• The Journal of Internal Korean Medicine
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• v.43 no.6
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• pp.1063-1074
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• 2022

Objectives: The aim of this study was to investigate the differences in the prevalence of metabolic syndrome (MetS) according to the Sasang constitution (SC) and cold and heat pattern identification (CHPI). Methods: SC, CHPI, MetS, and component data were obtained from 2,561 participants in 26 Korean medical clinics from 2007 to 2013. SC, diagnosed by Korean medicine doctors, was confirmed by positive responses to herbal medicines administered according to that constitution. The CHPI was verified by a questionnaire about thermal sensitivity and drinking habits. The diagnosis criteria for MetS were: 1) waist circumference (WC) ≥90 cm (male) and ≥80 cm (female); 2) triglycerides ≥150 mg/dL; 3) high density lipoprotein cholesterol (HDL) <40 mg/dL (male) and <50 mg/dL (female); 4) blood pressure ≧130/85 mmHg; and 5) fasting blood glucose ≥100 mg/dL. Odds ratios (ORs) and differences in MetS and its components were compared using logistic regression and ANCOVA. Results: The MetS prevalence rates were 54.1%, 22.0%, and 33.3% for Taeeumin (TE), Soeumin (SE), and Soyangin (SY), respectively, and 30.5% and 44.5% for the cold and heat patterns, respectively. ANCOVA for MetS components showed significantly higher WC in TE than in SE or SY, and all components except HDL were higher in the heat pattern group than in the cold pattern group. Logistic regression for MetS prevalence showed a significant association between TE and the heat pattern group (OR=1.653) but not for non-TE and the cold pattern group. Conclusions: Considering SC and CHPI together may be more effective in managing MetS than considering SC alone.

• Journal of Korea Spatial Information System Society
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• v.12 no.1
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• pp.49-56
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• 2010

Many researches of frequent pattern mining technique for detecting unknown patterns on spatial data have studied actively. Existing data structures have classified into tree-structure and array-structure, and those structures show the weakness of performance on dense or sparse data. Since spatial data have obtained the characteristics of dense and sparse patterns, it is important for us to mine quickly dense and sparse patterns using only single algorithm. In this paper, we propose novel data structure as compressed patricia frequent pattern tree and frequent pattern mining algorithm based on proposed data structure which can detect frequent patterns quickly in terms of both dense and sparse frequent patterns mining. In our experimental result, proposed algorithm proves about 10 times faster than existing FP-Growth algorithm on both dense and sparse data.

• The KIPS Transactions:PartD
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• v.17D no.4
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• pp.253-258
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• 2010

Traditional frequent pattern mining considers equal profit/weight value of every item. Weighted Frequent Pattern (WFP) mining becomes an important research issue in data mining and knowledge discovery by considering different weights for different items. Existing algorithms in this area are based on fixed weight. But in our real world scenarios the price/weight/importance of a pattern may vary frequently due to some unavoidable situations. Tracking these dynamic changes is very necessary in different application area such as retail market basket data analysis and web click stream management. In this paper, we propose a novel concept of dynamic weight and an algorithm DWFPM (dynamic weighted frequent pattern mining). Our algorithm can handle the situation where price/weight of a pattern may vary dynamically. It scans the database exactly once and also eligible for real time data processing. To our knowledge, this is the first research work to mine weighted frequent patterns using dynamic weights. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using dynamic weights.

• International Journal of Contents
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• v.13 no.1
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• pp.31-37
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• 2017

Using the network analysis method, this study investigates the communication structure of Open Data on the Twitter sphere. It addresses the communication path by mapping influential activities and comparing the contents of tweets about Open Data. In the years 2015 and 2016, the NodeXL software was applied to collect tweets from the Twitter network, containing the term "opendata". The structural patterns of social media communication were analyzed through several network characteristics. The results indicate that the most common activities on the Twitter network are related to the subjects such as new applications and new technologies in Open Data. The study is the first to focus on the structural and informational pattern of Open Data based on social network analysis and content analysis. It will help researchers, activists, and policy-makers to come up with a major realization of the pattern of Open Data through Twitter.

