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第43届中国控制会议

作者： Jiajian Wu Kanglin Zhu Han Wu Guohui Xiang Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science & Technology Shenzhen Huazhong University of Science and Technology Research Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Deep learning-based methods have shown their wide application prospects in the field of solid oxide fuel cell(SOFC)***,the irrationality of the prediction object and the lack of prediction accuracy have hindered the progress of the prediction methods for SOFC ***,two algorithms are proposed in this study,a feature selection algorithm based on maximum information coefficient and Bayesian information criterion for identifying key variables that are sensitive to changes in the SOFC system,and a prediction model based on a genetic algorithm optimized for sequence to sequence *** M/BIC algorithm reduces the number of features by 62.5%(from 40 to 15),which contributes to the acceleration of the algorithm training and the subsequent sensor layout *** prediction model incorporates a two-dimensional convolutional neural network and a multi-head attention mechanism to capture more information in the input sequence,an encoder-decoder architecture and a Gated recurrent unit to better predict performance *** with real experimental data show significant changes in feature dimensionality reduction and prediction accuracy.

关键词： SOFC Feature selection Performance prediction Deep learning

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Decoupling of nitrogen and oxygen sensors based on particle swarm optimization and fuzzy feedforward compensation

Decoupling of nitrogen and oxygen sensors based on particle ...

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第43届中国控制会议

作者： Can Sun Zhen Wang Jie Wang Yu Qiao Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and Technology Shenzhen Huazhong University of Science and Technology Research Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

A nitrogen oxide sensor that can accurately and quickly detect the content of nitrogen oxides in exhaust gas plays a key role in the treatment of automotive exhaust gas. Due to the characteristics of gas diffusion and chamber structure, nitrogen oxide sensors are difficult to achieve high-precision and fast measurement due to strong coupling. In order to solve this problem, this paper proposes a decoupling control strategy for nitrogen oxide sensors. On the one hand, the strategy uses fuzzy feedforward compensation, and on the other hand, it uses PID control based on particle swarm optimization to achieve control. This not only eliminates the coupling between loops, but also improves the control accuracy. The analysis shows that the design of the decoupling control strategy for nitrogen oxide sensors proposed in this paper is feasible, which allows the nitrogen oxide sensors to work in a stable and reliable state.

关键词： Nitrogen-oxygen sensor Coupling Adaptive PID Fuzzy feedforward decoupling

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Collaborative control for power and temperature tracking of the solid oxide fuel cell under maximum system efficiency

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ENERGY REPORTS 2024年 12卷 617-630页

作者： Huo, Haibo Xu, Sheng Zhu, Hongxiang Wang, Biao Lei, Zhengling Xu, Jingxiang Li, Xi Shanghai Ocean Univ Coll Engn Sci & Technol Shanghai Engn Res Ctr Marine Renewable Energy Shanghai 201306 Peoples R China Shanghai Ocean Univ Coll Engn Sci & Technol Shanghai Engn Res Ctr Hadal Sci & Technol Shanghai 201306 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Sch Artificial Intelligence & Automat Educ Minist Wuhan 430074 Peoples R China

Solid oxide fuel cell (SOFC) is a highly efficient and green power production technology with broad application prospects. The commercialization of the SOFC faces several control challenges, including load tracking, temperature safety, fuel starvation, and maximum efficiency. For solving these problems, a pre-reforming SOFC system model fueled by methane is first established and validated. Then, to optimize the efficiency of the system and find the relevant optimal operating points (OOPs) of the SOFC, a hybrid optimization approach that combines particle swarm optimization (PSO) with genetic algorithm (GA) is suggested. On this basis, an optimal thermo-electric coordination controller comprising OOPs feedforward controller, reference governor, and flow feedback controller is presented in this study. Simulation findings indicate the excellence of the presented cooperative control strategy for load power tracking and stack temperature control, while maximum efficiency, thermal safety and sufficient fuel are ensured.

关键词： Solid oxide fuel cell Optimal coordinated control Hybrid algorithm Efficiency optimization Fuel starvation

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Research on Fault Diagnosis of Solid Oxide Fuel Cell Based on Improved Random Forest Algorithm

Research on Fault Diagnosis of Solid Oxide Fuel Cell Based o...

