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Design and Implementation of Distributed Digital Twin network Based on Software-Defined networks 3rd

Design and Implementation of Distributed Digital Twin Netw...

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3rd International conference on Cyberspace Simulation and Evaluation, CSE 2024

作者： Feng, Hao Huang, Gan Cao, Kun School of Computer Science and Information Security Guilin University of Electronic Technology JinJi Road Guangxi Guilin541004 China

ISBN: (纸本)9789819645084

Digital Twin network (DTN) emerges as a novel network paradigm that can provide a twin network that is highly consistent with the real physical network. Although digital twin technology has been initially practiced in computer network applications, there is currently a lack of research on the design and implementation of distributed digital twin network system. Through the establishment of digital twins for SDN networks and the collection of data from physical networks, this paper designs and implements a distributed digital twin network system based on OMNeT++. The proposed system can achieve simulation, verification, optimization, and control of the physical network, providing substantial support for network operations and accelerating network innovation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software defined networking

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Operational Flexibility Enhancement with Aggregated Electric Vehicles based on Virtual Energy Storage Model

Operational Flexibility Enhancement with Aggregated Electric...

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Fully Actuated System Theory and Applications (CFASTA), conference on

作者： Shiwei Chen Hongfei Lang Muchun Wan Junjun Du Feng Bao Guangchao Geng Fuyang Rongda Complete-Set Electric Equipmnent Manufacturing Branch of Hangzhou Power Equipment Manufacturing Co. Ltd. Hangzhou P. R. China College of Electrical Engineering Zhejiang University Hangzhou P. R. China

ISBN: (数字)9798350373691

ISBN: (纸本)9798350373707

Distribution network (DN) operational flexibility refers to the adaptability of DNs to uncertainties in sources and loads, which is directly related to the reliability and economics of power supply. With the large-scale integration of distributed energy resources (DERs) into DNs, higher requirements are placed on the operational flexibility. As an important load component in DNs, the optimal control of electric vehicles (EVs) is the crucial mean to improve operational flexibility. However, optimizing the charging and discharging power of a large number of EVs requires a significant amount of computation time, making real-time control of EVs difficult. Therefore, this paper aggregates a large number of EVs based on a virtual energy storage model (VESM), and proposes an optimization method for the VESM to obtain its controllable power and capacity limits. In order to reduce computation time, an online tracking method of the controllable power and capacity without re-solving the optimization model is proposed. Finally, the effectiveness of the VESM and proposed online tracking method are verified in both residential and workplace area by case studies.

关键词： Uncertainty Power supplies optimization models Computational modeling Employment Electric vehicles Real-time systems

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Smart Contracts Refinement for Gas optimization 3

Smart Contracts Refinement for Gas Optimization

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3rd conference on Blockchain Research and Applications for Innovative networks and Services (BRAINS)

作者： Nelaturu, Keerthi Beillahi, Sidi Mohamed Long, Fan Veneris, Andreas Univ Toronto Dept Elect & Comp Engn Toronto ON Canada Univ Paris CNRS IRIF Paris France Univ Toronto Dept Comp Sci Toronto ON Canada

ISBN: (纸本)9781665439244

Smart contracts facilitate the execution of programmable code on a blockchain. The cost for executing smart contract code is metered using gas - the exact amount of which is based on the computational complexity of the underlying smart contract. Hence, it is imperative to optimize smart contract code to reduce gas consumption and, in some instances, to even avoid malicious attacks. In this paper, we propose an approach to optimize the gas consumption of smart contracts, specifically loop control structures. We present a prototype implementation of our approach using off-the-shelf tools for Solidity smart contracts. We experimentally evaluate our technique using 72 Solidity smart contracts. Our evaluation demonstrates the average gas cost savings per transaction to be around 23,943 gas units, or an equivalent 21% decrease in gas costs. Although the approach causes a slight increase in deployment costs due to the additional internal functions, this is only 16, 710 gas units on the average, or a 5% of the total deployment cost. As this overhead remains quite reasonable when compared to the gas cost savings for each transaction, it also confirms the efficacy, practicality and effectiveness of the proposed methodology.

关键词： Smart contracts Gas Verification Synthesis

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Robust Day-Ahead Scheduling of Source-network-Load-Storage Flexible Resources for Wind Power Accommodation 3

Robust Day-Ahead Scheduling of Source-Network-Load-Storage F...

