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3rd conference on Fully Actuated System Theory and Applications (FASTA)

作者： Deng, Yaoming Zhou, Min Chen, Minghua Yang, Zaiyue Southern Univ Sci & Technol Shenzhen Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350373707;9798350373691

Recently, Deep Reinforcement Learning (DRL) methods have become increasingly common in voltage control problems of power distribution systems. However, existing DRL methods either lack a theoretical guarantee of voltage stability or cannot maintain their optimality when the network topology changes. This paper proposes a DRL algorithm based on Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) framework, Flexible-MATD3, which can maintain the system stability and optimality even when the topology and impedance parameters change. We proposed a partial monotonic neural network to constrain the policy search space of our DRL algorithm so that our policy can always guarantee safety. In addition, the experimental results show that Flexible-MATD3 achieves better performance than the baseline controllers for different network topologies and line impedance parameters without retraining the neural network.

关键词： Reinforcement Learning Distribution system Voltage control Lyapunov stability

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SDN Enabled L2 Switch Implementation and Its performance Evaluation Through P4 Programming 1

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20th International conference on Computing and Information Technology (IC2IT2)

作者： Pudasaini, Sakar Dawadi, Babu R. Ghimire, Roshani Pau, Giovanni Sapkota, Binod Tribuvan Univ Inst Engn Pulchowk Campus Kathmandu Nepal Tribhuvan Univ Adv Coll Engn & Management Kathmandu Nepal Kore Univ Enna Fac Engn & Architecture Enna Italy

ISBN: (数字)9783031585616

ISBN: (纸本)9783031585609;9783031585616

The concept of SDN (Software-Defined networking) originally came with the purpose of disaggregating control plane and data plane. However, current scope of SDN has surpassed the original goal. It sets out to introduce programmability in the control plane for better visibility and manageability of networking devices. The idea has been recently extended to program the data plane. In the present scenario P4 (Programming Protocol-Independent Packet Processors) stands as the de-facto language to program the data plane. In this study, we implemented an L2 (Layer 2) switch and evaluated it performance using P4 programming language. The performance of the switch was evaluated in terms of throughput (Mbps) and latency (ms). The throughput was found to be nominal as the switch was implemented in software and the latency for first packet was also high since the switch's table was empty. However, once the table was filled, subsequent packets suffered very low latency.

关键词： SDN Programmable Data Plane P4 Data Plane control Plane network Programmability

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Development and Practice of Aviation Maintenance Training Simulator 43

Development and Practice of Aviation Maintenance Training Si...

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43rd Chinese control conference, CCC 2024

作者： Wenzhu, Sun Zhile, Wang Xiao, Xu Naval Aeronautical University Qingdao Branch Qingdao266041 China

ISBN: (纸本)9789887581581

This article aims to develop an aviation maintenance training simulator, and has designed an efficient maintenance simulator. Firstly, the development architecture of the system was described in detail, and the tool chain for system development and the roles of each tool were introduced. Then, the development process was described, including image model data collection, 3D model construction, virtual scene construction, network communication construction and teaching control system development. The exquisite system architecture makes the simulator have advantages such as fast response speed, small time delay, stable performance, and realistic visual effects. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Delay control systems

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A Novel Hybrid control Strategy for Grid-Connected PV-Battery systems under Adverse Environmental Conditions 13

A Novel Hybrid Control Strategy for Grid-Connected PV-Batter...

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13th IEEE International conference on Communication systems and network Technologies, CSNT 2024

作者： Pandey, Pawan Kumar Gyan Ganga Institute of Technology and Science Dept. of Electrical Engineering Jabalpur India

ISBN: (纸本)9798350305463

This work presents a new controller for gridconnected PV/Battery systems that combines a bidirectional battery controller with a voltage source converter (VSC) to solve problems caused by power fluctuations on the DC-link. The hybrid VSC controller is developed to maintain a continuous power supply on the DC-link. It does this by responding to changes in environmental factors and load fluctuations in the PV-generated output, which in turn triggers dynamic adjustments to the inverter pulse signals. To increase system stability, the bidirectional battery controller orchestrates the charge and discharge operations in collaboration with the hybrid VSC, optimizing power delivery to the grid. Through a detailed performance study of the proposed controller, faults on both the DC and AC sides of the system are analyzed and efficiently addressed. The controller proved important in enhancing power quality and decreasing harmonic distribution. Furthermore, the hybrid controller's performance is assessed under several one-day environmental situations. Ultimately, the results of simulation done in MATLAB/Simulink corroborate the usefulness of the suggested controller in providing optimal performance in the described grid-connected PV/Battery system. © 2024 IEEE.

关键词： controllers

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GAT-based Deep Reinforcement Learning Algorithm for Real-time Task Scheduling on Multicore Platform 36

GAT-based Deep Reinforcement Learning Algorithm for Real-tim...

