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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Wang, Baonan Wang, Hui Zhang, Dan College of Computer Science and Technology Shanghai University of Electric Power Shanghai China State Key Laboratory of Power System Operation and Control Dept. of Electrical Engineering Tsinghua University Beijing China Institute of Logistic Science and Engineering Shanghai Maritime University Shanghai China

ISBN: (纸本)9789819608966

Current optimization methods for microgrid scheduling face issues such as insufficient precision in energy distribution, high operational costs, and inefficiency. In response to these challenges, an optimization scheduling method for multi-energy microgrids based on the QUBO (Quadratic Unconstrained Binary Optimization) quantum computing model is proposed in this paper, alongside a discretization approach based on a grid partitioning strategy. To validate the feasibility of addressing microgrid optimization scheduling problems in a quantum computing environment, a QUBO model is constructed and simulation studies are conducted in the qbsolv environment provided by D-Wave. The results indicate that operational costs are significantly reduced with an increase in the number of grids, particularly a reduction of 4.52% when the number of grids increases from five to ten. This method not only effectively enhances the precision of electricity supply and energy utilization efficiency but also reduces unnecessary electrical waste and economic costs. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Microgrids

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Prior Knowledge-Driven Hybrid Prompter Learning for RGB-Event Tracking

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IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Wang, Mianzhao Shi, Fan Cheng, Xu Chen, Shengyong Tianjin University of Technology Engineering Research Center of Learning-Based Intelligent System Ministry of Education Key Laboratory of Computer Vision and System School of Computer Science and Engineering Tianjin300384 China Technical University of Denmark Department of Technology Management and Economics Kongens Lyngby Denmark

Event data can asynchronously capture variations in light intensity, thereby implicitly providing valuable complementary cues for RGB-Event tracking. Existing methods typically employ a direct interaction mechanism to fuse RGB and event data. However, due to differences in imaging mechanisms, the representational disparity between these two data types is not fixed, which can lead to tracking failures in certain challenging scenarios. To address this issue, we propose a novel prior knowledge-driven hybrid prompter learning framework for RGB-Event tracking. Specifically, we develop a frame-event hybrid prompter that leverages prior tracking knowledge from the foundation model as intermediate modal support to mitigate the heterogeneity between RGB and event data. By leveraging its rich prior tracking knowledge, the intermediate modal reduces the gap between the dense RGB and sparse event data interactions, effectively guiding complementary learning between modalities. Meanwhile, to mitigate the internal learning disparities between the lightweight hybrid prompter and the deep transformer model, we introduce a pseudo-prompt learning strategy that lies between full fine-tuning and partial fine-tuning. This strategy adopts a divide-and-conquer approach to assign different learning rates to modules with distinct functions, effectively reducing the dominant influence of RGB information in complex scenarios. Extensive experiments conducted on two public RGB-Event tracking datasets show that the proposed HPL outperforms state-of-the-art tracking methods, achieving exceptional performance. © 1991-2012 IEEE.

关键词： Object detection

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Harnessing Light Field Angular Cues and Spatial Geometries for Semantic Segmentation

Harnessing Light Field Angular Cues and Spatial Geometries f...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chen Jia Fan Shi Xu Cheng School of Computer Science and Engineering The Engineering Research Center of Learning-Based Intelligent System (Ministry of Education) The Key Laboratory of Computer Vision and System (Ministry of Education) Tianjin University of Technology Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

4D light field imaging captures rich spatial-angular information, providing essential geometric cues for semantic segmentation tasks. In this paper, we introduce a novel backbone network called the Light Field Extraction Interaction Network (LFEI-Net). LFEI-Net excels in extracting global structures and multi-scale spatial-angular features, capturing feature dependencies through channel modeling and diverse feature interactions. Unlike traditional methods that depend on pyramid and dilated feature extraction, LFEI-Net pioneers an efficient method by integrating large-scale horizontal depth-wise convolution (HDWC) and vertical depth-wise convolution (VDWC) with interactive operations for comprehensive spatial multi-scale feature extraction. Furthermore, we present the Multi-Angular Modeling (MAM) module, which effectively captures scene angle variations from multiple perspectives and precisely delineates object boundaries, thereby improving model adaptability. Our experimental evaluations on two datasets demonstrate that LFEI-Net significantly outperforms state-ofthe-art (SOTA) 2D and 4D light field semantic segmentation methods, achieving mean Intersection over Union (mIoU) of 83.72% and 86.88%, respectively.

