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True Time Dispatch of Experimental Reliability Resources Based on Hybrid Group Intelligent Algorithm Optimization 2

True Time Dispatch of Experimental Reliability Resources Bas...

引用

2nd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Li, Fang Heilongjiang International University Heilongjiang Harbin China

ISBN: (纸本)9798350347456

True time resource dispatch problem refers to how to reasonably arrange and allocate limited resources under given time constraints, so that one or several objectives are optimal, in terms of its essence, this is the process of decision-making. With the high-speed blossom of information technology, humans have higher demand for network performance, and the network itself has the characteristics of openness and dynamics, so Lots of network nodes has become more complex and diverse. Affected by many uncertain factors, the work load dynamic resource dispatch was always unexpected open environment. For the sake of enhance the robustness and dependability of the system and reduce resource consumption and task completion delay, an effective control strategy must be adopted. In the implementation of this new environment traditional control system design and implementation of the method, its characteristic is to control and dispatch phase separation, is unable to provide the necessary quality control (QOS) guarantees. Hence the need for an effective solution that can support the above requirements. On this basis, this paper will use mixed difference strategy multi-objective group intelligence algorithm and multi-direction update mixed multi-objective group intelligence method to solve the true time dispatch of resources. This model combines the advantages of two kinds of intelligent optimization algorithms and overcomes their respective disadvantages. The experiment result shows that the model based on hybrid group intelligent algorithm has higher positioning accuracy and strong generalization performance. The HIOA uses the multi-direction update strategy and the local search strategy based on the particle sequence, the convergence of the algorithm is improved obviously, and the searched solutions have good distribution performance. Therefore, the hybrid group intelligent algorithm can be more reasonable in true time resource dispatch. © 2023 IEEE.

关键词： Phase separation

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An Improved Ant Colony Algorithm based on Target-Driven Gravity Force 5

An Improved Ant Colony Algorithm based on Target-Driven Grav...

引用

5th international conference on Telecommunications and Communication Engineering, ICTCE 2022

作者： Chen, Siyang Sun, Yue Song, Haiyang Yang, Xi Yuan, Kunhao Chengdu University of Technology Chengdu China Loughborough University Loughborough United Kingdom

ISBN: (纸本)9781450397797

Addressing the issues of slow convergence, trapping into the locally optimal solutions, and blinding search in conventional ant colony algorithm (ACA) for mobile robot path-planning, we develop an improved ant colony algorithm based on target-driven gravity force (ACA-TDGF). The goal is quickly finding an optimal route in a grid map that connects the starting and ending points, avoiding encountering random obstacles. In the ACA-TDGF algorithm, the ants are classified as strong, medium, or weak according to the valid path length in each iteration, while the ant leaves the updated pheromone along the path. This novel pheromones updating strategy provides diversity in pheromone levels at the initial whilst improving the performance by selecting the strong class, reducing the probability of trapping into the locally optimal solutions. ACA-TDGF algorithm combines the gravitational function with the ant colony algorithm, replacing the first-order gravitational function as a higher-order exponential form so that the algorithm has a faster convergence speed and higher global search ability. Simulation results show superior performance in the tested environment. © 2022 Association for computing Machinery.

关键词： Motion planning

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Application Deployment in Mobile Edge computing Environment Based on Microservice Chain 25

Application Deployment in Mobile Edge Computing Environment ...

引用

25th IEEE international conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Li, Haiyan Tang, Bing Xu, Wei Guo, Feiyan Zhang, Xiaoyuan Hunan University of Science and Technology School of Computer Science and Engineering Hunan Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

ISBN: (纸本)9781665405270

Mobile edge computing (MEC) has become an extremely hot topic in recent years. Mobile edge cloud relies on storage and computing resources on network edge to provide users with delay-sensitive services. However, the transmission delay among microservices and the load of the servers tend to increase due to improper service placement and unreasonable resource allocation under MEC. In this paper, an edge service placement strategy based on an improved fast non-domination sorted genetic algorithm is proposed. First, a microservice placement optimization model is built with the goal of minimizing the average transmission delay and the load balance degree. Then, a genetic algorithm-based microservice placement approach called GA-MSP using improved NSGA-II is studied under the premise that a single service instance is deployed only on one container. The experiments show that the proposed GA-MSP approach is able to achieve low delay and load balance effectively, and ultimately deploy services based on the resulting sets after convergence, which outperforms several other existing representative methods. © 2022 IEEE.

关键词： Mobile edge computing

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Rail Camera: An Edge Resource Management for Smart Farm Application

Rail Camera: An Edge Resource Management for Smart Farm Appl...