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第43届中国控制会议

作者： Kanglin Zhu Hongfu Xiang Guohui Xiang Han Wu Xi Li School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and Technology Shenzhen Huazhong University of Science and Technology Research Institute

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Solid oxide fuel cell(SOFC) is a new energy power supply device that has been highly popular in recent years. It has the advantages of high efficiency, quietness, and no pollution. However, due to the long-term operation of the SOFC system in a high-temperature environment, various faults are inevitable, which will affect the normal operation of the system. Therefore,this paper mainly focuses on the rupture fault of the heat exchanger and the hydrogen shortage fault that occur during the operation of the SOFC. A novel method(PCA-RF) for SOFC system fault diagnosis is established by combining principal component analysis and random forest algorithm. This method not only has a simple process but also has high ***, this algorithm can well help researchers accurately identify whether there is a fault in the actual use scenario of the SOFC, so as to reduce or recover unnecessary losses.

关键词： SOFC Data-driven PCA RF Fault diagnosis

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control capacity and bimodality in target control

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EPL 2022年第5期139卷 51004-51004页

作者： Wang, Wenchu Cai, Defu Liu, Xueming Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China State Grid Hubei Elect Power Res Inst Wuhan Hubei Peoples R China

controlling large networks is a fundamental problem and a great challenge in network science. Typically, full control is not necessary and infeasible. In many cases, only a preselected subset of nodes is required to be controlled, which is the target control problem. Each node does not participate in controlling the target set with equal probability, prompting us to quantify their contributions for target control. Here we develop a random sampling method to estimate the likelihood of each node participating as a driver node in target control configurations and demonstrate the unbiased ness of sampling. Each node is assigned with a role of critical, intermittent or redundant as it appears in all, some and none of the minimum driver node sets accordingly. We apply the method to Erclos-Renyi (ER) and scale-free (SF) networks and find that the fractions of critical and intermittent nodes increase as the scale of the target set increases. Furthermore, when the size of target node is fixed in SF networks, the fraction of redundant nodes may show a bimodal behavior as the networks become denser, leading to two control modes: centralized control and distributed control. The findings help understand the dynamics of control and offer tools for target control in complex systems. Copyright (C) 2022 EPLA

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Vision Transformer Off-the-Shelf: A Surprising Baseline for Few-Shot Class-Agnostic Counting

arXiv

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arXiv 2023年

作者： Wang, Zhicheng Xiao, Liwen Cao, Zhiguo Lu, Hao Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Class-agnostic counting (CAC) aims to count objects of interest from a query image given few exemplars. This task is typically addressed by extracting the features of query image and exemplars respectively and then matching their feature similarity, leading to an extract-then-match paradigm. In this work, we show that CAC can be simplified in an extract- and-match manner, particularly using a vision transformer (ViT) where feature extraction and similarity matching are executed simultaneously within the self-attention. We reveal the rationale of such simplification from a decoupled view of the self-attention. The resulting model, termed CACViT, simplifies the CAC pipeline into a single pretrained plain ViT. Further, to compensate the loss of the scale and the order-of-magnitude information due to resizing and normalization in plain ViT, we present two effective strategies for scale and magnitude embedding. Extensive experiments on the FSC147 and the CARPK datasets show that CACViT significantly outperforms state-of-the-art CAC approaches in both effectiveness (23.60% error reduction) and generalization, which suggests CACViT provides a concise and strong baseline for CAC. Code will be available. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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3D Cinemagraphy from a Single image

3D Cinemagraphy from a Single Image

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xingyi Li Zhiguo Cao Huiqiang Sun Jianming Zhang Ke Xian Guosheng Lin Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology S-Lab Nanyang Technological University Adobe Research

We present 3D Cinemagraphy, a new technique that mar-ries 2D image animation with 3D photography. Given a single still image as input, our goal is to generate a video that contains both visual content animation and camera motion. We empirically find that naively combining existing 2D image animation and 3D photography methods leads to obvious artifacts or inconsistent animation. Our key insight is that representing and animating the scene in 3D space offers a natural solution to this task. To this end, we first convert the input image into feature-based layered depth images using predicted depth values, followed by unprojecting them to a feature point cloud. To animate the scene, we perform motion estimation and lift the 2D motion into the 3D scene flow. Finally, to resolve the problem of hole emer-gence as points move forward, we propose to bidirectionally displace the point cloud as per the scene flow and synthe-size novel views by separately projecting them into target image planes and blending the results. Extensive experiments demonstrate the effectiveness of our method. A user study is also conducted to validate the compelling rendering results of our method.

