There has been increasing interest in UHPC (Ultra-High Performance Concrete) materials in recent years. Owing to the superior mechanical properties and durability, the UHPC has been widely used for the design of various types of structures. In this paper, machine learning based compressive strength prediction methods of the UHPC are proposed. Various regression-based machine learning models were built to train dataset. For train and validation, 110 data samples collected from the literatures were used. Because the proportion between the compressive strength and its composition is a highly nonlinear, more advanced regression models are demanded to obtain better results. The complex relationship between mixture proportion and concrete compressive strength can be predicted by using the selected regression method.

트랙터의 보급이 늘어나는 만큼 올바른 사용법 및 점검이 중요하다. 특히 타이어 공기압에 따른 토양다짐 현상, 연료의 과소비, 안전사고를 예방하기 위해 간편한 측정기술이 필요하다. 따라서 본 연구에서는 대중적 모바일 영상 취득 장치인 스마트폰 카메라를 활용해 획득한 이미지 데이터로 타이어 압력을 예측하였다. 전통적 캘리브레이션을 응용하여 왜곡률을 보정하였다. 트랙터 타이어에 공기압을 0 kPa에서 300 kPa까지 주입하면서 구간별 타이어 촬영을 하였으며, 타이어 중심을 기준으로 상, 하, 좌, 우의 픽셀을 측정하였다. 하중을 받는 타이어의 기하학적 특성을 고려하여 중심과 바닥의 픽셀을 보정식을 통해 보정한 뒤 압력에 따른 픽셀의 변화를 도출하였다.

산지재해는 1차적으로 산지사면에서 산사태가 발생되어 2차적으로 계류를 따라 토석류로 이동 및 확산되면서 산지 하부지역의 시설지와 주거지에 피해를 발생시킨다. 따라서 본 연구는 전라북도 지역의 토석류 발생지 79개소를 조사 대상으로 현장조사를 통한 발생 길이에 영향을 미치는 인자를 구명하고, 수량화이론(I)을 이용하여 발생 길이에 대한 각 인자의 기여도 분석을 통해 예방적인 측면에서 전라북도 지역 내 토석류 발생 위험지역에 대한 예측기준을 작성하였다. 토석류의 발생 길이에 영향을 미치는 인자는 모암(화성암), 횡단사면(복합사면), 입목 흉고직경(6cm 이하), 표고(501m 이상), 발생위치(산록) 등이었다. 각 인자의 범위를 추정한 결과, 모암(0.5633)이 가장 높게 나타나 전라북도 지역의 토석류 발생 위험도에 큰 영향을 미치는 것으로 추정되었으며, 다음으로는 횡단사면(0.4565), 사면위치(0.3568), 흉고직경(0.3274), 표고(0.3052)순으로 나타났다. 전라북도 지역 산지에서 토석류 발생 위험도 판정식을 기준으로 5개 인자의 카테고리별 점수를 계산한 추정치 범위는 0점에서 2.0092점 사이에 분포하였다. 중앙값인 1.0046점을 기준으로 토석류 위험도 예측을 위한 등급을 분류한 결과 Ⅰ등급은 1.5070 이상, Ⅱ등급 1.0047 ∼ 1.5069, Ⅲ등급 0.5023 ∼ 1.0046, IV등급 0.5022 이하로 나타났고, Ⅰ등급과 Ⅱ등급에서 토석류 발생비율이 76%로서 비교적 높은 적중률을 보였다. 따라서 본 판정표는 전라북도의 산지에서 지역의 위험 비탈면에 있어서 토석류 발생 위험도 판정에 유용하게 활용할 수 있을 것으로 판단된다.

Interest rate spreads indicate the conditions of the economy and serve as an indicator of the recession. The purpose of this study is to predict Korea's interest rate spreads using US data with long-term continuity. To this end, 27 US economic data were used, and the entire data was reduced to 5 dimensions through principal component analysis to build a dataset necessary for prediction. In the prediction model of this study, three RNN models (BasicRNN, LSTM, and GRU) predict the US interest rate spread and use the predicted results in the SVR ensemble model to predict the Korean interest rate spread. The SVR ensemble model predicted Korea's interest rate spread as RMSE 0.0658, which showed more accurate predictive power than the general ensemble model predicted as RMSE 0.0905, and showed excellent performance in terms of tendency to respond to fluctuations. In addition, improved prediction performance was confirmed through period division according to policy changes. This study presented a new way to predict interest rates and yielded better results. We predict that if you use refined data that represents the global economic situation through follow-up studies, you will be able to show higher interest rate predictions and predict economic conditions in Korea as well as other countries.

The global trend is the application of heat-treated omission materials to reduce the manufacturing cost of automobile steering parts. Attempts have been made to apply heat-treated omission materials in domestic, but they are delayed due to concerns over rising cold forging process costs. For quantitative prediction of cold forging process cost, fatigue properties of forging die materials were evaluated. Based on this, the die life and cost were predicted quantitatively, and the manufacturing cost reduction of automobile steering parts using heat-treated material was found to be about 11%. Also, various methods to improve die life were additionally presented.

In this study, we performed algorithms to predict algae of Chlorophyll-a (Chl-a). Water quality and quantity data of the middle Nakdong River area were used. At first, the correlation analysis between Chl-a and water quality and quantity data was studied. We extracted ten factors of high importance for water quality and quantity data about the two weirs. Algorithms predicted how ten factors affected Chl-a occurrence. We performed algorithms about decision tree, random forest, elastic net, gradient boosting with Python. The root mean square error (RMSE) value was used to evaluate excellent algorithms. The gradient boosting showed 10.55 of RMSE value for the Gangjeonggoryeong (GG) site and 11.43 of RMSE value for the Dalsung (DS) site. The gradient boosting algorithm showed excellent results for GG and DS sites. Prediction value for the four algorithms was also evaluated through the Receiver operating characteristic (ROC) curve and Area under curve (AUC). As a result of the evaluation, the AUC value was 0.877 at GG site and the AUC value was 0.951 at DS site. So the algorithm‘s ability to interpret seemed to be excellent.