IEEE Systems Journal最新文献_第10页

Conceptual, Mathematical, and Analytical Foundations for Mission Engineering and System of Systems Analysis 任务工程和系统分析的概念、数学和分析基础

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2024-07-25 DOI: 10.1109/JSYST.2024.3409231

Ali K. Raz;Mohammed Bhuyian;Jose L. Bricio-Neto;Christopher Santos;Daniel Maxwell

{"title":"Conceptual, Mathematical, and Analytical Foundations for Mission Engineering and System of Systems Analysis","authors":"Ali K. Raz;Mohammed Bhuyian;Jose L. Bricio-Neto;Christopher Santos;Daniel Maxwell","doi":"10.1109/JSYST.2024.3409231","DOIUrl":"10.1109/JSYST.2024.3409231","url":null,"abstract":"Mission engineering (ME) is an emerging approach to designing and analyzing configurations of system-of-systems (SoS) for accomplishing one or more missions. ME seeks to flexibly leverage SoS capabilities and dynamically adapt their configuration to meet evolving mission needs. SoS configurations today, however, remain static and are carefully designed to accomplish a mission. The problem we address in this article is developing modeling and analysis techniques for flexible integration and adaptive selection of potential SoS configurations to achieve multiple missions with an overarching agility in the execution space. We propose a foundational framework for ME, complete with semantics and grammar to represent the ME design space (MEDS), along with a set of logical and mathematical modeling approaches that lends the MEDS to robust SoS analytical methods. Specifically, the framework proposes development of a mission-focused ontology and domain-specific language to enable consistent semantic representation of MEDS, which is then logically evaluated for spatial and temporal consistency in forming SoS configurations using set-based design principles and Allen's interval algebra. The resulting feasible SoS configurations are then evaluated for mission success using graph theory and multiattribute utility theory. The application of the framework is demonstrated on a simplified and notional sense-decide-effect problem for flexibly accomplishing multiple missions with SoS.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1549-1559"},"PeriodicalIF":4.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10609409","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}

引用次数: 0

Multiagent UAV-Aided URLLC Mobile Edge Computing Systems: A Joint Communication and Computation Optimization Approach 多代理无人机辅助 URLLC 移动边缘计算系统：通信与计算联合优化方法

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2024-07-23 DOI: 10.1109/JSYST.2024.3426096

Yijiu Li;Dang Van Huynh;Van-Linh Nguyen;Dac-Binh Ha;Hans-Jürgen Zepernick;Trung Q. Duong

{"title":"Multiagent UAV-Aided URLLC Mobile Edge Computing Systems: A Joint Communication and Computation Optimization Approach","authors":"Yijiu Li;Dang Van Huynh;Van-Linh Nguyen;Dac-Binh Ha;Hans-Jürgen Zepernick;Trung Q. Duong","doi":"10.1109/JSYST.2024.3426096","DOIUrl":"10.1109/JSYST.2024.3426096","url":null,"abstract":"In this article, we consider a multiagent unmanned aerial vehicle (UAV)-aided system employing mobile edge computing (MEC) servers to satisfy the requirement of ultrareliable low latency communications (URLLCs) in intelligent autonomous transport applications. Our MEC architecture aims to guarantee quality-of-service (QoS) by investigating task offloading and caching implemented in the nearby UAVs. To enhance system performance, we propose to minimize the network energy consumption by jointly optimizing communication and computation parameters. This includes decisions on task offloading, edge caching policies, uplink transmission power, and the processing rates of users. Given the nonconvex nature and high computational complexity of this optimization problem, an alternating optimization algorithm is proposed, where the three subproblems of caching, offloading, and power allocation are solved in an alternating manner. Our simulation results demonstrate the efficacy of the proposed method, showcasing significant reductions in user energy consumption and optimal resource allocation. This work serves as an initial exploration of the transformative potential of cutting-edge technologies, such as UAVs, URLLC, and MEC, in shaping the future landscape of intelligent autonomous transport systems.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 4","pages":"1828-1838"},"PeriodicalIF":4.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782841","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}

引用次数: 0

A Hybrid Traffic Flow Forecasting and Risk-Averse Decision Strategy for Hydrogen-Based Integrated Traffic and Power Networks 氢基综合交通和电力网络的混合交通流预测和风险规避决策策略

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2024-07-19 DOI: 10.1109/JSYST.2024.3420237

