• 한국의류산업학회지
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• 제22권5호
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• pp.551-560
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• 2020

Due to the nature of fashion design that responds quickly and sensitively to changes, accurate forecasting for upcoming fashion trends is an important factor in the performance of fashion product planning. This study analyzed the major phenomena of fashion trends by introducing text mining and a big data analysis method. The research questions were as follows. What is the key term of the 2010SS~2019FW fashion trend? What are the terms that are highly relevant to the key trend term by year? Which terms relevant to the key trend term has shown high frequency in news articles during the same period? Data were collected through the 2010SS~2019FW Pre-Trend data from the leading trend information company in Korea and 45,038 articles searched by "fashion+material" from the News Big Data System. Frequency, correlation coefficient, coefficient of variation and mapping were performed using R-3.5.1. Results showed that the fashion trend information were reflected in the consumer market. The term with the highest frequency in 2010SS~2019FW fashion trend information was material. In trend information, the terms most relevant to material were comfort, compact, look, casual, blend, functional, cotton, processing, metal and functional by year. In the news article, functional, comfort, sports, leather, casual, eco-friendly, classic, padding, culture, and high-quality showed the high frequency. Functional was the only fashion material term derived every year for 10 years. This study helps expand the scope and methods of fashion design research as well as improves the information analysis and forecasting capabilities of the fashion industry.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2018년도 춘계 학술논문 발표대회
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• pp.171-172
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• 2018

Performance measurement and management in the construction industry has traditionally been regarded as the major factor for successful business execution. In addition, there is emphasis on the function of performance measurement and management that can early warn potential risks and poor performance in project execution environment changes. Previous studies have made various attempts to measure the quantitative performance of construction projects, and the research to derive the link between the results and the issue that can meet the future needs is expected to provide valuable information. The purpose of this study is to analyze the research trends based on the keywords presented in previous studies on the performance measurement and management of construction projects. Considering that the results presented in the existing literature can be an indicator of the evolution of the sector before it is applied to industry, the result of this study is expected to be used as a basic data to support the establishment of research direction in the future.

• 대한조선학회 특별논문집
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• 대한조선학회 2015년도 특별논문집
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• pp.41-45
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• 2015

In this study reviewed the validity of the estimated optimum trim by the numerical analysis. For this purpose, the numerical analysis of the trim optimization for 6500TEU container carrier and capesize bulk carrier were carried out using Star-CCM+, which results were compared with the results of model tests. The reliability of results of the numerical analysis was confirmed via comparing the resistance determined by the numerical analysis and model test. The performance of self-propulsion at each trim conditions were estimated using the calculated resistance by numerical analysis. The BHP at each trim condition were calculated by estimated performance of self-propulsion, which trend of results were confirmed similar trend of result of model test.

• KIEAE Journal
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• 제17권5호
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• pp.77-86
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• 2017

Purpose: Because of the growing concern over fossil fuel use and increasing demand for greenhouse gas emission reduction since the 1990s, the building energy analysis field has produced various types of methods, which are being applied more often and broadly than ever. A lot of research products have been actively proposed in the area of the building energy simulation for over 50 years around the world. However, in the last 20 years, there have been only a few research cases where the trend of building energy analysis is examined, estimated or compared. This research aims to investigate a trend of the building energy analysis by focusing on methodology and characteristics of each method. Method: The research papers addressing the building energy analysis are classified into two types of method: engineering analysis and algorithm estimation. Especially, EPG(Energy Performance Gap), which is the limit both for the existing engineering method and the single algorithm-based estimation method, results from comparing data of two different levels- in other words, real time data and simulation data. Result: When one or more ensemble algorithms are used, more accurate estimations of energy consumption and performance are produced, and thereby improving the problem of energy performance gap.

• 지능정보연구
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• 제23권4호
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• pp.147-168
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• 2017

