• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2001.11a
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• pp.158-163
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• 2001

본 논문의 국내 패션 업체에서 상품기획 과정 중 많은 시간과 노력을 투자해야 했던 디자인 및 패턴 자료를 데이터베이스화하여 key word를 통해 효율적으로 찾아 사용할 수 있도록 한 디지털 여성 자켓 분류 데이터베이스 시스템을 개발하고자 실시되었다. 이를 위해 의류업체 종사자 48명과 의류학 전공 대학원생 54명, 패턴 전문가 11명을 대상으로 설문조사 및 인터뷰를 실시하여 디자인 및 패턴의 분류 기준과 의류 생산기획 업무의 디지털화 가능성을 검토하였다. 본 연구의 결과는 다음과 같다. 국내 의류업체에서는 상품기획시 국외패션잡지와 collection지를 가장 많이 활용하고 있었으며, 디자인 및 패턴 DB 프로그램에 대한 효용성 기대와 수용도 기대에는 집단간 유의한 차이가 있는 것으로 나타났다. 여성 자켓 디자인 DB를 위한 구성요소 분류 기준은 7가지고 선정되었고, 이미지 형용사 분류 기준은 6가지로 선정되었다. 또한 자켓 제작을 위한 block pattern 분류 기준은 4가지로 선정되었다. 본 연구를 통해 개발된 자켓 디자인 선택 프로그램의 모델을 제시한 후 실험 참가자들에게 효용성 및 사용가능성을 다시 검증한 결과, 프로그램 제시 전 조사결과보다 유의하게 긍정적으로 평가되었으며, 데이터베이스 자료 활용시 이미지 형용사를 통한 검색보다는 구성요소를 통한 검색에 더 만족하는 것으로 나타났다.

• The KIPS Transactions:PartD
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• v.11D no.3
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• pp.709-716
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• 2004

This paper describes an intelligent real-time monitoring system of a semiconductor processing equipment, which determines normal or not for a wafer in processing, using multiple time-series pattern recognition. The proposed system consists of three phases, initialization, learning and real-time prediction. The initialization phase sets the weights and tile effective steps for all parameters of a monitoring equipment. The learning phase clusters time series patterns, which are producted and fathered for processing wafers by the equipment, using LBG algorithm. Each pattern has an ACI which is measured by a tester at the end of a process The real-time prediction phase corresponds a time series entered by real-time with the clustered patterns using Dynamic Time Warping, and finds the best matched pattern. Then it calculates a predicted ACI from a combination of the ACI, the difference and the weights. Finally it determines Spec in or out for the wafer. The proposed system is tested on the data acquired from etching device. The results show that the error between the estimated ACI and the actual measurement ACI is remarkably reduced according to the number of learning increases.

• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.283-291
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• 2016

As the infrastructures and populations are the condensed in the mega city, urban flood management becomes very important due to the severe loss of lives and properties. For the more accurate calculation of runoff from the urban catchment, hourly or even minute rainfall data have been utilized. However, the time steps of the measured or forecasted data under climate change scenarios are longer than hourly, which causes the difficulty on the application. In this study, daily rainfall data was disaggregated into hourly using the stochastic method. Based on the historical hourly precipitation data, Gram Schmidt orthonormalization process and K-Nearest Neighbor Resampling (KNNR) method were applied to disaggregate daily precipitation into hourly. This method was originally developed to disaggregate yearly runoff data into monthly. Precipitation data has smaller probability density than runoff data, therefore, rainfall patterns considering the previous and next days were proposed as 7 different types. Disaggregated rainfall was resampled from the only same rainfall patterns to improve applicability. The proposed method was applied rainfall data observed at Seoul weather station where has 52 years hourly rainfall data and the disaggregated hourly data were compared to the measured data. The proposed method might be applied to disaggregate the climate change scenarios.

• Journal of Advanced Technology Convergence
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• v.1 no.1
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• pp.25-31
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• 2022

This study is a descriptive survey study conducted with the aim of analyzing differences in consumption patterns according to the personality types of the Enneagram and providing basic data that can be used for advertising and branding based on it. 43 people who explained the purpose of the study and agreed to participate were conveniently recruited. The research tools were investigated online with the type of personality of the Enneagram of Joo (2003) and the consumption pattern of Jeon (2013). The data analysis utilizes statistical methods such as descriptive statistics, frequency analysis, and one-way ANOVA in the IBM SPSS Statistics 23 program. For consumption patterns 1, 2, 3, and 6, there were statistically significant differences between personality types 1, 2, 3, 5, 7, and 9 (p<.050). Through this study, it was confirmed that consumption patterns vary depending on the type of personality of the Enneagram. The results of this study are expected to provide basic data in establishing effective strategies for branding and advertising of the design that consumers want.

• Korean Journal of Remote Sensing
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• v.16 no.1
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• pp.1-11
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• 2000

Multi-temporal data have been used frequently for analyzing dynamic characteristics of ecological environment. Little research, however, shows the characteristics and problems of the analysis of continental- or global-scale, multi-temporal satellite data. This research investigated the characteristics of large-area, multi-temporal data analysis and the problems of phenological difference of ground vegetation and scarcity of training data for a long term period. This research suggested a latitudinal image segmentation method and an invariant pixel method. As an application, the image segmentation and invariant pixel methods were applied to a set of AVHRR data covering most part of Asia from 1982 to 1993. Fuzzy classification results showed the decrease of forests and the increase of croplands at densely populated areas, however an opposite trend was detected at sparsely populated or depopulated areas.

