• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.234-241
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• 2021

By recognizing the importance of demand forecasting, the military is conducting many studies to improve the prediction accuracy for repair parts. Demand forecasting for repair parts is becoming a very important factor in budgeting and equipment availability. On the other hand, the demand for intermittent repair parts that have not constant sizes and intervals with the time series model currently used in the military is difficult to predict. This paper proposes a method to improve the prediction accuracy for intermittent repair parts of the Patriot. The authors collected intermittent repair parts data by classifying the demand types of 701 repair parts from 2013 to 2019. The temperature and operating time identified as external factors that can affect the failure were selected as input variables. The prediction accuracy was measured using both time series models and data mining models. As a result, the prediction accuracy of the data mining models was higher than that of the time series models, and the multilayer perceptron model showed the best performance.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.5
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• pp.429-436
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• 2009

Odd network was introduced as one model of graph theory. In [1], it was introduced as a class of fault-tolerant multiprocessor networks and analyzed so many useful properties such as simple routing algorithms, maximal fault tolerance, node axsjoint path, etc. In this paper, we sauw a construction algorithm of edge-axsjoint spanning trees in Odd network $O_d$. Also, we prove that edge-disjoint spanning tree generated by our algorithm is optimal edge-disjoint spanning tree.

• Proceedings of the Korean Society of Fisheries Technology Conference
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• 2004.10a
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• pp.30-33
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• 2004

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2000.05a
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• pp.311-321
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• 2000

본 논문에서는 정신분석과 두 종류의 정서이론, 인공지능과 신경회로망 그리고 퍼지 페트리 네트 등을 사용하여 사람의 인지과정을 모방한 인지모형시스템을 개발하였다. 먼저 프로이트의 정신분석을 사용하여 정신의 구조를 그래프로 표현한 후 이것을 '마음의 지도'라 명명하였다. 인지모형시스템을 구현하기 위한 첫 번째 작업으로 동적인 추론을 할 수 있는 지능 모델인 KNBN(Kohonen Network based Belief Network)을 제안하였다. KNBN으로 표현한 마음의 약도 내에서 연결강도 값으로 사용할 상대적 데이터를 만들기 위한 근거로서는 '정서'를 사용하였는데, 플라칙의 진화론에 근거한 정서이론과 오토니의 인지적 정서이론을 결합하여 데이터로 만든후 이 수치를 연결강도로 사용하였다. 이 두 개의 정서이론을 결합하는 알고리즘을 만들기 위해 페트리네트를 변형한 퍼지 페트리네트를 제안하였다. 또한 오토니가 주장하는 정서의 인지구조를 사람들이 그대로 이해하는지 여부를 알기 위해 대학생 100명을 대상으로 설문지를 사용해 정서의 인지구조에 대해 조사하였고 그 결과 값에 근거하여 두 개의 정서이론 결합 알고리즘을 만들었다. 이것으로 정서 발화에 대한 상대적인 수치가 산출되었고, 이것을 KNBN으로 표현한 마음의 약도에 결합하기 위해 0과 1사이의 수치로 정규화 하였다. 이렇게 정규화된 데이터를 이용해 인지 모형 시스템을 개발하였다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.1-10
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• 2004

Quite many papers about moving objects database (MOD) have been Published. Most of them are concerning of improving efficiency of updating policy, but none of them talks about verification of system's requirements. No matter how efficient updating policy is employed, a system designer still has to verify if the MOD satisfies user's requirement at the beginning of system lifestyle. For example, if a MOD serves n moving objects, then the designer must verity if it can update information for n moving objects and provide new information to moving objects within specified time limit. For the purpose of requirement analysis of MODs, we build a Petri net model for MOD using Design/CPN, and then we show how to verify whether the MOD satisfies user's requirements by simulation. The contribution of this paper is providing a simulation model for requirement analysis of MOD for the first time. The model is expansion of our previous fuzzy-timing Petri net model. The distance-based updating policy, and distributed database management system are reflected in this model and system analysis for moving objects is considered. It is built in Design/CPN so that the simulation can be automatically performed. The application of our model is not limited to requirement analysis, it is useful to study other MOD design issues, such as the trade-offs between update cost and information accuracy, and between the tim interval needed for updating database and MOD system resources, etc.

