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检索条件"任意字段=8th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2019"

共 336 条记录，以下是131-140 订阅

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Location and recognition system for lightning fault of transmission line based on data-driven technology 8

Location and recognition system for lightning fault of trans...

引用

8th ieee data driven control and learning systems conference, ddcls 2019

作者： Li, Rui Cao, Rongmin Wu, Yingnian Yu, Di University of Science and Technology Beijing100083 China Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9781728114545

In order to solve the complex and difficult identification problems of overhead transmission line fault diagnosis, and to improve the accuracy of classification effectively, a new method of fault diagnosis for overhead transmission line is proposed in this paper. Firstly, the collected traveling wave signals are processed by HHT (Hilbert-Huang Transform) to realize joint feature extraction in time-frequency domain. And a data-driven lightning strike warning model for transmission lines is adopted. the model includes PCA (principal component analysis), data acquisition and preprocessing, data analysis and prediction, and model online correction. For eliminating the influence of noise and singularity on fault diagnosis;then input training set and production rules to train the intelligent classification method, by which exact fault diagnosis model was obtained. Finally, apply the algorithm to the intelligent lightning traveling wave monitoring system of an actual 500 kV transmission line, the experimental results show that the proposed method can not only calculate the exact location of fault points, but also accurately classified them that classified both single fault and multi-fault, which opens up a new approach for overhead transmission line to intelligent fault diagnosis. © 2019 ieee.

关键词： Feature extraction

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Method of power grid fault diagnosis using intuitionistic fuzzy petri nets with inhibitor arcs 8

Method of power grid fault diagnosis using intuitionistic fu...

引用

8th ieee data driven control and learning systems conference, ddcls 2019

作者： Tan, Mingyue Li, Jiming Zhao, Shasha Cheng, Xuezhen State Key Laboratory of Mining Disaster Prevention Shandong University of Science and Technology Control Co-founded by Shandong Province Ministry of Science and Technology Qingdao266590 China

ISBN: (纸本)9781728114545

Aiming at the uncertainty of information in power grid fault diagnosis and meeting the requirements of real-time online fault diagnosis, A method of power grid fault diagnosis using intuitionistic fuzzy Petri Nets with inhibitor arcs is proposed, in which the effect of inhibitor arcs to the relay protection configuration is considered, and intuitionistic fuzzy logic is employed to deal with the uncertain alarm messages/information. Firstly, according to the topology analysis of power grid and relay protection configuration setting rules, the Petri Nets with inhibitor arcs is used to reflect the protection logic relationship on the model structure. then, the intuitionistic fuzzy set is taken into account to handle the complex fault data situation of certain information and uncertain information. Finally, the results of testing cases using the ieee-39 node system show that this method can effectively deal with the mis-operation of multiple circuit breakers in complex data environment and accurately diagnose complex faults of power grid under uncertain information. © 2019 ieee.

关键词： Failure analysis

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Aerodrome clearance monitoring and management based on multi-source high resolution remote sensing data 8

Aerodrome clearance monitoring and management based on multi...

引用

8th ieee data driven control and learning systems conference, ddcls 2019

作者： Xu, Yubin Ma, Yan Guo, Jing Wang, Xuhui Liu, Shasha Ma, Li China Academy of Civil Aviation Science and Technology Beijing100028 China

ISBN: (纸本)9781728114545

FAA and ICAO have clear specification for aerodrome clearance management, which is vital for ensuring safe takeoff, flight and landing to aircrafts. this work proposed a new method to identify obstacles and evaluate potential hazards, which will make great contribution for clearance management and help mitigate possible risk in aviation security framework. this came up with new fully automatic methodology to detect environmental building change and OISs surface penetration based on multi-source high resolution remote sensing data. Shape extraction and classification of obstacles will be figured out from stereo paired images. this kind of clearance remote spatial patrol can be done every a few days, which is very suitable for wide area where is huge difficult to reach and human patrol pattern may miss. the application in Changshui International Airport proved to be very effective strategy to update obstacles database and manage clearance. More importantly, it helps guarantee aerodrome terminal operation safety. © 2019 ieee.

关键词： Stereo image processing

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Self-balance control of electric unicycle based on active disturbance rejection control 8

Self-balance control of electric unicycle based on active di...

