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Cluster Cloud with SDN Virtual Network 13

Cluster Cloud with SDN Virtual Network

引用

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

作者： Cho, Sungchol Aung, Khin Chan Myae Han, Sunyung Sang Huh College Konkuk University Seoul Korea Republic of Konkuk University Department of Computer and Science Engineering Seoul Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Software Defined Network (SDN) refers to an approach to network infrastructure that virtualizes and abstracts network resources through software. In addition, software-defined networks are introduced to break free from the limitations of IP network infrastructure. Cloud computing refers to Internet-based computing. It is a service that places a program on a virtualized server on the Internet and calls it on a computer or smartphone whenever necessary. Currently, there is a proliferation of personal devices and cloud applications, which leads to increased network bandwidth. Cloud computing is a limitation in the expansion of cloud computing in IP networks, and SDN is applied to solve this problem. The structure of the cloud has a centralized cloud and a distributed cloud structure. Core Cloud is the most efficient for high-speed data processing because data processing in one place. However, the network length between Cloud and the user is the longest and most inefficient for data transmission. Distributed Cloud is not good for data processing speed than centralized cloud, but the network length between cloud and user is the most effective in high-speed data transmission. In this paper, we propose a Cluster Cloud with the Instance Exchange structure. Cluster Cloud exchanges Instance between the Cluster Cloud through instance information and the SDN information to selectively change to the core Cloud, Distributed Cloud structure. Therefore, although it is in the form of a centralized cloud, it is changed to a distributed form according to the movement of users to reduce the data usage of the overall network. © 2022 IEEE.

关键词： Edge computing

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Adaptive Servo System Parameter Tuning with Success-Driven Differential Evolution 8th

Adaptive Servo System Parameter Tuning with Success-Driven D...

引用

8th international conference on computing, Control and Industrial Engineering, CCIE 2024

作者： Zhou, Maoyu Hao, Qiang Zou, Weijun Liu, Zongkai Nanjing University of Science and Technology Nanjing210094 China China North Vehicle Research Institute Beijing100072 China

ISBN: (纸本)9789819769339

Addressing issues in system instability and low efficiency during the parameter tuning process of servo systems using traditional optimization algorithms, this paper proposes an improved Differential Evolution (DE) algorithm. Although the traditional Relay Feedback Method (RFB) can obtain theoretical parameters, it is susceptible to irregular disturbances within the system, leading to parameter fluctuations and affecting tuning accuracy. To enhance tuning efficiency and system robustness, this study employs the RFB results as the initial population for the improved DE algorithm and introduces a Success-Driven Mutation Strategy (SDMS) to accelerate algorithm convergence. The SDMS records and reuses historically successful mutation steps to guide the algorithm in searching along effective directions in subsequent iterations. Through simulation experiments, the improved DE algorithm’s rapid convergence and the effectiveness of the optimized parameters are validated. The algorithm significantly enhances the system’s dynamic performance and disturbance rejection capabilities, demonstrating its potential value in practical engineering applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Disturbance rejection

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An Improved Sine Cosine Algorithm 3

An Improved Sine Cosine Algorithm

引用

3rd international conference on Intelligent Communications and computing, ICC 2023

作者： Wang, Congqian Wang, Shasha He, Xuelei Networks and Data Centers Northwestern University Xi'an710100 China School of Information Science&Technology Northwestern University Xi'an710100 China

ISBN: (纸本)9798350308327

Addressing the challenges associated with the sine cosine algorithm (SCA), including slow convergence, low optimization accuracy, and easy falling into the local optimal in optimizing high-dimensional functions for SCA, this paper introduces an improved sine cosine algorithm (ISCA). This innovative approach combines the strengths of the differential evolution (DE) and the SCA. To tackle these challenges, the whale optimization algorithm's (WOA) spiral update strategy is integrated to refine the SCA's results, enhancing its local search capabilities. The ISCA strategically employs the SCA in its initial phase, followed by the later stages' implementation of DE. This strategic combination ensures a well-balanced utilization of the global search and local development capabilities of both algorithms. Simulation experiments on 23 benchmark functions illustrate that the proposed improved algorithm (ISCA) has a high optimization performance. © 2023 IEEE.

