This study suggests a machine learning model for predicting the production quality of free-machining 303-series stainless steel small rolling wire rods according to the manufacturing process's operation condition. The operation condition involves 37 features such as sulfur, manganese, carbon content, rolling time, and rolling temperature. The study procedure includes data preprocessing (integration and refinement), exploratory data analysis, feature selection, machine learning modeling. In the preprocessing stage, missing values and outlier are removed, and variables for the interaction between processes and quality influencing factors identified in existing studies are added. Features are selected by variable importance index of lasso regression, extreme gradient boosting (XGBoost), and random forest models. Finally, logistic regression, support vector machine, random forest, and XGBoost is developed as a classifier to predict good or defective products with new operating condition. The hyper-parameters for each model are optimized using k-fold cross validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963 and logarithmic loss of 0.0209. In this study, the quality prediction model is expected to be able to efficiently perform quality management by predicting the production quality of small rolling wire rods in advance.

통영 LNG 기지에서 방류되는 냉배수가 진해만에 미치는 영향을 알아보고, 냉배수의 활용 방안 모색을 위해 총 4개의 냉배수 방류량에 대한 진해만의 환경변화를 1년간(2018년) 모의하였다. 실제 냉배수 방류량인 Case1(10 m3 sec-1)의 모의 결과, 모든 분기에서 냉배 수에 의한 진해만의 환경변화는 매우 미미하게 나타났다. 모의 방류량인 Case2(100 m3 sec-1)의 경우 방류구 반경 5 km 범위에서 1 ~ 3℃의 수온 감소를 보였으며, Case3(1000 m3 sec-1)에서는 방류구 반경 8 km 범위에서 최대 4 ~ 5℃의 수온이 감소하였고 진해만 전 해역에 걸쳐 냉 배수가 확산하는 결과를 보였다. 플랑크톤의 성장 속도는 최대 15% 감소하였으며(11월), 대형조류의 성장 속도는 행암만 부근에서 최대 6 % 증가하는 결과를 보였다. 상기 결과로부터 통영 LNG 기지에서 방류되는 냉배수에 의한 진해만의 환경변화는 미미한 것을 확인하였 다. 또한 Case3 결과로부터 국소지역의 ‘적조 방재’, ‘해조류 성장’을 목적으로 냉배수의 활용이 가능할 것으로 기대된다.

본 연구는 기상청 현업모델(LDAPS)로부터 예측된 서울의 도시열섬 강도와 지상 기온을 AWS 관측과 비교 평가하였다. 관측된 서울의 열섬 강도는 봄과 겨울동안 증가하며 여름동안 감소한다. 열섬 강도의 시간적 변동 경향은 새벽 시간 최대, 오후에 최소를 보인다. 기상청 국지기상예측시스템(LDAPS)으로부터 예측된 열섬 강도는 여름철 과대모의, 겨울철 과소모의 특징을 보인다. 특히 여름철은 주간에 과대 모의 경향이 증가하며, 겨울은 새벽 시간 과소 모의 오차가 크게 나타난다. LDAPS에서 예측된 지면 기온의 오차는 여름철 감소하며 겨울철 증가한다. 겨울철 열섬 강도의 과소 모의는 도시 기온의 과소 모의와 관련되었으며, 여름철 열섬 강도의 과대 모의는 교외 지역 기온의 과소 모의로부터 기인하는것으로 판단된다. 도시 열섬강도 예측성 개선을 위하여 도시효과를 고려하는 도시캐노피모델을 LDAPS와 결합하여 2017년 여름 기간동안 모의하였다. 도시캐노피모델 적용 후 도시의 지면 기온의 오차는 개선되었다. 특히 오전시간 과소모의되는 기온의 오차 개선 효과가 뚜렷하였다. 도시캐노피모델은 여름동안 과대 모의하는 도시열섬강도를 약화시키는 개선 효과를 보였다.

