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Finite-time sliding-mode control for semi-Markov systems with delayed impulses

IEEE/CAA Journal of Automatica Sinica 2025年

作者： Ren, Fangmin Wang, Xiaoping Li, Yangmin Zeng, Zhigang The School of Artificial Intelligence and Automation The Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Hubei Key Laboratory of Braininspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China The Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong999077 Hong Kong

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Resilience-promoting day-ahead schedule in coastal integrated energy system considering subjective uncertainty

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Energy 2025年 328卷

作者： Yu, De-Qi Xiao, Jiang-Wen Wang, Yan-Wu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology) Ministry of Education Wuhan 430074 China

This paper studies the resilience enhancement of the energy and water supply in coastal multi-energy system under extreme weather events. A resilience-promoting day-ahead scheduling model is proposed for coastal integrated power-gas-water system under windstorms. Energy coupling and fragility curves related to wind speed are applied into the proposed model to improve system resilience. Specially, subjective uncertainties of operators are described by the mental accounting theory to address the impact of operators’ subjective perceptions on decision analysis, and to enhance psychological satisfaction of operators. The alternating direction method of multipliers algorithm with alternating optimization procedure is employed to obtain a feasible solution for the proposed model while preserving customer privacy. Case studies validate the effectiveness of the proposed model and method by comparative tests. © 2025 Elsevier Ltd

关键词： Day-ahead schedule Integrated power-gas-water system Mental accounting theory Resilience

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Stage-dependent proteomic alterations in aqueous humor of diabetic retinopathy patients based on data-independent acquisition and parallel reaction monitoring

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Journal of Translational Medicine 2025年第1期23卷 1-18页

作者： Jin, Yeanqi Liu, Junbin Zhang, Xueli Zhang, Liang Cui, Ying Luo, Xiaoyang Zhu, Haoxian Chen, Zhifan Liu, Mengya Wu, Xiyu Chen, Xinyu Liao, Shuoxin Wu, Guanrong Fang, Xiang Meng, Qianli Department of Ophthalmology Guangdong Eye Institute Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou China School of Medicine South China University of Technology Guangzhou China Medical Research Institute Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou China The Fourth Affiliated Hospital of Guangzhou Medical University Guangzhou China

Background: Diabetic retinopathy (DR), a microvascular complication of diabetes mellitus (DM), represents the predominant cause of preventable vision loss in working-age populations globally. While the pathophysiological mechanisms underlying DR progression remain incompletely understood, our study employs comprehensive proteomic profiling of aqueous humor (AH) to identify stage-specific biomarkers and therapeutic targets in type 2 diabetes mellitus (T2DM) patients across DR progression. Methods: Utilizing data-independent acquisition (DIA) mass spectrometry, we quantified AH proteomes in a discovery cohort comprising 24 subjects: 18 T2DM patients stratified by DR severity [6 non-DR, 6 non-proliferative DR (NPDR), 6 proliferative DR (PDR)] and 6 cataract controls without diabetes (non-DM). Validation cohort analysis (including 10 AH samples in each group) was performed using parallel reaction monitoring (PRM) strategy for verification of target proteins. Comprehensive bioinformatics analyses included gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, protein-protein interaction (PPI) network construction, receiver operating characteristic (ROC) curve analysis, and ConnectivityMap (Cmap)-based drug prediction. Results: Proteomic profiling identified 739 quantifiable AH proteins (62% extracellular) with clear separation among the four clinical stages in the discovery cohort. GSEA uncovered altered expression of proteins mainly related to complement and coagulation cascades, folate metabolism, and the selenium micronutrient network in patients with DR. WGCNA-derived protein modules yielded 83 PRM-validated targets, including 5 hub proteins differentiating NPDR from non-DR and 33 hub proteins showed significant upregulation in PDR versus NPDR comparison. Clinical correlation analysis identified F2, FGG, FGB, RBP4, AMBP, VTN, C8A, CPB2, and C2 associated with clini

关键词： Aqueous humor Data-independent acquisition Diabetic retinopathy Parallel reaction monitoring Proteomics

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New Insights on the Stability of Sampled-Data-Based LFC for Power Systems: A Delay-Interval-Dependent LKF Approach

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IEEE Transactions on Smart Grid 2025年

作者： Wang, Wei-Min Wang, Yan-Wu Wan, Qian Zeng, Hong-Bing Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Wuhan430074 China Hunan University of Technology School of Electrical and Information Engineering Zhuzhou412007 China

This paper analyzes the stability problem of load frequency control (LFC) for power systems under uncertain transmission delays. First, an argumented LFC system model accounting for uncertainties in transmission delays is proposed. Then, a novel delay-interval-dependent Lyapunov-Krasovskii functional (LKF) that takes into account both the upper and lower bounds of the transmission delay is proposed. Several less conservative stability criteria are derived using the proposed LKF. Based on the stability criterion obtained, the sampling interval and the upper and lower bounds of the transmission delay are taken as the conditions to guide the controller design. Finally, case studies are conducted based on a one-area power system and a three-area power system, both integrated with wind and solar power generation. The simulation comparison results demonstrate that the proposed LFC scheme exhibits strong robustness against uncertain transmission delays, load fluctuations, and the intermittent generation of wind and solar power. © 2010-2012 IEEE.