引用

8th ieee data driven control and learning systems conference, ddcls 2019

作者： Liang, Guangchao Ye, Lingjian Zhu, Minfeng Ningbo Institute of Technology Zhejiang University Taiyuan University of Science and Technology China Zhejiang University Department of Control Science and Engineering Hangzhou310027 China

ISBN: (纸本)9781728114545

For self-balance control of electric unicycle (EU), the conventional PID control often leads to poor dynamic performance. In this paper, an active disturbance rejection control (ADRC) based control design approach is proposed to improve the control performance. Firstly, the dynamic model of unicycle is explored, which builds up the fundamental relation between the input and output, namely motor output torque and the attitude pitch angle. Since the physical model is nonlinear and highly sensitive to external conditions such as the system load, a sophisticated control strategy is required for practical usage. To this end, a three-level cascaded control system is designed, which is composed of the current loop, the motor speed loop, and the attitude loop, respectively. the former two loops are for speed regulation of the unicycle, while the third loop controls the attitude based on the ADRC to deal with various uncertainties. the control performance is verified through simulation studies along with comparisons with the conventional PID control. Results show that the ADRC-based control system achieved better self-balance for attitude control, where the attitude adjustment period is shortened and the overshot of the attitude is also reduced. © 2019 ieee.

关键词： Dynamic models

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An improved extreme learning machine based on auto-encoder for production predictive modeling of industrial processes 8

An improved extreme learning machine based on auto-encoder f...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Geng, Zhiqiang Meng, Qingchao Han, Yongming Wei, Qin Ouyang, Zhi College of Information Science Technology Beijing University of Chemical Technology Beijing100029 China Guizhou Provincial Key Laboratory of Public Big Data Guiyang550025 China

ISBN: (纸本)9781728114545

Industrial process data has the characteristics of complexity, variability and noisy, which brings challenges to data-driven production predictive modeling for industrial processes basing on the traditional extreme learning machine (ELM). therefore, this paper proposes an improved ELM based on auto-encoder (AE) (AE-ELM). the AE can extract the main features with lower-dimension by eliminating the linear correlation among the original complex data. then, the main features are used as the inputs of the ELM. For the purpose of verifying the effectiveness of the proposed method, the AE-ELM model has been experimented on the production prediction of the pure terephthalic acid (PTA). the experimental results prove that the AE-ELM is less sensitive to the structure of the traditional ELM and principal components extraction based robust ELM (PCE-RELM). Moreover, the modeling accuracy can be improved by 2.4%, which has certain guiding significance for process modeling and production prediction. © 2019 ieee.

关键词： Knowledge acquisition

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A feature extraction method based on multi-rhythmic and co-space modes for p300 potential 8

A feature extraction method based on multi-rhythmic and co-s...

引用

8th ieee data driven control and learning systems conference, ddcls 2019

作者： Chen, Guo Deng, Xin Tang, Yun Mi, Jianxun Li, Danni Sun, Kaiwei Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781728114545

In order to extract the effective features of EEG data based on P300 potential and improve the classification accuracy of brain-computer interface (BCI) system, a novel method from spatial and frequency domain is proposed in this paper. that is, the multi-rhythm signal and Common Spatial Pattern (CSP) are combined to extract the feature of P300 potential. Meanwhile, this paper uses the elastic networks as classifier which combines with $l-data$ norm and $l-ieee$ norm that not only makes the coefficients sparse, but also avoids losing the inhere t structure among samples from the same class. In this paper, the public EEG data set is used to verify the proposed method and by comparing the traditional CSP method. the experimental results show that the new method provides more frequency-space-related feature information increased by 4.8% compared with the traditional CSP method. It indicates that the proposed method achieves the good performance for the P300 EEG analysis. © 2019 ieee.

