PURPOSES : This study aims to develop and evaluate computer vision-based algorithms that classify the road roughness index (IRI) of road specimens with known IRIs. The presented study develops and compares classifier-based and deep learning-based models that can effectively determine pavement roughness grades. METHODS : A set road specimen was developed for various IRIs by generating road profiles with matching standard deviations. In addition, five distinct features from road images, including mean, peak-to-peak, standard variation, and mean absolute deviation, were extracted to develop a classifier-based model. From parametric studies, a support vector machine (SVM) was selected. To further demonstrate that the model is more applicable to real-world problems, with a non-integer road grade, a deep-learning model was developed. The algorithm was proposed by modifying the MNIST database, and the model input parameters were determined to achieve higher precision. RESULTS : The results of the proposed algorithms indicated the potential of using computer vision-based models for classifying road surface roughness. When SVM was adopted, near 100% precision was achieved for the training data, and 98% for the test data. Although the model indicated accurate results, the model was classified based on integer IRIs, which is less practical. Alternatively, a deep-learning model, which can be applied to a non-integer road grade, indicated an accuracy of over 85%. CONCLUSIONS : In this study, both the classifier-based, and deep-learning-based models indicated high precision for estimating road surface roughness grades. However, because the proposed algorithm has only been verified against the road model with fixed integers, optimization and verification of the proposed algorithm need to be performed for a real road condition.

Ambient Air Vaporizer (AAV) is an essential facility in the process of generating natural gas that uses air in the atmosphere as a medium for heat exchange to vaporize liquid natural gas into gas-state gas. AAV is more economical and eco-friendly in that it uses less energy compared to the previously used Submerged vaporizer (SMV) and Open-rack vaporizer (ORV). However, AAV is not often applied to actual processes because it is heavily affected by external environments such as atmospheric temperature and humidity. With insufficient operational experience and facility operations that rely on the intuition of the operator, the actual operation of AAV is very inefficient. To address these challenges, this paper proposes an artificial intelligence-based model that can intelligent AAV operations based on operational big data. The proposed artificial intelligence model is used deep neural networks, and the superiority of the artificial intelligence model is verified through multiple regression analysis and comparison. In this paper, the proposed model simulates based on data collected from real-world processes and compared to existing data, showing a 48.8% decrease in power usage compared to previous data. The techniques proposed in this paper can be used to improve the energy efficiency of the current natural gas generation process, and can be applied to other processes in the future.

This study was conducted to determine the possibility of estimating the daily mean temperature for a specific location based on the climatic data collected from the nearby Automated Synoptic Observing System (ASOS) and Automated Weather System(AWS) to improve the accuracy of the climate data in forage yield prediction model. To perform this study, the annual mean temperature and monthly mean temperature were checked for normality, correlation with location information (Longitude, Latitude, and Altitude) and multiple regression analysis, respectively. The altitude was found to have a continuous effect on the annual mean temperature and the monthly mean temperature, while the latitude was found to have an effect on the monthly mean temperature excluding June. Longitude affected monthly mean temperature in June, July, August, September, October, and November. Based on the above results and years of experience with climate-related research, the daily mean temperature estimation was determined to be possible using longitude, latitude, and altitude. In this study, it is possible to estimate the daily mean temperature using climate data from all over the country, but in order to improve the accuracy of daily mean temperature, climatic data needs to applied to each city and province.

The objective of this study was to access the effect of climate and soil factors on alfalfa dry matter yield (DMY) by the contribution through constructing the yield prediction model in a general linear model considering climate and soil physical variables. The processes of constructing the yield prediction model for alfalfa was performed in sequence of data collection of alfalfa yield, meteorological and soil, preparation, statistical analysis, and model construction. The alfalfa yield prediction model used a multiple regression analysis to select the climate variables which are quantitative data and a general linear model considering the selected climate variables and soil physical variables which are qualitative data. As a result, the growth degree days(GDD) and growing days(GD), and the clay content(CC) were selected as the climate and soil physical variables that affect alfalfa DMY, respectively. The contributions of climate and soil factors affecting alfalfa DMY were 32% (GDD, 21%, GD 11%) and 63%, respectively. Therefore, this study indicates that the soil factor more contributes to alfalfa DMY than climate factor. However, for examming the correct contribution, the factors such as other climate and soil factors, and the cultivation technology factors which were not treated in this study should be considered as a factor in the model for future study.

