• Journal of Computing Science and Engineering
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• v.8 no.4
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• pp.199-206
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• 2014

The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

• The Journal of Industrial Distribution & Business
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• v.10 no.11
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• pp.71-80
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• 2019

Purpose: The purpose of this study is to design the curriculum of Basic College Software Programming to develop creative and logical-thinking. This course is guided by algorithmic thinking and logical thinking that can be solved by computing for problem-solving, and it helps to develop by software through basic programming education. Through the stage of problem analysis, abstraction, algorithm, data structure, and algorithm implementation, the curriculum is designed to help learners experience algorithm problem-solving in various areas to develop diffusion thinking. For Learners aim to achieve the balanced development of divergent and convergent-thinking needed in their creative problem-solving skills. Research design, data and methodology: This study is to design a basic software education for improving algorithm-thinking for non-major. The curriculum designed in this paper is necessary to non-majors students who have completed the 'Creative Thinking and Coding Course' Design Thinking based are targeted. For this, contents were extracted through advanced research analysis at home and abroad, and experts in computer education, computer engineering, SW education, and education were surveyed in the form of quasi-openness. Results: In this study, based on ADD Thinking's algorithm thinking, we divided the unit college majors into five groups so that students of each major could accomplish the goal of "the ability to internalize their own ideas into computing," and extracted and designed different content areas, content elements and sub-components from each group. Through three expert surveys, we established a strategy for characterization by demand analysis and major/textbook category and verified the appropriateness of the design direction to ensure that the subjects and contents of the curriculum are appropriate for each family in order to improve algorithm-thinking. Conclusions: This study helps develop software by enhancing the ability of students who practice various subjects and exercises to explore creative expressions in various areas, such as 'how to think like a computer' that can implement and execute their ideas in computing. And it helps increase the ability to think logical and algorithmic computing based on creative solutions, improving problem-solving ability based on computing thinking and fundamental understanding of computer coding and development of logical thinking ability through programming.

• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.295-305
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• 2022

In the modern artificial intelligence society, the goal of education is to cultivate convergent talents who can adapt well to and lead the artificial intelligence society. The core competency required of convergent future talents is to be able to understand and create software well. In this respect, algorithmic education is very important. Therefore, in this study, an algorithm education program was developed to improve convergent thinking ability. The effectiveness was verified through the paired samples t-test by conducting a pre-post test of convergent thinking ability, and it was found that students' convergent thinking ability was significantly improved in the areas of algorithm knowledge and function.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1930-1932
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• 2004

SOM(Self Organizing Map) algorithm which has some advantages such as data accumulation ability and the degradation trend trace ability was compared with conventionally used BP(Back Propagation) algorithm. For the purpose, partial discharge data were acquired and analysed from the artificial defects in GIS. As a result, basically the pattern recognition rate of BP algorithm was found out to be better than that of SOM algorithm. However, SOM algorithm showed a great on-site-applicability such as ability of suggesting new-pattern-possibility. Therefore, through increasing pattern recognition rate it is possible to apply SOM algorithm to partial discharge analysis. Also, for the image processing method it is required the normalization of the PRPDA graph. However, due to the normalization both BP and SOM algorithm have shown worse results, so that it is required further study to solve the problem.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.53 no.10
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• pp.515-522
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• 2004

In this study, we tried to investigate that the advantages of SOM(Self Organizing Map) algorithm such as data accumulation ability and the degradation trend trace ability would be adaptable to the analysis of partial discharge pattern recognition. For the purpose, we analyzed partial discharge data obtained from the typical artificial defects in GIS and XLPE power cable system through SOM algorithm. As a result, partial discharge pattern recognition could be well carried out with an acceptable error by use of Kohonen map in SOM algorithm. Also, it was clarified that the additional data could be accumulated during the operation of the algorithm. Especially, we found out that the data accumulation ability of Kohonen map could make it possible to suggest new patterns, which is impossible through the conventional BP(Back Propagation) algorithm. In addition, it is confirmed that the degradation trend could be easily traced in accordance with the degradation process. Therefore, it is expected to improve on-site applicability and to trace real-time degradation trends using SOM algorithm in the partial discharge pattern recognition

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2413-2417
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• 2003

This paper proposes an improvement of a QoS based routing protocol. The hierarchical source routing algorithm[4] was improved by including of the Crank-Back algorithm[2]. The Crank-Back capability is the ability to solve the problem of selecting the wrong route because of using unsynchronized routing information in the ingress router. In order to reduce time of reversing the call-setup process back to the ingress router, spare-route information is included with a call-setup packet. Spare-route information could be used by every router to find the next appropriated link itself when insufficient resource of the selected link was found. The proposed algorithm improves the performance of the source routing call-setup process significantly.

• Proceedings of the KIEE Conference
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• 2004.05b
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• pp.109-112
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• 2004

In this study, the applicability of SOM(Self Organizing Map) algorithm to partial discharge pattern recognition have been investigated. For the purpose, using acquired data from the artificial defects in GIS, SOM algorithm which has some advantages such as data accumulation ability and the degradation trend trace ability was compared with conventionally used BP(Back Propagation) algorithm. As a result, basically BP algorithm was found out to be better than SOM algorithm. Therefore, it is needed to apply SOM algorithm in combination with BP algorithm in order to improve on-site applicability using the advantages of SOM. Also, for the pattern recognition by use of PRPDA(Phase Resolved Partial Discharge Analysis) it is required the normalization of the PRPDA graph. However, in case of the normalization both BP and SOM algorithm have shown worse results, so that it is required further study to solve the problem.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.1057-1068
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• 2003

Results of using principal component analysis prior to cluster analysis are compared with results from applying agglomerative clustering algorithm alone. The retrieval ability of the agglomerative clustering algorithm is improved by using principal components prior to cluster analysis in some situations. On the other hand, the loss in retrieval ability for the agglomerative clustering algorithms decreases, as the number of informative variables increases, where the informative variables are the variables that have distinct information(or, necessary information) compared to other variables.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.735-748
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• 2014

The fault-tolerance routing problem is one of the most important issues in the application of the Internet of Things, and has been attracting growing research interests. In order to maintain the communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which alternate paths are created once the previous routing is broken. Then, we propose an improved efficient and intelligent fault-tolerance algorithm (IEIFTA) to provide the fast routing recovery and reconstruct the network topology for path failure in the Internet of Things. In the IEIFTA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by the immune mechanism, which can improve the ability of global search and improve the converging rate of the algorithm. The simulation results indicate that the IEIFTA-based fault-tolerance algorithm outperforms the EARQ algorithm and the SPSOA algorithm due to its ability of fast routing recovery mechanism and prolonging the lifetime of the Internet of Things.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1178-1191
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• 2014

The fault-tolerance routing problem is one of the most important issues in the application of the Internet of Things, and has been attracting growing research interests. In order to maintain the communication paths from source sensors to the macronodes, we present a hybrid routing scheme and model, in which alternate paths are created once the previous routing is broken. Then, we propose an improved efficient and intelligent fault-tolerance algorithm (IEIFTA) to provide the fast routing recovery and reconstruct the network topology for path failure in the Internet of Things. In the IEIFTA, mutation direction of the particle is determined by multi-swarm evolution equation, and its diversity is improved by the immune mechanism, which can improve the ability of global search and improve the converging rate of the algorithm. The simulation results indicate that the IEIFTA-based fault-tolerance algorithm outperforms the EARQ algorithm and the SPSOA algorithm due to its ability of fast routing recovery mechanism and prolonging the lifetime of the Internet of Things.