnosis, this study proposes two models. One is a cross domain feature extraction model constructed using a mixed attention mechanism, which recognizes and extracts high-level features of bearing faults through channel attention and spatial attention mechanisms. The other is a bearing cross domain fault diagnosis model based on multi-layer perception mechanism. This model takes the feature signals collected by the attention mechanism model as input to identify and align the differences between the source and target domain features, facilitating cross domain transfer of features. The experimental results show that the mixed attention mechanism model has a maximum accuracy of 97.3 % for feature recognition of different faults, and can successfully recognize corresponding signal values. The multi-layer perception model can achieve the highest recognition accuracy of 99.5 % in bearing fault diagnosis, and it can reach a stable state when it iterates to 26, and the final stable loss value is 0.28. Therefore, the two models proposed in this study have good application value.
      Citation: Journal of Measurements in Engineering
      PubDate: 2023-10-30T00:00:00Z
      DOI: 10.21595/jme.2023.23460
      Issue No: Vol. 11, No. 4 (2023)
       
  • Vehicle state and parameter estimation based on improved extend Kalman
           filter

    • Authors: Liu; Yingjie, Cui, Dawei, Peng, Wen
      Pages: 12
      Abstract: Journal of Measurements in Engineering, (in Press).
      Yingjie Liu, Dawei Cui, Wen Peng
      In order to reduce the influence of historical measurement data errors in the process of vehicle state estimation and improve the accuracy of the vehicle state estimation, a limited memory random weighted extended Kalman filter (LMRWEKF) algorithm is proposed. Firstly, a 3-DOF nonlinear vehicle dynamics model is established. Secondly, the limited memory extended Kalman filter is formed by fusing the limited memory filter and the extended Kalman filter. Then, according to the random weighting theory, the weighting coefficients that obey Dirichlet distribution are introduced to further improve the filtering estimation accuracy. Finally, a virtual test based on the ADAMS/CAR is used for the experimental verification. The results show that the error in the longitudinal velocity and the yaw rate is small, especially in the mean value of estimation error of side slip angle which is different in just 0.015 degrees between the virtual test and the simulation result. And also, the results compared with traditional methods indicate that the proposed LMRWEKF algorithm can solve the problem of vehicle state estimation with the performance of noise fluctuation suppression and higher estimation accuracy. The mean absolute error (MAE) and root mean square error (RMSE) are considered to verify the estimation accuracy of the proposed algorithm. And the comparison results indicate that the estimation accuracy of the LMRWEKF algorithm is significantly higher than those of the EKF and DEKF methods.
      Citation: Journal of Measurements in Engineering
      PubDate: 2023-10-30T00:00:00Z
      DOI: 10.21595/jme.2023.23475
      Issue No: Vol. 11, No. 4 (2023)
       
  • The application of fault diagnosis techniques and monitoring methods in
           building electrical systems – based on ELM algorithm

    • Authors: Liu; Guanghui
      Pages: 16
      Abstract: Journal of Measurements in Engineering, (in Press).
      Guanghui Liu
      The reliability of modern building electrical systems are receiving increasing attention as they become more intelligent and complex. As the majority of building electrical systems use neutral point grounding, earth faults or short circuits can get worse over time and damage both the distribution system and the electrical equipment. To this end, the corresponding three phases and four categories, namely three-phase voltage, three-phase current after fault, three-phase voltage distortion rate, three-phase current distortion rate, a total of 12 dimensional fault feature vectors and 10 fault simulation types, were summarised and extracted in conjunction with the actual operating conditions of the system. Using traditional fault identification ideas and neural network algorithm as reference, a 12-dimensional fault feature vector is used as the model input to construct a building electrical fault diagnosis and detection model based on ELM algorithm. Results showed that the ELM-based model’s classification accuracy for this experimental sample was 97.56 %, its AUC was 0.92, and its RMSE was 0.3521. These figures were higher than the classification accuracy and performance of the BP algorithm and GA-BP algorithm fault diagnosis models, and they also demonstrate better robustness and generalizability. The model also has a 97.27 % correct rate in fault discrimination, while the computation time is only 0.201 s, and its fault identification and diagnosis speed is faster than other algorithmic models. At the same time, this research model has a good fault monitoring accuracy of up to 98.6 % for building electrical systems. The research can provide a more sensitive, accurate and rapid fault monitoring method for the current building electrical system. It also improves the reliability of the building electrical system in a complex environment and achieves better protection of the system. This has a certain significance for the development of the building electrical industry.
      Citation: Journal of Measurements in Engineering
      PubDate: 2023-10-27T00:00:00Z
      DOI: 10.21595/jme.2023.23357
      Issue No: Vol. 11, No. 4 (2023)
       
