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Joint routing and scheduling optimization algorithm for airborne time-sensitive networking 3

Joint routing and scheduling optimization algorithm for airb...

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3rd International conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2024

作者： Li, Chuang Wang, Zhong Rocket Force University of Engineering Xi'an China

ISBN: (纸本)9781510680449

The application of drones in rescue, exploration and other fields is becoming increasingly frequent. The existing bus technology has low bandwidth performance, poor mounting capacity and poor interconnectivity, which cannot meet the increasingly large transmission needs of video, image, and control information in drone avionics systems, hindering the development of drone informatization and intelligence. Time-Sensitive networking are a set of standards specified by the IEEE 802 working group, which provide deterministic connections for network devices while being compatible with traditional Ethernet, and can be used to meet the information transmission requirements of Unmanned Aerial Vehicle avionics systems. This article proposes a novel optimization objective using a joint routing and scheduling optimization algorithm based on Integer Linear Programming, which can effectively solve the problems faced by bus technology in Unmanned Aerial Vehicle avionics systems. The simulation experiment results show that the proposed method performs well and reliably in terms of delay, and can provide good bandwidth performance. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Ethernet

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3rd Modeling, Estimation and control conference, MECC 2023 - Proceedings

3rd Modeling, Estimation and Control Conference, MECC 2023 -...

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3rd Modeling, Estimation and control conference, MECC 2023

ISBN: (纸本)9781713872344

The proceedings contain 107 papers. The topics discussed include: development of linear battery model for path planning with mixed integer linear programming: simulated and experimental validation;challenges in integrating low-level path following and high-level path planning over polytopic maps;experimental validation on aerial vehicles of real-time motion planning with continuous-time Q-learning;subspace structured neural network for rapid trajectory optimization;prediction of aircraft estimated time of arrival using a supervised learning approach;modelling and parameter estimation of cartridge probes for atomic force microscopy;wind field estimation using multiple quadcopters;active suspension parameters identification: an algebraic approach and its application to suspension travel control;and reinforcement learning enabled safety-critical tracking of automated vehicles with uncertainties via integrated control-dependent, time-varying barrier function, and control Lyapunov Function.

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Enhancing MPPT Efficiency in Standalone Photovoltaic Systems: Integration of Finite control Set Model Predictive control with Optimized Artificial Neural network 3

Enhancing MPPT Efficiency in Standalone Photovoltaic Systems...

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3rd International conference on Advanced Electrical Engineering, ICAEE 2024

作者： Benabdallah, Naima Belabbas, Belkacem Tahri, Ahmed University of Tiaret L2GEGI Laboratory Department of Electrical Engeneering Algeria

ISBN: (数字)9798350349351

ISBN: (纸本)9798350349351

This paper presents an advanced Finite control Set Model Predictive control (FSC-MPC) strategy tailored for optimizing Maximum Power Point Tracking (MPPT) in standalone photovoltaic systems. Leveraging a dynamic model of a boost converter, the FSC-MPC strategy integrates a unique cost function, which incorporates the absolute difference between the reference and predicted current to effectively regulate the boost switch, thereby enhancing reference tracking and minimizing losses. An advancement in this strategy involves the inclusion of an additional term in the cost function aimed at optimizing the switching frequency, thus reducing losses and attenuating undesirable oscillations. The FSC-MPC's current reference generation relies on an optimized Artificial Neural network (OP-ANN) architecture, wherein parameters such as the number of hidden layers, neurons per layer, and activation function are dynamically adjusted using a pattern-search algorithm (PSA). By minimizing the loss function through adjusting these parameters, the optimization algorithm ensures more effective learning and enhances the ANN's adaptability to changing environmental conditions. The proposed model is implemented, modeled, and simulated within the MATLAB/Simulink environment, with extensive discussion of simulation results providing a comprehensive evaluation of the performance of the proposed algorithms. © 2024 IEEE.

关键词： Cost functions

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3rd International Workshop on Human Brain and Artificial Intelligence, HBAI 2022, held in conjunction with 31st International Joint conference on Artificial Intelligence and 23rd european conference on Artificial Intelligence, IJCAI-ECAI 2022

3rd International Workshop on Human Brain and Artificial Int...