引用

international conference on Information and Communication technology convergence (ICTC)

作者： Seungwoo Kum Jaewon Moon Youngkee Kim Alejandro Barrera Carvajal Seungtaek Oh Francisco Andres Perez Information and Media Research Center Korea Electronics Technology Institute Seongnam Republic of Korea CT Engineering Group Madrid Spain

Edge computing is one of the most important technology for the industry to realize analysis with deep learning technology. The edge computing architecture provides many benefits over cloud computing, since it reduces cloud communication requirements by processing data in itself. Among many industries for edge computing applications, smart agriculture is a promising one since it needs to handle various kinds of data such as ones from sensors and/or video streams. However, applying edge computing has been remaining a challenging task since it has different environment than other industries. It has less computational resources in the field and it should be able to deal with the target crop change. In this paper, an edge management platform with a rail camera for a smart farm is presented. First, A rail camera is implemented as an edge device which has accelerator resource where the deep learning application container can be deployed. The edge resource is federated with other cloud resources. And then, software for the edge management are presented - the Container Deployment Service and AI Model Repository, for container deployment and model deployment management, respectively.

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CNN-LSTM-AE Based Predictive Maintenance Using STFT for Rotating Machinery 8

CNN-LSTM-AE Based Predictive Maintenance Using STFT for Rota...

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

作者： Gong, Seokhyun Lee, Jaehyeong Lee, Chae-Gyu Jeong, Jongpil Choi, Wonmin Kim, Chanyoung Park, Jungsoo Kim, Jongheon Sungkyunkwan University Dept. Smart Factory Convergence Suwon16419 Korea Republic of Sungkyunkwan University Dept. Electronic and Electrical Engineering Suwon16419 Korea Republic of Sungkyunkwan University Dept. Mechanical Engineering Suwon16419 Korea Republic of Sungkyunkwan University Dept. Systems Management Engineering Suwon16419 Korea Republic of

ISBN: (纸本)9798350373615

In modern manufacturing after the Fourth Industrial Revolution, fast and accurate production is essential, and it is important to maintain and improve process reliability and productivity. Beyond the traditional conservative maintenance method, it is important to monitor the condition of facilities and take preventive measures. This study aims to analyze vibration data of manufacturing equipment and predict rotor defects using deep learning technology. An anomaly detection model that distinguishes between normal and abnormal conditions of the equipment is trained, and proactive problem prediction through internal and external data (sensor data, operation logs, etc.) is emphasized. Vibration data is converted into two-dimensional image data through Short Time Fourier Transform (STFT), and feature extraction is performed through Convolutional Neural Network (CNN) algorithm. Then, an Autoencoder (AE) based time series anomaly detection technique is used to make efficient and accurate predictions. In order to strengthen the competitiveness of the manufacturing industry, a deep learning-based rotor defect prediction model was developed, which is expected to improve the stability and productivity of the manufacturing process. © 2023 IEEE.

关键词： Defects

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Chaotic Particle Swarm Optimization with Spiral-shaped Update Mechanism 23

Chaotic Particle Swarm Optimization with Spiral-shaped Updat...

引用

2nd international conference on Algorithms, Data Mining, and Information technology, ADMIT 2023

作者： Tian, Dongping Li, Bingchun Liu, Chen Wang, Yani School of Computer Science and Technology Kashi University Xinjiang Kashi City China

ISBN: (纸本)9798400707629

Particle swarm optimization (PSO), as one of the metaheuristic optimization methods, has the merits of fast convergence, few adjustable parameters and simple searching process, but it also has the disadvantages of easily falling into local optimal and low convergence precision. Hence, a chaotic PSO with spiral-shaped update strategy (CPSOS) is presented in this work. First, the chaotic mapping method is used to generate the initial swarm to enhance the quality of initial particles. Second, a method based on time-varying acceleration coefficients is employed to trade off the exploration and exploitation through the analysis of velocity and position changes for particle. In addition, an adaptive position updating strategy based on spiral-shaped mechanism is adopted in different evolutionary states to get better global convergence. Finally, simulation experiment of the CPSOS algorithm is carried out based on 12 classical benchmark functions, and the convergence analysis under 2-dimensional environment is also detailed. It can be concluded from the convergence figures that the CPSOS has much better robustness and stability by improving the shortcomings of standard particle swarm optimization. CCS CONCEPTS •computing methodologies •Artificial intelligence •Search methodologies © 2023 ACM.

关键词： Economic and social effects

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Deep Learning with Enhanced convergence and Its Application in MEC Task Offloading 21st

Deep Learning with Enhanced Convergence and Its Application ...

引用

21st international conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Wan, Zheng Dong, Xiaogang Deng, Changshou Jiangxi Univ Finance & Econ Sch Informat Management Nanchang 330013 Jiangxi Peoples R China Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China

ISBN: (纸本)9783030953881;9783030953874

As an emerging computing paradigm, the mobile edge computing (MEC) has become the top topic in various research fields. Nevertheless, task offloading, as a key issue in MEC environment, is still an immense challenge because it is often NP-hard. Currently, many researchers adopt deep learning frameworks to solve task offloading problem of MEC. Unfortunately, most of these works directly use various deep learning frameworks. It is insufficient consideration that how to improve the convergence performance of deep learning in solving MEC task offloading problem. To cope with this issue, we propose two methods to enhance the convergence of deep learning in this paper, which are named as uniform design method (UDM) and hadamard matrix method (HMM), respectively. UDM and HMM can enhance exploiting ability of the space near the specific offloading decision, benefiting to improve the convergence performance of deep learning algorithms. An improved deep learning algorithm is built by integrating UDM or HMM. The validity of our proposed algorithm is verified through extensive simulation experiments. The results show that our proposed algorithm can achieve better convergence performance than the benchmark algorithm under different learning rates and memory sizes.

