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Optimal Task Offloading with Deep Q-Network for Edge-Cloud computing Environment 13

Optimal Task Offloading with Deep Q-Network for Edge-Cloud C...

引用

13th international conference on Information and Communication technology convergence, ICTC 2022

作者： Ullah, Ihsan Lim, Hyun-Kyo Seok, Yeong-Jun Han, Youn-Hee University of Technology and Education Advanced Technology Research Center Cheonan Korea Republic of Korea University of Technology and Education Future Convergence Engineering Cheonan Korea Republic of Korea University of Technology and Education Department of Computer Science and Engineering Cheonan Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

The edge-cloud computing paradigm has become a key component of fifth-generation (5G) networks and beyond. It becomes difficult to meet the computation-intensive and delay-sensitive service requirements of intelligent IoT applications by using only edge computing or cloud computing alone. Therefore, an intelligent resource management technique is required to efficiently distribute the task offloading to the edge and cloud computing systems. In this paper, we aim to optimize the task offloading under delay constraints to maximize resource utilization and minimize the offloading rejection in the edge-cloud computing system. Considering optimal decisions for task offloading and resource allocation deep reinforcement learning is applied. We formulate this optimization problem by the Markov decision process and then use Deep Q-Network (DQN) to find the optimal policy for task offloading. Thus, we design the DQN-edge-cloud (DQNEC) computing scheme to update the policy and optimally offload the task in a dynamic edge-cloud environment with considering resource utilization. The simulation results show that DQNEC achieves good performance than the heuristic approaches in terms of maximizing resource utilization with minimum cost and maximizing task offloading with minimum task rejection. © 2022 IEEE.

关键词： Resource allocation

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The Full Coverage Criterion-Based Asynchronous Power Flow Method for Integrated Transmission and Distribution Networks 3

The Full Coverage Criterion-Based Asynchronous Power Flow Me...

引用

3rd international conference on New Energy and Power Engineering, ICNEPE 2023

作者： Yang, Jierui Chen, Julong Li, Qingsheng Zhang, Yu Power Grid Planning Research Center of Guizhou Power Grid Co. Ltd Guiyang China

ISBN: (纸本)9798350393224

Distribution networks (DNs) has more and more distributed generations (DGs), and the influence between transmission and distribution networks (T&DNs) is becoming increasingly severe. Analysis of the transmission and distribution coordination networks (TDCNs) is imperative. The forward-backward-splitting (FBS) method has the best convergence performance in asynchronous methods for TDCNs' power flow (PF) currently, but FBS method has the defect of unreasonable convergence criteria leading to misjudgment. Therefore, this paper proposes the full coverage criterion-based (FCC) method, which achieves correct convergence judgment after full coverage of DNs' information. The simulation results demonstrate that the convergence criterion of the proposed method is more reasonable than the FBS method. © 2023 IEEE.

关键词： Distributed computing Integrated transmission and distribution Power flow calculation

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Optimal Z-axis Find Algorithm in Ellipsometry Semiconductor Process based on Local Search using Machine Vision 18

Optimal Z-axis Find Algorithm in Ellipsometry Semiconductor ...

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18th international conference on Future Networks and Communications (FNC) / 20th international conference on Mobile Systems and Pervasive computing (MobiSPC) / 13th international conference on Sustainable Energy Information technology (SEIT)

作者： Lee, Jaehyeong Kim, Taeyong Ryu, Sehyeon Ahn, Jungeun Kim, Sungjun Jeong, Jongpil Sungkyunkwan Univ Dept Smart Factory Convergence Suwon 440746 South Korea AIM AI Res Lab Hanam South Korea

By the latest method, wafers of semiconductors have been sliced very thin for manufacturing efficiency, and the manufacturing process of stacking various thin films has been used. In order to measure such a thin film during the semiconductor manufacturing process, an Elipsometer, a non-destructive optical device, is used. Ellipsometer analyzes the thin film by checking the change in the polarization state of the incident light after the light irradiated to the wafer surface is reflected from the incident surface. However, thinly sliced wafers are often bent during the manufacturing process, so in industrial sites Therefore, it was difficult to efficiently measure the thin film by maintaining an accurate optical state. Accordingly, this study analyzed data based on the image of Machine Vision and compared algorithms that efficiently enable precise measurement on vented wafers by using it and changing the Z axis. Thus, we propose a focusing optimizing algorithm based on machine vision image processing and evaluate the data and features to support it, and we open data sets and algorithm codes that can prove this process in GitHub repository(1). In addition, the efficiency of these algorithms was interpreted through simulation figures, and through this, an optical system capable of precise measurement applying a method of efficiently moving the Z-axis is proposed. (c) 2023 The Authors. Published by Elsevier B.V.

