Pub Date : 2020-07-01DOI: 10.23919/CCC50068.2020.9188795
Haosi Zheng, H. Yokoi, Yinlai Jiang, Feng Duan
Seamless communication between human intended motions and robot actions is essential for Human-Robot Interaction (HRI). When tele-operating a robotic hand, it is a natural and effective way via surface electromyography (sEMG) signals. This paper proposes an online sEMG motion classification framework for tele-operating the robotic hand. The whole framework consists of offline training and online recognition phases. In the offline training phase, three features were selected from four candidates and Artificial Neural Network (ANN) won the election among three classifiers. Inthe online recognition phase, two-thresholds data segmentation and majority voting techniques were designed, and three subjects participated in online experiment to verify the feasibility of this framework. The online experimental results show that the average total accuracy is 73.56% and the average vote accuracy is 91.67%. The outcomes of this study have shown the promising potential of sEMG-based HRI.
{"title":"An Online sEMG Motion Classification Framework for Tele-operating the Robotic Hand","authors":"Haosi Zheng, H. Yokoi, Yinlai Jiang, Feng Duan","doi":"10.23919/CCC50068.2020.9188795","DOIUrl":"https://doi.org/10.23919/CCC50068.2020.9188795","url":null,"abstract":"Seamless communication between human intended motions and robot actions is essential for Human-Robot Interaction (HRI). When tele-operating a robotic hand, it is a natural and effective way via surface electromyography (sEMG) signals. This paper proposes an online sEMG motion classification framework for tele-operating the robotic hand. The whole framework consists of offline training and online recognition phases. In the offline training phase, three features were selected from four candidates and Artificial Neural Network (ANN) won the election among three classifiers. Inthe online recognition phase, two-thresholds data segmentation and majority voting techniques were designed, and three subjects participated in online experiment to verify the feasibility of this framework. The online experimental results show that the average total accuracy is 73.56% and the average vote accuracy is 91.67%. The outcomes of this study have shown the promising potential of sEMG-based HRI.","PeriodicalId":255872,"journal":{"name":"2020 39th Chinese Control Conference (CCC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125950820","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 : 2020-07-01DOI: 10.23919/CCC50068.2020.9188776
Wenjuan Qi, Zunbing Sheng
For a two-sensor linear discrete time-invariant stochastic system with time-delayed measurements, by the measurement transformation method, an equivalent system without measurement delays is obtained, and then using the covariance intersection (CI) fusion method, the covariance intersection steady-state Kalman fuser is presented. It can handle the estimation fusion problem between local estimation errors for the system with unknown cross-covariances and avoid a large computed burden and computational complexity of cross-covariances. It is proved that its accuracy is higher than that of each local estimator, and is lower than that of optimal Kalman fuser weighted by matrices with known cross-covariances. A Monte-Carlo simulation example shows the above accuracy relation, and indicates that its actual accuracy is close to that of the Kalman fuser weighted by matrices, hence it has good performances.
{"title":"Covariance Intersection Kalman Fuser with Time-delayed Measurements","authors":"Wenjuan Qi, Zunbing Sheng","doi":"10.23919/CCC50068.2020.9188776","DOIUrl":"https://doi.org/10.23919/CCC50068.2020.9188776","url":null,"abstract":"For a two-sensor linear discrete time-invariant stochastic system with time-delayed measurements, by the measurement transformation method, an equivalent system without measurement delays is obtained, and then using the covariance intersection (CI) fusion method, the covariance intersection steady-state Kalman fuser is presented. It can handle the estimation fusion problem between local estimation errors for the system with unknown cross-covariances and avoid a large computed burden and computational complexity of cross-covariances. It is proved that its accuracy is higher than that of each local estimator, and is lower than that of optimal Kalman fuser weighted by matrices with known cross-covariances. A Monte-Carlo simulation example shows the above accuracy relation, and indicates that its actual accuracy is close to that of the Kalman fuser weighted by matrices, hence it has good performances.","PeriodicalId":255872,"journal":{"name":"2020 39th Chinese Control Conference (CCC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125962783","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 : 2020-07-01DOI: 10.23919/CCC50068.2020.9188779
Wei Zhao, Li Xu, Ting He
In this paper, we present two speaker gating mechanisms for multi-speaker Tacotron, a popular end-to-end text-to- speech (TTS) neural system, to improve the performance of generating multiple voices. With our presented mechanisms, the model can work better in both generalization and accuracy. As a starting point, we introduce the original multi-speaker Tacotron as a baseline model because of its excellent performance and straightforward structure. Employing gated linear units (GLUs), two different speaker gating mechanisms are then proposed for this model. Extensive experiments on VCTK dataset are conducted to demonstrate the validity of our methods. Conclusively, we find that it is promising to incorporate the speaker identity information by using the proposed speaker gating mechanisms.
