• The Journal of Engineering Geology
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• v.31 no.1
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• pp.31-42
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• 2021

Database of Cut-slope management system (CSMS) has been constructed based on investigations of all slopes on the roads of the whole country. The investigation data is documented by human, so it is inevitable to avoid human-error such as missing-data and incorrect entering data into computer. The goal of this paper is constructing a prediction model based on several machine-learning algorithms to solve those imperfection problems of the CSMS data. First of all, the character-type data in CSMS data must be transformed to numeric data. After then, two algorithms, i.g., multinomial logistic regression and deep-neural-network (DNN), are performed, and those prediction models from two algorithms are compared. Finally, it is identified that the accuracy of DNN-model is better than logistic model, and the DNN-model will be utilized to improve data-quality.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.741-747
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• 2022

Fatigue lowers an individual's ability and makes it difficult to perform work. As fatigue accumulates, concentration decreases and thus the possibility of causing a safety accident increases. Awareness of fatigue is subjective, but it is necessary to quantitatively measure the level of fatigue in the actual field. In previous studies, it was proposed to measure the level of fatigue by expert judgment by adding objective indicators such as bio-signal analysis to subjective evaluations such as multidisciplinary fatigue scales. However this method is difficult to evaluate fatigue in real time in daily life. This paper is a study on the fatigue classification model that determines the fatigue level of workers in real time using speech data recorded in the field. Machine learning models such as logistic classification, support vector machine, and random forest are trained using speech data collected in the field. The performance evaluation showed good performance with accuracy of 0.677 to 0.758, of which logistic classification showed the best performance. From the experimental results, it can be seen that it is possible to classify the fatigue level using speech signals.

• Advanced Industrial SCIence
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• v.2 no.4
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• pp.11-16
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• 2023

The subway is a means of public transportation that plays an important role in the transportation system of modern cities. However, congestion often occurs due to sudden breakdowns and system outages, causing inconvenience. Therefore, in this paper, we conducted a study on failure prediction and prevention using machine learning to efficiently operate the subway system. Using UC Irvine's MetroPT-3 dataset, we built a subway breakdown prediction model using logistic regression. The model predicted the non-failure state with a high accuracy of 0.991. However, precision and recall are relatively low, suggesting the possibility of error in failure prediction. The ROC_AUC value is 0.901, indicating that the model can classify better than random guessing. The constructed model is useful for stable operation of the subway system, but additional research is needed to improve performance. Therefore, in the future, if there is a lot of learning data and the data is well purified, failure can be prevented by pre-inspection through prediction.

• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.3-9
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• 2022

본연구는 한국어 언어모델 트레이닝 단계에서 자주 사용되는 Fill-Mask 태스크와 직업 관련 키워드로 구성되는 각종 성별 유추 템플릿을 이용해 한국어 언어모델에서 발생하는 성별 편향 현상을 정량적으로 검증하고 해석한다. 결과를 봤을 때 현재 직업 키워드에서 드러나는 성별 편향은 각종 한국어 언어모델에서 이미 학습된 상태이며 이를 해소하거나 차단하는 방법을 마련하는 것이 시급한 과제이다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1395-1408
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• 2014

In this research, a River Recreation Index Model (RRIM) was developed to provide sufficient information on the water quality of rivers to the public in order to secure safety of publics. River Recreation Index (RRI) is an integrated water quality information for recreation activities in rivers and expressed as the point from 0 to 100. The proposed RRIM consisted of two sub models: Fecal Coliform Model (FCM) and Water Quality Index Model (WQIM). FCM predicted Fecal Coliform Grade (FCG) using a logistic regression and WQIM synthesized water quality parameters of, DO, pH, turbidity and chlorophyll a into Water Quality Index (WQI). FCG and WQI were integrated into RRI by the integrating algorithm. The proposed model was applied to upstream of Gangjeong Weir in Nakdong River, and compared with Real Time Water Quality Index (RTWQI) which is the existing water quality information system for recreation use. The results show that calculated RRI reflected change of integrated water quality parameters well. Especially chlorophyll a showed Pearson correlation coefficient -0.85 with RRI. Also, RRIM produced more conservative index than RTWQI because RRI was calculated considering uncertainty of water quality criteria. Further, RRI showed especially low values when fecal coliform was predicted as low grade.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.135-140
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• 2007

