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Flood Inundation Range Prediction Method Based on SRR-Informer 13th

Flood Inundation Range Prediction Method Based on SRR-Inform...

13th International Conference on AI and Multimodal Services, AIMS 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Liu, Han Chen, Zhihao Sun, Qi College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031776809

Flood forecasting methods based on deep learning rely on a large number of observational data, and are facing serious challenges in areas with scarce data. Aiming at the problems of flood inundated range prediction in areas with scarce data, this paper proposes a flood inundated range prediction method based on spatial reduction reconstruction (SRR) and improved deep attention neural network (Informer) to reduce the data requirements of the two core parts of water level simulation and spatial modeling. It alleviates the problem of insufficient observational data for flood inundation range prediction in areas with scarce data. This method obtains the dependency relationship of long time series data through Informer model, and inserts the built-in input selection layer to reduce the number of parameters in the model, reducing the data requirement of water level simulation. At the same time, the SRR algorithm is used to select the representative locations that are easy to be inundated in the basin, which reduces the number of locations required for spatial modeling of flood inundation range and the corresponding data requirements. The experimental results show that this method can improve the accuracy of flood inundation range prediction and speed up the efficiency of the model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Application and Optimization of Multi-agent Reinforcement Learning in Collaborative Decision-Making 8th

Application and Optimization of Multi-agent Reinforcement Le...

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8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Sun, Qi Chen, Zhihao Liu, Han College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031779534

With the rapid development of intelligent systems, Multi-Agent Systems (MAS) have shown unique advantages in solving complex decision-making problems. Particularly in the field of Multi-Agent Reinforcement Learning (MARL), Multiple agents can decompose complex tasks, process information and make decisions in parallel, share experiences, accelerate the learning process, and significantly improve decision quality and efficiency. This paper explores the theoretical underpinnings of MARL and its application to collaborative decision-making and analyzes practical cases in areas such as transportation system management, automated manufacturing, and smart grids. Additionally, it addresses challenges in strategy coordination, handling dynamic environments, and improving learning efficiency. This paper proposes several optimization strategies and introduces reservoir group optimization experiments. By comparing with single-agent algorithms, it verifies that multi-agent systems can coordinate multiple reservoirs, enhance convergence speed, and achieve higher power generation efficiency, demonstrating better practical application prospects. Furthermore, the future trends of MARL, including technological advancements, potential applications, and challenges, are discussed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reinforcement learning

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EchoGCN: An Echo Graph Convolutional Network for Skeleton-Based Action Recognition 27th

EchoGCN: An Echo Graph Convolutional Network for Skeleton-Ba...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Qian, Weiwen Huang, Qian Li, Chang Chen, Zhongqi Mao, Yingchi College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9783031783531

Graph Convolutional Networks (GCNs) have attracted considerable attention in the realm of human action recognition. However, conventional GCNs-based methods typically struggle to construct adjacency matrices that capture diverse semantics, thus leading to performance limitations. To tackle this issue, we propose the Echo Graph as a set of adjacency matrices, which includes both hierarchical and global graphs. Specifically, our hierarchical graph exploits the hierarchical information based on the selected central broadcast nodes, aggregating joints dispersed across considerable physical distances into a unified semantic space. The global graph we construct transcends the limitations imposed by physically defined topological structures, delving into comprehensive information exchange among nodes. Additionally, we propose node activation and hierarchical activation model. These activations aim to prominently highlight crucial nodes and edges for specific samples. Finally, we incorporate Margin ReLU distillation to improve computational efficiency and design a four-stream integration using only the joint and bone data streams from two central broadcast nodes. Based on the aforementioned components, we propose EchoGCN, capable of extracting representative skeleton features. Experimental results on three datasets (NTU-RGB+D 60, NTU-RGB+D 120, and NW-UCLA) demonstrate that our model achieves state-of-the-art performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Musculoskeletal system

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SSTHyper: Sparse Spectral Transformer for Hyperspectral Image Reconstruction 17th

SSTHyper: Sparse Spectral Transformer for Hyperspectral Ima...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Xu, Meng Lin, Mingying Ren, Qi Jia, Sen College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9789819609109

