• 한국안전학회지
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• 제36권5호
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• pp.52-60
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• 2021

As the prevention of fatal accidents is considered an essential part of social responsibilities, both government and individual have devoted efforts to mitigate the unsafe conditions and behaviors that facilitate accidents. Several studies have analyzed the factors that cause fatal accidents and compared them to those of non-fatal accidents. However, studies on mathematical and systematic analysis techniques for identifying the features of fatal accidents are rare. Recently, various industrial fields have employed machine learning algorithms. This study aimed to apply machine learning algorithms for the classification of fatal and non-fatal accidents based on the features of each accident. These features were obtained by text mining literature on accidents. The classification was performed using four machine learning algorithms, which are widely used in industrial fields, including logistic regression, decision tree, neural network, and support vector machine algorithms. The results revealed that the machine learning algorithms exhibited a high accuracy for the classification of accidents into the two categories. In addition, the importance of comparing similar cases between fatal and non-fatal accidents was discussed. This study presented a method for classifying accidents using machine learning algorithms based on the reports on previous studies on accidents.

• 정보과학회 논문지
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• 제44권11호
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• pp.1156-1164
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• 2017

기존의 버그 담당자 자동 배정 연구들은 대부분 기계학습 알고리즘을 기반으로 예측 시스템을 구축하는 방식이었다. 따라서, 고성능의 기계학습 모델을 적용하는 것이 담당자 자동 배정 시스템 성능의 핵심이 된다고 할 수 있으며 관련 연구에서는 높은 성능을 보이는 SVM, Naive Bayes 등의 기계학습 모델들이 주로 사용되고 있다. 본 논문에서는 기계학습 분야에서 최근 좋은 성능을 보이고 있는 딥 러닝을 버그 담당자 자동 배정에 적용하고 그 성능을 평가한다. 실험 결과, 딥 러닝 기반 Bug Triage 시스템이 활성 개발자 대상 실험에서 48%의 정확도를 달성했으며 이는 기존의 기계학습 대비 최대 69%향상된 결과이다.

• 한국정보시스템학회지:정보시스템연구
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• 제30권2호
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• pp.105-126
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• 2021

Purpose The purpose of this study is to implement a optimal machine learning model about the cancellation prediction performance in car sales business. It is to apply the data set of accumulated contract, cancellation, and sales information in sales support system(SFA) which is commonly used for sales, customers and inventory management by imported car dealers, to several machine learning models and predict performance of cancellation. Design/methodology/approach This study extracts 29,073 contracts, cancellations, and sales data from 2015 to 2020 accumulated in the sales support system(SFA) for imported car dealers and uses the analysis program Python Jupiter notebook in order to perform data pre-processing, verification, and modeling that is applying and learning to Machine learning model after then the final result was predicted using new data. Findings This study confirmed that cancellation prediction is possible by applying car purchase contract information to machine learning models. It proved the possibility of developing and utilizing a generalized predictive model by using data of imported car sales system with machine learning technology. It can reduce and prevent the sales failure as caring the potential lost customer intensively and it lead to increase sales revenue by predicting the cancellation possibility of individual customers.

• 디지털융복합연구
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• 제18권12호
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• pp.29-36
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• 2020

본 연구의 배경은 최근 포스트 코로나시대의 비대면 행정서비스를 위한 주요 정책수단으로 기계학습 행정서비스가 주목을 받고 있는 가운데 기계학습 행정서비스를 시범적으로 운영하고 있는 서울특별시를 대상으로 기계학습 행정서비스 도입 시 효과가 예상되는 업무유형에 대하여 살펴보았다. 연구방법으로는 2020년 7월 한 달 동안 기계학습 기반 행정서비스를 활용하거나 수행하고 있는 서울시 행정조직을 대상으로 설문조사를 실시하여 조직단위별 도입 가능한 기계학습 행정서비스 및 응용서비스를 분석하고, 지도학습, 비지도학습, 강화학습 등 기계학습 행정서비스의 업무유형별 특성을 분석하였다. 그 결과, 지도학습 및 비지도학습 업무유형의 특성에서 유의미한 차이가 있는 것으로 나타났고, 특히 강화학습 업무유형이 기계학습 행정서비스에 가장 적합한 업무적 특성요인을 포함하고 있는 것으로 밝혀져 그에 대한 정책적 시사점을 도출하였다. 본 연구결과는 기계학습 행정서비스를 도입하고자 하는 실무자들에게는 참고자료로 제공될 수 있고, 향후 기계학습 행정서비스를 연구하고자 하는 연구자들에게는 연구의 기초자료로 활용될 수 있을 것이다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제14권2호
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• pp.73-83
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• 2014