关键词： Optimization

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Slime Mould-Seagull Optimizer 7

Slime Mould-Seagull Optimizer

引用

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

作者： Das, Gyanesh Panda, Rutuparna Agrawal, Sanjay Vssut Department of Etce Odisha Burla768018 India

ISBN: (纸本)9781665421683

Biological swarm behaviour of slime mould is a popular application algorithm in engineering optimization problems. It's food searching behaviour is more around in two-dimensional plane i.e. plane surface only, while population decide for future displacement. This limit situation is investigated for additional better chances of investigation. Seagull Optimization Algorithm is utilized to further increase in exploration. A complete analysis is tested through 23 benchmark functions. The validation of our method is done by comparing 3 standard algorithms. The proposed Slime Mould-Seagull (SMS) Algorithm would be helpful for function optimization in taking care of optimization-based engineering design. © 2022 IEEE.

关键词： Soft computing

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Application of the Refactoring to the Understandability and the Cognitive Complexity of a Software 7

Application of the Refactoring to the Understandability and ...

引用

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

作者： Wijendra, Dinuka R. Hewagamage, K.P. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka University of Colombo School of Computing Department of Information Systems Colombo Sri Lanka

ISBN: (纸本)9781665421683

Cognitive complexity of a software determines the methodology of comprehending the internal logic of a given software by an individual, quantitatively. The procedure of handling a software by different users is different, which results the cognitive complexity as a subjective measurement. The quantification of the cognitive complexity is still not standardized due to the varied number of factors affected for the cognitive complexity determination and its nature of the subjectivity. This paper evaluates the relationship between the cognitive complexity and understandability as one of the qualitative factors to determine the cognitive complexity and the usage of refactoring techniques to reduce the cognitive complexity without refraining its calculation process with respective to the internal logic of the software as in other standard software complexity metrics perform. © 2022 IEEE.

关键词： Computer circuits

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A Survey on Deep Reinforcement Learning-driven Task Offloading in Aerial Access Networks 13

A Survey on Deep Reinforcement Learning-driven Task Offloadi...

引用

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

作者： Nguyen, Tri-Hai Park, Laihyuk Seoul National University of Science and Technology Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Internet of Things computation offloading is a challenging problem, particularly in distant places where mobile edge computing (MEC) or cloud infrastructure is absent. Fortunately, aerial access networks (AANs), which include unmanned aerial vehicles and satellite communications, are employed as effective aerial platforms to deliver ubiquitous and reliable access. Furthermore, deep reinforcement learning (DRL) is a viable method to boost the efficiency of edge network resource management in achieving energy-efficient, low-delay MEC services. This paper investigates recent advances in DRL-based task offloading strategies in the MEC-based AANs. Research challenges and directions are also discussed. © 2022 IEEE.

关键词： Reinforcement learning

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A Mutation Lion Swarm Optimization Algorithm Based on Proportional Strategy 24

A Mutation Lion Swarm Optimization Algorithm Based on Propor...

引用

3rd Asia conference on Algorithms, computing and Machine Learning, CACML 2024

作者： Zhao, Ze Jiang, Mingyan Yuan, Dongfeng Jiang, Keqin Wang, Feng Zhou, Xiaotian School of Information Science and Engineering Shandong University China Qilu Institute of Technology China

ISBN: (纸本)9798400716416

During the iterative process, the probability of selection is directly linked to the fitness magnitude, as each iteration of swarm intelligence optimization algorithms progressively converges towards an optimal solution. Building upon this premise, we introduce the Lion Swarm Optimization Algorithm based on the Proportional Strategy (PLSO), designed to enhance convergence speed and achieve superior optima. Inspired by the roulette strategy, our approach integrates the concept into the Lion Swarm Optimization (LSO) algorithm. In essence, it mimics the behavior of lion cubs, who learn hunting by following lionesses;however, their choice of lioness to follow is governed by rationality, favoring those with superior hunting skills. This fosters knowledge transfer among lionesses, strengthening the link between global and local optima, thereby enhancing local search capabilities and significantly accelerating convergence. In this paper, we evaluate the performance of the PLSO algorithm against six single-peak test functions, four multi-peak test functions, and four additional functions, chosen randomly. Comparative analyses are conducted with classical optimization algorithms, and the PLSO algorithm is applied to address the image segmentation problem. Our findings demonstrate the superior efficacy and robustness of the PLSO algorithm. © 2024 ACM.

关键词： Optimization algorithms

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Cognitive Complexity Reduction through Control Flow Graph Generation 7

Cognitive Complexity Reduction through Control Flow Graph Ge...

