Pub Date : 2022-07-22DOI: 10.1109/ISPDS56360.2022.9874122
Jun Yu Li, Yuejun Pan, Hao Wang, Y. Yuan, T. Guan
In order to design braces that are more in line with patient characteristics, and help clinicians achieve rapid and accurate diagnosis and treatment. Starting from practical application, this paper crawls AIS brace-related knowledge from medical websites, combines electronic cases and expert knowledge, builds AIS brace knowledge graph, and summarizes the main knowledge of AIS. Due to the complexity of the knowledge of AIS braces, this paper proposes a joint entity and relation extraction method based on the FS-E-BIESO annotation method. By comparing the two knowledge extraction algorithms BERT-BiLSTM-CRF and BiLSTM-CRF, it is concluded that BiLSTM-CRF has a better F1 value. By comparing the two knowledge extraction algorithms BERT-BiLSTM-CRF and BiLSTM-CRF, it is concluded that BiLSTM-CRF has a better F1 value. The extracted knowledge is merged to eliminate the interference knowledge, and imported into neo4j in the form of triples to construct the knowledge graph of AIS orthopedic braces.
{"title":"Research on the construction of knowledge graph of AIS orthopedic braces","authors":"Jun Yu Li, Yuejun Pan, Hao Wang, Y. Yuan, T. Guan","doi":"10.1109/ISPDS56360.2022.9874122","DOIUrl":"https://doi.org/10.1109/ISPDS56360.2022.9874122","url":null,"abstract":"In order to design braces that are more in line with patient characteristics, and help clinicians achieve rapid and accurate diagnosis and treatment. Starting from practical application, this paper crawls AIS brace-related knowledge from medical websites, combines electronic cases and expert knowledge, builds AIS brace knowledge graph, and summarizes the main knowledge of AIS. Due to the complexity of the knowledge of AIS braces, this paper proposes a joint entity and relation extraction method based on the FS-E-BIESO annotation method. By comparing the two knowledge extraction algorithms BERT-BiLSTM-CRF and BiLSTM-CRF, it is concluded that BiLSTM-CRF has a better F1 value. By comparing the two knowledge extraction algorithms BERT-BiLSTM-CRF and BiLSTM-CRF, it is concluded that BiLSTM-CRF has a better F1 value. The extracted knowledge is merged to eliminate the interference knowledge, and imported into neo4j in the form of triples to construct the knowledge graph of AIS orthopedic braces.","PeriodicalId":280244,"journal":{"name":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122035678","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 : 2022-07-22DOI: 10.1109/ISPDS56360.2022.9874093
Jingya Yu, Guoyou Wang, Shenghua Cheng
Liquid-based thin-layer cell smears are very important for the early screening and prevention of cervical cancer, and computer-aided diagnosis can reduce the workload of pathologists. The cell classification method based on deep learning can process data efficiently. However, most classification methods are based on a single resolution for recognition. When the resolution is low, the processing speed of the whole slide image is faster, but lack of picture details, which makes the identification inaccurate. When the resolution is high, it takes more time to process the whole slide image, but with more image detail. To this end, we propose a model based on Attention Mechanism and Multi-resolution Feature Fusion Module (AMFM), which combine the advantages of various resolutions to classify cervical cells. Experiments show that the accuracy is increased by 3.93% and the AUC is improved by 0.022 on the four-classification task of the cervical cell compared to the model based on a single resolution.