Transformer-based methods have improved the quality of hyperspectral images (HSIs) reconstructed from RGB by effectively capturing their remote relationships. The self-attention mechanisms in existing Transformer models have not fully considered the spatial sparsity and spectral continuity characteristics of HSIs and fail to effectively filter out significant features, resulting in lower-quality reconstruction results. This paper proposes a sparse spectral transformer model for HSI reconstruction (SSTHyper) to address this limitation, adaptively preserving crucial features. The network consists of an encoder-decoder structure capable of learning shallow and deep spatial-spectral priors, primarily composed of sparse spectral self-attention groups. Introducing a sparse spectral self-attention mechanism allows adaptive masking of non-significant details, enhancing reconstruction accuracy. Meanwhile, a lightweight cross-level fusion network is proposed to reduce model parameters and computational costs to enhance spatial-spectral feature extraction. Experimental results on two benchmark datasets demonstrate the outstanding performance of the proposed method. The code will be released at SSTHyper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image reconstruction

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Spectral Modality-Aware Interactive Fusion Network for HSI Super-Resolution 17th

Spectral Modality-Aware Interactive Fusion Network for HSI ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Xu, Meng Mao, Jiayou Mo, Ziqian Fu, Xiyou Jia, Sen College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9789819609109

Due to the limitations of current spectral imaging equipment in acquiring high-resolution hyperspectral images (HR-HSIs), a common approach is to fuse low-resolution hyperspectral images (LR-HSIs) with high-resolution multispectral images (HR-MSIs). However, most existing methods have not fully taken into account the correlation and discrepancy in modality between hyperspectral images (HSIs) and multispectral images (MSIs). To address this limitation, we propose an innovative spectral modality-aware interactive fusion network (SMIF-NET) for comprehensive extraction of spectral information and seamless feature fusion. First, we introduce the spectral modality-aware transformer (SMAT) with a dual-attention mechanism to compute spectral self-similarity and cross-spectral correlation. Second, we apply the interactive spatial-spectral feature fusion (IS2F2) to fuse the acquired high-level spectral and spatial features. This fusion technique combines spatial-wise and channel-wise squeeze and excitation to achieve seamless integration of spatial-spectral information. Finally, the extensive experiments on three datasets demonstrate the superior performance of SMIF-NET in both visual and quantitative assessments compared to eight state-of-the-art (SOTA) fusion-based methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image fusion

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GTE: learning code AST representation efficiently and effectively

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science China(Information sciences) 2025年第3期68卷 393-394页

作者： Yihao QIN Shangwen WANG Bo LIN Kang YANG Xiaoguang MAO College of Computer Science and Technology National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology

With the development of deep learning in recent years, code representation learning techniques have become the foundation of many software engineering tasks such as program classification [1] and defect detection. Earlier approaches treat the code as token sequences and use CNN, RNN, and the Transformer models to learn code representations.

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Leveraging computer Vision for Automatic Modulation Classification: Insights from Spectrum and Constellation Diagram Analysis 27th

Leveraging Computer Vision for Automatic Modulation Classifi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhao, Wenjie Luo, Qiuming College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9783031783821

Automated modulation recognition is a challenging task in communication systems. Leveraging recent advancements in transfer learning, this paper proposes a novel method for automatic modulation recognition using transferred computer vision models. The method allows fine-tuning of the vision models to recognize modulation signals through spectrum and constellation diagrams. Experiments on the Radioml dataset demonstrate that the proposed method outperforms recent traditional methods by 8.97%, with an average accuracy of 0.5732. An ablation study confirms the effectiveness of using spectrum and constellation diagrams. This study verifies the feasibility of transferring vision models to AMC tasks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signal modulation

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Service Function Chain Deployment with Intrinsic Dynamic Defense Capability

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5418-5432页

作者： Wang, Ran Cai, Lundan Wu, Qiang Niyato, Dusit Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing China Nanyang Technological University School of Computer Science and Engineering Singapore

