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Resilient Energy Hub Management: A Consensus-Based Approach for Lossy Communication Networks 25

Resilient Energy Hub Management: A Consensus-Based Approach ...

引用

25th international Middle East Power System conference, MEPCON 2024

作者： El-Afifi, Magda I. Osman, Samaa F. Sedhom, Bishoy E. Eladl, Abdelfattah A. Nile Higher Institute for Eng. and Technology Dept. of Elect. and Comm. Eng. Mansoura Egypt Mansoura University Faculty of Engineering Electrical Engineering Department El-Mansoura35516 Egypt

ISBN: (纸本)9798350379648

A distributed consensus strategy is presented in this paper to address the energy hub (EH) dispatch problem in the context of packet loss and EH uncertainty. This approach was motivated by the difficulty of concentrated energy management approaches and massive amounts of data that were exchanged, which also included a problem with packet loss. Each unit of generation and consumption is responsible for computing power generation, load, and mismatches, and the coordination of energy nodes is controlled in a decentralized manner. Workload sharing, robustness, effective decision-making, rapid convergence, and node autonomy are some of the benefits that can be gained from utilizing this distributed technique. The basic objective, which is applicable to both connected and isolated grids, is to accomplish the reduction of operational costs and emissions while simultaneously satisfying demand and operating within system restrictions. Significant gains are demonstrated by two case studies with the presences packet loss and without packet loss. These studies show that there is a reduction of power mismatch by one hundred percent for the electricity grid, 99.96 percent for heating, and 99.95% for gas. A further decrease of 13.27% was seen in overall operating expenses, while a 30.37% reduction was seen in total emissions. © 2024 IEEE.

关键词： Packet loss

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A Weight Vector Bi-Objective Evolutionary Algorithm with Bi-criterion Evolution for Many-Objective Optimization 4

A Weight Vector Bi-Objective Evolutionary Algorithm with Bi-...

引用

4th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2022

作者： Wang, Jiangtao Chen, Hanning School of Artificial Intelligence Tiangong University Tianjin China School of Computer Science and Technology Tiangong University Tianjin China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

In the multi-objective optimization process, its main purpose is to obtain Pareto non-dominated solutions with well convergence and diversity, but in most cases the convergence and diversity of solutions are conflicting. In order to solve this problem, this paper proposes a new convergence and diversity evaluation method to convert the multi-objective optimization problem into a bi-goal, more effectively evaluate the dominant relationship between individuals through the weight vectors, and then increase the selection pressure. We introduce the bi-criterion evolution can better balance the convergence and diversity of Pareto optimal solutions. Based on the proposed method, a new multi-objective optimization algorithm BiGE-BEW is proposed. Experimental results show that the proposed algorithm shows strong competitiveness in solving multi-objective optimization problems and greatly improves the performance of algorithms for solving multi-objective optimization problems. © 2022 IEEE.

关键词： Multiobjective optimization

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Enhancing Offline Coverage Path Planning with Deep Reinforcement Learning 2

Enhancing Offline Coverage Path Planning with Deep Reinforce...

引用

2nd international conference on Machine Learning, Cloud computing, and Intelligent Mining, MLCCIM 2023

作者： Lian, Yaoning Yang, Shaowu Jin, Songchang Zheng, Yuxi Shi, Dianxi National University of Defense Technology Changsha China Beijing China

ISBN: (纸本)9798350328530

Coverage path planning is a critical challenge in various domains, including drones, robots, and automation devices. Traditional methods heavily rely on manually crafted heuristic rules, which struggle to adapt to complex environments and task requirements. This paper proposes a deep reinforcement learning-based algorithm for coverage path planning to achieve automated and intelligent solutions. Three significant contributions are presented. Firstly, curriculum learning is integrated to adjust the training difficulty, facilitating rapid convergence in deep reinforcement learning. Secondly, a coverage heat map is introduced, providing a visual representation of target areas and guiding the convolutional neural network to focus on relevant features, enhancing coverage performance. Lastly, a more reasonable reward function is designed, promoting desirable behavior and facilitating effective exploration and exploitation during learning. Experimental evaluations demonstrate the versatility of the training framework, successfully extending its application to larger and more complex environments. The proposed algorithm is thoroughly tested and exhibits stable and efficient coverage in maps with varying initial positions and random obstacles. © 2023 IEEE.