{"title":"Cervical cell classification based on attention mechanism and multi-resolution feature fusion","authors":"Jingya Yu, Guoyou Wang, Shenghua Cheng","doi":"10.1109/ISPDS56360.2022.9874093","DOIUrl":"https://doi.org/10.1109/ISPDS56360.2022.9874093","url":null,"abstract":"Liquid-based thin-layer cell smears are very important for the early screening and prevention of cervical cancer, and computer-aided diagnosis can reduce the workload of pathologists. The cell classification method based on deep learning can process data efficiently. However, most classification methods are based on a single resolution for recognition. When the resolution is low, the processing speed of the whole slide image is faster, but lack of picture details, which makes the identification inaccurate. When the resolution is high, it takes more time to process the whole slide image, but with more image detail. To this end, we propose a model based on Attention Mechanism and Multi-resolution Feature Fusion Module (AMFM), which combine the advantages of various resolutions to classify cervical cells. Experiments show that the accuracy is increased by 3.93% and the AUC is improved by 0.022 on the four-classification task of the cervical cell compared to the model based on a single resolution.","PeriodicalId":280244,"journal":{"name":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131620454","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 : 2022-07-22DOI: 10.1109/ISPDS56360.2022.9874087
Jianming Wu, Yu Hu, Dan-mei Xu, Yaosheng Tan, Chunfeng Liu, Lei Pei, Zhuo Li
Intelligent construction in the process of application of extra-high arch dams, a large number of monitoring instruments buried inside the project during the construction process, reasonable use of good monitoring data to promote the construction of seamless dams, has important research significance. Monitoring data can sense the true state of the dam in all aspects and improve engineering guidance. In this paper, through the current stage of monitoring data of the extra-high arch dam, the objective, continuity, timeliness, accuracy and dynamics of the monitoring data, the visualization of the seepage, temperature and stress field monitoring data of the extra-high arch dam, the whole process analysis, and the application of monitoring data to provide comprehensive feedback on the construction process are conducive to promoting the progress of the design and construction of the extra-high arch dam, and promoting the construction of the extra-high arch dam with higher quality.
{"title":"Intelligent analysis and decision making for monitoring data of ultra-high arch dams","authors":"Jianming Wu, Yu Hu, Dan-mei Xu, Yaosheng Tan, Chunfeng Liu, Lei Pei, Zhuo Li","doi":"10.1109/ISPDS56360.2022.9874087","DOIUrl":"https://doi.org/10.1109/ISPDS56360.2022.9874087","url":null,"abstract":"Intelligent construction in the process of application of extra-high arch dams, a large number of monitoring instruments buried inside the project during the construction process, reasonable use of good monitoring data to promote the construction of seamless dams, has important research significance. Monitoring data can sense the true state of the dam in all aspects and improve engineering guidance. In this paper, through the current stage of monitoring data of the extra-high arch dam, the objective, continuity, timeliness, accuracy and dynamics of the monitoring data, the visualization of the seepage, temperature and stress field monitoring data of the extra-high arch dam, the whole process analysis, and the application of monitoring data to provide comprehensive feedback on the construction process are conducive to promoting the progress of the design and construction of the extra-high arch dam, and promoting the construction of the extra-high arch dam with higher quality.","PeriodicalId":280244,"journal":{"name":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133616732","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 : 2022-07-22DOI: 10.1109/ISPDS56360.2022.9874014
Wenyue Li, Lingchun Meng
With the rapid development of the power industry, the scale of our country's power grid continues to expand, and transmission lines spread all over the country. The stability of transmission lines is one of the important factors to ensure stable power supply. The working environment of power transmission equipment is relatively harsh. Compared with other industrial sectors, transmission line failures are more dangerous and require higher stability. In order to ensure the stable operation of the power system, it is necessary to conduct regular inspections on the transmission lines. With the development of UAV technology and image processing technology, UAV line inspection technology based on video processing has become the most popular way of inspection of transmission lines. As a new operation and maintenance method in the power industry, the UAV line inspection system not only reduces the work intensity of transmission line operation and maintenance personnel, but also improves the quality, benefit and efficiency of inspections. It will be the main focus of transmission line operation and maintenance management in the future. Aiming at the problems such as unclear inspection interface and inconspicuous target focus caused by limited bandwidth when UAV patrols the line, this paper proposes a UAV image transmission system based on region of interest (ROI) coding. The system can effectively improve the clear reading of the target, thereby improving the effect and quality of the line inspection without increasing the total bandwidth.
