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Short-term wind speed prediction method based on prior wind direction knowledge and multi-period decoupling

engineering Applications of Artificial intelligence 2025年 153卷

作者： Shang, Zewen Li, Xuewei Liu, Zhiqiang Sun, Yingzhou Yu, Jian Yu, Mei Xu, Tianyi Xiong, Wei College of Intelligence and Computing Tianjin University Tianjin300350 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China Tianjin Key Laboratory of Advanced Networking Tianjin300350 China TCU School of Civil Engineering Tianjin Chengjian University Tianjin300384 China

Accurate wind speed prediction improves power system stability and efficiency. Wind direction contains airflow dynamic information, but existing methods fail to fully capture it. The multi-periodicity of meteorological data causes irregular wind speed fluctuations, complicating the capture of correlations and variances across periods. To address these issues, this paper proposes the Multi-Period Wind Direction Graph Network(MWGN). Gaussian Spatial Module(GSM) calculates the relationship between wind turbines using Gaussian distribution along the crosswind direction, thus enhancing airflow motion capture. Multi-Period Time Series Decoupling Module(MPTSDM) selects the primary periods by analyzing the frequency domain and extracts local correlation and periodic correlation, to better model the changing pattern of time series. Compared with the existing models, MWGN achieves the consistent state-of-the-art in three datasets. © 2025 Elsevier Ltd

关键词： Wind forecasting

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Exploring Edge-Driven Collaborative Fine-Tuning Toward Customized AIGC Services

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IEEE Network 2025年第3期39卷 293-301页

作者： Zhang, Qinnan Zong, Tianqi Xiong, Zehui Sun, Pangbo Zhu, Jianming Qiu, Wangjie Zheng, Zhiming Poor, H. Vincent Beihang University Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Zhongguancun Laboratory Beijing100191 China Singapore University of Technology and Design Information Systems Technology and Design Tampines 487372 Singapore Qingcheng.AI Ltd. Beijing100084 China Central University of Finance and Economics School of Information Beijing100081 China Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

With the popularization of artificial intelligence-generated content (AIGC), the explosion of end users is spurring an unprecedented diversity in preferences. To cope with this challenge, the AIGC paradigm is shifting from cloud-driven pre-trained large models to edge-driven personalized customization models. The former are large-scale models with more than 100 billion parameters trained based on massive data, while the latter introduce heterogeneous user preferences or professional knowledge to fine-tune the former. However, such fine-tuning incurs costly resource consumption and privacy disclosure. In this paper, we offer a holistic perspective on the AIGC fine-tuning framework spanning from end user to edge to cloud. Specifically, we first investigate how to deploy an edge-driven collaborative fine-tuning task through federated learning. Then, we discuss a verifiable model consensus protocol and fairness incentive design for edge servers to participate in a collaborative learning task. In addition, a case study focuses on the medical scenario, where we develop a practical X-ray diagnostic demo through collaborative fine-tuning of a multimodal pre-training model, and the diagnostic dialogue and performance results are compared. Finally, potential research directions are identified to advance the edge-driven customized AIGC services. © 1986-2012 IEEE.

关键词： Phase change materials

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iManip: Skill-Incremental Learning for Robotic Manipulation

arXiv

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arXiv 2025年

作者： Zheng, Zexin Cai, Jia-Feng Wu, Xiao-Ming Wei, Yi-Lin Tang, Yu-Ming Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Pengcheng Lab China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

The development of a generalist agent with adaptive multiple manipulation skills has been a long-standing goal in the robotics community. In this paper, we explore a crucial task, skill-incremental learning, in robotic manipulation, which is to endow the robots with the ability to learn new manipulation skills based on the previous learned knowledge without re-training. First, we build a skill-incremental environment based on the RLBench benchmark, and explore how traditional incremental methods perform in this setting. We find that they suffer from severe catastrophic forgetting due to the previous methods on classification overlooking the characteristics of temporality and action complexity in robotic manipulation tasks. Towards this end, we propose an incremental Manipulation framework, termed iManip, to mitigate the above issues. We firstly design a temporal replay strategy to maintain the integrity of old skills when learning new skill. Moreover, we propose the extendable PerceiverIO, consisting of an action prompt with extendable weight to adapt to new action primitives in new skill. Extensive experiments show that our framework performs well in Skill-Incremental Learning. Codes of the skill-incremental environment with our framework will be open-source. Copyright © 2025, The Authors. All rights reserved.