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BokehMe: When Neural Rendering Meets Classical Rendering

BokehMe: When Neural Rendering Meets Classical Rendering

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Peng, Juewen Cao, Zhiguo Luo, Xianrui Lu, Hao Xian, Ke Zhang, Jianming Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan Peoples R China Adobe Res San Jose CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose BokehMe, a hybrid bokeh rendering framework that marries a neural renderer with a classical physically motivated renderer. Given a single image and a potentially imperfect disparity map, BokehMe generates high-resolution photo-realistic bokeh effects with adjustable blur size, focal plane, and aperture shape. To this end, we analyze the errors from the classical scattering-based method and derive a formulation to calculate an error map. Based on this formulation, we implement the classical renderer by a scattering-based method and propose a two-stage neural renderer to fix the erroneous areas from the classical renderer. The neural renderer employs a dynamic multi-scale scheme to efficiently handle arbitrary blur sizes, and it is trained to handle imperfect disparity input. Experiments show that our method compares favorably against previous methods on both synthetic image data and real image data with predicted disparity. A user study is further conducted to validate the advantage of our method.

关键词： Photography Computer vision Shape Apertures Rendering (computer graphics) Pattern recognition

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Alzheimer’s Disease Diagnosis Based on Collaborative Learning Augmented Algorithms 18th

Alzheimer’s Disease Diagnosis Based on Collaborative Learn...

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18th Chinese intelligent Systems Conference, CISC 2022

作者： Gao, Libin Hu, Zhongyi Li, Zuoyong Lu, Xingjin Li, Rui Tong, Yinsheng Lin, Dehou College of Computer Science and Aritificial Intelligence Wenzhou University Wenzhou325035 China Key Laboratory of Intelligent Image Processing and Analysis Wenzhou325035 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China

ISBN: (纸本)9789811962028

In this study, a novel training method is innovatively proposed to address the problem of poor generalisation of trained models due to imbalance in Alzheimer’s disease (AD) data. The method alternates AD image data with dog and cat data from natural images for collaborative learning training across datasets, enhancing the generalisation ability of the models. At the same time, inspired by current work related to attention mechanisms under the field of computer vision, the model is reconstructed and an attention module is added to the model to further improve the accuracy of the model. The experimental results show that the method has good classification performance and provides new ideas for the diagnosis of other brain diseases. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Stability Enhancement of Inverter-Current Feedback Grid-Following Inverters Using a Passivity-Based Voltage controller

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IEEE Transactions on Energy Conversion 2025年

作者： Huang, Yukai Xu, Jinbang Ye, Jie Huang, Songtao Ren, Ziao Shen, Anwen Hu, Yihua Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multispectral Information Processing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China King's College London Department of Engineering LondonWC2R 2LS United Kingdom

Grid-following inverters remain the primary interface for modern renewable energy systems due to their strong power output performance. As the global demand for clean and efficient energy conversion increases, optimizing the stability of inverter systems is crucial for integrating renewable sources into the power grid. In certain energy conversion scenarios, current sensors are placed on the inverter side due to equipment constraints and space limitations, which can impact the stability of the inverter. Although the stability of inverter-side current feedback (ICF) configurations has been studied, methods to further enhance their stability remain underexplored. In this paper, the stability of ICF grid-following inverters is analyzed using a passivity-based design approach, focusing on a point of common coupling voltage controller. The proposed controller is thoroughly analyzed, and a detailed design strategy is presented to ensure stability under varying grid impedance conditions. Experimental results validate the theoretical analysis, demonstrating the effectiveness of the proposed design in enhancing the stability of ICF grid-following inverters. These advancements contribute to improving energy conversion efficiency and reliability. © 2025 IEEE.

关键词： Energy conversion efficiency

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