  • Genetic algorithm-based error correction algorithm for CNC turning
           machining of mechanical parts

    • Authors: Xue; Qinghong, Miao, Ying, Xue, Zijian
      Pages: 15
      Abstract: Journal of Measurements in Engineering, (in Press).
      Qinghong Xue, Ying Miao, Zijian Xue
      This paper discusses how to improve the machining precision in the turning of slender shaft. The main cause of dimensional error in slender shaft machining is analyzed by establishing dimensional error model and using genetic algorithm to optimize cutting parameter selection. Based on this, the proportional-integral-differential control error compensation is proposed to reduce the error in the turning process of slender shaft. Through the simulation experiment, the machining size error of slender shaft under different cutting parameters is obtained. It is found that the increase of back blowing and feed rate will aggravate the dimensional error, while the increase of CS will reduce the dimensional error. The error after the proportional-integral-differential control error compensation is much smaller than that without the error compensation. The experimental results show that the method is reliable in reducing the errors in the turning of slender shaft, and can realize the machining mode with higher precision and efficiency. This is of great significance to the development of machinery manufacturing industry.
      Citation: Journal of Measurements in Engineering
      PubDate: 2023-10-19T00:00:00Z
      DOI: 10.21595/jme.2023.23501
      Issue No: Vol. 11, No. 4 (2023)
       
  • Extraction and diagnosis of rolling bearing fault signals based on
           improved wavelet transform

    • Authors: Cheng; Zhiqing
      Pages: 16
      Abstract: Journal of Measurements in Engineering, (in Press).
      Zhiqing Cheng
      As the continuous growth of the machinery industry, the importance of rolling bearings as key connecting parts in machinery movement is also increasing. However, the extraction and diagnosis of rolling bearing fault signals are difficult, and how to use modern transform analysis methods to raise the extraction efficiency and diagnostic accuracy becomes the focus. For this, a rolling bearing fault signal extraction and diagnosis model is designed based on empirical wavelet transform. The diagnostic model is optimized by using support vector machine and quantum genetic algorithm to design a rolling bearing fault signal extraction and diagnosis model based on improved empirical wavelet transform-support vector machine. The test results show that the research method can obtain four component signals showing different anomalies when generating time domain diagrams. Only five component peaks are generated and one group is extracted as output when generating component peaks. The abnormal amplitude of envelope spectrum basically reaches 0.40×10-6 or above. The judgment accuracy of component diagnosis reaches 98.12%. The above results show that the research method has better fault signal extraction ability and better diagnostic accuracy when performing fault signal diagnosis, which can provide new technical support for rolling bearing fault signal extraction and diagnosis.
      Citation: Journal of Measurements in Engineering
      PubDate: 2023-10-09T00:00:00Z
      DOI: 10.21595/jme.2023.23442
      Issue No: Vol. 11, No. 4 (2023)
       
  • DIC measurement method based on binocular stereo vision for image 3D
           displacement detection

    • Authors: Dong; Xue
      Pages: 15
      Abstract: Journal of Measurements in Engineering, (in Press).
      Xue Dong
      The deformation detection of large machinery is usually achieved using three-dimensional displacement measurement. Binocular stereo vision measurement technology, as a commonly used digital image correlation method, has received widespread attention in the academic community. Binocular stereo vision achieves the goal of three-dimensional displacement measurement by simulating the working mode of the human eyes, but the measurement is easily affected by light refraction. Based on this, the study introduces particle swarm optimization algorithm for target displacement measurement on Canon imaging dataset, and introduces backpropagation neural network for mutation processing of particles in particle swarm algorithm to generate fusion algorithm. It combines the four coordinate systems of world, pixel, physics, and camera to establish connections. Taking into account environmental factors and lens errors, the camera parameters and deformation coefficients were revised by shooting a black and white checkerboard. Finally, the study first conducted error analysis on binocular stereo vision technology in three dimensions, and the relative error remained stable at 1 % within about 60 seconds. At the same time, three algorithms, including the spotted hyena algorithm, were introduced to conduct performance comparison experiments using particle swarm optimization and backpropagation network algorithms. The experiment shows that the three-dimensional error of the fusion algorithm gradually stabilizes within the range of [–0.5 %, 0.5 %] over time, while the two-dimensional error generally hovers around 0 value. Its performance is significantly superior to other algorithms, so the binocular stereo vision of this fusion algorithm can achieve good measurement results.
      Citation: Journal of Measurements in Engineering
      PubDate: 2023-10-09T00:00:00Z
      DOI: 10.21595/jme.2023.23448
      Issue No: Vol. 11, No. 4 (2023)
       
