计算机科学最新文献

{"title":"Convolutional neural networks for accurate and robust noninvasive pressure measurements in sealed systems","authors":"D. Pereira,&nbsp;M. Prisbrey,&nbsp;E.S. Davis,&nbsp;P. Vakhlamov,&nbsp;A. Saini,&nbsp;C. Chavez,&nbsp;C. Pantea,&nbsp;J. Greenhall","doi":"10.1016/j.engappai.2025.111729","DOIUrl":"10.1016/j.engappai.2025.111729","url":null,"abstract":"<div><div>Accurate noninvasive pressure measurement in sealed systems is essential for many applications in process control, chemical transport, and chemical safety assessment. This paper introduces a pioneering approach to enhance pressure estimation using Acoustic Resonance Spectroscopy (ARS), a promising technique that analyzes the system's exterior vibrations to infer internal pressure. Despite its potential, extracting precise measurements from noisy ARS data remains challenging. We present a novel method employing a convolutional neural network (CNN) to improve the accuracy and generalizability of the pressure prediction. The CNN performance is compared against benchmark ARS processing methods such as k-nearest neighbors (kNN) and a peak tracking method. The models were trained and tested using labeled data from laboratory-controlled tests on vacuum vessels subjected to varying internal pressures. Evaluation under various signal processing and field measurement scenarios was conducted to assess the accuracy and generalizability of the proposed models. The CNN outperformed other models by significant margins, achieving a mean absolute error (MAE) of approximately 15 Torr, compared to approximately 30 Torr for kNN and 60 Torr for peak tracking. Furthermore, we tested the generalizability of the models by introducing synthetic data augmentations such as spectrum shift and dropout, which approximate real-world measurement errors in ARS measurements. We found that the CNN maintained high accuracy under shifting and dropout data scenarios, showcasing its robustness, while the other models showed larger increases in error. This suggests CNN as a strong candidate for noninvasive pressure measurements, as well as a wide range of other applications detectible via spectral measurements, such as corrosion detection, structural integrity monitoring, or reaction tracking in closed vessels, offering high accuracy and resilience to environmental fluctuations.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111729"},"PeriodicalIF":7.5,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711690","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}

{"title":"Self-adaptive uncertainty modeling and relation reasoning for cross-modal egocentric human action recognition in sports","authors":"Zhangzhi Zhao ,&nbsp;Chen He ,&nbsp;Xun Jiang ,&nbsp;Xing Xu","doi":"10.1016/j.compeleceng.2025.110583","DOIUrl":"10.1016/j.compeleceng.2025.110583","url":null,"abstract":"<div><div>Human action recognition (HAR) aims to interpret human actions from provided data and has found widespread application in the sports domain. However, HAR in sports presents unique challenges, as egocentric video often lacks complete body information. Additionally, individual variations and aleatoric errors during data collection can further corrupt cross-modal interactions. For example, the movement habits of different athletes, differences in body types, and changes in the wearing positions of sensors (such as IMUs) can lead to individual differences and random noise in inertial data. To address these issues, we emphasize the distinctiveness of human actions in sports and propose a novel framework for robust feature embedding and enhanced cross-modal interaction, termed <em><strong>S</strong>elf-adaptive <strong>U</strong>ncertainty <strong>M</strong>odeling and <strong>R</strong>elation <strong>R</strong>easoning (<strong>SUMRR</strong>)</em>, specifically designed for egocentric human action recognition in sports. Our approach begins with the sampling of robust unimodal features from an uncertainty perspective, which helps mitigate individual variance and reduce aleatoric errors. Furthermore, by meticulously modeling relation-level interactions across modalities, we construct robust cross-modal features that significantly enhance recognition performance. We evaluate our proposed SUMRR framework on notable cross-modal, egocentric sports datasets using various backbone architectures and achieve a remarkable 89.49% recognition precision. Experimental results demonstrate the portability and robustness of our SUMRR for egocentric human recognition in sports.