Pub Date : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021526
Xiang Yue, Yanhua Liu, Hongguang Wang, Yan Feng
According to the characteristics of the transmission line environment and the requirements of the inspection task, a robot mechanism for inspection based on the transformation of the transmission line is proposed. The configuration of the robot and the magnetic online charging device are analyzed. The obstacle surmounting process of typical obstacles is analyzed by using finite state machine. The motion sequence of the robot crossing obstacles is planned and the obstacle crossing test is carried out. The test results show that the mechanism can cross the drainage line, tension clamp and other complex obstacles, which verifies the rationality of the mechanism design and the feasibility of the motion planning. With the transformation of the transmission line environment, the robot can efficiently and quickly cross the typical obstacles.
{"title":"Development of a power line Inspection Robot Capable of automatically crossing Obstacles","authors":"Xiang Yue, Yanhua Liu, Hongguang Wang, Yan Feng","doi":"10.1109/WCMEIM56910.2022.10021526","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021526","url":null,"abstract":"According to the characteristics of the transmission line environment and the requirements of the inspection task, a robot mechanism for inspection based on the transformation of the transmission line is proposed. The configuration of the robot and the magnetic online charging device are analyzed. The obstacle surmounting process of typical obstacles is analyzed by using finite state machine. The motion sequence of the robot crossing obstacles is planned and the obstacle crossing test is carried out. The test results show that the mechanism can cross the drainage line, tension clamp and other complex obstacles, which verifies the rationality of the mechanism design and the feasibility of the motion planning. With the transformation of the transmission line environment, the robot can efficiently and quickly cross the typical obstacles.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133523111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The analysis and prediction of apparel fashion colors are very important for the production and sales activities of the apparel industry. In the field of fashion color analysis of apparel images, existing image algorithms have problems such as poor segmentation effects in complex backgrounds and poor data real-time. In this paper, the YOLOv5 algorithm is applied to garment detection, the histogram equalization is used to enhance the image of garment pictures, the KMeans clustering algorithm is used to get the approximate foreground area of the garment, the GrabCut algorithm is used to segment the image with the processed pictures to get the final foreground color area of the garment, and then the KMeans clustering algorithm is used to get the main color of the garment. thus analyzing the pattern between colors. The study of fashionable colors of clothing in video surveillance scenes has higher real-time data volumes, larger data capacities, and faster analysis speeds than the current research methods.
{"title":"Combining the YOLOv5 and Grabcut Algorithms for Fashion Color Analysis of Clothing","authors":"Feng Liu, Zhaoqi Liu, Weiguang Liu, Hongsheng Zhao","doi":"10.1109/WCMEIM56910.2022.10021426","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021426","url":null,"abstract":"The analysis and prediction of apparel fashion colors are very important for the production and sales activities of the apparel industry. In the field of fashion color analysis of apparel images, existing image algorithms have problems such as poor segmentation effects in complex backgrounds and poor data real-time. In this paper, the YOLOv5 algorithm is applied to garment detection, the histogram equalization is used to enhance the image of garment pictures, the KMeans clustering algorithm is used to get the approximate foreground area of the garment, the GrabCut algorithm is used to segment the image with the processed pictures to get the final foreground color area of the garment, and then the KMeans clustering algorithm is used to get the main color of the garment. thus analyzing the pattern between colors. The study of fashionable colors of clothing in video surveillance scenes has higher real-time data volumes, larger data capacities, and faster analysis speeds than the current research methods.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132703596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021404
Siwei Shao, Chenglin Yang, Lin Feng
The conditions of crude fuel can reflect the reaction degree in a blast furnace, and the real-time monitoring and analysis of the crude fuel can improve production performance and stabilize the conditions of the furnace. The results of crude fuel conditions obtained by traditional manual sampling detection are low in accuracy, and have danger and hysteresis. In order to reduce the workload of personnel and improve detection accuracy and timeliness, this paper proposes an intelligent monitoring method of crude fuel images based on deep learning. According to the method, attention mechanisms are added on the basis of a Mask R-CNN algorithm, so that the detection accuracy is improved, and besides, the problem of overfitting is solved. In order to ensure the detection accuracy under high-speed motion blurred images, a DeblurGAN-v2 algorithm is used to deblur the images; and when a dataset is built, data enhancement is used to increase the number and types of samples, so that the algorithm can adapt to the actual production environment of a factory. Through a crude fuel detection experiment, the effectiveness of the algorithm in the aspect of improving the detection accuracy of clear and blurred images is verified.