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3rd International Workshop on Human Brain and Artificial Intelligence, HBAI 2022, held in conjunction with 31st International Joint conference on Artificial Intelligence and 23rd european conference on Artificial Intelligence, IJCAI-ECAI 2022

ISBN: (纸本)9789811982217

The proceedings contain 19 papers. The special focus in this conference is on Human Brain and Artificial Intelligence. The topics include: Brain network Analysis of Hand Motor Execution and Imagery Based on Conditional Granger Causality;a Hybrid Brain-Computer Interface for Smart Car control;a Spiking Neural network for Brain-Computer Interface of Four Classes Motor Imagery;virtual Drone control Using Brain-Computer Interface Based on Motor Imagery Brain Magnetic Fields;brain controlled Manipulator System Based on Improved Target Detection and Augmented Reality Technology;optimization of Stimulus Color for SSVEP-Based Brain-Computer Interfaces in Mixed Reality;white Matter Maturation and Hemispheric Asymmetry During Childhood Based on Chinese Population;A Digital Gaming Intervention Combing Multitasking and Alternating Attention for ADHD: A Preliminary Study;A BCI Speller with 120 Commands Encoded by Hybrid P300 and SSVEP Features;Delving into Temporal-Spectral Connections in Spike-LFP Decoding by Transformer networks;a Mask Image Recognition Attention network Supervised by Eye Movement;DFC-SNN: A New Approach for the Recognition of Brain States by Fusing Brain Dynamics and Spiking Neural network;DSNet: EEG-Based Spatial Convolutional Neural network for Detecting Major Depressive Disorder;SE-1DCNN-LSTM: A Deep Learning Framework for EEG-Based Automatic Diagnosis of Major Depressive Disorder and Bipolar Disorder;Emotion Recognition from EEG Using All-Convolution Residual Neural network;Salient Object Detection with Fusion of RGB Image and Eye Tracking Data;multi-source Domain Adaptation Based on Data Selector with Soft Actor-Critic.

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Multi-time scale optimal operation of virtual power plants with integrated EV charging and swapping stations 3

Multi-time scale optimal operation of virtual power plants w...

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3rd International conference on Power Systems and Electrical Technology, PSET 2024

作者： Yuan, Hongtao Xu, Xiaoyuan Yan, Zheng Liu, Jinsong Shen, Bing Xu, Bingyan Shanghai Jiao Tong University Key Laboratory of Control of Power Transmission and Conversion Shanghai China State Grid Shanghai Electric Power Company Shanghai Electric Power Company Electric Power Science Research Institute Shanghai China State Grid Shanghai Electric Power Company Shanghai Electric Power Company Jinshan Power Supply Company Shanghai China

ISBN: (纸本)9798350353204

In response to deal with the challenges for power system by the increasing EV penetration rate, this paper proposes an optimal operation strategy for virtual power plants which comprehensively considers the adjustment capabilities of regional adjustable resources. Based on the dynamic characteristics of road traffic flow, this paper takes the spatial and temporal distribution characteristics of EV charging load in account and establishes a mathematical model of EV charging and swapping integrated station (CSIS). Moreover, an optimal scheduling model of virtual power plant(VPP) is proposed considering renewable energy sources (RES), micro gas turbines (MT), CSIS, energy storage power stations, static var compensators (SVC) and other components. To reduce the prediction errors' impact on regional RES output, load demand, and road traffic flow, the multi-time scale optimization scheduling strategy based on model predictive control technology is proposed in this paper to improve the adaptability of the model in practice. The case study takes the actual road conditions of bus lines in a city and the improved 12-node transportation network as an example to verify the proposed strategy. The calculation results prove the rationality and effectiveness of the proposed model. © 2024 IEEE.

关键词： Static Var compensators

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Research on regional coordination control of urban road traffic based on improved particle swarm optimization algorithm 3

Research on regional coordination control of urban road traf...