오래 전부터 학계에서는 정확한 주식 시장의 예측에 대한 많은 연구가 진행되어 왔고 현재에도 다양한 기법을 응용한 예측모형들이 연구되고 있다. 특히 최근에는 딥러닝(Deep-Learning)을 포함한 다양한 기계학습기법(Machine Learning Methods)을 이용해 주가지수를 예측하려는 많은 시도들이 진행되고 있다. 전통적인 주식투자거래의 분석기법으로는 기본적 분석과 기술적 분석방법이 사용되지만 보다 단기적인 거래예측이나 통계학적, 수리적 기법을 응용하기에는 기술적 분석 방법이 보다 유용한 측면이 있다. 이러한 기술적 지표들을 이용하여 진행된 대부분의 연구는 미래시장의 (보통은 다음 거래일) 주가 등락을 이진분류-상승 또는 하락-하여 주가를 예측하는 모형을 연구한 것이다. 하지만 이러한 이진분류로는 추세를 예측하여 매매시그널을 파악하거나, 포트폴리오 리밸런싱(Portfolio Rebalancing)의 신호로 삼기에는 적합치 않은 측면이 많은 것 또한 사실이다. 이에 본 연구에서는 기존의 주가지수 예측방법인 이진 분류 (binary classification) 방법에서 주가지수 추세를 (상승추세, 박스권, 하락추세) 다분류 (multiple classification) 체계로 확장하여 주가지수 추세를 예측하고자 한다. 이러한 다 분류 문제 해결을 위해 기존에 사용하던 통계적 방법인 다항로지스틱 회귀분석(Multinomial Logistic Regression Analysis, MLOGIT)이나 다중판별분석(Multiple Discriminant Analysis, MDA) 또는 인공신경망(Artificial Neural Networks, ANN)과 같은 기법보다는 예측성과의 우수성이 입증된 다분류 Support Vector Machines(Multiclass SVM, MSVM)을 사용하고, 이 모델의 성능을 향상시키기 위한 래퍼(wrapper)로서 유전자 알고리즘(Genetic Algorithm)을 이용한 최적화 모델을 제안한다. 특히 GA-MSVM으로 명명된 본 연구의 제안 모형에서는 MSVM의 커널함수 매개변수, 그리고 최적의 입력변수 선택(feature selection) 뿐만이 아니라 학습사례 선택(instance selection)까지 최적화하여 모델의 성능을 극대화 하도록 설계하였다. 제안 모형의 성능을 검증하기 위해 국내주식시장의 실제 데이터를 적용해본 결과 ANN이나 CBR, MLOGIT, MDA와 같은 기존 데이터마이닝 기법들이나 인공지능 알고리즘은 물론 현재까지 가장 우수한 예측 성과를 나타내는 것으로 알려져 있던 전통적인 다분류 SVM 보다도 제안 모형이 보다 우수한 예측성과를 보임을 확인할 수 있었다. 특히 주가지수 추세 예측에 있어서 학습사례의 선택이 매우 중요한 역할을 하는 것으로 확인 되었으며, 모델의 성능의 개선효과에 다른 요인보다 중요한 요소임을 확인할 수 있었다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2019년도 추계학술발표대회
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• pp.145-148
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• 2019

Principal Component Analysis (PCA) is a powerful technique in data analysis and widely used to detect anomalies in Wireless Sensor Networks. However, the performance of conventional PCA is not high on time-series data collected by sensors. In this paper, we propose a Joint Exponential Smoothing and Trend-based Principal Component Analysis (JES-TBPCA) for Anomaly Detection which is based on conventional PCA. Experimental results on a real dataset show a remarkably higher performance of JES-TBPCA comparing to conventional PCA model in detection of stuck-at and offset anomalies.

• Industrial Engineering and Management Systems
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• 제11권2호
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• pp.134-141
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• 2012

It is difficult to find an appropriate ordering policy for a many types of items. One of the reasons for this difficulty is that each item has a different demand trend. We will classify items by shipment trend and then decide the ordering policy for each item category. In this study, we indicate that categorizing items from their statistical characteristics leads to an ordering policy suitable for that category. We analyze the ordering policy and shipment trend and propose a new method for selecting the ordering policy which is based on finding the strongest relation between the classification of the items and the ordering policy. In our numerical experiment, from actual shipment data of about 5,000 items over the past year, we calculated many statistics that represent the trend of each item. Next, we applied the canonical correlation analysis between the evaluations of ordering policies and the various statistics. Furthermore, we applied the cluster analysis on the statistics concerning the performance of ordering policies. Finally, we separate items into several categories and show that the appropriate ordering policies are different for each category.

• 한국항공우주학회지
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• 제42권6호
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• pp.478-487
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• 2014

개념설계는 복합시스템인 무인기의 성공적인 개발을 위해 가장 중요한 단계로써 간단한 성능해석과 형상설계가 수행된다. 개념설계 단계에서의 성능해석은 복잡한 해석도구를 사용하기 보다는 주로 경험식이나 통계적 데이터를 이용한 추세방정식을 사용한다. 무인기의 형상은 매우 다양하여 개념설계 단계에서 이러한 모든 항공기 형상을 고려하기에는 어려움이 있다. 본 연구에서는 무인기 개념설계를 위해 주요 성능변수에 대한 추세방정식을 도출하였고, 자주 사용되는 형상 선정을 위해 최대이륙중량 50-1,500kg 급의 중소형 무인기에 대한 데이터베이스를 구축하였다. 또한 주요 성능변수들에 대한 파라미터 분석을 수행하였으며, 이들 성능변수에 대한 상관도 분석결과에 따라 높은 상관도를 보이는 최대이륙중량과 날개폭을 기준으로 각 성능요소별 회귀분석을 수행하여 추세방정식을 도출하였다.

• 한국항공우주학회지
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• 제42권12호
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• pp.1057-1064
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• 2014

정지궤도위성은 다수의 탑재체를 하나의 위성체 플랫폼에 탑재하고 2010년 6월 26일에 발사되었다. 태양기간 동안 인공위성에서 요구되는 전력은 태양전지 배열기 윙에서 생성된다. 태양전지는 Spectrolab사의 적층법을 사용한 RWE Space사의 Gaget 2로 명명되는 GaInP/GaAs/Ge 3중 접합 셀이다. 본 논문은 정지궤도 비행 데이터에 대한 경향을 분석한 결과를 바탕으로 설계수명 말기에서 태양전지 배열기의 전력 성능을 평가하였다. 설계수명 말기에서 예측한 태양전지 배열기의 전력 성능은 태양전지 배열기 제작사가 제공한 전력 성능과 비교하였다. 경향분석 결과를 통해 태양전지 셀은 현저한 성능감소 없이 정상적으로 동작되고 있다.

• 대한원격탐사학회지
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• 제33권4호
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• pp.445-454
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• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.