关键词： Robot learning

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Audio-Visual Collaborative Learning for Weakly Supervised Video Anomaly Detection

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IEEE Transactions on Multimedia 2025年

作者： Meng, Jingke Tian, Huilin Lin, Ge Hu, Jian-Fang Zheng, Wei-Shi Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Peng Cheng Laboratory Shenzhen518005 China Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education China

Weakly supervised anomaly detection is to identify the time window when an anomaly event happened based on the video-level label indicating whether the video contains anomaly event or not. Recent efforts have focused on leveraging multi-modal data, specifically combining visual and audio information, to enhance detection accuracy. While some studies have explored intra-video separation techniques, the primary emphasis remains on distinguishing potentially anomalous events scoring highest from those scoring lowest as normal events. Nevertheless, challenges persist in delineating boundaries between normal and abnormal events, particularly when visual differences are subtle. Our proposed framework, called Audio-Visual Collaborative Learning (AVCL), addresses the challenge of ambiguity in weakly supervised anomaly detection. Our core idea centers around utilizing both audio track variations and the perceptual robustness of visual information to detect and differentiate challenging cases, which composed of two essential modules: the Audio-Visual collaborative Hard cases Separation (AVHS) module and the Multi-modal Mutual Learning (MML) module. The AVHS module aims to address the challenge of discerning visually ambiguous clips in anomaly videos, differentiating between normal and abnormal events. To further improve detection accuracy, we introduce the Multi-modal Mutual Learning (MML) module, and this module enables a process of mutual learning to facilitate the exchange of knowledge and expertise between the single-modal branch and the multi-modal branch. We demonstrate that the proposed approach achieves state-of-the-art detection performance on benchmarks of XD-Violence and CCTV-Fightssub datasets. © 1999-2012 IEEE.

关键词： Self-supervised learning

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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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Non-Markovian effect enhanced quantum noises in a coherent Ising machine

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Optics Express 2025年第11期33卷 22208-22221页

作者： Chen-Rui Fan Bo Lu Yi-Xuan Yao Qing Ai Chuan Wang School of Artificial Intelligence Beijing Normal University Beijing 100875 China Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou 450001 China lubo@*** School of Physics and Astronomy Applied Optics Beijing Area Major Laboratory Beijing 100875 China Key Laboratory of Multiscale Spin Physics Ministry of Education Beijing Normal University Beijing 100875 China wangchuan@***

Combinatorial optimization problems (COPs) constitute a fundamental class of computational challenges with extensive applications across scientific and industrial domains. The emergence of the coherent Ising machine (CIM) as a computational paradigm has demonstrated exceptional capabilities in rapidly generating arbitrary spin configurations and effectively solving large-scale COPs. Despite its promising potential, the CIM framework encounters an inherent limitation related to amplitude heterogeneity. Current approaches to this limitation primarily rely on Gaussian approximations to model quantum noise in the system’s amplitude dynamics within the original CIM framework. However, these methods generally fail to account for the temporal and periodic non-Markovian characteristics of pulses in practical environments. In this study, we introduce temporally correlated non-Markovian noise into the dynamics of both conventional and spiking neural network-based CIM systems, investigating their performance through comprehensive hyperparameter analysis. Our method incorporates a detailed examination of hyperparameter selection and its influence on algorithmic efficiency. Through extensive numerical simulations, we demonstrate that the integration of non-Markovian noise significantly accelerates variance evolution in both canonical coordinates and amplitude dynamics over time. The proposed approach not only exhibits enhanced computational performance but also shows superior scalability for large-scale problem instances. These findings suggest that our approach offers distinct advantages in addressing complex COPs, potentially opening new avenues for efficient optimization in various applications.

关键词： Neural networks Nonlinear optical fibers Numerical simulation Optical parametric oscillators Optical systems Quantum noise

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A Multi-Dimensional Fusion Method for Identifying Key Nodes in New Energy Vehicle Supply Chains

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Tehnicki Vjesnik 2025年第3期32卷 1185-1194页

作者： Gao, Haiwei Zhu, Xiaomin Yang, Xiaobo Liu, Tianyue Guo, Binghui Xu, Mingzhe School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing100044 China School of Food and Health Beijing Technology and Business University Beijing100048 China National Engineering Research Centre for Agri-Product Quality Traceability Beijing Technology and Business University Beijing100048 China LMIB and NLSDE Beihang University Beijing100191 China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Institute of Artificial Intelligence Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100191 China Peng Cheng Laboratory Beijing100191 China School of Economics and Management Beijing Jiaotong University Beijing100044 China

This paper proposes a multi-dimensional fusion method for identifying key nodes in new energy vehicle supply chains, considering the structural characteristics and risk propagation properties of the network. The method integrates network centrality analysis, the SIR model for risk propagation, and the cascading failure model to comprehensively evaluate the importance of nodes in the supply chain network. The proposed method is applied to the supply chain networks of Tesla and Xpeng brands, constructed from industry data. The results reveal that the key nodes with a strong impact on risk propagation include not only core enterprises such as batteries but also accessory industries with hidden leading positions. The proposed method provides a more comprehensive approach to identifying potential and critical risk control nodes in new energy vehicle supply chains, which has significant practical implications for supply chain risk management. © 2025, Strojarski Facultet. All rights reserved.

