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DESIGN AND ANALYSIS OF WIND TURBINE FAULT DIAGNOSIS SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK

Diagnostyka 2025年第1期26卷

作者： Luo, Xiaoli Ning, Jinye Wu, Min Scientific Research Department Hunan Electrical College of Technology Xiangtan411101 China Institute of Big Data and Artificial Intelligence Application Technology Hunan Electrical College of Technology Xiangtan411101 China School of Mechanical Engineering Hunan University of Science and Technology Xiangtan City411201 China

Wind turbines are apt to diverse faults during long-term operation in natural environments, which affect their power generation efficiency and lifespan. Therefore, based on convolutional neural networks, gradient descent method was introduced to optimize their parameter training. Meanwhile, synchronous compressed wavelet transform was utilized to enhance the fault signal's time-frequency information. The fault detection correlation operation was optimized through Pearson correlation coefficient. Finally, a new type of fan fault detection model was proposed. The average fault detecting accuracy of this model was the highest at 98.98%, the average loss value was the lowest at 0.08%, and the average time consumption was the shortest at 16.52s. The minimum mean square error for detecting inner and outer ring pitting of fan bearings was 0.016 and 0.018, respectively. As a result, the proposed new model performs excellently in terms of accuracy and reliability in fault detection, with detection accuracy generally superior to other existing models. This model can significantly improve wind turbine fault detection, reduce false alarm and false alarm rates, and provide effective guarantees for wind turbines' stable operation. © 202x by the Authors.

关键词： Convolutional neural networks

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Joint Task Partitioning and Resource Allocation in UAV-Enabled Vehicular Edge Computing Based on Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年第11期12卷 15453-15466页

作者： Liang, Hongbin Zhang, Han Ale, Laha Hong, Xintao Wang, Lei Jia, Qiong Zhao, Dongmei Southwest Jiaotong University School of Transportation and Logistics National United Engineering Laboratory of Integrated and Intelligent Transportation National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China Southwest Jiaotong University School of Information Science and Technology Chengdu611756 China Chinese Academy of Sciences Laboratory of Noise and Vibration Research Institute of Acoustics Beijing100190 China McMaster University Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada

Vehicle Edge Computing (VEC) leverages compact cloud computing at the mobile network edge to meet the processing and latency needs of vehicles. By bringing computation closer to the vehicles, VEC reduces data transmission, minimizes latency, and boosts performance for compute-intensive applications. However, during peak hours of urban road traffic, the scarce computational resources available at edge servers could pose challenges in fulfilling the processing needs of vehicles. Introducing Unmanned Aerial Vehicles (UAVs) as supplementary edge computing nodes could significantly mitigate the aforementioned issue. In this paper, we propose a flexible edge computing framework in which a fleet of UAVs function as mobile computational service providers, offering computation offloading services to multiple vehicles. We design and optimize a computation offloading model for the UAV-enabled vehicle edge computing environment. The proposed model tackles the task offloading challenge, aiming to optimize UAV revenue and task processing efficiency while considering the constraints of UAVs' restricted computational power and energy resources. Towards this end, our model jointly considers two key factors: task partitioning and computational resource allocation. To tackle the challenges posed by the aforementioned non-convex optimization problem, we construct a Markov Decision Process (MDP) model for the multi-UAV-enabled mobile edge computing system and introduce an innovative Multi-Agent Deep Reinforcement Learning (MADRL) framework addressing the decision-making challenge represented by MDP model. Comprehensive simulation outcomes illustrate that our devised task offloading technique outperforms other optimization methods. © 2014 IEEE.

