선박 건조 시장의 요구를 맞추기 위하여 신속한 건조의 목표로 노력하고 있는 조선소가 선박 정도관리에 대한 요구가 갈수록 높아지고 있다. 선박 건조 공정에서 생산성의 향상과 생산 주기의 단축을 위하여 선박 부재의 정도평가를 전 주기에서 수행해야 하는 것은 중요하다. 선박의 품질을 높이기 위하여 조선소에서 블록의 정도제어를 수행하는 것은 선박의 건조 주기를 단축할 수 있을 뿐만 아니라 건조 비용도 줄일 수 있다. 선박 블록의 정도를 제어하는 중심은 선박 블록 통합 정도관리시스템을 만들어야 한다. 이 시스템은 “Non-allowance Shipbuilding”의 목표로 정도관리의 총괄성, 블록 정도의 향상, 정도관리 과정의 표준화 등이 이루어져야 한다. 일반적으로 정도관리를 수행하는 관리자가 광파측정기를 이용하여 선박 블록의 접합면에 있는 주요 포인트(vital point)를 측정하고 수집하지만 무거운 계측장비를 가지고 블록의 정도관리를 수행하는 것은 불편할 뿐만 아니라 시간도 오래 걸린다. 본 논문에서는 선박 블록의 정도관리 시간을 단축할 수 있는 포인트 클라우드 기반으로 3차원 레이저 스캐너를 이용한 선박 블록 탑재 전에 오차예측 방법을 제안하였다. 이 방법은 ICP(iterative closest point) 알고리즘으로 측정된 포인트 클라우드와 설계된 점들의 비교 작업을 수행한 다음에 허용범위 내의 오차를 만족하는 지를 판단한다.

Aggregate Production Planning determines levels of production, human resources, inventory to maximize company’s profits and fulfill customer's demands based on demand forecasts. Since performance of aggregate production planning heavily depends on accuracy of given forecasting demands, choosing an accurate forecasting method should be antecedent for achieving a good aggregate production planning. Generally, typical forecasting error metrics such as MSE (Mean Squared Error), MAD (Mean Absolute Deviation), MAPE (Mean Absolute Percentage Error), and CFE (Cumulated Forecast Error) are utilized to choose a proper forecasting method for an aggregate production planning. However, these metrics are designed only to measure a difference between real and forecast demands and they are not able to consider any results such as increasing cost or decreasing profit caused by forecasting error. Consequently, the traditional metrics fail to give enough explanation to select a good forecasting method in aggregate production planning. To overcome this limitation of typical metrics for forecasting method this study suggests a new metric, WACFE (Weighted Absolute and Cumulative Forecast Error), to evaluate forecasting methods. Basically, the WACFE is designed to consider not only forecasting errors but also costs which the errors might cause in for Aggregate Production Planning. The WACFE is a product sum of cumulative forecasting error and weight factors for backorder and inventory costs. We demonstrate the effectiveness of the proposed metric by conducting intensive experiments with demand data sets from M3-competition. Finally, we showed that the WACFE provides a higher correlation with the total cost than other metrics and, consequently, is a better performance in selection of forecasting methods for aggregate production planning.

PURPOSES : This study is to investigate the relationship of socioeconomic characteristics and road network structure with traffic growth patterns. The findings is to be used to tweak traffic forecast provided by traditional four step process using relevant socioeconomic and road network data. METHODS: Comprehensive statistical analysis is used to identify key explanatory variables using historical observations on traffic forecast, actual traffic counts and surrounding environments. Based on statistical results, a multiple regression model is developed to predict the effects of socioeconomic and road network attributes on traffic growth patterns. The validation of the proposed model is also performed using a different set of historical data. RESULTS : The statistical analysis results indicate that several socioeconomic characteristics and road network structure cleary affect the tendency of over- and under-estimation of road traffics. Among them, land use is a key factor which is revealed by a factor that traffic forecast for urban road tends to be under-estimated while rural road traffic prediction is generally over-estimated. The model application suggests that tweaking the traffic forecast using the proposed model can reduce the discrepancies between the predicted and actual traffic counts from 30.4% to 21.9%. CONCLUSIONS : Prediction of road traffic growth patterns based on surrounding socioeconomic and road network attributes can help develop the optimal strategy of road construction plan by enhancing reliability of traffic forecast as well as tendency of traffic growth.

다 자유도를 가 진 구조계 로 부터 얻어진 동적거동의 측정치를 이용하여 구조물 의 모우드 셰수를 추정 하는 시간영역방볍에 대해 연구하였다. 이를 위해 운동방정식올 실험적 모 우드식으로 변환한 다음 이를 다시 이산시간 영역의 식인 ARMAX식으로 나타내었다. 순차적 예측 오차 방법 을 이용하여 ARMAX 식의 계수들 을 추 정 한 후， 이들로 부 터 구조볼의 모 우 드 계수들을 계산하였다. 지 진 하중을 받는 3충 빌딩 구조볼 의 실험치 를 이용하여 얻은 모 우드 계수들 은 서로 다른 실험 간에 좋은 일치 를 보 였 으 며， 또한 계산된 계수들을 이용하여 다시 구한 구조품 웅답의 시간이력들은 실험치들과 좋은 일 치 를 보였다.

Concrete has recently been modified to have various performance and properties. However, the conventional method for predicting the compressive strength of concrete has been suggested by considering only a few influential factors. so, In this study, nine influential factors (W/B ratio, Water, Cement, Aggregate(Coarse, Fine), Fly ash, Blast furnace slag, Curing temperature, and humidity) of papers opened for 10 years were collected at 4 conferences in order to know the various correlations among data and the tendency of data. The selected mixture and compressive strength data were used for learning the Deep Learning Algorithm to derive a prediction model. The purpose of this study is to suggest a method of constructing a prediction model that predicts the compression strength with high accuracy based on Deep Learning Algorithms.