• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.388-392
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• 1995

The Electronic Speckle Pattern Interferometry(ESPI) has been applied to many technical problems such as deformation and displacement measurement, strain visualization and surface roughness monitoring. Composite materials have various complicated characteristics depending on the ply materials,ply orientations,ply stacking sequences and boundary conditions. Therefore, it is difficult to analyze composite material. For efficient use of composit materials in engineering applications, the dynamic behavior such as, natural frequencies and modal patterns should be identified. This studying presents FEM results for the free vibration of symmetrically laminated composite as [30/-30/90] $_{s}$. The natural frequencies of laminated composite rectangular plates having the boundary condition(:2-edge clamped) are experimentally obtained. In order to demonstrate the validity of the experiment,FEM analysis using ANSYS was performed and natural frequencies experimentally obtained is compared with calculated by FEM analysis. The results obtained from both experiment and FEM analysis show a good agreement.t.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.61
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• pp.115-126
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• 2000

In General, data mining is defined as the knowledge discovery or extracting hidden necessary information from large databases. Its technique can be applied into decision making, prediction, and information analysis through analyzing of relationship and pattern among data. One of the most important works is to find association rules in data mining. Association Rule is mainly being used in basket analysis. In addition, it has been used in the analysis of web-log and user-pattern. This paper provides the application method in the field of marketing through the analysis of data using association rule as a technique of data mining.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.77-83
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• 2015

Frequent pattern mining, which is one of the major areas actively studied in data mining, is a method for extracting useful pattern information hidden from large data sets or databases. Moreover, frequent pattern mining approaches have been actively employed in a variety of application fields because the results obtained from them can allow us to analyze various, important characteristics within databases more easily and automatically. However, traditional frequent pattern mining methods, which simply extract all of the possible frequent patterns such that each of their support values is not smaller than a user-given minimum support threshold, have the following problems. First, traditional approaches have to generate a numerous number of patterns according to the features of a given database and the degree of threshold settings, and the number can also increase in geometrical progression. In addition, such works also cause waste of runtime and memory resources. Furthermore, the pattern results excessively generated from the methods also lead to troubles of pattern analysis for the mining results. In order to solve such issues of previous traditional frequent pattern mining approaches, the concept of representative pattern mining and its various related works have been proposed. In contrast to the traditional ones that find all the possible frequent patterns from databases, representative pattern mining approaches selectively extract a smaller number of patterns that represent general frequent patterns. In this paper, we describe details and characteristics of pattern condensing techniques that consider the maximality or closure property of generated frequent patterns, and conduct comparison and analysis for the techniques. Given a frequent pattern, satisfying the maximality for the pattern signifies that all of the possible super sets of the pattern must have smaller support values than a user-specific minimum support threshold; meanwhile, satisfying the closure property for the pattern means that there is no superset of which the support is equal to that of the pattern with respect to all the possible super sets. By mining maximal frequent patterns or closed frequent ones, we can achieve effective pattern compression and also perform mining operations with much smaller time and space resources. In addition, compressed patterns can be converted into the original frequent pattern forms again if necessary; especially, the closed frequent pattern notation has the ability to convert representative patterns into the original ones again without any information loss. That is, we can obtain a complete set of original frequent patterns from closed frequent ones. Although the maximal frequent pattern notation does not guarantee a complete recovery rate in the process of pattern conversion, it has an advantage that can extract a smaller number of representative patterns more quickly compared to the closed frequent pattern notation. In this paper, we show the performance results and characteristics of the aforementioned techniques in terms of pattern generation, runtime, and memory usage by conducting performance evaluation with respect to various real data sets collected from the real world. For more exact comparison, we also employ the algorithms implementing these techniques on the same platform and Implementation level.

• Fire Science and Engineering
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• v.20 no.1 s.61
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• pp.55-70
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• 2006

The purpose of this study is to analyze an adequate deployment of fire service force to be prepared to respond appropriately and effectively in Korea by settlement pattern. In order to examine the deployment of fire service force by the present standard, we analyzed the logical basis and the deployment of fire service force by city and province. We also classified clusters for settlement pattern through the statistical methods and raised several points for the existing deployment model of fire service force by the classified settlement pattern. As a result, it was confirmed that the deployment of fire service force by the settlement pattern was irrelevant to fire service need.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.31-31
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• 2017

On farm evaluation for the control of airborne diseases like FMD and flu virus has been done in past but control of disease in process of transportation of livestock and manures is still needed. The objective of this study was to predict a common pattern of livestock vehicles movement. The analysis were done on GPS data, collected from drivers of livestock vehicles in Jeju Island, South Korea in year 2012 and 2013. The GPS data include the coordinates of moving vehicles according to time and dates, livestock farms and manure keeping sites. 2012 year data was added to ArcGIS and different tools were used for predicting common vehicle moving pattern. The common pattern of year 2012 were determined and considered as predicted common pattern for year 2013. To compare with actual pattern of year 2013 the same analysis was done to find the difference in 2012 and 2013 pattern. When the manure keeping sites and livestock farms were same in both years, as a result common pattern of 2012 and 2013 were similar but difference were found in patterns when the manure keeping sites and livestock farms were changed. In future for more accurate results and to predict the accurate pattern of vehicles movement, more dependent and independent variables will be required to make a suitable model for prediction.