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36th Chinese control and Decision conference (CCDC)

作者： Zhao, Mingjun Mo, Lei Liu, Ji Han, Jie Niu, Dan Southeast Univ Sch Automat Nanjing 210096 Peoples R China Hithink RoyalFlush Informat Network Co Ltd Hangzhou 310023 Peoples R China

ISBN: (纸本)9798350387780;9798350387797

The scheduling of real-time and dependent tasks on multicore systems plays an important role in influencing system performance. During the scheduling process, multiple constraints should be taken into account, e.g., task dependency, real-time response, and energy efficiency. The complex task scheduling problem makes it difficult to balance solution quality and computation time. Existing works either use time-consuming methods to find optimal solutions or get feasible solutions through heuristic methods. In this paper, we propose a GAT (graph attention network)-based deep reinforcement learning (DRL) algorithm to solve task scheduling problems. This method combines the benefits of deep learning (DL) networks and reinforcement learning (RL) algorithms. It can achieve adaptive learning and adjust information features by constructing the GAT to model the dependencies between tasks. At the same time, we use the soft actor-critic (SAC) algorithm to optimize the task scheduling policy to minimize the makespan of scheduling results. The experimental results show that our method outperforms other scheduling methods regarding scheduling efficiency (the quality of task scheduling problem) and algorithm running time.

关键词： Deep reinforcement learning graph attention network multicore system dependent real-time task task scheduling

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control Theory-Inspired Model Design for Single Image De-Raining

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IEEE TRANSACTIONS ON CIRCUITS AND systems ii-EXPRESS BRIEFS 2022年第2期69卷 649-653页

作者： Zhou, Man Wang, Rujing Chinese Acad Sci Inst Intelligent Machines Intelligent Agr Engn Lab Anhui Prov Hefei 230031 Peoples R China Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China

While existing image de-raining methods have obtained satisfactory performance, lacking theoretical basis prevents them from further improvement. In this brief, we propose an efficient image de-raining network empowered by control theory to address this issue. To the best of our knowledge, it is the first attempt to integrate the control theory into image de-raining model design. Different from previous methods that roughly construct complicated neural networks, our method is theoretical and provides a new perspective for model design. Specifically, by mimicking the signal processing flow of state observable and controllable standard form, we expand them to two network modules, named C-IM and O-IM with every component in the module one-to-one corresponding to each operation involved in state equation. Equipped with C-IM and O-IM, our proposed network could efficiently explore and exploit the features of rain streaks in a recursive fashion. Extensive experiments demonstrate that control theory equipped method is capable of obtaining promising performance and speeding up the model training.

关键词： Mathematical model control theory Training Rain Circuits and systems Convolution Feature extraction control theory single image de-raining neural network

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Real-Time Intrusion Detection Method Based on Frequency Domain Simplified Dual-PINN 24

Real-Time Intrusion Detection Method Based on Frequency Doma...

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8th International conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhang, Bohan Wang, Zhiwen Miao, Wei Lanzhou University of Science and Technology Gansu Lanzhou China Gansu Ziguang Intelligent Transportation and Control Technology Co Gansu Lanzhou China

ISBN: (纸本)9798400710094

To address the limitations of traditional intrusion detection systems that do not conform to the physical laws governing actual industrial processes, we propose a frequency domain simplified Dual-Physical-Informational Neural network (Dual-PINN) methodology. This methodology reduces training costs and enhances real-time detection performance, allowing the lightweight detection system to be applicable in resource-constrained industrial environments. Additionally, drawing on the concept of meta-learning, we have partitioned the model into a teacher network and a student network to enhance the robustness of detection. Experimental results demonstrate that our method achieves over 99% detection accuracy on a real-world network traffic dataset, with an average detection time of 0.098 seconds per data packet, effectively enhancing the performance and efficiency of intrusion detection. © 2024 Copyright held by the owner/author(s).

关键词： network intrusion

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Surveying Lightweight Neural network Architectures for Enhanced Mobile performance 5th

Surveying Lightweight Neural Network Architectures for Enhan...

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5th International conference on Smart Applications and Data Analysis for Smart Cyber Physical systems

作者： Briouya, Hasnae Briouya, Asmae Choukri, Ali Ibn Tofail Univ Comp Sci Lab Fac Sci Kenitra Morocco

ISBN: (纸本)9783031770425;9783031770432

This study explores the creation of lightweight neural network architectures specifically designed for mobile devices. The main focus is on MobileNet, but we also examine other models like ShuffleNet, SqueezeNet, EfficientNet, MnasNet, and NASNet Mobile. The main challenge is to carefully evaluate these models to find the best balance between simplicity, accuracy, and efficiency, considering the diverse needs of mobile applications. We assess important metrics such as simplicity, accuracy, and efficiency within these architectures. The main goal is to provide guidance to practitioners in choosing the most suitable architecture. We offer insights into the trade-offs and advantages of each model through both quantitative and qualitative assessments. We consider factors like computational resources, accuracy requirements, and processing speed. The findings of this research provide valuable insights for practitioners who want to make informed decisions about the best neural network architecture for mobile devices. This guidance is tailored to their specific computational limitations and application requirements, helping them make strategic decisions in this specialized field.