• Journal of Korean Society of Transportation
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• v.34 no.2
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• pp.158-167
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• 2016

This study evaluates potential sources of errors in k-NN(k-nearest neighbor) algorithm such as procedures, variables, and input data. Previous research has been thoroughly reviewed for understanding fundamentals of k-NN algorithm that has been widely used for short-term traffic states prediction. The framework of this algorithm commonly includes historical data smoothing, pattern database, similarity measure, k-value, and prediction horizon. The outcomes of this study suggests that: i) historical data smoothing is recommended to reduce random noise of measured traffic data; ii) the historical database should contain traffic state information on both normal and event conditions; and iii) trial and error method can improve the prediction accuracy by better searching for the optimum input time series and k-value. The study results also demonstrates that predicted error increases with the duration of prediction horizon and rapidly changing traffic states.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.101-107
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• 2013

Is generated in real time in the real world, a large amount of time series data from a wide range of business areas. But it is not easy to determine the optimal model for the description and understanding of the time series data is represented as a dynamic feature. In this study, through the HMM suitable for estimating the short and long-term forecasting model of time-series data to estimate a model that can explain the characteristics of these time series data, it was estimated to predict future patterns of movement. The actual stock market through various materials, information criterion and optimal model estimation for the length of the most efficient data was found to accurately estimate the state of the model. Similar movement patterns predictive than the long-term prediction is more similar to the short-term prediction of the experimental result were found to be.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.28-38
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• 2019

The smart card data stores the transit usage records of individual passengers. By using this, it is possible to analyze the traffic demand by station and time. However, since the purpose of the trip is not recorded in the smart card data, the demand for each purpose such as commuting, school, and leisure is estimated based on the survey data. Since survey data includes only some samples, it is difficult to predict public transport demand for each purpose close to the complete enumeration survey. In this study, we estimates the purposes of trip for individual passengers using the smart card data corresponding to the complete enumeration survey of public transportation. We estimated trip purposes such as commute, school(university) considering frequency of O-D, duration, and departure time of a passenger. Based on this, the passengers are classified as workers and university students. In order to verify our methodology, we compared the estimation results of our study with the patterns of the survey data.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.673-675
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• 2002

각 단체나 직장등에서 프레젠테이션은 필수적인 요소이다. 그리고 근래에는 프로젝터의 보급률이 늘어나면서 과거의 OHP필름을 사용하는 방법보다는 직접 컴퓨터를 연결하여 자료를 영사하면서 프리젠테이션을 진행하는 사례가 늘어나고 있다 이러한 환경에서 사용자는 컴퓨터의 제어에 있어서 거리라는 제약을 받아야만 했다. 프리젠테이션을 진행하는 동안 사용자는 컴퓨터를 제어할 수 있는 위치에서 발표를 해야 하지만 레이서포인터라는 일반적인 도구를 사용하여 컴퓨터에 명령을 내린 수 있다면 더 효율적으로 거리에 구애받지 않고 자료의 이동이나 다른 작업을 수행할 수 있다. 또한 컴퓨터가 학습하는 패턴을 늘려감으로서 더욱 다양한 명령을 지시할 수 있게 함으로써 프레젠테이션환경의 개선 할 수 있다.

• Journal of Korean Society of Transportation
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• v.20 no.3
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• pp.193-207
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• 2002

실시간 교통신호 제어시스템은 검지기 체계로부터 교통소통자료를 수집하고, 이를 중앙시스템에서 실시간으로 분석, 처리하여 신호시간을 신호주기별로 산출하는 시스템이다. 신호제어시스템의 운영성과는 시스템의 신호제어기능 또는 제어 알고리즘을 활용할 수 있는 신호제어전략의 효율성 여부에 의하여 크게 좌우된다. 본 연구에서는 신신호 시스템의 기본 제어알고리즘과 개선된 신호제어 알고리즘을 개관하고, 신신호시스템의 운영성과를 현장자료를 토대로 분석하였다. 신신호 시스템의 운영성과는 시스템의 제어기능과 알고리즘을 토대로 비 중요교차로의 패턴 Table을 조정하여 신호제어전략을 수립하고, 이의 적용성을 분석하는 과정을 통하여 분석되었다. 운영성과는 시범지역의 남북 교통축(영동대로)과 동서 교통축(도곡동길)을 대상으로 분석하였다. 분석결과 영동대로는 비 중요 교차로의 패턴 Table을 조정함에 따라 시스템 운영성과가 현저하게 개선되는 것을 확인할 수 있었으며, 도곡동길은 현재의 신호운영 전략과 검토대안이 비슷한 수준의 운영성과를 도출하는 것으로 분석되었다. 시범 운영지역의 교통축은 1개의 중요 교차로와 5-6개의 비 중요 교차로로 구성된다. 영동대로의 운영성과 분석결과, 교통축의 운영효과는 중요교차로의 교통대응 제어기능과 동시에 비 중요 교차로의 패턴 제어기능이 적절하게 연계 운영될 때 운영효율성을 높일 수 있음을 확인하였다. 따라서 향후 신호운영의 효율성을 높이기 위해서는 중요교차로의 교통대응 제어기능의 개선과 동시에 비중요교차로의 패턴 Table의 개선이 필수적인 것으로 판단된다.