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Research on tactical edge cloud service model for joint intelligence 3

Research on tactical edge cloud service model for joint inte...

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3rd international conference on Electronics, Electrical and Information Engineering, ICEEIE 2023

作者： Du, Bo Yuan, Chen Yu, Zhaojun Engineering University of PAP Urumqi830049 China

ISBN: (纸本)9781510671423

To cope with the problems of insufficient level of intelligence in attempting combat missions under tactical edge conditions, high command, and control delay, and difficulty in sharing data across domains, the joint intelligent tacti-cal edge cloud service model is proposed, the definition and formal description of joint intelligence are given, and the synergistic mechanism of the joint intel-ligent cloud service model is analyzed, to provide theoretical references for distributed combat at the tactical edge under intelligent conditions. © 2023 SPIE.

关键词： distributed database systems

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Design and application of multi-level grid agc collaborative control scheme adapting to distributed new energy control 3

Design and application of multi-level grid agc collaborative...

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2024 3rd international conference on New Energy, Energy Storage and Power Engineering, NESP 2024

作者： Jiang, Xinfan Hu, Dijun Zhang, Tailei Wu, Jinbo Liu, Li Wang, Mingli State Grid Hunan Electrical Power Company Limited Changsha410004 China

The proportion of installed capacity and output of new energy generation in China has been increasing year by year. Due to the volatility, randomness, and unpredictability of new energy, the load fluctuation in the power grid is more frequent, and the demand for AGC regulation is intensifying. The proportion of conventional power AGC resources capacity is decreasing, and new energy will increasingly become a new type of AGC resource. However, the low voltage and distributed characteristics of new energy pose a challenge to the existing power grid AGC system targeting high voltage and large capacity AGC resources. This paper designs a multi-level grid AGC collaborative architecture suitable for new energy access control, proposes a collaborative strategy between conventional power AGC and new energy AGC, and proposes a provincial and prefectural AGC collaborative control scheme including model design, information exchange, and security verification. The scheme presented in the paper has been successfully applied in provincial and prefectural power grid dispatch automation systems. © Published under licence by IOP Publishing Ltd.

关键词： Power grids

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3rd international conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

3rd International Conference on Artificial-Business Analytic...

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3rd international conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

ISBN: (纸本)9789819748594

The proceedings contain 60 papers. The special focus in this conference is on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects. The topics include: Particle Swarm Optimization for Efficient Data Dissemination in VANETs;a Reactive Approach for High-Accuracy and Data-Driven Customer Behaviour Analysis and Prediction;a Brief Survey on Fabric Defect Detection;CNN Based Real Time Detection of Words from Lip Movements and Automated into Text;application of Artificial Intelligence for the Diagnosis of Dementia (Alzheimer): A Systematic Evaluation;shortest Job First with Gateway-Based Resource Management Strategy for Fog Enabled Cloud computing;software Vulnerability Analysis Based on Statistical Characteristics;hate Speech Detection on Twitter: A Comparative Evaluation of Different Machine Learning Techniques;detection of Heart Disease Using Machine Learning;SVM-Based Framework for Breast Cancer Detection;hand Gesture Recognition System Using Machine Learning;deep Learning Method for Plant Disease Recognition and Prediction;Lung Cancer Detection by Using CNN Architecture Models;deep Fake Analyser: A Review Based on Detecting the Deepfakes;predicting the Outcomes of La Liga Matches;Artifact Detection and Removal in EEG: A Review of Methods and Contemporary Usage;analyzing the Employee Attrition Rate: A Comparative Study of Various Machine Learning Approaches to Foresee Employee Attrition;satellite Image Classification Using Deep Learning for Big Earth Data;tackling Misinformation Through Tweets: A Comparative Study of Various Machine Learning Approaches;feature-Based Anomaly Detection in Static Social Networks;framework for Optimising Supply Chain Analysis Using Machine Learning;evaluation of Load Forecasting in Intelligent Grid systems.Through Machine Learning Techniques.

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Multi-obje Optimization Strategy for distributed Energy Resources in Distribution Networks Based on a Two-Stage Model 3

Multi-obje Optimization Strategy for Distributed Energy Reso...