关键词： Stability criteria

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Cross-Modal Causal Representation Learning for Radiology Report Generation

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IEEE transactions on image processing : a publication of the IEEE Signal processing Society 2025年 PP卷 PP页

作者： Weixing Chen Yang Liu Ce Wang Jiarui Zhu Guanbin Li Cheng-Lin Liu Liang Lin School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Science Sun Yat-sen University Shenzhen China Department of Health Technology and Informatics The Hong Kong Polytechnic University Hung Hom Hong Kong State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

Radiology Report Generation (RRG) is essential for computer-aided diagnosis and medication guidance, which can relieve the heavy burden of radiologists by automatically generating the corresponding radiology reports according to the given radiology image. However, generating accurate lesion descriptions remains challenging due to spurious correlations from visual-linguistic biases and inherent limitations of radiological imaging, such as low resolution and noise interference. To address these issues, we propose a two-stage framework named Cross-Modal Causal Representation Learning (CMCRL), consisting of the Radiological Cross-modal Alignment and Reconstruction Enhanced (RadCARE) pre-training and the Visual-Linguistic Causal Intervention (VLCI) fine-tuning. In the pre-training stage, RadCARE introduces a degradation-aware masked image restoration strategy tailored for radiological images, which reconstructs high-resolution patches from low-resolution inputs to mitigate noise and detail loss. Combined with a multiway architecture and four adaptive training strategies (e.g., text postfix generation with degraded images and text prefixes), RadCARE establishes robust cross-modal correlations even with incomplete data. In the VLCI phase, we deploy causal front-door intervention through two modules: the Visual Deconfounding Module (VDM) disentangles local-global features without fine-grained annotations, while the Linguistic Deconfounding Module (LDM) eliminates context bias without external terminology databases. Experiments on IU-Xray and MIMIC-CXR show that our CMCRL pipeline significantly outperforms state-of-the-art methods, with ablation studies confirming the necessity of both stages. Code and models are available at.

关键词： Visualization Linguistics Radiology Heart Training Correlation Feature extraction Lesions Accuracy Lungs

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FIE-Net: Foreground Instance Enhancement Network for Domain Adaptation Object Detection in Remote Sensing imagery

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Zhang, Jun Zhang, Xupeng Liu, Shuai Pan, Bin Shi, Zhenwei Hebei University of Technology School of Artificial Intelligence Tianjin300401 China Hebei Province Key Laboratory of Big Data Calculation Tianjin300401 China KLMDASR LEBPS LPMC Nankai University School of Statistics and Data Science Tianjin300071 China Beihang University Image Processing Center School of Astronautics State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China

Domain adaptation methods can mitigate performance degradation in remote sensing image object detection that arises from interdomain differences. However, current approaches often overlook the focused attention on foreground features, and the differences between the foreground and the background in remote sensing images implicitly diminish the characteristics of the foreground. To address this challenge, we propose a foreground instance enhancement network (FIE-Net) to balance the differences between the foreground and the background in remote sensing images, while enhancing the alignment and application of foreground features. The FIE-Net cooperates with the foreground-focused multigranularity feature alignment (FMA) module with the label filtering and application (LFA) module, progressively focusing on the salient foreground features during the processes of feature alignment and label application. Specifically, FMA directs feature alignment toward the foreground focus during the multigranularity feature alignment process, through foreground-focus perception attention and instance-centered emphasis approach. LFA balances the foreground and background difference information contained in labels, through the hybrid threshold label filtering method and the progressive label switching strategy. The experimental results indicate superior performance and generalization capabilities of our proposed FIE-Net in multiple remote sensing adaptation scenarios. © 1980-2012 IEEE.

关键词： Optical remote sensing

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Joint Masked Reconstruction and Contrastive Learning for Mining Interactions Between Proteins

arXiv

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arXiv 2025年

作者： Li, Jiang Wang, Xiaoping School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Brain-Inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

Protein-protein interaction (PPI) prediction is an instrumental means in elucidating the mechanisms underlying cellular operations, holding significant practical implications for the realms of pharmaceutical development and clinical treatment. Presently, the majority of research methods primarily concentrate on the analysis of amino acid sequences, while investigations predicated on protein structures remain in the nascent stages of exploration. Despite the emergence of several structure-based algorithms in recent years, these are still confronted with inherent challenges: (1) the extraction of intrinsic structural information of proteins typically necessitates the expenditure of substantial computational resources;(2) these models are overly reliant on seen protein data, struggling to effectively unearth interaction cues between unknown proteins. To further propel advancements in this domain, this paper introduces a novel PPI prediction method jointing masked reconstruction and contrastive learning, termed JmcPPI. This methodology dissects the PPI prediction task into two distinct phases: during the residue structure encoding phase, JmcPPI devises two feature reconstruction tasks and employs graph attention mechanism to capture structural information between residues;during the protein interaction inference phase, JmcPPI perturbs the original PPI graph and employs a multi-graph contrastive learning strategy to thoroughly mine extrinsic interaction information of novel proteins. Extensive experiments conducted on three widely utilized PPI datasets demonstrate that JmcPPI surpasses existing optimal baseline models across various data partition schemes. The associated code can be accessed via https://***/lijfrankopen/JmcPPI. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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CMCRD: Cross-Modal Contrastive Representation Distillation for Emotion Recognition

arXiv

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arXiv 2025年

作者： Kan, Siyuan Wu, Huanyu Cui, Zhenyao Huang, Fan Xu, Xiaolong Wu, Dongrui 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 Wuhan Institute of Digital Engineering Wuhan430074 China Shanghai Jiao Tong University Shanghai200240 China

