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2019 2nd International Conference on Advanced Materials, Intelligent Manufacturing and Automation, AMIMA 2019

作者： Huang, Li Liao, Shangchun College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan Hubei430081 China Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan Hubei430081 China

With the development of artificial intelligence technology, robotics technology has become more and more mature. Ground walking robots not only develop rapidly, but also have been applied in actual production and life. However, the development of wall climbing robot technology is still in the laboratory research and small-scale application. We live in a world where progress is continuing. Large-scale buildings, bridges and ships are becoming more and more common. In these places, it is inevitable to involve the construction, maintenance and clarity of high-rise buildings and ships. In the case of dangerous and inefficient manpower work, the application of wall climbing robots can play a very good role. Therefore, the development of wall climbing robots is of vital importance both now and in the future. Starting from the performance characteristics of wall climbing robot, this paper studies and summarizes the moving mode, control mode, conditions to be satisfied and various adsorption forms of wall climbing robot, and introduces the basic research situation in the field of wall climbing robot. © Published under licence by IOP Publishing Ltd.

关键词： Robot applications

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An unsupervised hourly weather status pattern recognition and blending fitting model for PV system fault detection

引用

Applied Energy 2022年 319卷

作者： Jiaqi Qu Zheng Qian Yan Pei Lu Wei Hamidreza Zareipour Qiang Sun School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China School of Electronics and Information Engineering Beihang University Beijing China The Department of Electrical and Computer Engineering University of Calgary Calgary Canada State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing China

Detecting PV system faults in a timely fashion is important to ensure the safe operation of equipment and reduce their impact on the economy of the PV systems. It is necessary to further improve the time-sensitive performance evaluation of the system. However, the hourly weather scenario segmentations are seldom considered during the hour-level online monitoring process. Therefore, a hybrid method based on unsupervised hourly weather status pattern recognition and blending fitting model is proposed for hourly fault detection to improve the performance evaluation of PV systems. The proposed solution includes three parts, firstly, in the data preprocessing stage, the measured power with the errors and noise under normal operation situation caused by the environment changes is corrected by monthly linear fitting. Secondly, an unsupervised hourly weather status pattern recognition method is constructed using the measured radiation data, including unsupervised clustering and the Multiclass-GBDT-LR classification process. Finally, after eliminating the anomalies and errors, the blending fitting model of the hourly sub-weather status is established. Through the analysis of power plants in Australia and China, the proposed solutions are validated and evaluated to be superior to existing data-driven solutions in terms of fitting accuracy, detection validity, and response time. Numerical results of case studies indicate that the developed methodology under sub-weather has improved the detection accuracy up to 97.71% and 99.29% compared to benchmark models.

关键词： Photovoltaic systems performance Hourly fault detection Unsupervised hourly weather status pattern recognition Blending fitting model

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A Novel Person Re-ID Method based on Multi-Scale Feature Fusion

A Novel Person Re-ID Method based on Multi-Scale Feature Fus...

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Chinese control Conference (CCC)

作者： Yuanyuan Li Xiaofei Wang Zhiqin Zhu Xinghua Huang Penghua Li Guanqiu Qi Yujun Rong Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Complex System Safety and Control Ministry of Education Chongqing University Chongqing China Computer Information Systems Department Buffalo State College Buffalo NY USA China Mobile (Hangzhou) Information Technology Co. Ltd. Hangzhou China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Person re-identification (Re-ID) is a challenging but significant research topic. Previous methods based on global features are not pay much attention to the fine-grained feature information. While the methods based on local features strictly rely on the accurate pedestrian detection bounding-boxes to align the image pairs. In this paper, the Multi-Scale Feature Fusion Network is proposed which can extract global and local features simultaneously and to learn jointly. The network structure consists of two branches. One is a global branch for global feature learning, in this branch, we extract the whole body feature information of human body to express the pedestrian image as a whole. The other is a local branch for local feature learning. In the local branch, according to the characteristics of pedestrian's human structure, we segment the image into 6 horizontal strips to bring the local feature representation of the pedestrian image. In addition, we use a novel integration of multiple loss functions to further improve the recognition accuracy of the network. Experiments on Market-1501 and DukeMTMC-reID datasets show that our proposed method can achieve state-of-the-art results.

关键词： Feature extraction Task analysis Training Measurement Smoothing methods Electronic mail Machine learning

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A TextCNN Based Approach for Multi-label Text Classification of Power Fault Data

A TextCNN Based Approach for Multi-label Text Classification...

