• 대기
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• 제27권2호
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• pp.235-249
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• 2017

This paper describes methodology verifying near-surface predictability of numerical weather prediction models against the surface synoptic weather station network (SYNOP) observation. As verification variables, temperature, wind, humidity-related variables, total cloud cover, and surface pressure are included in this tool. Quality controlled SYNOP observation through the pre-processing for data assimilation is used. To consider the difference of topographic height between observation and model grid points, vertical inter/extrapolation is applied for temperature, humidity, and surface pressure verification. This verification algorithm is applied for verifying medium-range forecasts by a global forecasting model developed by Korea Institute of Atmospheric Prediction Systems to measure the near-surface predictability of the model and to evaluate the capability of the developed verification tool. It is found that the verification of near-surface prediction against SYNOP observation shows consistency with verification of upper atmosphere against global radiosonde observation, suggesting reliability of those data and demonstrating importance of verification against in-situ measurement as well. Although verifying modeled total cloud cover with observation might have limitation due to the different definition between the model and observation, it is also capable to diagnose the relative bias of model predictability such as a regional reliability and diurnal evolution of the bias.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.119-127
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• 2022

Most of the construction works are conducted outdoors, so the construction workers are affected by weather conditions such as temperature, humidity, and wind velocity which can be evaluated the thermal comfort as environmental factors. In our previous researches, it was found that construction accidents are usually occurred in the discomfort ranges. The safety management, therefore, should be planned in consideration of the thermal comfort and measured by a specialized simulation tool. However, it is very complex, time-consuming, and difficult to model. To address this issue, this study is aimed to develop a framework of a prediction model for improving the prediction accuracy about outdoor thermal comfort considering environmental factors using machine learning algorithms with hyperparameter tuning. This study is done in four steps: i) Establishment of database, ii) Selection of variables to develop prediction model, iii) Development of prediction model; iv) Conducting of hyperparameter tuning. The tree type algorithm is used to develop the prediction model. The results of this study are as follows. First, considering three variables related to environmental factor, the prediction accuracy was 85.74%. Second, the prediction accuracy was 86.55% when considering four environmental factors. Third, after conducting hyperparameter tuning, the prediction accuracy was increased up to 87.28%. This study has several contributions. First, using this prediction model, the thermal comfort can be calculated easily and quickly. Second, using this prediction model, the safety management can be utilized to manage the construction accident considering weather conditions.

• 디지털융복합연구
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• 제17권5호
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• pp.217-223
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• 2019

자동차 분야에서 저항 점용접의 결함 및 품질을 실시간으로 예측할 수 있는 연구는 원가절감과 고품질 생산을 위한 필수 불가결한 연구 분야라 할 수 있다. 용접 품질은 전단강도와 너깃의 크기에 의해서 결정되며 여러 가지 독립변수에 따라 결과가 달라진다. 실시간 예측시스템을 개발하기 위하여 다중 회귀분석을 실시하여 3개의 독립변수로 두 가지 종속변수를 충분한 통계적 결과로 구하였으나 회귀식에 의한 품질 예측은 정확도를 보장할 수 없었다. 본 연구에서는 다층 신경망 회로를 구축하였다. 10가지의 동저항 변수에 의한 신경망은 3개의 은닉층을 구축하여 실행 함수와 가중치 행렬을 구하였다. 그러나 이 경우, 입력 변수가 너무 많아 실시간 제어에 어려움이 있을 수 있으므로 회귀분석에 의한 3개의 독립변수로 신경망을 구축하였다. 그 결과 모든 시험데이터를 불량, 부분 불량, 양품으로 구분하는데 성공하였다. 따라서 다중 회귀분석에 의해서 구한 3개의 독립변수에 의한 실시간 용접 품질 판정 시스템을 완성할 수 있었다.

• The Journal of Asian Finance, Economics and Business
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• 제8권8호
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• pp.399-407
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• 2021

Stock movement is difficult to predict because it has dynamic characteristics and is influenced by many factors. Even so, there are some approaches to predict stock price movements, namely technical analysis, fundamental analysis, and sentiment analysis. Many researches have tried to predict stock price movement by utilizing these analysis techniques. However, the results obtained are varied and inconsistent depending on the variables and object used. This is because stock price movement is influenced by a variety of factors, and it is likely that those studies did not cover all of them. One of which is that no research considers the use of fundamental analysis in terms of currency exchange rates and the use of foreign stock price index movement related to the technical analysis. This research aims to predict stock price movements in Indonesia based on sentiment analysis, technical analysis, and fundamental analysis using Support Vector Machine. The result obtained has a prediction accuracy rate of 65,33% on an average. The inclusion of currency exchange rate and foreign stock price index movement as a predictor in this research which can increase average prediction accuracy rate by 11.78% compared to the prediction without using these two variables which only results in average prediction accuracy rate of 53.55%.