关键词： Geometry Adaptation models Convolution Semantic segmentation Imaging Feature extraction Light fields Acoustics Speech processing

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VAGeo: View-specific Attention for Cross-View Object Geo-Localization

VAGeo: View-specific Attention for Cross-View Object Geo-Loc...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhongyang Li Xin Yuan Wei Liu Xin Xu School of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Cross-view object geo-localization (CVOGL) aims to locate an object of interest in a captured ground- or drone-view image within the satellite image. However, existing works treat ground-view and drone-view query images equivalently, overlooking their inherent viewpoint discrepancies and the spatial correlation between the query image and the satellite-view reference image. To this end, this paper proposes a novel View-specific Attention Geo-localization method (VAGeo) for accurate CVOGL. Specifically, VAGeo contains two key modules: view-specific positional encoding (VSPE) module and channel-spatial hybrid attention (CSHA) module. In object-level, according to the characteristics of different viewpoints of ground and drone query images, viewpoint-specific positional codings are designed to more accurately identify the click-point object of the query image in the VSPE module. In feature-level, a hybrid attention in the CSHA module is introduced by combining channel attention and spatial attention mechanisms simultaneously for learning discriminative features. Extensive experimental results demonstrate that the proposed VAGeo gains a significant performance improvement, i.e., improving acc@0.25/acc@0.5 on the CVOGL dataset from 45.43%/42.24% to 48.21%/45.22% for ground-view, and from 61.97%/57.66% to 66.19%/61.87% for drone-view.

关键词： Location awareness Accuracy Attention mechanisms Image coding Signal processing Encoding Satellite images Object recognition Speech processing Drones

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Gesture Recognition of sEMG Based on Res-LSTM 17th

Gesture Recognition of sEMG Based on Res-LSTM

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17th International Conference on Intelligent Robotics and Applications, ICIRA 2024

作者： Zhao, Yujia Zou, Chunlong Yun, Juntong Jiang, Du Huang, Li Liu, Ying Jiang, Guozhang Xie, Yuanmin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for their convenience, noninvasiveness, and ease of access. However, compared with high-density EMG signals, sparse EMG signals lack rich feature information, which in turn affects gesture recognition accuracy. In order to reduce the loss of feature information of sparse EMG signals in the spatio-temporal dimension, this paper proposes a hybrid neural network Res-LSTM combining residual network and long short-term memory network. The ordinary convolutional blocks in the CNN network are replaced with residual blocks, and the final fully connected layer is removed and a constant mapping layer is added to adequate extraction of spatial feature information of the data. The Res layer’s output is utilized as the input for the long and short-term memory (LSTM) network, which further extracts the features of the data in the temporal dimension, and finally completes the categorization output through a fully connected layer. The average accuracy of 91.11% for gesture-motion classification was verified by training on a homebrew dataset;and tested on a public dataset, NinaPro DB1, with an average accuracy of 91.03%. The experimental findings indicate that the proposed Res-LSTM network framework contributes to solving the problem of lack of feature information of sparse sEMG signals and improves the pattern recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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A third-party platoon coordination service: Pricing under government subsidies

A third-party platoon coordination service: Pricing under go...

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作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Mårtensson, Jonas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease. © 2023 The Authors. Asian Journal of Control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic Control Society.

关键词： Trucks

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Disentangle Nighttime Lens Flares: Self-supervised Generation-based Lens Flare Removal

arXiv

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arXiv 2025年

作者： He, Yuwen Wang, Wei Wang, Wanyu Jiang, Kui Wu, Wanyu Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology China School of Computer Science and Technology Harbin Institute of Technology China

Lens flares arise from light reflection and refraction within sensor arrays, whose diverse types include glow, veiling glare, reflective flare and so on. Existing methods are specialized for one specific type only, and overlook the simultaneous occurrence of multiple typed lens flares, which is common in the real-world, e.g. coexistence of glow and displacement reflections from the same light source. These co-occurring lens flares cannot be effectively resolved by the simple combination of individual flare removal methods, since these coexisting flares originates from the same light source and are generated simultaneously within the same sensor array, exhibit a complex interdependence rather than simple additive relation. To model this interdependent flares’ relationship, our Nighttime Lens Flare Formation model is the first attempt to learn the intrinsic physical relationship between flares on the imaging plane. Building on this physical model, we introduce a solution to this joint flare removal task named Self-supervised Generation-based Lens Flare Removal Network (SGLFR-Net), which is self-supervised without pre-training. Specifically, the nighttime glow is detangled in PSF Rendering Network(PSFR-Net) based on PSF Rendering Prior, while the reflective flare is modelled in Texture Prior Based Reflection Flare Removal Network (TPRR-Net). Empirical evaluations demonstrate the effectiveness of the proposed method in both joint and individual glare removal tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Glare

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Robust intrusion detection based on personalized federated learning for IoT environment

