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arXiv 2020年

作者： Zhao, Shizhen Gao, Changxin Shao, Yuanjie Li, Lerenhan Yu, Changqian Ji, Zhong Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electrical and Information Engineering Tianjin University

We propose a Generative Transfer Network (GTNet) for zero-shot object detection (ZSD). GTNet consists of an Object Detection Module and a Knowledge Transfer Module. The Object Detection Module can learn large-scale seen domain knowledge. The Knowledge Transfer Module leverages a feature synthesizer to generate unseen class features, which are applied to train a new classification layer for the Object Detection Module. In order to synthesize features for each unseen class with both the intra-class variance and the IoU variance, we design an IoU-Aware Generative Adversarial Network (IoUGAN) as the feature synthesizer, which can be easily integrated into GTNet. Specifically, IoUGAN consists of three unit models: Class Feature Generating Unit (CFU), Foreground Feature Generating Unit (FFU), and Background Feature Generating Unit (BFU). CFU generates unseen features with the intra-class variance conditioned on the class semantic embeddings. FFU and BFU add the IoU variance to the results of CFU, yielding class-specific foreground and background features, respectively. We evaluate our method on three public datasets and the results demonstrate that our method performs favorably against the state-of-the-art ZSD approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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A new crossbar architecture based on two serial memristors with threshold

A new crossbar architecture based on two serial memristors w...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiaoping Wang Min Chen Yi Shen Xiaoya Hu Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China School of Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781479919611

This paper presents a memory crossbar based on two serial memristors with threshold characteristic to eliminate the effect of sneak paths, which is a key issue in crossbar memory system leading to great degradation in their performance and power efficiency. At first, we analyze the threshold characteristic of memristor and propose a memristor model with threshold. Based on this model, the paper presents the design and simulation of a non-volatile memory system utilizing two serial memristors with different polarities as a memory cell. This scheme solves the sneak-path problem by taking advantage of the threshold characteristic and the performance with having always high resistance state in all the memory cells, which is validated by simulation results. The scheme also possesses the superior properties of remarkable compatibility and high density.

关键词： Memristors Nonvolatile memory

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Energy management of solid oxide fuel cell/lithium battery hybrid system

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CSEE Journal of Power and Energy Systems 2022年 1-14页

作者： Li, Dong Zhong, Xiao Bo Xu, Yuan Wu Wei, Chong Yang Zhao, Song Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei 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 College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430081 China

The research of fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission. However, the lower efficiency of energy management has been a critical factor that obstruct the commercialization of hybrid system. In this work, based on finite element ideas, a solid oxide full cell node model is developed to accurately estimate the operating status of the fuel cell. As for lithium battery, previous energy management studies generally only focused on state of charge, however, state of health is also a key parameter for lithium battery. In this paper, considering the strong coupling between variables we use particle filter algorithm to jointly estimate the state of charge and state of health. To reasonably distribute and manage the energy in the hybrid system, a T-S fuzzy controller for the hybrid system is designed by controlling the state variables in the SOFC model and the state of lithium battery. Finally, our algorithm is verified by DSPACE simulation platform. The result shows that the hybrid system with the energy management strategy not only meets the real-time sharply changing demand but also ensures the hybrid system working efficiently and safely. © 2022 China Electric Power Research Institute. All rights reserved.

关键词： Hybrid systems

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Semi-global containment control of multi-agent systems with intermittent input saturation

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Journal of the Franklin Institute 2015年第9期352卷 3504-3525页

作者： Housheng Su Gui Jia Michael Z.Q. Chen School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 China Department of Mechanical Engineering The University of Hong Kong Hong Kong

This paper investigates containment control of multi-agent systems with intermittent communications and input saturation on fixed undirected networks . Under the assumption that each agent is asymptotically null controllable with bounded controls and there exists at least one leader that has directed path to each followers, both state feedback and output feedback control protocols are designed by utilizing the algebraic Riccati equation . For any a priori given bounded set , semi-global state feedback and output feedback containment control of multi-agent systems with intermittent communication can be attained. Numerical simulations are provided to ensure the effectiveness of results.

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EDGE DETECTION USING SUPPORT VALUE TRANSFROM

EDGE DETECTION USING SUPPORT VALUE TRANSFROM

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2007 International Conference on Machine Learning and Cybernetics(IEEE第六届机器学习与控制论国际会议)

作者： SHENG ZHENG JINWEN TIAN JIAN LIU Electronics and Information Engineering Department Huazhong University of Science and Technology W State Education Commission Key Lab for Image Processing and Intelligent Control HUST Wuhan 430074 Electronics and Information Engineering Department Huazhong University of Science and Technology W

An innovative edge detection algorithm using the support value transform is presented in this *** on the support value transform, the multi scale support value images are extracted from the *** resolution of an image is directly related to the proper scale for edge detection, and the second level support value image is used to locate the edge positions by their zero *** experiments are carried out and sharp image edges are obtained from a variety of sample *** with many other existing methods, including LoG and Canny detectors, the proposed algorithm is superior to the LOG and Roberts approach.