关键词： Mobile edge computing Task offloading Deep learning Uniform design Hadamard matrix

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Fed-STP: An Improved Federated Learning Approach via Stage-wise Training and Pruning 2

Fed-STP: An Improved Federated Learning Approach via Stage-w...

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2nd international conference on Cloud computing, Big Data Application and Software Engineering, CBASE 2023

作者： Huang, Youxin Chen, Weizhe Zhu, Shunzhi Huang, Zhicai Xiamen University of Technology School of Computer and Information Engineering Xiamen China Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou China Xiamen Huaxia University College of Information and Smart Electromechanical Engineering Xiamen China

ISBN: (纸本)9798350331448

Federated Learning (FL) is a transformative machine learning paradigm, allowing multiple clients to train a global model while maintaining local data privacy collaboratively. However, each client's unique local data distribution introduces challenges, leading to imbalances in individual model optimization and affecting model performance and convergence in non-Independent and Identically Distributed (non-IID) settings. To mitigate these issues, we propose an innovative federated learning algorithm, 'Federated Learning with Stage-wise Training and Pruning' (Fed-STP), aiming to optimize model performance under non-IID conditions. Fed-STP employs client-specific model pruning and a stage-wise training strategy to enhance local models' adaptability to their unique data characteristics. This approach also minimizes the information loss resulting from model parameter variations during the server aggregation process, thus indirectly enhancing the overall performance of the federated global model. Comprehensive experiments and detailed analyses confirm Fed-STP's superior performance over conventional FL methods, showing significant improvements in diverse image classification tasks, mainly when dealing with non-IID client data, with performance enhancements reaching up to 9.25%. © 2023 IEEE.

关键词： Data privacy

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Data to Diagnosis: Harnessing STRING Insights to Unravel the Biomarkers of Alzheimer’s

Data to Diagnosis: Harnessing STRING Insights to Unravel the...

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international conference on computing and Networking technology (ICCNT)

作者： Anagha Rajeev Bijoy A Jose Sinoy Johnson Sayuj Koyyappurath Department of Computer Science Cochin University of Science and Technology Kochi India Department of Biotechnology Cochin University of Science and Technology Kochi India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Alzheimer’s disease, shrouded in complexity, poses a challenge to predictive modeling due to extensive missing data in gene and protein interaction analyses. Identifying pivotal “hub genes” crucial for cellular networks becomes elusive, hampering effective disease understanding. Machine learning models, essential for accurate predictions, exacerbated by diverse disease manifestations. The challenge intensifies with fragmented, inconsistent data sources laden with gaps and format disparities. Our research introduces the ‘Hub Gene convergence Schema,’ a novel approach that systematically identifies pivotal hub genes, significantly enhancing predictive accuracy for Alzheimer’s disease, enabling early diagnosis and personalized treatment. The intended ‘Hub Gene convergence Schema,’ a diagram integral to our project, further enhances our approach. The ultimate goal is adaptive models facilitating early diagnosis, personalized treatment, and a profound comprehension of Alzheimer’s, marking a crucial step in our relentless pursuit of combating this formidable adversary.

关键词： Accuracy Databases Soft sensors Data integrity Pathogenesis Predictive models Biomarkers Complexity theory Alzheimer's disease convergence

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TWAFR-GRU: An Integrated Model for Real-time Charging Station Occupancy Prediction 19

TWAFR-GRU: An Integrated Model for Real-time Charging Statio...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Chen, Qiyang Liu, Sheng Qu, Haohao Zhu, Rui You, Linlin Sun Yat-Sen University School of Intelligent Systems Engineering Shenzhen China Institute of High Performance Computing Agency for Science Technology and Research Systems Science Department Singapore

ISBN: (纸本)9798350346558

The fast-growing number of electric vehicles has resulted in a variety of charging-related issues, such as the shortage of public charging piles, the inconvenience of finding available charging piles, the additional traffic congestion caused by unnecessary cruising to search for available charging piles, etc. Since only constructing new charging piles may not significantly improve the charging efficiency, methods of predicting charging station occupancy are widely discussed as an effective means to address these issues. However, several challenges are encountered in training an efficient and effective model by utilizing distributed data, improving convergence speed, and enhancing model generalization ability. To address these issues, this paper proposes a novel mechanism, named TWAFR-GRU, which integrates Temporally Weighted Asynchronous Federated Learning (TWAFL) with Reptile and Gated Recurrent Unit (GRU). As shown by the holistic evaluation based on the charging station occupancy dataset, compared with other state-of-the-art baselines, TWAFR-GRU can 1) decrease MAE, RMSE and RAE by 19%, 15% and 17% separately and improve R2, by 67%;2) cut rounds for the model to converge by 75%;3) save training time by 44%;and 4) reduce 17% in forecasting error after the personalization of the initial model to serve a specific charging station. © 2022 IEEE.

关键词： Piles

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