关键词： Curricula

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Network Softwarization: Developments and Challenges 14

Network Softwarization: Developments and Challenges

引用

14th international conference on Communications, COMM 2022

作者： Popescu, Adrian Westerhagen, Alexander School of Computing Blekinge Institute of Technology Karlskrona Sweden Saab Ab Business Area Surveillance Karlskrona Sweden

ISBN: (纸本)9781665494854

The paper provides an overview of network softwarization. This refers to network changes and adaptations regarding the convergence to Software Defined Networking and Network Function Virtualisation as well as moving the network control to Data Centers and Mobile Edge. For doing this, basic concepts and elements used in the performance provision in softwarized networking environments are used. Adequate performance frame-works can be defined, including relevant metrics and models, to be used in the development, deployment and operation of cloud and edge computing architectures as well as 5G network slicing. The paper provides an overview of network slicing, backbone of 5G technology, as well. © 2022 IEEE.

关键词： 5G mobile communication systems

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CNN-BiLSTM Based Federated Learning for Field Worker Safety 8

CNN-BiLSTM Based Federated Learning for Field Worker Safety

引用

8th IEEE/ACIS international conference on Big Data, Cloud computing, and Data Science, BCD 2023

作者： Kim, Namji Ahn, Jisu Jeong, Jongpil Sungkyunkwan University Department of Smart Factory Convergence Suwon-si16419 Korea Republic of

ISBN: (纸本)9798350373615

Since the fourth industrial revolution, industries have become increasingly interested in human-centered technology and management. In particular, worker safety in industrial sites has become an important topic as discussions on ensuring the right to survival and corporate social responsibility have expanded. In this context, this study aims to develop a worker safety management system considering the characteristics of industrial sites. To this end, we propose a federated learning (FL) technique utilizing the convolutional neural networks and bidirectional long short-Term memory (CNN-BiLSTM) mechanism, which addresses the heterogeneous resource constraints of edge devices and ensures performance, data confidentiality, and system stability. The proposed technique uses a deep learning algorithm, CNN-BiLSTM, to detect subtle activity anomalies and contribute to predicting potentially dangerous situations. It utilizes wearable sensors to analyze workers' biometric indicators and serves to increase industrial safety and proactively prevent potential threats. This research is expected to provide a new approach to industrial safety and improve safety management at industrial sites. © 2023 IEEE.

关键词： Accident prevention

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Automation using Artificial Intelligence in Business Landscape 2

Automation using Artificial Intelligence in Business Landsca...

引用

2nd international conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Unni, Manu Vasudevan Rudresh, S. Bh, Rashmi Krishnan, Renjith K. Kar, Rohit Devichandrika, S. Department of Management St. Claret College Karnataka Bengaluru India Karnataka Bengaluru India College of Economics and Business Administration University of Technology and Applied Sciences Shinas Oman Department of Business Administration St. Francis De Sales College Karnataka Bangalore India

ISBN: (纸本)9798350340235

The integration of Artificial Intelligence (AI) with automation has sparked a remarkable transformation in the contemporary business landscape, promising elevated efficiency and quality. However, this convergence encounters multifaceted challenges, notably in the adoption of recent AI techniques such as deep learning, reinforcement learning, and natural language processing. These techniques, while potent, grapple with challenges in data quality, interpretability, and ethical considerations. In this study, we aim to delineate the intricate interplay between AI and automation, illuminating their collective potential to augment operational efficiency and confer a competitive advantage. Through a comprehensive review, we will explore the effective integration of these technologies, navigating hurdles such as data bias, system compatibility, and human-machine collaboration. Here, the primary research objective is to provide insights on optimizing the outcomes by synergizing AI and automation while addressing the inherent challenges, ultimately fostering sustainable and impactful implementations in organizational frameworks. © 2023 IEEE.

关键词： Artificial Intelligence Automation Companies Machine learning Management Technologies

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A Review of Efficient Resource Allocation for mm-Wave D2D Communication in Cellular Networks using ML Algorithm 3

A Review of Efficient Resource Allocation for mm-Wave D2D Co...

引用

3rd international conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Bilal, N. Md Velmurugan, T. School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350322842

Recently, mobile data traffic is rapidly increasing due to large data transmission in the cellular network. Researchers have proposed several techniques to handle this situation and increase the network capacity of the system. However, data path loss, unexpected blockage in the network, and other features make the network implementation of mm-Wave bands more challenging. Therefore, resource allocation is considered as a significant task to reduce the mutual interference, transmission delay, and offload data traffic from the base station. In this research study, the effective resource allocation techniques for D2D in mm-Wave communication are analyzed using Machine Learning (ML) based algorithms. This study develops a unique framework for deploying and operating D2D mm-Wave communication networks in wireless cellular systems, UAV networks, and wearable device networks by utilizing advanced mathematical techniques from ML. The network throughput, energy efficiency, and convergence rate are considered as key parameters for defining the effectiveness of resource allocation. This comprehensive research study supports the researchers to obtain the best solution for the current issues in D2D mm-Wave communication in a cellular network. © 2023 IEEE.