{"title":"Design of UAV Image Transmission System Based on Region of Interest Coding","authors":"Wenyue Li, Lingchun Meng","doi":"10.1109/ISPDS56360.2022.9874014","DOIUrl":"https://doi.org/10.1109/ISPDS56360.2022.9874014","url":null,"abstract":"With the rapid development of the power industry, the scale of our country's power grid continues to expand, and transmission lines spread all over the country. The stability of transmission lines is one of the important factors to ensure stable power supply. The working environment of power transmission equipment is relatively harsh. Compared with other industrial sectors, transmission line failures are more dangerous and require higher stability. In order to ensure the stable operation of the power system, it is necessary to conduct regular inspections on the transmission lines. With the development of UAV technology and image processing technology, UAV line inspection technology based on video processing has become the most popular way of inspection of transmission lines. As a new operation and maintenance method in the power industry, the UAV line inspection system not only reduces the work intensity of transmission line operation and maintenance personnel, but also improves the quality, benefit and efficiency of inspections. It will be the main focus of transmission line operation and maintenance management in the future. Aiming at the problems such as unclear inspection interface and inconspicuous target focus caused by limited bandwidth when UAV patrols the line, this paper proposes a UAV image transmission system based on region of interest (ROI) coding. The system can effectively improve the clear reading of the target, thereby improving the effect and quality of the line inspection without increasing the total bandwidth.","PeriodicalId":280244,"journal":{"name":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132335712","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 : 2022-07-22DOI: 10.1109/ISPDS56360.2022.9874105
Yifan Xu, Yong Bai
In recent years, object detection has been expanded to drone scenes, where remote sensing images contain a greater variety and arbitrary-oriented targets. In order to solve the problem of detection difficulty and computational intensity for remote sensing images, oriented object detection is needed and the network model is expected to be deployed on resource-limited devices. This paper proposes a lightweight object detection method for oriented object detection by leveraging and compressing YOLOv5 network model. We integrate the fine-tuning stage in network slimming with knowledge distillation to enhance the accuracy of the detection model and save training time by transferring the important feature information to the student network. Loss function is redesigned by combining Theta loss with other detection and distillation losses to make the compression model more accurate. Extensive experiments are conducted to verify the effectiveness of our proposed method on the remote sensing public dataset DOTA. The compressed model achieves an accuracy of 76.18% on the DOTA dataset, 1.7% increase compared to the original YOLOv5 model. The FLOPs are decreased by 37.0%, the number of parameters is decreased by 58.9%, the weight file size is decreased by 57.6%, and the inference time is decreased by 17.4%.
{"title":"Compressed YOLOv5 for Oriented Object Detection with Integrated Network Slimming and Knowledge Distillation","authors":"Yifan Xu, Yong Bai","doi":"10.1109/ISPDS56360.2022.9874105","DOIUrl":"https://doi.org/10.1109/ISPDS56360.2022.9874105","url":null,"abstract":"In recent years, object detection has been expanded to drone scenes, where remote sensing images contain a greater variety and arbitrary-oriented targets. In order to solve the problem of detection difficulty and computational intensity for remote sensing images, oriented object detection is needed and the network model is expected to be deployed on resource-limited devices. This paper proposes a lightweight object detection method for oriented object detection by leveraging and compressing YOLOv5 network model. We integrate the fine-tuning stage in network slimming with knowledge distillation to enhance the accuracy of the detection model and save training time by transferring the important feature information to the student network. Loss function is redesigned by combining Theta loss with other detection and distillation losses to make the compression model more accurate. Extensive experiments are conducted to verify the effectiveness of our proposed method on the remote sensing public dataset DOTA. The compressed model achieves an accuracy of 76.18% on the DOTA dataset, 1.7% increase compared to the original YOLOv5 model. The FLOPs are decreased by 37.0%, the number of parameters is decreased by 58.9%, the weight file size is decreased by 57.6%, and the inference time is decreased by 17.4%.","PeriodicalId":280244,"journal":{"name":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130772091","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 : 2022-07-22DOI: 10.1109/ISPDS56360.2022.9874076
Fei Li
In order to improve the accuracy of sentiment classification of online product reviews, a model for sentiment analysis of unbalanced reviews is proposed. Firstly, the LDA model is used to balance the original review text set, and then the word vector model and convolution neural network are combined to construct the review text vectorization feature extraction process to obtain the word feature vector, which is used as the input matrix of the balanced review set. Finally, the BiLSTM algorithm is used for sentiment classification to obtain product reviews of positive and negative sentiment categories. The results show that LDA sampling unbalance processing method is a high accuracy unbalanced text processing method. BiLSTM algorithm has better effect of sentiment classification than other deep learning algorithms. CNN-BiLSTM model based on LDA unbalance processing obtains the optimal model performance evaluation index value in the comparative experiment of different sentiment classification models, which verifies the advantages and effectiveness of the model and effectively realizes sentiment analysis on unbalanced review texts.
