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Two-dimensional temperature distribution estimation for a cross-flow planar solid oxide fuel cell stack

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2020年第3期45卷 2257-2278页

作者： Wu, Xiaodong Jiang, Jianhua Zhao, Weiqi Li, Xi Li, Jian Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Educ Minist Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Mat Sci & Engn State Key Lab Mat Proc & Dye & Mold Technol Wuhan 430074 Peoples R China

The uniform temperature distribution of a cross-flow planar solid oxide fuel cell (SOFC) stack plays an essential role in stack thermal safety and electrical property. However, because of the strict requirements in stack sealing struture, it is hard to acquire the temperature inside the stack using thermal detection devices within an acceptable cost. Therefore, accurately estimating the two-dimensional (2-D) temperature distribution of the cross-flow stack is crucial for its thermal management. In this paper, Firstly, a 2-D mechanism model of a cross-flow planar SOFC stack is established. The stack is divided into 5*5 nodes along the gas flow directions, which can reflect the stack states with moderate computational burden. Then, experimental test data is utilized to modify and validate the stack model, guaranteeing the model accuracy as well as the reliability of model-based state estimator design. Finally, easily-measured stack inputs and outputs are selected, and a temperature distribution estimator combined with unscented kalman filter (UFK) approach is developed to achieve accurate and fast temperature distribution estimation of a cross-flow SOFC stack. Simulation results demonstrate that the UKF-based temperature distribution estimator can precisely and quickly achieve the temperature distribution estimation of the cross-flow stack under both static state and dynamic state changes and is applicable to cross-flow stacks with different size or cell number as well, the maximum estimated absolute error is less than 0.15 K with an absolute error rate of 0.015%, which indicates the developed estimator has good estimation performances. (C) 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Cross-flow SOFC stack Modeling and validation Unscented kalman filter Temperature distribution estimation

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Simplified Fourier Series Based Transistor Open-Circuit Fault Location Method in Voltage-Source Inverter Fed Induction Motor

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IEEE ACCESS 2020年 8卷 83953-83964页

作者： Wu, Feng Sun, Jianwen Zhou, Dehong Liu, Yang Geng, Tao Zhao, Jin Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Univ Alberta Elect & Comp Engn Dept Edmonton AB T6G 2R3 Canada Huazhong Univ Sci & Technol Guangdong HUST Ind Technol Res Inst Guangdong Prov Key Lab Digital Mfg EquipmentSch Key Lab Image Proc & Intelligent ControlMinist E Wuhan 430074 Peoples R China

Transistors in three-phase voltage-source inverter often suffer from open-circuit failures due to the lifting of bonding wires caused by thermic cycling, resulting in performance degradation with ripple torque and current harmonics. Current-spectral-analysis based methods are widely applied to failure diagnosis;however, high calculation consumption and complex implementation limit their application in some real-time occasion. In this paper, a simplified Fourier series method is proposed by the product between reconstructed phase currents and reference signals. Meanwhile, a novel normalized method for DC and fundamental components of simplified Fourier series are proposed to locate twenty-one transistor open-circuit faults. Numerical results show that the proposed Fourier series method coincides with that of Fast Fourier Transform. Experimental results and the comparison with previous methods show high efficiency and merits of its application to transistor open-circuit fault location in the voltage-source inverter.

关键词： Voltage-source inverter real-time current spectral open-circuit fault location

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A mini-review of noble-metal-free electrocatalysts for overall water splitting in non-alkaline electrolytes

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Materials Reports(Energy) 2021年第2期1卷 48-60页

作者： Jie Yu Yawen Dai Qijiao He Dongqi Zhao Zongping Shao Meng Ni Department of Building and Real Estate The Hong Kong Polytechnic UniversityHung HomKowloonHong Kong999077PR China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and TechnologyWuhan430074PR China State Key Laboratory of Materials-Oriented Chemical Engineering College of Chemical EngineeringNanjing Tech UniversityNanjing210009PR China Department of Chemical Engineering Curtin UniversityPerthWestern Australia6845Australia Environmental Energy Research Group Research Institute for Sustainable Urban Development(RISUD)The Hong Kong Polytechnic UniversityHung HomKowloonHong Kong999077PR China

Development of noble-metal-free materials with remarkable electrocatalytic water-splitting performance in acidic or neutral media has sparked considerable attention in recent ***,we review the latest research on design and fabrication of precious-metal-free catalytic materials for overall water electrolysis in non-alkaline environment,especially highlighting several optimizing approaches to enhance the catalytic behavior and to realize effective bifunctional *** these involved noble-metal-free electrocatalysts are classified into transition-metal oxides(TMOs),transition-metal nitrides(TMNs),transition-metal carbides(TMCs),transition-metal phosphides(TMPs),transition-metal chalcogenides,metal complexes,and metal-free carbons,as shown in the main ***,the paper also offers an introduction of the fundamental electrochemistry of water splitting before entering the subject,as well as a prospective discussion on mechanism understanding,novel catalysts fabrication,and standardized performance measurements/evaluation in the last section.

