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Task Scheduling Based on Improved Particle Swarm Optimization for Cloud computing 8th

Task Scheduling Based on Improved Particle Swarm Optimizatio...

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8th international conference on Artificial Intelligence and Security , ICAIS 2022

作者： Zhang, Qiming Xie, Xiaolan Wang, Jiaming Guangxi Key Laboratory of Embedded Technology and Intelligent System Guangxi Guilin541006 China School of Information Science and Engineering Guilin University of Technology Guangxi Guilin541006 China

ISBN: (纸本)9783031067600

Particle Swarm Optimization (PSO) algorithm is widely used in cloud computing task scheduling. As PSO algorithm tends to fall into local optimum and has poor convergence accuracy in the later stage, as a result, in this paper i integrates Simulated Annealing algorithm (SA) into PSO algorithm and adopts a strategy of mixing random and nonlinear decreasing inertia weight. At the same time, the idea of "accept the bad solution with a certain probability" in simulated annealing is used to improve the global search ability of the algorithm. Finally, the chaotic disturbance strategy is added to make the algorithm search for a better solution as far as possible, so as to improve the convergence accuracy of the algorithm in the later stage. The improved PSOSA algorithm and the standard particle swarm optimization algorithm are applied to the task scheduling test in the cloud environment through the Cloudsim simulation platform. The results show that the improved algorithm can achieve better scheduling results and faster convergence speed. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Simulated annealing

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Decision-Making Platform for SMART Plantation Agriculture Using Machine Learning and Image Processing 7

Decision-Making Platform for SMART Plantation Agriculture Us...

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7th IEEE international conference for convergence in technology, I2CT 2022

作者： Swarnakantha, Supunya Chathurika, Bhagyanie Weragoda, K.V. Bowatte, W.M.I.K. Thalawala, E.V. Bandara, M.M.U.L. Faculty of Computing Sliit Malabe Campus Sri Lanka

ISBN: (纸本)9781665421683

Plantation agriculture plays a crucial role in the Sri Lankan economy in terms of both values of production and employment, even though the relative contribution has declined in recent years. Climate variability and volatile commodity prices influence agricultural production and revenue. Production and marketing decisions are frequently based on insufficient knowledge of the specific outcome of that decision. Therefore, most planters are having difficulty with the decision-making process since they are not using high-level technologies and are relying on conventional approaches. As a result, Sri Lankan agriculture and plantation industries are operating at a lower production capacity. The objective of this study is to analyze and propose appropriate solutions to the challenges that the planters face daily based on their environmental characteristics, previous data, and using their mobile phone cameras, planters will be able to make the most precise decisions using high-level technologies. This system presents a software-enabled platform for predicting future yields, forecasting the future market and intermediate buying selling prices, recognizing pests, and providing appropriate treatments, forecasting a fertilizer plan and water delivery according to soil type, and selecting the most suitable crops for cultivation. Aside from that, introducing a platform where planters can sell their crop to local and international customers and planters can communicate with experts and other planters through an agricultural forum. Machine learning, deep learning, and image processing techniques are employed to develop this system. © 2022 IEEE.

关键词： Fertilizers

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Implementation of stochastic computing in activation functions using stochastic arithmetic components

Implementation of stochastic computing in activation functio...

引用

international conference for convergence of technology (I2CT)

作者： P. Ashok B. Bala Tripura Sundari Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

A new computing method using stochastic-based numbers is gaining importance as an approximate computing method to save area, energy, and computation time based on the accuracy required. This works uses stochastic computing, which is suitable for enhancing the efficiency of neural network. Herein we focus on developing activation functions that are essential parameters in the design of neural networks. The activation function in stochastic computing is typically a threshold function that maps the input bits to a binary output based on a probability distribution. This paper presents the development of modified activation functions tanh and COS using SC-based arithmetic components. Two different types of stochastic number generators (SNGs) have been used. Error analysis has been done based on the computation using two SNGs. Also, accuracy measurement is performed using error analysis for these complex functions mentioned above.

关键词： Error analysis Stochastic systems Neural networks Measurement uncertainty Approximate computing Probability distribution Generators

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Task Offloading and Resource Allocation based on Enhanced Artificial Hummingbird Algorithm in NOMA-Assisted Mobile Edge computing

Task Offloading and Resource Allocation based on Enhanced Ar...

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international conference on Communication technology (ICCT)

作者： Jie Yang Di Chen Xuehong Cao Jing Zeng Nanjing Institute of Technology Nanjing China Nanjing University of Posts and Telecommunications Nanjing China Nanjing Institute of Technology Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

Mobile edge computing (MEC) is a key technology of the Internet of Vehicles (IoV). Applying non-orthogonal multiple access (NOMA) technology to MEC can improve spectrum and avoid serious computing delay. This paper studies the optimization problem of task offloading and resource allocation under the NOMA-MEC system. By jointly optimizing the task partition ratio, transmit power allocation and computing resource allocation on the MEC server, the goal is to minimize the weighted total delay of the system. An enhanced artificial hummingbird algorithm (EAHA) is proposed to solve the optimization problem. Considering the task priority weights of each vehicle user, the SIC decoding mechanism of the NOMA technology is improved, and the chaos map, Levy flight strategy and whale algorithm are added to improve the population initialization and migration strategy in the AHA algorithm. The problem of uneven population distribution and repeated individuals in the original algorithm is solved, and the limitation of the algorithm that it is easy to fall into the local optimal solution is improved. Finally, the optimization solution of task offloading and resource allocation problems is realized, so that vehicle users can obtain the optimal offloading strategy. Simulation results show that the algorithm proposed in this paper can effectively reduce the total delay of the system and has considerable convergence speed and accuracy.

关键词： NOMA Multi-access edge computing Simulation Delays Partitioning algorithms Resource management Servers Optimization Internet of Vehicles convergence

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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...

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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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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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Engineering research on multi-strategy improvement of the grey wolf optimizer

Engineering research on multi-strategy improvement of the gr...

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Information Systems and computing technology (ISCTech), international conference on

作者： Mingming Ma Bo Wang Chenyan Yang College of Science Shenyang University of Technology Liaocheng China College of Science Shenyang University of Technology Anshan China College of Science Shenyang University of Technology Yuncheng China

In order to address the shortcomings of traditional grey wolf optimization algorithm, such as the lack of population diversity, early convergence, and poor global optimization ability, a new algorithm (Improved Gray Wolf Optimizer IGWO) has been proposed based on Cauchy mutation and multi-strategy improvement. Firstly, the Tent chaotic map is utilized to initialize the population to avoid insufficient exploration of random populations. Secondly, the convergence factor is changed to a non-linear decreasing mode to flexibly balance the development and exploration stages. Finally, the Cauchy mutation operator is used to perturb the position updates, suppress premature convergence, and improve global optimization performance. In the simulation part, six test functions are used to verify the superiority of the IGWO. The time complexity analysis further proves its effectiveness. According to the experimental results, the IGWO algorithm demonstrates a significant effectiveness in solving two engineering design problems, and therefore can be applied to practical engineering optimization problems.

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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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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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