关键词： Machine learning

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An Iterative RBSF Kalman Equalizer for MIMO ISI Channels 15

An Iterative RBSF Kalman Equalizer for MIMO ISI Channels

引用

15th international conference on Cyber-Enabled Distributed computing and Knowledge Discovery, CyberC 2023

作者： Zhang, Lingfeng Ni, Liangfang Ma, Qing Zhou, Fang Anhui University of Technology School of Electrical Engineering and Information Ma'anshan China

ISBN: (纸本)9798350308693

This paper proposes an iterative reliability-based soft feedback (RBSF) Kalman equalizer (KE) for multiple-input multiple output inter symbol interference (MIMO-ISI) channels. According to the Gaussian assumption, the RBSFKE iterative uses the successive interference suppression to form the minimum mean-square error (MMSE) filtered estimate so as to achieve the extrinsic log-likelihood ratios (LLRs) of the code bits of the concerned symbol. To improve the convergence of property of the iterative KE, the items of the state vector corresponding to received symbols are divided into two blocks based on reliability characterized by a posteriori LLRs of encoded bits, is larger than a certain threshold, is iteratively cancelled with the RBSFKE so as to further obtain the residual signal for evaluating the symbols in the unreliable block. This is done until the unreliable block is void, resulting in the extrinsic information for a coded bit with the greatest likelihood. The simulation results also show that the proposed scheme outperforms the other equalizers considered in this paper. © 2023 IEEE.

关键词： Frequency selective fading

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Efficient DOA Estimation Algorithm Based on Modified Recursive Least Squares Algorithm 5

Efficient DOA Estimation Algorithm Based on Modified Recursi...

引用

5th IEEE international conference on Power, Intelligent computing and Systems, ICPICS 2023

作者： Zhu, Hongxi Cai, Xingyan Chang, Fengyi College of Internet of Things Engineering Hohai University at Changzhou Changzhou China College of Sydney Smart Technology Northeastern University at Qinhuangdao Qinhuangdao China College of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9798350333442

Traditional DOA algorithms for estimating based on subspace decomposition have high estimation accuracy, but the number of signal sources should be given and the eigenvalue decomposition of the covariance matrix is required, which has high computational complexity. Existing DOA estimation algorithms based on adaptive filtering overcome the drawbacks of the subspace-based decomposition algorithms. However, they have limitations in practical applications, as the DOA estimation error and resolution probability are significantly worse than that of subspace decomposition class algorithms. In this paper, a modified recursive least squares (mRLS) algorithm is put forward for DOA estimation. We define a new cost function, which introduces the minimum disturbance constraint and generalized convex penalty function into the traditional RLS algorithm. It has better convergence speed and less mean square error compared with the RLS algorithm. Simulations demonstrate that the proposed algorithm has higher estimation accuracy and resolution probability than existing adaptive filtering-based DOA estimation algorithms, and that its performance approximates that of subspace decomposition algorithms with a significant reduction in computational complexity. © 2023 IEEE.

关键词： Adaptive filtering

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Flexible LAN-WAN Orchestration for Communication Efficient Federated Learning over Large-Scale Mobile Devices 30

Flexible LAN-WAN Orchestration for Communication Efficient F...

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30th IEEE international conference on Parallel and Distributed Systems, ICPADS 2024

作者： Yuan, Jinliang Li, Qing Dang, Fan Mu, Xiaofang Qi, Hui Xu, Mengwei Wang, Shangguang Tsinghua University Department of Automation China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China China Taiyuan Normal University College of Computer Science and Technology China Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology China

ISBN: (纸本)9798331515966

Federated learning (FL) has been widely adopted as a privacy-preserving model training paradigm. However, traditional FL protocol heavily relies on data transmission between clients and servers across the wide-area network (WAN), which is tightly constrained and unreliable, therefore causing expensive communication and slow convergence. To this end, we propose a LAN-aware FL (LanFL) protocol, which can efficiently leverage the network capacity of the local-area network (LAN). By frequent model aggregation among the devices within the same LAN, we can significantly reduce the global aggregation across WAN, thus accelerating the training process. However, due to the unique challenges introduced by LAN, it's not easy to efficiently utilize LAN resources while preserving the original dignity of FL performance. Therefore, LanFL also incorporates several critical techniques: LAN-aware hierarchical aggregation, intraLAN device topology construction, and inter-LAN heterogeneous bandwidth coordination. Extensive real-world experiments are conducted and the experimental results show that LanFL can significantly accelerate FL training up to 6.0 ×, while preserving the model accuracy. © 2024 IEEE.

关键词： Network topology

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