{"title":"Improving Multi-Speaker Tacotron with Speaker Gating Mechanisms","authors":"Wei Zhao, Li Xu, Ting He","doi":"10.23919/CCC50068.2020.9188779","DOIUrl":"https://doi.org/10.23919/CCC50068.2020.9188779","url":null,"abstract":"In this paper, we present two speaker gating mechanisms for multi-speaker Tacotron, a popular end-to-end text-to- speech (TTS) neural system, to improve the performance of generating multiple voices. With our presented mechanisms, the model can work better in both generalization and accuracy. As a starting point, we introduce the original multi-speaker Tacotron as a baseline model because of its excellent performance and straightforward structure. Employing gated linear units (GLUs), two different speaker gating mechanisms are then proposed for this model. Extensive experiments on VCTK dataset are conducted to demonstrate the validity of our methods. Conclusively, we find that it is promising to incorporate the speaker identity information by using the proposed speaker gating mechanisms.","PeriodicalId":255872,"journal":{"name":"2020 39th Chinese Control Conference (CCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126174222","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 : 2020-07-01DOI: 10.23919/CCC50068.2020.9188955
Yong Li, Jun Xiao, Qidan Zhu
In the measurement of overlapping particle size, it is necessary to perform image processing on the stacked particle image to obtain accurate measurement results. In this paper morphological filtering is used to remove the isolated small area and fill the holes. The distance image combined with h-minima transform is used to get the seed points. Then the seed points and background are marked on the distance image. Finally, the distance image is segmented by watershed. Due to the partial missing after segmentation of conglutinated particles, according to the prior knowledge that the shape of particles is similar to ellipse, this paper reconstructs the contour of the incomplete particles by ellipse fitting technology. Finally, the measurement algorithm of particle shape and particle size characteristics is determined. Two groups experiments are carried out for particle size measurement, and the error of particle size measurement is analyzed. It is proved that the measurement is accurate.
{"title":"The method on stacked particle image segmentation and particle size measurement","authors":"Yong Li, Jun Xiao, Qidan Zhu","doi":"10.23919/CCC50068.2020.9188955","DOIUrl":"https://doi.org/10.23919/CCC50068.2020.9188955","url":null,"abstract":"In the measurement of overlapping particle size, it is necessary to perform image processing on the stacked particle image to obtain accurate measurement results. In this paper morphological filtering is used to remove the isolated small area and fill the holes. The distance image combined with h-minima transform is used to get the seed points. Then the seed points and background are marked on the distance image. Finally, the distance image is segmented by watershed. Due to the partial missing after segmentation of conglutinated particles, according to the prior knowledge that the shape of particles is similar to ellipse, this paper reconstructs the contour of the incomplete particles by ellipse fitting technology. Finally, the measurement algorithm of particle shape and particle size characteristics is determined. Two groups experiments are carried out for particle size measurement, and the error of particle size measurement is analyzed. It is proved that the measurement is accurate.","PeriodicalId":255872,"journal":{"name":"2020 39th Chinese Control Conference (CCC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126181259","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}
Human behavior prediction is an interdisciplinary research direction, involving image processing, computer vision, pattern recognition, machine learning, and artificial intelligence, which is one of the important research topics in the field of computer vision. This paper introduces a model for predicting human skeletal motion sequence, which is composed of LSTM main network and structured prediction layer. We have verified its performance on h3.6m dataset, and this structure has achieved good results in the short-term prediction of human motion.