이탈 고객 예측은 데이터 마이닝에서 다루는 주요한 문제 중에 하나이다. 이탈 고객 예측은 일종의 분류(classification) 문제로 의사결정나무추론, 로지스틱 회귀분석, 인공신경망 등의 기법이 많이 활용되어왔다. 일반적으로 이탈 고객 예측을 위한 모델은 고객의 인구통계학적 정보와 계약이나 거래 정보를 입력변수로 하여 이탈 여부를 목표변수로 보는 형태로 분류 모델을 생성하게 된다. 본 연구에서는 고객과의 지속적인 접촉으로 발생되는 추가적인 사건 정보를 활용하여 연관성 규칙을 생성하고 이 결과를 기존의 방식으로 생성된 분류 모델과 결합하는 이탈 고객 예측 방법을 제시한다. 제시한 방법의 유용성을 확인하기 위해서 특정 국내 신용카드사의 실제 데이터를 활용하여 실험을 수행하였다. 실험 결과 제시된 방법이 기존의 전통적인 분류 모델에 비해서 향상된 성능을 보이는 것을 확인할 수 있었다. 제시된 예측 방법의 장점은 기존의 이탈 예측을 위한 입력 변수들 이외에 고객과 회사간의 접촉을 통해서 생성된 동적 정보들을 통합적으로 활용하여 예측 정확도를 높이고 실시간으로 이탈 확률을 갱신할 수 있다는 점이다.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.05a
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• pp.404-407
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• 2021

석유개발 현장에서 시추장비의 고장으로 인한 장비교체 및 시추시간 증가는 막대한 비용소모를 발생시킨다. 본 논문은 딥러닝 기반의 시추장비 중 드릴비트의 동력을 구동시키는 디젤엔진의 고장 요소를 분류하고 이 요소에 따른 고장여부를 판별하는 딥러닝 기반의 이상 예측 및 진단 모델을 개발하였다. 또한 제안한 모델의 우수성을 확인하기 위해 로지스틱 회귀분석 분류모델과의 예측성능 비교분석도 수행하였다.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.6
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• pp.607-611
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• 2001

Ultrasonic inspection system consist of the operator, equipment and procedure. The reliability of results in ultrasonic inspection is affected by its ability. Furthermore, the reliability of nondestructive testing is influenced by the inspection environment, materials and types of defect. Therefore, it is very difficult to estimate the reliability of NDT due to the various factors. In this study, the probability of detection by logistic probability model and Monte Carlo simulation is used for the reliability assessment of ultrasonic inspection. The utility of the NDT reliability assesment is verified by the analysis of the data from round robin test nth these models.

• Journal for History of Mathematics
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• v.27 no.3
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• pp.197-210
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• 2014

Species like insects and fishes have, in many cases, non-overlapping time intervals of one generation and their descendant one. So the population dynamics of such species can be formulated as discrete models. In this paper various discrete population models are introduced in chronological order. The author's investigation starts with the Malthusian model suggested in 1798, and continues through Verhulst model(the discrete logistic model), Ricker model, the Beverton-Holt stock-recruitment model, Shep-herd model, Hassell model and Sigmoid type Beverton-Holt model. We discuss the mathematical and practical significance of each model and analyze its properties. Also the stability properties of stationary solutions of the models are studied analytically and illustratively using GSP, a computer software. The visual outputs generated by GSP are compared with the analytical stability results.

• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.667-680
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• 2021

We investigated changes in frost days and frost-free periods and to comparatively assess frost event prediction models developed using logistic regression (LR), random forest (RF), and long short-term memory (LSTM) networks. The meteorological variables for the model development were collected from the Suwon, Cheongju, and Gwangju stations for the period of 1973-2019 for spring (March - May) and fall (September - November). The developed models were then evaluated by Precision, Recall, and f-1 score and graphical evaluation methods such as AUC and reliability diagram. The results showed that significant decreases (significance level of 0.01) in the frequencies of frost days were at the three stations in both spring and fall. Overall, the evaluation metrics showed that the performance of RF was highest, while that of LSTM was lowest. Despite higher AUC values (above 0.9) were found at the three stations, reliability diagrams showed inconsistent reliability. A further study is suggested on the improvement of the predictability of both frost events and the first and last frost days by the frost event prediction models and reliability of the models. It would be beneficial to replicate this study at more stations in other regions.