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IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Qiang Zhang Rongrong Zheng Ziyan Zhao Bo Chai Jiangui Li Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory (GEIRI) Global Energy Interconnection Research Institute Co. Ltd Beijing China State Grid Information & Telecommunication Co. Ltd School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160252

With the increasing number of power terminal devices in power systems, fault diagnosis based on textual fault data has become a challenging task. Classification technology plays an important role in power system fault diagnosis. In this paper, we propose a TextCNN based approach to multi-label text classification for analyzing fault text data of power terminal devices. First, the main category labels of faults and failure were constructed. The dataset was preprocessed for feature representation. Next, we adopted the TextCNN based model for multi-label classification of power fault texts. The experimental results show that the proposed method is better than the typical multi-label classification models for power fault text multi-label classification. The constructed label categories were also experimentally evaluated, and these labels reflect the main types of power faults and failure.

关键词： Text categorization Internet of Things Power system faults Feature extraction Fault diagnosis

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Opacity of Nondeterministic Transition systems: A (Bi)Simulation Relation Approach

Opacity of Nondeterministic Transition Systems: A (Bi)Simula...

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作者： Zhang, Kuize Yin, Xiang Zamani, Majid ACCESS Linnaeus Center School of Electrical Engineering KTH Royal Institute of Technology Stockholm10044 Sweden Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai200240 China Computer Science Department University of Colorado Boulder BoulderCO80309 United States

In this paper, we propose several opacity-preserving (bi)simulation relations for nondeterministic transition systems (NTSs) in terms of initial-state opacity, current-state opacity, K-step opacity, and infinite-step opacity. We also show how one can leverage quotient constructions to compute such relations. As a result, although the opacity verification problem for infinite NTSs is generally undecidable, if one can find such an opacity-preserving relation from an infinite NTS to a finite one, the (lack of) opacity of the infinite NTS can be easily verified over the finite one, which is decidable. © 1963-2012 IEEE.

关键词： Opacity

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A pre-allocation design for cost minimization and delay constraint in vehicular offloading system

arXiv

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arXiv 2019年

作者： Chen, Zhijie Yang, Bo Chen, Cailian Guan, Xinping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China China

To accommodate exponentially increasing traffic demands of vehicle-based applications, operators are utilizing offloading as a promising technique to improve quality of service (QoS), which gives rise to the application of Mobile Edge Computing (MEC). While the conventional offloading paradigms focus on delay and energy tradeoff, they either fail to find efficient models to represent delay, especially the queueing delay, or underestimate the role of MEC Server. In this paper, we propose a novel Pre-Allocation Design for vehicular Offloading (PADO). A task delay queue is constructed based on an allocate-execute separate (AES) mechanism. Due to the dynamics of vehicular network, we are inspired to utilize Lyapunov optimization to minimize the execution cost of each vehicle and guarantee task delay. The MEC Server with energy harvesting devices is also taken into consideration of the system. The transaction between vehicles and server is decided by a Stackelberg Game framework. We conduct extensive experiments to show the property and superiority of our proposed framework. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy harvesting

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Compound control method for DC–DC converter

Compound control method for DC–DC converter

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2019 Jiangsu Annual Conference on Automation, JACA 2019

作者： Tang, Yongwei Li, Juan Li, Shengquan Lin, Zhongwei Wang, Shuwang Department of Electrical Engineering School of Electrical Energy and Power Engineering Yangzhou University Yangzhou225127 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China

Considering the problems of internal and external disturbances, i.e. time-varying load, inaccurate system model and external uncertainties etc., a compound control strategy based on model predictive control (MPC) and non-linear extended state observer (ESO) are proposed to attenuate the output voltage fluctuation under continuous conduction mode (CCM) mode of the buck circuits. In addition, a non-linear ESO is used to observe all kinds of uncertainties caused by internal and external disturbances and to eliminate their effects by compensating these disturbances in the feed-forward channel. Compared with the pure MPC control, the simulation results show that the proposed composite control method has the better disturbance suppression effect and also can effectively improve the stability of the buck circuit. © 2019 Institution of Engineering and Technology. All rights reserved.

关键词： Model predictive control

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A Hierarchical Fine-Tuning Based Approach for Multi-label Text Classification

A Hierarchical Fine-Tuning Based Approach for Multi-label Te...

