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2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)最新文献

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Low-rank Shared Dictionary Learning with Incoherence Constraint for Endoscopic Gastrointestinal Image Classification 基于非相干约束的低秩共享字典学习用于内镜胃肠道图像分类
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275161
Yue Ma, Zixin Shen, Sheng Li, Liping Chang, Jinhui Zhu, Xiongxiong He
{"title":"Low-rank Shared Dictionary Learning with Incoherence Constraint for Endoscopic Gastrointestinal Image Classification","authors":"Yue Ma, Zixin Shen, Sheng Li, Liping Chang, Jinhui Zhu, Xiongxiong He","doi":"10.1109/DDCLS49620.2020.9275161","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275161","url":null,"abstract":"Endoscope has been widely used in clinical examination of gastrointestinal diseases. Many automatic endoscopic image classification algorithms based on dictionary learning are proposed to assist doctors in diagnosing diseases, where the learning method of shared dictionary and class-specific dictionaries enables training dictionary to be more discriminative. Nevertheless, in the process of dictionary learning, the appearance of common features in class-specific dictionaries may cause low classification accuracy. To remedy this deficiency, herein we introduce a coherence constraint between low-rank shared dictionary and class-specific dictionaries. The proposed dictionary learning method is applied to the classification system of endoscopic gastrointestinal images, including normal, polyp and ulcer images, whose experimental results prove that it has promising classification performance.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115151276","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}
引用次数: 2
State Estimator Design for Genetic Regulatory Networks with Discrete and Leakage Delays 具有离散延迟和泄漏延迟的遗传调控网络的状态估计器设计
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275095
Shasha Xiao, Tingru Xu, Xian Zhang, Xiaona Yang, Xin Wang
{"title":"State Estimator Design for Genetic Regulatory Networks with Discrete and Leakage Delays","authors":"Shasha Xiao, Tingru Xu, Xian Zhang, Xiaona Yang, Xin Wang","doi":"10.1109/DDCLS49620.2020.9275095","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275095","url":null,"abstract":"In this article, the state estimation issue is addressed for a class of genetic regulatory networks with discrete and leakage delays. The main purpose is to calculate and complete the system state information through the significant measurement outputs. Firstly, the original nonlinear error system is translated into a linearly uncertain one by applying the Lagrange’s Mean–Value Theorem. Secondly, a sufficient condition is established to ensure the robust asymptotic stability of error system by resorting to Lyapunov–Krasovskii functional, convex combination technique, Jensen’s inequality, linear matrix inequality combined with Barbalat’s lemma. Meantime, the state observer gains are derived in term of the feasible solutions to inequalities. Finally, a group of numerical examples are given to verify the effect of leakage delay on system stability and the effectiveness of the devised state observer.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123109157","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}
引用次数: 0
Adaptive Terminal Sliding Mode Backstepping Control for Virtual Synchronous Generators 虚拟同步发电机的自适应终端滑模反步控制
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275251
Yongbao Sun, Dezhi Xu, Weilin Yang, Kaitao Bi, Wenxu Yan
{"title":"Adaptive Terminal Sliding Mode Backstepping Control for Virtual Synchronous Generators","authors":"Yongbao Sun, Dezhi Xu, Weilin Yang, Kaitao Bi, Wenxu Yan","doi":"10.1109/DDCLS49620.2020.9275251","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275251","url":null,"abstract":"A novel control strategy based on microgrid (MG) is proposed in this paper to improve the low inertia characteristics of traditional voltage source three-phase converters (VSC). The proposed control strategy consists of the Virtual Synchronous Generator (VSG), backstepping control and adaptive terminal sliding mode control. The VSG can improve the virtual inertia of the system in the application of inverter, the sliding mode method can improve the robust performance of the system, and the adaptive control method can be used to compensate the parameter transient error of the system. The low inertia characteristic of MG system in traditional VSC is improved. The simulation results has shown that the designed controller has better antiinterference and inertia performance.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116695297","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}
引用次数: 1
On Driver's Workload in Dangerous Scenes Based on EEG Data 基于脑电图数据的危险场景驾驶员工作负荷研究
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275085
Jiyuan Tan, Rui Bi, Weiwei Guo, Li Li, Yueqin Wang
