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Modeling and simulation of temperature distribution for planar cross-flow solid oxide fuel cell

Energy Procedia 2019年 158卷 1585-1590页

作者： Yuan-wu Xu Xiao-long Wu Hang You Tao Xue Dong-qi Zhao Jian-hua Jiang Zhong-hua Deng Xiao-wei Fu Li Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan 430065 China Research Institute of Huazhong University of Science & Technology in Shenzhen Shenzhen 518057 China

Solid oxide fuel cell is a device that can convert chemical energy directly into electricity. Its advantages, such as high efficiency, low emission, quiet operation, fuel flexibility, bring about the broad application prospect. However, it is difficult to obtain the temperature distribution in the existing planar cross flow solid oxide fuel cell through the experiment. In this paper, a control orient two-dimensional differential equation model is established for a planar cross flow solid oxide fuel cell based on the finite node method, and an iterative algorithm for calculating the real time voltage of the fuel cell is proposed for the model. Based on the model, the temperature distribution of the fuel cell in the test and system configuration is simulated. The simulation results show that the model can reflect the thermoelectric characteristics of the planar cross flow solid oxide fuel cell, especially the temperature distribution of the fuel cell. The SOFC temperature distribution modeling in this paper is helpful for the development of temperature distribution observers and design related control methods in later studies.

关键词： modeling simulation temperature distribution planar cross flow solid oxide fuel cell

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Standby and Shutdown Cycles Modeling of SOFC Lifetime Prediction

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Energy Procedia 2019年 158卷 1573-1578页

作者： Wu Xiao-long Xu Yuanwu Xue Tao Zhao Dongqi Jiang Jianhua Deng Zhonghua Fu Xiaowei Li Xi School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Wuhan 430065 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518057 China

In order to more accurately predict the impact of solid oxide fuel cells to stack life on standby and shutdown, this paper proposes a modeling method based on experimental data. Which is based on Elman neural network(NN). At the same time, a main factor is considered in the modeling process which is the effect of cooling rate on the stack. In the process of modeling, the most obvious cooling rate is used to modeling. There are two different influencing factors on classify, the training set and the verification set respectively. After the reliability of the model, the Solid Oxide Fuel cell (SOFC) stack life prediction is carried out. From the predict results and the experimental results, it is found that the prediction results are good and the high precision.

关键词： Solid Oxide Fuel Cell(SOFC) Stack useful lifetime predict Cooling speed Gas composition Cycle standby shutdown

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An Overlapping Community Detection Based Multi-Objective Evolutionary Algorithm for Diversified Social Influence Maximization

An Overlapping Community Detection Based Multi-Objective Evo...

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Congress on Evolutionary Computation

作者： Lei Zhang Fengiiao Sun Fan Cheng Haiping Ma Xiaoyan Sun Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China Institutes of Physical Science and Information Technology Anhui University Hefei China School of Information and Control Engineering China University of Mining and Technology Xuzhou China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

Influence maximization refers to selecting a group of nodes from a social network, which obtains the largest influence spread under a cascade model. However, most of the existing works only focused on the influence and ignored the diversity of influenced crowd. Thus, scholars have raised the issue of diversified social influence maximization recently, using the category information of nodes to design diversity indicator and introducing a trade-off parameter to balance the two objectives influence and diversity as one single objective for optimization. In fact, the category information of nodes in the network is usually difficult to be collected, thus the definition of diversity based on nodes' categories is not very general and accurate. In addition, it is very difficult to set the trade-off parameter, especially when there is no prior knowledge in real applications. To this end, we employ overlapping community structure information to design the diversity of nodes without any node's additional (e.g. category) information. Due to the two objectives of influence and diversity may be conflicting, a multi-objective evolutionary algorithm named MOEA-DIM is proposed to optimize the two objectives simultaneously, which does not need to set the tradeoff parameter between the two objectives. In MOEA-DIM, a network reduction strategy based on overlapping community structure is suggested to greatly reduce the search space. In addition, a population initialization strategy based on random walk is designed to accelerate the convergence of the algorithm. Experiments on six real-world datasets show that the proposed algorithm MOEA-DIM has promising performance in terms of both effectiveness and efficiency.

