Pub Date : 2023-05-01DOI: 10.1109/MIM.2023.10121409
Bingxin Hu, Zhiyuan Li, Hongsheng Liu, Bin Zhang, Shengxi Zhou
Energy harvesting from flow-induced vibrations has been a hot spot in recent years. In this study, a flutter-based piezoelectric energy harvester (FPEH) connected with a self-powered rectifier-less S-SSHI interface circuit is working at the limit cycle oscillation (LCO) state to efficiently harvest wind-induced vibration energy. First, an FPEH is designed, and the theoretical model is derived. The dynamic response of the FPEH is tested and measured in a wind tunnel, and results show that flutters start at the wind speed of 7.3 m/s. Meanwhile, the root mean square (RMS) output voltage increases with the increase of the wind speed which is also proved by the numerical simulations and the experiment. A self-powered optimized series synchronized switch harvesting on inductor circuit (SP-OSSHI) is proposed to efficiently harvest the electrical energy according to the output characteristic from flutter. The proposed circuit reduces the number of components and the circuit size by improving the positive and negative peak detection switches, which reduces the internal energy loss and thus improves the energy harvesting efficiency. The energy harvester is verified by the experiment, and a maximum output power of 36 μ W is obtained.
{"title":"A Self-Powered Rectifier-Less Series-Synchronized Switch Harvesting on Inductor (S-SSHI) Interface Circuit for Flutter-Based Piezoelectric Energy Harvesters","authors":"Bingxin Hu, Zhiyuan Li, Hongsheng Liu, Bin Zhang, Shengxi Zhou","doi":"10.1109/MIM.2023.10121409","DOIUrl":"https://doi.org/10.1109/MIM.2023.10121409","url":null,"abstract":"Energy harvesting from flow-induced vibrations has been a hot spot in recent years. In this study, a flutter-based piezoelectric energy harvester (FPEH) connected with a self-powered rectifier-less S-SSHI interface circuit is working at the limit cycle oscillation (LCO) state to efficiently harvest wind-induced vibration energy. First, an FPEH is designed, and the theoretical model is derived. The dynamic response of the FPEH is tested and measured in a wind tunnel, and results show that flutters start at the wind speed of 7.3 m/s. Meanwhile, the root mean square (RMS) output voltage increases with the increase of the wind speed which is also proved by the numerical simulations and the experiment. A self-powered optimized series synchronized switch harvesting on inductor circuit (SP-OSSHI) is proposed to efficiently harvest the electrical energy according to the output characteristic from flutter. The proposed circuit reduces the number of components and the circuit size by improving the positive and negative peak detection switches, which reduces the internal energy loss and thus improves the energy harvesting efficiency. The energy harvester is verified by the experiment, and a maximum output power of 36 μ W is obtained.","PeriodicalId":55025,"journal":{"name":"IEEE Instrumentation & Measurement Magazine","volume":"26 1","pages":"5-13"},"PeriodicalIF":2.1,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43554969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/MIM.2023.10121411
Stefan Büttner, Julian Windisch, M. März
In recent decades, research in the field of cryogenic power electronics has gained increasing interest, as it promises advantages such as higher power density and higher efficiency. Particularly in the mobility sector, lower weight and smaller size are essential to advance electrification [1]. Another incentive and benefit of lower power losses is the reduction in operating costs. However, the study in [2] has shown that energetic profitability of low-temperature cooling is achieved, in particular, in applications where the necessary cooling for the power electronics is available for free and synergy effects can be realized within the overall system. Interesting areas of application are, therefore, in the field of aviation, where the cold ambient temperature of -55 °C is available, and in the mobility sector. Cryogenically stored fuels such as liquid hydrogen (LH2) or liquid natural gas (LNG) must be heated before they can be used, for example LH2 for application in a fuel cell, for which the power losses generated in a power electronic converter can be used perfectly. This saves energy for extra heaters and increases the efficiency of the power electronics [2]. One challenge when operating power electronics at temperatures below -40 °C is that most electronic components are not specified from the manufacturer for these temperatures. Therefore, a comprehensive characterization of all required electronic components for a deep temperature operation is essential, for which a suitable environment—a cryogenic cooling system—with variably adjustable ambient temperature is required.
