• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.51-58
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• 2021

Microservices are not only developed independently, but can also be run and deployed independently, ensuring more flexible scaling and efficient collaboration in a cloud computing environment. This impact has led to a surge in migrating to microservices-oriented application environments in recent years. In order to introduce microservices, the problem of identifying microservice units in a single application built with a single architecture must first be solved. In this paper, we propose an algorithm-based approach to identify microservices from legacy systems. A graph is generated using the meta-information of the legacy code, and a microservice candidate is extracted by applying a clustering algorithm. Modularization quality is evaluated using metrics for the extracted microservice candidates. In addition, in order to validate the proposed method, candidate services are derived using codes of open software that are widely used for benchmarking, and the level of modularity is evaluated using metrics. It can be identified as a smaller unit of microservice, and as a result, the module quality has improved.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.93-104
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• 2024

Demand response is a strategy that encourages customers to adjust their consumption patterns at times of peak demand with the aim to improve the reliability of the power grid and minimize expenses. The integration of renewable energy sources into smart grids poses significant challenges due to their intermittent and unpredictable nature. Demand response strategies, coupled with reinforcement learning techniques, have emerged as promising approaches to address these challenges and optimize grid operations where traditional methods fail to meet such kind of complex requirements. This research focuses on investigating the application of reinforcement learning algorithms in demand response for renewable energy integration. The objectives include optimizing demand-side flexibility, improving renewable energy utilization, and enhancing grid stability. The results emphasize the effectiveness of demand response strategies based on reinforcement learning in enhancing grid flexibility and facilitating the integration of renewable energy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.5C
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• pp.498-504
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• 2009

A high-performance low-complexity decoding algorithm for LDPC codes is proposed in this paper, which has the advantages of both bit-flipping (BF) algorithm and sum-product algorithm (SPA). The proposed soft bit-flipping algorithm requires only simple comparison and addition operations for computing the messages between bit and check nodes, and the amount of those operations is also small. By increasing the utilization ratio of the computed messages and by adopting nonuniform quantization, the signal-to-noise ratio (SNR) gap to the SPA is reduced to 0.4dB at the frame error rate of 10-4 with only 5-bit assignment for quantization. LDPC codes with high column or row weights, which are not suitable for the SPA decoding due to the complexity, can be practically implemented without much worsening the error performance.

• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.33-40
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• 2009

Edge detection is very significant in low level image processing. However, majority edge detection methods are not only effective enough cause of the noise points' influence, even not flexible enough to different input images. In order to sort these problems, in this paper an algorithm is presented that has an extra noise reduction stage at first, and then automatically selects the both thresholds depending on gradient amplitude histogram and intra class minimum variance. Using this algorithm, can fade out almost all of the sensitive noise points, and calculate the propose thresholds for different images without setting up the practical parameters artificially, and then choose edge pixels by fuzzy algorithm. In finally, get the better result than the former Canny algorithm.

• Journal of Internet Computing and Services
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• v.5 no.4
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• pp.115-124
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• 2004

This paper presents a weighted fuzzy backward reasoning algorithm for rule-based systems based on weighted fuzzy Petri nets. The fuzzy production rules in the knowledge base of a rule-based system are modeled by weighted fuzzy Petri nets, where the truth values of the propositions appearing in the fuzzy production rules and the certainty factors of the rules are represented by fuzzy numbers. Furthermore, the weights of the propositions appearing in the rules are also represented by fuzzy numbers. The proposed weighted fuzzy backward reasoning generates the backward reasoning path from the goal node to the initial nodes and then evaluates the certainty factor of the goal node. The algorithm we proposed can allow the rule-based systems to perform weighted fuzzy backward reasoning in more flexible and human-like manner.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1481-1494
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• 1997

In hard real-time systems, a timing fault may yield catastrophic results. Dynamic scheduling provides the flexibility to compensate for unexpected events at runtime; however, scheduling overhead at runtime is relatively large, constraining both the accuracy of the timing and the complexity of the scheduling analysis. In contrast, static scheduling need not have any runtime overhead. Thus, it has the potential to guarantee the precise time at which each instruction implementing a control action will execute. This paper presents a new approach to the problem of analyzing high-level language code, augmented by arbitrary before and after timing constraints, to provide a valid static schedule. Our technique is based on instruction-level complier code scheduling and timing analysis, and can ensure the timing of control operations to within a single instruction clock cycle. Because the search space for a valid static schedule is very large, a novel adaptive genetic search algorithm was developed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.2
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• pp.397-406
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• 2000