关键词： Wafer stage Semiconductor Ellipsometer Machine Vision Auto-Focusing

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Multipath Information Announcement Algorithm for computing Power Network based on Self-Organizing Map Network

Multipath Information Announcement Algorithm for Computing P...

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2023 IEEE/CIC international conference on Communications in China, ICCC 2023

作者： Li, Kaixiang Zhang, Xing Wang, Wenbo Beijing University of Posts and Telecommunications Wireless Signal Processing and Network Laboratory Beijing100876 China

ISBN: (纸本)9798350345384

Information announcement is a critical technology in the computing Power Network (CPN) for monitoring real-time multi-dimensional resources of computing nodes and synchronizing them with other nodes. This paper first models the problem of minimizing the delay of information announcement as a Traveling Salesman Problem (TSP). Then, the proposed Inverse Euclidean distance Algorithm (IEDA) is utilized to determine the Euclidean space coordinates of each node based on their network delay. Finally, the proposed SOM-based Multi-route Synchronization Algorithm (SMSA) is employed to identify multiple synchronization routes for completing the Com-Net synchronization. The simulation results demonstrate that compared to traditional algorithms, the proposed method can reduce convergence time by nearly 17% and decrease the Age of Information by nearly 4%. © 2023 IEEE.

关键词： computing power

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Modified Long Short Term Memory-Puzzle Optimization Algorithm for Task Offloading Prediction in Mobile Edge computing 9

Modified Long Short Term Memory-Puzzle Optimization Algorith...

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9th IEEE international conference on Smart Structures and Systems, ICSSS 2023

作者： Jebamani, S Anitha Winster Sathianesan, Godfrey Srm Institute of Science and Technology Department of Computing Technologies Tamilnadu Kattankulathur603202 India Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Tamilnadu Kattankulathur603202 India

ISBN: (纸本)9798350384208

Task offloading may increase transmission latency as well as transmission energy use in IoT edge computing. The paper describes the task offloading problem as a joint decision-making problem for cost reduction that also encompasses processing energy consumption. Processing latency, and the task throw rate associated with latency-sensitive tasks in order to lower the cost of resources needed for task offloading and enhance the utilization of server resources. The MLSTM networks-based POA algorithm is suggested to resolve the aforementioned task offloading decision dilemma. This approach uses the MLSTM method to anticipate the load on the edge server in real-time during the model's training stage, which significantly enhances speed and convergence accuracy throughout the offloading procedure. In the testing stage, the next task's characteristics are predicted using an MLSTM framework, and next the task's computing resources are assigned, thereby decreasing the task's reaction time and improving the system's offloading effectiveness. According to the experimental assessment, this approach may successfully lower offloading costs, average latency, and task throw rates. © 2023 IEEE.

关键词： Energy utilization

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Optimized CNN Architecture based on Mutant Particle Swarm Optimization and Opposition Based Learning for Image Classification 1

Optimized CNN Architecture based on Mutant Particle Swarm Op...

引用

1st international conference on Cyber Security and computing, CyberComp 2024

作者： Saleem, Muhammad Asif Senan, Norhalina Aamir, Muhammad Universiti Tunn Hussein Onn Malaysia Faculty of Computer Science and Information Technology Batu Pahat Malaysia University of Oxford Department of Computer Science United Kingdom

ISBN: (纸本)9798350387728

Recent improvements in Convolution Neural Networks (CNN) have demonstrated extraordinary performance in solving real-world problems. However, the performance of CNN depends purely on its architectural parameters and their arrangements. Designing CNN architecture for a particular problem is time consuming and requires a deep understanding of Neural networks, which is not possible for each researcher. In this research, an automated CNN architecture based on the Particle Swarm Optimization algorithm empowered with an iterative mutation operator and Opposition-based Learning strategy is proposed for image classification problems. The proposed technique significantly enhances performance in terms of accuracy, precision-recall, and convergence rate. The effectiveness of the proposed method is evaluated on benchmark datasets, including CIFAR-10, MNIST-Fashion, and Paddy Doctor Dataset. It demonstrates superior performance compared to traditional models which are Alex-Net, VGG19, GA-CNN, and PSO-CNN. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Image Caption Validation for Public Complaints on Social Media 8

Image Caption Validation for Public Complaints on Social Med...

引用

8th IEEE international conference for convergence in technology, I2CT 2023

作者： Purwantono Scudetto, Naufal Edy Romadhony, Ade Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350334012

Image Caption Validation is the task of validating whether a caption matches the image provided. The result of this task can reduce distress by validating complaints conveyed from the local community. In this paper, we introduce a custom data set of Indonesian public complaints that consists of floods and littering complaints from Twitter. We used a fine-tuned VGG16 model for image classification and an IndoBERT Model (Indonesian Bert Model) for text classification. Both of these models were combined to into a system that validates whether a given caption accurately describes the image. The experimental result showed that the fine-tuned VGG16 had an accuracy score of 93%, the IndoBERT Model had an accuracy of 89%, and the Validation Output Prediction Model had an accuracy of 56%. © 2023 IEEE.

