Existing reinforced concrete buildings with seismically deficient column details affect the overall behavior depending on the failure type of column. This study aims to develop and validate a machine learning-based prediction model for the column failure modes (shear, flexure-shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used, considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model represents the highest average value of the classification model performance measurements among the considered learning methods, and it can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with simple column details.

Fouling is an inevitable problem in membrane water treatment plant. It can be measured by trans-membrane pressure (TMP) in the constant flux operation, and chemical cleaning is carried out when TMP reaches a critical value. An early fouilng alarm is defined as warning the critical TMP value appearance in advance. The alarming method was developed using one of machine learning algorithms, decision tree, and applied to a ceramic microfiltration (MF) pilot plant. First, the decision tree model that classifies the normal/abnormal state of the filtration cycle of the ceramic MF pilot plant was developed and it was then used to make the early fouling alarm method. The accuracy of the classification model was up to 96.2% and the time for the early warning was when abnormal cycles occurred three times in a row. The early fouling alram can expect reaching a limit TMP in advance (e.g., 15-174 hours). By adopting TMP increasing rate and backwash efficiency as machine learning variables, the model accuracy and the reliability of the early fouling alarm method were increased, respectively.

A machine learning-based algorithms have used for constructing species distribution models (SDMs), but their performances depend on the selection of backgrounds. This study attempted to develop a noble method for selecting backgrounds in machine-learning SDMs. Two machine-learning based SDMs (MaxEnt, and Random Forest) were employed with an example species (Spodoptera litura), and different background selection methods (random sampling, biased sampling, and ensemble sampling by using CLIMEX) were tested with multiple performance metrics (TSS, Kappa, F1-score). As a result, the model with ensemble sampling predicted the widest occurrence areas with the highest performance, suggesting the potential application of the developed method for enhancing a machine-learning SDM.

고성능 콘크리트(HPC) 압축강도는 추가적인 시멘트질 재료의 사용으로 인해 예측하기 어렵고, 개선된 예측 모델의 개발이 필수적 이다. 따라서, 본 연구의 목적은 배깅과 스태킹을 결합한 앙상블 기법을 사용하여 HPC 압축강도 예측 모델을 개발하는 것이다. 이 논 문의 핵심적 기여는 기존 앙상블 기법인 배깅과 스태킹을 통합하여 새로운 앙상블 기법을 제시하고, 단일 기계학습 모델의 문제점을 해결하여 모델 예측 성능을 높이고자 한다. 단일 기계학습법으로 비선형 회귀분석, 서포트 벡터 머신, 인공신경망, 가우시안 프로세스 회귀를 사용하고, 앙상블 기법으로 배깅, 스태킹을 이용하였다. 결과적으로 본 연구에서 제안된 모델이 단일 기계학습 모델, 배깅 및 스태킹 모델보다 높은 정확도를 보였다. 이는 대표적인 4가지 성능 지표 비교를 통해 확인하였고, 제안된 방법의 유효성을 검증하였다.

본 연구는 갈색날개매미충의 기계유유제 처리 방법별 부화율과 산란에 의한 사과 열매의 품질 변화에 대해 조사하였다. 기계유유제 처리 효과를 보면 기계유유제 20배를 도포한 것이 평균 0.57%로 가장 적은 부화율을 보였고, 분무한 가지에서는 평균 1%의 부화율을 보였다. 기계유유제를 50배 도포 처리시 부화율이 약 35%를 보인반면 분무처리는 약 77%를 보여 편차를 고려하면 무처리와 차이가 없는 것으로 보인다. 홍로와 후지 품종에서 갈색날개매미충이 산란한 결과지와 산란되지 않은 결과지에 이듬해 사과 열매가 결실되어도 과실의 품질 차이는 통계적 유의성은 없었다. 또한 갈색날개매미충의 산란에 의한 가지의 부러짐도 없었고, 결과지 생육도 통계적 유의성은 없었다.