{"title":"Design and Operation of a Cost-Effective Cooling Chamber for Testing Power Electronics at Cryogenic Temperatures","authors":"Stefan Büttner, Julian Windisch, M. März","doi":"10.1109/MIM.2023.10121411","DOIUrl":"https://doi.org/10.1109/MIM.2023.10121411","url":null,"abstract":"In recent decades, research in the field of cryogenic power electronics has gained increasing interest, as it promises advantages such as higher power density and higher efficiency. Particularly in the mobility sector, lower weight and smaller size are essential to advance electrification [1]. Another incentive and benefit of lower power losses is the reduction in operating costs. However, the study in [2] has shown that energetic profitability of low-temperature cooling is achieved, in particular, in applications where the necessary cooling for the power electronics is available for free and synergy effects can be realized within the overall system. Interesting areas of application are, therefore, in the field of aviation, where the cold ambient temperature of -55 °C is available, and in the mobility sector. Cryogenically stored fuels such as liquid hydrogen (LH2) or liquid natural gas (LNG) must be heated before they can be used, for example LH2 for application in a fuel cell, for which the power losses generated in a power electronic converter can be used perfectly. This saves energy for extra heaters and increases the efficiency of the power electronics [2]. One challenge when operating power electronics at temperatures below -40 °C is that most electronic components are not specified from the manufacturer for these temperatures. Therefore, a comprehensive characterization of all required electronic components for a deep temperature operation is essential, for which a suitable environment—a cryogenic cooling system—with variably adjustable ambient temperature is required.","PeriodicalId":55025,"journal":{"name":"IEEE Instrumentation & Measurement Magazine","volume":"26 1","pages":"46-51"},"PeriodicalIF":2.1,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47145202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/MIM.2023.10121412
K. Welch, R. Pennington, Saipruthvi Vanaparthy, H. Do, Rohit Narayanan, Dan Popa, G. Barnes, Grace M. Kuravackel
Individuals with autism spectrum disorder (ASD) often face barriers in accessing opportunities across a range of educational, employment, and social contexts. One of these barriers is the development of effective communication skills sufficient for navigating the social demands of everyday environments. Fortunately, researchers have established evidence-based practices (EBP) for teaching critical communication skills to individuals with ASD [1]. One EBP that has received a great deal of attention over the last few decades is technology-aided instruction and intervention (TAII) [1], [2]. TAII is an instructional practice in which technology is an essential component and is used to facilitate behavior change. Further, it encompasses a wide range of applications including computer-assisted instruction, virtual and augmented reality, augmentative and alternative communication, and robot-assisted intervention [2].
{"title":"Using Physiological Signals and Machine Learning Algorithms to Measure Attentiveness During Robot-Assisted Social Skills Intervention: A Case Study of Two Children with Autism Spectrum Disorder","authors":"K. Welch, R. Pennington, Saipruthvi Vanaparthy, H. Do, Rohit Narayanan, Dan Popa, G. Barnes, Grace M. Kuravackel","doi":"10.1109/MIM.2023.10121412","DOIUrl":"https://doi.org/10.1109/MIM.2023.10121412","url":null,"abstract":"Individuals with autism spectrum disorder (ASD) often face barriers in accessing opportunities across a range of educational, employment, and social contexts. One of these barriers is the development of effective communication skills sufficient for navigating the social demands of everyday environments. Fortunately, researchers have established evidence-based practices (EBP) for teaching critical communication skills to individuals with ASD [1]. One EBP that has received a great deal of attention over the last few decades is technology-aided instruction and intervention (TAII) [1], [2]. TAII is an instructional practice in which technology is an essential component and is used to facilitate behavior change. Further, it encompasses a wide range of applications including computer-assisted instruction, virtual and augmented reality, augmentative and alternative communication, and robot-assisted intervention [2].","PeriodicalId":55025,"journal":{"name":"IEEE Instrumentation & Measurement Magazine","volume":"26 1","pages":"39-45"},"PeriodicalIF":2.1,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49461020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-01DOI: 10.1109/MIM.2023.10121386
Q. Jiang, Jialiang Yan
Due to the increasing demand for tactile sensing of robotic hands, this paper designs a soft robot hand that can realize object shape recognition and hardness detection. Taking advantage of the high sensitivity and wavelength division multiplexing of Fiber Bragg Gratings (FBG), a distributed detection method using a single fiber in series with multiple FBG is proposed, which can demodulate bending and pressure at the same time, reduce wiring and improve efficiency.