关键词： Convex optimization

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Semantic Feature Division Multiple Access for Digital Semantic Broadcast Channels

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IEEE Internet of Things Journal 2025年第11期12卷 17123-17136页

作者： Ma, Shuai Sun, Zhiye Shen, Bin Wu, Youlong Li, Hang Shi, Guangming Li, Shiyin Al-Dhahir, Naofal Peng Cheng Laboratory Shenzhen518066 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China Xidian University School of Artificial Intelligence Shaanxi Xi'an710071 China The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

In this paper, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multi-user broadcast (BC) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multi-user semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in the same time-frequency resource. Specifically, for inference tasks, we design a SFDMA digital BC network based on robust information bottleneck (RIB), which can achieve a tradeoff between inference performance, data compression and multi-user interference. Moreover, for image reconstruction tasks, we develop a SFDMA digital BC network by utilizing a Swin Transformer, which significantly reduces multi-user interference. More importantly, SFDMA can protect the privacy of users' semantic information, in which each receiver can only decode its own semantic information. Furthermore, we establish a relationship between performance and signal to interference plus noise ratio (SINR), which is fitted by an Alpha-Beta-Gamma (ABG) function. Furthermore, an optimal power allocation method is developed for the inference and reconstruction tasks. Extensive simulations verify the effectiveness and superiority of our proposed SFDMA scheme. © 2014 IEEE.

关键词： Image reconstruction

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Omics sciences for cervical cancer precision medicine from the perspective of the tumor immune microenvironment

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Oncology Research 2025年第4期33卷 821-836页

作者： GUANTING PANG YAOHAN LI QIWEN SHI JINGKUI TIAN HANMEI LOU YUE FENG College of Pharmaceutical Science Zhejiang University of TechnologyHangzhou310014China College of Artificial Intelligence and Big Data for Medical Sciences Shandong First Medical University&Shandong Academy of Medical SciencesJinan250000China Collaborative Innovation Center for Green Pharmaceuticals Zhejiang University of TechnologyHangzhou310014China Hangzhou Institute of Medicine(HIM) Chinese Academy of SciencesHangzhou310022China Department of Gynecological Oncology Zhejiang Cancer HospitalHangzhou310022China

Immunotherapies have demonstrated notable clinical benefits in the treatment of cervical cancer(CC).However,the development of therapeutic resistance and diverse adverse effects in immunotherapy stem from complex interactions among biological processes and factors within the tumor immune microenvironment(TIME).Advanced omic technologies offer novel insights into a more expansive and thorough layer of the ***,integrating multidimensional omics within the frameworks of systems biology and computational methodologies facilitates the generation of interpretable data outputs to characterize the clinical and biological trajectories of tumor *** this review,we present advanced omics technologies that utilize various clinical samples to address scientific inquiries related to immunotherapies for CC,highlighting their utility in identifying metastasis dissemination,recurrence risk,and therapeutic resistance in patients treated with immunotherapeutic *** review elaborates on the strategy for integrating multi-omics data through artificial intelligence ***,an analysis of the obstacles encountered in the multi-omics analysis process and potential avenues for future research in this domain are presented.

关键词： Omics Tumor immune microenvironment(TIME) Precision medicine Cervical cancer(CC)

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Distributed Signal Processing for Extremely Large-Scale Antenna Array Systems: State-of-the-Art and Future Directions

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IEEE Journal on Selected Topics in Signal Processing 2025年第2期19卷 304-330页

作者： Xu, Yanqing Larsson, Erik G. Jorswieck, Eduard A. Li, Xiao Jin, Shi Chang, Tsung-Hui The Chinese University of HongKong School of Science and Engineering Shenzhen518172 China Linköping University Department of Electrical Engineering Linköping581 83 Sweden Technical University of Braunschweig Institute for Communications Technology Braunschweig38106 Germany Southeast University National Mobile Communication Research Laboratory Nanjing210096 China The Chinese University of HongKong School of Artificial Intelligence Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China

Extremely large-scale antenna arrays (ELAA) play a critical role in enabling the functionalities of next generation wireless communication systems. However, as the number of antennas increases, ELAA systems face significant bottlenecks, such as excessive interconnection costs and high computational complexity. Efficient distributed signal processing (SP) algorithms show great promise in overcoming these challenges. In this paper, we provide a comprehensive overview of distributed SP algorithms for ELAA systems, tailored to address these bottlenecks. We start by presenting three representative forms of ELAA systems: single-base station ELAA systems, coordinated distributed antenna systems, and ELAA systems integrated with emerging technologies. For each form, we review the associated distributed SP algorithms in the literature. Additionally, we outline several important future research directions that are essential for improving the performance and practicality of ELAA systems. © 2025 IEEE.

