IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2025-07-02 DOI: 10.1109/jiot.2025.3585120

Haozhen Li, Xinyu Gu, Zhiling Du, Jiahui Chen, Zhenyu Liu, Lin Zhang

引用次数: 0

An efficient Anomaly Detection Model Based on Tensor Decomposition and VARIMA for High-Dimensional Multivariate Time Series 基于张量分解和VARIMA的高维多元时间序列异常检测模型

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2025-07-02 DOI: 10.1109/jiot.2025.3570876

Cong Gao, Liru Shi, Hong Sun, Ting Ma, Yuzhe Chen, Qingqi Pei, Yanping Chen

引用次数: 0

Aerial STAR-RIS-based Symbiotic Systems with Semi-NOMA Transmission: Performance Analysis and Optimization 基于星- ris的半noma传输共生系统：性能分析与优化

IF 10.6 1区计算机科学

IEEE Internet of Things Journal Pub Date : 2025-07-02 DOI: 10.1109/jiot.2025.3585253

Thai-Hoc Vu, Khac-Tuan Nguyen, Daniel Benevides da Costa, Hyundong Shin, Sunghwan Kim

引用次数: 0

Dissecting Blockchain Network Partitioning Attacks and Novel Defense for Bitcoin and Ethereum 区块链网络分区攻击与比特币和以太坊新防御

IF 6.8 1区计算机科学

IEEE Transactions on Information Forensics and Security Pub Date : 2025-07-02 DOI: 10.1109/tifs.2025.3585468

Ruonan Chen, Dawei Li, Yang Zhang, Yizhong Liu, Jianwei Liu, Zhenyu Guan, Min Xie, Qianhong Wu, Jianying Zhou, Willy Susilo

引用次数: 0

Integrated Sensing and Communication for Intelligent Transportation: OTFS-Based Traffic Channel Reconstruction and Signal Detection 面向智能交通的集成传感与通信：基于otfs的交通通道重构与信号检测

IF 6.8 2区计算机科学

IEEE Transactions on Vehicular Technology Pub Date : 2025-07-02 DOI: 10.1109/tvt.2025.3585187

Tongmin Xiong, Yi Liao, Ying-Chang Liang

引用次数: 0

Joint Beam-Target Assignment and Communication-User Association as well as Power Allocation in a Radar-Communication Coexistence Network 雷达-通信共存网络中的联合波束目标分配、通信用户关联及功率分配

IF 6.8 2区计算机科学

IEEE Transactions on Vehicular Technology Pub Date : 2025-07-02 DOI: 10.1109/tvt.2025.3581172

Haowei Zhang, Bo Wang, Weijian Liu, Qun Zhang, Taiyong Fei, Lin Zhang, Tao Song, Youwei Meng, Baobao Liu

引用次数: 0

Space-Frequency Transmit Sequence Design for Dual-Function RadCom System 双功能RadCom系统的空频发射序列设计

IF 6.3 3区计算机科学

IEEE Wireless Communications Letters Pub Date : 2025-07-02 DOI: 10.1109/lwc.2025.3585364

Junhui Qian, Jiheng Liu, Yuhang He, Fengde Jia, Guobing Qian, Shiyuan Wang

引用次数: 0

CorNet: A deep learning method based on physics-guided and attention mechanism for predicting flow field of coronary arterial tree CorNet：一种基于物理引导和注意机制的冠状动脉树流场预测深度学习方法

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-07-02 DOI: 10.1016/j.engappai.2025.111460

Xiaoyu Liu , Shengze Cai , Hongtao Lin , Xingli Liu , Xiuhua Hu , Longjiang Zhang , Qi Gao

{"title":"CorNet: A deep learning method based on physics-guided and attention mechanism for predicting flow field of coronary arterial tree","authors":"Xiaoyu Liu , Shengze Cai , Hongtao Lin , Xingli Liu , Xiuhua Hu , Longjiang Zhang , Qi Gao","doi":"10.1016/j.engappai.2025.111460","DOIUrl":"10.1016/j.engappai.2025.111460","url":null,"abstract":"<div><div>Replacing traditional computational fluid dynamics (CFD) with deep learning techniques has become a prevalent approach for studying blood flow and diagnosing diseases. Nevertheless, no neural network has been specifically designed for tree-like blood vessel structures that can effectively capture their bifurcation patterns and branching dependencies. Coronary neural network (CorNet) was proposed for predicting the pressure field and fractional flow reserve (FFR<span><math><msub><mrow></mrow><mrow><mi>CorNet</mi></mrow></msub></math></span>) of the coronary artery tree. The novelty of this framework lies in utilizing self-attention mechanisms to address long-term spatial dependencies within the vascular tree. Our dataset comprised 295 coronary arterial trees from 273 patients, each including point clouds reconstructed from medical images and fractional flow reserve values calculated by CFD (FFR<span><math><msub><mrow></mrow><mrow><mi>CT</mi></mrow></msub></math></span>). Physical constraints were incorporated to mitigate data sparsity and enhance the interpretability of the neural network. The pressure results predicted by CorNet are consistent with the pressure calculated by CFD (mean relative error = 3.96%). There is also a good consistency between FFR<span><math><msub><mrow></mrow><mrow><mi>CT</mi></mrow></msub></math></span> and FFR<span><math><msub><mrow></mrow><mrow><mi>CorNet</mi></mrow></msub></math></span>.Compared to invasive fractional flow reserve, which is considered the “gold standard”, FFR<span><math><msub><mrow></mrow><mrow><mi>CorNet</mi></mrow></msub></math></span> demonstrates accuracy comparable to FFR<span><math><msub><mrow></mrow><mrow><mi>CT</mi></mrow></msub></math></span> (88% vs. 90%) while reducing computation time by several thousand-fold. Compared to previous studies, CorNet eliminates the need to identify specific lesion sites or manually extract geometric parameters of stenotic segments. This is the first computational method to predict hemodynamics in three-dimensional vascular tree structures using attention mechanisms within a deep learning model. We foresee that this framework will enable near-real-time flow field predictions for arterial trees and offer valuable insights for cardiovascular disease treatment.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111460"},"PeriodicalIF":7.5,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review on data-driven prognostics and health management for wind turbine systems 风力发电系统数据驱动预测与健康管理综述