关键词： Edge detection mapped least squares support vector machine (mapped LS-SVM) support value transform (SVT) zero crossings

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Relevant Region Prediction for Crowd Counting

arXiv

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arXiv 2020年

作者： Chen, Xinya Bin, Yanrui Gao, Changxin Sang, Nong Tang, Hao Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Department of Information Engineering and Computer Science University of Trento

Crowd counting is a concerned and challenging task in computer vision. Existing density map based methods excessively focus on the individuals’ localization which harms the crowd counting performance in highly congested scenes. In addition, the dependency between the regions of different density is also ignored. In this paper, we propose Relevant Region Prediction (RRP) for crowd counting, which consists of the Count Map and the Region Relation-Aware Module (RRAM). Each pixel in the count map represents the number of heads falling into the corresponding local area in the input image, which discards the detailed spatial information and forces the network pay more attention to counting rather than localizing individuals. Based on the Graph Convolutional Network (GCN), Region Relation-Aware Module is proposed to capture and exploit the important region dependency. The module builds a fully connected directed graph between the regions of different density where each node (region) is represented by weighted global pooled feature, and GCN is learned to map this region graph to a set of relation-aware regions representations. Experimental results on three datasets show that our method obviously outperforms other existing state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Directed graphs

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Analysis of MFCS topology for high efficiency and long lifetime

Analysis of MFCS topology for high efficiency and long lifet...

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第43届中国控制会议

作者： Hongfu Xiang Jiajian Wu Zhen Wang Jie Wang 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

Multi-stack fuel cell system(MFCS) are an important basis for large-scale application of solid oxide fuel cell(SOFC)technology,MFCS can provide higher system power and longer service *** the number of stacks increases,different topologies can be formed between stacks,including parallel structures and series structures,and the physical characteristics of MFCS will also *** this study,the degradation of the stacks is first considered,and the system model of series structure and parallel structure including the degradation of the stacks is *** parallel structure proposed in this paper is the key to ensure the high efficiency and long lifetime of *** the simulation of systems with different topologies,it is found that the power generation efficiency of the systems caused by the topologies varies greatly in different output power *** efficiency of the parallel structure in the low power stage is significantly better than that of the series structure,and the parallel structure is more conducive to the high efficiency of the system in the whole *** topological structures also have a greater impact on the degradation of *** degradation rates of stacks in parallel structures are close to each other,and the state of stacks is more *** overall life of the system in parallel structures is also better than that in series structures.

关键词： SOFC MFCS topological structure stack degradation

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Predictive control of Vehicle Based on Echo state Network

Predictive Control of Vehicle Based on Echo State Network

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International Conference on Intelligence Science and Information Engineering (ISIE)

作者： Shaobo Ni Lei Liu Zhishen Wang Zirui Wang School of AeroSpace Engineering Beijing Institute of technology Beijing China Key Laboratory of Education Ministry for Image Processing and Intelligent Control Department of Control Science & Engineering Huazhong University of Science and Technology Wuhan China

An Echo state Network (ESN) based predictive control method for aircraft system is proposed to increase the flight quality. Aero craft is a nonlinear and time-varying system and its controllers are usually designed by trajectory linearization method. Furthermore, the predictive controller is designed to adjust the current controlled variables by the trend of output to improve the quick responsiveness and traceability. Then, the stability of the system is proved by Lyapunov function. Finally, the numerical simulation results of step-response and attitude tracking illustrate the robustness and effect of the method.

关键词： Predictive control Aerospace control Training Sparse matrices Neurons Aircraft Mathematical model

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Adaptive control for power and thermal management of an integrated 5kW solid oxide fuel cell system

Adaptive control for power and thermal management of an inte...

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第十五届全国氢能会议暨第7届两岸三地氢能研讨会

作者： Lin Zhang Jianhua Jiang Xi Li Huan Cheng Jie Yang Jian Li School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent ControlHuazhong University of Science & TechnologyWuhan430074China School of Materials Science and Engineering State Key Laboratory of Material Processing and Die and Mould TechnologyHuazhong University of Science and TechnologyWuhan 430074China

A 5-kW dynamic solid oxide fuel cell(SOFC)system with a second air bypass has been developed with a model to perform both steady-state and dynamic analysis in this *** identifies and addresses the control challenges a... 详细信息

关键词： Solid oxide fuel cell (SOFC) Optimal set-points Load transitions RBF network based direct adaptive controller

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High Confidence Tracking with Offline Historical Learning and Online Correlation Filter Updating

High Confidence Tracking with Offline Historical Learning an...

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作者： Shou-dong HAN Hong-wei WANG Xin-xin XIA Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Research Institute of Huazhong University of Science and Technology in Shenzhen

Target tracking is one of the most challenging tasks in computer vision. In this paper, the high confidence tracking(HCT) algorithm is proposed by combining the offline historical learning network with online correlation filter updating model. First, the weighted historical targets are introduced into the offline learning network, which solves the problem of target loss caused by inaccurate tracking of the previous frame. Second, the target's confidence detection mechanism is proposed, and added to the correlation filter tracking algorithm, so that the model drift is avoided. Finally, we form a new high confidence tracking algorithm with offline learning. Compared with the state-of-the-art tracking algorithm, our algorithm performs outstandingly on benchmark OTB13 and OTB15, while ensuring real-time performance.

关键词： High confidence Target tracking Offline learning Online updating

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