Emotion recognition is an important component of affective computing, and also human-machine interaction. Unimodal emotion recognition is convenient, but the accuracy may not be high enough;on the contrary, multi-modal emotion recognition may be more accurate, but it also increases the complexity and cost of the data collection system. This paper considers cross-modal emotion recognition, i.e., using both electroencephalography (EEG) and eye movement in training, but only EEG or eye movement in test. We propose cross-modal contrastive representation distillation (CMCRD), which uses a pre-trained eye movement classification model to assist the training of an EEG classification model, improving feature extraction from EEG signals, or vice versa. During test, only EEG signals (or eye movement signals) are acquired, eliminating the need for multi-modal data. CMCRD not only improves the emotion recognition accuracy, but also makes the system more simplified and practical. Experiments using three different neural network architectures on three multi-modal emotion recognition datasets demonstrated the effectiveness of CMCRD. Compared with the EEG-only model, it improved the average classification accuracy by about 6.2%. © 2025, CC BY.

关键词： Modal analysis

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Progressive Semi-Decoupled Detector for Accurate Object Detection

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IEEE Transactions on Multimedia 2025年

作者： Han, Bo He, Lihuo Ke, Junjie Wu, Jinjian Gao, Xinbo Xidian University Visual Information Processing Laboratory School of Electronic Engineering Xi'an710071 China Xidian University School of Artificial Intelligence Xi'an710071 China Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Image Cognition Chongqing400065 China

Inconsistent accuracy between classification and localization tasks is a common challenge in modern object detection. Task decoupling, which employs distinct features or labeling strategies for each task, is a widely used approach to address this issue. Although it has led to noteworthy advancements, this approach is insufficient as it neglects task interdependence and lacks an explicit consistency constraint. To bridge this gap, this paper proposes the Progressive Semi-Decoupled Detector (ProSDD) to enhance both classification and localization accuracy. Specifically, a new detection head is designed that incorporates feature suppression and enhancement mechanism (FSEM) and bidirectional interaction module (BIM). Compared with the decoupled head, it not only filters out task-irrelevant information and enhances task-related information, but also avoids excessive decoupling at the feature level. Moreover, both FSEM and BIM are used multiple times, thus forming a progressive semi-decoupled head. Then, a novel consistency loss is proposed and integrated into the loss function of object detection, ensuring harmonic performance in classification and localization. Experimental results demonstrate that the proposed ProSDD effectively alleviates inconsistent accuracy and achieves high-quality object detection. Taking the pretrained ResNet-50 as the backbone, ProSDD achieves a remarkable 43.3 AP on the MS COCO dataset, surpassing contemporary state-of-the-art detectors by a substantial margin under the equivalent configurations. © 1999-2012 IEEE.

关键词： Object detection

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Generation Maintenance Scheduling for Power Systems Considering the Risk Quantification of Hybrid Uncertainty

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IEEE Transactions on Power Systems 2025年

作者： Yang, Xiao Zhao, Yong Li, Yuanzheng Huang, Cheng Ding, Qiang Key Laboratory of Image Information Processing and Intelligent Control of Ministry of Education of China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Power Science Research Institute of State Grid Jiangsu Electric Power Company China China Electric Power Research Institute Company China

The accurate quantification of risk caused by uncertainty forms a crucial foundation for formulating the generation maintenance scheduling (GMS) of power systems. However, the probability distribution functions (PDFs) of uncertain variables such as wind power and load are challenging to model accurately or are unknown, which makes it difficult to measure their economic risk and formulate appropriate GMS for power systems. To address this issue, we consider hybrid uncertainty from wind power and load, and propose a novel interval-probabilistic worst conditional value-at-risk (IP-WCVaR)-based generation maintenance scheduling method. Firstly, a novel IP-WCVaR method is proposed to measure the risk of the interval and probabilistic hybrid uncertainty, and the analytical mathematical model of the IP-WCVaR is derived through typical scenarios of probability correction. On this basis, the positive and negative spinning reserve models are established using the IP-WCVaR and then integrated into the GMS model, which enhances the resilience of the power system. Finally, the new risk-averse GMS model is formulated as the lower and upper boundary optimal models, which are transformed into tractable mixed integer linear programming problems based on the interval extreme value theory. The effectiveness and superiority of the proposed IP-WCVaR method are verified on the modified IEEE 24-bus and IEEE 118-bus power systems. © 1969-2012 IEEE.

关键词： Mixed-integer linear programming

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