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"127 ","pages":"Article 110583"},"PeriodicalIF":4.0,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A unified multi-task model for leaf disease region detection and segmentation","authors":"Tian Zhang ,&nbsp;Yanfeng Lu ,&nbsp;Chenshuang Li ,&nbsp;Rundong Hong ,&nbsp;Hengqiang Su ,&nbsp;Ce Yang ,&nbsp;Ning Yang ,&nbsp;Haiying Zhang ,&nbsp;Changji Wen","doi":"10.1016/j.engappai.2025.111853","DOIUrl":"10.1016/j.engappai.2025.111853","url":null,"abstract":"<div><div>Crop diseases challenge agricultural production. Pinpointing disease spots, assessing infection areas, and gauging infection severity are crucial for effective disease control. However, lesion variations, blurred boundaries, and small, dense lesions make precise detection and segmentation difficult. This paper presents an end-to-end unified multi-task model based on Detection Transformer (DETR) for leaf disease region detection and segmentation. It integrates Convolutional Neural Networks (CNNs) and Transformer, uses the Contextual Transformer Network (CoTNet) for feature extraction, and incorporates innovative mechanisms like box-attention and reference window update. Additionally, we have devised a novel instance segmentation head. This head effectively addresses the misclassification issue between minute disease spots and leaf surfaces. Experiments show the model achieves Average Precision (<span><math><mrow><mtext>AP</mtext></mrow></math></span>). The Average Precision of Bounding Box (<span><math><mrow><msup><mtext>AP</mtext><mtext>box</mtext></msup></mrow></math></span>) of 73.9 %, the Average Precision of Mask (<span><math><mrow><msup><mtext>AP</mtext><mtext>mask</mtext></msup></mrow></math></span>) of 68.2 %, the Average Precision of Small-sized Bounding Box (<span><math><mrow><msubsup><mtext>AP</mtext><mi>s</mi><mtext>box</mtext></msubsup></mrow></math></span>) of 29.0 %, and the Average Precision of Small-sized Mask (<span><math><mrow><msubsup><mtext>AP</mtext><mi>s</mi><mtext>mask</mtext></msubsup></mrow></math></span>) of 27.1 % for four diseases, with detection recall reaching 76.8 % and segmentation recall reaching 73.4 %. Meanwhile. The model architecture demonstrates practical feasibility with 40.1 million (m) parameters and 169 Giga Floating-point Operations Per Second (GFLOP) computational complexity. The accuracy of disease grading reaches 92.07 %. In this study, a model based on Artificial Intelligence (AI) was implemented to address the challenges in leaf disease detection and segmentation. The proposed model, which integrates object detection and instance segmentation tasks, can be applied to accurately identify and grade leaf diseases, providing support for disease control strategies in agriculture.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111853"},"PeriodicalIF":7.5,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711692","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}

{"title":"Continual Learning Inspired by Brain Functionality: A Comprehensive Survey","authors":"Muhammad Azeem Aslam,&nbsp;Muhammad Hamza,&nbsp;Zhu Shuangtong,&nbsp;Hu Hongfei,&nbsp;Xu Wei,&nbsp;Muhammad Irfan,&nbsp;Zheng Jiangbin,&nbsp;Saba Aslam","doi":"10.1155/int/3145236","DOIUrl":"https://doi.org/10.1155/int/3145236","url":null,"abstract":"<div>\u0000 <p>Neural network–based models have shown tremendous achievements in various fields. However, standard AI-based systems suffer from catastrophic forgetting when undertaking sequential learning of multiple tasks in dynamic environments. Continual learning has emerged as a promising approach to address catastrophic forgetting. It enables AI systems to learn, transfer, augment, fine-tune, and reuse knowledge for future tasks. The techniques used to achieve continual learning are inspired by the learning processes of the human brain. In this study, we present a comprehensive review of research and recent developments in continual learning, highlighting key contributions and challenges. We discuss essential functions of the biological brain that are pivotal for achieving continual learning and map these functions to the recent machine-learning methods to aid understanding. Additionally, we offer a critical review of five recent types of continual learning methods inspired by the biological brain. We also provide empirical results, analysis, challenges, and future directions. We hope that this study will benefit both general readers and the research community by offering a complete picture of the latest developments in this field.