引用

3rd International conference on Electrical Engineering and control Technologies, CEECT 2021

作者： Zhou, Qian Xu, Xiaochuan Xiao, Wenhao Xie, Yanbin Zhu, Yuan Mu, Fan State Grid Chongqing Electric Power Company Chongqing China State Grid Chongqing Urban Electric Power Supply Branch Chongqing China

ISBN: (纸本)9781665440417

In view of the large-scale development of wind power around the world, the coordination scheduling of source-network-load-storage (SNLS) flexible resources is beneficial to utilize wind power. Firstly, considering uncertainties of both wind power and price-based demand response (PDR), this paper constructs a day-ahead scheduling model of SNLS flexible resources based on robust optimization framework. Secondly, the constructed model considers the operation characteristics of SNLS flexible resources, with the minimum total cost as the optimization goal, and considers life constraints on battery energy storage (BES) and congestion management on transmission lines. After linearization, the model constructed in this paper is formulated as a mixed-integer linear programming (MILP) problem. Finally, numerical test is carried out on the modified version of the IEEE 30-bus test system. Through comparative analysis, the test results verify the feasibility and effectiveness of the proposed method for scheduling SNLS flexible resources to accommodate wind power. © 2021 IEEE.

关键词： Wind power

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Performance Prediction of Low-Concentration Photovoltaic Photothermal Components

Performance Prediction of Low-Concentration Photovoltaic Pho...

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Energy Engineering and Power Systems (EEPS), International conference on

作者： Yong Zhang Chao Cheng Yang Zhao Sicong Zhang Zetong Yuan Yutong Li Kun Wang Heng Zhang School of Energy Power and Mechanical Engineering North China Electric Power University Beijing China National Institute of Energy Development Strategy North China Electric Power University Beijing China State Grid Tianjin Electric Power Company Tianjin China Beijing Key Laboratory of Pollutant Monitoring and Control in Thermoelectric Production Process North China Electric Power University Beijing China

In this paper, there are two ways for the performance prediction of CPV/T components, heat transfer analysis and BP neural network, based on the nonlinearity of its performance fluctuation and the addition of heat transfer process, with the low-concentration photovoltaic photothermal complex utilization (LCPV/T) system as the research object. The comparison results show that both prediction methods show good prediction accuracy when the irradiance fluctuation is low, and the relative error is controlled within 10%; when the irradiation value fluctuates, the neural network prediction has higher prediction accuracy in some abnormal output determined by special experimental conditions, while the heat transfer analysis has higher overall prediction accuracy. Combining the advantages and disadvantages of both, the heat transfer analysis is suitable for component performance optimization, and the neural network is suitable for component performance prediction at longer time and space scales.

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3rd International conference on Electrical, Communication and Computer Engineering, ICECCE 2021

3rd International Conference on Electrical, Communication an...

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3rd International conference on Electrical, Communication and Computer Engineering, ICECCE 2021

The proceedings contain 213 papers. The topics discussed include: computer vision and artificial intelligence based navigation on top-view virtual plane;computer assisted grade diagnosis using feature fusion and network algorithm flow;research on wind speed prediction model of least squares support vector machine through genetic algorithm optimization;research on architecture for long-tailed genre computer intelligent classification with music information retrieval and deep learning;research on an intelligent path planning algorithm for robot using electrical control technology;computer mathematical statistics optimization of conversion between blockchain integral and token;harmonic response analysis for ball grid array package using computer finite element simulation;research on obstacle avoidance path planning of electrical control robot in unknown environment;and appearance design of intelligent garbage robot through computer visual recognition technology.

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PPO Improvement in Different Environments

PPO Improvement in Different Environments

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International conference on Electronics and Information Technology (EIT)

作者： Simon Nakibinge Yongrui Liu Qiang Gao Griffiths Anusu Luyali School of Electronic and Information Engineering Beihang University Beijing P.R. China School of Economics and Management Beihang University Beijing P.R. China

ISBN: (数字)9798350369151

ISBN: (纸本)9798350369168

This study investigates strategies to improve the performance of the Proximal Policy optimization (PPO) algorithm in different environments. The PPO algorithm is a widely used method in reinforcement learning, known for its robustness and efficiency in various tasks. However, its performance can vary significantly depending on the environment, and there is a continuous need for optimization to enhance its learning capabilities and overall effectiveness. The primary objective of this study is to evaluate whether specific strategies i.e. adaptive learning rate, neural network initialization, state normalization, and reward clipping can lead to better learning and eventually better overall performance of the PPO algorithm. Three distinct environments are utilized for the evaluation i.e. robotic control environments from OpenAI Gym (HalfCheetah, Walker2D) and a Futures Trading environment. Our results demonstrate that the proposed strategies lead to a PPO variant that significantly outperforms the baseline PPO in all the studied environments. These findings underscore the potential of the improved PPO performance for more effective reinforcement learning applications.

