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international Electrical engineering congress (iEECON)

作者： Wallop Tipwong Adisorn Sirikham Jessada Konpang Putthiphong Kirdpipat Yutana Chongjarearn Oradee Preutisrunyanont Department of Electrical and Telecommunication Engineering Rajamangala University of Technology Krungthep Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Thammasat School of Engineering Thammasat University Pathum Thani Thailand Department of Robotic Engineering and Automation System Dhurakij Pundit University Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This study investigates the application of clustering algorithms to data collected from color sensors providing frequency signals during the cultivation process. Continuous sampling allowed for real-time data collection, and variations in temperature and humidity were observed to influence signal reception. The dataset's large volume and varying sample sizes necessitated the evaluation of several clustering models for efficient data processing. The experiment tested K-Means, K-Means++, Mini-Batch K-Means, and Bisecting K-Means algorithms. The results reveal that the computation time for each model increased with the number of groups. Specifically, the K-Means, K-Means++, and Mini-Batch K-Means models exhibited shorter computation times, ranging from 0.02 to 0.03 seconds for two-group scenarios, while the Bisecting K-Means algorithm took significantly longer, approximately 0.10 seconds. Despite the variation in processing times, all models performed similarly in terms of clustering quality, with an average Silhouette Score of 0.82. This score indicates that the algorithms effectively separated data points into well-defined groups while maintaining high accuracy in clustering. Additionally, the analysis highlighted the importance of balancing computation time and clustering quality, particularly as the number of clusters increases. These findings suggest that while computational efficiency may vary across models, the clustering quality remains robust, providing valuable insights for data analysis in agricultural sensor networks. The study's results demonstrate that each clustering model is capable of handling large datasets with high accuracy in grouping, making them applicable to real-world agricultural data processing scenarios. In conclusion, the experiment confirms that all tested models, despite differing in computation time, successfully partitioned the data with high-quality results, offering promising applications for data analysis and optimization in agricult

关键词： Temperature sensors Analytical models Image color analysis computational modeling Clustering algorithms Approximation algorithms Data models Sensor systems computational efficiency Optical sensors

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30th international Conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

30th International Conference on Parallel and Distributed Pr...

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30th international Conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031856372

The proceedings contain 24 papers. The special focus in this conference is on Parallel and Distributed Processing Techniques. The topics include: Parallel N-Body Performance Comparison: Julia, Rust, and More;REFT: Resource-Efficient Federated Training Framework for Heterogeneous and Resource-Constrained Environments;An Efficient Data Provenance Collection Framework for HPC I/O Workloads;using Minicasts for Efficient Asynchronous Causal Unicast and Byzantine Tolerance;a Comparative Study of Two Matrix Multiplication algorithms Under Current Hardware Architectures;Is Manual Code Optimization Still Required to Mitigate GPU Thread Divergence? Applying a Flattening Technique to Observe Performance;towards Automatic, Predictable and High-Performance Parallel Code Generation;Attack Graph Generation on HPC Clusters;analyzing the Influence of File Formats on I/O Patterns in Deep Learning;inference of Cell–Cell Interactions Through Spatial Transcriptomics Data Using Graph Convolutional Neural Networks;natural Product-Like Compound Generation with Chemical Language Models;improved Early–Modern Japanese Printed Character Recognition Rate with Generated Characters;Improved Method for Similar Music Recommendation Using Spotify API;Reconfigurable Virtual Accelerator (ReVA) for Large-Scale Acceleration Circuits;Building Simulation Environment of Reconfigurable Virtual Accelerator (ReVA);vector Register Sharing Mechanism for High Performance Hardware Acceleration;Efficient Compute Resource Sharing of RISC-V Packed-SIMD Using Simultaneous Multi-threading;introducing Competitive Mechanism to Differential Evolution for Numerical Optimization;hyper-heuristic Differential Evolution with Novel Boundary Repair for Numerical Optimization;jump Like a Frog: Optimization of Renewable Energy Prediction in Smart Gird Based on Ultra Long Term Network;vision Transformer-Based Meta Loss Landscape Exploration with Actor-Critic Method;Fast computation Method for Stopping Condition of Range Restricted

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Introducing Competitive Mechanism to Differential Evolution for Numerical Optimization 30th

Introducing Competitive Mechanism to Differential Evolution...

