• Asian Pacific Journal of Cancer Prevention
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• 제16권3호
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• pp.1277-1280
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• 2015

Aim: To report epidemiologic and anatomoclinical transitions of inflammatory breast cancer (IBC) in Tunisia. Materials and Methods: Data including clinico-pathological data for 208 cases of T4d or PEV 3 non-metastatic breast cancer diagnosed between 2005 and 2010 were collected from patient records. Chi2 and Z tests were used to compare variables with two Tunisian historical series and a series about Arab-American patients. Results: Thirty three percent of our patients had their first child before 23 years of age and 56% had their menarche before 12 years, 75% never receiving oral contraception. Obesity was observed in 42% of women and IBC occurred during pregnancy in 13% of cases. Tumor grade was II-III in 90% of cases, HR was negative in 52%, HER2 was over expressed in 31% and invasion of more than 3 axillary nodes occurred in 18% of patients. We observed a pCR rate of 19% after neoadjuvant treatment (anthracyline-taxane used in 79%, trastuzumab in 27% ). Compared to historical Tunisian series (since 1996), IBC epidemiology remained stable in terms of median age, menopausal status and obesity. However we observed a significant decrease in median clinical tumor size and number of positive axillary lymph nodes. Comparison to IBC in Arab-Americans showed a significant difference in terms of median age, menopausal status, positivity of hormonal receptors and educational level. Conclusions: Our assessment of epidemiologic transition showed a reduction of clinco-pathological stage of IBC, keeping the same characteristics as compared to Tunisian historical series over a period of 14 years. Features seem to be different in Arab-American patients, probably related to migration, "occidentalization" of life style and improvement in socio-economic level.

• 한국건설관리학회논문집
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• 제7권5호
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• pp.85-93
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• 2006

이 연구는 최근 건설 프로젝트의 불확실성이 증가하고, 국내 건설 산업이 다변화되는 상황에서 시작되었다 프로젝트의 Pre-Design 단계와 Construction 단계에서 얻을 수 있는 변수들을 도출해냄으로써 적절한 노무량을 예측할 수 있는 모델을 제시하는 데에 이 연구의 목적이 있다. 표준품셈과 같은 기존의 방법으로는 퇴직공제금과 같은비용을 정확하게 산출하는 데에 어려움이 있기 때문에 본 연구에서는 수도권 지역에서 2000년부터 현재까지 공사가 완료 된 공동주택 38곳의 실적자료를 이용한 통계적 방법을 사용하여, 실제 공사에 투입되고 있는 노무량과 공사의 전체적인 개요와의 상관관계를 분석하고, 회귀모델을 제시하였다. 회귀모델의 검증에서는 몇몇의 현장을 제외하고는 결과값이 통계적으로 비교적 유의한 것으로 확인되었다. 이 회귀모델은 기존의 방법보다 퇴직공제금의 적절한 산정에 도움을 줄 수 있을 것으로 기대된다.

• 정보처리학회논문지D
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• 제18D권4호
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• pp.225-236
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• 2011

무선 통신 기술의 발달과 컴퓨터의 소형화에 힘입어 사용자의 위치에 따라 맞춤형 서비스를 제공하기 위하여 다양한 위치 기반 서비스 응용들이 개발되고 있다. 그리고 대용량의 차량 위치 데이터를 효과적으로 처리하기 위하여 차량 위치 감지 및 전송, 데이터의 삽입 및 검색과 사용자 질의 처리 기술이 요구된다. 이 논문에서는 대용량의 과거 차량 위치 정보를 빠르게 입력, 검색하는 과거 위치 색인을 설계하고 상황에 따라 입력과 검색 비용을 조절할 수 있는 가변 버퍼 노드인 기법을 제안한다. 설계된 색인은 GIP+와 같이 효과적인 입력을 위해 버퍼 노드를 사용하고 빠른 검색을 위해 프로젝션 스토리지를 사용한다. 그리고 사용자가 지정한 시간 간격에 따라 버퍼 노드에 저장되는 데이터의 개수를 조절하여 입력과 검색 비용을 조절할 수 있다. 실험에서는 버퍼 노드 크기에 따라 비단말 노드 수가 달라지며, 이로 인해 입력과 검색 성능이 달라짐을 확인할 수 있다. 제안된 가변 버퍼 노드 방식은 위치 기반 서비스 응용에 따라 과거 위치 색인의 성능을 조절하는데 효과적으로 사용 가능하다.