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3rd International conference on Electronics and Communication;network and Computer Technology (ECNCT)

作者： Wen, Ling Dong, Baotian Li, Pengcheng Beijing Jiaotong Univ Transportat Beijing Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

In order to alleviate the pressure of urban road traffic, the coordinated control strategy of regional traffic signals is studied. Taking the maximum capacity of the traffic control sub-area as the optimization objective, the regional signal coordinated control model is constructed, and the particle swarm optimization algorithm is used to solve the problem. The performance of the particle swarm algorithm is improved by adding interference items, decreasing inertia weight and adjusting learning factors. The above model and algorithm are verified by the traffic flow data of the actual road network. The experimental results show that the improved particle swarm algorithm has faster convergence speed and higher solution accuracy, and the proposed signal control model can effectively improve the overall traffic capacity of the region.

关键词： Urban road traffic Regional coordination control strategy Improved particle swarm optimization algorithm

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FPGA-Based Hardware optimization and Implementation of YOLOv4-tiny 3

FPGA-Based Hardware Optimization and Implementation of YOLOv...

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3rd International conference on Communications, Information System, and Data Science, CISDS 2024

作者： Wang, Kai Bai, Yanhong Li, Xiaosong Department of Electronic Information Engineering Taiyuan University of Science and Technology Taiyuan030024 China Department of Intelligent Manufacturing Industry Shanxi University of Electronic Science and Technology Linfen041000 China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

To address the challenges associated with the YOLOv4-Tiny algorithm's complex structure, high computational resource requirements, and extensive parameter count-which collectively impede efficient FPGA deployment-we propose a hardware-software co-optimization strategy. This approach replaces the YOLOv4-Tiny backbone with the MobileNetv1 network and integrates the Convolutional Block Attention Module (CBAM) into the enhanced feature extraction network. Channel pruning is applied to streamline the network structure, and weights and biases are quantized to 16-bit fixed-point representations. Compared to the original YOLOv4-Tiny, this optimized network reduces parameters by 40% while retaining nearly identical recognition *** high-level synthesis tools, we generate FPGA IP cores, design a parallel pipelined convolutional architecture, and implement inter-layer blocking between convolutional layers to enhance computational efficiency. This improved algorithm is deployed on a Zynq-7020 FPGA chip. Experimental results show that the optimized algorithm achieves a computational performance of 43.4 GOP/s, offering a speedup of 1.6 to 4.1 times compared to existing studies, with an energy efficiency ratio 4.8 to 10.7 times greater than current implementations. These findings indicate that the proposed strategy significantly improves algorithm deployment efficiency on resource-limited FPGA platforms. © 2025 SPIE.

关键词： High level synthesis

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Deep Reinforcement Learning for Cost-Efficient Power control in Multi-Cell networks 3

Deep Reinforcement Learning for Cost-Efficient Power Control...

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3rd IEEE Industrial Electronics Society Annual On-Line conference, ONCON 2024

作者： Sun, Chang Nie, Xiaokai Zhao, Xin Yu, Wenwu School of Automation Southeast University Nanjing210096 China Shenzhen Research Institute Southeast University Shenzhen518057 China School of Mathematics Southeast University Nanjing211189 China

ISBN: (纸本)9798331540319

Energy consumption is a critical concern in multi-cell networks. To reduce the energy cost, renewable energy has been accommodated to offer cellular networks with auxiliary energy supply. Most existing studies of cellular networks powered by hybrid energy sources are based on ideal battery models. However, the energy storage efficiency of batteries in reality is influenced by environmental factors and the natural degradation of batteries over time. In this paper, we investigate the cost-efficient transmit power control in multi-cell networks with hybrid energy sources and time-varying energy storage efficiency. Considering such dynamic networks characterized by the stochastic property of energy harvesting, channel conditions, and time-varying energy storage efficiency, deep reinforcement learning (DRL) is adopted to learn the optimal power control strategy without requiring prior knowledge of the environment. To enhance the robustness and accommodate varying levels of exploration and exploitation during different training phases of DRL, an enhanced proximal policy optimization algorithm is proposed. Specifically, a dynamic clipping threshold is designed based on an adaptive adjustment mechanism, which allows modifying the clipping threshold in accordance with the agent's performance. Simulation results show that our proposed DRL algorithm can significantly improve the cost efficiency of the multi-cell network compared with other DRL algorithms. © 2024 IEEE.