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作者： Zhong, Rui Cao, Yang Zhang, Enzhi Munetomo, Masaharu Graduate School of Information Science and Technology Hokkaido University Sapporo Japan Information Initiative Center Hokkaido University Sapporo Japan

ISBN: (纸本)9783031856372

This paper introduces a novel competitive mechanism into differential evolution (DE), presenting an effective DE variant named competitive DE (CDE). CDE features a simple yet efficient mutation strategy: DE/winner-to-best/1. Essentially, the proposed DE/winner-to-best/1 strategy can be recognized as an intelligent integration of the existing mutation strategies of DE/rand-to-best/1 and DE/cur-to-best/1. The incorporation of DE/winner-to-best/1 and the competitive mechanism provide new avenues for advancing DE techniques. Moreover, in CDE, the scaling factor F and mutation rate Cr are determined by a random number generator following a normal distribution, as suggested by previous research. To investigate the performance of the proposed CDE, comprehensive numerical experiments are conducted on CEC2017 and engineering simulation optimization tasks, with CMA-ES, JADE, and other state-of-the-art optimizers and DE variants employed as competitor algorithms. The experimental results and statistical analyses highlight the promising potential of CDE as an alternative optimizer for addressing diverse optimization challenges. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Competitive Mechanism Differential Evolution (DE) Evolutionary computation (EC) Novel Mutation Strategy Numerical Optimization

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Yin-Yang-pair Optimization: A novel lightweight optimization algorithm

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engineering APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2016年 54卷 62-79页

作者： Punnathanam, Varun Kotecha, Prakash Indian Inst Technol Guwahati Dept Chem Engn Gauhati 781039 Assam India

In this work, a new metaheuristic, Yin-Yang-Pair Optimization (YYPO), is proposed which is based on maintaining a balance between exploration and exploitation of the search space. It is a low complexity stochastic algorithm which works with two points and generates additional points depending on the number of decision variables in the optimization problem. It has three user defined parameters that provide flexibility to the users to govern its search. The performance of the proposed algorithm is evaluated on the set of problems used for the Single Objective Real Parameter Algorithm competition that was held as part of the congress on Evolutionary computation 2013. The results are compared with that of other traditional and recent algorithms such as Artificial Bee Colony, Ant Lion Optimizer, Differential Evolution, Grey Wolf Optimizer, Multidirectional Search, Pattern Search and Particle Swarm Optimization. Based on nonparametric statistical tests, YYPO is shown to provide highly competitive performance relative to the other algorithms while having a significantly lower time complexity. In addition, the performance of YYPO is showcased on three classical constrained engineering problems from literature. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Yin-Yang-Pair Optimization Evolutionary computation Single objective optimization Metaheuristic congress on Evolutionary computation 2013

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Adaptive backtracking search optimization algorithm with pattern search for numerical optimization

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Journal of Systems engineering and Electronics 2016年第2期27卷 395-406页

作者： Shu Wang Xinyu Da Mudong Li Tong Han Information and Navigation College Air Force Engineering University Aeronautics and Astronautics Engineering College Air Force Engineering University

The backtracking search optimization algorithm（BSA） is one of the most recently proposed population-based evolutionary algorithms for global optimization. Due to its memory ability and simple structure, BSA has powerful capability to find global optimal solutions. However, the algorithm is still insufficient in balancing the exploration and the exploitation. Therefore, an improved adaptive backtracking search optimization algorithm combined with modified Hooke-Jeeves pattern search is proposed for numerical global optimization. It has two main parts： the BSA is used for the exploration phase and the modified pattern search method completes the exploitation phase. In particular, a simple but effective strategy of adapting one of BSA＇s important control parameters is introduced. The proposed algorithm is compared with standard BSA, three state-of-the-art evolutionary algorithms and three superior algorithms in IEEE congress on Evolutionary computation 2014（IEEE CEC2014） over six widely-used benchmarks and 22 real-parameter single objective numerical optimization benchmarks in IEEE CEC2014. The results of experiment and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.