{"title":"Shape and Hardness Perception of Robot Soft Finger Based on Fiber Bragg Grating","authors":"Q. Jiang, Jialiang Yan","doi":"10.1109/MIM.2023.10121386","DOIUrl":"https://doi.org/10.1109/MIM.2023.10121386","url":null,"abstract":"Due to the increasing demand for tactile sensing of robotic hands, this paper designs a soft robot hand that can realize object shape recognition and hardness detection. Taking advantage of the high sensitivity and wavelength division multiplexing of Fiber Bragg Gratings (FBG), a distributed detection method using a single fiber in series with multiple FBG is proposed, which can demodulate bending and pressure at the same time, reduce wiring and improve efficiency.","PeriodicalId":55025,"journal":{"name":"IEEE Instrumentation & Measurement Magazine","volume":"26 1","pages":"26-32"},"PeriodicalIF":2.1,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44932673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the phenomenon of mode mixing and information redundancy when Hilbert-Huang transform (HHT) is used for non-stationary signal measurement data processing, an optimized HHT algorithm is proposed in the study. The processing effect is improved by setting complementary ensemble empirical mode decomposition instead of empirical mode decomposition, using a frequency-domain smoothing vector to smooth and marginal spectrum feedback to optimize the time-frequency spectrum. The optimized algorithm is applied to the measurement data processing of acoustic signals of Penaeus vannamei. The duration, the range of the frequency, and the relative intensity of the frequency within 0~24 kHz of the signals are obtained. Mean-while, the optimized time-frequency spectrums obtained by processing the signals and the distribution diagrams of the number of key information points obtained under different smoothing vectors and feedback times prove that the optimized performance of the algorithm is affected by the signal quality and the selection of smoothing vectors. Besides, the primary and secondary feedback results need to be integrated when extracting signal features.
{"title":"A Time-Frequency Domain Detection Method for Measurement Data of Non-Stationary Signals Based on Optimized Hilbert-Huang Transform","authors":"Caiyun Zhu, Tianyu Cao, Xiaoqun Zhao, Yichen Yang, Zhongwei Xu","doi":"10.1109/MIM.2023.10083022","DOIUrl":"https://doi.org/10.1109/MIM.2023.10083022","url":null,"abstract":"Aiming at the phenomenon of mode mixing and information redundancy when Hilbert-Huang transform (HHT) is used for non-stationary signal measurement data processing, an optimized HHT algorithm is proposed in the study. The processing effect is improved by setting complementary ensemble empirical mode decomposition instead of empirical mode decomposition, using a frequency-domain smoothing vector to smooth and marginal spectrum feedback to optimize the time-frequency spectrum. The optimized algorithm is applied to the measurement data processing of acoustic signals of Penaeus vannamei. The duration, the range of the frequency, and the relative intensity of the frequency within 0~24 kHz of the signals are obtained. Mean-while, the optimized time-frequency spectrums obtained by processing the signals and the distribution diagrams of the number of key information points obtained under different smoothing vectors and feedback times prove that the optimized performance of the algorithm is affected by the signal quality and the selection of smoothing vectors. Besides, the primary and secondary feedback results need to be integrated when extracting signal features.","PeriodicalId":55025,"journal":{"name":"IEEE Instrumentation & Measurement Magazine","volume":"26 1","pages":"29-39"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46170780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.1109/MIM.2023.10083002
Jinjun Duan, Bingcheng Wang, Baolin Ji, Weidong Sun, Zhouyi Wang, Z. Dai
With the rapid development of global industry, the storage capacity of oil tanks and wind turbine towers worldwide is gradually increasing and with it the problem of their maintenance: how to achieve a stable attachment of maintenance equipment to such vertical arcs? Wall-climbing robots are the ideal delivery platform due to their interface bonding capabilities. However, the robot is susceptible to the curvature of the curved surface. If the contact between its attachments and the crawling surface is inadequate, the closed chain system formed by the stance phase is unable to resist the force impact from the sticky release, and the risk of the robot destabilizing and tipping over is great. To improve the robot's adaptive capacity and anti-disturbance capability, this paper proposes an adaptive external force-softening motion strategy for the limbs of the inner and outer curved stance phases to ensure the stability of the robot body. The foot end motion is orthogonally decoupled into the forward direction and the arc surface fitting direction, and the stance phase adopts a virtual mass-damping control model to realize the spring cushioning behavior of the system during the forward motion. The experimental results show that the algorithm proposed in this paper can effectively improve the stability of the robot in the process of vertical arc crawling and avoid the phenomenon of unstable fall.