• Journal of the Korea Convergence Society
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• v.6 no.2
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• pp.25-30
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• 2015

The purpose of this study was to analyze the factors that affects the participating in a smoking cessation program. Data were from the A Study on the Seoul Welfare Panel Study 2010. Subjects were 1,326 smokers aged 19 and older living in the community. Dependent variable was defined as experience of smoking cessation. Explanatory variables were included as age, gender, level of education, employment status, household income, marital status, drinking, self-reported health status, depression, disease, and physical activity. A prediction model was developed by the use of a Decision Tree and Neural Network Algorithm. In the Prediction model, self reported health status, disease, income, household income were significantly associated with participating in a smoking cessation program. Based this study, systematic education and development of programs are required.

• The Journal of the Korea Contents Association
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• v.22 no.2
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• pp.545-558
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• 2022

The purpose of this study is to explore a prediction model for accurately predicting Financial Difficulties of Chinese Cultural Industry Enterprises through Traditional Statistics and Machine Learning. To construct the prediction model, the data of 128 listed Cultural Industry Enterprises in China are used. On the basis of data groups composed of 25 explanatory variables, prediction models using Traditional Statistical such as Discriminant Analysis and logistic as well as Machine Learning such as SVM, Decision Tree and Random Forest were constructed, and Python software was used to evaluate the performance of each model. The results show that the Random Forest model has the best prediction performance, with an accuracy of 95%. The SVM model was followed with 93% accuracy. The Decision Tree model was followed with 92% accuracy.The Discriminant Analysis model was followed with 89% accuracy. The model with the lowest prediction effect was the Logistic model with an accuracy of 88%. This shows that Machine Learning model can achieve better prediction effect than Traditional Statistical model when predicting financial distress of Chinese cultural industry enterprises.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.725-728
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• 2002

현재, 웹기반 원격교육 시스템에서는 많은 학습자의 컨텐츠 및 서비스가 요구되고 있다. 기본 학습자료의 제공에 대한 많은 모형이 대두되고 있으나 기본 학습을 뒷받침해 줄 수 있는 보충학습 자료의 모형은 제시되지 않고 있다. 따라서, 본 논문에서는 학습자의 학습 휴리스틱에 의하여 기계학습된 보충학습 내용과 위치를 웹과 이메일로 자동 푸쉬해 줄 수 있는 시스템을 제안한다. 휴리스틱에 의하여 보충학습 데이터의 트리를 구성한 후 시멘틱 네트를 이용한 속성을 정의하고 기계학습된 학습자의 반복 학습 경로를 분석하여 보충학습을 원활히 진행할 수 있도록 시스템을 설계하는 것이 본 논문의 목적이다.

• The KIPS Transactions:PartA
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• v.17A no.3
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• pp.113-120
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• 2010

Even networks were introduced as a class of fault-tolerant multiprocessor networks and analyzed so many useful properties and algorithms such as simple routing algorithms, maximal fault tolerance, node disjoint path. Introduced routing algorithms and node disjoint path algorithms are proven to be optimal. However, it has not been introduced to constructing scheme for edge-disjoint spanning trees in even networks. The design of edge-disjoint spanning trees is a useful scheme to analyze for measuring the efficiency of fault tolerant of interconnection network and effective broadcasting. Introduced routing algorithm or node disjoint path algorithm are for the purpose of routing or node disjoint path hence they are not applicable to constitute edge disjoint spanning tree. In this paper, we show a construction algorithm of edge-disjoint spanning trees in even network $E_d$.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.2
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• pp.76-88
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• 2017

The urban park plays important roles in protecting the urban landscape and improving citizens' health, recreation, and the emotional life. Above and beyond these roles, the urban park is expected to rearrange urban spatial structures as a kind of urban system. The purpose of this paper is to identify empirically to change urban spatial structures by construction of the urban park. This study regards gentrification around the urban park as a process to change urban spatial structures. The gentrification means the regeneration and upgrading of deteriorated urban property by the middle class or commercial developers. The site of case analysis is the Seoul Forestry Park in Sungdong-gu, Seoul. The Seoul Forestry Park is regarded as a representative urban park of Seoul, and caused gentrification around park after the 2005 opening. This study operationally defines the gentrification index and the accessibility index from an urban park and offers an empirical analysis of relation among the urban park, the gentrification and urban spatial structure in a statistic district which is the minimum unit of Korean statistic data in 2000, 2005, and 2010, using Difference-in-Difference method and linear probability model. The results of this empirical study show that the Seoul Forestry Park changes urban spatial structures by gentrification. It reverses a trend of migration of gentrifiers before and after construction of the Seoul Forestry Park. It suggests urban park construction as an alternative method for urban regeneration by inducing the middle class into the inner city of Seoul.