引用

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

ISBN: (纸本)9781665421683

The cognitive complexity of a software determines the human comprehension effort to determine its underlying logic. The human comprehension effort preliminary depends on the person, who deals with the software, the source code and the problem to be addressed. The understandability of a given source code is varying with each user such that the cognitive complexity results with a subjective measurement. The graphical representation of the logical behavior of a source code implies the individual to comprehend the logic easily rather than referring to its original code base. This paper evaluates the possibility of using control flow graph representation to reduce the cognitive complexity of a given source code, thereby ensuring a less software complexity. © 2022 IEEE.

关键词： Graphic methods

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Analysis of Big Data Research Hotspots Based on Keyword Co-occurrence 9

Analysis of Big Data Research Hotspots Based on Keyword Co-o...

引用

9th IEEE international conference on Data Science in Cyberspace, DSC 2024

作者： Zhang, Niuniu Feng, Guohe National University of Defense Technology National Key Laboratory of Information Systems Engineering Changsha China South China Normal University College of Economics And Management Guangzhou China

ISBN: (纸本)9798350391367

In the era of mobile Internet and cloud computing, the rise of big data has profoundly impacted diverse industries and sectors, drawing significant scholarly attention. The objective of this paper is to delineate an overview of the international research landscape of big data, identifying key research hotspots and emerging trends. To gain a comprehensive overview of the current state of research in the field of big data, we employed a range of analytical techniques, including co-word analysis, clustering analysis, social network analysis, and dynamic strategy coordinate mapping. Our review encompassed articles published between 2017 and 2019, spanning a three-year period. The data were sourced from the SCI, SSCI, SPCI, and CPCI-SSH databases. The literature was subjected to a sysytematic analysis using Bibexcel, SPSS, and Gephi software, with the objective of uncovering distinct patterns and trends. The findings reveal 11 thematic clusters within international big data research. These are text mining, data fusion, distributed computing, Industry 4.0, data privacy, business intelligence, precision medicine, bioinformatics, and data mining techniques such as classification and clustering. It is noteworthy that Industry 4.0, data privacy, and text mining emerged as significant research hotspots. Furthermore, the fields of artificial intelligence, natural language processing, smart grids, energy big data, and wireless sensor networks are regarded as emerging areas of interest. It is anticipated that the convergence of bioinformatics and other pivotal areas of big data research, will serve as the impetus for future research hotspots, necessitating sustained scholarly investigation and discourse. © 2024 IEEE.

关键词： big data clustering analysis co-words analysis social network analysis

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Adaptive Control of Three-Phase Induction Generator for Power Quality With PI Gains Optimization 3

Adaptive Control of Three-Phase Induction Generator for Powe...

引用

3rd Odisha international conference on Electrical Power Engineering, Communication and computing technology, ODICON 2024

作者： Dawar, Bindra Arya, Sabha Raj Chilipi, Rajasekharareddy Giri, Ashutosh K Sardar Vallabhbhai National Institute of Technology Department of Electrical Engineering Surat395007 India Government Engineering College Department of Electrical Engineering Bharuch392002 India

ISBN: (纸本)9798350354379

This article discusses implementation of variable step size gradient adaptive least means square (VSSGA-LMS) control scheme for shunt connected distribution static compensator (DSTATCOM). DSTATCOM is connected in 3-phase self-excited induction generator (SEIG) based system feeding to Nonlinear Load. The entire operation of 3-phase SEIG such as harmonics reduction in generated current, reactive power control and load balancing is obtained through the parallel operation of DSTATCOM. The control is employed to obtain the reactive and active element of weight from non-ideal load currents. This is further utilized to trigger the voltage source converter (VSC). It also employs gradient information in the weight updating rule and is incorporated to keep the frequency and terminal voltage within the acceptable limit. The resulting VSSGA-LMS control strategy attains rapid convergence and minimizes steady-state errors. A comparison between the synchronous reference frame (SRF), and least mean square (LMS) control algorithms confirms the superiority of the proposed VSSGA-LMS approach. This is evident in faster settling times and reduced peak overshoot. Furthermore, for optimal PI gains, the particle swarm optimization (PSO) algorithm is integrated with the aforementioned control algorithm to stabilize the dynamics. System analysis has been developed to evaluate the performance of the PSO algorithm using different standard objective functions. This control scheme along with ITAE-based objective function, PSO-optimized gains improves the system performance and maintains the source current distortion within the standard limits. The performance of the suggested control system is assessed through MATLAB. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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