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43rd Chinese control Conference, CCC 2024

作者： Xiang, Hongfu Wu, Jiajian Wang, Zhen Wang, Jie Li, Xi Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen518055 China

ISBN: (纸本)9789887581581

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 life. As 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 change. In 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 built. The parallel structure proposed in this paper is the key to ensure the high efficiency and long lifetime of MFCS. Through 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 stages. The 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 stage. Different topological structures also have a greater impact on the degradation of stacks. The degradation rates of stacks in parallel structures are close to each other, and the state of stacks is more consistent. The overall life of the system in parallel structures is also better than that in series structures. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Topology

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Cross-Domain Few-Shot Incremental Learning for Point-Cloud Recognition

Cross-Domain Few-Shot Incremental Learning for Point-Cloud R...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Yuwen Tan Xiang Xiang Key Lab of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Sensing 3D objects is critical when 2D object recognition is not accessible. A robot pre-trained on a large point-cloud dataset will encounter unseen classes of 3D objects after deploying it. Therefore, the robot should be able to learn continuously in real-world scenarios. Few-shot class-incremental learning (FSCIL) requires the model to learn from few-shot new examples continually and not forget past classes. However, there is an implicit but strong assumption in the FSCIL that the distribution of the base and incremental classes is the same. In this paper, we focus on cross-domain FSCIL for point-cloud recognition. We decompose the catastrophic forgetting into base class forgetting and incremental class forgetting and alleviate them separately. We utilize the base model to discriminate base samples and new samples by treating base samples as in-distribution samples, and new objects as out-of-distribution samples. We retain the base model to avoid catastrophic forgetting of base classes and train an extra domain-specific module for all new samples to adapt to new classes. At inference, we first discriminate whether the sample belongs to the base class or the new class. Once classified at the model level, test samples are then passed to the corresponding model for class-level classification. To better mitigate the forgetting of new classes, we adopt the soft label and hard label replay together. Extensive experiments on synthetic-to-real incremental 3D datasets show that our proposed method can balance the performance between the base and new objects and outperforms the previous state-of-the-art methods.

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A2B: Anchor to Barycentric Coordinate for Robust Correspondence

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2023年第10期131卷 2582-2606页

作者： Zhao, Weiyue Lu, Hao Cao, Zhiguo Li, Xin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China West Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown VA 15461 USA

There is a long-standing problem of repeated patterns in correspondence problems, where mismatches frequently occur because of inherent ambiguity. The unique position information associated with repeated patterns makes coordinate representations a useful supplement to appearance representations for improving feature correspondences. However, the issue of appropriate coordinate representation has remained unresolved. In this study, we demonstrate that geometric-invariant coordinate representations, such as barycentric coordinates, can significantly reduce mismatches between features. The first step is to establish a theoretical foundation for geometrically invariant coordinates. We present a seed matching and filtering network (SMFNet) that combines feature matching and consistency filtering with a coarse-to-fine matching strategy in order to acquire reliable sparse correspondences. We then introduce Degree, a novel anchor-to-barycentric (A2B) coordinate encoding approach, which generates multiple affine-invariant correspondence coordinates from paired images. Degree can be used as a plug-in with standard descriptors, feature matchers, and consistency filters to improve the matching quality. Extensive experiments in synthesized indoor and outdoor datasets demonstrate that Degree alleviates the problem of repeated patterns and helps achieve state-of-the-art performance. Furthermore, Degree also reports competitive performance in the third image Matching Challenge at CVPR 2021. This approach offers a new perspective to alleviate the problem of repeated patterns and emphasizes the importance of choosing coordinate representations for feature correspondences.

关键词： Robust correspondence Feature matching Coordinate representations Barycentric coordinates Anchor-to-barycentric (A2B)

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Coupled Urban Risks:A Complex Systems Perspective with a People-Centric Focus

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工程（英文） 2025年第1期44卷 44-50页

作者： Min Ouyang Zekai Cheng Jiaxin Ma Hongwei Wang Stergios Aristoteles Mitoulis School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Processing and Intelligent Control Ministry of EducationHuazhong University of Science and TechnologyWuhan 430074China School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Management Huazhong University of Science and TechnologyWuhan 430074China School of Engineering University of BirminghamBirmingham B15 2TTUK