关键词： Deep reinforcement learning

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3rd Electric Power and Renewable Energy conference, EPREC 2022

3rd Electric Power and Renewable Energy Conference, EPREC 20...

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3rd Electric Power and Renewable Energy conference, EPREC 2022

ISBN: (纸本)9789811983528

The proceedings contain 19 papers. The special focus in this conference is on Electric Power and Renewable Energy. The topics include: Sooty Tern optimization Algorithm for Economic Emission Dispatch Problem Integration with Wind Energy;bid-Based Economic Load Dispatch in Coordination with Virtual Power Plant;development of Fractional Order controller for Water Level Coupled Tank System Using Different optimization Techniques;a Novel White Shark Optimizer for Optimal Parameter Selection of Power System Oscillation Damper;Transient Analysis of a HOMER Designed Renewable Energy Sources (RES) Using MATLAB/Simulink;Monitoring and control of EV-to-Grid Load with REAL-TIME Data Communication by Using RSLINX-OPC Server Among MATLAB, PLC, and SCADA;artificial Neural network-Based Designing of Solar and Wind System with Modified Power Filter;optimal Allocation of Capacitors for Loss Reduction in Distribution System;ioT-Based Kalman Filtering and Particle Swarm optimization for Detecting Skin Lesion;Detection of Fault Disturbances in a DG Integrated Hybrid Power System Using HS-Transform and Wavelet Transform;clonal Assortment optimization Procedure to Unravel Cost-Effective Power Dispatch Problem;Classification of Fault Disturbances in a DG Integrated Hybrid Power System Using Support Vector Machine and Decision Tree;intelligent Excitation System for Efficient control of Dual Stator Winding Permanent Magnet Synchronous Generator in Hybrid Power System Applications;investigation of New Fuzzy Cascade controller for Frequency Deviation in Hybrid Power Systems;frequency Regulation in Microgrid Considering Virtual Inertia with Firefly Algorithm Optimized controller;a Report on Multi-agent System Application in Power System.

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Accelerating 5G Service-Based Architecture with eBPF 12

Accelerating 5G Service-Based Architecture with eBPF

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12th International conference on networks, Communication and Computing (ICNCC)

作者： Yong Xuan Huang Kung Chun Wang Bi Shun Ke Chunghwa Telecom Labs Cloud Comp Lab Taipei Taiwan

ISBN: (纸本)9798400709265

Service-Based Architecture (SBA) serves as a foundational methodology for 5G core networks, as delineated by the 3rd Generation Partnership Project (3GPP). This modular approach segments the 5G Core network into discrete, functionally autonomous network Functions (nfs), deployed via either virtual machines or container technologies. The efficacy of the SBA execution platform significantly impacts the service capabilities of these nfs. In this study, we present an enhanced architecture that employs extended Berkeley Packet Filter (eBPF) and eXpress Data Path (XDP) technologies to fine-tune the network, security, service, and scheduling aspects within a Kubernetes framework. Additionally, eBPF's monitoring and packet control features augment the service capabilities of 5G components, thereby improving encrypted data transmission. eBPF's real-time monitoring aids the scheduler in making nuanced decisions, while an eBPF-driven load balancer ensures equitable service distribution. Comprehensive testing reveals that the enhanced architecture increases transmission efficiency by 17%, reduces average latency by 43%, and alleviates latency anomalies compared to conventional 5G nf platforms. It also enhances the inherent connectivity of 5G nfs by approximately 15%. For encryption-reliant applications, the architecture achieves a 17% performance gain in latency, transmission, and connectivity, while its scheduling and service balancing capabilities demonstrate a roughly 19% performance enhancement.

关键词： 5G Core network Virtual network Acceleration eBPF/XDP

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