{"title":"Control Strategy of Stable Climbing Mechanics for Gecko-Inspired Robot on Vertical Arc Surface","authors":"Jinjun Duan, Bingcheng Wang, Baolin Ji, Weidong Sun, Zhouyi Wang, Z. Dai","doi":"10.1109/MIM.2023.10083002","DOIUrl":"https://doi.org/10.1109/MIM.2023.10083002","url":null,"abstract":"With the rapid development of global industry, the storage capacity of oil tanks and wind turbine towers worldwide is gradually increasing and with it the problem of their maintenance: how to achieve a stable attachment of maintenance equipment to such vertical arcs? Wall-climbing robots are the ideal delivery platform due to their interface bonding capabilities. However, the robot is susceptible to the curvature of the curved surface. If the contact between its attachments and the crawling surface is inadequate, the closed chain system formed by the stance phase is unable to resist the force impact from the sticky release, and the risk of the robot destabilizing and tipping over is great. To improve the robot's adaptive capacity and anti-disturbance capability, this paper proposes an adaptive external force-softening motion strategy for the limbs of the inner and outer curved stance phases to ensure the stability of the robot body. The foot end motion is orthogonally decoupled into the forward direction and the arc surface fitting direction, and the stance phase adopts a virtual mass-damping control model to realize the spring cushioning behavior of the system during the forward motion. The experimental results show that the algorithm proposed in this paper can effectively improve the stability of the robot in the process of vertical arc crawling and avoid the phenomenon of unstable fall.","PeriodicalId":55025,"journal":{"name":"IEEE Instrumentation & Measurement Magazine","volume":"26 1","pages":"48-56"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44183435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In actual engineering applications, the mechanical machine is exposed to uncertain conditions such as noise interference and various loads. The commonly used fault diagnosis models suffer degradation in the prediction accuracy in such complex industrial environments where the available label samples are insufficient and the conditions are varied. To combat this challenge, a cross-domain mechanical fault diagnosis method based on the deep-learning networks is proposed. It utilizes small samples, i.e., 10% of the total, and operates on the time-series signal collected from the mechanical equipment. It provides a classification accuracy of more than 97% on the dataset from Case Western Reserve University (CWRU) under variable conditions and 97.56% with the noise interference of 0 dB. The one-dimensional vibration signal is first converted into an image through RGB mapping. Then, the derived RGB image is capable of the time dependent and spatial properties of the time sequence signal and can be directly used as the input of the deep-learning networks. The deep-learning networks model, i.e., the ResNet, is adopted for the fault feature extraction and additional dense connections are added among the residual blocks to supplement the insufficient labeled samples within the networks. Then, an RGB-DResNet is constructed, capable of retaining the robust features for the classification of the mechanical faults in different working conditions. Finally, through retraining the model by use of transfer learning, the derived RGB-TDResNet model gives a fine adaption to the feature distribution with a small amount of target domain information. The performance of the proposed fault diagnosis model was validated on the dataset from CWRU. The results show that it provides a high identification accuracy and strong robustness in variable operating conditions as well as the noise environment. It is a rather promising approach for dealing with the cross-domain tasks of mechanical fault diagnosis.