</p>\u0000 </div>","PeriodicalId":14089,"journal":{"name":"International Journal of Intelligent Systems","volume":"2025 1","pages":""},"PeriodicalIF":5.0,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/int/3145236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A personalized human–machine cooperative approach with transformer-based recognition for longitudinal and lateral control of intelligent vehicles","authors":"Yan Ma ,&nbsp;Jingjing Xie ,&nbsp;Liang He ,&nbsp;Kailong Zhang ,&nbsp;Xiongmei Zeng ,&nbsp;Quan Ouyang ,&nbsp;Danwei Wang","doi":"10.1016/j.engappai.2025.111816","DOIUrl":"10.1016/j.engappai.2025.111816","url":null,"abstract":"<div><div>Human–machine interaction brings challenges for vehicle control design due to individual differences, a personalized cooperative approach with driving style recognition is proposed to achieve lateral and longitudinal control of intelligent vehicles in this paper. An improved Transformer-based method with an unsupervised pre-training and window-based multi-head self-attention is proposed to enhance the recognition accuracy and speed of driving styles, and thereby to capture the controller parameters under various driving styles. To achieve the lateral and longitudinal control of human-machine cooperative system, an integrated driver–vehicle model is established by considering driving styles and vehicle planar dynamics. Then, a Takagi–Sugeno fuzzy controller is developed to handle time-varying parameters and eliminate human-machine conflicts. Especially, stability conditions are exploited by Lyapunov arguments to achieve the control objective. Finally, simulation results show that the designed Transformer-based method has better classification accuracy and computational efficiency than other baselines on the same dataset. Based on recognition results, the designed controller can effectively improve the driving performance under various driving styles and time-varying parameters compared with other methods.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111816"},"PeriodicalIF":7.5,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711049","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}

{"title":"Cost-effective and real-time landslide monitoring method based on ultra-wideband using ultra-wideband transformer neural network","authors":"Yu Si ,&nbsp;Zhaofeng He ,&nbsp;Fan Zhang ,&nbsp;Xiaoyun Sun ,&nbsp;Yong Chen ,&nbsp;Haiqing Zheng","doi":"10.1016/j.engappai.2025.111851","DOIUrl":"10.1016/j.engappai.2025.111851","url":null,"abstract":"<div><div>Landslides rank among the most destructive natural phenomena, posing substantial risks to human safety, infrastructure, and ecological systems. Their frequent occurrence in topographically complex regions demands urgent development in real-time monitoring solutions. Current monitoring methodologies, however, are constrained by prohibitive costs, limited temporal resolution, and high-power consumption. These factors create substantial implementation barriers to implementing landslide monitoring systems. To address these limitations, this study proposes an economical real-time monitoring method leveraging ultra-wideband (UWB) technology for landslide detection. The implementation of a dual-Microcontroller Unit (MCU) distributed hardware architecture enables high-accuracy ranging capabilities and high real-time performance. To enhance the spatial resolution of UWB systems in landslide monitoring, we propose an optimized sensor deployment structure and a novel deep learning architecture called Ultra-wideband Transformer (UWBformer). This network utilizes differential UWB-ranging data to predict spatial displacement at monitoring locations, specifically the displacement distance, horizontal angle, and pitch angle. UWBformer incorporates a spatial multi-head attention mechanism and a dual-channel architecture processing both time-domain and frequency-domain features. It is specifically designed to mitigate ranging error propagation and enhance prediction stability by focusing on relative distance changes rather than absolute ranging accuracy. Empirical results demonstrate UWBformer's superior performance in predicting displacement distance, horizontal angle, and pitch angle, outperforming the conventional Caffery-Taylor (C-T) localization approach and established deep learning benchmarks. Field tests incorporated <span><math><mrow><mn>3</mn><mi>σ</mi></mrow></math></span> criterion and Kalman filtering alongside to pre-process raw measurements, thereby enhancing data stability. Comprehensive validation across field tests demonstrates UWBformer's capability to maintain accurate spatial displacement estimation under harsh environments.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111851"},"PeriodicalIF":7.5,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711691","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}