  • Characterization of 3D-Radar images of pavement devoid damage based on
           FDTD

    • Authors: Li; Y. X., Kang, X. T., Sheng, S. M., Fu, C. J.
      Pages: 14
      Abstract: Journal of Measurements in Engineering, (in Press).
      Y. X. Li, X. T. Kang, S. M. Sheng, C. J. Fu
      Accurate judgement of devoid damage information by 3D-Radar is an effective way of repairing damage in nondestructive pavements. In order to systematically analyse the characteristics of devoid damage under nondestructive pavements in 3D-Radar response. In this study, the 3D-Radar response to devoid damage of different sizes, locations and moisture contents was quantified by FDTD orthorectified simulations. Data acquisition of the pre-buried devoid damage on site was carried out using 3D-Radar, compared with the orthorectified simulation results and numerical analysis. The detection effect was also verified by relying on the project. The results show that the radar wave characteristics of the devoid damage are obvious. Different colour and waveform image characteristics in B-Scan in the presence and absence of water at the location; the size of the devoid also has an impact on the image characteristics. It depends on the footprints and size of the devoid. It creates “upward-convex”, “down-concave” and straight features; the presence of the devoid characteristics in the 3D-Radar mapping will enhance the confidence of the devoid identification through field tests and engineering verification.
      Citation: Journal of Measurements in Engineering
      PubDate: 2023-10-06T00:00:00Z
      DOI: 10.21595/jme.2023.23469
      Issue No: Vol. 11, No. 4 (2023)
       
  • The analysis of the destabilizing motion of a hyperbolic cooling tower
           during demolition blasting

    • Authors: Jia; Haipeng, Song, Qianqian
      Pages: 13
      Abstract: Journal of Measurements in Engineering, (in Press).
      Haipeng Jia, Qianqian Song
      The destabilizing motion characteristics of the hyperbolic cooling tower in demolition blasting are thoroughly investigated through the establishment of a numerical simulation calculation model, leading to the following conclusions regarding its destabilizing motion. The tensile-compression elastic-plastic model, which possesses the characteristics of parameter modification function and independence from unit size, can more effectively capture the mechanical properties of concrete materials and find better application in the simulation and calculation research of reinforced concrete structures. The self-oscillation frequency check and collapse morphological analysis are employed to validate the accuracy of the simulation calculation model for hyperbolic cooling towers, as well as to assess the rationality of parameters in the tensile-compression elastic-plastic model. The collapse of a cooling tower induces flexural deformation in the lateral wall, tensile disturbance in the upper and middle sections of the cylinder, and compressive disturbance in the vertical cross-section. The cylinder body has incurred damage as a result of the tower wall’s front end striking the ground at the directional window position on the front side of the throat, leading to a significant extrusion deformation issue. The buckling deformation in the central and lower sections of the rear wall propagated towards the back side of the tower wall upon reaching the ground, ultimately resulting in an “inverted V-shaped” damage along the buckling deformation line. The research findings hold significant relevance for future endeavors.
      Citation: Journal of Measurements in Engineering
      PubDate: 2023-10-06T00:00:00Z
      DOI: 10.21595/jme.2023.23470
      Issue No: Vol. 11, No. 4 (2023)
       
 
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  Subjects -> MANUFACTURING AND TECHNOLOGY (Total: 363 journals)
    - CERAMICS, GLASS AND POTTERY (31 journals)
    - MACHINERY (34 journals)
    - MANUFACTURING AND TECHNOLOGY (223 journals)
    - METROLOGY AND STANDARDIZATION (6 journals)
    - PACKAGING (19 journals)
    - PAINTS AND PROTECTIVE COATINGS (4 journals)
    - PLASTICS (42 journals)
    - RUBBER (4 journals)

METROLOGY AND STANDARDIZATION (6 journals)

Showing 1 - 6 of 6 Journals sorted alphabetically
Aeolian Research     Hybrid Journal   (Followers: 7)
International Journal of Instrumentation Technology     Hybrid Journal   (Followers: 9)
Journal of Applied Meteorology and Climatology     Hybrid Journal   (Followers: 40)
Journal of Measurements in Engineering     Open Access   (Followers: 5)
Nanomanufacturing and Metrology     Hybrid Journal  
NCSLI Measure : The Journal of Measurement Science     Hybrid Journal  
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JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


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