• Spatial Information Research
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• 제17권3호
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• pp.329-339
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• 2009

역사문화지도는 한 지역의 개별적인 유적에 대한 단순한 집성이 아니라, 다양한 구성요소들의 시.공간적 연관성에 대한 종합적인 반영이 이루어져야만 한다. 문명의 발상지라고 할 수 있는 수계권의 역사와 문화, 그리고 생활환경의 연구는 지금까지의 지역단위 차원을 넘어서 타 문화권의 비교, 나아가 국가 및 대륙간의 문명사 연구에도 일익을 담당할 수 있을 것이다. 이러한 맥락에서 본 연구는 우리 고유의 역사와 문화를 토대로 문화적 유형화 및 지역적 정체성 파악을 위한 공용의 문화정보시스템 구축에 중점을 두었다. 즉, 영산강유역권 역사문화정보시스템은 수계권의 다양한 역사문화정보를 전자지도의 형태로 담아내고, 인터넷을 통해 공유하는 Web GIS 기반의 통합정보시스템인 것이다. 상기의 시스템은 고고, 건축 미술, 생태환경, 역사, 민속 문학, 음식 등 각 분야별 기초조사 자료에 의한 종합적인 산출물이며, 학제간 연구에서의 활용뿐만 아니라 공공의 자료로써 새로운 가치창출에 그 목적을 두고 있다. 결과적으로 Web GIS 기반의 영산강유역권 역사문화정보시스템 구축에 따른 기대효과는 첫째, 영산강유역 분야의 연구에 대한 로드맵을 확인하는데 공헌할 수 있으며, 둘째, 영산강유역의 역동적 구조와 특성에 대한 학제적 탐구를 촉진하는 역할을 한다는 것이다. 그리고 셋째, 역사문화지도 연구를 위한 전문화 된 전자 학술자원 제공의 본격적 발판을 마련하는 효과를 기대할 수 있다.

• 한국정보시스템학회지:정보시스템연구
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• 제31권3호
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• pp.177-195
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• 2022

Purpose This paper aims to prepare a full operational readiness by establishing an optimal flight plan considering the weather conditions in order to effectively perform the mission and operation of military aircraft. This paper suggests a flight prediction model and rules by analyzing the correlation between flight implementation and cancellation according to weather conditions by using big data collected from historical flight information of military aircraft supplied by Korean manufacturers and meteorological information from the Korea Meteorological Administration. In addition, by deriving flight rules according to weather information, it was possible to discover an efficient flight schedule establishment method in consideration of weather information. Design/methodology/approach This study is an analytic study using data mining techniques based on flight historical data of 44,558 flights of military aircraft accumulated by the Republic of Korea Air Force for a total of 36 months from January 2013 to December 2015 and meteorological information provided by the Korea Meteorological Administration. Four steps were taken to develop optimal flight prediction models and to derive rules for flight implementation and cancellation. First, a total of 10 independent variables and one dependent variable were used to develop the optimal model for flight implementation according to weather condition. Second, optimal flight prediction models were derived using algorithms such as logistics regression, Adaboost, KNN, Random forest and LightGBM, which are data mining techniques. Third, we collected the opinions of military aircraft pilots who have more than 25 years experience and evaluated importance level about independent variables using Python heatmap to develop flight implementation and cancellation rules according to weather conditions. Finally, the decision tree model was constructed, and the flight rules were derived to see how the weather conditions at each airport affect the implementation and cancellation of the flight. Findings Based on historical flight information of military aircraft and weather information of flight zone. We developed flight prediction model using data mining techniques. As a result of optimal flight prediction model development for each airbase, it was confirmed that the LightGBM algorithm had the best prediction rate in terms of recall rate. Each flight rules were checked according to the weather condition, and it was confirmed that precipitation, humidity, and the total cloud had a significant effect on flight cancellation. Whereas, the effect of visibility was found to be relatively insignificant. When a flight schedule was established, the rules will provide some insight to decide flight training more systematically and effectively.

• 한국농공학회논문집
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• 제47권3호
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• pp.3-13
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• 2005

The conceptual rainfall-runoff models are used to predict complex hydrological effects of a basin. However, to obtain reliable results, there are some difficulties and problems in choosing optimum model, calibrating, and verifying the chosen model suitable for hydrological characteristics of the basin. In this study, Genetic Algorithm and SCE-UA method as global optimization methods were applied to compare the each optimization technique and to analyze the application for the rainfall-runoff models. Modified TANK model that is used to calculate outflow for watershed management and reservoir operation etc. was optimized as a long term rainfall-runoff model. And storage-function model that is used to predict real-time flood using historical data was optimized as a short term rainfall-runoff model. The optimized models were applied to simulate runoff on Pyeongchang-river watershed and Bocheong-stream watershed in 2001 and 2002. In the historical data study, the Genetic Algorithm and the SCE-UA method showed consistently good results considering statistical values compared with observed data.