Mohammad Jadidbonab;Hussein Abdeltawab;Yasser Abdel-Rady I. Mohamed

{"title":"A Hybrid Traffic Flow Forecasting and Risk-Averse Decision Strategy for Hydrogen-Based Integrated Traffic and Power Networks","authors":"Mohammad Jadidbonab;Hussein Abdeltawab;Yasser Abdel-Rady I. Mohamed","doi":"10.1109/JSYST.2024.3420237","DOIUrl":"10.1109/JSYST.2024.3420237","url":null,"abstract":"This article develops an operational framework for hydrogen microgrids integrated with traffic and power networks to optimize decision-making strategies. It tackles challenges in traffic flow prediction exacerbated by the rise of electric and hydrogen vehicles, which significantly affect power systems and hydrogen microgrids. We employ a risk-averse information gap decision theory to ensure secure operations under uncertain traffic conditions. Our framework utilizes a hybrid deep-learning forecasting method, combining a 1-D convolutional neural network and bidirectional long short-term memory to accurately predict traffic flow for origin–destination pairs in Edmonton, Canada. Enhanced by a Bayesian algorithm for hyperparameter tuning, this method improves prediction accuracy and operational efficiency. The framework also integrates operational strategies with urban travel plans to optimize charging for electric and hydrogen vehicles, thereby enhancing energy efficiency and supporting thermal demands. Validated in Edmonton's power and traffic networks, our framework enhances optimal charging, routing, and operation conditions, surpassing traditional methods to maintain secure operations during outages and improve the overall system robustness.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1581-1592"},"PeriodicalIF":4.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737277","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}

引用次数: 0

DT-LSMAS: Digital Twin-Assisted Large-Scale Multiagent System for Healthcare Workflows DT-LSMAS：用于医疗保健工作流程的数字双胞胎辅助大规模多代理系统

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2024-07-18 DOI: 10.1109/JSYST.2024.3424259

Abdullah Lakhan;Mazin Abed Mohammed;Dilovan Asaad Zebar;Karrar Hameed Abdulkareem;Muhammet Deveci;Haydar Abdulameer Marhoon;Jan Nedoma;Radek Martinek

{"title":"DT-LSMAS: Digital Twin-Assisted Large-Scale Multiagent System for Healthcare Workflows","authors":"Abdullah Lakhan;Mazin Abed Mohammed;Dilovan Asaad Zebar;Karrar Hameed Abdulkareem;Muhammet Deveci;Haydar Abdulameer Marhoon;Jan Nedoma;Radek Martinek","doi":"10.1109/JSYST.2024.3424259","DOIUrl":"10.1109/JSYST.2024.3424259","url":null,"abstract":"Digital healthcare has garnered much attention from academia and industry for health and well-being. Many digital healthcare architectures based on large-scale edge and cloud multiagent systems (LSMASs) have recently been presented. The LSMAS allows agents from different institutions to work together to achieve healthcare processing goals for users. This article presents a digital twin large-scale multiagent strategy (DT-LSMAS) comprising mobile, edge, and cloud agents. The DT-LSMAS comprised different schemes for healthcare workflows, such as added healthcare workflows, application partitioning, and scheduling. We consider healthcare workflows with different biosensor data such as heartbeat, blood pressure, glucose monitoring, and other healthcare tasks. We partitioned workflows into mobile, edge, and cloud agents to meet the deadline, total time, and security of workflows in large-scale edge and cloud nodes. To handle the large-scale resource for real-time sensor data, we suggested digital twin-enabled edge nodes, where delay-sensitive workflow tasks are scheduled and executed under their quality of service requirements. Simulation results show that the DT-LSMAS outperformed in terms of total time by 50%, minimizing the risk of resource leakage and deadline missing during scheduling on heterogeneous nodes. In conclusion, the DT-LSMAS obtained optimal results for workflow applications.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 4","pages":"1883-1892"},"PeriodicalIF":4.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141746436","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}

引用次数: 0

LiDAR From the Sky: UAV Integration and Fusion Techniques for Advanced Traffic Monitoring 来自天空的激光雷达：用于高级交通监控的无人机集成与融合技术

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2024-07-17 DOI: 10.1109/JSYST.2024.3425541

