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IEEE Conference on Wireless Communications and Networking

作者： Haobo Huang Yusun Fu Junpeng Yin Ningbo Artificial Intelligence Institute Shanghai Jiao Tong University Ningbo China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the Internet of Things (IoT) playing an increasingly crucial role in connecting diverse devices, applications have different requirements for communication and various evaluation metrics have emerged, such as Age of information (AoI) and estimation error. Considering the fundamental trade-off between reliability and latency of the communication system, the relationship among reliability, latency, AoI and estimation error is complex. This paper proposes a remote state observation system with application-layer rateless codes to explore above relationships, addressing how to design the system for optimal performance based on varying requirements by adjusting coding parameters. By emphasizing flexible and adaptable coding strategies, we aim to convey the idea that the error-correction codes can be designed to optimize the communication system for diverse practical metrics. It is different from the traditional designs that focus on approaching the Shannon limit.

关键词： Measurement Estimation error Correlation Communication systems Reliability engineering information age Error correction codes Internet of Things Channel coding State estimation

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RISE-Based Prescribed Performance control for Multimotor Driving Systems

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IEEE Transactions on Industrial Informatics 2025年

作者： Wang, Shubo Chen, Qiang Kunming University of Science and Technology Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming650500 China Zhejiang University of Technology College of Information Engineering Hangzhou310023 China

In this article, a novel approximation free control method was developed for multimotor driving systems with prescribed performance. The developed controller includes multimotor synchronization controller and the load tracking controller. First, the synchronous controller was designed by using the robust integral of the sign of the error to guarantee that all the motors states can converge their average value. Second, to achieve the load tracking control, an approximation free controller was developed based on the prescribed performance function. This method can avoid the function approximators and reduce the reduced computational burden and complexity. With the developed control strategy, the tracking error remains in a prescribed boundary, and the multimotor synchronous error achieves the asymptotic convergence. Finally, comparative experiments are conducted on a four-motor drive system to test the enhanced performance and robustness of the designed controller. © 2005-2012 IEEE.

关键词： Robust control

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Mixture-of-Experts for Open Set Domain Adaptation: A Dual-Space Detection Approach

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2025年

作者： Du, Zhenbang An, Jiayu Tu, Yunlu Hong, Jiahao Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

Open Set Domain Adaptation (OSDA) copes with the distribution and label shifts between the source and target domains simultaneously, performing accurate classification for known classes while identifying unknown class samples in the target domain. Most existing OSDA approaches, depending on the final image feature space of deep models, require manually-tuned thresholds, and may easily misclassify unknown samples as known classes. Mixture-of-Experts (MoE) could be a remedy. Within an MoE, different experts handle distinct input features, producing unique expert routing patterns for various classes in a routing feature space. As a result, unknown class samples may display different expert routing patterns to known classes. This paper proposes Dual-Space Detection, which exploits the inconsistencies between the image feature space and the routing feature space to detect unknown class samples without any threshold. A Graph Router is further introduced to better make use of the spatial information among the image patches. Experiments on three datasets validated the effectiveness and superiority of our approach. © 2020 IEEE.

关键词： Mixture-of-Experts Open Set Domain Adaptation Routing Feature

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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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An accurate forecasting model for key water quality factors based on Transformer with multi-scale attention mechanism

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Environmental Modelling and Software 2025年 191卷

作者： Li, Dashe Ji, Xiaodong Liu, Lu School of Computer Science and Technology Shandong Technology and Business University Shandong Yantai264005 China Key Laboratory of Intelligent Information Processing Shandong Technology and Business University Shandong Yantai264005 China