{"title":"Intelligent Monitoring Method of Crude Fuel Images Based On Deep Learning","authors":"Siwei Shao, Chenglin Yang, Lin Feng","doi":"10.1109/WCMEIM56910.2022.10021404","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021404","url":null,"abstract":"The conditions of crude fuel can reflect the reaction degree in a blast furnace, and the real-time monitoring and analysis of the crude fuel can improve production performance and stabilize the conditions of the furnace. The results of crude fuel conditions obtained by traditional manual sampling detection are low in accuracy, and have danger and hysteresis. In order to reduce the workload of personnel and improve detection accuracy and timeliness, this paper proposes an intelligent monitoring method of crude fuel images based on deep learning. According to the method, attention mechanisms are added on the basis of a Mask R-CNN algorithm, so that the detection accuracy is improved, and besides, the problem of overfitting is solved. In order to ensure the detection accuracy under high-speed motion blurred images, a DeblurGAN-v2 algorithm is used to deblur the images; and when a dataset is built, data enhancement is used to increase the number and types of samples, so that the algorithm can adapt to the actual production environment of a factory. Through a crude fuel detection experiment, the effectiveness of the algorithm in the aspect of improving the detection accuracy of clear and blurred images is verified.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133234795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021377
Hongyuan Jiao, Chuang Ma
The hearth temperature is an important index to measure the condition of blast furnace (BF). A good and reasonable hearth temperature can maintain the stable production of BF, which is a direct guarantee to realize the longevity and efficiency of BF. Therefore, an image-based multiple rank matrix regression (MRMR) temperature prediction method is proposed to study the change of tuyere raceway temperature. This method takes matrix as the input of the model and considers the spatial position information of the matrix elements. According to the intrinsic property of the projection vectors, the relevant constraints are applied to avoid the overfitting problem. Finally, the prediction performance of the model is validated by using BF data.
{"title":"A New Image-Based Temperature Prediction Method","authors":"Hongyuan Jiao, Chuang Ma","doi":"10.1109/WCMEIM56910.2022.10021377","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021377","url":null,"abstract":"The hearth temperature is an important index to measure the condition of blast furnace (BF). A good and reasonable hearth temperature can maintain the stable production of BF, which is a direct guarantee to realize the longevity and efficiency of BF. Therefore, an image-based multiple rank matrix regression (MRMR) temperature prediction method is proposed to study the change of tuyere raceway temperature. This method takes matrix as the input of the model and considers the spatial position information of the matrix elements. According to the intrinsic property of the projection vectors, the relevant constraints are applied to avoid the overfitting problem. Finally, the prediction performance of the model is validated by using BF data.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131689676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Channel state information (CSI)-based wireless device fingerprinting provides an effective authentication scheme at the physical layer of wireless communication devices using CSI short-time invariance, but appears to be inadequate for long-time communication authentication or scenarios that require intermittent authentication. To address this problem, we propose to construct a wireless device fingerprint using frame response time interval (FRTI) and combine a CSI-based wireless dynamic device fingerprint to form a multi-domain fusion wireless device identification and authentication method, which can effectively identify and authenticate wireless devices at the time of wireless device access and during continuous wireless device communication to ensure safe and reliable operation of wireless devices. The experimental results under different scenarios show that using the multi-domain fusion method of frame response interval and channel state information for wireless device identification can significantly improve the accuracy of calculation and transmission delay, and the identification rate reaches more than 95%.
