• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1415-1421
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• 2016

There are considerable concern on the methods for the efficient utilization of the test-resources as increasing of the number of the tests for functionality and performance verification of weapon systems. Furthermore, with an increase in the complexity of the resource assignment the decision support is required. Test resource allocation is basically the same problems as conventional NP-hard FJSP(Flexible Job Shop Problem), therefore empirical test resource allocation method that has been used in many decades is limited in the time performance. Although research has been conducted applying the genetic algorithm to the FJSP, it is limited in the test resource allocation domain in which more than one machine is necessary for a single operation. In this paper, a variable group genetic algorithm is proposed. The algorithm is expected to improve the test plan efficiency by automating and optimizing the existing manual based allocation. The simulation result shows that the algorithm could be applicable to the test plan.

• Journal of Korean Society of societal Security
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• v.4 no.2
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• pp.29-40
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• 2011

The objective of this paper is to develop an intelligent decision support system that is able to advise disaster countermeasures and degree of incidents on the basis of the collected and analyzed signs of disasters. The concepts derived from ontology, text mining and case-based reasoning are adapted to design the system. The functions of this system include term-document matrix, frequency normalization, confidency, association rules, and criteria for judgment. The collected qualitative data from signs of new incidents are processed by those functions and are finally compared and reasoned to past similar disaster cases. The system provides the varying degrees of how dangerous the new signs of disasters are and the few countermeasures to the disaster for the manager of disaster management. The system will be helpful for the decision-maker to make a judgment about how much dangerous the signs of disaster are and to carry out specific kinds of countermeasures on the disaster in advance. As a result, the disaster will be prevented.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.352-356
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• 2007

키그래프는 데이터 패턴 속에서 인간의 의사결정이나 미래에 닥쳐올 변화에 영향을 주지만 자주 발생하지 않는 희소성이 있는 사건을 발견하기 위한 알고리즘이다. 키그래프는 지진예측, 논문, 파일탐색, 그리고 중요한 URL 추출 등에 이용되었다. 데이터 분할을 통한 클러스터의 형성은 키그래프의 성능에 가장 큰 영향을 끼치는 요소 중의 하나이다. 본 논문에서는 유전자 알고리즘을 이용하여 키그래프의 성능을 향상시킬 수 있는 최적의 데이터 분할을 찾아내는 방법을 제안한다. 제안한 방법의 가능성을 보여주기 위하여 모바일 기기 사용자로부터 수집한 방문 장소 데이터에 제안하는 방법을 적용하여 키그래프의 성능이 향상되는 것을 보인다.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.412-414
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• 2019

경로 계획은 에이전트가 로봇이 특정 목적지에 도착할 수 있도록 에이전트가 수집한 정보를 바탕으로 경로를 설정하는 작업을 뜻한다. 부분 관측만 가능한 맵인 경우 에이전트 이동마다 새로 수집되는 정보들을 바탕으로 마르코프 의사결정 과정을 사용한 가치 반복 알고리즘이 널리 사용되지만, 제안된 가치 반복 알고리즘 사용 시 매 행동마다 모든 공간의 최적 경로를 계산하기 때문에 시간이 오래 걸리는 문제점이 있다. 이에 본 논문에서는 에이전트가 한 번에 탐색하는 범위가 제한되어 있다는 점에 착안하여 탐색 반경 내에 속하는 공간의 가치 함수 값을 미리 추정하여 효율적으로 최적의 경로를 추정하는 가치 반복 알고리즘을 제안한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.326-327
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• 2022

This study is an algorithm for clinical decision-making using machine learning, and it is an algorithm to classify pressure sores to be used in the development of a system to help prevent pressure sores when nursing staff care for patients who lie down for a long time. As a result of machine learning, the learning accuracy of the algorithm was 82.14% and the test accuracy was 82.58%.

• Convergence Security Journal
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• v.14 no.7
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• pp.59-64
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• 2014

We investigate $p(n){\cdot}2^{O(\sqrt{n})}$ algorithm for 0/1 knapsack problem where x is the total bit length of a list of sizes of n objects. The algorithm is adaptable of method that achieves a similar complexity for the partition and Subset Sum problem. The method can be applied to other optimization or decision problem based on a list of numerics sizes or weights. 0/1 knapsack problem can be used to solve NP-Complete Problems with pseudo-polynomial time algorithm. We try to apply this technique to bio-informatics problem which has pseudo-polynomial time complexity.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.139-155
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• 2003

This paper presents the new methodology of analyzing and classifying patterns of customers in mobile telecommunication market to enhance the performance of predicting the credit information based on the decision tree and neural network. With the application of variance selection process from decision tree, the systemic process of defining input vector's value and the rule generation were developed. In point of customer management, this research analyzes current customers and produces the patterns of them so that the company can maintain good customer relationship and makes special management on the customer who has huh potential of getting out of contract in advance. The real implementation of proposed method shows that the predicted accuracy is higher than existing methods such as decision tree(CART, C4.5), regression, neural network and combined model(CART and NN).

• Smart Media Journal
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• v.13 no.1
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• pp.9-17
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• 2024

This study aims to develop an integrated agricultural distribution network management system to improve the quality, profit, and decision-making efficiency of agricultural products. We adopt two key techniques: crop maturity detection based on the YOLOX target detection algorithm and market price prediction based on the Prophet model. By training the target detection model, it was possible to accurately identify crops of various maturity stages, thereby optimizing the shipment timing. At the same time, by collecting historical market price data and predicting prices using the Prophet model, we provided reliable price trend information to shipping decision makers. According to the results of the study, it was found that the performance of the model considering the holiday factor was significantly superior to that of the model that did not, proving that the effect of the holiday on the price was strong. The system provides strong tools and decision support to farmers and agricultural distribution managers, helping them make smart decisions during various seasons and holidays. In addition, it is possible to optimize the distribution network of agricultural products and improve the quality and profit of agricultural products.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.57-62
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• 2010

Recently, decision tree techniques have been studied in terms of quick searching and extracting of massive data in medical fields. Although many different techniques have been developed such as CART, C4.5 and CHAID which are belong to a pie in Clermont decision tree classification algorithm, those methods can jeopardize remained data by the binary method during procedures. In brief, C4.5 method composes a decision tree by entropy levels. In contrast, CART method does by entropy matrix in categorical or continuous data. Therefore, we compared C4.5 and CART methods which were belong to a same pie using breast cancer data to evaluate their performance respectively. To convince data accuracy, we performed cross-validation of results in this paper.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.67-75
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• 2023

This paper deals with the operation of air taxis, which is one of the latest innovative technologies aimed at solving the issue of traffic congestion in cities. A key challenge for the successful introduction of the technology and efficient operation is a vertiport location problem. This paper employs a discrete choice model to calculate choice probabilities of transportation modes for each route, taking into account factors such as cost and travel time associated with different modes. Based on this probability, a mathematical formulation to maximize the utilization rate for air taxi is proposed. However, the proposed model is NP-hard, effective and efficient solution methodology is required. Compared to previous studies that simply proposed the optimization models, this study presents a solution methodology using the cross-entropy algorithm and confirms the effectiveness and efficiency of the algorith through numerical experiments. In addition to the academic excellence of the algorithm, it suggests that decision-making that considers actual data and air taxi utilization plans can increase the practial usability.