• Journal of the Korean Society of School Health
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• v.22 no.2
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• pp.137-146
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• 2009

Purpose: This study was performed to identify the socioeconomic factors, the health behavior factors and dietary pattern that have an influence on abdominal obesity, as measured by using the waist circumference in children and adolescents. Methods: This study used data collected from 1998, 2001, 2005 Korean National Health and Nutrition Examination Survey (NHANES). Final sample included 3,596 subjects from 10 to 19 years old who had completed necessary health examinations, health behaviors survey, and nutrition survey. Results: 12 dietary patterns emerged from factor analysis with different factor loading. Logistic regression analysis of the factors that affect abdominal obesity showed that the education level, BMI(Body Mass Index) and dietary pattern, 'Kimchi' including Korean cabbage and radish affected the abdominal obesity in children and adolescents. Conclusions: Abdominal obesity is a risk factor for morbidity and mortality, and it is associated with chronic diseases, including cardiovascular disease and diabetes. Based on the findings, it is essential to modify dietary pattern for preventing abdominal obesity, which is a condition associated with the incidence of chronic disease in adults.

• Fashion & Textile Research Journal
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• v.13 no.3
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• pp.382-389
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• 2011

Pants is an item of clothes of which physical suitability for improvement of appearance and movement is greatly emphasized. The pattern design of pants is very important because the fitness of crotch region, which is difficult to measure, is structurally essential in pants. This study gives detailed pattern development data for the future pattern design by considering design factors related with crotch region of pants and by suggesting related data. For this study, we measured and analyzed the measuring size of each parts of pattern and the design methods related with crotch region selecting 20 textbooks used in universities and institutes of higher education. Through this study, crotch region of pants pattern related design data were obtained and this result could be a primary information in development and education of pants pattern.

• Journal of Korean Society for Quality Management
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• v.49 no.1
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• pp.81-95
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• 2021

Purpose: The purpose of this study was to analysis pattern of nonconforming farmers who is one of the factors of unconformity in residual pesticides. Methods: Pattern analysis of nonconforming farmers were analyzed through convergence of safety data and farmer's DB data. Exploratory data analysis and association rule analysis were used for extracting factors related to unconformity. Results: The results of this study are as follows; regarding the exploratory data analysis, it was found that factors of farmers influencing unconformity in residual pesticides by total 9 factors; sampling time, gender, age, cultivation region, farming career, agricultural start form, type of agriculture, cultivation area, classification of agricultural products. Regarding the association rule analysis, non-conformity association rules were found over the past three years. There was a difference in the pattern of nonconforming farmers depending on the cultivation period. Conclusion: Exploratory data analysis and association rule analysis will be useful tools to establish more efficient and economical safety management plan for agricultural products.

• Fashion & Textile Research Journal
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• v.2 no.4
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• pp.353-359
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• 2000

The purpose of this study was to develop the jacket pattern drafting method for Korean middle-aged women. The study was composed as follows : 1. Body measurement and statistical analysis : Two hundreds and forty four middle-aged women between the ages of forty five and fifty nine were measured on 52 items. 2. Development of new jacket pattern drafting method : Measurement items necessary to draft new jacket pattern were back waist length, bust girth, waist girth, hip girth, back interscye breadth, front interscye breadth, neck base girth, nipple length, nipple to nipple breadth, 1/2 posterior shoulder length and sleeve length. Basic jackets constructed from the experimental patterns were examined through the fitting test. 3. Evaluation of the new pattern drafting method : The sensory evaluations for appearance and comfort were applied to evaluate the new jacket pattern by comparing it with one of the conventional jacket patterns (Lee Hyung-Suk's). According to the result of sensory evaluation, the new jacket pattern was more positive.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.7
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• pp.1424-1434
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• 2009

DNA computing-inspired pattern classification based on the hypernetwork model is a novel approach to pattern classification problems. The hypernetwork model has been shown to be a powerful tool for multi-class data analysis. However, the ordinary hypernetwork model has limitations, such as operating sequentially only. In this paper, we propose a efficient implementing method of DNA computing-inspired pattern classifier using GPU. We show simulation results of multi-class pattern classification from hand-written digit data, DNA microarray data and 8 category scene data for performance evaluation. and we also compare of operation time of the proposed DNA computing-inspired pattern classifier on each operating environments such as CPU and GPU. Experiment results show competitive diagnosis results over other conventional machine learning algorithms. We could confirm the proposed DNA computing-inspired pattern classifier, designed on GPU using CUDA platform, which is suitable for multi-class data classification. And its operating speed is fast enough to comply point-of-care diagnostic purpose and real-time scene categorization and hand-written digit data classification.