关键词： Risk management

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Quantum Multi-Path Communication Protocol Based on Maximum Flow Theory

Quantum Multi-Path Communication Protocol Based on Maximum F...

引用

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

作者： Jihao Fan Liang Huang Jun Li Long Shi Yuwen Qian School of Cyber Science and Engineering Nanjing University of Science and Technology Jiangyin China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Quantum networks are an actively researched and promising field, aiming to achieve efficient quantum information transmission by interconnecting quantum nodes. In large-scale quantum networks, end-to-end throughput is a critical factor that affects the overall performance of the network. The maximum flow problem, extensively studied in classical network theory, identifies a set of paths between the source and destination nodes that maximizes the total flow. This study extends the maximum flow problem to quantum networks, focusing on coordinating multiple paths for multi-path quantum communication. We propose a Quantum Multi-Path Communication Protocol (QMCP) that employs maximum flow theory to allocate transmission resources across multiple nodes efficiently, thus maximizing the total transmission capacity from the source to the destination. Our evaluation demonstrates that QMCP significantly enhances end-to-end throughput in quantum networks.

关键词： Quantum entanglement Simulation Signal processing algorithms Focusing Information processing Signal processing Throughput Routing protocols Quantum networks Speech processing

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Differential-Trust-Mechanism Based Trade-off Method Between Privacy and Accuracy in Recommender Systems

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5054-5068页

作者： Xu, Guangquan Feng, Shicheng Xi, Hao Yan, Qingyang Li, Wenshan Wang, Cong Wang, Wei Liu, Shaoying Tian, Zhihong Zheng, Xi Qingdao Huanghai University School of Big Data Qingdao China Tianjin University College of Intelligence and Computing Tianjin300350 China KLISS and School of Software Beijing100084 China Sichuan University School of Cyber Science and Engineering Chengdu610207 China Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China East China Normal University Shanghai200062 China Hiroshima University School of Informatics and Data Science Higashihiroshima739-8511 Japan Guangzhou University Cyberspace Institute of Advanced Technology Guangdong Key Laboratory of Industrial Control System Security Huangpu Research School of Guangzhou University China Macquarie University School of Computing SydneyNSW2109 Australia

In the era where Web3.0 values data security and privacy, adopting groundbreaking methods to enhance privacy in recommender systems is crucial. Recommender systems need to balance privacy and accuracy, while also having the ability to overcome cold start problems. The Differential Trust Mechanism (DTM) introduced in this paper is such an approach. The DTM provides a unique use of Gaussian distributions in modeling trust relationships within data, offering a novel way to balance recommendation accuracy with user privacy. This mechanism innovatively applies differential privacy principles, using Gaussian noise addition to protect individual user data from inference attacks, while maintaining the integrity and utility of the overall dataset. Unlike traditional anonymization techniques that often compromise data utility or vulnerability to reverse engineering, DTM provides a robust solution by dynamically adjusting privacy levels based on the trustworthiness of data requests. By combining DTM with existing mainstream recommendation algorithms, the prediction accuracy of MAE and RMSE increases by at least 6.60% and 2.69%, respectively. This dual benefit positions DTM as a significant advancement in secure data processing, especially relevant for online businesses and platforms where personalized recommendations are crucial yet privacy concerns are paramount. © 2005-2012 IEEE.

关键词： Sensitive data

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RoGSplat: Learning Robust Generalizable Human Gaussian Splatting from Sparse Multi-View Images

arXiv

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arXiv 2025年

作者： Xiao, Junjin Zhang, Qing Nie, Yonewei Zhu, Lei Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China South China University of Technology China China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

This paper presents RoGSplat, a novel approach for synthesizing high-fidelity novel views of unseen human from sparse multi-view images, while requiring no cumbersome per-subject optimization. Unlike previous methods that typically struggle with sparse views with few overlappings and are less effective in reconstructing complex human geometry, the proposed method enables robust reconstruction in such challenging conditions. Our key idea is to lift SMPL vertices to dense and reliable 3D prior points representing accurate human body geometry, and then regress human Gaussian parameters based on the points. To account for possible misalignment between SMPL model and images, we propose to predict image-aligned 3D prior points by leveraging both pixel-level features and voxel-level features, from which we regress the coarse Gaussians. To enhance the ability to capture high-frequency details, we further render depth maps from the coarse 3D Gaussians to help regress fine-grained pixel-wise Gaussians. Experiments on several benchmark datasets demonstrate that our method outperforms state-of-the-art methods in novel view synthesis and cross-dataset generalization. Our code is available at https: //***/iSEE-laboratory/RoGSplat. Copyright © 2025, The Authors. All rights reserved.

关键词： Pixels

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