关键词： Training Measurement Adaptive learning Neural networks Reinforcement learning Performance gain Robustness optimization Tuning Robots

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An optimization Scheme for Consortium Blockchain in Airport Collaborative Decision Making 3

An Optimization Scheme for Consortium Blockchain in Airport ...

引用

3rd IEEE International conference on Civil Aviation Safety and Information Technology, ICCASIT 2021

作者： Zhang, Xiaohui Miao, Xianghua Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Faculty of Information Engineering and Automation Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming China

ISBN: (纸本)9781665425186

Consortium blockchain-based A-CDM (Airport Collaborative Decision Making) was a decentralized technology solution proposed by some researchers in recent years to solve robustness, privacy, and security issues in the traditional centralized architecture. The key idea of blockchain-based A-CDM is to use blockchain technology to store the flight operation data in a distributed, consistent, and tamper-proof ledger, further improve the capability of flight management and reduce the trust barriers among the multi-stakeholders such as airport, air traffic management bureau, airlines, and ground service agency, etc. The RAFT consensus was adopted in many blockchain-based data sharing solutions to meet the need for efficiency in data transmission. However, the RAFT algorithm can only be used in a non-byzantine network because security requirements such as preventing malicious nodes are not fully considered. It will bring an immeasurable impact on flight operation when the leader node of RAFT-based A-CDM was caused by malicious control. In response to the above problems, this paper proposes an optimization scheme to improve the security performance of the consortium blockchain in A-CDM. © 2021 IEEE

关键词： Blockchain

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An Aeromagnetic Compensation Algorithm Based on Broad Learning System

An Aeromagnetic Compensation Algorithm Based on Broad Learni...

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Robotics, Artificial Intelligence and Intelligent control (RAIIC), International conference on

作者： Qilong Wang Junlei Song Chao Zuo Kaifeng Dong Fang Jin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo- Exploration Ministry of Education Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China

ISBN: (数字)9798350352627

ISBN: (纸本)9798350352634

Aeromagnetic surveys, renowned for their operational flexibility and high efficiency, serve as a crucial technique for measuring the geomagnetic field. However, aeromagnetic surveys are easily affected by magnetic interference from navigation platforms, making the compensation of aeromagnetic interference a crucial step in the measurement process. To address the inadequate consideration of nonlinear magnetic field interference in traditional compensation algorithms, this paper introduces an aeromagnetic compensation approach based on broad learning system. The broad learning system employs an incremental learning mechanism aimed at enhancing the precision of the network alongside the increase in nodes. With each expansion of the network node, computation is streamlined to calculating the pseudo-inverse of the expansion node, eliminating the necessity for retraining the entire network structure. Leveraging the nonlinear fitting characteristics of the broad learning system, this paper improves the accuracy of aeromagnetic interference compensation. Through UAV flight experiments, the broad learning system is compared with methodologies using particle swarm optimization (PSO) and BP neural network. Compared with PSO, training time was reduced by $21.3 \%$ and magnetic interference by $33.6 \%$. Compared with BP neural networks, training time was reduced by $34.9 \%$ and magnetic interference by $28.6 \%$. This paper provides references and ideas for the selection of aeromagnetic interference compensation algorithms.

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Algorithm for Identifying Violations in Electrical Power Operations by Integrating Spatial Attention Mechanism and YOLOv5

Algorithm for Identifying Violations in Electrical Power Ope...

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Electrical Engineering and control Science (IC2ECS), International conference on

作者： Mingke Zhang Yiran Zhao Faculty of Electrical and Control Engineering Liaoning Technical University Huludao China

Electric power operations have problems such as complex scenarios, frequent operations, and high-risk factors, which makes the research of intelligent on-site action monitoring methods crucial. Therefore, this paper proposes an improved YOLOv5 algorithm model for the accurate identification of power operation violations. Firstly, the YOLOv5 image recognition network is constructed and the spatial attention mechanism is fused in this algorithmic framework to effectively capture the global dependency between spatial features and characters, and to enhance the saliency of the target region in complex scenes; secondly, in the feature fusion stage, the multi-scale target recognition based on Bayes hyper-parameter optimization is constructed, to enhance the multi-target detection ability in the scene. Finally, this paper takes the operator's helmet, safety rope, and insulated safety suit worn in different operation scenes as the monitoring target of safe operation to carry out the verification of the improved algorithm, and analyses and verifies the effectiveness of the algorithm model. The experimental results show that the mAP accuracy of the proposed method in this paper is improved from 91.1 % to 95.1 % over the unoptimized YOLOv5 algorithm, the memory occupancy is reduced by 20.22%, and the monitoring speed is improved by 99.65%.

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