Pub Date : 2025-02-13DOI: 10.1109/JSYST.2025.3533880
Zihan Chen;Shengda Tang
This study delves into the distributed linear quadratic tracking (LQT) problem within interconnected multimachine power systems (IMMPSs), and proposes a model-free policy iteration (PI) algorithm based on data compression technology for designing sparse controllers that align with the actual communication links in IMMPSs. Specifically, to address the practical limitation that communication links between subsystems of IMMPSs may be unavailable, we first formulate a sparse LQT problem in which the sparse patterns of controllers match the actual communication links. Meanwhile, in order to be applicable to real-time applications while overcoming model uncertainty caused by parameter variability common in IMMPSs models, we subsequently develop a data compression-based model-free PI algorithm for the abovementioned sparse LQT problem. The main advantages of this algorithm over existing algorithms for IMMPSs control are threefold: first, it has the ability to operate without a prior knowledge of system model, second, its embedded data compression significantly reduces the time consumption for controller design, making it suitable for real-time applications, and third, it designs controllers based on actual communication links, making it practical for applications where communication infrastructure may be constrained. Finally the efficacy of the proposed algorithm is verified through the IEEE 39-bus New England Power System.
{"title":"Data Compression-Based Model-Free PI Algorithm for Sparse LQT Control in Interconnected Multimachine Power Systems","authors":"Zihan Chen;Shengda Tang","doi":"10.1109/JSYST.2025.3533880","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3533880","url":null,"abstract":"This study delves into the distributed linear quadratic tracking (LQT) problem within interconnected multimachine power systems (IMMPSs), and proposes a model-free policy iteration (PI) algorithm based on data compression technology for designing sparse controllers that align with the actual communication links in IMMPSs. Specifically, to address the practical limitation that communication links between subsystems of IMMPSs may be unavailable, we first formulate a sparse LQT problem in which the sparse patterns of controllers match the actual communication links. Meanwhile, in order to be applicable to real-time applications while overcoming model uncertainty caused by parameter variability common in IMMPSs models, we subsequently develop a data compression-based model-free PI algorithm for the abovementioned sparse LQT problem. The main advantages of this algorithm over existing algorithms for IMMPSs control are threefold: first, it has the ability to operate without a prior knowledge of system model, second, its embedded data compression significantly reduces the time consumption for controller design, making it suitable for real-time applications, and third, it designs controllers based on actual communication links, making it practical for applications where communication infrastructure may be constrained. Finally the efficacy of the proposed algorithm is verified through the IEEE 39-bus New England Power System.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"176-187"},"PeriodicalIF":4.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637951","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}
Pub Date : 2025-02-06DOI: 10.1109/JSYST.2025.3532511
Wenfeng Hu;Yulong Jiang;Biao Luo;Tingwen Huang
This article analyzes the consensus of double-integrator multiagent systems subjected to constant disturbances. First, we propose a proportional–integral (PI)-based consensus protocol with a linear integrator, under which the system can achieve consensus without any steady-state error. By directly analyzing the closed-loop system matrix, a necessary and sufficient condition for parameter selection is derived. Subsequently, to overcome the phase lag defect of the linear integrator, we propose a new PI-based protocol with a split-path nonlinear integrator. The nonlinear consensus protocol can not only ensure that the system achieves asymptotic consensus, but also enhance the transient performance with respect to overshoot. Finally, some simulation comparisons are conducted to validate the effectiveness of the proposed protocols.