{"title":"The Frame Response Time Interval Based Device Fingerprinting Identification","authors":"Jing Guo, Yajuan Guo, Haitao Jiang, Fan Wu, Ziying Wang, Zhimin Gu","doi":"10.1109/WCMEIM56910.2022.10021485","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021485","url":null,"abstract":"Channel state information (CSI)-based wireless device fingerprinting provides an effective authentication scheme at the physical layer of wireless communication devices using CSI short-time invariance, but appears to be inadequate for long-time communication authentication or scenarios that require intermittent authentication. To address this problem, we propose to construct a wireless device fingerprint using frame response time interval (FRTI) and combine a CSI-based wireless dynamic device fingerprint to form a multi-domain fusion wireless device identification and authentication method, which can effectively identify and authenticate wireless devices at the time of wireless device access and during continuous wireless device communication to ensure safe and reliable operation of wireless devices. The experimental results under different scenarios show that using the multi-domain fusion method of frame response interval and channel state information for wireless device identification can significantly improve the accuracy of calculation and transmission delay, and the identification rate reaches more than 95%.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133788846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021365
Jinyan Li, Xiangde Liu, Yi Zhang, Yunchuan Hu
Traditional simultaneous localization and mapping (SALM) algorithms are based on static environments. If there are dynamic objects in the environment, it will cause inaccurate positioning or problems that cannot be located. In order to solve this problem, the method of SegNet lightweight neural network and sparse optical flow combined with multi-view geometry is proposed to eliminate dynamic feature points. Firstly, the SegNet network is used to obtain the mask of potential moving objects. Secondly, sparse optical flow and geometric methods detect dynamic feature points. Finally, the dynamic feature points detected by semantics, optical flow, and geometric methods are combined to reject the feature points. This method can improve the positioning accuracy of the SLAM system in a dynamic environment.
{"title":"Dynamic scene SLAM algorithm based on semantic information and joint constraints of optical flow and geometry","authors":"Jinyan Li, Xiangde Liu, Yi Zhang, Yunchuan Hu","doi":"10.1109/WCMEIM56910.2022.10021365","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021365","url":null,"abstract":"Traditional simultaneous localization and mapping (SALM) algorithms are based on static environments. If there are dynamic objects in the environment, it will cause inaccurate positioning or problems that cannot be located. In order to solve this problem, the method of SegNet lightweight neural network and sparse optical flow combined with multi-view geometry is proposed to eliminate dynamic feature points. Firstly, the SegNet network is used to obtain the mask of potential moving objects. Secondly, sparse optical flow and geometric methods detect dynamic feature points. Finally, the dynamic feature points detected by semantics, optical flow, and geometric methods are combined to reject the feature points. This method can improve the positioning accuracy of the SLAM system in a dynamic environment.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":"434 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124244094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021453
Li Shi, Hong He
In recent years mobile phones and hand-held video cameras are gaining increasing popularity. They allow people to easily film videos but also bring unwanted camera shakes and jitters that affect the video quality. Although mechanical devices, optical devices, and electronic devices can help remove unwanted shakes, those methods are usually expensive and impractical for mobile phones and hand-held cameras. Whereas digital video stabilization techniques only require raw footage maybe plus the gyro data. In this paper, we focus on and compare the effects of different approaches to motion estimation and motion compensation, the two crucial parts of video stabilization algorithms that have a large impact on the quality of video stabilization. Based on that, we conclude that the future of video stabilization lies within using a gyroscope to get accurate camera motion and using neural networks to achieve amazing video quality.
{"title":"A Review and Comparison on Video Stabilization Alorithms","authors":"Li Shi, Hong He","doi":"10.1109/WCMEIM56910.2022.10021453","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021453","url":null,"abstract":"In recent years mobile phones and hand-held video cameras are gaining increasing popularity. They allow people to easily film videos but also bring unwanted camera shakes and jitters that affect the video quality. Although mechanical devices, optical devices, and electronic devices can help remove unwanted shakes, those methods are usually expensive and impractical for mobile phones and hand-held cameras. Whereas digital video stabilization techniques only require raw footage maybe plus the gyro data. In this paper, we focus on and compare the effects of different approaches to motion estimation and motion compensation, the two crucial parts of video stabilization algorithms that have a large impact on the quality of video stabilization. Based on that, we conclude that the future of video stabilization lies within using a gyroscope to get accurate camera motion and using neural networks to achieve amazing video quality.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116406271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a compound control system of fast steering mirror based on fuzzy adaptive PI and Kalman filter is designed. Based on MATLAB/Simulink software simulation experiment, the designed control system is compared with traditional PI controller and digital low-pass filter respectively. The experimental results show that it greatly improves the adaptive ability and anti noise ability of the system. The filtered curve also has no obvious phase lag.
