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检索条件"任意字段=10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021"

共 359 条记录，以下是211-220 订阅

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An Intelligent Recommendation System for Performance Equipment Operation and Maintenance via Deep Neural Network and Attention Mechanism 10

An Intelligent Recommendation System for Performance Equipme...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Li, Huimin Chen, Yongyi Zhang, Dan Wu, Lifeng Zhejiang University of Technology Department of Automation Hangzhou310023 China

ISBN: (纸本)9781665424233

the theater has a huge number and variety of performing arts equipment, so the artificial maintenance will take a great time and energy. How to reduce the cost of manual maintenance is particularly important. this paper proposes an intelligent recommendation system for performance equipment operation and maintenance based on deep learning network and attention mechanism. First, according to the research main goal, the performance equipment is used to construct dataset and a preprocess scheme is adopted for the raw data, then we use the deep neural network to train the feature data, and integrate the attention mechanism into the recommendation system. Attention mechanism can increase attention to important information. Finally, through the cosine similarity between the calculated features, the recommended results are generated. An illustrative example is given to show the effectiveness of main results. © 2021 ieee.

关键词： Recommender systems

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Feature Extraction of Sequence data Based on LSTM and its Application to Fault Diagnosis of Industrial Process 11

Feature Extraction of Sequence Data Based on LSTM and its Ap...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Yang, Xiaojun Wan, Chuan Zhang, Tongshuai Xiong, Zhihua Tsinghua University Department of Automation Beijing100084 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

data with sequential relationships such as text, speech, and sensor data, belong to sequence data. Many deep learning models are proposed to extract features and learn inherent information among the sequence data. We investigate the similarities and differences of the modeling methods of sequence data in various fields, and then use deep learning neural network to build a model for the fault diagnosis of sequence data in process industry. Long-short-term memory (LSTM) network and feature extraction of contrastive predictive coding (CPC) are used in this paper for building deep learning models. CPC can learn representations of data, and LSTM can learn the information contained in sequence data. For application to fault diagnosis, the whole model combining CPC and LSTM can appropriately distinguish normal data from various fault data. Experiments on the Tennessee-Eastman process show that the LSTM model combining with proper feature extraction methods can improve the performance of fault diagnosis. © 2022 ieee.

关键词： Failure analysis

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Robust ILC Approach for 2-D Linear Time-varying Continuous-discrete systems 11

Robust ILC Approach for 2-D Linear Time-varying Continuous-d...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Wan, Kai Wei, Xiaohui Long, Dafeng Xu, Qingyuan School of Electronic Information and Electrical Engineering Huizhou University Huizhou516007 China School of Electronic and Information Guangdong Polytechnic Normal University Guangzhou510665 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

this paper first discusses the robust iterative learning control (ILC) problem on 2-D linear time-varying continuous-discrete systems with iteration-dependent boundary states. A continuous-discrete ILC law is proposed to ensure the ultimate tracking error converging to a bounded range. the upper bound of its is proportional to the boundedness parameter on iteration-dependent boundary states. In particular, while these iteration-dependent uncertainties are disappear, the ILC tracking error converges to zero. Finally, an illustrative test can be provided to illustrate the effectiveness of the proposed ILC law. © 2022 ieee.

关键词： Two term control systems

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A 2-Dimensional Linear Non-minimum Phase Estimator Based on Dynamic Exceeded Regressor 10

A 2-Dimensional Linear Non-minimum Phase Estimator Based on ...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Que, Haoyi Sun, Pei Su, Hongye Xu, Zhaowen Institute of Intelligence Science and Engineering Shenzhen Polytechnic Shenzhen518055 China National Laboratory of Industrial Control Technology Institute of Cyber-Systcms and Control Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665424233

In the paper, we proposed a 2-dimensional non-minimum phase linear regressor, to verify that in the procedure of dynamic extended regressor and mixing, with bounded functions of time, which persistence of excitation(PE) condition is absent, the parameter estimation errors could converge to 0. And a proposition of available form is given for the systems to meet the requirements of the dynamical extended regressor. Numeral and simple pendulum examples examine the results and simulations show the effectiveness. © 2021 ieee.

关键词： Linear systems

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SCA-SVM Fault Diagnosis of Analog Circuits Based on Transfer learning 10

SCA-SVM Fault Diagnosis of Analog Circuits Based on Transfer...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Yu, Danlu Zhang, Aihua Mu, Wei College of Engineering Bohai University Jinzhou121013 China

ISBN: (纸本)9781665424233

To solve the problem that there are Lack of sufficient fault samples in circuit fault diagnosis methods meanwhile generations for obtaining failure samples are too expensive is proposed, this paper proposes sine and cosine algorithm and support vector machine (SCA-SVM), a challenging research method of circuit fault diagnosis, that is, modeling is carried out under the premise of using a small number of target fault samples. the algorithm uses the idea of transfer learning to diagnose the fault of analog circuit. First, the feature is extracted from the data by wavelet packet, then the dimension is reduced by principal component analysis (PCA), then the fault sets are classified by SCA-SVM, and finally the accuracy is determined by Sallen-Key band pass filter circuit and CSTV filter circuit. Overall, compared with back-propagation algorithm (BP) genetic algorithm (GA) learning vector quantization algorithm (LVQ) and PCA, SCA performance in few-shot learning (FSL) is better than other algorithms. © 2021 ieee.