{"title":"Consensus of Double-Integrator Multiagent Systems Under Disturbances: Two Types of PI-Based Protocols","authors":"Wenfeng Hu;Yulong Jiang;Biao Luo;Tingwen Huang","doi":"10.1109/JSYST.2025.3532511","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3532511","url":null,"abstract":"This article analyzes the consensus of double-integrator multiagent systems subjected to constant disturbances. First, we propose a proportional–integral (PI)-based consensus protocol with a linear integrator, under which the system can achieve consensus without any steady-state error. By directly analyzing the closed-loop system matrix, a necessary and sufficient condition for parameter selection is derived. Subsequently, to overcome the phase lag defect of the linear integrator, we propose a new PI-based protocol with a split-path nonlinear integrator. The nonlinear consensus protocol can not only ensure that the system achieves asymptotic consensus, but also enhance the transient performance with respect to overshoot. Finally, some simulation comparisons are conducted to validate the effectiveness of the proposed protocols.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"87-95"},"PeriodicalIF":4.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637970","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}
Pub Date : 2025-02-03DOI: 10.1109/JSYST.2025.3527627
Jianquan Zhu;Haojiang Huang;Wenmeng Zhao;Qiyuan Zheng;Wenhao Liu;Jiajun Chen;Yuhao Luo
The growth of distributed renewable energy in microgrids (MGs) raises challenges in energy management and consumption. As an innovative approach, peer-to-peer (P2P) energy trading offers a promising solution to address these problems. In this article, we propose a bi-layer decentralized (BLD) optimization algorithm for P2P energy trading in multimicrogrids (MMG). Compared with traditional optimization algorithms that are single-layer decentralized (i.e., decentralizing solely at inter-MG trading and typically centralizing prosumers within the MG), the proposed algorithm achieves bi-layer decentralization (i.e., decentralization extends to both inter-MG and intra-MG trading). In this way, the BLD algorithm can significantly preserve the information privacy and decision independence of prosumers. In addition, the proposed algorithm can efficiently manage power flow in a decentralized manner at both layers, whereas existing decentralized algorithms frequently neglect this critical feature. Furthermore, an accelerated BLD (ABLD) algorithm is proposed to address time-consuming issues in this nested P2P trading for MMG. Numerical simulations on various test systems demonstrate the effectiveness of the proposed algorithm. The results indicate that the error of the proposed algorithm is below 0.1%. In addition, BLD requires 15602.03 s to converge with 560 prosumers, while ABLD only requires 228.05 s.
{"title":"Bi-Layer Decentralized Optimization Algorithm for Peer-to-Peer Energy Trading in Multimicrogrids","authors":"Jianquan Zhu;Haojiang Huang;Wenmeng Zhao;Qiyuan Zheng;Wenhao Liu;Jiajun Chen;Yuhao Luo","doi":"10.1109/JSYST.2025.3527627","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3527627","url":null,"abstract":"The growth of distributed renewable energy in microgrids (MGs) raises challenges in energy management and consumption. As an innovative approach, peer-to-peer (P2P) energy trading offers a promising solution to address these problems. In this article, we propose a bi-layer decentralized (BLD) optimization algorithm for P2P energy trading in multimicrogrids (MMG). Compared with traditional optimization algorithms that are single-layer decentralized (i.e., decentralizing solely at inter-MG trading and typically centralizing prosumers within the MG), the proposed algorithm achieves bi-layer decentralization (i.e., decentralization extends to both inter-MG and intra-MG trading). In this way, the BLD algorithm can significantly preserve the information privacy and decision independence of prosumers. In addition, the proposed algorithm can efficiently manage power flow in a decentralized manner at both layers, whereas existing decentralized algorithms frequently neglect this critical feature. Furthermore, an accelerated BLD (ABLD) algorithm is proposed to address time-consuming issues in this nested P2P trading for MMG. Numerical simulations on various test systems demonstrate the effectiveness of the proposed algorithm. The results indicate that the error of the proposed algorithm is below 0.1%. In addition, BLD requires 15602.03 s to converge with 560 prosumers, while ABLD only requires 228.05 s.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"282-293"},"PeriodicalIF":4.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645242","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}
Pub Date : 2025-02-03DOI: 10.1109/JSYST.2025.3528976
Weiwei Liu;Wenxuan Hu;Wei Jing;Lanxin Lei;Lingping Gao;Yong Liu
Autonomous vehicles trained through multiagent reinforcement learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with diverse driving styles and personalities, particularly in highly interactive situations. This is because conventional MARL algorithms usually operate under the assumption of fully cooperative behavior among all agents and focus on maximizing team rewards during training. To address this issue, we introduce the personality modeling network (PeMN), which includes a cooperation value function and personality parameters to model the varied interactions in high-interactive scenarios. The PeMN also enables the training of a background traffic flow with diverse behaviors, thereby improving the performance and generalization of the ego vehicle. Our extensive experimental studies, which incorporate different personality parameters in high-interactive driving scenarios, demonstrate that the personality parameters effectively model diverse driving styles and that policies trained with PeMN demonstrate better generalization than traditional MARL methods.