{"title":"Human Action prediction based on skeleton data","authors":"Qipeng Zhang, Tian Wang, Huai‐Ning Wu, Mingmin Li, Jianpeng Zhu, H. Snoussi","doi":"10.23919/CCC50068.2020.9189122","DOIUrl":"https://doi.org/10.23919/CCC50068.2020.9189122","url":null,"abstract":"Human behavior prediction is an interdisciplinary research direction, involving image processing, computer vision, pattern recognition, machine learning, and artificial intelligence, which is one of the important research topics in the field of computer vision. This paper introduces a model for predicting human skeletal motion sequence, which is composed of LSTM main network and structured prediction layer. We have verified its performance on h3.6m dataset, and this structure has achieved good results in the short-term prediction of human motion.","PeriodicalId":255872,"journal":{"name":"2020 39th Chinese Control Conference (CCC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126182329","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 : 2020-07-01DOI: 10.23919/CCC50068.2020.9189135
Jing Hu, Yueying Liu, Xiaowei Gao, Hefeng Wu
This paper discusses the H∞ control problem for uncertain discrete-time infinite Markov jump systems. Firstly, some sufficient conditions for existence of state feedback H∞ controller are given to ensure that the closed-loop system is exponentially mean square stable with conditioning (EMSS-C) for the zero exogenous disturbance with H∞ performance level. Further, the backward iterative algorithm of four coupled matrix Riccati equations (CMREs) is presented to design H2/ H∞ controller. Finally, some numerical simulations are provided to show the applicability of developed approaches.
{"title":"Discrete-Time H∞ Control for Infinite Markov Jump Systems with Uncertainty","authors":"Jing Hu, Yueying Liu, Xiaowei Gao, Hefeng Wu","doi":"10.23919/CCC50068.2020.9189135","DOIUrl":"https://doi.org/10.23919/CCC50068.2020.9189135","url":null,"abstract":"This paper discusses the H∞ control problem for uncertain discrete-time infinite Markov jump systems. Firstly, some sufficient conditions for existence of state feedback H∞ controller are given to ensure that the closed-loop system is exponentially mean square stable with conditioning (EMSS-C) for the zero exogenous disturbance with H∞ performance level. Further, the backward iterative algorithm of four coupled matrix Riccati equations (CMREs) is presented to design H2/ H∞ controller. Finally, some numerical simulations are provided to show the applicability of developed approaches.","PeriodicalId":255872,"journal":{"name":"2020 39th Chinese Control Conference (CCC)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123778156","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 : 2020-07-01DOI: 10.23919/CCC50068.2020.9188699
Yaxue Ren, Jinfeng Lv, Fucai Liu
To solve the problem of fuzzy identification of nonlinear systems, a novel fuzzy identification method based on fuzzy c-means clustering (FCM) algorithm and Gaussian function is proposed. Firstly, fuzzy clustering algorithm is used to divide the input space to obtain the clustering center, then the clustering center is used as the gaussian function center to determine the membership function to obtain the premise parameters of the fuzzy model, and the conclusion parameters of the fuzzy model are identified by recursive least squares (RLS). Finally, three simulation examples are given to verify the effectiveness of the proposed method in identifying T-S fuzzy model.
