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Fault diagnosis for PV arrays considering dust impact based on transformed graphical features of characteristic curves and convolutional neural network with CBAM modules

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Applied Energy 2024年 355卷

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

Various faults can occur during the operation of photovoltaic (PV) arrays, and both dust-affected operating conditions and various diode configurations complicate the faults. However, current methods for fault diagnosis based on I-V characteristic curves usually do not effectively use all the distinguishable information contained in the I-V curves or often rely on calibrating the field characteristic curves to standard test conditions (STC). It is difficult to apply these methods in practice and accurately identify multiple complex faults with similarities in different blocking diode configurations of PV arrays under the influence of dust. Therefore, a novel fault-diagnosis method for PV arrays that considers the impact of dust is proposed. In the pre-processing stage, the Isc-Voc normalized Gramian angular difference field (GADF) method is presented, which normalizes and transforms the resampled PV array characteristic curves from the field, including I-V and P-V, to obtain transformed graphical feature matrices. Subsequently, in the fault diagnosis stage, the convolutional neural network (CNN) model with convolutional block attention modules (CBAM) is designed to classify faults, which identifies complex fault types from the transformed graphical matrices containing complete discriminative fault information. In addition, the performances of different graphical feature transformation and CNN-based classification methods are compared using case studies. The results indicate that the developed method for PV arrays with different blocking diode configurations under various operating conditions has high fault diagnosis accuracy and reliability.

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Fast all-optical random number generator

arXiv

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arXiv 2022年

作者： Zhao, Jiamin Li, Pu Zhang, Xing Gao, Zhensen Jia, Zhiwei Bogris, Adonis Alan Shore, K. Wang, Yuncai Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China Guangdong Provincial Key Laboratory of Photonics Information Technology Guangzhou510006 China Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University Shanghai200444 China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China Department of Informatics and Computer Engineering University of West Attica Athens12243 Greece School of Electronic Engineering Bangor University WalesLL57 1UT United Kingdom

We propose a simple and all-optical method for fast random number generation based on the laser mode hopping. Through periodically restarting a two-mode laser operating in the bistable state, a random number stream can be generated due to the spontaneous emission noise. To validate the feasibility of this method, we perform a theoretical simulation using the common vertical-cavity surface-emitting laser (VCSEL) with two polarization modes. Numerical results demonstrate that fast 2.5 Gb/s random number streams can be continuously obtained with verified randomness. This scheme provide a fully monolithic solution for random number generator, due to its simple and all-optical structure. Copyright © 2022, The Authors. All rights reserved.

关键词： Random number generation

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Age-of-Task Aware Sampling Rate Optimization in Edge-Assisted Industrial Network systems

Age-of-Task Aware Sampling Rate Optimization in Edge-Assiste...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Xiaojing Wen Cailian Chen Mingyan Li Ling Lyu Xinping Guan Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Ministry of Education of China Shanghai P.R. China College of Computer Science Chongqing University Chongqing P. R. China School of Information Science and Technology Dalian Maritime University Dalian P. R. China

ISBN: (纸本)9781728181059

Multivariate-information and computation-intensive tasks play an important role in Wireless Sensor Network systems (WSNSs), where information freshness has an important impact on the system performance of state analysis. Recently, the Age of Information (AoI) has been studied extensively as a promising metric to evaluate the freshness of state packets. However, most of the existing research focuses on optimizing the average AoI of a single information source, which can not be directly applied to the scenario with multi-source tasks. In this paper, we firstly present a novel definition of the Age of Task (AoT) for edge-assisted industrial WSNSs. Furthermore, the expressions of the sensing time and arrival time that determine the AoT tail distribution are given in detail. Then, we propose an AoT tail violation probability minimization problem to find the optimal sampling rate and give the feasible region of the sampling rate. Since it is difficult to obtain the exacted expression of the formulated problem, the Upper Bound Minimization Problem (UBMP) and the more tractable α-relaxed UBMP are proposed to obtain the near-optimal sampling rate. Finally, simulation results show that the sampling rate obtained by α-relaxed UBMP is nearly optimal for the AoT tail violation probability minimization problem.

关键词： Wireless sensor networks Analytical models Upper bound system performance Simulation Stochastic processes Minimization

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Towards the Future Data Market: Reward Optimization in Mobile Data Subsidization 3rd

Towards the Future Data Market: Reward Optimization in Mobil...