关键词： Antenna arrays

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EAPDS: Efficient Auditable and Privacy-preservation data Sharing Scheme Based on Attribute-Based Encryption for IoMT

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IEEE Internet of Things Journal 2025年

作者： Wang, Hui Xie, Yong Luo, Min Liu, Yining Shirazi, Syed Hamad Qinghai University Department of Information Technology Xining810016 China Qinghai Institute of Technology School of Computer and Information Science Xining810016 China Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology Xining810016 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325000 China Hazara University Department of Information Technology Mansehra21300 Pakistan

data sharing schemes based on the Internet of Medical Things (IoMT) have emerged as a more convenient way to monitor and manage individuals’ health. However, this scenario faces challenges such as privacy preservation, effectiveness, and practicality, which hinder its further development. To the best of our knowledge, there is no agreed-upon data-sharing method that addresses all of these problems. In this paper, we make a step ahead by designing an Efficient and Auditable Privacy-preservation data Sharing scheme (EAPDS) based on multi-authority attribute-based encryption. EAPDS designs an auditable anonymous authentication mechanism to realize identity privacy protection, as well as an efficient multi-authority attribute-based encryption mechanism to achieve the efficiency and practicability of data-sharing. Formal security analysis demonstrates EAPDS can resist replayable chosen-ciphertext attack. Many performance evaluation experiments and functional analyses show that EAPDS not only performs better than existing medical data-sharing schemes in terms of efficiency, but also in terms of privacy protection and feasibility. Consequently, our EAPDS scheme holds promising application prospects. © 2014 IEEE.

关键词： data consistency

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Hyperspectral Remote Sensing Images Salient Object Detection: The First Benchmark dataset and Baseline

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Liu, Peifu Bai, Huiyan Xu, Tingfa Wang, Jihui Chen, Huan Li, Jianan Beijing Institute of Technology Beijing100081 China Ministry of Education of China Key Laboratory of Photoelectronic Imaging Technology and System Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Big Data and Artificial Intelligence Laboratory Chongqing401151 China

The objective of hyperspectral remote sensing image salient object detection (HRSI-SOD) is to identify objects or regions that exhibit distinct spectrum contrasts with the background. This area holds significant promise for practical applications;however, progress has been limited by a notable scarcity of dedicated datasets and methodologies. To bridge this gap and stimulate further research, we introduce the first HRSI-SOD dataset, termed hyperspectral remote sensing saliency dataset (HRSSD), which includes 704 hyperspectral images (HSIs) and 5327 pixel-level annotated salient objects. The HRSSD dataset poses substantial challenges for SOD algorithms due to large-scale variation, diverse foreground-background relations, and multisalient objects. Additionally, we propose an innovative and efficient baseline model for HRSI-SOD, termed the deep spectral saliency network (DSSN). The core of DSSN is the cross-level saliency assessment block (CSAB), which performs pixel-wise attention and evaluates the contributions of multiscale similarity maps at each spatial location, effectively reducing erroneous responses in cluttered regions and emphasizes salient regions across scales. Additionally, the high-resolution fusion module (HRFM) combines bottom-up fusion strategy and learned spatial upsampling to leverage the strengths of multiscale saliency maps, ensuring accurate localization of small objects. Experiments on the HRSSD dataset robustly validate the superiority of DSSN, underscoring the critical need for specialized datasets and methodologies in this domain. Further evaluations on the HSOD-BIT and HS-SOD datasets demonstrate the generalizability of the proposed method. © 1980-2012 IEEE.