{"title":"A Novel Fuzzy Model Identification Approach Based on FCM and Gaussian Membership Function","authors":"Yaxue Ren, Jinfeng Lv, Fucai Liu","doi":"10.23919/CCC50068.2020.9188699","DOIUrl":"https://doi.org/10.23919/CCC50068.2020.9188699","url":null,"abstract":"To solve the problem of fuzzy identification of nonlinear systems, a novel fuzzy identification method based on fuzzy c-means clustering (FCM) algorithm and Gaussian function is proposed. Firstly, fuzzy clustering algorithm is used to divide the input space to obtain the clustering center, then the clustering center is used as the gaussian function center to determine the membership function to obtain the premise parameters of the fuzzy model, and the conclusion parameters of the fuzzy model are identified by recursive least squares (RLS). Finally, three simulation examples are given to verify the effectiveness of the proposed method in identifying T-S fuzzy model.","PeriodicalId":255872,"journal":{"name":"2020 39th Chinese Control Conference (CCC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125544573","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 : 2020-07-01DOI: 10.23919/CCC50068.2020.9188806
Zhanpeng Zheng, Zhong Yin, Jianhua Zhang
Evaluating operator cognitive workload (CW) levels in human-machine systems based on neurophysiological signals is becoming the basis to prevent serious accidents due to abnormal state of human operators. This study proposes an inter-subject CW classifier, extreme learning machine (ELM)-based deep stacked denoising autoencoder ensemble (ED-SDAE), to adapt the variations of the electroencephalogram (EEG) feature distributions across different subjects. The ED-SDAE consists of two cascade-connected modules, which are termed as high level personalized feature abstractions and abstraction fusion. The combination of SDAE and locality preserving projection (LPP) technique is regarded as base learner to obtain ensemble members for training meta-classifier by stacking-based approach. The ELM model with Q-statistics diversity measurement is acted as meta-classifier to fuse above inputs to improve classification performance. The feasibility of the SD-SDAE is tested by two EEG databases. The multi-class classification rate achieves 0.6353 and 0.6747 for T1 and T2 respectively, and significantly outperforms several shallow and deep CW estimators. By computing the main time complexity, the computational workload of the ED-SDAE is also acceptable for high-dimensional EEG features.
{"title":"An ELM-based Deep SDAE Ensemble for Inter-Subject Cognitive Workload Estimation with Physiological Signals","authors":"Zhanpeng Zheng, Zhong Yin, Jianhua Zhang","doi":"10.23919/CCC50068.2020.9188806","DOIUrl":"https://doi.org/10.23919/CCC50068.2020.9188806","url":null,"abstract":"Evaluating operator cognitive workload (CW) levels in human-machine systems based on neurophysiological signals is becoming the basis to prevent serious accidents due to abnormal state of human operators. This study proposes an inter-subject CW classifier, extreme learning machine (ELM)-based deep stacked denoising autoencoder ensemble (ED-SDAE), to adapt the variations of the electroencephalogram (EEG) feature distributions across different subjects. The ED-SDAE consists of two cascade-connected modules, which are termed as high level personalized feature abstractions and abstraction fusion. The combination of SDAE and locality preserving projection (LPP) technique is regarded as base learner to obtain ensemble members for training meta-classifier by stacking-based approach. The ELM model with Q-statistics diversity measurement is acted as meta-classifier to fuse above inputs to improve classification performance. The feasibility of the SD-SDAE is tested by two EEG databases. The multi-class classification rate achieves 0.6353 and 0.6747 for T1 and T2 respectively, and significantly outperforms several shallow and deep CW estimators. By computing the main time complexity, the computational workload of the ED-SDAE is also acceptable for high-dimensional EEG features.","PeriodicalId":255872,"journal":{"name":"2020 39th Chinese Control Conference (CCC)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126601591","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 : 2020-07-01DOI: 10.23919/CCC50068.2020.9189302
Xiaolin Tang, Xiaogang Wang, Jin Hou, Huafeng Wu, Dan Liu
In this paper, an improved Sobel edge detection algorithm is proposed to overcome the shortcomings of traditional Sobel edge detection operators, such as the limitation of detection direction in horizontal and vertical directions, and the need to set detection threshold artificially. Firstly, the detection direction is improved, based on the horizontal and vertical detection directions, two directions of 45 degree and 135 degree are added, which can detect the edge information of multiple gradient directions of the image. Secondly, considering the overall and local gray level of the input image, an edge judgment threshold is adaptively generated to make the detected image edge more complete. Finally, the multi-directional detection and adaptive threshold generation are combined. The experimental results show that the improved Sobel edge detection algorithm can extract more direction edge information, and the edge boundary is clear, which has better robustness to noise interference.