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3rd EAI International Conference on 6G for Future Wireless Networks, 6GN 2020

作者： Xiong, Zehui Zhao, Jun Kang, Jiawen Niyato, Dusit Deng, Ruilong Xie, Shengli Alibaba-NTU Joint Research Institute NTU Singapore Singapore School of Computer Science and Engineering NTU Singapore Singapore Energy Research Institute NTU Singapore Singapore College of Control Science and Engineering School of Cyber Science and Technology Zhejiang University Hangzhou China Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou China Joint International Research Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou China

ISBN: (纸本)9783030639402

Mobile data subsidization launched by network operators is a promising business model to provide some economic insights on the evolving direction of the 4G/5G and beyond mobile data market. The scheme allows content providers to partly subsidize mobile data consumption of mobile users in exchange for displaying a certain amount of advertisements. The users are motivated to access and consume more content without being concerned about overage charges, yielding higher revenue to the data subsidization ecosystem. For each content provider, how to provide appropriate data subsidization (reward) competing with others to earn more revenue and gain higher profit naturally becomes the key concern in such a ecosystem. In this paper, we adopt a hierarchical game approach to model the reward optimization process for the content providers. We formulate an Equilibrium Programs with Equilibrium Constraints (EPEC) problem to characterize the many-to-many interactions among multiple providers and multiple users. Considering the inherent high complexities of the EPEC problem, we propose to utilize the distributed Alternating Direction Method of Multipliers (ADMM) algorithm to obtain the optimum solutions with fast-convergence and decomposition properties of ADMM. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Game theory

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Generating infrared image from visible image using Generative Adversarial Networks

Generating infrared image from visible image using Generativ...

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2019 IEEE International Conference on Unmanned systems, ICUS 2019

作者： An, Lei Zhao, Jiajia Di, Huijun Beijing Institute of Technology School of Computer Science and Technology Beijing China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Coorperation Laboratory Beijing China

ISBN: (纸本)9781728137926

Infrared images have properties that are unaffected by illumination compared to visible images, object can be clearly recognized at day or night. Therefore, it is a better choice to use infrared images when training deep learning algorithms in unmanned systems. However, how to obtain a large number of infrared images makes it possible to train deep learning algorithm, which is the focus of our attention. In contrast to difficulties in direct shooting, computer simulations can generate large infrared images from visible images. But models that are trained on simulated data usually do not translate well to real scenarios. To bridge the domain gap between simulated and real infrared images, we exploit recent advances in paired image-to-image translation. We extend Pixel2Pixel to generate infrared image from visible image and propose a novel way of multilayer semantic information fusion, which significantly improves the quality of generated images. Finally, the image generated by our proposed PixelPro network is almost consistent with the real image distribution, achieved the purpose of expanding the dataset. © 2019 IEEE.

关键词： Generative adversarial networks

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A Distributed Observer Based on Event-Triggered Communication for LTI systems

A Distributed Observer Based on Event-Triggered Communicatio...

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International Conference on control and Automation (ICCA)

作者： Hui Tian Xin Cai Feng Xiao Bo Wei State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources and with the School of Control and Computer Engineering North China Electric Power University Beijing China School of Electrical Engineering Xinjiang University Urumqi China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

This paper focuses on the design of a distributed observer based on event-triggered communication for continuous-time linear time-invariant (LTI) systems. In order to implement discrete-time communication among (sub-) observers, a dynamic event-triggered mechanism is proposed to reduce the communication frequency among neighboring observers as well as to ensure accurate estimations of the designed observers. In this case, each observer broadcasts its information to its neighbors only when the relevant event-triggering condition is met. Finally, numerical simulations are given to show the effectiveness of the proposed approach.

关键词： Observers Linear systems Vehicle dynamics Sensors Couplings Real-time systems Performance analysis

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A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders

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Biomedical Signal Processing and control 2022年第PartB期72卷 103370-103370页

作者： Lei, Yu Belkacem, Abdelkader Nasreddine Wang, Xiaotian Sha, Sha Wang, Changming Chen, Chao Beijing Institute of Basic Medical Sciences Beijing China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates School of Artificial Intelligence Xidian University Xian China National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China Brain-inspired Intelligence and Clinical Translational Research Center/Department of Neurosurgery Xuanwu Hospital Capitap Medical University Beijing China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China

Background: Early and accurate diagnosis of bipolar and major depressive disorders is important in clinical practice. However, no diagnostic biomarkers can discriminate bipolar from major depressive disorder with high accuracy at present. Methods: We propose a novel convolutional neural network architecture using multichannel raw resting-state electroencephalograph signals to differentiate bipolar disorder from major depressive disorder. This method has great potential in diagnosing mental disorders. In total, 101 patients with major depressive disorder, 82 patients with bipolar disorder, and 81 healthy controls were assessed. Clinical diagnosis was performed by psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Participants were instructed to fix their eyes on a cross on the monitor during the collection of resting-state electroencephalograph signals. Results: A classification mean accuracy of 96.88% was achieved using the resting-state electroencephalograph signals with the 10-fold cross-validation method. The results indicate that deep neural networks could learn efficient feature patterns automatically without manual feature selections. Both statistical analysis and feature visualization on different brain regions showed that the classification performance and the learned features of the proposed model were consistent with that obtained in neurobiological studies on bipolar and major depressive disorders. Conclusions: The combined use of deep neural networks and electroencephalograph signals is an effective approach for the computer-aided diagnosis of bipolar and major depressive disorders. © 2021 Elsevier Ltd