关键词： MobileNet Lightweight neural networks Computational efficiency Semantic segmentation Mobile applications

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A modified fractional-order-based future search algorithm for performance enhancement of a PEMFC-based CCHP

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2023年第4期45卷 12821-12843页

作者： Lu, Biao Razmjooy, Navid Xuzhou Kinder Garten Teachers Coll Informat Network Ctr Xuzhou Jiangsu Peoples R China SIMATS Saveetha Sch Engn Dept Comp Sci & Engn Div Res & Innovat Chennai Tamil Nadu India Xuzhou Kinder Garten Teachers Coll Informat Network Ctr Xuzhou 221004 Jiangsu Peoples R China SIMATS Saveetha Sch Engn Dept Comp Sci & Engn Div Res & Innovat Chennai 602105 Tamil Nadu India

Load control and cost optimization are considered to be crucial in tri-generation or combined cooling, heating, and power (CCHP) systems. In this study, an inventive CCHP system employs an FC system as its first mover and includes a heat exchanger, a heat recovery, as well as an auxiliary boiler, an electric chiller, and an absorption chiller. The electrical grid is linked to this system. The idea here is to maximize the system's performance from a financial perspective and to make the annual expenditure of the system minimum over a 20-year period that is considered as the cycle life-span. It is a multi-objective optimization problem which is optimized using a newly introduced metaheuristic optimization method and a Fractional-order future search optimizer. The findings of this study are used to divine an ideal configuration of the CCHP. Finally, to demonstrate the higher efficiency of the suggested method, a comparison should be conducted among the optimization results of the fractional-order-based future search algorithm, the results of Non-dominated Sorting Genetic Algorithm ii (NSGA-ii), and standard future search algorithms in previous studies. Based on the results presented, the proposed Fractional-order Future Search Algorithm (FOFSA) was able to optimize the performance of a PEMFC-based CCHP system more effectively than conventional methods. The system's exergy efficiency was found to decrease from 52% at 793 mA/cm2 current density to 36% at 1000 mA/cm2 current density. However, with the application of FOFSA, the suggested optimal system had a higher exergy efficiency of 41.6% and a yearly cost of $2765, resulting in the maximum annual greenhouse gas (GHG) reduction of 4.48E6 g. Therefore, in summary, the proposed FOFSA yielded an optimized CCHP system configuration that had higher energy efficiency, lower annual cost, and reduced GHG emissions. These findings highlight the effectiveness of the FOFSA method in optimizing the performance of PEMFC-based CCHP s

关键词： Combined heating cooling and power cycle proton exchange membrane fuel cell economic performance annual cost fractional-order future search algorithm

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TinyLid: a RISC-V accelerated Neural network For LiDAR Contaminant Classification in Autonomous Vehicle 21

TinyLid: a RISC-V accelerated Neural Network For LiDAR Conta...

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21st ACM International conference on Computing Frontiers (CF)

作者： Jati, Grafika Molan, Martin Barchi, Francesco Bartolini, Andrea Acquaviva, Andrea Univ Bologna DEI Dept Bologna Italy

ISBN: (纸本)9798400705977

LiDAR plays a critical role in autonomous car perception. Hence, the robustness of LiDAR data is imperative. However, malfunctions resulting from sensor cover contaminants are unavoidable and can lead to erroneous data that slowly degrade performance. As such, detecting contamination in LiDAR is essential but remains an open challenge due to varying contaminant types, changing properties over time, and deployment aspects. Automatic classification of the contaminants would enable the automated response (like cleaning the sensor) to ensure the integrity of the data collected by the LiDAR sensor. To minimize the effect on the whole vehicle perception system, the contamination classification has to be performed near the sensor and in a computationally efficient way. To address these challenges, we have conducted a feasibility study of developing an efficient near-sensor machine learning-powered contaminant classification running on the RISC-V architecture. This paper proposes a lightweight 2D CNN network, TinyLid, trained to classify contaminants based on the most comprehensive LiDAR contaminant dataset. The results presented in this paper show that the proposed solution can achieve high classification performance while being computationally efficient and running on hardware with negligible power consumption compared to the LiDAR sensor itself. Specifically, implementing a proposed ML model on a reference RISC-V architecture GAP8 achieves the inference time of 2.575 milliseconds, 6.138 operations/cycle, and uses only 6.8% of 512 KiB L2 memory. The results presented in this work showcase the possibility of increasing the reliability and integrity of the LiDAR-collected sensor data without significant computational or energy consumption impact on the broader system.

关键词： Advanced driver assistance systems (ADAS) autonomous vehicle perception systems LiDAR reliability LiDAR contamination classification autonomous vehicle safety RISC-V GAP8 TinyML

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