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computers and Security 2025年 154卷

作者： Sun, Shimin Zhou, Le Wang, Ze Han, Li Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong University Tianjin300387 China School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

In the dynamic and complex realm of the Internet of Things (IoT) and artificial intelligence (AI), it is a significant challenge to design a network intrusion detection system that balances accuracy, efficiency, and data privacy. Federated learning offers a solution by enabling the sharing of high-quality attack samples to enhance local models’ intrusion detection capabilities without compromising local data privacy. However, most existing research on federated learning for intrusion detection assumes homogeneity among local models, which can reduce detection accuracy in real-world scenarios where local datasets are often non-independent and identically distributed (Non-IID). The Non-IID characteristic, marked by varied distributional properties and correlations, impacts model convergence and stability. To address this challenge, we propose a personalized federated cross learning framework (pFedCross) for intrusion detection, to manage imbalanced and heterogeneous data distributions. First, we present a collaborative model cross aggregation algorithm for personalized local model update, to solve the problem that one global model cannot always accommodate all the incompatible convergence directions of local models. Then, we introduce a gradient approximation α-fairness algorithm for global model generation to achieve a well-generalization. Finally, the experiments show that pFedCross outperforms baseline methods in improving model accuracy and reducing loss, highlighting its promise for enhancing IoT security. © 2025 Elsevier Ltd

关键词： Federated learning

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Multilevel features cascade fusion network for infrared video human behavior recognition

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Displays 2025年 87卷

作者： Han, Huiyan Ouyang, Nannan Kuang, Xinhe Wang, Song Xiong, Fengguang School of Computer Science and Technology North University of China Taiyuan030051 China Sydney Smart Technology College Northeastern University Qinhuangdao066004 China Shanxi Key Laboratory of Machine Vision and Virtual Reality Taiyuan030051 China

Human behavior recognition stands as a pivotal task in video analysis and understanding. While this field has garnered considerable attention within the visible spectrum, infrared imagery introduces distinct challenges, primarily due to its inherently blurry surface textures and less distinct features. Current mainstream methods typically rely on complex multi-stream networks to extract a range of human behavioral features. However, these methods frequently fall short in effectively utilizing complementary feature information directly, which can hinder the accuracy of behavior recognition. This study presents an end-to-end approach to address these challenges by proposing a Multilevel Features Cascade Fusion (MFCF) network. By using raw infrared video data as the direct input, this method simplifies the model architecture, thereby enhancing efficiency without sacrificing accuracy. Our approach incorporates a Non-local attention module to capture both global and hierarchical feature information—essential for decoding complex behaviors within infrared imagery. This is complemented by a deepwise features fusion module that integrates and cascades features at various levels. Through this multistage fusion process, the model develops a comprehensive understanding of the features, significantly improving its ability to interpret and classify nuanced human behaviors. Experimental results from benchmark datasets validate the effectiveness of our proposed method. Comparative analyses reveal that our approach surpasses existing state-of-the-art methods, demonstrating an average recognition accuracy improvement of 3.3% on the InfAR dataset and 2.3% on the IITR-IAR dataset, achieving overall accuracies of 90.00% and 85.56%, respectively. © 2024 The Author(s)

关键词： Infrared imaging

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Knowledge Hypergraph Link Prediction Based on GAT and Convolutional Neural Network

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computer Engineering and Applications 2025年第9期61卷 194-201页

作者： Pang, Jun Ma, Zhifen Lin, Xiaoli Wang, Mengxiang School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China China Institute of Standardization Beijing100088 China

Knowledge hypergraph (KHG) is a hypergraph structured knowledge graph. Knowledge hypergraph link prediction is based on known entities and relationships to predict missing entities or relationships, which is of great significance and value. However, the existing neural network-based methods of knowledge hypergraph link prediction only focus on the semantic features localized to the relationship facts and lack the representation learning of the association features between the relationship facts. To address the above problems, a knowledge hypergraph link prediction method based on graph attention network and convolutional neural network (HPGC) is proposed. On the one hand, an improved convolutional network is used to extract local features of node entity representations in the knowledge hypergraph;on the other hand, an improved GAT is used for attention modeling of nodes and relations, capturing global feature relations between nodes and merging them, so as to obtain a more comprehensive neighborhood structure of the relation facts, and to enrich the semantic representations of the relation facts in the hypergraph. In addition, to address the GAT layer output vector problem of HPGC, which introduces a multilayer perceptron (MLP) and regularization techniques to enhance the generalization ability of model training. Extensive experimental results on real datasets validate that the prediction performances of the proposed methods outperform the baseline methods. © 2025 Journal of computer Engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

关键词： Convolutional neural networks

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