{"title":"On Driver's Workload in Dangerous Scenes Based on EEG Data","authors":"Jiyuan Tan, Rui Bi, Weiwei Guo, Li Li, Yueqin Wang","doi":"10.1109/DDCLS49620.2020.9275085","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275085","url":null,"abstract":"Scientific measurement of the risk degree of traffic scenes and accurate assessment of driver's workload are conducive to reducing driving risk and road traffic accidents. In this paper, EEG signal evaluation method based on \"driver's\" perspective is used to describe the risk of traffic scene objectively and quantitatively. The traffic scenes with dynamic traffic environment factors are taken as the research objects, including the pedestrian scene and the variable-speed vehicle scene. The drivers’ EEG signals are used as the indicators to evaluate the risk degree of the traffic scene. Based on research objects and indicators, the internal relationship between drivers' EEG signals and traffic dangerous environment factors are explored, and the evaluation models of traffic scene risk degree based on drivers' EEG signals are established.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121028020","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}
引用次数: 1
Root Cause Diagnosis Framework Based on Granger Causality with the Combination of Normal and Fault Data 基于正、故障数据结合的格兰杰因果关系根本原因诊断框架
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275285
X. Ye, Jianguo Wang, Fei Wang, Yuan Yao, Junjiang Liu
{"title":"Root Cause Diagnosis Framework Based on Granger Causality with the Combination of Normal and Fault Data","authors":"X. Ye, Jianguo Wang, Fei Wang, Yuan Yao, Junjiang Liu","doi":"10.1109/DDCLS49620.2020.9275285","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275285","url":null,"abstract":"Granger causality analysis is one of the most widely used methods in root cause diagnosis. This method can get effective results in many cases, but there are still some problems and underutilization of data is one of them. Granger causality analysis only used the fault relate data segment. This paper proposes a novel root cause diagnosis framework based on Granger causality analysis, and attempts to combine the normal and fault data to make the result more accurate. The main ideal is to test the change of causality intensity before and after the fault to optimize the result of the fault propagation paths. Tennessee Eastman(TE) process data and TE data was used to verify the effectiveness of the method.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121226705","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}
引用次数: 0
Granger Causality Detection Based on Neural Network 基于神经网络的格兰杰因果关系检测
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275129
Jing-Ru Su, Jianguo Wang, Long-Fei Deng, Yuan Yao, Jian-Long Liu
{"title":"Granger Causality Detection Based on Neural Network","authors":"Jing-Ru Su, Jianguo Wang, Long-Fei Deng, Yuan Yao, Jian-Long Liu","doi":"10.1109/DDCLS49620.2020.9275129","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275129","url":null,"abstract":"Plant-wide oscillations are very common in industrial processes. When a control unit oscillates during the process, the oscillations will propagate through the connectivity between the units, which will cause poor product quality and higher energy consumption. It is important to diagnose the root cause of plant-wide oscillations. Generally, methods for estimating Granger causality use linear models such as autoregressive models. This paper proposes using Granger causality analysis based on the neural network for root cause diagnosis, which effectively solves the problem that Granger causality analysis based on linear models cannot handle non-linear data. The Granger causality detection model based on neural network is successfully applied to the plant-wide oscillation root location of industrial process, and the correct root cause is detected, which proves the feasibility and effectiveness of the method.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125084710","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}
引用次数: 1
Path-Following Control for Unmanned Rollers: A Composite Disturbance Rejection-based Framework 无人滚轮路径跟踪控制:基于复合干扰抑制的框架
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275192
K. Song, H. Xie
{"title":"Path-Following Control for Unmanned Rollers: A Composite Disturbance Rejection-based Framework","authors":"K. Song, H. Xie","doi":"10.1109/DDCLS49620.2020.9275192","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275192","url":null,"abstract":"The drum roller, as a widely used engineering vehicle, has higher degree of freedom in motion relative to conventional passenger vehicles. The special operating condition that has large rocks on road for compaction introduces severe disturbances in path-following. In this paper, a composite disturbance rejection-based framework, for the path-following control of rollers, is proposed. The external disturbances caused by rocks on road are rejected by correcting the coordinates of rollers from Global Position System (GPS) using measured attitude information. The nonlinearities from the complex articulation structure are compensated using a kinematic model-based feedforward control. All other uncertainties, internal and external, are lumped as an augmented state - \"total disturbance\", estimated hence rejected in real-time via the extended state observer (ESO). As compliment to ESO with the limited performance due to low sampling rate of GPS, a model parameters self-learning algorithm is added. The proposed solution is validated both in simulation and experiments, showing satisfactory performance. The maximum lateral error is ~0.1m for unmanned rollers, out-performing the average level of human driven rollers when working on road with maximum diameter of rocks up to 1m.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123578513","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}