Baya Cherif;Hakim Ghazzai;Ahmad Alsharoa

{"title":"LiDAR From the Sky: UAV Integration and Fusion Techniques for Advanced Traffic Monitoring","authors":"Baya Cherif;Hakim Ghazzai;Ahmad Alsharoa","doi":"10.1109/JSYST.2024.3425541","DOIUrl":"10.1109/JSYST.2024.3425541","url":null,"abstract":"Light detection and ranging (LiDAR) technology's expansion within the autonomous vehicles industry has rapidly motivated its application in numerous growing areas, such as smart cities, agriculture, and renewable energy. In this article, we propose an innovative approach for enhancing aerial traffic monitoring solutions through the application of LiDAR technology. The objective is to achieve precise and real-time object detection and tracking from aerial perspectives by integrating unmanned aerial vehicles with LiDAR sensors, thereby creating a potent Aerial LiDAR (A-LiD) solution for traffic monitoring. First, we develop a novel deep learning algorithm based on pointvoxel-region-based convolutional neural network (RCNN) to conduct road user detection. Then, we implement advanced LiDAR fusion techniques, including raw data fusion and decision data fusion, in an endeavor to improve detection performance through the combined analysis of multiple A-LiD systems. Finally, we employ the unscented Kalman Filter for object tracking and position estimation. We present selected simulation outcomes to demonstrate the effectiveness of our proposed solution. A comparison between the two fusion methods shows that raw point cloud fusion provides better detection performance than decision fusion.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1639-1650"},"PeriodicalIF":4.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737276","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}

引用次数: 0

Multiagent Detection System Based on Spatial Adaptive Feature Aggregation 基于空间自适应特征聚合的多代理检测系统

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2024-07-15 DOI: 10.1109/JSYST.2024.3423752

Hongbo Wang;He Wang;Xin Zhang;Runze Ruan;Yueyun Wang;Yuyu Yin

{"title":"Multiagent Detection System Based on Spatial Adaptive Feature Aggregation","authors":"Hongbo Wang;He Wang;Xin Zhang;Runze Ruan;Yueyun Wang;Yuyu Yin","doi":"10.1109/JSYST.2024.3423752","DOIUrl":"10.1109/JSYST.2024.3423752","url":null,"abstract":"Detection systems based on computer vision play important roles in Large-Scale Multiagent Systems. In particular, it can automatically locate and identify key objects and enhance intelligent collaboration and coordination among multiple agents. However, classification and localization in object detection may produce inconsistent prediction results due to different learning focus. Therefore, we propose a Spatial Decoupling and Boundary Feature Aggregation Network (SDBA-Net) to achieve spatial decoupling and task alignment. SDBA-Net includes a spatially sensitive region-aware module (SSRM) and a boundary feature aggregation module (BFAM). SSRM predicts sensitive regions for each task while minimizing computational cost. BFAM extracts valuable boundary features within sensitive regions and aligns them with corresponding anchors. These two modules are combined to spatially decouple and align the features of two tasks. In addition, a significance dependency complementary module (SDCM) is introduced. It enables SSRM to quickly adjust the sensitive region of the classification task to the significant feature region. Experiments are conducted on a large-scale complex real-world dataset MS COCO (Lin et al., 2014). The results show that SDBA-Net achieves better results than the baselines. Using the ResNet-50 backbone, our method improves the average precision (AP) of the single-stage detector VFNet by 1.0 point (from 41.3 to 42.3). In particular, when using the Res2Net-101-DCN backbone, SDBA-Net achieves an AP of 51.8 on the MS COCO test-dev.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 4","pages":"1849-1859"},"PeriodicalIF":4.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141718853","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}

引用次数: 0

RESP: A Real-Time Early Stage Prediction Mechanism for Cascading Failures in Smart Grid Systems RESP：智能电网系统级联故障的早期实时预测机制

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2024-07-08 DOI: 10.1109/JSYST.2024.3420950

Ali Salehpour;Irfan Al-Anbagi

{"title":"RESP: A Real-Time Early Stage Prediction Mechanism for Cascading Failures in Smart Grid Systems","authors":"Ali Salehpour;Irfan Al-Anbagi","doi":"10.1109/JSYST.2024.3420950","DOIUrl":"10.1109/JSYST.2024.3420950","url":null,"abstract":"Cascading failures resulting from cyberattacks are one of the main concerns in smart grid systems. The use of machine learning (ML) algorithms has become more relevant in identifying and forecasting such cascading failures. In this article, we develop a real-time early stage mechanism (RESP) to predict cascading failures due to cyberattacks in smart grid systems using supervised ML algorithms. We use a realistic methodology to create a dataset to train the algorithms and predict the state of all components of the system after failure propagation. We utilize the extreme gradient boosting (XGBoost) algorithm and consider the features of both the power and communication networks to improve the failure prediction accuracy. We use the real-time digital simulator (RTDS) to simulate the power system and make the system more applicable. We evaluate the mechanism's effectiveness using the IEEE 14-bus system, which results in the XGBoost algorithm achieving a 96.25% prediction accuracy rate in random attacks. We show that RESP can accurately predict the state of a power system in the early stages of failure propagation using real-time data. Furthermore, we show that RESP can identify the initial failure locations, which can aid in further protection plans and decisions.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1593-1604"},"PeriodicalIF":4.0,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570300","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}

