본 연구는 도로 노면결빙 판정 알고리즘에 대해 알고리즘을 개선하고 실제 현장 측정 자료와 알고리즘 예측값을 비교하였을 때 알고리즘에 대한 적중률을 분석하였다. 분석을 위하여 포천시 신북면 금 동리의 도로 및 기상을 측정하였다. 알고리즘은 기존 도로 결빙 알고리즘을 선정하여 실제 결빙 조건 및 측정 수치에 맞춰 4차 알고리즘까지 개선하였다. 최종적으로 응결에 의한 결빙, 강수에 의한 결빙, 적설에 의한 결빙, 결빙상태의 지속, 풍속에 의한 결빙 5개의 알고리즘을 제작하였다. 포천 현장에서 알고리즘을 활용하여 예측할 경우 경우 결빙 적중률이 93.22%까지 개선되었다. 결빙 알고리즘에 대한 조합 비율에 대 해 도출하였을 때 응결에 의한 결빙과 결빙상태의 지속에 대한 알고리즘이 96%를 차지하였다.

Odor is a type of sensory pollution that can stimulate the human sense of smell when it occurs, causing discomfort and making it difficult to create a pleasant environment. For this reason, there is a high possibility of complaints regarding odors if odors occur in pigsties near residential properties, and the number of such complaints is also increasing. In addition, odors emanating from pigsties around military installations can cause physical and psychological harm, not only to the soldiers living in these type of facilities but also to the families belonging to military personnel living there as well. Because the concentration of odors varies due to diverse factors such as temperature, humidity, wind direction, wind speed, and interaction between causative materials, predicting odors based on only one factor is not proper or appropriate. Therefore, in this work, we sought to construct models that are based on several regression techniques of machine learning using data collected in field. And we selected and utilized the model that has the highest-accuracy in order to notify and warn residents of odors in advance. In this work, 3672 data items were used to train and test the model. The several machine learning algorithms to build the models are polynomial regression, ridge regression, K-nearest neighbor regression (KNN Regression), and random forest. Comparing the performance of models based on each algorithm, the study found that KNN Regression was the most suitable model, and the result obtained from KNN regression was significant.

본 연구는 머신러닝 기법을 토대로 15개 환경 변수를 활용하여 소나무재선충병의 위험지역 분포를 예측하였다. 연구는 최대 엔트로피 모델을 머신러닝 기법으로 활용하였고, 연구 지역은 경주이며 연구 기간은 2018∼2020년이다. 모델의 평가에는 AUC(area under the curve)를 이용하였다. 연구 지역에서 소나무재선충병의 감염목 핵심 분포 지역은 2018년 대비 2019년과 2020년에 각각 2.5배와 4.7배 확대되었다. 소나무재선충병의 감염목 분포 추정 모델의 AUC는 모든 해에 최소 0.86 이상이었다. 모델에서 가장 중요한 변수는 직전 해의 감염목 근접도 이었다. 지형과 도로와의 인접성, 목조건물 인접성, 5월 평균 기온도 중요한 변수이었다. 인간 활동과 매개충의 생장 환경이 소나무재선충병의 공간적 분포에 중요한 역할을 한다는 것을 의미한다. 나아가 연구의 결과는 감염목 분포 정보의 지속적인 구축과 공유가 소나무재선충병 예방을 위한 정책과 연구에 중요하다는 것을 시사한다.

This article suggests the machine learning model, i.e., classifier, for predicting the production quality of free-machining 303-series stainless steel(STS303) small rolling wire rods according to the operating condition of the manufacturing process. For the development of the classifier, manufacturing data for 37 operating variables were collected from the manufacturing execution system(MES) of Company S, and the 12 types of derived variables were generated based on literature review and interviews with field experts. This research was performed with data preprocessing, exploratory data analysis, feature selection, machine learning modeling, and the evaluation of alternative models. In the preprocessing stage, missing values and outliers are removed, and oversampling using SMOTE(Synthetic oversampling technique) to resolve data imbalance. Features are selected by variable importance of LASSO(Least absolute shrinkage and selection operator) regression, extreme gradient boosting(XGBoost), and random forest models. Finally, logistic regression, support vector machine(SVM), random forest, and XGBoost are developed as a classifier to predict the adequate or defective products with new operating conditions. The optimal hyper-parameters for each model are investigated by the grid search and random search methods based on k-fold cross-validation. As a result of the experiment, XGBoost showed relatively high predictive performance compared to other models with an accuracy of 0.9929, specificity of 0.9372, F1-score of 0.9963, and logarithmic loss of 0.0209. The classifier developed in this study is expected to improve productivity by enabling effective management of the manufacturing process for the STS303 small rolling wire rods.