{"title":"Improving the Performance of Fast Steering Mirror Based on Kalman Filter and Fuzzy PI Control","authors":"Dingqian Tan, Qinyong Zeng, Dazhong Wang, Chenlong Liang","doi":"10.1109/WCMEIM56910.2022.10021347","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021347","url":null,"abstract":"In this paper, a compound control system of fast steering mirror based on fuzzy adaptive PI and Kalman filter is designed. Based on MATLAB/Simulink software simulation experiment, the designed control system is compared with traditional PI controller and digital low-pass filter respectively. The experimental results show that it greatly improves the adaptive ability and anti noise ability of the system. The filtered curve also has no obvious phase lag.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122023487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021521
Shen Zhongjie, Li Pan, Li Zitong, Gong Baogui
The quality inspection of gearbox has always been a difficult problem in the industry. A quality inspection method is proposed based on Q-switched wavelet sparse decomposition for the railway vehicle gearbox. It uses Q-switched wavelet transform to construct a sparse dictionary, establishes a sparse decomposition objective function, and uses the Lagrange contraction algorithm to solve the objective function, so as to decompose the monitoring signal into harmonic signal and shock signal, and finally extracts the pulse factor and skewness from the harmonic signal. The gear box fault is identified by using the pulse factor and skewness, and the fault is located by using the envelope spectrum of harmonic signal and shock signal. The bench test verifies the effectiveness of the quality inspection method for railway vehicle gearbox based on Q-switched wavelet sparse decomposition.
{"title":"The quality inspection of railway vehicle gearbox based on Q-switched wavelet sparse decomposition","authors":"Shen Zhongjie, Li Pan, Li Zitong, Gong Baogui","doi":"10.1109/WCMEIM56910.2022.10021521","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021521","url":null,"abstract":"The quality inspection of gearbox has always been a difficult problem in the industry. A quality inspection method is proposed based on Q-switched wavelet sparse decomposition for the railway vehicle gearbox. It uses Q-switched wavelet transform to construct a sparse dictionary, establishes a sparse decomposition objective function, and uses the Lagrange contraction algorithm to solve the objective function, so as to decompose the monitoring signal into harmonic signal and shock signal, and finally extracts the pulse factor and skewness from the harmonic signal. The gear box fault is identified by using the pulse factor and skewness, and the fault is located by using the envelope spectrum of harmonic signal and shock signal. The bench test verifies the effectiveness of the quality inspection method for railway vehicle gearbox based on Q-switched wavelet sparse decomposition.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123928540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-18DOI: 10.1109/WCMEIM56910.2022.10021350
Xianlin Ren, Chengrui Han, Yiduo Tian, Laixian Chen, B. Liu
A quality defect analysis and prediction method based on association rule mining is proposed to address the coupling and ambiguity between multiple quality data in the process of product manufacturing quality control and diagnosis. It overcomes the shortcomings of the traditional quality defect analysis method which can only trace the quality from a single chain and can simultaneously analyze and predict the specific quality characteristics data that lead to the output quality defects and the multiple input parameters of the manufacturing process that have an impact on it. By dividing the quality characteristics data intervals through K-means and using the Apriori algorithm to explore the correlation between the quality characteristics data, we can construct the rules to judge the loss of product quality. A GA-SVR based manufacturing process quality defect prediction model is built using the cloud server plus local terminal technology structure. Finally, through example analysis, it is proved the effectiveness of the proposed method.
{"title":"Research on quality defect analysis and prediction model based on association rule mining","authors":"Xianlin Ren, Chengrui Han, Yiduo Tian, Laixian Chen, B. Liu","doi":"10.1109/WCMEIM56910.2022.10021350","DOIUrl":"https://doi.org/10.1109/WCMEIM56910.2022.10021350","url":null,"abstract":"A quality defect analysis and prediction method based on association rule mining is proposed to address the coupling and ambiguity between multiple quality data in the process of product manufacturing quality control and diagnosis. It overcomes the shortcomings of the traditional quality defect analysis method which can only trace the quality from a single chain and can simultaneously analyze and predict the specific quality characteristics data that lead to the output quality defects and the multiple input parameters of the manufacturing process that have an impact on it. By dividing the quality characteristics data intervals through K-means and using the Apriori algorithm to explore the correlation between the quality characteristics data, we can construct the rules to judge the loss of product quality. A GA-SVR based manufacturing process quality defect prediction model is built using the cloud server plus local terminal technology structure. Finally, through example analysis, it is proved the effectiveness of the proposed method.","PeriodicalId":202270,"journal":{"name":"2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124879197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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