{"title":"Learning to Model Diverse Driving Behaviors in Highly Interactive Autonomous Driving Scenarios With Multiagent Reinforcement Learning","authors":"Weiwei Liu;Wenxuan Hu;Wei Jing;Lanxin Lei;Lingping Gao;Yong Liu","doi":"10.1109/JSYST.2025.3528976","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3528976","url":null,"abstract":"Autonomous vehicles trained through multiagent reinforcement learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with diverse driving styles and personalities, particularly in highly interactive situations. This is because conventional MARL algorithms usually operate under the assumption of fully cooperative behavior among all agents and focus on maximizing team rewards during training. To address this issue, we introduce the personality modeling network (PeMN), which includes a cooperation value function and personality parameters to model the varied interactions in high-interactive scenarios. The PeMN also enables the training of a background traffic flow with diverse behaviors, thereby improving the performance and generalization of the ego vehicle. Our extensive experimental studies, which incorporate different personality parameters in high-interactive driving scenarios, demonstrate that the personality parameters effectively model diverse driving styles and that policies trained with PeMN demonstrate better generalization than traditional MARL methods.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"317-326"},"PeriodicalIF":4.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645182","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}
Pub Date : 2025-01-31DOI: 10.1109/JSYST.2025.3528748
Elham Hojati;Alan Sill;Susan Mengel;Sayed Mohammad Bagher Sayedi;Argenis Bilbao;Konrad Schmitt
Maintaining service reliability, achieving sustainability, and ensuring energy efficiency are crucial for green high-performance computing systems. Balancing these factors is a key challenge for modern green data centers. In this research, we propose a monitoring, visualization, and management system for green data centers (MVMS-GDC). Our comprehensive automated platform includes “monitoring system” and “rules and policy management” modules. The “monitoring system” gathers and visualizes time series data from all resources of a green data center, tracking essential metrics and measurements. It audits green energy, microgrid, climate conditions, workloads, hardware, CPU and memory usage, cluster component health, computing node activities, and network health and quality metrics. The “rules and policy management” module defines and enforces policies to balance resources, ensuring a reliable, sustainable, scalable, and efficient computing environment. We implemented, tested, and evaluated the MVMS-GDC system using green energy at the Zephyr data center located at the GLEAMM site. Our results demonstrate at least a 4.9% improvement in performance, at least a 4% increase in energy efficiency, and a reduction of at least 4% in job losses. The MVMS-GDC system also enhances scalability by employing a policy machine for each compute node, which automates power state control (on, off, or hibernation) based on monitoring observations. This automated approach ensures efficient and dynamic scaling, making MVMS-GDC suitable for large and highly distributed data centers. Overall, MVMS-GDC provides a robust solution for balancing energy availability and computational needs, optimizing performance, and maintaining energy efficiency in green data centers.