Recently, the importance of preventive maintenance has been emerging since failures in a complex system are automatically detected due to the development of artificial intelligence techniques and sensor technology. Therefore, prognostic and health management (PHM) is being actively studied, and prediction of the remaining useful life (RUL) of the system is being one of the most important tasks. A lot of researches has been conducted to predict the RUL. Deep learning models have been developed to improve prediction performance, but studies on identifying the importance of features are not carried out. It is very meaningful to extract and interpret features that affect failures while improving the predictive accuracy of RUL is important. In this paper, a total of six popular deep learning models were employed to predict the RUL, and identified important variables for each model through SHAP (Shapley Additive explanations) that one of the explainable artificial intelligence (XAI). Moreover, the fluctuations and trends of prediction performance according to the number of variables were identified. This paper can suggest the possibility of explainability of various deep learning models, and the application of XAI can be demonstrated. Also, through this proposed method, it is expected that the possibility of utilizing SHAP as a feature selection method.

In this study, the machine learning which has been widely used in prediction algorithms recently was used. the research point was the CD(chudong) point which was a representative point of Daecheong Lake. Chlorophyll-a(Chl-a) concentration was used as a target variable for algae prediction. to predict the Chl-a concentration, a data set of water quality and quantity factors was consisted. we performed algorithms about random forest and gradient boosting with Python. to perform the algorithms, 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. as a result of the algorithm performance index, the gradient boosting showed that RMSE was 2.72 mg/m³ and MSE was 7.40 mg/m³ and R² was 0.66. as a result of the residual analysis, the analysis result of gradient boosting was excellent. as a result of the algorithm execution, the gradient boosting algorithm was excellent. the gradient boosting algorithm was also excellent with 2.44 mg/m³ of RMSE in the machine learning hyperparameter adjustment result.

The effect of the process conditions of high-velocity oxygen fuel (HVOF) thermal spray coating on the porosity of the coating layer is investigated. HVOF coating layers are formed by depositing amorphous FeMoCrBC powder. Oxygen pressure varies from 126 to 146 psi and kerosene pressure from 110 to 130 psi. The Microstructural analysis confirms its porosity. Data analysis is performed using experimental data. The oxygen pressure-kerosene pressure ratio is found to be a key contributor to the porosity. An empirical model is proposed using linear regression analysis. The proposed model is then validated using additional test data. We confirm that the oxygen pressure-kerosene pressure ratio exponentially increases porosity. We present a porosity prediction model relationship for the oxygen pressure-kerosene pressure ratio.

Algal bloom is an ongoing issue in the management of freshwater systems for drinking water supply, and the chlorophyll-a concentration is commonly used to represent the status of algal bloom. Thus, the prediction of chlorophyll-a concentration is essential for the proper management of water quality. However, the chlorophyll-a concentration is affected by various water quality and environmental factors, so the prediction of its concentration is not an easy task. In recent years, many advanced machine learning algorithms have increasingly been used for the development of surrogate models to prediction the chlorophyll-a concentration in freshwater systems such as rivers or reservoirs. This study used a light gradient boosting machine(LightGBM), a gradient boosting decision tree algorithm, to develop an ensemble machine learning model to predict chlorophyll-a concentration. The field water quality data observed at Daecheong Lake, obtained from the real-time water information system in Korea, were used for the development of the model. The data include temperature, pH, electric conductivity, dissolved oxygen, total organic carbon, total nitrogen, total phosphorus, and chlorophyll-a. First, a LightGBM model was developed to predict the chlorophyll-a concentration by using the other seven items as independent input variables. Second, the time-lagged values of all the input variables were added as input variables to understand the effect of time lag of input variables on model performance. The time lag (i) ranges from 1 to 50 days. The model performance was evaluated using three indices, root mean squared error-observation standard deviation ration (RSR), Nash-Sutcliffe coefficient of efficiency (NSE) and mean absolute error (MAE). The model showed the best performance by adding a dataset with a one-day time lag (i=1) where RSR, NSE, and MAE were 0.359, 0.871 and 1.510, respectively. The improvement of model performance was observed when a dataset with a time lag up of about 15 days (i=15) was added.

본 논문은 FRCM 공법으로 보강된 철근콘크리트 보의 휨 성능을 예측하기 위한 해석적 연구결과를 제시한다. FRCM 공법으로 보강된 철근콘크리트 보의 휨 성능 예측을 위해 상용구조해석 프로그램인 LS-DYNA를 이용하여 유한요소해석이 수행되었다. 유한요소해석시 콘크리트와 모르타르는 Solid 요소로 모델링 되었으며, 철근과 FRP 그리드는 각각 Beam 요소 및 Shell 요소로 모델링되었다. 또한, 콘크리트와 철근은 완전부착하는 것으로 가정되었으며, 콘크리트와 모르타르 경계면의 부착파괴를 모사하기 위하여 Contact_Tiebreak_Surface_to_Surface 요소가 사용되었다. 이후, 국내⋅외 여러 연구자들에 의해 수행된 실험의 재현해석을 통해 제안된 유한요소해석 모델의 신뢰성이 검증되었다. 실험결과와 해석결과의 파괴양상을 분석하였을 때, 본 연구에서 제안된 유한요소해석 모델은 실험체의 부착파괴를 적절히 모사할 수 있는 것으로 나타났다. 또한, 해석을 통해 예측된 극한 강도에 대한 실험결과의 비는 평균 1.04, 표준편차 0.064로 제안된 해석모델은 FRCM 공법으로 보강된 철근콘크리트 보의 휨 성능을 비교적 잘 예측할 수 있는 것으로 나타났다.