引用次数: 0

Heterogeneous Unknown Multiagent Systems of Different Relative Degrees: A Distributed Optimal Coordination Design 不同相对度的异构未知多代理系统：分布式优化协调设计

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2024-07-01 DOI: 10.1109/JSYST.2024.3417255

Hossein Noorighanavati Zadeh;Reza Naseri;Mohammad Bagher Menhaj;Amir Abolfazl Suratgar

{"title":"Heterogeneous Unknown Multiagent Systems of Different Relative Degrees: A Distributed Optimal Coordination Design","authors":"Hossein Noorighanavati Zadeh;Reza Naseri;Mohammad Bagher Menhaj;Amir Abolfazl Suratgar","doi":"10.1109/JSYST.2024.3417255","DOIUrl":"10.1109/JSYST.2024.3417255","url":null,"abstract":"This study delves into the distributed optimal coordination (DOC) problem, where a network comprises agents with different relative degrees. Each agent is equipped with a private cost function. The goal is to steer these agents towards minimizing the global cost function, which aggregates their individual costs. Existing literature often leans on known agent dynamics, which may not faithfully represent real-world scenarios. To bridge this gap, we delve into the DOC problem within a network of linear time-invariant (LTI) agents, where the system matrices remain entirely unknown. Our proposed solution introduces a novel distributed two-layer control policy: the top layer endeavors to find the minimizer and generates tailored reference signals for each agent, while the bottom layer equips each agent with an adaptive controller to track these references. Key assumptions include strongly convex private cost functions with local Lipschitz gradients. Under these conditions, our control policy guarantees asymptotic consensus on the global minimizer within the network. Moreover, the control policy operates fully distributedly, relying solely on private and neighbor information for execution. Theoretical insights are substantiated through simulations, encompassing both numerical and practical examples involving speed control of a multimotor network, thereby affirming the efficacy of our approach in practical settings.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1570-1580"},"PeriodicalIF":4.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503104","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}

引用次数: 0

Interval-Partitioned and Correlated Uncertainty Set Based Robust Optimization of Microgrid 基于区间划分和相关不确定性集的微电网鲁棒优化

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2024-06-26 DOI: 10.1109/JSYST.2024.3406698

Zuqing Zheng;Guo Chen;Zixiang Shen

{"title":"Interval-Partitioned and Correlated Uncertainty Set Based Robust Optimization of Microgrid","authors":"Zuqing Zheng;Guo Chen;Zixiang Shen","doi":"10.1109/JSYST.2024.3406698","DOIUrl":"10.1109/JSYST.2024.3406698","url":null,"abstract":"The dramatic increase in renewable energy sources has created significant uncertainties in the operation of power systems. This article investigates a day-ahead economic dispatch problem for a typical microgrid, considering the uncertainties of renewable energy sources and load demand. An interval-partitioned and temporal-correlated uncertainty set based robust optimization model is proposed, which allows a more accurate characterization of the distribution of uncertainties. The proposed robust optimization model can reduce the conservativeness of the optimal solution by avoiding scenarios that are low-probability or even impossible in reality. The model is then decomposed into a master problem and a nonlinear bi-level subproblem and solved by the \u0000<inline-formula><tex-math>$C & CG$</tex-math></inline-formula>\u0000 method and Big-M method. However, this method requires the introduction of a large number of auxiliary variables and related constraints, significantly increasing the computation burden. To tackle this problem, an efficient solution method, Improved-\u0000<inline-formula><tex-math>$C & CG$</tex-math></inline-formula>\u0000, is developed by integrating an outer approximation method into the \u0000<inline-formula><tex-math>$C & CG$</tex-math></inline-formula>\u0000 method. Finally, case studies verify the effectiveness of the proposed model, uncertainty set, and solution methods.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"18 3","pages":"1516-1527"},"PeriodicalIF":4.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503103","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}

引用次数: 0

IEEE Systems Journal Information for Authors IEEE 系统期刊作者信息

IF 4 3区计算机科学

IEEE Systems Journal Pub Date : 2024-06-20 DOI: 10.1109/JSYST.2024.3380721

引用次数: 0