Many recent theoretical developments in the field of machine learning and control have rapidly expanded its relevance to a wide variety of applications. In particular, a variety of portfolio optimization problems have recently been considered as a promising application domain for machine learning and control methods. In highly uncertain and stochastic environments, portfolio optimization can be formulated as optimal decision-making problems, and for these types of problems, approaches based on probabilistic machine learning and control methods are particularly pertinent. In this paper, we consider probabilistic machine learning and control based solutions to a couple of portfolio optimization problems. Simulation results show that these solutions work well when applied to real financial market data.

• International journal of advanced smart convergence
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• 제5권4호
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• pp.32-39
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• 2016

Machine learning is a field of artificial intelligence (AI), and a technology that collects, forecasts, and analyzes securities data is developed upon machine learning. The difference between using machine learning and not using machine learning is that machine learning-seems similar to big data-studies and collects data by itself which big data cannot do. Machine learning can be utilized, for example, to recognize a certain pattern of an object and find a criminal or a vehicle used in a crime. To achieve similar intelligent tasks, data must be more effectively collected than before. In this paper, we propose a method of effectively collecting data.

• International journal of advanced smart convergence
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• 제10권1호
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• pp.56-61
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• 2021

Neighbor path selection is to pre-select alternate routes in case geographically correlated failures occur simultaneously on the communication network. Conventional heuristic-based algorithms no longer improve solutions because they cannot sufficiently utilize historical failure information. We present a novel solution model for neighbor path selection by using machine learning technique. Our proposed machine learning neighbor path selection (ML-NPS) model is composed of five modules- random graph generation, data set creation, machine learning modeling, neighbor path prediction, and path information acquisition. It is implemented by Python with Keras on Tensorflow and executed on the tiny computer, Raspberry PI 4B. Performance evaluations via numerical simulation show that the neighbor path communication success probability of our model is better than that of the conventional heuristic by 26% on the average.

• Computers and Concrete
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• 제32권5호
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• pp.513-525
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• 2023

Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of RC columns using a regression algorithm. Therefore, this study develops a successive machine learning process for predicting multiple nonlinear behaviors of rectangular RC columns. This process consists of three stages: single machine learning, bagging ensemble, and stacking ensemble. In the case of strength prediction, sufficient prediction accuracy is confirmed even in the first stage. In the case of displacement, although sufficient accuracy is not achieved in the first and second stages, the stacking ensemble model in the third stage performs better than the machine learning models in the first and second stages. In addition, the performance of the final prediction models is verified by comparing the backbone curves and hysteresis loops obtained from predicted outputs with actual experimental data.

• 한국표면공학회지
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• 제56권6호
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• pp.353-370
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• 2023

Corrosion detection and analysis is a very important topic in reducing costs and preventing disasters. Recently, image processing techniques have been widely applied to corrosion identification and analysis. In this work, we briefly introduces traditional image processing techniques and machine learning algorithms applied to detect or analyze corrosion in various fields. Recently, machine learning, especially CNN-based algorithms, have been widely applied to corrosion detection. Additionally, research on applying machine learning to region segmentation is very actively underway. The corrosion is reddish and brown in color and has a very irregular shape, so a combination of techniques that consider color and texture, various mathematical techniques, and machine learning algorithms are used to detect and analyze corrosion. We present examples of the application of traditional image processing techniques and machine learning to corrosion detection and analysis.

• 한국BIM학회 논문집
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• 제6권4호
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• pp.35-41
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• 2016

Learning effect is an observation that the more times a task is performed, the less time is required to produce the same amount of outcomes. The construction industry heavily relies on repeated tasks where the learning effect is an important measure to be used. However, most construction durations are calculated and applied in real projects without considering the learning effects in each of the repeated activities. This paper applied the learning effect to the repeated activities in a small sized apartment construction project. The result showed that there was about 10 percent of difference in duration (one approach of the total duration with learning effects in 41 days while the other without learning effect in 36.5 days). To make the comparison between the two approaches, a large number of BIM based computer simulations were generated and useful patterns were recognized using machine learning algorithm named Decision Tree (See5). Machine learning is a data-driven approach for pattern recognition based on observational evidence.