引用

3rd international conference on Robotics, Automation and Intelligent Control, ICRAIC 2023

作者： Wang, Zhichao Sun, Xianbo Ke, Yiluo Hubei Minzu University College of Intelligent Systems Science and Engineering Enshi China

ISBN: (纸本)9798350360813

With the rapid development of new energy, a large number of distributed generation (DG) are connected to the distribution network. As the number of DGs connected to the grid increases, it will exacerbate voltage fluctuations and instability in the power grid, which will directly lead to network losses and increased operating costs. To effectively reduce the operational costs for the power supply company and optimize the network's losses, a two- stage model-based day-ahead optimization scheduling approach has been proposed. The first stage is the optimization scheduling stage of DG, which determines the unit combination of DG based on market electricity prices, DG operating costs, and the price of signing interruptible load (IL) contracts, as well as the purchase of electricity from the large power grid and IL reduction. The second stage focuses on reactive power optimization. In this stage, considering the reactive power characteristics of DG, the outputs of DG units and reactive power compensation devices are jointly optimized to ensure DG outputs remain within specified ranges while reducing distribution network losses. Simulation results conducted on a modified IEEE 33- node system demonstrate that the proposed two-stage day- ahead optimization scheduling model has reduced the network loss rate by 45%, further contributing to the reduction of operational costs for the power supply company. © 2023 IEEE.

关键词： Electric power distribution

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PGS-BFT: A Pipeline-Based Graph Structure Byzantine Fault Tolerance Consensus Algorithm 3

PGS-BFT: A Pipeline-Based Graph Structure Byzantine Fault To...

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3rd international conference on Computer Science and Blockchain, CCSB 2023

作者： Chen, Jingwen Liu, Yang Liu, Xinlei Peng, Song Henan university of technology Distributed Computing & BlockChain Lab Zhengzhou China

ISBN: (纸本)9798350359077

The Byzantine fault-tolerant (BFT) consensus algorithm is widely used in blockchain systems.due to its ability to tolerate malicious nodes and ensure system consistency. However, existing consensus protocols still face significant challenges. This paper proposes a pipeline-based graph structure Byzantine Fault Tolerant (PGS-BFT) consensus algorithm. It leverages parallel pipeline execution to accelerate block generation efficiency. By synchronizing block dependencies among nodes, all validated blocks eventually form an ordered graph structure within a cycle, enabling nodes to achieve final consensus. The proposed PGS-BFT algorithm effectively improves system consensus efficiency and reduces transaction latency while ensuring Byzantine fault-tolerant security. It can be applied to existing consortium blockchain platforms. © 2023 IEEE.

关键词： Blockchain

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Blockchain Technology and its Impact in Stock Exchange 3

Blockchain Technology and its Impact in Stock Exchange

引用

3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Sathya, D. Siddique Ibrahim, S.P. Jagadeesan, D. RV University School of Computer Science and Engineering Bangalore India VIT-AP University Beside AP Secretariat Andhra Pradesh Amaravati India Kamadhenu Arts and Science College Dept. of Commerce PA and IT Tamil Nadu India

ISBN: (纸本)9798350343632

The traditional centralized stock exchange systems.are having many issues, such as steep transaction costs, centralized control and governance, and vulnerability to various forms of attack and abuse, including front-running. So, a system that provides the accessibility, integrity, reliability, and security for stock transactional data is much needed in any trading environment. Blockchain is an emerging technology that is used for sharing data in a decentralized manner in a huge network of untrusted participants. Blockchain-based systems.guarantee accessibility, availability, integrity, reliability, security and easy data management. The execution of smart contracts in stock exchange will eliminate the need of central authority and facilitates various stock trading-related requests and inquiries. The data stored in the transactional record is accessible by all the stakeholders without compromising its authenticity. The blockchain based stock exchange system will eliminate the broker commission and transaction fee but it will have the mining fee which is negligible compared to other fees of centralized system. This study has prepared a report on the previous research work that was implemented on blockchain based stock exchange system and a comparison is made on the blockchain type, its implementation and performance. © 2023 IEEE.

关键词： Blockchain Cryptocurrency distributed systems Private ledger Smart contract Stock exchange

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An Intrusion Detection Framework with Optimized Feature Selection and Classification Combination Using Support Vector Machine 3

An Intrusion Detection Framework with Optimized Feature Sele...

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3rd IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Tianyao, Luo Huadong, Huang Run, Li Guangxi International Business Vocational College Nanning530007 China

ISBN: (纸本)9798350398410

As intelligent devices and related technologies continue to develop, abnormal traffic detection has become a major issue in the field of internet security. Malicious attacks can have a negative impact on systems.and cause a decline in computational performance. One of the technologies used to identify intruder activity and assess system security by sending out notifications is intrusion detection systems. In this endeavor, we employ the diversified NSL-KDD dataset for intrusion detection to introduce a novel feature selection and classification merging technique using support vector machines (SVM). The goal of this technique is to increase the capability of intrusion classification by considerably reducing the input feature set from the training data. The process of choosing significant input training features and discarding unimportant ones in supervised learning results in a feature subset that can produce greater classification accuracy. In our tests, we used the KDDTest+ and KDDTest-21 data sets' various input feature subsets as input features for the SVM classifier. According to the experimental findings, the suggested technique obtains classification accuracy of 88.25% when using only KDDTest+ and 72.42% when using KDDTest-21. © 2023 IEEE.