• 산업경영시스템학회지
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• 제45권2호
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• pp.20-29
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• 2022

This study intends to present a traffic node-based and link-based accident prediction models using XGBoost which is very excellent in performance among machine learning models, and to develop those models with sustainability and scalability. Also, we intend to present those models which predict the number of annual traffic accidents based on road types, weather conditions, and traffic information using XGBoost. To this end, data sets were constructed by collecting and preprocessing traffic accident information, road information, weather information, and traffic information. The SHAP method was used to identify the variables affecting the number of traffic accidents. The five main variables of the traffic node-based accident prediction model were snow cover, precipitation, the number of entering lanes and connected links, and slow speed. Otherwise, those of the traffic link-based accident prediction model were snow cover, precipitation, the number of lanes, road length, and slow speed. As the evaluation results of those models, the RMSE values of those models were each 0.2035 and 0.2107. In this study, only data from Sejong City were used to our models, but ours can be applied to all regions where traffic nodes and links are constructed. Therefore, our prediction models can be extended to a wider range.

• Computers and Concrete
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• 제24권5호
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• pp.399-412
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• 2019

In this investigation, Elman neural networks were utilized for predicting the mechanical properties of Self-Compacting Concretes (SCCs). Elman models were designed by using experimental data of many different concrete mixdesigns of various types of SCC that were collected from the literature. In order to investigate the effectiveness of the selected input variables on the network performance in predicting intended properties, utilized data in artificial neural networks were considered in two sets of 8 and 140 input variables. The obtained outcomes showed that not only can the developed Elman ANNs predict the mechanical properties of SCCs with high accuracy, but also for all of the desired outputs, networks with 140 inputs, compared to ones with 8, have a remarkable percent improvement in the obtained prediction results. The prediction accuracy can significantly be improved by using a more complete and accurate set of key factors affecting the desired outputs, as input variables, in the networks, which is leading to more similarity of the predicted results gained from networks to experimental results.

• 한국공작기계학회논문집
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• 제13권6호
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• pp.64-73
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• 2004

A fatigue damage accumulation model based on the continuum damage mechanics theory was developed where modulus decay ratios in tension and shear were used as indicators for damage variables D. In the model, the damage variables are considered to be second-order tensors. Then, the maximum principal damage variable, $D^*$ is introduced. According to the similarity to the principal stress, $D^*$ is obtained as the maximum eigen value of damage tensor [D]. Under proportional tension and torsion loadings, fatigue lives were satisfactorily predicted at any combined stress ratios using the present model in which the Fatigue characteristics only under uniaxial tension and pure torsion loadings were needed. Fatigue life prediction under uniaxial tension and pure torsion loadings, was performed based on the damage mechanics using boundary element method.

• 한국정보전자통신기술학회논문지
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• 제10권6호
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• pp.594-600
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• 2017

본 논문에서는 매우 높은 차원을 가진 데이터에서 의미 있는 특징 벡터 추출하여 입력 공간의 차원을 줄이기 위하여 주성분 분석법을 사용하였다. 주성분 분석법을 이용하여 축소된 차원을 가진 입력 데이터를 이용하여 회귀 다항식의 입력벡터로 사용하는 모델과 패턴 분류기의 설계 방법을 제안하였다. 제안된 모델 및 패턴 분류기는 매우 단순한 구조를 가진 회귀다항식을 기반으로 설계하여 모델 및 패턴 분류기의 과적합 문제를 해결 하고자 하였다. 제안된 설계방법을 적용하여 설계된 모델과 패턴 분류기의 성능을 비교 및 평가하기 위하여, 다양한 기계 학습 데이터 집합을 사용하였다.

• 한국융합학회논문지
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• 제13권5호
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• pp.237-247
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• 2022

본 연구는 급속한 고령화 시대에서 우리나라의 고령층의 경제활동 수준을 다양한 머신러닝 기법으로 정확히 예측하고자 하였다. 고령층의 경제활동 수준과 기존 연구들은 고령층의 삶의 만족도, 사회보장제도 등과 연관된 인과성 검증을 중심으로 이루어진 데 반해, 본 연구는 다양한 머신러닝 기법으로 고령층의 경제활동 수준을 예측하였으며, 특히 예측조합법을 함께 사용함으로써 예측의 안정성을 도모하였다. 60세 이상의 경제활동참가율, 취업률 등을 종속변수로 하고 가구 특성, 소득, 평균임금 등을 설명변수로 설정하여 서로 다른 특성을 지닌 5가지의 머신러닝 기법과 2가지의 예측조합법을 적용하여 예측결과들을 비교하였다. 분석 결과, 종속변수별, 예측구간별로 예측성능이 높은 머신러닝 기법 및 예측조합법은 상이하였으나, 예측의 안정성 측면에서는 예측조합법이 상대적으로 우수한 것으로 나타났다. 이에 따라, 본 연구는 고령층의 경제활동 수준을 정확히 예측하고 예측의 안정성을 도모하여 정책적 관점에서도 실용성을 제고한다고 볼 수 있다.

• Korean Journal of Radiology
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• 제22권7호
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• pp.1213-1224
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• 2021

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.