{"title":"Social impact of recommendation algorithm in crisis: Forming algorithmic experience through group information interaction and algorithm task fit","authors":"Xiwei Wang ,&nbsp;Siguleng Wuji ,&nbsp;Mali Li ,&nbsp;Yutong Liu ,&nbsp;Ran Luo","doi":"10.1016/j.ipm.2025.104323","DOIUrl":"10.1016/j.ipm.2025.104323","url":null,"abstract":"<div><div>This study explores the societal impact of recommendation algorithms during crisis situations, specifically examining the dynamic interaction between users, algorithms, and tasks in disaster contexts. By integrating the Task-Technology Fit (TTF) model, Stress and Coping theory, and Social Identity theory, the research constructs a comprehensive analytical framework to better understand group information behavior and algorithmic experiences. Addressing the theoretical limitations of existing research that separates human-algorithm interaction from human-human interaction, this study innovatively incorporates algorithmic performance and interaction purpose into a unified analysis model. Through an experimental design, the study manipulates the \"personalized content recommendation\" feature by enabling and disabling it to observe how different algorithm configurations influence user perceptions and behaviors. The findings reveal that a strong task-technology fit enhances group information interaction intentions, with personalized content recommendations playing a dual moderating role. They not only bridge perceived disaster threats and task-technology fit but also impact the relationship between perceived disaster threat, perceived interactive support, and overall algorithm experience. This study contributes to the theoretical expansion of task-technology fit applications in disaster contexts and provides practical insights for designing recommendation algorithms in crisis situations. It highlights the importance of algorithmic forming and contextual fitness in improving public engagement and crisis response efficiency.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"63 1","pages":"Article 104323"},"PeriodicalIF":7.4,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forecasting biogas potential in Türkiye’s Central Anatolia region with artificial neural networks and geographical information system-based analysis","authors":"Halil Şenol ,&nbsp;Emre Çolak ,&nbsp;Volkan Başer","doi":"10.1016/j.engappai.2025.111843","DOIUrl":"10.1016/j.engappai.2025.111843","url":null,"abstract":"<div><div>The global utilization of biogas energy generated via anaerobic digestion has been steadily increasing, emphasizing the importance of assessing biomass resources and estimating biogas generation potential before implementing large-scale production. This study focuses on Türkiye’s Central Anatolia Region (CAR), which had a bovine population of 3.95 million in 2021, accounting for over 5 % of Europe’s bovine population. A key novelty of this research lies in the estimation of the region’s bovine manure-based biogas potential (BMBP) using 2021 data, which revealed a remarkable increase to 3162 GW-hour (GWh) compared to 1370 GWh in 2004. This significant growth not only highlights the region’s untapped renewable energy potential but also underscores the critical role of advanced methodologies in accurately assessing and forecasting energy resources over time. To forecast future potential, artificial neural networks (ANNs) were employed to estimate the BMBP of all provinces in the CAR up to 2035. Among these, Konya is projected to have the highest BMBP in 2035, with 919 GWh, contributing approximately 50 % of the electricity consumed by its habitations and 4.5 times the electricity expended for lighting. Additionally, Arc Geographical Information System (ArcGIS) was utilized to perform geographical and temporal analyses of the region, providing a comprehensive spatial perspective. Both the ArcGIS findings and the BMBP findings highlight the significant contributions of BMBP to renewable energy resources in the CAR, offering critical insights for shaping regional energy policies. Future research should expand to include biogas potential from other agricultural wastes in the region.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"160 ","pages":"Article 111843"},"PeriodicalIF":7.5,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711568","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}