PURPOSES : Arterial work zones, particularly at signalized intersections, have several characteristics and effects different from freeways. This paper presents three significant work zone effects on signalized intersections: (1) saturation headway change (saturation flow rate change), (2) green time (g/C ratio) change, and (3) progression speed degradation impacts on bandwidth performance. METHODS : Both saturation flow rate reduction and g/C reduction were selected as the work zone impact variables for a signalized intersection, while bandwidth capacity reduction was chosen to measure the impact of work zones on arterials. The authors established a statistical model and normalized g/C table to estimate saturation headways and the g/C ratio at signalized intersection work zones based on the work intensity, pavement condition, ledge presence, turn percentages from shared lanes, and number of closed exclusive turn lanes. In addition, the dynamic bandwidth capacity and bandwidth solution space change based on the progression speed were introduced in this study. RESULTS : A normalized g/C ratio distribution was developed to estimate both the non-work zone and work zone g/C ratios under different work zone configurations. The results of the estimated work zone capacity using the work zone saturation headway model and the g/C ratio distribution showed that the estimated capacity reduction ranged from 32.78%~2.93%. In addition, arterial dynamic bandwidth and its capacity were both critically influenced by the progression speed. CONCLUSIONS : The proposed model and method will help practitioners understand the factors that cause a decrease in the saturation flow rate and g/C and influence progression quality on the urban arterial street due to work zones. Moreover, the proposed model and method can guide the calibration of simulation tools to properly represent the resulting capacity effects of work zones on arterial streets.

본 연구에서는 3차원 네트워크 폴리아크릴산나트륨 겔의 가교환경을 변화시켜 기계적 강도 및 팽윤거동을 제어하고 그 물성을 평가하는 연구를 진행하였다. 일반적으로 겔 용액의 가교도가 증가함에 따라 3차원 네트워크 겔의 팽윤비는 감소하고 겔의 기계적 강도는 증가한다. 본 연구에서는 3차원 네트워크 겔 상의 가교개수밀도를 산출하여, 겔화 과정에서 가교환경에 의존하는 중합효율 및 가교효율을 확인하였다. 그 결과, 겔 용액에서 단량체와 가교제의 중량비가 동일하더라도 가교환경이 달라지면 실제 제조된 겔 내부의 가교개수밀도가 3.6배 이상 달라질 수 있음을 확인하였다. 본 연구에서 시도한 가교개수밀도 기반 겔 평가 방법을 활용하면 효과적인 VOCs 흡수제로써 3차원 네트워크 겔을 최적화 할 수 있으리라 기대된다.

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.

Near infrared reflectance spectroscopy (NIRS) is routinely used for the determination of nutrient components of forages. However, little is known about the impact of sample preparation and wavelength on the accuracy of the calibration to predict minerals. This study was conducted to assess the effect of sample preparation and wavelength of near infrared spectrum for the improvement of calibration and prediction accuracy of Calcium (Ca) and Phosphorus (P) in imported hay using NIRS. The samples were scanned in reflectance in a monochromator instrument (680–2,500 nm). Calibration models (n = 126) were developed using partial least squares regression (PLS) based on cross-validation. The optimum calibrations were selected based on the highest coefficients of determination in cross validation (R2) and the lowest standard error of cross-validation (SECV). The highest R2 and the lowest SECV were obtained using oven-dry grinded sample preparation and 1,100-2,500 nm wavelength. The calibration (R2) and SECV were 0.99 (SECV: 468.6) for Ca and 0.91 (SECV: 224.7) for P in mg/kg DM on a dry weight, respectively. Results of this experiment showed the possibility of NIRS method to predict mineral (Ca and P) concentration of imported hay in Korea for routine analysis method to evaluate the feed value.

This ammonia prediction study was performed using the time-series artificial neural network model, Long-short term memory (LSTM), after long-term monitoring of ammonia and environmental factors (ventilation rate (V), temperature (T), humidity (RH)) from a slurry finishing pig farm on mechanical ventilation system. The difference with the actual ammonia concentration was compared through prediction of the last three days of the entire breeding period. As a result of the analysis, the model which had a low correlation (ammonia concentration and humidity) was confirmed to have less error values than the models that did not. In addition, the combination of two or more input values [V, RH] and [T, V, RH] showed the lowest error value. In this study, the sustainability period of the model trained by multivariate input values was analyzed for about two days. In addition, [T, V, RH] showed the highest predictive performance with regard to the actual time of the occurrence of peak concentration compared to other models . These results can be useful in providing highly reliable information to livestock farmers regarding the management of concentrations through artificial neural network-based prediction models.