关键词： Noble-metal-free electrocatalyst Overall water splitting Oxygen evolution reaction Hydrogen evolution reaction Non-alkaline electrolyte Bifunctional catalytic electrode

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A Self-Reproduction Hyperchaotic Map With Compound Lattice Dynamics

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2022年第10期69卷 10564-10572页

作者： Li, Yongxin Li, Chunbiao Zhang, Sen Chen, Guanrong Zeng, Zhigang Nanjing Univ Informat Sci & Technol Dept Informat & Commun Engn Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Atmospher Environm & Equip Nanjing 210044 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence Inst Artificial Intelligence Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Educ Minist China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China

In this article, sinusoidal functions are introduced to a discrete map for hyperchaos generation and attractor self-reproduction. The constructed map shares a unique structure with controllable symmetry and conditional symmetry, which exhibits compound lattice dynamics, including 1-D and 2-D attractor growth. The direction of attractor growth can be controlled under polarity balance. STM32-based circuit realization verifies the results with numerical simulation and theoretical analysis. A pseudorandom number generator is built finally based on the newly proposed hyperchaotic map proving the high performance in application.

关键词： Chaotic communication Lattices Compounds Trajectory Generators Artificial intelligence Eigenvalues and eigenfunctions Attractor growth attractor self-reproduction (ASR) hyperchaotic map lattice dynamics

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A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial image Anomaly Detection

arXiv

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arXiv 2024年

作者： Lin, Yuxuan Chang, Yang Tong, Xuan Yu, Jiawen Liotta, Antonio Huang, Guofan Song, Wei Zeng, Deyu Wu, Zongze Wang, Yan Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai200438 China Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai200438 China Faculty of Computer Science Free University of Bozen-Bolzano Bolzano39100 Italy College of Information Technology Shanghai Ocean University Shanghai201306 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

In the advancement of industrial informatization, unsupervised anomaly detection technology effectively overcomes the scarcity of abnormal samples and significantly enhances the automation and reliability of smart manufacturing. As an important branch, industrial image anomaly detection focuses on automatically identifying visual anomalies in industrial scenarios (such as product surface defects, assembly errors, and equipment appearance anomalies) through computer vision techniques. With the rapid development of Unsupervised industrial image Anomaly Detection (UIAD), excellent detection performance has been achieved not only in RGB setting but also in 3D and multimodal (RGB and 3D) settings. However, existing surveys primarily focus on UIAD tasks in RGB setting, with little discussion in 3D and multimodal settings. To address this gap, this artical provides a comprehensive review of UIAD tasks in the three modal settings. Specifically, we first introduce the task concept and process of UIAD. We then overview the research on UIAD in three modal settings (RGB, 3D, and multimodal), including datasets and methods, and review multimodal feature fusion strategies in multimodal setting. Finally, we summarize the main challenges faced by UIAD tasks in the three modal settings, and offer insights into future development directions, aiming to provide researchers with a comprehensive reference and offer new perspectives for the advancement of industrial informatization. Corresponding resources are available at https://***/Sunny5250/Awesome-Multi-Setting-UIAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Smart manufacturing

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Simultaneous Gait Generation and Path Following control of Snake Robot Using MPC

Simultaneous Gait Generation and Path Following Control of S...

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IEEE International Conference on Robotics and Biomimetics

作者： Yichen Liang Chao Ren Yongchen Tang Xiaohan Li Shugen Ma School of Electrical and Information Engineering Tianjin University Tianjin China Key Lab of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Tangshan Ritsumeikan University Shiga Japan

ISBN: (纸本)9781665405362

The path following control with given forward velocity for snake robot is important in real applications. Most of traditional model based snake robot control methods rely on predefined gaits. In this paper, the simultaneous gait generation and path following control with given forward velocity are investigated under the MPC scheme. Firstly, two cost functions are respectively studied for the snake robot, to realize path convergence and to maximize the forward velocity. Then a MPC scheme is proposed by combining the two cost functions, to achieve better path following control performance and higher forward velocity of the snake robot. Moreover, the forward velocity control of snake robot is realized by viewing the desired velocity as one constraint. Simulation results verify the effectiveness of the proposed control scheme.

关键词： Simulation Conferences Biomimetics Snake robots Velocity control Cost function Convergence

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Multiconsensus of Second-Order Multiagent Networks via Pulse-Modulated Intermittent control

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COMPLEXITY 2020年第1期2020卷

作者： Chi, Ming Wang, Xu-Long He, Ding-Xin Liu, Zhi-Wei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

This paper studies the multiconsensus problem of multiagent networks based on sampled data information via the pulse-modulated intermittent control (PMIC) which is a general control framework unifying impulsive control, intermittent control, and sampling control. Two kinds of multiconsensus, including stationary multiconsensus and dynamic multiconsensus of multiagent networks, are taken into consideration in such control framework. Based on the eigenvalue analysis and algebraic graph theory, some necessary and sufficient conditions on the feedback gains and the control period are established to ensure the multiconsensus. Finally, several simulation results are included to show the theoretical results.

关键词： Eigenvalues and eigenfunctions

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SOFC Stack Internal Temperature Estimation Based on Spatial-temporal Distribution Laws

SOFC Stack Internal Temperature Estimation Based on Spatial-...