음악은 오직 시간 안에서 표현이 되며 리듬의 물리학적 해석은 진동의 시간 패턴에 있다. 이 진동은 인간의 청각기관 내에서 전기 화학적 정보로 변환되어 청각체계의 신경 중계를 통해 뇌에 도달하며 인식과 지각의 작용을 통해 리듬을 분석하고 저장하며 프로세스를 한다. 이 논문은 리듬을 인식하고 지각 할 때 어떠한 요소에 의해 그루브가 결정되는지에 대해 연구하였으며 이 요소들을 활용하여 음악을 만들고 연주하고 가창하는 뮤지션들이 좀 더 리드미컬한 표현이 가능할 수 있도록 하는 목적을 가지고 있다. 연구방법은 신경학 관점에서 뇌가 리듬을 어떻게 프로세스 하는 지에 대해 기술하고 리듬의 활용 경험이 풍부한 전문가들에게 가창이나 연주, 디렉팅 그리고 작⋅ 편곡 등의 음악 경험에서 어떠한 요소에 의해 리듬 그루브의 변화를 인지 하였는지에 대해 조사하 였다. 연구결과 우리의 뇌는 리듬을 시간적 비율구조로 인지 및 지각을 하고 프로세스를 하는 것 으로 분석되었고 ‘리듬 그루브에 영향을 끼치는 요소’에 대한 조사에서는 음악 전문가 214명 중 76.5%가 악센트, 76.1%가 타이밍, 75.6%가 음의 길이, 41.3%가 딕션 등이 가장 큰 영향을 끼친 다고 인식하고 있는 것으로 나타났다. 마지막으로 이 연구는 이번 조사에서 나타난 요소들을 샘플 로 만들어 음악 전문가들이 직접 체험을 하는 실험연구로 확장될 것이며 이것을 통해 좀 더 명확 한 리듬 그루브의 요소를 제시할 수 있을 것이라 예측하였다.

편심가새골조(EBF)의 역량설계법에 의하면, 링크가 완전항복 및 변형경화 상태일 때 기둥, 링크외부보, 가새(비소산 부재)는 탄성 거동해야 한다. 현행 AISC 341은 역량설계에 필요한 변형도경화계수(SHF)를 1.25로 제시하고 있으나, 실제로 건물이 고층 규모일수 록 모든 링크가 이처럼 동등한 수준의 초과강도에 도달할 가능성은 매우 낮아진다. 본 연구에서는 링크의 SHF를 정밀하게 예측하는 방법을 제안함으로써, 역량설계법의 목적을 달성하면서 구조물량을 절감하고자 하였다. 제안한 방법의 효과를 검증하기 위해 선형해 석을 2회 수행하여 SHF를 예측하고, 이를 비선형 해석결과와 비교하였다. 다음으로 비선형 해석에 의한 응답을 분석하여 구조물의 한 계상태에서 비소산 부재들의 항복 여부를 확인하였다. 그 결과, 본 연구의 방법으로 설계된 구조물은 링크의 SHF를 정확히 예측함으 로 인해 물량이 큰 폭으로 절감되었으며, 비소산 부재들도 모두 탄성상태를 만족하는 것으로 나타났다.

The high level of lithium storage in synthetic porous carbons has necessitated the development of accurate models for estimating the specific capacity of carbon-based lithium-ion battery (LIB) anodes. To date, various models have been developed to estimate the storage capacity of lithium in carbonaceous materials. However, these models are complex and do not take into account the effect of porosity in their estimations. In this paper, a novel model is proposed to predict the specific capacity of porous carbon LIB anodes. For this purpose, a new factor is introduced, which is called normalized surface area. Considering this factor, the contribution of surface lithium storage can be added to the lithium stored in the bulk to have a better prediction. The novel model proposed in this study is able to estimate the lithium storage capacity of LIB anodes based on the porosity of porous carbons for the first time. Benefiting porosity value (specific surface area) makes the predictions quick, facile, and sensible for the scientists and experts designing LIBs using porous carbon anodes. The predicted capacities were compared with that of the literature reported by experimental works. The remarkable consistency of the measured and predicted capacities of the LIB anodes also confirms the validity of the approach and its reliability for further predictions.