{"title":"An Improved Sobel Face Gray Image Edge Detection Algorithm","authors":"Xiaolin Tang, Xiaogang Wang, Jin Hou, Huafeng Wu, Dan Liu","doi":"10.23919/CCC50068.2020.9189302","DOIUrl":"https://doi.org/10.23919/CCC50068.2020.9189302","url":null,"abstract":"In this paper, an improved Sobel edge detection algorithm is proposed to overcome the shortcomings of traditional Sobel edge detection operators, such as the limitation of detection direction in horizontal and vertical directions, and the need to set detection threshold artificially. Firstly, the detection direction is improved, based on the horizontal and vertical detection directions, two directions of 45 degree and 135 degree are added, which can detect the edge information of multiple gradient directions of the image. Secondly, considering the overall and local gray level of the input image, an edge judgment threshold is adaptively generated to make the detected image edge more complete. Finally, the multi-directional detection and adaptive threshold generation are combined. The experimental results show that the improved Sobel edge detection algorithm can extract more direction edge information, and the edge boundary is clear, which has better robustness to noise interference.","PeriodicalId":255872,"journal":{"name":"2020 39th Chinese Control Conference (CCC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126626767","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 : 2020-07-01DOI: 10.23919/CCC50068.2020.9189056
Dan Shan, Xiaoxu Zhang, W. Lu
The traditional moving object recognition and tracking systems are usually based on software environment of PC, so the performance of processing speed, real-time and the size have limitations. In view of this, the circuit design based on Field Programmable Gate Array(FPGA) is proposed, which improves the performance of processing speed and real-time and miniaturization. Aiming at the recognition, tracking and feature point extraction of moving object, 300,000 pixel camera is used for image acquisition, and hardware circuit is used for image processing, including real-time image caching, gray-scale processing, improved bit plane median filtering, segmentation of self-adaptive threshold binarizing, fusing of frame difference and background difference methods for moving object detecting, tracking and feature point extracting. Finally, the recognition results are displayed by HDMI interface displayer. The design takes full advantage of the high-speed and parallel processing ability of FPGA, and combines the high-speed on-chip RAM with the large capacity off-chip SDRAM to realize the processing and storage of video data. After written by Verilog HDL and verified by Modelsim, the physical circuit is implemented in FPGA. This system has the characteristics of strong anti-interference, compact, flexibility, high speed, low power consumption, versatility and scalability, which is suitable for both industrial field and home use.
{"title":"Circuit Design of Moving Object Recognition System","authors":"Dan Shan, Xiaoxu Zhang, W. Lu","doi":"10.23919/CCC50068.2020.9189056","DOIUrl":"https://doi.org/10.23919/CCC50068.2020.9189056","url":null,"abstract":"The traditional moving object recognition and tracking systems are usually based on software environment of PC, so the performance of processing speed, real-time and the size have limitations. In view of this, the circuit design based on Field Programmable Gate Array(FPGA) is proposed, which improves the performance of processing speed and real-time and miniaturization. Aiming at the recognition, tracking and feature point extraction of moving object, 300,000 pixel camera is used for image acquisition, and hardware circuit is used for image processing, including real-time image caching, gray-scale processing, improved bit plane median filtering, segmentation of self-adaptive threshold binarizing, fusing of frame difference and background difference methods for moving object detecting, tracking and feature point extracting. Finally, the recognition results are displayed by HDMI interface displayer. The design takes full advantage of the high-speed and parallel processing ability of FPGA, and combines the high-speed on-chip RAM with the large capacity off-chip SDRAM to realize the processing and storage of video data. After written by Verilog HDL and verified by Modelsim, the physical circuit is implemented in FPGA. This system has the characteristics of strong anti-interference, compact, flexibility, high speed, low power consumption, versatility and scalability, which is suitable for both industrial field and home use.","PeriodicalId":255872,"journal":{"name":"2020 39th Chinese Control Conference (CCC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126855880","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：