{"title":"A Comprehensive Monitoring, Visualization, and Management System for Green Data Centers","authors":"Elham Hojati;Alan Sill;Susan Mengel;Sayed Mohammad Bagher Sayedi;Argenis Bilbao;Konrad Schmitt","doi":"10.1109/JSYST.2025.3528748","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3528748","url":null,"abstract":"Maintaining service reliability, achieving sustainability, and ensuring energy efficiency are crucial for green high-performance computing systems. Balancing these factors is a key challenge for modern green data centers. In this research, we propose a monitoring, visualization, and management system for green data centers (MVMS-GDC). Our comprehensive automated platform includes “monitoring system” and “rules and policy management” modules. The “monitoring system” gathers and visualizes time series data from all resources of a green data center, tracking essential metrics and measurements. It audits green energy, microgrid, climate conditions, workloads, hardware, CPU and memory usage, cluster component health, computing node activities, and network health and quality metrics. The “rules and policy management” module defines and enforces policies to balance resources, ensuring a reliable, sustainable, scalable, and efficient computing environment. We implemented, tested, and evaluated the MVMS-GDC system using green energy at the Zephyr data center located at the GLEAMM site. Our results demonstrate at least a 4.9% improvement in performance, at least a 4% increase in energy efficiency, and a reduction of at least 4% in job losses. The MVMS-GDC system also enhances scalability by employing a policy machine for each compute node, which automates power state control (<sc>on</small>, <sc>off</small>, or hibernation) based on monitoring observations. This automated approach ensures efficient and dynamic scaling, making MVMS-GDC suitable for large and highly distributed data centers. Overall, MVMS-GDC provides a robust solution for balancing energy availability and computational needs, optimizing performance, and maintaining energy efficiency in green data centers.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"119-129"},"PeriodicalIF":4.0,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645229","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}
Pub Date : 2025-01-29DOI: 10.1109/JSYST.2025.3529502
Liwen Weng;Zhitao Li;Lixin Gao
In the affine formation maneuver control for multiagent systems with a leader-follower structure, external disturbances easily cause deformation of the formation shape, thereby affecting a series of cascading reactions. Hence, robustness against disturbances in affine formation control has been a subject remaining to be determined. To address this problem, a continuous robust controller is introduced in this study, leveraging the robust integral of the sign of the error (RISE) approach, aiming to suppress external disturbances while ensuring efficient convergence speed of the system under various collective formation maneuvers determined by the leader. The controller is designed to handle two types of disturbance models: one involving general disturbances and the other considering time-delay disturbances. It consists of formation tracking terms based on stress matrices and graph theory, as well as disturbance suppression terms utilizing RISE. Sufficient conditions for the stability of affine formations under both types of disturbances are derived. By designing a Lyapunov function that integrates a class-P function, the exponential stability of the closed-loop system is rigorously demonstrated. Finally, simulation results are provided to verify the performance and effectiveness of the proposed control strategy.
在具有领导者-追随者结构的多代理系统的仿射编队操纵控制中，外部干扰很容易导致编队形状变形，从而影响一系列级联反应。因此，仿射编队控制中抗扰动的鲁棒性一直是一个有待确定的课题。针对这一问题，本研究利用误差符号的鲁棒积分（RISE）方法，引入了一种连续鲁棒控制器，旨在抑制外部干扰，同时确保系统在领导者决定的各种集体编队机动下的有效收敛速度。控制器设计用于处理两类干扰模型：一类涉及一般干扰，另一类考虑时延干扰。它包括基于应力矩阵和图论的编队跟踪项，以及利用 RISE 的干扰抑制项。推导出了两种干扰下仿射编队稳定性的充分条件。通过设计一个整合 P 类函数的 Lyapunov 函数，闭环系统的指数稳定性得到了严格证明。最后，还提供了仿真结果，以验证所提控制策略的性能和有效性。
{"title":"Affine Formation Maneuver Control of Multiagent Systems With Disturbances Based on RISE Controller","authors":"Liwen Weng;Zhitao Li;Lixin Gao","doi":"10.1109/JSYST.2025.3529502","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3529502","url":null,"abstract":"In the affine formation maneuver control for multiagent systems with a leader-follower structure, external disturbances easily cause deformation of the formation shape, thereby affecting a series of cascading reactions. Hence, robustness against disturbances in affine formation control has been a subject remaining to be determined. To address this problem, a continuous robust controller is introduced in this study, leveraging the robust integral of the sign of the error (RISE) approach, aiming to suppress external disturbances while ensuring efficient convergence speed of the system under various collective formation maneuvers determined by the leader. The controller is designed to handle two types of disturbance models: one involving general disturbances and the other considering time-delay disturbances. It consists of formation tracking terms based on stress matrices and graph theory, as well as disturbance suppression terms utilizing RISE. Sufficient conditions for the stability of affine formations under both types of disturbances are derived. By designing a Lyapunov function that integrates a class-P function, the exponential stability of the closed-loop system is rigorously demonstrated. Finally, simulation results are provided to verify the performance and effectiveness of the proposed control strategy.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"142-151"},"PeriodicalIF":4.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637865","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}