关键词： Optimization Encoding

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DeepACC:Automate Chromosome Classification based on Metaphase images using Deep Learning Framework Fused with Prior Knowledge

arXiv

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arXiv 2020年

作者： Luo, Chunlong Yu, Tianqi Luo, Yufan Wang, Manqing Yu, Fuhai Li, Yinhao Tian, Chan Qiao, Jie Xiao, Li Key Laboratory of Intelligent Information Processing Advanced Computer Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China Peking University Third Hospital Beijing China School of Computer and Control Engineering University of Chinese Academy of China

Chromosome classification is an important but difficult and tedious task in karyotyping. Previous methods only classify manually segmented single chromosome, which is far from clinical practice. In this work, we propose a detection based method, DeepACC, to locate and fine classify chromosomes simultaneously based on the whole metaphase image. We firstly introduce the Additive Angular Margin Loss to enhance the discriminative power of model. To alleviate batch effects, we transform decision boundary of each class case-by-case through a siamese network which make full use of prior knowledges that chromosomes usually appear in pairs. Furthermore, we take the clinically seven group criterion as a prior knowledge and design an additional Group Inner-Adjacency Loss to further reduce inter-class similarities. 3390 metaphase images from clinical laboratory are collected and labelled to evaluate the performance. Results show that the new design brings encouraging performance gains comparing to the state-of-the-art baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Chromosomes

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Enhanced gear fault diagnosis via heterodyne downconversion: Theoretical verification and optimized ultrasonic signal acquisition

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Measurement 2025年 256卷

作者： Jingchao Zhang Shaoqun Zhang Yixue Dong Chen Li Guoqian Jiang Yingwei Li Xiaoli Li School of Information Science and Engineering Yanshan University Qinhuangdao 066004 China Hebei Key Laboratory of Information Transmission and Signal Processing Qinhuangdao 066004 China Intelligent Control System R&D Department Computer Application Technology Research Institute China Ordnance Industry Beijing 100089 China School of Electrical Engineering Yanshan University Qinhuangdao 066004 China School of Automation Science and Engineering South China University of Technology Guangzhou 510641 China Pazhou Lab Guangzhou 510330 China

Ultrasonic fault diagnosis has emerged as a promising technique for gear fault detection, owing to its capability to capture modulated high-frequency transients induced by incipient or localized defects. However, its practical application is constrained by the requirement for extremely high sampling rates. Heterodyne downconversion offers a potential solution by translating ultrasonic spectral components to lower frequencies, though its effectiveness in preserving diagnostic features remains insufficiently validated. This study conducts a theoretical analysis of the influence of heterodyne downconversion on ultrasonic signal characteristics and proposes an optimized heterodyne-based ultrasonic acquisition system with enhanced charge amplification and frequency conversion circuits. Experimental evaluations using a gear fault test platform demonstrate that the downconverted signals preserve the envelope spectral features of the original ultrasonic signals. Furthermore, comparative analyses indicate that the proposed method achieves superior fault detection sensitivity compared to conventional vibration-based techniques, particularly under high rotational speeds. These findings validate the feasibility and diagnostic advantages of the proposed heterodyne-based approach for efficient and accurate ultrasonic condition monitoring.

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Improving magnetic nanothermometry accuracy through mixing-frequency excitation

arXiv

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arXiv 2020年

作者： Guo, Silin Liu, Jay Du, ZhongZhou Liu, Wenzhong School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China Ningbo Chuanshanjia Electrical and Mechanical Co. Ltd. Ningbo315400 China

In this study, we proposed a temperature model of magnetic nanoparticle relaxation and a phase measurement method under a mixing-frequency excitation field, which can improve the temperature accuracy of magnetic nanothermometry. According to the Debye-based magnetization model for magnetic nanoparticles, the phases at the mixing frequencies are used to solve the relaxation phase delay to the magnetic field with the higher frequency. The method could improve the signal-to-noise ratio of the magnetic response signal, and also weaken the phase shift of the detection coils caused by temperature changes. Experimental results show that the method can achieve static temperature measurement error less than 0.1K and dynamic temperature measurement error less than 0.2K. © 2020, CC-BY.

关键词： Temperature measurement

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Dynamic analysis of high-dimensional fractional-order gene regulation networks with time delay

Dynamic analysis of high-dimensional fractional-order gene r...