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Chinese Automation Congress (CAC)

作者： Zhao, Weiqi Jiang, Jianhua Zhang, Haobo Qin, Hongchuan Li, Xi Li, Jian Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat China EU Inst Clean & Renewable Energy Key LabEduc Minist Image Proc & Intelligent Cont Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Educ Minist Image Proc & Intelligent Control Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Sch Mat Sci & Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9781728176871

Solid oxide fuel cell (SOFC) stack internal temperature distribution is an important index for stack performance management. Due to the strict gas sealing requirement of SOFC, temperature measuring devices cannot be installed within an acceptable cost and designing an observer to estimate stack inner temperature is an efficient way. However, as for the hydrocarbon fueled SOFC system, the dimension of system input and state variables is very high due to the complex fuel composition, which challenges the observer design. In this work, from the perspective of practical application, a novel method that quickly obtain the stack temperature distribution by extracting spatial-temporal stack temperature distribution features under different operating conditions is proposed. Firstly, the temperature distribution data is generated by a validated mechanism SOFC system model, and then the spatial and temporal features of the temperature distribution are grasped by employing Multivariable Linear Regression (MLR) algorithm. Fusing the information of SOFC center node temperature and the spatial- temporal temperature distribution laws, the SOFC temperature distribution can be obtained. Simulation results show that the MLR model can accurately describe the temperature distribution characteristics under various operation conditions, which paves the way for quickly and accurately obtaining of SOFC stack internal temperature distribution.

关键词： SOFC system stack temperature distribution spatial-temporal distribution features Multivariable Linear Regression

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城市耦合风险——以人为本的复杂系统视角（英文）

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Engineering 2025年第1期 44-50页

作者：欧阳敏程泽楷马佳欣王红卫 Stergios Aristoteles Mitoulis School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology School of Management Huazhong University of Science and Technology School of Engineering University of Birmingham

The complexity of coupled risks, which refer to the compounded effects of interacting uncertainties across multiple interdependent objectives, is inherent to cities functioning as dynamic, interdependent systems. A disruption in one domain ripples across various urban systems, often with unforeseen consequences. Central to this complexity are people, whose behaviors, needs, and vulnerabilities shape risk evolution and response effectiveness. Realizing cities as complex systems centered on human needs and behaviors is essential to understanding the complexities of coupled urban risks. This paper adopts a complex systems perspective to examine the intricacies of coupled urban risks, emphasizing the critical role of human decisions and behavior in shaping these dynamics. We focus on two key dimensions: cascading hazards in urban environments and cascading failures across interdependent exposed systems in cities. Existing risk assessment models often fail to capture the complexity of these processes, particularly when factoring in human decision-making. To tackle these challenges, we advocate for a standardized taxonomy of cascading hazards, urban components, and their interactions. At its core is a people-centric perspective, emphasizing the bidirectional interactions between people and the systems that serve them. Building on this foundation, we argue the need for an integrated, people-centric risk assessment framework that evaluates event impacts in relation to the hierarchical needs of people and incorporates their preparedness and response capacities. By leveraging real-time data, advanced simulations, and innovative validation methods, this framework aims to enhance the accuracy of coupled urban risk modeling. To effectively manage coupled urban risks, cities can draw from proven strategies in real complex systems. However, given the escalating uncertainties and complexities associated with climate change, prioritizing people-centric strategies is crucial. This ap

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ImPLoc: a multi-instance deep learning model for the prediction of protein subcellular localization based on immunohistochemistry images

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BIOINFORMATICS 2020年第7期36卷 2244-2250页

作者： Long, Wei Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong Univ Ctr Brain Comp & Machine Intelligence Dept Comp Sci & Engn Shanghai 200240 Peoples R China Key Lab Shanghai Educ Commiss Intelligent Interac Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Key Lab Syst Control & Informat Proc Minist Educ China Shanghai 200240 Peoples R China

Motivation: The tissue atlas of the human protein atlas (HPA) houses immunohistochemistry (IHC) images visualizing the protein distribution from the tissue level down to the cell level, which provide an important resource to study human spatial proteome. Especially, the protein subcellular localization patterns revealed by these images are helpful for understanding protein functions, and the differential localization analysis across normal and cancer tissues lead to new cancer biomarkers. However, computational tools for processing images in this database are highly underdeveloped. The recognition of the localization patterns suffers from the variation in image quality and the difficulty in detecting microscopic targets. Results: We propose a deep multi-instance multi-label model, ImPLoc, to predict the subcellular locations from IHC images. In this model, we employ a deep convolutional neural network-based feature extractor to represent image features, and design a multi-head self-attention encoder to aggregate multiple feature vectors for subsequent prediction. We construct a benchmark dataset of 1186 proteins including 7855 images from HPA and 6 subcellular locations. The experimental results show that ImPLoc achieves significant enhancement on the prediction accuracy compared with the current computational methods. We further apply ImPLoc to a test set of 889 proteins with images from both normal and cancer tissues, and obtain 8 differentially localized proteins with a significance level of 0.05.

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