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.

여러 센서를 이용한 구조물의 구조 응답을 모니터링하는 사례가 증가하고 있다. 그러나 비용과 관리 문제로 인해 제한된 센서만이 구조물에 설치되어 일부의 구조 응답만을 수집하는 경우가 대부분이다. 이는 구조물의 전체 거동을 분석하는데 장애요소로 작용하게 된다. 따라서 제한된 센서를 이용해 센서가 설치되지 않은 위치에서의 응답을 신뢰할 수 있는 수준으로 예측하는 기술이 필요하다. 본 연구에서는 제한된 정보를 이용해 저층 건물 구조물의 지진 응답을 예측하는 해석적 연구를 수행한다. 활용 가능한 응답 정보는 1층과 최상층의 가속도 응답만을 사용할 수 있다고 가정한다. 두 정보를 이용하면 구조물의 1차 고유진동수를 얻을 수 있다. 1층 가속도 정보는 구조물의 가력 정보로 활용한다. 최상층의 가속도이력응답에 대한 오차와 대상 구조물의 1차 고유진동수 오차를 최소화하는 구조물의 질량과 강성 정보를 유전자알고리즘을 이용해 예측하는 기법을 제시한다. 제약조건은 고려하지 않는다. 탐색공간을 의미하는 설계변수의 범위를 결정하기 위해 인공신경망 기반의 파라미터 예측기법을 제시한다. 또한 유전자알고리즘을 통해 얻게 되는 해를 개선시키기 위해 앞서 언급한 인공신경망을 활용한다. 제시한 기법을 검증하기 위해 5층 구조물 예제를 사용한다.

This study was conducted to investigate the quality of kimchi cabbages stored under a pallet unit-controlled atmosphere (PUCA), containing 2% O2 and 5% CO2, and to develop quality prediction models for cabbages stored under such conditions. Summer and winter cabbage samples were divided into PUCA-exposed groups and atmospheric airexposed control groups (in a cold storage). The control summer cabbages lost up to 8.31% of their weight, whereas the PUCA-exposed summer cabbages lost only 1.23% of their weight. Additionally, PUCA storage effectively delayed the reduction in cabbage moisture content compared with the control storage. After storage for 60 and 120 days of the summer and winter samples, respectively, the reducing sugar contents were higher in the PUCA groups than in the control groups. The linear regression analysis-derived equations for predicting the storage period, weight loss, and moisture content in the control groups, as well as those for predicting the storage period and weight loss in the PUCA groups, were appropriate according to the adjusted coefficient of determination, root mean square error, accuracy factor, and bias factor values. Therefore, this PUCA system would be useful for improving the shelf life of the postharvest summer and winter cabbages used in the commercial kimchi industry.

FRP 시트(Sheet)를 활용한 보강 공법은 제작 과정에서의 간편함과 시공의 용이성으로 현장에서 다수 적용되고 있으며, 기존 연구자들은 FRP 시트로 보강한 철근콘크리트의 휨강도를 예측하기 위한 연구를 진행하였다. 그러나 이는 주로 탄소 섬유와 유리 섬유에 한정되어 있었다. 이 연구에서는 바잘트 섬유시트의 역학적 성질을 파악하기 위하여 물성 시험을 수행하였으며, 바잘트 섬유시트로 보강한 철근콘크리트 보의 휨실험을 수행하였다. 또한 그 결과 값을 비교 분석하여 기존 연구를 바탕 으로 바잘트 섬유 시트로 보강한 철근콘크리트 보의 휨모멘트 예측식을 제안하였다. 강도설계법, ACI440.2R (2017) 그리고 Park et al. (2005)의 예측값을 검토한 결과, 강도설계법은 실험값과 예측값의 비가 0.88로 나타났으며, ACI440.2R (2017) 설계식은 0.92, Park et al. (2005)은 0.97로 나타나 기존의 해석 방법은 휨모멘트를 과대평가하는 것으로 나타났다. 본 연구의 제안식은 실험값과 예측값의 비가 1.00으로 나타나 휨모멘트를 안전측으로 예측하는 것으로 나타났다.

Research suggests that prediction is important for language comprehension and learning. Accordingly, it becomes crucial to understand factors that can influence prediction. In this regard, speakers’ prior linguistic experience such as parsing bias has been claimed to affect prediction in the error-based learning account. To test this claim, the current study, using the visual world eye-tracking paradigm, investigated if L2 speakers’ anticipatory eye movements are influenced by their parsing bias, and if individuals’ parsing bias interacts with their working memory capacity and/or vocabulary size for the prediction. The results showed no main effect of the parsing bias on the prediction overall, and the parsing bias did not interact with the working memory capacity and/or the vocabulary size for the prediction. Importantly, however, the speakers’ parsing bias significantly interacted with the trials. The influence of the parsing bias over the course of this experiment suggests that L2 speakers’ prediction is guided by their recent experience with linguistic input as well as long-term linguistic experience.

Because the inner environment of greenhouse has a direct impact on crop production, many studies have been performed to develop technologies for controlling the environment in the greenhouse. However, it is difficult to apply the technology developed to all greenhouses because those studies were conducted through empirical experiments in specific greenhouses. It takes a lot of time and cost to develop the models that can be applicable to all greenhouse in real situation. Therefore studies are underway to solve this problem using computer-based simulation techniques. In this study, a model was developed to predict the inner environment of glass greenhouse using CFD simulation method. The developed model was validated using primary and secondary heating experiment and daytime greenhouse inner temperature data. As a result of comparing the measured and predicted value, the mean temperature and uniformity were 2.62°C and 2.92%p higher in the predicted value, respectively. R2 was 0.9628, confirming that the measured and the predicted values showed similar tendency. In the future, the model needs to improve by applying the shape of the greenhouse and the position of the inner heat exchanger for efficient thermal energy management of the greenhouse.