引用次数: 2
On Chaos Control of Small-scale Unmanned Helicopter Based Upon HODFC 基于HODFC的小型无人直升机混沌控制
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275046
Xitong Guo, Guoyuan Qi, Xia Li, Shengli Ma
{"title":"On Chaos Control of Small-scale Unmanned Helicopter Based Upon HODFC","authors":"Xitong Guo, Guoyuan Qi, Xia Li, Shengli Ma","doi":"10.1109/DDCLS49620.2020.9275046","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275046","url":null,"abstract":"The small-scale unmanned helicopter has the high main rotor speed and the light body, which makes it highly sensitive to disturbances. In the case of improper assembly and wind interference, the fuselage will shake violently, and even produce chaotic angular velocity oscillation behavior. In this paper, the situation of chaotic oscillation of the angular velocity is given. The high order differential feedback controller (HODFC) for the angular velocity of the helicopter is designed. The controller is independent of the mathematical model of the system, with simple structure and mathematical significance for parameter adjustment. Finally, the controller is loaded after the helicopter generates oscillations in chaotic situation. The angular velocity of the helicopter tends to the reference value rapidly after the controller is loaded, indicating that the HODFC constructed can stabilize the chaotic oscillation of the helicopter’s angular velocity.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123588074","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}
引用次数: 0
Satellite Micro Anomaly Detection Based on Telemetry Data 基于遥测数据的卫星微异常检测
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275260
Chao Sun, Shaojun Chen, M. E, Ying Du, Chuanmin Ruan
{"title":"Satellite Micro Anomaly Detection Based on Telemetry Data","authors":"Chao Sun, Shaojun Chen, M. E, Ying Du, Chuanmin Ruan","doi":"10.1109/DDCLS49620.2020.9275260","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275260","url":null,"abstract":"The military requirements of space security defense and space fast response are increasingly urgent. Accurate and effective micro anomaly detection of on-orbit satellites is an important technical way of satellites life cycle health management. Under this military background, the micro anomaly detection of the key components of the satellite is proposed and carried out. In order to solve the problems of low diagnostic accuracy of the traditional Voherra series model in satellite telemetry signal micro anomaly detection, o-Voherra series anomaly detection model for the feature extraction of telemetry data based on the optimized sequence model is proposed. Firstly, the feature of satellite telemetry data is extracted by using the constructed optimized sequence model. Secondly, phase space reconstruction of telemetry data after preprocessing and feature extraction. Finally, the telemetry data micro anomaly detection are realized by the proposed o-Voherra series model. Through the remote sensing data experiment of the key components of the satellite after desensitization, the proposed model can accurately realize the micro anomaly detection of the key components of the satellite.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115063298","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}
引用次数: 3
Observer-based Optimal Adaptive Control for Multi-motor Driving Servo System 基于观测器的多电机驱动伺服系统最优自适应控制
2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2020-11-20 DOI: 10.1109/DDCLS49620.2020.9275157
Shuangyi Hu, X. Ren
{"title":"Observer-based Optimal Adaptive Control for Multi-motor Driving Servo System","authors":"Shuangyi Hu, X. Ren","doi":"10.1109/DDCLS49620.2020.9275157","DOIUrl":"https://doi.org/10.1109/DDCLS49620.2020.9275157","url":null,"abstract":"In this paper, an improved optimal sliding mode control strategy is proposed for multi-motor driving servo system. Some states of multi-motor drive system are not measurable and there exists unknown nonlinearity. To solve this problem, the disturbance observer and extended state observer are both applied to estimate the unknown states and nonlinearity. Based on optimal control theory, the optimal sliding surface is selected to guarantee the optimal dynamic performance of the sliding mode of the system. The effectiveness of designed control methods is illustrated by simulation results.","PeriodicalId":420469,"journal":{"name":"2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115435528","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}
引用次数: 2
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