关键词： Deep neural networks

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Board-level Intelligent Built-in Test Design of Digital Input Module by Improved BP Neural Network

Board-level Intelligent Built-in Test Design of Digital Inpu...

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Data Driven control and Learning systems (DDCLS)

作者： Yasong Wang Congzhi Huang Jianhua Zhang Guolian Hou School of Control and Computer Engineering North China Electric Power University Beijing China Key Laboratory of Power Station Energy Transfer Conversion and System North China Electric Power University Beijing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

The built-in test (BIT) technology plays a major role in heavy-duty gas turbine control system. The accuracy of BIT is a guarantee for the normal operation of the equipment. In the actual production process, conventional BIT has a relatively high false alarm rate. In this paper, an intelligent built-in test (BIT) design method was presented for digital input (DI) module using a back propagation (BP), which was optimized by improved adaptive gravitational search algorithm (IAGSA) for quantitative analysis. In order to improve defect of the basic gravitational search algorithm (GSA), a tent map for population chaos initialization was proposed. Firstly, the status information that can reflect the failure of the DI module was chosen as experimental data. Then, these data were handled by normalization. After that, multiple IAGSA-BP models were established with status information about DI module. The output of these models can fully represent the fault status of the DI module. Finally, the experimental results are shown to validate the effectiveness of the proposed approaches.

关键词： Biological neural networks Neurons control systems Training Hidden Markov models Turbines Optimization

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Resource allocation for millimeter-wave train-ground communications in high-speed railway scenarios

arXiv

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arXiv 2021年

作者： Zhang, Xiangfei Niu, Yong Mao, Shiwen Cai, Yunlong He, Ruisi Ai, Bo Zhong, Zhangdui Liu, Yiru State Key Laboratory of Rail Traffic Control and Safety Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University Beijing100044 China National Mobile Communications Research Laboratory Southeast University Nanjing211189 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of ISEE Zhejiang University China State Key Laboratory of Rail Traffic Control and Safety Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

With the development of wireless communication, higher requirements arise for train-ground wireless communications in high-speed railway (HSR) scenarios. The millimeter-wave (mm-wave) frequency band with rich spectrum resources can provide users in HSR scenarios with high performance broadband multimedia services, while the full-duplex (FD) technology has become mature. In this paper, we study train-ground communication system performance in HSR scenarios with mobile relays (MRs) mounted on rooftop of train and operating in the FD mode. We formulate a nonlinear programming problem to maximize network capacity by allocation of spectrum resources. Then, we develop a sequential quadratic programming (SQP) algorithm based on the Lagrange function to solve the bandwidth allocation optimization problem for track-side base station (BS) and MRs in this mm-wave train-ground communication system. Extensive simulation results demonstrate that the proposed SQP algorithm can effectively achieve high network capacity for train-ground communication in HSR scenarios while being robust to the residual self-interference (SI). Copyright © 2021, The Authors. All rights reserved.

关键词： Resource allocation

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Online reinforcement learning control by direct heuristic dynamic programming: from time-driven to event-driven

arXiv

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arXiv 2020年

作者： Zhao, Qingtao Si, Jennie Sun, Jian Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing100081 China Department of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States

In this paper time-driven learning refers to the machine learning method that updates parameters in a prediction model continuously as new data arrives. Among existing approximate dynamic programming (ADP) and reinforcement learning (RL) algorithms, the direct heuristic dynamic programming (dHDP) has been shown an effective tool as demonstrated in solving several complex learning control problems. It continuously updates the control policy and the critic as system states continuously evolve. It is therefore desirable to prevent the time-driven dHDP from updating due to insignificant system event such as noise. Toward this goal, we propose a new event-driven dHDP. By constructing a Lyapunov function candidate, we prove the uniformly ultimately boundedness (UUB) of the system states and the weights in the critic and the control policy networks. Consequently we show the approximate control and cost-to-go function approaching Bellman optimality within a finite bound. We also illustrate how the event-driven dHDP algorithm works in comparison to the original time-driven dHDP. Copyright © 2020, The Authors. All rights reserved.

关键词： Learning algorithms

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