{"title":"An Sentiment Analysis Model of Online Product Reviews Based on Deep Learning","authors":"Fei Li","doi":"10.1109/ISPDS56360.2022.9874076","DOIUrl":"https://doi.org/10.1109/ISPDS56360.2022.9874076","url":null,"abstract":"In order to improve the accuracy of sentiment classification of online product reviews, a model for sentiment analysis of unbalanced reviews is proposed. Firstly, the LDA model is used to balance the original review text set, and then the word vector model and convolution neural network are combined to construct the review text vectorization feature extraction process to obtain the word feature vector, which is used as the input matrix of the balanced review set. Finally, the BiLSTM algorithm is used for sentiment classification to obtain product reviews of positive and negative sentiment categories. The results show that LDA sampling unbalance processing method is a high accuracy unbalanced text processing method. BiLSTM algorithm has better effect of sentiment classification than other deep learning algorithms. CNN-BiLSTM model based on LDA unbalance processing obtains the optimal model performance evaluation index value in the comparative experiment of different sentiment classification models, which verifies the advantages and effectiveness of the model and effectively realizes sentiment analysis on unbalanced review texts.","PeriodicalId":280244,"journal":{"name":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130069825","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 : 2022-07-22DOI: 10.1109/ISPDS56360.2022.9874221
Yu Wang, Jiandong Fang, Yudong Zhao
Automatic monitoring and evaluation of cattle welfare status in smart pastures requires tracking and identification of the target cattle's ear area and morphology based on video images. Most of the traditional methods use contact detection, which is somewhat invasive and easy to cause cattle stress reaction. In this paper, we design a dynamic small area tracking discrimination algorithm based on the marker less posture estimation method, which includes the tracking matching model of key points, the relative fluctuation model of cattle ear and the fluctuation behavior evaluation model in turn, and finally realize the automatic recognition of motion cattle ear area and morphological features. Through simulation experiments, the effectiveness and feasibility of the method are demonstrated.
{"title":"Dynamic small area visual target tracking discrimination algorithm and animal welfare evaluation applications","authors":"Yu Wang, Jiandong Fang, Yudong Zhao","doi":"10.1109/ISPDS56360.2022.9874221","DOIUrl":"https://doi.org/10.1109/ISPDS56360.2022.9874221","url":null,"abstract":"Automatic monitoring and evaluation of cattle welfare status in smart pastures requires tracking and identification of the target cattle's ear area and morphology based on video images. Most of the traditional methods use contact detection, which is somewhat invasive and easy to cause cattle stress reaction. In this paper, we design a dynamic small area tracking discrimination algorithm based on the marker less posture estimation method, which includes the tracking matching model of key points, the relative fluctuation model of cattle ear and the fluctuation behavior evaluation model in turn, and finally realize the automatic recognition of motion cattle ear area and morphological features. Through simulation experiments, the effectiveness and feasibility of the method are demonstrated.","PeriodicalId":280244,"journal":{"name":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125189088","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 : 2022-07-22DOI: 10.1109/ISPDS56360.2022.9874019
Liangming Huang, Jun Jiang, Xiuwu Gao
The huge gap between the speed of processors and their memory has become one major bottleneck in modern computer systems. In order to achieve higher performance, sophisticated techniques are increasingly needed to improve the data locality of the programs. In this paper, the structure peeling optimization based on LTO is implemented in the GCC compiler for that purpose. The structure types suitable for peeling are selected through adequate escape analysis and then split into multiple pieces, each containing one field corresponding to that in the original structure. This optimization is placed in the stage after whole program analysis of LTO so that can handle functions' parameters and global variables which cannot be handled from a local perspective. The experimental result shows that by adopting our optimization the geometric mean performance acceleration ratio of five SPEC CPU benchmarks can be achieved by 1.23, with individual benchmark performance increasing by up to 59.29%.