Pub Date : 2025-01-29DOI: 10.1109/JSYST.2025.3529720
Alireza Meidani;Moein Abedini;Majid Sanaye-Pasand
The security of distance relays in zone 3 is critical for preventing catastrophic blackouts, especially during stressed conditions. However, the challenge lies in distinguishing between short-circuit faults and system disturbances, e.g., load encroachment, power swing, out-of-step condition, and voltage instability, which can mimic fault conditions and lead to improper relay operation. To address this issue, this article presents a new local protection method by employing three innovative indices: the out-of-step detection index, the positive-sequence impedance angle (PSIA) index, and the superimposed PSIA index. These indices are derived from theoretical principles and are tailored to effectively discriminate between faults and system disturbances. As a major novelty, the determination of indices threshold values is extracted based on the theoretical relationships and further validated through comprehensive static and dynamic analyses. The algorithm's effectiveness is evaluated under various stressed conditions through multiple simulations, showing its ability to distinguish between stressed conditions and short-circuit faults, even in the presence of inverter-based resources. This leads to enhanced power system reliability and security. The proposed method is also cost-effective as it is implemented in the local distance relay, eliminating the need for synchrophasor devices and communication infrastructure.
{"title":"Enhancing Performance of Distance Relay Zone 3 Under Stressed Conditions Using an Angle-Based Algorithm","authors":"Alireza Meidani;Moein Abedini;Majid Sanaye-Pasand","doi":"10.1109/JSYST.2025.3529720","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3529720","url":null,"abstract":"The security of distance relays in zone 3 is critical for preventing catastrophic blackouts, especially during stressed conditions. However, the challenge lies in distinguishing between short-circuit faults and system disturbances, e.g., load encroachment, power swing, out-of-step condition, and voltage instability, which can mimic fault conditions and lead to improper relay operation. To address this issue, this article presents a new local protection method by employing three innovative indices: the out-of-step detection index, the positive-sequence impedance angle (PSIA) index, and the superimposed PSIA index. These indices are derived from theoretical principles and are tailored to effectively discriminate between faults and system disturbances. As a major novelty, the determination of indices threshold values is extracted based on the theoretical relationships and further validated through comprehensive static and dynamic analyses. The algorithm's effectiveness is evaluated under various stressed conditions through multiple simulations, showing its ability to distinguish between stressed conditions and short-circuit faults, even in the presence of inverter-based resources. This leads to enhanced power system reliability and security. The proposed method is also cost-effective as it is implemented in the local distance relay, eliminating the need for synchrophasor devices and communication infrastructure.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"270-281"},"PeriodicalIF":4.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645231","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}
Pub Date : 2025-01-29DOI: 10.1109/JSYST.2025.3534536
{"title":"List of Reviewers 2024","authors":"","doi":"10.1109/JSYST.2025.3534536","DOIUrl":"https://doi.org/10.1109/JSYST.2025.3534536","url":null,"abstract":"","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 1","pages":"3-6"},"PeriodicalIF":4.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10857644","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637972","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}
Pub Date : 2025-01-28DOI: 10.1109/JSYST.2024.3524025
Mohammad Hijji;Kaleem Ullah;Mohammed Alwakeel;Ahmed Alwakeel;Fahad Aradah;Faouzi Alaya Cheikh;Muhammad Sajjad;Khan Muhammad
This article introduces a comprehensive multiagent prototype system designed to enhance the autonomous navigation capabilities of vehicles by incorporating numerous sensors and components. The system includes features such as an ultrasonic sensor for precise distance measurement, a specially crafted “SonarSpinner” with a wide 160° field of view, a vision sensor for road sign detection and steering angle estimation, and an infrared obstacle avoidance sensor, operating with a predefined obstacle-halting threshold of 150 cm. Data collection for model training and evaluation is accomplished using a virtual reality-based self-driving car simulator, resulting in a diverse dataset. The proposed system harnesses knowledge distillation from teacher models, such as the Nvidia model, to create a lightweight student model optimized for real-time inference while retaining competitive accuracy. Additionally, a custom Haar cascade classifier enhances traffic sign detection capabilities. The distilled model is then converted to TensorFlow Lite for efficient deployment on edge devices within autonomous vehicles, ensuring a secure and efficient navigation system. This innovative approach combines optimized distillation methods with specialized classifiers to facilitate the development of robust and real-time self-driving car systems.