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IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Qiang Zhang Bo Chai Bochuan Song Jingpeng Zhao Artificial Intelligence on Electric Power System State Grid Corporation Joint Laboratory (GEIRI) Global Energy Interconnection Research Institute Co. Ltd SGCC-HUST Future Grid Research Institute Beijing China School of Control and Computer Engineering North China Electric Power University Changpin District Beijing China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160252

Hierarchical Text classification has recently become increasingly challenging with the growing number of classification labels. In this paper, we propose a hierarchical fine-tuning based approach for hierarchical text classification. We use the ordered neurons LSTM (ONLSTM) model by combining the embedding of text and parent category for hierarchical text classification with a large number of categories, which makes full use of the connection between the upper-level and lower-level labels. Extensive experiments show that our model outperforms the state-of-the-art hierarchical model at a lower computation cost.

关键词： Training Text categorization Logic gates Neurons Computational modeling Mathematical model Taxonomy

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Performance Evaluation of CFB Unit Combustion system Based on Comprehensive Economics and Entropy Weight Method

Performance Evaluation of CFB Unit Combustion System Based o...

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International Conference on Sensing, Diagnostics, Prognostics, and control (SDPC)

作者： Ce Qin Mingming Gao Hongfu Zhang The State Key Laboratory of Alternate Electric Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9781728101996

ISBN: (纸本)9781728102009

A performance evaluation method for the combustion system in the variable load process of circulating fluidized bed (CFB) unit based on the minimum variance control is proposed. Each operating parameter are initially weighted according to the dynamic economic index of the operating parameters, and entropy weight method is used to adjust the weights of operating parameter under different operating conditions. Then, the minimum variance index based on comprehensive economic and entropy weight method is proposed to evaluate the performance of the combustion system of CFB unit, which reduces the polarization effect generated by the single weighting performance evaluation method and improves the objective scientificality of the operating parameter weighting. Finally, a case study on the combustion system of a 300MW CFB unit reveals that the minimum variance index based on the comprehensive economy and entropy weight method can better evaluate the performance of the combustion system in the dynamic process of the unit, compared with the traditional performance evaluation method. It is of great significance to the operational safety and economy of CFB boiler units.

关键词： Combustion Time series analysis Data models Performance evaluation Entropy Indexes Boilers

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Classification of Cognitive Ability of Healthy Elderly Individuals Using Resting-State Functional Connectivity Magnetic Resonance Imaging and An Extreme Learning Machine

Research Square

引用

Research Square 2021年

作者： Yang, Zekun Ge, Manling Abdelkader, Nasreddine Belkacem Fu, Xiaoxuan Zhang, Qirui Chen, Shenghua Xie, Chong Song, Zibo Chen, Chao State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Hebei Province Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability Hebei University of Technology Tianjin300130 China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain15551 United Arab Emirates Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Harvard Medical School CharlestownMA02129 United States Department of Medical Imaging Jinling Hospital Nanjing University School of Medicine General Hospital of Eastern Theater Nanjing210002 China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300003 China

Purpose Quantitative determination of the correlation between cognitive ability and functional biomarkers in the elderly brain is essential. To identify biomarkers associated with cognitive performance in the elderly, this study combined an index model specific for resting-state functional connectivity (FC) magnetic resonance imaging (fcMRI) with a supervised machine learning method. Methods Performance scores on conventional cognitive test batteries and MRI data were obtained for 98 healthy elderly individuals and 90 healthy youth from two public databases. Based on the test scores, the elderly cohort was categorized into two groups: excellent and poor. An fcMRI index model was constructed for each elderly individual to determine the relative differences in FC among brain regions compared with that in the youth cohort. Brain areas sensitive to test scores could then be identified using the fcMRI indexes. To confirm the effectiveness of constructed model, the fcMRI indexes of these brain areas were used as feature matrix inputs for training an extreme learning machine. Classification accuracy was then tested in separate groups and confirmed by N-fold cross-validation. Results This learning study could effectively classify the cognitive status of healthy elderly individuals according to frontal lobe, temporal lobe, and parietal lobe FC values with a mean accuracy of 83.5%, which is substantially higher than that achieved using conventional correlation analysis. Conclusion This fcMRI classification study may facilitate early detection of age-related cognitive decline as well as help reveal the underlying pathological mechanisms. © 2021, CC BY.

关键词： Magnetic resonance imaging

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