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Chinese control Conference (CCC)

作者： Zhe Zhang Can Ding Jing Zhang Feng Liu Yingjie Zhang Fanyong Cheng College of Electrical and Information Engineering Hunan University Changsha China School of Automation China University of Geosciences Wuhan China College of Computer Science and Electronic Engineering Hunan University Changsha China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fujian China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

This paper mainly analyzes the stability of high-dimensional fractional-order gene regulatory network systems with time delay. Based on the judgment of eigenvalues, this paper proposes sufficient information about the stability of high-dimensional fractional-order gene regulatory network systems with time delay. Conditions, and by analyzing the characteristic equations, the bifurcation conditions of the high-dimensional fractional-order gene regulatory network system with time delay are derived. Finally, the theoretical part of this paper is verified by numerical simulation.

关键词： Bifurcation Timing Stability analysis Mathematical model Delay effects Numerical stability Electronic mail

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Three-dimensional spin Hall effect of light in tight focusing

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Physical Review A 2020年第2期101卷 023819-023819页

作者： Weixing Shu Chuyang Lin Jun Wu Shizhen Chen Xiaohui Ling Xinxing Zhou Hailu Luo Shuangchun Wen Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices School of Physics and Electronics Hunan University Changsha 410082 China Key Laboratory of Low Dimensional Quantum Structures and Quantum Control of Ministry of Education Hunan Normal University Changsha 410081 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application College of Physics and Electronic Engineering Hengyang Normal University Hengyang 421002 China

We theoretically propose the realization of a three-dimensional (3D) spin Hall effect (SHE) of light by tightly focusing a linearly polarized light. Owing to the spin-orbital interaction in focusing, the left and right circularly polarized components of light are partly converted into each other, acquiring opposite vortex phases. As the superposition of the vortex and the initial vortex-free light, the two polarized components exhibit spiral intensity patterns which rotate oppositely around the propagation axis. Consequently, the two spin components are distributed alternatively in both the azimuthal and the longitudinal directions, leading to a 3D array of split spin components, and the transverse intensity is also spit asynchronously. Moreover, that array is rotatable by the incident polarization, and the longitudinal spacing in the array can be controlled by the numerical aperture of the lens. The 3D SHE of light finds an additional degree of freedom available to control the spin state of the photon, which may be valuable in optical manipulation and quantum information.

关键词： Classical optics

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Spherical interpolated convolutional network with distance-feature density for 3D semantic segmentation of point clouds

arXiv

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arXiv 2020年

作者： Wang, Guangming Yang, Yehui Zhang, Huixin Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Computer Science and Technology University of Cambridge United Kingdom

The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3D convolution kernel to extract features from raw 3D point clouds because of the unstructured property of point clouds. In this paper, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3D convolution operator. This newly proposed feature extraction operator improves the accuracy of the network and reduces the parameters of the network. In addition, this paper analyzes the defect of point cloud interpolation methods based on the distance as the interpolation weight and proposes the self-learned distance-feature density by combining the distance and the feature correlation. The proposed method makes the feature extraction of spherical interpolated convolution network more rational and effective. The effectiveness of the proposed network is demonstrated on the 3D semantic segmentation task of point clouds. Experiments show that the proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Pining control Algorithm for Complex Networks

Pining Control Algorithm for Complex Networks

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第三十八届中国控制会议

作者： Bingjun Wang Hui Liu Jiangnqiao Xu Jiaqi Liu School of Artificial Intelligence and Automation & Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology

In this paper, we quote the concept of resistance distance for Pinning control, and discuss the relationship between the upper bound of the minimum eigenvalue of the grounded Laplacian Matrix and the resistance distance of the Pinning control for one single node. Our simulation results in various networks like BA network, ER network, NW network etc. show that the ranking of the minimum eigenvalue of the grounded Laplacian Matrix is highly consistent with the ranking of resistance distance,much better than the degree ranking, which provides us another approach to estimate the node for Pinning control. In the case of controlling multiple(l) nodes, we use the iterative algorithm to calculate the grounded Laplacian Matrix for better combination of the controlled nodes, through which the computation of NP-hard problem of controlling l nodes can be greatly reduced, and the effect of the iterative algorithm are presented by simulation calculations in different networks,and the smaller l the better effect.

关键词： Complex dynamical network grounded Laplacian matrix pinning control spectral properties

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