본 연구에서는 농산물에서 오염 가능성이 있는 병원성 식중독 균 L. monocytogenes에 대해 신선편의 샐러드, 파인애플, 냉동망고에서 예측 모델을 개발하고, 본 연구에서 개발된 예측 모델을 다른 제품에서 적용 여부를 검증하였다. 시료에 L. monocytogenes를 접종하여 각각의 저장 온도에 보관 시 샐러드는 13oC, 파인애플은 10oC 이상에서 성장하였으며, 두 식품 중 파인애플에서 L. monocytogenes 가 더 빠르게 성장하는 것으로 확인 되었다. 또한, 냉동망 고에 접종한 L. monocytogenes는 -2, -10, -18oC의 저장온 도에서 온도가 낮아질수록 delta 값이 커지며 생존력이 높아지는 양상을 보였다. 본 실험 검증을 통해 같은 신선편 의 과일, 채소 식품 그룹에 속하더라도 식품 각각의 특성에 따라 L. monocytogenes의 성장 패턴은 일정하지 않으며 각기 다른 행동 패턴을 보이는 것으로 확인하였다. 신 선편의 샐러드 및 절단된 과일류는 냉장유통 되며 추가세 척 없이 소비되는 제품 특성상 공정과정에서 L. monocytogenes에 의한 오염이 일어나지 않도록 위생관리 에 주의하고 유통과정에서 온도 남용이 되지 않도록 유통 온도 관리에도 유의해야할 것으로 사료된다.

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.

As the damage caused by earthquakes gradually increases, seismic retrofitting for existing public facilities has been implemented in Korea. Several types of structural analysis methods can be used to evaluate the seismic performance of structures. Among them, for nonlinear dynamic analysis, the hysteresis model must be carefully applied because it can significantly affect the behavior. In order to find a hysteresis model that predicts rational behavior, this study compared the experimental results and analysis results of the existing non-seismic reinforced concrete frames. For energy dissipation, the results were close to the experimental values in the order of Pivot, Concrete, Degrading, and Takeda models. The Concrete model underestimated the energy dissipation due to excessive pinching. In contrast, the other ones except the Pivot model showed the opposite results with relatively little pinching. In the load-displacement curves, the experimental and analysis results tended to be more similar when the column axial force was applied to columns.

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.

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.

There have been a lot of studies in the past for the method of predicting the failure of a machine, and recently, a lot of researches and applications have been generated to diagnose the physical condition of the machine and the parts and to calculate the remaining life through various methods. Survival models are also used to predict plant failures based on past anomaly cycles. In particular, special machine that reflect the fluid flow and process characteristics of chemical plants are connected to hundreds or thousands of sensors, so there are not many factors that need to be considered, such as process and material data as well as application of derivative variables. In this paper, the data were preprocessed through time series anomaly detection based on unsupervised learning to predict the abnormalities of these special machine. Next, clustering results reflecting clustering-based data characteristics were applied to produce additional variables, and a learning data set was created based on the history of past facility abnormalities. Finally, the prediction methodology based on the supervised learning algorithm was applied, and the model update was confirmed to improve the accuracy of the prediction of facility failure. Through this, it is expected to improve the efficiency of facility operation by flexibly replacing the maintenance time and parts supply and demand by predicting abnormalities of machine and extracting key factors.

벼에 줄무늬잎마름병을 유발하는 애멸구(Laodelphax striatellus)의 온도에 따른 산란 등 성충 활동 특성을 12.5~35.0℃ 10개 항온조건 광주기 14L:10D에서 조사하였다. 산란모델을 만들기 위한 단위 함수를 개발하고 DYMEX를 이용하여 개체군 밀도 변동 모델을 구축하였다. 성충 수명은 15.0℃에서 56.0일로 가장 길었고, 35.0℃에서 17.7일로 가장 짧았으며 온도가 올라감에 따라 수명도 짧아지는 경향을 보였다. 암컷 한 마리당 총산란수는 22.5℃에서 515.9개로 가장 많았으며, 35℃에서 18.6개로 가장 적었다. 산란 모델 개발을 위해 성충발육율, 총산란수, 성충사망율 및 누적산란율 단위모델을 추정한 결과, 단위모델 모두에서 높은 수준의 모델 적합성을 보였다(r2=0.94~0.97). 개체군 밀도 변동 모델은 포트와 포장 실험을 통하여 예측 정확도를 평가하였다. 포트 및 포장 실험 결과 접종 후 30일까지는 각 조사 시점에서 밀도 및 영기 분포 비율의 예측 정확도가 비교적 높았으나 이후에는 1, 2령의 조사 밀도와 예측 밀도 간에 큰 차이가 발생하였고, 영기 분포 변화의 경우도 모델에서 실제 조사 자료보다 1~2단계의 발육 영기가 빠르게 추정되는 경향을 보였다.