{"title":"A Dense ResNet Model with RGB Input Mapping for Cross-Domain Mechanical Fault Diagnosis","authors":"Xiaozhuo Xu, Chaojun Li, Xinliang Zhang, Yunji Zhao","doi":"10.1109/MIM.2023.10083021","DOIUrl":"https://doi.org/10.1109/MIM.2023.10083021","url":null,"abstract":"In actual engineering applications, the mechanical machine is exposed to uncertain conditions such as noise interference and various loads. The commonly used fault diagnosis models suffer degradation in the prediction accuracy in such complex industrial environments where the available label samples are insufficient and the conditions are varied. To combat this challenge, a cross-domain mechanical fault diagnosis method based on the deep-learning networks is proposed. It utilizes small samples, i.e., 10% of the total, and operates on the time-series signal collected from the mechanical equipment. It provides a classification accuracy of more than 97% on the dataset from Case Western Reserve University (CWRU) under variable conditions and 97.56% with the noise interference of 0 dB. The one-dimensional vibration signal is first converted into an image through RGB mapping. Then, the derived RGB image is capable of the time dependent and spatial properties of the time sequence signal and can be directly used as the input of the deep-learning networks. The deep-learning networks model, i.e., the ResNet, is adopted for the fault feature extraction and additional dense connections are added among the residual blocks to supplement the insufficient labeled samples within the networks. Then, an RGB-DResNet is constructed, capable of retaining the robust features for the classification of the mechanical faults in different working conditions. Finally, through retraining the model by use of transfer learning, the derived RGB-TDResNet model gives a fine adaption to the feature distribution with a small amount of target domain information. The performance of the proposed fault diagnosis model was validated on the dataset from CWRU. The results show that it provides a high identification accuracy and strong robustness in variable operating conditions as well as the noise environment. It is a rather promising approach for dealing with the cross-domain tasks of mechanical fault diagnosis.","PeriodicalId":55025,"journal":{"name":"IEEE Instrumentation & Measurement Magazine","volume":"26 1","pages":"40-47"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45812867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Industrial defect detection is gaining importance in the control of industrial product quality. Highly accurate and efficient defect detection with complex and variable industrial defect types is therefore an interesting but challenging problem. Vision transformers have been highly successful in a variety of computer vision tasks, due to their ability to capture global information in images. Nevertheless, simply capturing global information is problematic. On the one hand, because they are incapable of inductive bias as Convolutional Neural Network (CNN), transformers will have difficulty focusing on local features of defects in industrial defect image inspection tasks. On the other hand, using global computation leads to excessive memory and computational cost. To mitigate these issues, we propose a new vision transformer architecture which contains Hybrid Window Attention (HWA) and Dynamic Token Normalization (DTN). HWA, which combines pooling attention and window attention, makes the computational complexity reduced to improve efficiency. DTN enables transformers to focus on both the global information and the local features of defects, thus providing improved accuracy of industrial surface defect detection. Extensive experiments demonstrate that our Dynamic Vision Transformer (DHT) achieves 96.8% and 98.5% classification accuracy on the NEU dataset and the DAGM dataset, respectively, with a low computational complexity.
{"title":"DHT: Dynamic Vision Transformer Using Hybrid Window Attention for Industrial Defect Images Classification","authors":"Chao Ding, Donglin Teng, Xianghua Zheng, Qiang Wang, Yuanyuan He, Zhang Long","doi":"10.1109/MIM.2023.10083000","DOIUrl":"https://doi.org/10.1109/MIM.2023.10083000","url":null,"abstract":"Industrial defect detection is gaining importance in the control of industrial product quality. Highly accurate and efficient defect detection with complex and variable industrial defect types is therefore an interesting but challenging problem. Vision transformers have been highly successful in a variety of computer vision tasks, due to their ability to capture global information in images. Nevertheless, simply capturing global information is problematic. On the one hand, because they are incapable of inductive bias as Convolutional Neural Network (CNN), transformers will have difficulty focusing on local features of defects in industrial defect image inspection tasks. On the other hand, using global computation leads to excessive memory and computational cost. To mitigate these issues, we propose a new vision transformer architecture which contains Hybrid Window Attention (HWA) and Dynamic Token Normalization (DTN). HWA, which combines pooling attention and window attention, makes the computational complexity reduced to improve efficiency. DTN enables transformers to focus on both the global information and the local features of defects, thus providing improved accuracy of industrial surface defect detection. Extensive experiments demonstrate that our Dynamic Vision Transformer (DHT) achieves 96.8% and 98.5% classification accuracy on the NEU dataset and the DAGM dataset, respectively, with a low computational complexity.","PeriodicalId":55025,"journal":{"name":"IEEE Instrumentation & Measurement Magazine","volume":"26 1","pages":"19-28"},"PeriodicalIF":2.1,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45991416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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