关键词： evolutionary algorithm backtracking search optimization algorithm(BSA) Hooke-Jeeves pattern search parameter adaption numerical optimization

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ON THE COMPLEXITY OF COMPOSITE NUMBERS 3rd

ON THE COMPLEXITY OF COMPOSITE NUMBERS

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3RD international SYMP ON algorithms AND computation ( ISAAC 92 )

作者： ITOH, T HORIKAWA, K Department of Information Processing Interdisciplinary Graduate School of Science and Engineering Tokyo Institute of Technology 4259 Nagatsuta Midori-ku Yokohama 227 Japan

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TOPOLOGICALLY CONSISTENT algorithms RELATED TO CONVEX POLYHEDRA 3rd

TOPOLOGICALLY CONSISTENT ALGORITHMS RELATED TO CONVEX POLYHE...

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3RD international SYMP ON algorithms AND computation ( ISAAC 92 )

作者： SUGIHARA, K Department of Mathematical Engineering and Information Physics Faculty of Engineering University of Tokyo 7-3-1 Itongo Bnnkyo-ku Tokyo 113 Japan

ISBN: (纸本)3540562796

The paper presents a general method for the design of numerically robust and topologically consistent geometric algorithms concerning convex polyhedrain the three-dimensional space. A graph is the vertex-edge graph of a convex polyhedron if and only if it is planar and triply connected (Steinitz’ theorem). On the basis of this theorem, conventional geometric algorithms are revised in such a way that, no matter how poor the precision in numerical computation may be, the output graph is at least planar and triply connected. The resultant algorithms are robust in the sense that they do not fail in finiteprecision arithmetic, and are consistent in the sense that the output is the true solution to a perturbed input. © Springer-Verlag Berlin Heidelberg 1992.

关键词： Geometry

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Sparse-Reduced computation for Large-Scale Spectral Clustering

Sparse-Reduced Computation for Large-Scale Spectral Clusteri...

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IEEE international Conference on Industrial engineering and engineering Management (IEEM)

作者： Baumann, Philipp Univ Bern Dept Business Adm Schuetzenmattstr 14 CH-3012 Bern Switzerland

ISBN: (纸本)9781509036653

Clustering is a fundamental task in machine learning and data analysis. A large number of clustering algorithms has been developed over the past decades. Among these algorithms, the recently developed spectral clustering methods have consistently outperformed traditional clustering algorithms. Spectral clustering algorithms, however, have limited applicability to large-scale problems due to their high computational complexity. We propose a new approach for scaling spectral clustering methods that is based on the idea of replacing the entire data set with a small set of representative data points and performing the spectral clustering on the representatives. The main contribution is a new approach for efficiently identifying the representative data points. First results indicate that the proposed scaling approach achieves high-quality clusterings and is substantially faster than existing scaling approaches.

关键词： Big Data Large-scale Clustering Sparse-reduced computation Spectral Clustering Unsupervised Learning

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THE K-EDGE-CONNECTIVITY AUGMENTATION PROBLEM OF WEIGHTED GRAPHS 3rd

THE K-EDGE-CONNECTIVITY AUGMENTATION PROBLEM OF WEIGHTED GRA...

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3RD international SYMP ON algorithms AND computation ( ISAAC 92 )

作者： WATANABE, T MASHIMA, T TAOKA, S Department of Circuits and Systems Faculty of Engineering Hiroshima University 4-1 Kagamiyama 1-chome Higashi-Hiroshima 724 Japan

The k-edge-eonneetivity augmentation problem (k-ECA) is the subject of the paper. Four approximation algorithms FSA, FSM, SMC and HBD for k-ECA are proposed, and both theoretical and experimental evaluation are given.... 详细信息

ISBN: (纸本)3540562796

关键词： Approximation algorithms

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Recursive algorithms for direct computation of generalized sliding discrete cosine transforms

Recursive algorithms for direct computation of generalized s...

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2010 3rd international congress on Image and Signal Processing, CISP 2010

作者： Qian, Li Luo, Shouhua He, Siyuan Chen, Gong School of Biological Science and Medical Engineering Southeast University Nanjing China Dept. of Information Engineering Jiangsu Provincial Hospital of TCM Nanjing China

ISBN: (纸本)9781424465149

Generalized sliding discrete cosine transforms refers to computation of the Discrete Cosine Transforms (DCT) of a sequence as the sequence slides over a window function multiple sample at a time. Recursive algorithms for direct computation of generalized sliding discrete cosine transforms are proposed. The computational performance of the algorithms for both software and hardware implementation is compared with some of the known algorithms for computing discrete cosine transforms in short-time domain. ©2010 IEEE.

关键词： Discrete cosine transforms

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