{"title":"Development of a type of cross-scale piezoelectric screw motor operating in quasi-static and resonant states","authors":"Xiaolong Shu ,&nbsp;Yifang Zhang ,&nbsp;Jianfa Lin ,&nbsp;Bingliang Guan ,&nbsp;Min Qian ,&nbsp;Qiaosheng Pan","doi":"10.1016/j.mechatronics.2025.103391","DOIUrl":"10.1016/j.mechatronics.2025.103391","url":null,"abstract":"<div><div>In this study, a cross-scale piezoelectric screw motor was proposed, designed, fabricated and tested. The proposed motor can operate in quasi-static and resonant states, and achieves cross-scale motion output through mode conversion. The motor is comprised of a stator and a rotor, with the same internal and external screws. The motor’s motion is achieved by friction between the stator and the rotor. Structure and working principle of the motor are introduced. The vibration modes of the stator in different modes were studied through finite element analysis. The motor's dynamic model was established. Finally, the prototype was fabricated, and the output performance was tested. Experimental results demonstrate a minimum resolution of 12.5 nm and a maximum load capacity of 12 N in quasi-static mode. When operating in resonant state, the motor achieves a maximum speed of 10.4mm/min (32.8 rpm), the maximum load capacity is 30 N and the maximum efficiency is 0.36 % when the prototype is rotated forward. When the motor is reversed, the maximum speed is 20.8 mm/min (65.5 rpm), the load capacity reaches 33 N, and the maximum efficiency is 0.46 %. The proposed piezoelectric motor promotes the development of cross-scale actuators.</div></div>","PeriodicalId":49842,"journal":{"name":"Mechatronics","volume":"110 ","pages":"Article 103391"},"PeriodicalIF":3.1,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CIL: Cyber security index level of cyber-physical systems: A robust stochastic game approach using MITRE ATT&CK framework","authors":"Zahra Azimi,&nbsp;Ahmad Afshar","doi":"10.1016/j.sysconle.2025.106207","DOIUrl":"10.1016/j.sysconle.2025.106207","url":null,"abstract":"<div><div>This paper addresses the challenge of cyber security assessment in interdependent cyber-physical systems (CPS) under model uncertainty and partial observability by developing a Hybrid Zero-Sum Multi-Stage Robust Stochastic-Bayesian (HZMRS) game model. In the cyber layer, HZMRS models attack infiltration dynamics by incorporating tactics and techniques from the ICS MITRE ATT&amp;CK framework, thereby systematically enhancing the modeling of adversarial progression. For the power network as the physical layer, HZMRS employs the Transient Energy Function (TEF) approach, instead of linear approximations and small-signal stability criteria, to effectively capture the severe transient disturbances triggered by DoS attacks. To solve the HZMRS game, we propose a Robust Accelerated Value Iteration (RAVI) algorithm that ensures robust performance against worst-case transition probabilities and employs prioritized sweeping to accelerate convergence. We also provide a proof of convergence for this algorithm. Unlike classic algorithms, RAVI is designed to handle model uncertainties arising from zero-day vulnerabilities and incomplete information about attacker capabilities. Based on the outcome of the HZMRS, we introduce a novel metric called <em>Cyber Security Index Level</em> (<span><math><mrow><mi>C</mi><mi>I</mi><mi>L</mi></mrow></math></span>), which quantifies the probability of successful physical layer intrusion after breaching the cyber layer. The proposed model is validated through simulations conducted on the IEEE 9-bus power network, considering an attack scenario adapted from the BlackEnergy v3 malware. Comparative results show that RAVI achieves successful convergence under uncertainty, and the derived security metrics offer improved reliability and practical relevance for real-world CPS applications.</div></div>","PeriodicalId":49450,"journal":{"name":"Systems & Control Letters","volume":"204 ","pages":"Article 106207"},"PeriodicalIF":2.1,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}