• 경영과학
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• 제31권3호
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• pp.1-12
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• 2014

This study suggests the two types DEA models such as DEA CCR model and Super Efficiency model to evaluate the value of a company and to apply them for the investments. 14 kinds of real data of companies such as EV/EBITDA, EPS growth rate, PCR, PER, dividend yield, PBR, stock price/net current asset, debt ratio, current ratio, ROE, operating margin, inventory turnover, accounts receivable turnover, and sales growth ratio were used as input variables of DEA models. 12 year data from December 30, 2000 up to December 30, 2012 were collected, and the data with negative, missing and 0 values were removed reflecting the characteristics of the DEA. In order to verify the effectiveness of the models, we compared the historical variability and rate of return of both models those of the market. Study results are as follows. First, two DEA models are more stable than market in terms of rate of return because the historical variability of both models are less than that of market. Second, Super Efficiency model is more stable than CCR model. Lastly, the cumulative rate of return of Super Efficiency model (434%) is greater than that of the CCR model (420%) and that of the market (269%).

• Journal of Electrical Engineering and Technology
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• 제13권3호
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• pp.1089-1098
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• 2018

This study aims at developing and applying a hybrid model to the wind power prediction (WPP). The hybrid model for a very-short-term WPP (VSTWPP) is achieved through analytical data, multiple linear regressions and least square methods (MLR&LS). The data used in our hybrid model are based on the historical records of wind power from an offshore region. In this model, the WPP is achieved in four steps: 1) transforming historical data into ratios; 2) predicting the wind power using the ratios; 3) predicting rectification ratios by the total wind power; 4) predicting the wind power using the proposed rectification method. The proposed method includes one-step and multi-step predictions. The WPP is tested by applying different models, such as the autoregressive moving average (ARMA), support vector machine (SVM), and artificial neural network (ANN). The results of all these models confirmed the validity of the proposed hybrid model in terms of error as well as its effectiveness. Furthermore, forecasting errors are compared to depict a highly variable WPP, and the correlations between the actual and predicted wind powers are shown. Simulations are carried out to definitely prove the feasibility and excellent performance of the proposed method for the VSTWPP versus that of the SVM, ANN and ARMA models.

• 한국조경학회지
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• 제25권1호
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• pp.82-99
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• 1997

The purpose of this study is to establish work classification system of landscape construction in order to offer the basis of new estimation system of public landscape construction. New estimation system is based on historical construction data. For application of this system, the standard work classification system is necessary. Because extensive cost data should be accumulated under an unified construction work classification system. In the study of new estimation system carried by KICT(Korea Institute of Construction Technology), landscaping works belong to earth work of civil engineering. It looks very unreasonable work classification, because landscape archtecture has its own specialties and professional domain. In this study, information classification systems in the construction industry and various landscaping works of housing developments are analysed. As a result. a standard work classification system of housing landscape construction is proposed in section VI-3. This standard work classification structure consists of three levels divisions (i.e large work division, middle work division, small work division) . Now in this study, housing landscape construction works are divided into four large works and twenty six middle works. According to work attributes, middle and small work division is possible to subdivide into details.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.150-150
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• 2018

The Numerical Weather Prediction (NWP) models provide information for weather forecasts. The highly nonlinear and complex interactions in the atmosphere are simplified in meteorological models through approximations and parameterization. Therefore, the simplifications may lead to biases and errors in model results. Although the models have improved over time, the biased outputs of these models are still a matter of concern in meteorological and hydrological studies. Thus, bias removal is an essential step prior to using outputs of atmospheric models. The main idea of statistical bias correction methods is to develop a statistical relationship between modeled and observed variables over the same historical period. The Model Output Statistics (MOS) would be desirable to better match the real time forecast data with observation records. Statistical post-processing methods relate model outputs to the observed values at the sites of interest. In this study three methods are used to remove the possible biases of the real-time outputs of the Weather Research and Forecast (WRF) model in Imjin basin (North and South Korea). The post-processing techniques include the Linear Regression (LR), Linear Scaling (LS) and Power Scaling (PS) methods. The MOS techniques used in this study include three main steps: preprocessing of the historical data in training set, development of the equations, and application of the equations for the validation set. The expected results show the accuracy improvement of the real-time forecast data before and after bias correction. The comparison of the different methods will clarify the best method for the purpose of the forecast skill enhancement in a real-time case study.