본 연구는 화재진압 및 피난활동을 지원하는 딥러닝 기반의 알고리즘 개발에 관한 기초 연구로 선박 화재 시 연기감지기가 작동하기 전에 검출된 연기 데이터를 분석 및 활용하여 원격지까지 연기가 확산 되기 전에 연기 확산거리를 예측하는 것이 목적이다. 다음과 같은 절차에 따라 제안 알고리즘을 검토하였다. 첫 번째 단계로, 딥러닝 기반 객체 검출 알고리즘인 YOLO(You Only Look Once)모델에 화재시뮬레이션을 통하여 얻은 연기 영상을 적용하여 학습을 진행하였다. 학습된 YOLO모델의 mAP(mean Average Precision)은 98.71%로 측정되었으며, 9 FPS(Frames Per Second)의 처리 속도로 연기를 검출하였다. 두 번째 단계로 YOLO로부터 연기 형상이 추출된 경계 상자의 좌표값을 통해 연기 확산거리를 추정하였으며 이를 시계열 예측 알고리즘인 LSTM(Long Short-Term Memory)에 적용하여 학습을 진행하였다. 그 결과, 화재시뮬레이션으로부터 얻은 Fast 화재의 연기영상에서 경계 상자의 좌표값으로부터 추정한 화재발생~30초까지의 연기 확산거리 데이터를 LSTM 학습모델에 입력하여 31초~90초까지의 연기 확산거리 데이터를 예측하였다. 그리고 추정한 연기 확산거리와 예측한 연기 확산거리의 평균제곱근 오차는 2.74로 나타났다.

Forward osmosis (FO) process is a chemical potential driven process, where highly concentrated draw solution (DS) is used to take water through semi-permeable membrane from feed solution (FS) with lower concentration. Recently, commercial FO membrane modules have been developed so that full-scale FO process can be applied to seawater desalination or water reuse. In order to design a real-scale FO plant, the performance prediction of FO membrane modules installed in the plant is essential. Especially, the flux prediction is the most important task because the amount of diluted draw solution and concentrate solution flowing out of FO modules can be expected from the flux. Through a previous study, a theoretical based FO module model to predict flux was developed. However it needs an intensive numerical calculation work and a fitting process to reflect a complex module geometry. The idea of this work is to introduce deep learning to predict flux of FO membrane modules using 116 experimental data set, which include six input variables (flow rate, pressure, and ion concentration of DS and FS) and one output variable (flux). The procedure of optimizing a deep learning model to minimize prediction error and overfitting problem was developed and tested. The optimized deep learning model (error of 3.87%) was found to predict flux better than the theoretical based FO module model (error of 10.13%) in the data set which were not used in machine learning.

The increased turbidity in rivers during flood events has various effects on water environmental management, including drinking water supply systems. Thus, prediction of turbid water is essential for water environmental management. Recently, various advanced machine learning algorithms have been increasingly used in water environmental management. Ensemble machine learning algorithms such as random forest (RF) and gradient boosting decision tree (GBDT) are some of the most popular machine learning algorithms used for water environmental management, along with deep learning algorithms such as recurrent neural networks. In this study GBDT, an ensemble machine learning algorithm, and gated recurrent unit (GRU), a recurrent neural networks algorithm, are used for model development to predict turbidity in a river. The observation frequencies of input data used for the model were 2, 4, 8, 24, 48, 120 and 168 h. The root-mean-square error-observations standard deviation ratio (RSR) of GRU and GBDT ranges between 0.182~0.766 and 0.400~0.683, respectively. Both models show similar prediction accuracy with RSR of 0.682 for GRU and 0.683 for GBDT. The GRU shows better prediction accuracy when the observation frequency is relatively short (i.e., 2, 4, and 8 h) where GBDT shows better prediction accuracy when the observation frequency is relatively long (i.e. 48, 120, 160 h). The results suggest that the characteristics of input data should be considered to develop an appropriate model to predict turbidity.