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42nd Chinese control Conference, CCC 2023

作者： Zhao, Wei Liu, Guoqiang Li, Zexin Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China

ISBN: (纸本)9789887581543

Reversible solid oxide cells (rSOC) can operate in both electrolysis mode and in fuel cell mode with high efficiency and reduced cost using the same device for both functions. When used in dynamic operation with intermittent electrical power sources, rSOC system switches from the fuel cell (SOFC) to electrolyzer (SOEC) and vice versa depending on load and grid peculiarities. This can lead to temperature profiles within the stack that can potentially lead to the failure of the stack and eventually the system. In the present work, a system-level dynamic model of rSOC is established and validated against experimental data. Subsequently, detailed dynamic thermal behavior of stack during the switching between the two modes is analyzed, and a temperature management controller based on Model Predict control method (MPC) is proposed. Gas flow and temperature at the stack inlet are controlled by air flow and bypass valves, avoiding problems associated with temperature overshoot during transient operation. The simulation results show that the temperature controller has the ability to follow fast thermal changes while maintaining thermal safety, which indicates the competitiveness of the controller. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Gas temperature

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Trimmed Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles 15th

Trimmed Data-Driven Evolutionary Optimization Using Selectiv...

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15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Shan, Yawen Hou, Yongjin Wang, Mengzhen Xu, Fei Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

Data-driven evolutionary algorithms (DDEAs) have attracted much attention in recent years, due to their effectiveness and advantages in solving expensive and complex optimization problems. In an offline data-driven evolutionary algorithm, no additional information is available to update surrogate models, so we need to ensure the quality of the surrogate models built before optimization. Based on the offline data-driven evolutionary algorithm using selective surrogate ensembles (DDEA-SE), we propose a trimmed bagging based DDEA-SE (TDDEA-SE) to construct a more accurate model pool. To this end, we use trimmed bagging to prune models with large errors calculated by out-of-bag samples, thus improving the accuracy of the selective surrogate ensembles and promoting the optimization. The experimental results on benchmark problems show that the proposed algorithm can strike a balance between diversity and accuracy of models, and its competitiveness on solving offline data-driven optimization problems. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Evolutionary algorithms

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A Physiological Signal Emotion Recognition Method Based on Domain Adaptation and Incremental Learning 1

A Physiological Signal Emotion Recognition Method Based on D...

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1st International Conference on Cyber-Energy Systems and intelligent Energy, ICCSIE 2022

作者： Li, Junnan Wang, Xiaoping Huazhong University of Science and Technology The School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China

ISBN: (纸本)9781665452151

Temporal concept shift (TCS) is an unavoidable problem in physiological signal-based emotion recognition tasks, i.e., the data distribution of physiological signals is constantly changing over time, which gradually degrades the model accuracy. To this end, we propose a method based on a combination of domain adaptation and incremental learning to reduce the impact of temporal concept drift. In this paper, domain adaptation is used to reduce the distribution differences and incremental learning is used to prevent the learned knowledge from being forgotten. Finally, we validate the effectiveness of our approach on two real datasets. © 2023 IEEE.

关键词： Emotion Recognition

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SAR image segmentation based on immune greedy spectral clustering

SAR image segmentation based on immune greedy spectral clust...

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2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Gou, S.P. Zhang, J. Jiao, L.C. Key Laboratory of Intelligent Perception and Image Understanding for the Ministry of Education Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9781424427314

In this paper we propose a novel spectral clustering algorithm called Immune Greedy Spectral Clustering Algorithm, which introduces immune clone selection algorithm instead of greedy selection to choose a subset before greedy spectral embedding without any prior experiment knowledge. As we know, Nyström algorithm is a random selecting method which depends so much on the selecting result so that the clustering result fluctuates obviously. Greedy spectral clustering algorithm acquires a subset called dictionary by greedy selection algorithm so that the result could be more stable and better. However, to choose an appropriate input tolerance, we need prior experiment knowledge about the relationship between tolerance and select number. Moreover, since the criterion used for greedy selection is the distance in feature space between a candidate example and its projection on the subspace spanned by selected examples, we need to compute every example point one by one to get the error of using the selected examples to approximate the candidate example and then decide whether to choose it. So the time expense increases inevitable. Considering all above, we present a new method called Immune Greedy Spectral Clustering Algorithm. The experimental results show that Immune Greedy Spectral Clustering Algorithm need no prior experiment knowledge and could save time compared with greedy spectral clustering while getting a better accuracy rate compared with Nyström algorithm. ©2009 IEEE.

关键词： Cloning

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Multiobjective Brainstorm Optimization with Diversity Preservation Mechanism for Nonlinear Equation Systems 15th

Multiobjective Brainstorm Optimization with Diversity Preser...

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15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Zhao, Yi Zhu, Zelin Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811613531

In many fields of engineering and science, it is necessary to solve nonlinear equation systems (NESs). When using multiobjective optimization to solve NESs, there are two problems: 1) how to transform an NES into a multiobjective optimization problem and 2) how to design an algorithm to solve the transformed problems. In this work, we propose a multilayer bi-objective transformation technique, which can transform an NES into a bi-objective optimization problem, and it overcomes the curse of dimensionality and the problem of missing roots caused by the decrease of solutions discernibility in previous transformation techniques. Then, combining the multilayer bi-objective transformation technique, we design a multiobjective brainstorm optimization with a diversity preservation mechanism, which can effectively locate multiple roots of NESs in a single run. Compared with several state-of-art methods on 30 NESs with different features, our approach provides very competitive performance with the highest root ratio and success ratio. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Multiple Unmanned Surface Vehicle Coverage control in Dynamic Environments

Multiple Unmanned Surface Vehicle Coverage Control in Dynami...