The Service Function Chain (SFC) leverages Network Function Virtualization (NFV) and software-Defined Networking (SDN) for flexible deployment, creating customized service chains tailored to specific applications. As NFV and SDN technologies play crucial roles in the SFC implementation, any security risk that arises in an NFV/SDN network can potentially pose a threat to SFC. Thus, SFC becomes vulnerable to network security attacks. To address this, intrinsic security technologies, including moving target defense and mimic defense, offer proactive protection against both known and unknown threats. It is expected to break through traditional security protection mechanisms such as "enhanced", "plug-in"and "passive"defense. This paper proposes an intrinsic dynamic defense architecture to equip SFC with active defense capabilities, shifting from passive reactive mechanism based on prior knowledge to an active defense against various attacks. The architecture comprises two models and five modules, including a sub-pool partitioning algorithm that enhances heterogeneity across sub-pools by splitting the heterogeneous replica pool into several sub-pools among replica VNFs. To meet Quality of Service (QoS) requirements like latency, cost, and security, we formulate a multi-objective optimization problem with three objectives: latency, cost, and defense success rate. Following that, we propose a dynamic Deep Reinforcement Learning (DRL)-based deployment algorithm. This algorithm selects appropriate VNFs based on heterogeneity and historical information, improving SFC and VNF security against external attacks. Extensive experiments validate that our architecture significantly enhances network security, provided that this improvement comes at the expense of limited cost and latency. © 2002-2012 IEEE.

关键词： Quality of service

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Human Action Recognition in Multi-Level Convolutional Temporal Attention Network

Human Action Recognition in Multi-Level Convolutional Tempor...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Qian Chen, ZhongQi Li, Chang Qian, WeiWen College of Computer Science and Software Engineering Hohai University NanJing China

ISBN: (纸本)9798350368741

Human Action Recognition (HAR) has widespread applications in areas such as human-computer interaction, elderly care, and home healthcare. However, current sensor-based HAR faces challenges of low fine-grained recognition performance and difficulty in distinguishing similar actions. To solve this problem, this paper proposes a model based on Multilevel Convolutional Time Series Attention Network (MCTSANet). By Multi-ResCNN to pay attention to different levels of features and using Time Series Attention (TSA) to pay attention to the more important data in the channel, so as to improve the ability of confusable action *** on three public datasets show that the proposed method outperforms state-of-the-art sensor-based HAR approaches. © 2025 IEEE.

关键词： Attention Mechanism Deep Learning Human Action Recognition Wearable Sensors

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Automatic Positioning of Intelligent Mobile Robot Based on Improved Front-End Scanning Matching

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 559-572页

作者： Wang, Hong Wang, Yi-Gui Zhang, Jie Department of Computer and Software Engineering Shandong College of Electronic Technology Jinan250200 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China College of Agricultural Science and Technology Yana Royal Polytechnic University Chiang Mai50000 Thailand

Traditional autonomous navigation methods for mobile robots mainly rely on geometric feature-based LiDAR scan-matching algorithms, but in complex environments, this method is often affected due to the presence of moving objects, occlusions, and other interfering factors, resulting in a decrease in positioning accuracy. With the growing demand for robotics applications in logistics, security, exploration and other fields, the need for robust autonomous navigation and high-precision mapping in highly dynamic and complex environments is becoming more and more urgent. To solve this problem, an improved front-end scan matching algorithm based on Frequency Modulated Continuous Wave (FMCW) LiDAR is proposed in this paper. Firstly, by using the target Doppler velocity information provided by the FMCW LiDAR, we design a novel point cloud segmentation algorithm based on velocity clustering, which is able to effectively distinguish between stationary and moving objects, and avoid the dynamic interference affecting the position estimation. Secondly, we introduce the Gaussian Mixture Model Sampling Consistency (GMMSC) algorithm, which is more robust to reject the mis-matched pairs in the scanning matching process and improve the alignment accuracy. Finally, based on the residual high-quality matched pairs, we combine the classical ICP algorithm with the robust kernel function to further enhance the stability of the position estimation in the case of occlusion and local mismatch. The experimental results show that the proposed improved algorithm significantly improves the mapping capability of mobile robots in complex environments compared with the existing techniques. The average relative error of the improved front-end scanning matching algorithm is reduced by 40.6 % compared with the pre-improved one. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Mobile robots

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