Genetic algorithm is used as a means of search, optimization md machine learning, its structure is simple but it is applied to various areas. And it is about an active and effective controller which can flexibly prepare for changeable circumstances. For this study, research about an action base system evolving by itself is also being considered. There is to have a problem that depended entirely on heuristic knowledge of expert forming membership function and control rule for fuzzy controller design. In this paper, for forming the fuzzy control to perform self-organization, we tuned the membership function to the most optimal using a genetic algorithm(GA) and improved the control efficiency by the self-correction and generation of control rules.

• Journal of Korean Society of Transportation
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• v.20 no.4
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• pp.109-122
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• 2002

본 연구에서는 교차로의 비용 및 특성을 고려한 도로선형최적화 모형을 유전자 알고리즘(Genetic Algorithms)을 이용하여 개발하였다. 기존의 도로선형최적화 모형은 교차로 특성을 고려하지 못해서 실제 적용에 심대한 문제점을 내재하고 있다. 본 논문에서는 특정 도로선형에 교차로 건설의 필요가 있을 경우, 민감(Sensitive)하고 지배적인(Dominating) 교차로 비용 항목들 즉, 토공비용, 보상비, 포장비, 사고비용, 지체 및 연료소모비용 등의 산정이 시도되었다. 또한 비교적 우수한 도로선형 대안을 유전자 알고리즘을 이용한 탐색과정 중에서 비효율적으로 강제 퇴화시키는 단점 보완을 위한 교차로 국소 최적화 방법(Local Optimization of Intersections)이 개발되어 기존 모형을 보완하였다. 공간상의 도로선형은 매개변수적 묘사(Parametric Representation)를 통하여 구현하였으며 벡터운영(Vector Manipulation)을 통해 교차로비용 산정의 근간인 교차점과 다른 중요점들의 좌표를 찾을 수 있었다. 개발된 교차로 비용산정 모형이 보다 정밀하게 교차로 비용을 산정함이 증명되었으며 궁극적으로는 기존의 최적화 모형의 단점을 보완할 수 있음이 제시되었다. 또한, 새로이 제시된 교차로 국소 최적화 방법이 최적대안 탐색과정의 유연성을 증대하였으며, 결과적으로 효율적인 교차로의 유지에 기여함을 알 수 있었다. 제시된 교차로 국소 최적화 방법은 추후 단일노선이 아닌 도로망 최적화시의 기초를 제시함은 주목할 만 하다. 두개의 예제에서 도출된 최적노선 및 교차로 비용 등의 검토 결과, 도로상의 교차로 건설비용은 도로선형 최적화에 큰 영향을 미치는 실질적이며 민감한 비용 항목임이 검증되었으며 이는 도로선형최적화 모형이 교차로 비용을 반드시 검토 및 평가할 수 있어야 함을 반증한다.

• Journal of the Korea Society for Simulation
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• v.17 no.4
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• pp.249-257
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• 2008

This paper shows the Applied Q-learning Algorithm which supports selecting the waiting position of a robot and the part serviced next in the Flexible Manufacturing Cell (FMC) that consists of one robot and various types of facilities. To verify the performance of the suggested algorithm, we design the general FMC made up of single transfer robot and multiple machines with a simulation method, and then compare the output with other control methods. As a result of the analysis, the algorithm we use improve the average processing time and total throughputs as well by increasing robot utilization, reversely, by decreasing robot waiting time. Furthermore, because of ease of use compared with other complex ways and its adoptability to real world, we expect that this method contribute to advance total FMC efficiency as well.

• Journal of the Korean School Mathematics Society
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• v.7 no.1
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• pp.83-102
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• 2004

The purpose of this study was to investigate addition strategies of the 1st year elementary school students compared to the strategies recommended by the 7th national curriculum. We used interviewed children's worksheets to analyze the children's strategies. The results of the study showed that the formal strategies the textbook recommended and the children's strategies were so different. Teachers need to articulately comment two strategies when they teach mathematics in the classrooms.