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 *** to this complexity are people,whose behaviors,needs,and vulnerabilities shape risk evolution and response *** cities as complex systems centered on human needs and behaviors is essential to understanding the complexities of coupled urban *** 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 *** focus on two key dimensions:cascading hazards in urban environments and cascading failures across interdependent exposed systems in *** risk assessment models often fail to capture the complexity of these processes,particularly when factoring in human *** tackle these challenges,we advocate for a standardized taxonomy of cascading hazards,urban components,and their *** its core is a people-centric perspective,emphasizing the bidirectional interactions between people and the systems that serve *** 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 *** leveraging real-time data,advanced simula-tions,and innovative validation methods,this framework aims to enhance the accuracy of coupled urban risk *** effectively manage coupled urban risks,cities can draw from proven strategies in real complex ***,given the escalating uncertainties and complexities associated with climate change,prioritizing people-centric strategies is *** approach will empower cities to bui

关键词： Coupled urban risks People-centric Risk management

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Distributed Predefined-Time control for Hybrid AC/DC Microgrid

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2023年第8期70卷 8324-8333页

作者： Zhang, Yu Wang, Yan-Wu Liu, Xiao-Kang Yang, Wu Liang, Shu-Ming Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Guangxi Univ Sch Elect Engn Nanning 530004 Peoples R China

Due to the popularization of distributed generators (DGs) and diversity of loads, hybrid microgrid, mixing AC/DC subgrids, has gradually become a popular research topic. However, there is lack of research results to achieve the control objectives of precise AC/DC bus voltage/frequency restoration and global power sharing among DGs in hybrid microgrid within a predefined time. In this article, a distributed predefined-time controller (DPTC) is designed to achieve the above objectives with unknown load power by employing a classK1 function and defining a unified error. The convergence time of bus voltage and output power of converters can be adjusted by a predefined parameter. Moreover, a distributed predefined-time observer is proposed to rely less on direct load measurements and guarantee the controller implementation in emergencies when load information is unavailable. Through hardwarein-the-loop experiment tests, the effectiveness of the DPTC is verified in scenarios of load change and plug-and-play. Comparative experiment indicates that the proposed controller has the potential for convenient adjustment of convergence time and the advantage of small overshoot.

关键词： Microgrids Voltage control Reactive power Hybrid power systems Frequency control Convergence Renewable energy sources AC/DC bus voltage regulation hybrid microgrid power sharing predefined-time control

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Research of spatial context convolutional neural networks for early diagnosis of Alzheimer's disease

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JOURNAL OF SUPERCOMPUTING 2024年第4期80卷 5279-5297页

作者： Tong, Yinsheng Li, Zuoyong Huang, Hui Gao, Libin Xu, Minghai Hu, Zhongyi Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Key Lab Intelligent Image Proc & Anal Wenzhou 325035 Zhejiang Peoples R China Minjiang Univ Coll Comp & Control Engn Fuzhou 350108 Fujian Peoples R China Wenzhou Univ Technol Coll Intelligent Mfg & Elect Engn Wenzhou 325035 Zhejiang Peoples R China

The early and effective diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) has received increasing attention in recent years. However, currently available deep learning methods often ignore the contextual spatial information contained in structural MRI images used for early diagnosis and classification of Alzheimer's disease. This may lead us to miss important structural details by failing to adequately capture the potential connections between each slice and its neighboring slices. This lack of contextual information may cause the accuracy of the network model to suffer, which in turn affects its generalization ability and application in real-life scenarios. To explore deeper the connection between spatial context slices, this research is designed to develop a new network model to effectively detect or predict AD by digging into the deeper spatial contextual structural information. In this paper, we design a spatial context network based on 3D convolutional neural network to learn the multi-level structural features of brain MRI images for AD classification. The experimental results show that the model has good stability, accuracy and generalization ability. Our experimental method had a classification accuracy of 92.6% in the AD/CN comparison, 74.9% in the AD/MCI comparison, and 76.3% in the MCI/CN comparison. In addition, this paper demonstrates the effectiveness of the proposed network model through ablation experiments.

关键词： Alzheimer's disease Structural magnetic resonance image Spatial context Classification

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Disassembly of DNA origami dimers controlled by programmable polymerase primers

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CHEMICAL COMMUNICATIONS 2022年第92期58卷 12879-12882页

作者： Liao, Kangchao Chen, Kuiting Xie, Chun Chen, Zhekun Pan, Linqiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Hubei Peoples R China

Dynamic regulation of DNA origami nanostructures is important for the fabrication of intelligent DNA nanodevices. Toehold-mediated strand displacement is a common regulation strategy, which utilizes trigger strands to assemble and disassemble nanostructures. Such trigger strands are required to be completely complementary to the corresponding substrate strands, which strictly demands orthogonality and accuracy of the sequence design. Herein, we present a disassembly strategy of DNA origami dimers based on polymerase-triggered strand displacement, where the polymerase primers, as the trigger strands, were only partially complementary to the toehold region of the substrate strands. To demonstrate the programmability of trigger strands, we utilized primers with different sequence combination patterns to disassemble DNA origami dimers. The statistical summary of AFM images and fluorescence curves proved the feasibility of the new strategy. The utilization of polymerase-triggered strand displacement on the disassembly of DNA origami structures enriches the toolbox for the dynamic regulation of DNA nanostructures.

