• Proceedings of the Korean Information Science Society Conference
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• 2000.10c
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• pp.662-664
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• 2000

병렬 컴퓨팅에서 중요 문제의 하나는 다중 태스크를 다중 프로세서 병렬 시스템의 여러 노드에 대한 최적의 매핑을 찾는 것이다. 이러한 매핑의 목적은 솔루션 품질에 손상 없이 총 실행시간을 최소화시키는 것이다. 이 분야에서는 많은 휴리스틱 방법들을 사용하여 나름대로 매핑 문제를 해결해 왔다. 본 논문에서는 효율적인 클러스터 데이터 매핑을 위한 혼합형 휴리스틱 기법에 대하여 기술한다. 제시하는 휴리스틱 기법은 유전알고리즘과 평균장어닐링 알고리즘을 혼합시킨 것으로 두 가지 방법의 장점들을 합하여 성능을 향상시킬 수 있음을 보여준다. 혼합형 휴리스틱 알고리즘의 솔루션과 실행시간을 기존 매핑 알고리즘들과 비교한 시뮬레이션 결과를 보고한다.

• 대한예방의학회:학술대회논문집
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• 2002.10a
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• pp.447-448
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• 2002

• Journal of Information Technology Applications and Management
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• v.23 no.2
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• pp.143-152
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• 2016

We can organize, manage, search, and process the documents efficiently by a document clustering. In general, the documents are clustered in a high dimensional feature space because the documents consist of many terms. In this paper, we propose a new method to cluster the documents efficiently in a low dimensional feature space by finding the core concepts from a domain ontology corresponding to the particular area documents. The experiment shows that our clustering method has a good performance.

• 한국IT서비스학회:학술대회논문집
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• 2009.11a
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• pp.453-458
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• 2009

The purpose of this study was to detect early children with hyperactivity which is one of the symptoms of Attention Deficit - Hyperactivity Disorder (:ADHD). For this Purpose, This study used two methods; K-CBCL and observation of children`s behavior. K-CBCL was done online by parents at home. For observation of children's behavior, the school asked children to wear a 3 - axis accelerometer on their wrists. The data from K - CBCL and 3 - axis accelerometer were analyzed and clustered to separate hypersensitive children from ordinary children.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.95-108
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• 2011

TThis is a study of the personalization method that intelligently adapts the level of clustering considering purchasing index of a customer. In the e-biz era, many companies gather customers' demographic and transactional information such as age, gender, purchasing date and product category. They use this information to predict customer's preferences or purchasing patterns so that they can provide more customized services to their customers. The previous Customer-Segmentation method provides customized services for each customer group. This method clusters a whole customer set into different groups based on their similarity and builds predictive models for the resulting groups. Thus, it can manage the number of predictive models and also provide more data for the customers who do not have enough data to build a good predictive model by using the data of other similar customers. However, this method often fails to provide highly personalized services to each customer, which is especially important to VIP customers. Furthermore, it clusters the customers who already have a considerable amount of data as well as the customers who only have small amount of data, which causes to increase computational cost unnecessarily without significant performance improvement. The other conventional method called 1-to-1 method provides more customized services than the Customer-Segmentation method for each individual customer since the predictive model are built using only the data for the individual customer. This method not only provides highly personalized services but also builds a relatively simple and less costly model that satisfies with each customer. However, the 1-to-1 method has a limitation that it does not produce a good predictive model when a customer has only a few numbers of data. In other words, if a customer has insufficient number of transactional data then the performance rate of this method deteriorate. In order to overcome the limitations of these two conventional methods, we suggested the new method called Intelligent Customer Segmentation method that provides adaptive personalized services according to the customer's purchasing index. The suggested method clusters customers according to their purchasing index, so that the prediction for the less purchasing customers are based on the data in more intensively clustered groups, and for the VIP customers, who already have a considerable amount of data, clustered to a much lesser extent or not clustered at all. The main idea of this method is that applying clustering technique when the number of transactional data of the target customer is less than the predefined criterion data size. In order to find this criterion number, we suggest the algorithm called sliding window correlation analysis in this study. The algorithm purposes to find the transactional data size that the performance of the 1-to-1 method is radically decreased due to the data sparity. After finding this criterion data size, we apply the conventional 1-to-1 method for the customers who have more data than the criterion and apply clustering technique who have less than this amount until they can use at least the predefined criterion amount of data for model building processes. We apply the two conventional methods and the newly suggested method to Neilsen's beverage purchasing data to predict the purchasing amounts of the customers and the purchasing categories. We use two data mining techniques (Support Vector Machine and Linear Regression) and two types of performance measures (MAE and RMSE) in order to predict two dependent variables as aforementioned. The results show that the suggested Intelligent Customer Segmentation method can outperform the conventional 1-to-1 method in many cases and produces the same level of performances compare with the Customer-Segmentation method spending much less computational cost.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.1
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• pp.108-117
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• 2017

This paper presents a distribution simulation model for dual purpose improved conventional munitions based on flight test data. A systematic procedure for designing a dispersion simulation model is proposed. A new accumulated broken line graph was suggested for designing the distribution shape. In the process of verification and simulation for the distribution simulation model, verification was performed by first comparing data with firing test results, and an application simulation was then conducted. The Monte Carlo method was used in the simulations, which reflected the relationship between ejection conditions and real distribution data. Before establishing the simulation algorithm, the dominant ejection parameter of the submunitions was examined. The relationships between ejection conditions and distribution results were investigated. Five key distribution parameters were analyzed with respect to the ejection conditions. They reflect the characteristics of clustered particle dynamics and aerodynamics.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.81-86
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• 2018

Software defect severity is very important in projects with limited historical data or new projects. But general software defect prediction is very difficult to collect the label information of the training set and cross-project defect prediction must have a lot of data. In this paper, an unclassified data set with defect severity is clustered according to the distribution ratio. And defect severity-based prediction model is proposed by way of labeling. Proposed model is applied CLAMI in JM1, PC4 with the least ambiguity of defect severity-based NASA dataset. And it is evaluated the value of ACC compared to original data. In this study experiment result, proposed model is improved JM1 0.15 (15%), PC4 0.12(12%) than existing defect severity-based prediction models.