关键词： Support vector machines

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DRLMS: a multipath scheduler based on deep reinforcement learning 3

DRLMS: a multipath scheduler based on deep reinforcement lea...

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3rd international conference on Algorithms, Microchips, and Network Applications, AMNA 2024

作者： Zhang, Mengyang Yuan, Kaiguo Li, Xiaoyong Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781510680098

Most current network devices have multiple network interfaces, and multipath transport protocols can utilize multiple network paths (e.g., WiFi and cellular) to improve the performance and reliability of network transmission. The scheduler of the multipath transmission protocol determines the path to which each data packet should be transmitted, and is a key module that affects multipath transmission. However, current multipath schedulers cannot adapt well to various user usage scenarios. In this paper, we propose DRLMS, a deep reinforcement learning based multipath scheduler. DRLMS uses deep reinforcement learning to train neural networks to generate packet scheduling policies. It optimizes the scheduling strategy through feedback to the neural network through the reward function based on the current user usage scenario and QoS. We implement DRLMS in the MPQUIC protocol and compared it with current multipath schedulers. The results show that DRLMS's adaptability to user usage scenarios is significantly outperforms other schedulers. © 2024 SPIE.

关键词： Reinforcement learning

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Neuro-Symbolic Program Synthesis for Multi-Hop Natural Language Navigation 3

Neuro-Symbolic Program Synthesis for Multi-Hop Natural Langu...

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3rd international conference on Assured Autonomy, ICAA 2024

作者： English, William Simon, Dominic Ahmed, Rubel Jha, Sumit Ewetz, Rickard University of Florida Ece GainesvilleFL32611 United States University of Central Florida Ece OrlandoFL32816 United States Florida International University Cec MiamiFL33199 United States

ISBN: (纸本)9798331521011

Solving navigation problems from natural language descriptions is essential for advancing humanrobot interaction and enhancing the usability of autonomous systems. Symbolic approaches to path planning excel in well defined environments but cannot cope with the ambiguity of natural language inputs. On the other hand, neural solutions centered on large language models (LLMs) can parse free-form natural language but lack the reasoning capabilities for solving complex multihop path planning problems. In this paper, we propose a neuro-symbolic framework based on program synthesis for multi-hop natural language navigation called NSPS. The framework uses an LLM to parse the problem definition in natural language, a graph of the environment, and an API of a graph library. Next, the code generation capabilities of the LLM are used to synthesize a program for path planning and verification. The path planning program is executed to generate a solution path that is checked by the verification program. A selfcorrection loop is used to fix both syntax and value errors. The framework is evaluated using 600 multihop navigation tasks with 1 to 10 hops. Compared with neural approaches, the NSPS framework improves the success rate and path efficiency by an average of 64.3% and 19.4% across all tasks, respectively. © 2024 IEEE.

关键词： Motion planning

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SP-ADMM: a distributed optimization method of SFC placement for 5G-MEC networks 3

SP-ADMM: a distributed optimization method of SFC placement ...

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3rd international conference on Algorithms, Microchips, and Network Applications, AMNA 2024

作者： Zhang, Zhibo Wang, Huiqiang Niu, Shuangyue Lv, Hongwu College of Computer Science and Technology Harbin Engineering University Harbin China AVIC Chengdu Aircraft Design and Research Institute Chengdu China

ISBN: (纸本)9781510680098

Recently, the combination of a service function chain (SFC) with network function virtualization (NFV) and software-defined networking (SDN) has provided customers with flexible and efficient services. The emergence of multi-access edge computing (MEC) further enhances the level of service customization. However, achieving joint optimization of virtual network function (VNF) deployment and flow allocation in resource-constrained scenarios while meeting the diverse requirements of 5G verticals is challenging. Current research rarely addresses dedicated service provisioning for edge servers and considers the additional instantiation overhead introduced by adjusting cloud server parameters. In fact, this is a non-negligible issue during SFC deployment in 5G-MEC scenarios. Based on the above considerations, this paper constructs a joint SFC deployment problem for edge-cloud networks with the goal of maximizing network utility. We first propose a univariate modeling method based on meta-links that effectively avoids the variable coupling problem in traditional multivariate modeling approaches and reduce the problem size by at least half. Subsequently, to solve the NP-hard integer nonlinear problem (INLP), we propose a distributed computing architecture named SP-ADMM, which improves the speed and quality of SFC deployment in large-scale scenarios via convex combinatorial formulations and a Viterbi-based heuristic algorithm (PAC-GREP). Finally, we experimentally verify the convergence and approximation of the algorithms. Our solution demonstrates advantages in terms of network utility and convergence speed under the same network resources, increasing service capacity by at least 39%. © 2024 SPIE.

关键词： Network function virtualization

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