{"title":"Structure Peeling Based on Link Time Optimization in the GCC compiler","authors":"Liangming Huang, Jun Jiang, Xiuwu Gao","doi":"10.1109/ISPDS56360.2022.9874019","DOIUrl":"https://doi.org/10.1109/ISPDS56360.2022.9874019","url":null,"abstract":"The huge gap between the speed of processors and their memory has become one major bottleneck in modern computer systems. In order to achieve higher performance, sophisticated techniques are increasingly needed to improve the data locality of the programs. In this paper, the structure peeling optimization based on LTO is implemented in the GCC compiler for that purpose. The structure types suitable for peeling are selected through adequate escape analysis and then split into multiple pieces, each containing one field corresponding to that in the original structure. This optimization is placed in the stage after whole program analysis of LTO so that can handle functions' parameters and global variables which cannot be handled from a local perspective. The experimental result shows that by adopting our optimization the geometric mean performance acceleration ratio of five SPEC CPU benchmarks can be achieved by 1.23, with individual benchmark performance increasing by up to 59.29%.","PeriodicalId":280244,"journal":{"name":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116724922","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 : 2022-07-22DOI: 10.1109/ISPDS56360.2022.9874070
Jiale Wang, W. Zhuang, Di Shang
In scenes with low lighting at night, the outline of objects that need to be recognized, such as vehicles, is not clear, and cannot be accurately recognized by the automatic driving system. At present, there are many researches on instance segmentation models, but there are few researches on the instance segmentation application of automatic driving night scenes. According to BDD100K dataset, the automatic driving daytime scene dataset is marked. First, we perform data augmentation by using gamma correction to simulate the night driving scene in the training phase. Then we use our improved low-light enhancement algorithm with gradient increment based on RetinexNet in the prediction phase to brighten night driving scene images. Furthermore, we evaluated our proposed method on YOLACT++ model. The results show that the improved YOLACT++ automatic driving night segmentation ability has been significantly improved, the segmentation of vehicles at night is more accurate and robust, and it has better application value in night automatic driving scenarios.
{"title":"Light Enhancement Algorithm Optimization for Autonomous Driving Vision in Night Scenes based on YOLACT++","authors":"Jiale Wang, W. Zhuang, Di Shang","doi":"10.1109/ISPDS56360.2022.9874070","DOIUrl":"https://doi.org/10.1109/ISPDS56360.2022.9874070","url":null,"abstract":"In scenes with low lighting at night, the outline of objects that need to be recognized, such as vehicles, is not clear, and cannot be accurately recognized by the automatic driving system. At present, there are many researches on instance segmentation models, but there are few researches on the instance segmentation application of automatic driving night scenes. According to BDD100K dataset, the automatic driving daytime scene dataset is marked. First, we perform data augmentation by using gamma correction to simulate the night driving scene in the training phase. Then we use our improved low-light enhancement algorithm with gradient increment based on RetinexNet in the prediction phase to brighten night driving scene images. Furthermore, we evaluated our proposed method on YOLACT++ model. The results show that the improved YOLACT++ automatic driving night segmentation ability has been significantly improved, the segmentation of vehicles at night is more accurate and robust, and it has better application value in night automatic driving scenarios.","PeriodicalId":280244,"journal":{"name":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130571651","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 : 2022-07-22DOI: 10.1109/ISPDS56360.2022.9874214
Xiao-Fang Li, Yuanxun Fan, Luhui Xu
For a linear steering gear electric linear load simulation system (ELLS), a sliding mode variable structure control strategy based on linear matrix inequality (LMI) design is proposed. First of all, aiming at the problem of redundant force interference in the actual dynamic loading process of the system, on the basis of establishing the state space equation of the ELLS, the LMI sliding mode variable structure controller is designed, which can be compensated only by the calculation of LMI. Secondly, in order to solve the problem of high frequency noise caused by the differential of the traditional sliding mode to the measured output value, the sliding mode controller (SMC) designed based on LMI can control the system accurately only by measuring the output value of the system, and the convergence of the designed controller is proved by Lyapunov function. Finally, a Simulink simulation model is built to verify the accurate control of the system by the SMC based on LMI.
{"title":"LMI Design of Sliding Mode Robust Control for Electric Linear Load Simulator","authors":"Xiao-Fang Li, Yuanxun Fan, Luhui Xu","doi":"10.1109/ISPDS56360.2022.9874214","DOIUrl":"https://doi.org/10.1109/ISPDS56360.2022.9874214","url":null,"abstract":"For a linear steering gear electric linear load simulation system (ELLS), a sliding mode variable structure control strategy based on linear matrix inequality (LMI) design is proposed. First of all, aiming at the problem of redundant force interference in the actual dynamic loading process of the system, on the basis of establishing the state space equation of the ELLS, the LMI sliding mode variable structure controller is designed, which can be compensated only by the calculation of LMI. Secondly, in order to solve the problem of high frequency noise caused by the differential of the traditional sliding mode to the measured output value, the sliding mode controller (SMC) designed based on LMI can control the system accurately only by measuring the output value of the system, and the convergence of the designed controller is proved by Lyapunov function. Finally, a Simulink simulation model is built to verify the accurate control of the system by the SMC based on LMI.","PeriodicalId":280244,"journal":{"name":"2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126198462","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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