{"title":"Multiagent Sensor Integration and Knowledge Distillation System for Real-Time Autonomous Vehicle Navigation","authors":"Mohammad Hijji;Kaleem Ullah;Mohammed Alwakeel;Ahmed Alwakeel;Fahad Aradah;Faouzi Alaya Cheikh;Muhammad Sajjad;Khan Muhammad","doi":"10.1109/JSYST.2024.3524025","DOIUrl":"https://doi.org/10.1109/JSYST.2024.3524025","url":null,"abstract":"This article introduces a comprehensive multiagent prototype system designed to enhance the autonomous navigation capabilities of vehicles by incorporating numerous sensors and components. The system includes features such as an ultrasonic sensor for precise distance measurement, a specially crafted “SonarSpinner” with a wide 160° field of view, a vision sensor for road sign detection and steering angle estimation, and an infrared obstacle avoidance sensor, operating with a predefined obstacle-halting threshold of 150 cm. Data collection for model training and evaluation is accomplished using a virtual reality-based self-driving car simulator, resulting in a diverse dataset. The proposed system harnesses knowledge distillation from teacher models, such as the Nvidia model, to create a lightweight student model optimized for real-time inference while retaining competitive accuracy. Additionally, a custom Haar cascade classifier enhances traffic sign detection capabilities. The distilled model is then converted to TensorFlow Lite for efficient deployment on edge devices within autonomous vehicles, ensuring a secure and efficient navigation system. This innovative approach combines optimized distillation methods with specialized classifiers to facilitate the development of robust and real-time self-driving car systems.","PeriodicalId":55017,"journal":{"name":"IEEE Systems Journal","volume":"19 2","pages":"382-391"},"PeriodicalIF":4.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308399","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}
Pub Date : 2025-01-22DOI: 10.1109/JSYST.2024.3524880
Mahmoud A. Albreem;Alaa H. Al Habbash;Ammar M. Abu-Hudrouss;M.-T. EL Astal
The design of low-complexity data detection techniques for massive multiple-input multiple-output (mMIMO) systems continues to attract considerable industry and research attention due to the critical need to achieve the right tradeoff between complexity and performance, especially with the signed quadrature spatial modulation (SQSM) scheme. However, the SQSM scheme attains a high spectral efficiency and good performance but suffers from a high computational complexity with mMIMO systems. In this article, we propose an efficient low-complexity detection framework for the SQSM scheme. Sparsity detection is amalgamated in this article with minimum mean-square error (MMSE) detector by decoupling the detection of the real and imaginary vector streams. Unfortunately, the MMSE-based detector has a matrix inversion which incurs a high computational complexity. Therefore, we employed several iterative methods; i.e., conjugate gradient and Gauss–Seidel, to avoid the exact matrix inversion, and hence, the computational complexity is significantly reduced. Moreover, the proposed framework can host other iterative methods such as the JA, successive over relaxation, accelerated over relaxation, Neumann series, Newton iteration, two-parameter over relaxation, and Richardson methods. The proposed detection framework attains a significant complexity reduction with a small or insignificant deterioration in the performance.
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