The purpose of this study was to improve the noise measurement method of noise sources and the corresponding noise reduction measures during each manufacturing process closest to the workers in the large and hige power machine. To this end, the noise generated in the large and high power machine was measured and analyzed, and the frequency characteristics of noise sources and the causes of noise were identified. The noise map was used to predict the noise reduction effect. Moreover, it is expected that this will ultimately contribute to the reduction of human risks caused by the noise of the large and high power machine.

본 연구는 운항선의 운항 빅데이터를 활용하여 머신러닝 기법의 선박 마력 예측에 관한 것이다. 현재 신조선에는 ISO15016법을 이용하여 외부환경 요인에 대하여 수식을 통해 저항을 예측하나 관련 계산식이 복잡하고 요구하는 입력변수들이 많아 운항하는 실선 적 용에 많은 시간과 비용이 필요하다. 본 연구에서는 최근 예측, 인식 등에서 우수한 성능을 보이는 SVM(Support Vector Machine) 알고리즘을 이용하여 우수한 성능의 선박 출력 예측이 가능한 모델을 제안한다. 제안 예측 모델은 실선 운항 빅데이터만 확보된다면 ISO15016법 대비 우수한 성능의 예측이 가능한 장점이 있다. 본 연구에서는 178K 벌크캐리어의 운항 DATA를 활용하여 ISO15016 기법과 본 연구에서 제안 하는 SVM 알고리즘 기반의 마력해석법을 비교하여 ISO15016의 단점인 선박 모델 데이터 준비 부분을 줄이고 부정확한 마력 예측 성능을 개선하였다.

In this research, we evaluate on the disassemblability of recycling process for vehicle front door using the symbolic chart method and machine-learning algorithm. It is applied to the front door of 1600cc class vehicle, and then the conventional steel door and CFRP door were compared. Based on the principle symbolic chart method, the number of processes can be different according to decomposer proficiency of suitability of recycling process, so the evaluation method is required to supply this issue. The machine learning algorithm, and artificial intelligence method were applied and the applicable tools for each experiment were used to compensate the variations in the number of processes according to different proficiencies. Because CFRP front door has integrated components compare to steel door, so its disassemblability processes were decreased to 80 from 103 of the conventional steel door’s. It can be confirmed that the disassemblability was increased from the suitability of recycling equation. In case of the steel, disassemblability was approximately 60.6, in case of the CFRP is approximately 72 for car front door. Therefore, it can be concluded that the disassemblability of CFRP was better in the evaluation of suitability of recycling.

It is known that the growth and development of the mosquito are greatly affected by the change of the meteorological factors. In particular, temperature and precipitation are closely related to the life cycle of the mosquito, and their effects have different characteristics for each species of mosquito. Therefore, to develop a mosquito activity index based on mosquito density, it is essential to develop a prediction model based on weather data. In this study, we developed a functional formula that can estimate the change of mosquito density according to the change of meteorological factors using the mosquito classification data of Incheon region collected from 2011 to 2017. Also, using the data of the digital mosquito monitoring system (DMS) from April to October 2018, mosquito activity index according to characteristics of space in city was developed. In order to reflect the temporal characteristics of the mosquito life-cycle, we used data of temperature and precipitation prior to 1-2 weeks, and used land cover data to reflect the spatial characteristics of mosquito density. Density of Culex pipiens collected in the Incheon area were gradually increased when the average temperature increased two weeks ago after adjusting the precipitation. However, when the average temperature reached 22°C, the density of Culex pipiens marked a peak, and above the 22°C, the density was decreased. The predicted mosquito activity index calculated by applying the machine learning method to the DMS data of the Incheon area is designed to calculate from 1 to 10 grades. The accuracy of the mosquito activity index was 87% when the one grade error was allowed.

An agriculture which is one of the national security industry, has recognized the importance. International country have attention to the automatization of machine for the improvement of agriculture. However, because of modernization of agriculture, the rate of accurrence about agriculture accident has increased dramatically. Especially the rate of accurence of agricultural accident is higher than the other industrial accident in the developed country. Because of these reasons, developed country has efforted to the agriculture system about safety. Australia law for the safety system of agriculture is very well and the rate of accident in the self-management agriculture is included in the statistics of industrial But The rate of accident in self-management agriculture is very higher In future we have to try new method about Agricuture Safety of Korea.