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-07-02 DOI: 10.1016/j.engappai.2025.111484

Mi Yan , Siu Cheung Hui , Na Jiang , Ning Li

{"title":"A review on data-driven prognostics and health management for wind turbine systems","authors":"Mi Yan , Siu Cheung Hui , Na Jiang , Ning Li","doi":"10.1016/j.engappai.2025.111484","DOIUrl":"10.1016/j.engappai.2025.111484","url":null,"abstract":"<div><div>The wind power industry has developed rapidly due to the transformation of the global energy matrix. Prognostics and health management have attracted significant attention from industries and academia to ensure the reliability and safety of wind turbines, reduce maintenance costs, and increase productivity. Currently, most wind farms use supervisory control and data acquisition systems to collect, record, and store wind turbine operating data. Data-driven prognostics and health management of wind turbine systems have become the most commonly used methods for real-time monitoring and fault alarm prediction. Although some reviews of studies on data-driven prognostics and health management for wind turbine systems are available, they are structured mainly based on the processing tasks or key components of wind turbine systems, and have overlooked the challenges of data quality behind these processes. Different from previous works, this paper reviews the current developments of data-driven prognostics and health management for wind turbine systems from the perspective of data challenges, which include data availability, data labeling, data scarcity, data imbalance, data inconsistency, dynamic data and fleet-based data. In this paper, we discuss some open datasets and current data challenges in data-driven prognostics and health management for wind turbine systems, and review the related methods proposed for addressing these data issues. We then provide practical applications to help engineers understand data-driven prognostics and health management better. Finally, future research directions are suggested for further work on data-driven prognostics and health management for wind turbine systems.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"159 ","pages":"Article 111484"},"PeriodicalIF":7.5,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144523214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Safe and time-efficient exploration in Reinforcement Learning-based control of a vehicle thermal systems 基于强化学习的车辆热系统控制的安全性和时效性探索

IF 5.4 2区计算机科学

Control Engineering Practice Pub Date : 2025-07-02 DOI: 10.1016/j.conengprac.2025.106458

Prasoon Garg , Emilia Silvas , Frank Willems

{"title":"Safe and time-efficient exploration in Reinforcement Learning-based control of a vehicle thermal systems","authors":"Prasoon Garg , Emilia Silvas , Frank Willems","doi":"10.1016/j.conengprac.2025.106458","DOIUrl":"10.1016/j.conengprac.2025.106458","url":null,"abstract":"<div><div>Reinforcement Learning has achieved huge success with various applications in controlled environments. However, limited application is seen in real-world applications due to challenges in guaranteeing safe system operation, required experiment time, and required a-priori system knowledge and models in existing methods. In this work, we propose a novel exploration method, which addresses simultaneously the challenges associated with safe and time-efficient exploration while dealing with system uncertainty. This method integrates a reciprocal Control Barrier Function and an on-line learned Gaussian Process Regression model. For safe system operation, we leverage the information from the reciprocal Control Barrier Function to limit the step size of the agent’s actions, when approaching the safety boundary. To make this exploration process time-efficient, we use the information gain metrics that are calculated using the estimation of the action-values by an on-line learned Gaussian Process Regression model to determine the direction of the agent’s actions. We demonstrate the potential of our exploration method in simulation and on a vehicle test-bench for efficiency-optimal calibration of a thermal management system for battery electric vehicles. To quantify the benefits in terms of safety, optimality, and time efficiency, we benchmark our exploration method with random and uncertainty-driven exploration methods in a simulation environment. For the studied test case, the proposed exploration method satisfies the safety constraint and it converges to within 1.25% of the true optimal action while requiring 28% and 18% lower experiment time compared to the random and uncertainty-driven exploration methods, respectively. For the proposed method, its performance is also demonstrated on a vehicle test bench. Experimental results show that the maximal thermal system efficiency is realized within 2% of the true optimum, while effectively dealing with the safety constraints.</div></div>","PeriodicalId":50615,"journal":{"name":"Control Engineering Practice","volume":"164 ","pages":"Article 106458"},"PeriodicalIF":5.4,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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