关键词： Image classification

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Distributed Robust State Estimation of Uncertain Complex Networked Systems

Distributed Robust State Estimation of Uncertain Complex Net...

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2023 international conference on Neuromorphic computing, ICNC 2023

作者： Zhao, Yufan Lv, Yuezu Yu, Hao Beijing Institute of Technology School of Automation Beijing China Advanced Research Institute for Multidisciplinary Science Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350316889

This paper studies the consensus problem of multi-agent systems with unknown inputs under directed communication topologies. The structure of a specific-time observer is designed which takes a pulsative form at some time instants, and then the distributed attack-free protocol is presented, which only utilizes relative output measurement. To test the validity of the algorithm, the proposed algorithm is compared with existing Kalman filtering algorithm through simulation analysis, thereby drawing a conclusion on the advantages of reducing calculation costs, improving estimation accuracy, and convergence speed. © 2023 IEEE.

关键词： Multi agent systems

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Optimal Privacy Preserving in Wireless Federated Learning over Mobile Edge computing

Optimal Privacy Preserving in Wireless Federated Learning ov...

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IEEE international conference on Communications (IEEE ICC)

作者： Nguyen, Hai M. Chu, Nam H. Nguyen, Diep N. Dinh Thai Hoang Minh Hoang Ha Dutkiewicz, Eryk Univ Technol Sydney Sch Elect & Data Engn Sydney NSW Australia Vietnam Natl Univ VNU Univ Engn & Technol JTIRC Hanoi Vietnam Phenikaa Univ ORLab Fac Comp Sci Hanoi Vietnam

ISBN: (纸本)9781538674628

Federated Learning (FL) with quantization and deliberately added noise over wireless networks is a promising approach to preserve the user differential privacy while reducing the wireless resources. Specifically, an FL learning process can be fused with quantized Binomial mechanism-based updates contributed by multiple users to reduce the communication overhead/cost as well as to protect the privacy of participating users. However, the optimization of wireless transmission and quantization parameters (e.g., transmit power, bandwidth, and quantization bits) as well as the added noise while guaranteeing the privacy requirement and the performance of the learned FL model remains an open and challenging problem. In this paper, we aim to jointly optimize the level of quantization, parameters of the Binomial mechanism, and devices' transmit powers to minimize the training time under the constraints of the wireless networks. The resulting optimization turns out to be a Mixed Integer Non-linear Programming (MINLP) problem, which is known to be NP-hard. To tackle it, we transform this MINLP problem into a new problem whose solutions are proved to be the optimal solutions of the original one. We then propose an approximate algorithm that can solve the transformed problem with an arbitrary relative error guarantee. Intensive simulations show that for the same wireless resources the proposed approach achieves the highest accuracy, close to that of the conventional FL with no quantization and no noise added. This suggests the faster convergence/training time of the proposed wireless FL framework while optimally preserving users' privacy.

关键词： federated learning quantization level differential privacy communication constraints Binomial mechanism convergence time optimization

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WingSpan: Advanced Butterfly Classification using ResNet-18 Networks 3

WingSpan: Advanced Butterfly Classification using ResNet-18 ...

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3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Kaur, Jashanpreet Rani, Shalli Sharma, Ankita Dogra, Ayush Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

ISBN: (纸本)9798331532420

Butterfly classification is a challenging task due to the intricate patterns and diverse morphological characteristics exhibited by various species. This study leverages the ResNet-18 DL model to address the complexities of butterfly classification, aiming to achieve high accuracy and robust performance across diverse datasets. The ResNet-18 model, known for its residual learning framework, is particularly effective in training deeper networks by mitigating the vanishing gradient problem. A dataset comprising high resolution images of 75 butterfly species was used, with images split into training, validation, and test sets. The model was trained over 40 epochs, incorporating data augmentation and optimization techniques to enhance generalization. The results demonstrate that the ResNet-18 model achieved a training and validation accuracy approximately 98%, with minimal overfitting as indicated by the convergence of the training and validation loss curves. The findings underscore the efficacy of the ResNet-18 model in accurately classifying butterfly species, providing a foundation for future research in automated species identification and contributing to ecological monitoring and biodiversity conservation efforts. The study illustrates the potential of DL models in addressing complex classification tasks in entomology and beyond. © 2024 IEEE.

关键词： Abiotic

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