In this paper, I analyzed the industrial system and agriculture system about safety between korea and australia. An agriculture which is one of the national security industry, has recognized the importance. International country have attention to the automatization of machine for the improvement of agriculture. However, because of modernization of agriculture, the rate of accurrence about agriculture accident has increased dramatically. Especially the rate of accurence of agricultural accident is higher than the other industrial accident in the developed country. Because of these reasons, developed country has efforted to the agriculture systemabout safety. Australia law for the safety system of agriculture is very well and the rate of accident in the self-management agriculture is included in the statistics of industrial but The rate of accident in self-management agriculture is very higher. Korea has many middle size factory to make new goods and factory accurre very many accident. Because Korea government research the theory and practical affairs for the industrial system about safety and health to protect industrial accident In future we have to try new method about Agriculture Safety of Korea.

과채류의 재배에 있어서 접목은 작물의 안정적인 생산과 품질 향상을 위해 없어서는 안되는 기술이다. 그러나 지속적인 숙련 노동력의 감소와 농촌 인구 노령화 등으로 나타나는 노동력 부족 현상은 집목묘의 대량생산 공급에 어려움이 되고 있다. 본 연구에서는 기계접목에 있어서 접목법이 간단하며 접합부자재가 불필요한 삽접법을 이용하는 기계접목 메카니즘 개발의 기초연구를 수행하였다. 트레이상의 육묘상태에서 자동으로 접목하기 위한 기계접목의 기초연구로써 1본씩 수동 공급하는 반자동 기계삽접의 메카니즘 개발에 관한 연구로서 얻어진 결과를 요약하면 다음과 같다. 1. 접합부자재를 사용하지 않고 대부분 과채류에 적용가능한 삽접 기계접목장치의 메카니즘을 구성하였다. 2. 오이를 접수로 하고 신토좌와 흑종을 대목으로 하는 기계접목시험에서 모두 98%의 접목성공율을 나타냈다. 3. 1본 수동 공급에 의한 기계접목 시스템은 10초 정도로 접목 성능은 저조하지만 기계접목 메카니즘의 가능성을 확인하였다.

In meteorological data, various studies are being conducted to improve the prediction performance of rainfall with irregular patterns, unlike temperature and solar radiation with certain patterns. Especially in the case of the short-term forecast model for Dong-Nae Forecasts provided by the Korea Meteorological Administration (KMA), forecast data are provided at 6-hour intervals, and there is a limit to analyzing the impact of disasters. In this study, Hydrological Quantitative Precipitation Forecast (HQPF) information was generated by applying the machine learning method to Local ENsemble prediction system (LENS), Radar-AWS Rainrates (RAR), AWS and ASOS observation data and Dong-Nae Forecast provided by the KMA. Through the preprocessing process, the temporal and spatial resolutions of all the data were converted to the same resolution, and the predictor of machine learning was derived through the factor analysis of the predictor. Considering the processing speed and expandability, the XGBoost method of machine learning was applied, and the Probability Matching (PM) method was applied to improve the prediction accuracy of heavy rainfall. As a result of evaluating the HQPF performance produced for 14 heavy rainfall events that occurred in 2020, it was found that the predicted performance of HQPF was improved quantitatively and qualitatively.

In this study, we compared the prediction performances according to the bias and dispersion of temperature using ensemble machine learning. Ensemble machine learning is meta-algorithm that combines several base learners into one prediction model in order to improve prediction. Multiple linear regression, ridge regression, LASSO (Least Absolute Shrinkage and Selection Operator; Tibshirani, 1996) and nonnegative ride and LASSO were used as base learners. Super learner (van der Lann et al ., 1997) was used to produce one optimal predictive model. The simulation and real data for temperature were used to compare the prediction skill of machine learning. The results showed that the prediction performances were different according to the characteristics of bias and dispersion and the prediction error was more improved in temperature with bias compared to dispersion. Also, ensemble machine learning method showed similar prediction performances in comparison to the base learners and showed better prediction skills than the ensemble mean.