关键词： Failure analysis

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Cascade Smith control of SCR Flue Gas Denitrification System Based on Active Disturbance Rejection 10

Cascade Smith Control of SCR Flue Gas Denitrification System...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Pu, Cuiping Ren, Jie Su, Jianbo College of Mechanical and Electrical Engineering Kunming University Kunming650214 China Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9781665424233

Selective catalytic reduction (SCR) denitrification system has complex reaction process, which is characterized by large inertia, large delay, strong disturbance and uncertainty. In order to improve the robustness of traditional linear active disturbance rejection controller (LADRC), an improved active disturbance rejection controller (ILADRC) is designed by adding an identical linear extended state observer(LESO) and introducing the estimated error value of total disturbance. In order to better solve the problem of large delay in SCR denitrification system, Smith predictor is used to offset the delay of output variables before entering LESO. Smith-ILADRC control of SCR denitrification system is designed. the tracking performance, anti-jamming performance and robustness of Smith-ILADRC are simulated under the action of external disturbance and the change of model parameters. the results show that the tracking performance and anti-jamming ability of Smith-ILADRC are obviously improved. © 2021 ieee.

关键词： controllers

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Prediction of Social Ownership of Typical Household Appliances Based on Improved Grey Models 10

Prediction of Social Ownership of Typical Household Applianc...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Sun, Xiaoan Luan, Xiaoli Liu, Fei Key Laboratory for Advanced Process Control of Light Industry of the Ministry of Education Jiangnan University Jiangsu Wuxi214122 China

ISBN: (纸本)9781665424233

With the advent of complicated system of electronic and electrical household appliances, the prediction of the social ownership based on a single time series does not conform to its nonlinear growth law. In the present study, the main factors affecting the social ownership of household electronics and electrical appliances are initially investigated based on the grey relational model. then the main influencing factors are considered to construct a multiple grey prediction model. Moreover, the model is optimized by various intelligent algorithms to improve prediction accuracy. Finally, the model accuracy is evaluated through the social ownership data of four typical household appliances in Jiangsu province. the optimized model is applied to predict the social ownership of household appliances in Jiangsu province in the period 2020 to 2022. the results of this article may provide a basis for promoting the resource utilization of e-wastes and assisting managers to improve electronic waste recycling. © 2021 ieee.

关键词： Domestic appliances

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Tracking control of Two Virtually Coupled Trains via Prescribed Performance control Method 11

Tracking Control of Two Virtually Coupled Trains via Prescri...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Jia, Yuqi Huang, Deqing Li, Xuefang Institute of System Science and Technology Southwest Jiaotong University Chengdu610031 China School of Intelligent Systems Engineering Sun Yat-Sen University Guangzhou510275 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

As a new concept in railways, virtual coupling has been proven that could reduce the separation between two adjacent trains, and thus increase the railway capacity effectively. However, the shorter separation distance will lead to the stricter requirements for the tracking accuracy. the prescribed performance control method could restrict the convergence rate and the convergence range of the tracking error, thus could improve the tracking accuracy effectively. Based on this, a novel tracking control scheme for two virtually coupled trains under the framework of the prescribed performance control is developed in this paper. To deal with the system uncertainties, a fuzzy logic system is applied to approximate the unknown resistance. At last, the convergence of the proposed controller is proved by virtue of the Lyapunov stability theorem and its effectiveness is analyzed through numerical simulations. © 2022 ieee.

关键词： Fuzzy logic

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the problem of identification parameter data saturation in repetitive control and its solution 10

The problem of identification parameter data saturation in r...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Song, YongPan Zeng, ShuiPing Zhang, YuTao North China University of Technology Beijing100144 China

ISBN: (纸本)9781665424233

In the repetitive control of tracking periodic signals based on the principle of internal model, the control effect has a great relationship with the parameters of the controlled system. If the system is affected by noise and causes the internal parameters to change, failure to obtain the repeated control of the internal parameters in time will cause the system to lose stability. therefore, how to quickly identify the parameters of the controlled system is particularly important in the field of repetitive control. In the actual process, the traditional least square method is often used to identify the parameters of the controlled system. However, the convergence of the algorithm to parameter identification is very slow. Once the controlled system parameters are changed, the parameter information provided by the new data cannot be updated in time, and the convergence of the identification results is very slow. In order to overcome the data saturation phenomenon of the least squares algorithm, this paper uses three methods of forgetting factor algorithm, variable gain matrix algorithm, and introducing additional matrix R algorithm to improve the traditional least squares identification algorithm, and verified these three through MATLAB simulation. Effectiveness of the method. Compared with traditional methods, the improved three identification methods can speed up the convergence of parameter identification and improve the accuracy of parameter identification. © 2021 ieee.

关键词： Least squares approximations

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Study on Underwater Simultaneous Localization and Mapping Based on Different Sensors 10

Study on Underwater Simultaneous Localization and Mapping Ba...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Yin, Jiye Wang, Yu Lv, Jiaqi Ma, Jin School of Control and Computer Engineering North China Electric Power University Beijing102206 China Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781665424233

Simultaneous localization and mapping (SLAM) is important for autonomous underwater vehicles (AUVs). An AUV can be equipped with corresponding function sensors to obtain the information of underwater environment and its own position. Affected by many factors of water, many sensors that can be used normally on the ground perform poor in the marine scenario. this paper presents a review of the underwater SLAM implemented by different sensors: monocular camera, stereo camera, single-beam sonar, multi-beam sonar, side-scan sonar and laser. © 2021 ieee.

关键词： Autonomous underwater vehicles

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