关键词： DNA

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A Novel Data-Driven LSTM-SAF Model for Power Systems Transient Stability Assessment

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第7期20卷 9083-9097页

作者： Shao, Zonghe Wang, Qichao Cao, Yuzhe Cai, Defu You, Yang Lu, Renzhi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China State Grid Hubei Elect Power Res Inst Wuhan 430079 Peoples R China Huazhong Univ Sci & Technol Engn Res Ctr Autonomous Intelligent Unmanned Syst Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Transient stability is an important metric for assessing the operational state of a power system. However, due to the inherent complexity of the power systems, it is difficult to achieve stable and precise transient stability assessment (TSA). This article proposes a novel data-driven long short-term memory with self-attention mechanism and focal loss function (LSTM-SAF) model to achieve a rapid and reliable TSA scheme. First, an improved wrapper approach involving a genetic algorithm is established to obtain concise and effective input features, which can enhance model performance and efficiency. Then, an LSTM network combined with a self-attention mechanism is developed to learn reliable TSA paradigms, in which the self-attention mechanism can further explore the information relationships of temporal features extracted from the LSTM, thereby significantly improving TSA accuracy. In addition, to resolve the lack of insufficient training related to sample imbalance, a new focal loss function is designed to guide model training. This article provides a complete TSA scheme (including offline training and online execution) that considers both assessment performance and response speed. The effectiveness of the proposed model is verified by the numerical testing results on IEEE 39 bus system, NPCC 140 bus system, IEEE 145 bus system and IEEE 300 bus system.

关键词： Power system stability Feature extraction Long short term memory Data models Numerical stability Stability criteria Numerical models Focal loss long short-term memory (LSTM) network self-attention transient stability wrapper approach

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MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition

MoLo: Motion-augmented Long-short Contrastive Learning for F...

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

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Gao, Changxin Zhang, Yingya Zhao, Deli Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Current state-of-the-art approaches for few-shot action recognition achieve promising performance by conducting frame-level matching on learned visual features. However, they generally suffer from two limitations: i) the matching procedure between local frames tends to be inaccurate due to the lack of guidance to force long-range temporal perception;ii) explicit motion learning is usually ignored, leading to partial information loss. To address these issues, we develop a Motion-augmented Long-short Contrastive Learning (MoLo) method that contains two crucial components, including a long-short contrastive objective and a motion autodecoder. Specifically, the long-short contrastive objective is to endow local frame features with long-form temporal awareness by maximizing their agreement with the global token of videos belonging to the same class. The motion autodecoder is a lightweight architecture to reconstruct pixel motions from the differential features, which explicitly embeds the network with motion dynamics. By this means, MoLo can simultaneously learn long-range temporal context and motion cues for comprehensive few-shot matching. To demonstrate the effectiveness, we evaluate MoLo on five standard benchmarks, and the results show that MoLo favorably outperforms recent advanced methods. The source code is available at https://***/alibaba-mmai-research/MoLo.

关键词： Video: Action and event understanding

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Frequency Information Matters for image Matting 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Luo, Rongsheng Wei, Rukai Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory For Optoelectronics Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783031476341

ISBN: (纸本)9783031476334

image matting aims to estimate the opacity of foreground objects in order to accurately extract them from the background. Existing methods are only concerned with RGB features to obtain alpha mattes, limiting the perception of local tiny details. To address this issue, we introduce frequency information as an auxiliary clue to accurately distinguish foreground boundaries and propose the Frequency Matting Network (FMN). Specifically, we deploy a Frequency Boosting Module (FBM) in addition to the Discrete Cosine Transform (DCT) to extract frequency information from input images. The proposed FBM is a learnable component that empowers the model to adapt to complex scenarios. Furthermore, we design a Domain Aggregation Module (DAM) to effectively fuse frequency features with RGB features. With the assistance of frequency clues, our proposed FMN achieves significant improvements in matting accuracy and visual quality compared with state-of-the-art methods. Extensive experiments on Composition-1k and Distinctions-646 datasets demonstrate the superiority of introducing frequency information for image matting. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Discrete cosine transforms

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