The prediction of water quality parameters is vital for sustainable aquaculture. Dissolved oxygen (DO), a key factor influencing the health and growth of aquatic organisms, is challenging to predict due to its non-linearity and significant time lag. This study proposed a DO time-series prediction model based on Transformer architecture. A dynamic interpretable time-series decomposition strategy was proposed to extract the key feature information of the DO. A multi-scale decomposition attention mechanism was then designed to better understand the nonstationary characteristics in the time series and capture key features at different scales. Finally, the multi-scale temporal fusion attention mechanism reduced the loss of key information by integrating information from different scales to comprehensively capture complex patterns and dynamic changes in the data. Experimental results show that the prediction performance of the proposed model on six datasets including BaffleCreek is better than that of seven deep learning models. © 2025 Elsevier Ltd

关键词： Aquaculture

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Trend-Aware Patch Feature Fusion Transformer for Fault Diagnosis of Vibration Signals

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IEEE Sensors Journal 2025年

作者： Zhang, Ruicong Chu, Fei Qiu, Yazhi He, Dakuo Wang, Fuli China University of Mining and Technology School of Information and Control Engineering Xuzhou221000 China State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing100160 China Northeastern University College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang110000 China

The Vision Transformer (ViT) has demonstrated reliable performance in industrial equipment fault diagnosis by capturing global features and long-range dependencies in vibration signals. Nonetheless, when processing vibration signals, ViT primarily focuses on the correlations between patches, while neglecting the trend variation information hidden between signal units. This limitation hinders the model’s ability to extract latent fault information. To address this issue, we propose a trend-aware patch feature fusion Transformer (TAPformer) for fault diagnosis of vibration signals. Specifically, we design both patch-level and signal-level trend-aware self-attention blocks and adopt a weighted approach to integrate coarse-grained patch-level trend features with fine-grained signal-level trend features at the output of each self-attention head. This approach enables the model to capture fault information hidden within trend variations, effectively enhancing its fault detection capabilities. TAPformer establishes optimal weighted fusion hyperparameters through ablation experiments and is tested on four public datasets. Compared to eight advanced models, TAPformer achieves the highest accuracy across all four datasets, demonstrating the effectiveness and generalization capability of the proposed method. © 2001-2012 IEEE.

关键词： Fault detection

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Uncertainty-Aware Dynamic Fusion for Multimodal Clinical Prediction Tasks

Uncertainty-Aware Dynamic Fusion for Multimodal Clinical Pre...

引用

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

作者： Jiayu Guo Ying Cheng Wen He Yuejie Zhang Rui Feng Xiaobo Zhang School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China National Children’s Medical Center Children’s Hospital of Fudan University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal fusion offers significant potential for enhancing medical diagnosis, particularly in the Intensive Care Unit (ICU), where integrating diverse data sources is crucial. Traditional static fusion models often fail to account for sample-wise variations in modality importance, which can impact prediction accuracy. To address this issue, we propose a dynamic Uncertainty-Aware Weighting (UAW) strategy that adaptively adjusts the importance of different modalities based on their reliability. This strategy is coupled with an Expert Ensemble Fusion (EEF) module, which leverages self-attention mechanisms and modality-specific FeedForward Networks (FFNs) to preserve and integrate critical information from various modalities. The proposed method demonstrates its efficacy through extensive experiments on phenotype classification and mortality prediction tasks, showing improved accuracy and robustness in handling diverse clinical data.

关键词： Accuracy Phenotypes Soft sensors Mortality Signal processing Predictive models Robustness Medical diagnosis Speech processing X-ray imaging

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Zero-shot image denoising with hollow pair sampling and noise-aware attention

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Pattern Recognition 2025年 167卷

作者： Wang, Sheng Zhao, Chaoyue Wang, Qiao Liu, Mingzhi Mou, Chao Xu, Fu School of Information Science and Technology Beijing Forestry University Beijing100083 China Engineering Research Center for Forestry-oriented Intelligent Information Processing National Forestry and Grassland Administration Beijing100083 China State Key Laboratory of Efficient Production of Forest Resources Beijing100083 China