• Journal of Korean Academy of Nursing
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• v.20 no.3
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• pp.307-323
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• 1990

This Study was done to design and test an instrument to measure the health status of the elderly including physical, psychologyical and social dimensions. Data collection was done from July 18 to August 17, 1990. Subjects were 412 older persons in Korea. A convenience sample was used but the place of residence was stratified into large, medium and small city and rural areas. Participants located in Sudaemun-Gu, Mapo-Gu, and Kangnam-Gu, Seoul were interviewed by brained nursing students, and those in Chungju, Jonju, Chuncheon, and Jinju by professors of nursing colleges. Rural residents were interviewed by community health practioners working in Kungsang-Buk-Do, Kyngsang- Nam - Bo, Jonla Buk -Do, and Kyung Ki- Do. The tool developed for this study was a structured questionnaire based on previous literature and then tested for reliability and validity. This tool contained 20 physical health status items, 17 mental-emotional health status items and 38 social health status items. Physical health status items clustered in to six factors such as personal hygiene, activity, home management, digestive, sexual, sensory, and climination functions. Mental-emotional health status items clustered into two factors, mental health and emotional health. Social health status items clustered into seven factors, grandparent, parent, spouse, friend, kinships, group member and religious role functions. Data analysis included percentage, average, S.D., t-test and ANOVA. The results of the analysis were as follows : 1. The tool measuring the health status of the elderly and developed for this research had a relatively high reliavility indicated by a cronbach＝0.97793. 2. Average score of the subjects physical health status was 4, 054 in a 5 point likert scale, mentalemotional health status was 3.803, social health status was 2.939 and the total average was 3.521. The social status of the subjects was the lowest and the next was mental-emotional health status ; physical health status was the highest. 3. Educational background, perceived health status, the amount of pocket money were related to physical and mental-emotional health status and family structure was related mental-emotional physical and social health status. Occupation was related to physical and mental-emotional status. Area of residence was related to metal-emotional and social status. Source of living in the expeneses was related to physical and mental-emotional health status marital status to mental-emotional and social health status, and the number living in the home physical health status and religion to social health status. The following conciusions were derived from the above results ; 1. The health status of Korean elderly was relatively sound but social health status was the most vulnerable. The Social activity for Korean elderly is needed to improve social health. 2. Educational background, perceived health status and the amount of pocket money must be considered in the health assessment criteria of the elderly, Family structure, marial status, occupation, residence variables and sources of living expense must also be considered as significant. 3. A health education program based on the educational background of the elderly, and provision of an occupational socioeconomic welfare policy will be useful in order to increase social health status of Korean elderly.

• The Journal of Engineering Geology
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• v.24 no.4
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• pp.643-648
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• 2014

Understanding of fracture networks and rock mass properties during tunnel construction is extremely important for the prediction of dangers during excavation, and for deciding on appropriate excavation techniques and support. However, rapid construction process do not allow sufficient time for surveys and interpretations for spatial distributions of fractures and rock mass properties. This study introduces a new statistical approach for predicting joint distributions at foreside of current excavation face during the excavation process. The proposed methodology is based on a cumulative space diagram for joint sets. The diagram displays the cumulative spacing between adjacent joints on the vertical axis and the sequential position of each joint plotted at equally spaced intervals on the horizontal axis. According to the diagram, the degree of linearity of points representing the regularity of joint spacing; a linear trend of the points indicates that the joints are evenly spaced, with the slope of the line being directly related to the spacing. The linear points which are stepped indicates that the fracture set show clustered distribution. A clustered pattern within the linear group of points indicates a clustered joint distribution. Fractures surveyed from an excavated space can be plotted on this diagram, and the diagram can then be extended further according to the plotted diagram pattern. The extension of the diagram allows predictions about joint spacing in areas that have not yet been excavated. To test the model, we collected and analyzed data during excavation of a 10-m-long tunnel. Fractures in a 3-m zone behind the excavation face were predicted during the excavation, and the predictions were compared with observations. The methodology yielded reasonably good predictions of joint locations.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1349-1365
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• 2020

In recent years, in the fields of smart cities and digital twins, research on model generation is increasing due to the advantage of acquiring actual 3D coordinates by using point clouds. In addition, there is an increasing demand for a solid model that can easily modify the shape and texture of the building. In this paper, we propose a method to create a clustered solid building model based on point cloud data. The proposed method consists of five steps. Accordingly, in this paper, we propose a method to create a clustered solid building model based on point cloud data. The proposed method consists of five steps. In the first step, the ground points were removed through the planarity analysis of the point cloud. In the second step, building area was extracted from the ground removed point cloud. In the third step, detailed structural area of the buildings was extracted. In the fourth step, the shape of 3D building models with 3D coordinate information added to the extracted area was created. In the last step, a 3D building solid model was created by giving texture to the building model shape. In order to verify the proposed method, we experimented using point clouds extracted from unmanned aerial vehicle images using commercial software. As a result, 3D building shapes with a position error of about 1m compared to the point cloud was created for all buildings with a certain height or higher. In addition, it was confirmed that 3D models on which texturing was performed having a resolution of less than twice the resolution of the original image was generated.