关键词： Brain computer interface

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Aluminum electrolysis multi-fault diagnosis using wavelet packet decomposition and directed acyclic graph support vector machine 8

Aluminum electrolysis multi-fault diagnosis using wavelet pa...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Zhou, Kaibo Chen, Xudong Xu, Gaofeng Ge, Yaobao Zhang, Zhixin MOE Key Laboratory of Image Processing and Intelligence Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728114545

the operation condition of aluminum electrolytic cells is critical to the stability of the aluminum electrolysis process. Under the tough working environment, there will be many abnormal states in the cells, detrimental to aluminum quality, aluminum electrolysis efficiency and electrolytic cells life. therefore, it's of great significance to develop an effective process monitoring and multi-fault diagnosis method. the diagnosis accuracy and the speed of traditional multi-classification methods are limited. To solve these problems, a fault detection and multi-fault diagnosis framework based on wavelet packet decomposition (WPD) and directed acyclic graph support vector machine (DAG-SVM) is proposed. First, features are extracted from aluminum electrolytic cell voltage signals. then, DAG-SVM is used to detect and diagnose abnormal states. the result shows that the proposed method can detect and diagnose three abnormal states, anode effect state, voltage instability and liquid aluminum fluctuation with a high precision. the accuracy can reach about 86.8%. © 2019 ieee.

关键词： Failure analysis

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A weighted gene co-expression network analysis based on iterative learning 8

A weighted gene co-expression network analysis based on iter...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Yu, Meijuan Li, Yan Gong, Fengjun School of Control Science and Engineering Shandong University Jinan250061 China

ISBN: (纸本)9781728114545

Gene co-expression networks are widely used in field of exploring gene function and describe connections not only between genes in the network, but also between gene and trait of biological significance. As a part of building gene co-expression network, the adjacency function plays an important role in describing the connections between genes, which converts the co-expressive measure of each gene pair to its connective weight. A suitable parameter of adjacency function is determined by a biologically motivated criterion known as the scale-free topology fitting index in the gene co-expression network. In generally, the parameter of adjacency function is selected by manually looking up parameter table that is generated with the R software package WGCNA (Weighted Gene Co-Expression Network Analysis - WGCNA). therefore, the revised algorithm of selecting parameter of adjacency function is proposed. Iterative learning algorithm is used to automatically find the required parameter of adjacency function and the Mouse module-trait relationships are constructed. © 2019 ieee.

关键词： Iterative methods

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A regional logistics system based on connected vehicles 8

A regional logistics system based on connected vehicles

引用

8th ieee data driven control and learning systems conference, ddcls 2019

作者： Fang, Jing Deng, Xiaohui Ni, Haoyuan Li, Xinyu Bai, Lu Wang, Pangwei Beijing Key Lab of Urban Intelligent Traffic Control Technology North China University of Technology Beijing100144 China China Highway Engineering Consultants Corporation Beijing100089 China

ISBN: (纸本)9781728114545

With the development of intelligent transportation system, connected vehicles (CV) have become an important technology of regional logistics system. However, the increasing number of express delivery and the higher service level demanded by users have become the urgent problems to be solved. At present, the Last Station of Logistics between delivery point and delivery destination needs a lot of labors to complete, and the delivery mode cannot fully meet the demands of customers. this paper takes regional logistics transportation system as the main research object. By constructing dynamic path optimization algorithm and customized logistics mode of sending and receiving goods, a regional shared logistics transportation system is built based on CV and intelligent terminal. the application of the system in residential areas, campuses and other regional locations can improve the traffic situation to a certain extent, improve customer satisfaction, provide new delivery options for the logistics industry, optimize the overall logistics system, and realize the real intelligent logistics model. © 2019 ieee.

关键词： Vehicles

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Multi-agent cooperative-competitive environment with reinforcement learning 8

Multi-agent cooperative-competitive environment with reinfor...

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8th ieee data driven control and learning systems conference, ddcls 2019

作者： Huang, Si-Yu Hu, Bin Liao, Rui-Quan Xiao, Jiang-Wen He, Ding-Xin Guan, Zhi-Hong School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Petroleum and Engineering College Yangtze University Jingzhou420400 China

ISBN: (纸本)9781728114545

this paper studies the multi-agent pursuit-evasion problem. When the mathematical model of agent is unknown, it's effective to use machine learning algorithm to design the policy of each agent. According to the cooperation among pursuers and competition between evaders and pursuers, we choose deterministic policy gradient of reinforcement learning as our basic approach. In this study, we redesign the reward function and the structure of neural network to adapt to the actual environment where evader has greater speed and accelerated speed than pursuers. the character of this algorithm is that it only takes coordinates of agents as controller input without other information like speed, in particular, this algorithm would keep effective even the environment transform to higher dimensional space. Finally, we verify the validity of our algorithm in experiment. © 2019 ieee.

关键词： Reinforcement learning

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