• 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 Korea Society of Computer and Information
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• v.23 no.11
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• pp.111-122
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• 2018

The purpose of this study was to investigate the bodice prototype method suitable for the upper body shape of old-old aged women using the 3D anthropometric data. And it was to provide the basic data for the upper body garments of old-old aged women. In the overall appearance evaluation, the B pattern was rated as 4.00, and it was evaluated as the most suitable for the bodice prototype of the old-old aged woman. The E pattern was rated lower than normal, and the L pattern and the S pattern were found to be inadequate for older female bodice prototypes. As a result of the measurement of the waist and bust air gap of bodice prototype, the air gap of the bust was not significantly different between the patterns. But the waist air gap showed the largest difference between the L pattern and the S pattern. As a result of evaluating the appearance, the amount of space in the state of 3D simulation, and the air gap, the pattern B appeared to be the most appropriate prototype for the old-old aged women's body shape. However, there is a tendency that the shoulder end point is biased toward the back, so it is necessary to set the inclination of the back shoulder line to be more gentle. Conversely, the front shoulder should be more inclined. In the case of the 3D simulation, the B pattern showed that the other parts generally fit well. In the case of the 3D simulation program used in this study, it was evaluated that it is suitable only for the normal body shape because it is impossible to set the isometric angle which is one of the characteristics of the older female body shape. A study on the bodice prototype suitable for the bent body shape should be carried out through experiments on the actual body shape of various elderly women. In order to cope with the increase of elderly people who are familiar with digital, I think it is necessary to develop an avatar that reflects the old female body shape.

• The Journal of Information Systems
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• v.31 no.3
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• pp.197-220
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• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

• The Journal of Information Systems
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• v.26 no.4
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• pp.419-442
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• 2017

Purpose The purpose of this study is to explore the pattern of user response and it's duration time through social media content response analysis. We also analyze the characteristics of content quality factors which are associate with the user response pattern. The analysis results will provide some implications to develop strategies and schematic plans for the operator of regional marketing on the SNS. Design/methodology/approach This study used mixed methods to verify the effects and responses of social media contents on the users who have concerns about regional events such as local festival, cultural events, and city tours etc. Big data analysis was conducted with the quantitative data from regional government SNSs. The data was collected through web crawling in order to analyze the social media contents. We especially analyzed the contents duration time and peak level time. This study also analyzed the characteristics of contents quality factors using expert evaluation data on the social media contents. Finally, we verify the relationship between the contents quality factors and user response types by cross correlation analysis. Findings According to the big data analysis, we could find some content life cycle which can be explained through empirical distribution with peak time pattern and left skewed long tail. The user response patterns are dependent on time and contents quality. In addition, this study confirms that the level of quality of social media content is closely relate to user interaction and response pattern. As a result of the contents response pattern analysis, it is necessary to develop high quality contents design strategy and content posting and propagation tactics. The SNS operators need to develop high quality contents using rich-media technology and active response contents that induce opinion leader on the SNS.

• The Korean Journal of Applied Statistics
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• v.33 no.3
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• pp.269-280
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• 2020

Time course gene expression data is a large amount of data observed over time in microarray experiments. This data can also simultaneously identify the level of gene expression. However, the experiment process is complex, resulting in frequent missing values due to various causes. In this paper, we propose a pattern consistency index adaptive nearest neighbors as a method of missing value imputation. This method combines the adaptive nearest neighbors (ANN) method that reflects local characteristics and the pattern consistency index that considers consistent degree for gene expression between observations over time points. We conducted a Monte Carlo simulation study to evaluate the usefulness of proposed the pattern consistency index adaptive nearest neighbors (PANN) method for two yeast time course data.

• Journal of the Korean Society of Clothing and Textiles
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• v.30 no.5 s.153
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• pp.722-732
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• 2006

The pattern making of the tight-fitting collars which often used in diving suits, dance wear, or cycle wear has not been fully established. To develop tight-fitting collar pattern directly from 3D images from the representative somatotypes, dressforms developed by Jaeun Jung were used. The 3D scan data of the four male dressforms were obtained using Exyma-1200. Triangle Simplification and the Runge-Kutta method were applied to reduce the 3D scan data points and to make the segmented triangular patches in a plane from 3D data. As results, apparent differences between the tight-fitting collar patterns obtained from the 3D scan data and the ordinary 2D collar patterns were found around the center back line. The curvatures of the center back line were higher in all types of the tight-fitting collar than in the ordinary collar pattern. Relative differences in the shape of collar lines among four representative Korean men were reported. To fit the curved shape of the back neckline, 1.8 cm should be reduced from the upper neckline in average. We suggested the direct pattern making method for the 2D tight-fitting collar patterns considering the 3D shape of various types of men's dressform.