Image acquisition, compression, or transmission processes frequently introduce noise distortions, which can significantly degrade the visual quality of images. Existing zero-shot denoising methods face challenges in effectively differentiate between noise and subtle image content variations, especially when dealing with structured noise, non-zero-mean noise, and high-noise environments. As a result, these methods may lead to over-smoothing or the loss of fine details, ultimately degrading the overall image quality. In this paper, we propose a novel image denoising technique that does not rely on clean reference images for training. Instead, we utilize secondary sampling and convolution from the original noisy images to generate denoised images with enhanced contrast. Our sampling strategy expands upon the Zero Noise2Noise (ZS-N2N) approach, eliminating the need for additional noise models or parameters. By employing a straightforward hollow filter and noise-aware attention, our method achieves high-quality denoising across various noise types and levels while effectively distinguishing meaningful image features from noisy patterns. Experimental evaluations on visible light and infrared images demonstrate the effectiveness of our approach. Notably, our method excels in restoring image details. Under Gaussian noise, the visible image achieves the PSNR of 37.78 and an SSIM of 0.9460, while the infrared image attains the PSNR of 36.61 and an SSIM of 0.9415. Overall, our method successfully mitigates noise distortion while preserving rich image details, significantly enhancing visual quality. © 2025

关键词： Thermography (imaging)

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Efficient Algorithms for Minimizing the Kirchhoff Index via Adding Edges

arXiv

引用

arXiv 2025年

作者： Zhou, Xiaotian Zehmakan, Ahad N. Zhang, Zhongzhi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Research Institute of Intelligent Complex Systems Fudan University Shanghai200433 China School of Computing Australian National University Canberra Australia

The Kirchhoff index, which is the sum of the resistance distance between every pair of nodes in a network, is a key metric for gauging network performance, where lower values signify enhanced performance. In this paper, we study the problem of minimizing the Kirchhoff index by adding edges. We first provide a greedy algorithm for solving this problem and give an analysis of its quality based on the bounds of the submodularity ratio and the curvature. Then, we introduce a gradient-based greedy algorithm as a new paradigm to solve this problem. To accelerate the computation cost, we leverage geometric properties, convex hull approximation, and approximation of the projected coordinate of each point. To further improve this algorithm, we use pre-pruning and fast update techniques, making it particularly suitable for large networks. Our proposed algorithms have nearly-linear time complexity. We provide extensive experiments on ten real networks to evaluate the quality of our algorithms. The results demonstrate that our proposed algorithms outperform the state-of-the-art methods in terms of efficiency and effectiveness. Moreover, our algorithms are scalable to large graphs with over 5 million nodes and 12 million edges. Copyright © 2025, The Authors. All rights reserved.

关键词： Consensus algorithm

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Research on Early Warning of Drug Crime Behaviors Based on Data-driven Method 24

Research on Early Warning of Drug Crime Behaviors Based on D...

引用

Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management

作者： Shaobing Wu Changmei Wang Institute of Information Security Key Laboratory of Intelligent Drug Control Yunnan Police College Kunming Yunnan China Education Ministry Key Laboratory of Advanced Technology and Preparation for Renewable Energy Materials Yunnan Normal University Kunming Yunnan China

ISBN: (纸本)9798400709753

The identification of target suspects in images and videos from multi-source heterogeneous data is a difficult task in criminal investigation. In criminal scene analysis during law enforcement, images and videos from surveillance applications and security systems play an important role in providing visual files for the scene. With the rise of big data, artificial intelligence, the Internet of Things, blockchain, and cyberspace security, it is particularly important to improve the application of computer technology in drug crime cases and achieve data-driven drug crime behavior prediction. This paper builds police subject-specific databases such as drug addicts, drug traffickers, key personnel, and fugitives, and achieves drug crime behavior prediction and warning based on identity information, facial recognition and storage and picture comparison using a combination of Python and OpenCV. It provides a useful exploration for combating Drug Crime.

关键词： data-driven

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