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2023 International Conference on Neuromorphic Computing, ICNC 2023

作者： Chen, Hong He, Dingxin Mao, Huantao Yang, Yong Liu, Zhi-Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China

ISBN: (纸本)9798350316889

This paper investigates the area coverage problem for multiple Unmanned Surface Vehicles (USVs) in dynamic environments. Multiple USVs are employed to execute the coverage task using the Voronoi Partition method for task allocation. Parameter adaptive laws are employed to estimate the parameters of the sensing function and the Voronoi cell centroids for updating the target positions in dynamic environments. Additionally, considering the influence of water surface disturbances, a nonholonomic underactuated model analysis is conducted for the USV under non-full constraints, resulting in a motion strategy for USVs. This enables the multiple USVs to effectively cover the task area. Finally, simulation results are presented to validate the effectiveness of the coverage scheme designed in this paper and the ability of USVs to effectively cover dynamic environments. © 2023 IEEE.

关键词： Unmanned surface vehicles

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Enhanced Geometric Map:a 2D&3D Hybrid City Model of Large Scale Urban Environment for Robot Navigation

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机器人 2016年第3期38卷 311-321页

作者： LI Haifeng HU Zunhe LIU Jingtai College of Computer Science and Technology Civil Aviation University of China Tianjin 300300 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou 350121 China Institute of Robotics and Information Automatic System Nankai University Tianjin 300071 China Tianjin Key Laboratory of Intelligent Robotics Tianjin 300071 China

To facilitate scene understanding and robot navigation in large scale urban environment, a two-layer enhanced geometric map(EGMap) is designed using videos from a monocular onboard camera. The 2D layer of EGMap consists of a 2D building boundary map from top-down view and a 2D road map, which can support localization and advanced map-matching when compared with standard polyline-based maps. The 3D layer includes features such as 3D road model,and building facades with coplanar 3D vertical and horizontal line segments, which can provide the 3D metric features to localize the vehicles and flying-robots in 3D space. Starting from the 2D building boundary and road map, EGMap is initially constructed using feature fusion with geometric constraints under a line feature-based simultaneous localization and mapping(SLAM) framework iteratively and progressively. Then, a local bundle adjustment algorithm is proposed to jointly refine the camera localizations and EGMap features. Furthermore, the issues of uncertainty, memory use, time efficiency and obstacle effect in EGMap construction are discussed and analyzed. Physical experiments show that EGMap can be successfully constructed in large scale urban environment and the construction method is demonstrated to be very accurate and robust.

关键词：被提高的几何学的地图都市模型机械手航运同时发生的定位而且映射局部的包裹调整

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Efficient Fabric Defect Detection Algorithm Based on Improved YOLOv5

Efficient Fabric Defect Detection Algorithm Based on Improve...

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2023 China Automation Congress, CAC 2023

作者： Tian, LiHua Wang, AoZe Sun, ZhiGang Xiao, Li Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350303759

In order to address the issues of real-time performance and the low dependency between feature channels in fabric defect detection networks, this paper proposes the ESE-YOLOv5 network based on YOLOv5. Firstly, to address the relative redundancy of the neck detection network feature channels, a relatively lightweight and efficient convolution module is adopted to ensure accuracy while reducing computation and parameter volume. Furthermore, the Efficient Squeeze-Excitation (ESE) module is introduced into the backbone to optimize the dependency of feature channels, which enhances the model's feature extraction capacity and improves detection accuracy. Experimental results show that compared to YOLOv5, the proposed ESE-YOLOv5 model reduces computation and parameter volume while improving accuracy, meeting the needs of fabric defect detection for recognizing fabric defects that have similar characteristics to the background while maintaining real-time performance. © 2023 IEEE.

关键词： Feature extraction

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A parallel artificial immune model for optimization

A parallel artificial immune model for optimization

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2009 International Conference on Computational Intelligence and Security, CIS 2009

作者： Qi, Yutao Liu, Fang Jiao, Licheng School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Xidian University Xi'an China Inst. of Intelligent Information Processing and Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China

ISBN: (纸本)9780769539317

This paper presents a parallel artificial immune model termed as tower master-slave model (TMSM) for solving optimising problems. Based on TMSM, the parallel immune memory clonal selection algorithm (PIMCSA) is also proposed. TMSM is a two level coarse-grained parallel artificial immune model with distributed immune response and distributed immune memory. In PIMCSA, vaccines are extracted and migrated between populations rather than individual migration as has been done in parallel genetic algorithms. It is a good balance between population diversity and the convergent speed. Experimental results on the numerical optimization and TSP problems show that PIMCSA achieves good performance in terms of both solution quality and computation time. © 2009 IEEE.

关键词： Genetic algorithms

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Transfer network learning based remote sensing target recognition

Transfer network learning based remote sensing target recogn...

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MIPPR 2009 - Automatic Target Recognition and image Analysis: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Gou, Shuiping Wang, Yuqin Jiao, Licheng Key Laboratory of Intelligent Perception and Image Understanding for the Ministry of Education Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

ISBN: (纸本)9780819478061

The target recognition accuracy of remote sensing images is not satisfied. The labels of images acquisition and recollecting are difficult and expensive. In order to solve the problem, we introduce transfer learning into Network Boosting algorithm (NB) and propose Transfer Network Learning algorithm (TNL), in which other out-date data are reused to instruct the remote sensing target recognition. TNL is suitable to improve the performance of remote sensing target recognition, in which instances transfer learning is adopted for domain adaptation. The experimental results on the MSTAR SAR data set and remote sensing data set including two-class planes show that the proposed algorithm has better performance and achieves different domains learning. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Learning algorithms

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