关键词： Optical remote sensing

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MCT-Net: a multi-branch hybrid CNN-transformer model for medical image segmentation

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Pattern Analysis and applications 2025年第2期28卷 1-21页

作者： Shen, Longfeng Diao, Liangjin Peng, Rui Chen, Jiacong Lu, Zhengtian Ge, Fangzhen Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB) College of Computer Science and Technology Huaibei Normal University Huaibei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Big-Data Research Center on University Management Huaibei Normal University Huaibei China

In recent years, neural networks have demonstrated substantial progress in medical image segmentation. However, accurately segmenting objects in medical images is often restricted by edge blurring, which complicates the delineation of structures of interest. The purpose of this study is to develop a multi-branch hybrid CNN-transformer model to address the issue of edge blurring in medical image segmentation. This approach enables more precise edge detection and segmentation. Integrating hybrid CNN and transformer decoders facilitates detailed recovery while preserving global structures, thus minimizing errors in regions with blurred edges. Additionally, we incorporate a hybrid attention mechanism at the model’s bottleneck to enhance feature transfer between the encoder and decoder. The model was trained using the ACDC, Synapse, and polyp datasets, with a focus on improving segmentation accuracy in regions with blurry edges. Experimental results reveal that the proposed model surpasses existing state-of-the-art methods on the ACDC, Synapse, and polyp datasets, demonstrating its superior performance and effectiveness. These results suggest that the proposed model offers a significant improvement in handling edge blurring, with potential applications in enhancing medical image analysis and diagnostics.

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Reflection of Episodes: Learning to Play Game from Expert and Self Experiences

arXiv

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arXiv 2025年

作者： Xu, Xiaojie Li, Zongyuan Lu, Chang Qi, Runnan Ni, Yanan Jiang, Lumin Liu, Xiangbei Zhang, Xuebo Fang, Yongchun Huang, Kuihua Guo, Xian Wu, Zhanghua Li, Zhenya College of Artificial Intelligence Nankai University Tianjing China Laboratory for Big Data and Decision National University of Defense Technology Changsha China Jiangsu Automation Research Institute Jiangsu China

StarCraft II is a complex and dynamic real-time strategy (RTS) game environment, which is very suitable for artificial intelligence and reinforcement learning research. To address the problem of Large Language Model(LLM) learning in complex environments through self-reflection, we propose a Reflection of Episodes(ROE) framework based on expert experience and self-experience. This framework first obtains key information in the game through a keyframe selection method, then makes decisions based on expert experience and self-experience. After a game is completed, it reflects on the previous experience to obtain new self-experience. Finally, in the experiment, our method beat the robot under the Very Hard difficulty in TextStarCraft II. We analyze the data of the LLM in the process of the game in detail, verified its effectiveness. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Toward Secure Trajectory Similarity Range Query Under Multiuser Setting

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IEEE Internet of Things Journal 2025年第11期12卷 16336-16348页

作者： Ningning Cui Xun Sun Lili Pei Mengxiang Wang Dong Wang Jianxin Li Hulin Jin Jie Cui Hong Zhong School of Computer Science and Technology Anhui University Hefei China School of Data Science and Artificial Intelligence Chang’an University Xi’an China China National Institute of Standardization Beijing China School of Information Technology Deakin University Geelong VIC Australia School of Big Data Anhui University Hefei China

The widespread availability of similarity queries over trajectory data has led to numerous real-world applications, such as traffic management and path planning. With the proliferation of trajectory data, data owners often outsource storage and computation tasks to the cloud due to limited computing and storage resources. However, this scenario raises sharp security concerns, where it is critical to ensure both the integrity of query results and privacy during query processing. Furthermore, most existing works assume a single-user setting where all query users share the same key, which may lead to query privacy leakage. Therefore, in this article, we take the first step in studying the issue of multiuser and secure trajectory similarity range query (MSRQ). Specifically, inspired by the M-tree, we propose a secure index based on a distributed two-trapdoor public-key cryptosystem (DT-PKC), called M*-tree, and devise secure protocols to support multiuser query processing. We also carefully design a filtering strategy and verification scheme to ensure fast search and integrity guarantees. Finally, we theoretically analyze the security and complexity and empirically evaluate the performance and feasibility of our proposed approach.

关键词： Trajectory Indexes Security Query processing Protocols Privacy Dogs data privacy Cloud computing Public key cryptography

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