• 대한인간공학회지
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• 제19권1호
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• pp.1-9
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• 2000

This study was performed to investigate some characteristics on human error proneness in the computerized work environment. Our concerning theme was on human error likelihood according to personal temperament. Two experiments were performed. The first experiment was to study the effect of field- independence/dependence on error likelihood. The second experiment was on error proneness. These experiments were performed in information search task. which was most frequent task in computerized work environment such as the control room of nuclear power plant. Ten subjects were participated in this study. Analyzed results are as follows. Field-independence/dependence had a significant effect in both information search time and error frequency. Error proneness had a significant effect in both factors, too. And, a positive correlation was found between error frequency and information search time. These results will be utilized as a basis to study operator's error proneness in the computerized control room of nuclear power plant. later on.

• Toxicological Research
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• 제33권4호
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• pp.265-272
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• 2017

Aryl hydrocarbons such as 3-nitrobenzanthrone (NBA), 4-aminobiphenyl (ABP), acetylaminofluorene (AAF), benzo(a)pyrene (BaP), and 1-nitropyrene (NP) form bulky DNA adducts when absorbed by mammalian cells. These chemicals are metabolically activated to reactive forms in mammalian cells and preferentially get attached covalently to the $N^2$ or C8 positions of guanine or the $N^6$ position of adenine. The proportion of $N^2$ and C8 guanine adducts in DNA differs among chemicals. Although these adducts block DNA replication, cells have a mechanism allowing to continue replication by bypassing these adducts: translesion DNA synthesis (TLS). TLS is performed by translesion DNA polymerases-Pol ${\eta}$, ${\kappa}$, ${\iota}$, and ${\zeta}$ and Rev1-in an error-free or error-prone manner. Regarding the NBA adducts, namely, 2-(2'-deoxyguanosin-$N^2$-yl)-3-aminobenzanthrone (dG-$N^2$-ABA) and N-(2'-deoxyguanosin-8-yl)-3-aminobenzanthrone (dG-C8-ABA), dG-$N^2$-ABA is produced more often than dG-C8-ABA, whereas dG-C8-ABA blocks DNA replication more strongly than dG-$N^2$-ABA. dG-$N^2$-ABA allows for a less error-prone bypass than dG-C8-ABA does. Pol ${\eta}$ and ${\kappa}$ are stronger contributors to TLS over dG-C8-ABA, and Pol ${\kappa}$ bypasses dG-C8-ABA in an error-prone manner. TLS efficiency and error-proneness are affected by the sequences surrounding the adduct, as demonstrated in our previous study on an ABP adduct, N-(2'-deoxyguanosine-8-yl)-4-aminobiphenyl (dG-C8-ABP). Elucidation of the general mechanisms determining efficiency, error-proneness, and the polymerases involved in TLS over various adducts is the next step in the research on TLS. These TLS studies will clarify the mechanisms underlying aryl hydrocarbon mutagenesis and carcinogenesis in more detail.

• 한국IT서비스학회지
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• 제11권1호
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• pp.211-221
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• 2012

Many metric-based classification models have been proposed to predict fault-proneness of software module. This paper presents two prediction models using Bayesian classifier which is one of the most popular modern classification algorithms. Bayesian model based on Bayesian probability theory can be a promising technique for software quality prediction. This is due to the ability to represent uncertainty using probabilities and the ability to partly incorporate expert's knowledge into training data. The two models, Na$\ddot{i}$veBayes(NB) and Bayesian Belief Network(BBN), are constructed and dimensionality reduction of training data and test data are performed before model evaluation. Prediction accuracy of the model is evaluated using two prediction error measures, Type I error and Type II error, and compared with well-known prediction models, backpropagation neural network model and support vector machine model. The results show that the prediction performance of BBN model is slightly better than that of NB. For the data set with ambiguity, although the BBN model's prediction accuracy is not as good as the compared models, it achieves better performance than the compared models for the data set without ambiguity.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제3권2호
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• pp.57-64
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• 2014

입력 모듈의 결함경향성을 결정하는 결함 예측 모델 연구들은 대부분 훈련 데이터 집합을 사용하는 감독형 모델에 관련된 것들이었다. 하지만 과거 데이터 집합이 없거나 데이터 집합이 있더라도 현재 프로젝트와 성격이 다른 경우는 비감독형 모델이 필요하며, 이들에 관한 연구들은 모델 구축의 어려움 때문에 극소수 존재한다. 본 논문에서는 기존 비감독형 모델 연구들에서 사용하지 않은 대표적인 클러스터링 알고리즘인 EM, DBSCAN을 사용한 비감독형 모델들을 제작하여, 기존 연구들에서 사용한 K-means 모델과 성능을 비교하였다. 그 결과 오류율 면에서 EM이 K-means보다 약간 나은 성능을 보였으며, DBSCAN은 두 모델에 떨어지는 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1566-1582
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• 2019

Silent Data Corruptions (SDCs) is a serious reliability issue in many domains of computer system. The identification and protection of the program instructions that cause SDCs is one of the research hotspots in computer reliability field at present. A lot of solutions have already been proposed to solve this problem. However, many of them are hard to be applied widely due to time-consuming and expensive costs. This paper proposes an intelligent approach named SDCPredictor to identify the instructions that cause SDCs. SDCPredictor identifies SDC-causing Instructions depending on analyzing the static and dynamic features of instructions rather than fault injections. The experimental results demonstrate that SDCPredictor is highly accurate in predicting the SDCs proneness. It can achieve higher fault coverage than previous similar techniques in a moderate time cost.

• 한국산학기술학회논문지
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• 제16권1호
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• pp.180-194
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• 2015

도로 상에서 차량은 강자이고 보행자들은 약자의 처지이다. 본 논문은 보행자의 안전에 기여하고자 하는 목적을 가진다. 먼저, 본 논문은 인구 모수들과 TPB(계획된 행동의 이론)의 변수들(도로에서 보행자의 길 건너는 행동에 있어서의 태도, 개인적 가치, 인지된 행동 통제, 등), 길을 건너고자 하는 의도, 건너는 동안의 인지된 위험, 그리고 과거 교통사고의 경험 사이의 연관성을, 대학생 샘플을 대상으로, 연구하였다. TPB 변수들을 고려한 보행자 연구는 한국에서 통상 수행되지 않았었다. 더 나아가 본 연구는, 도로를 건너는 보행자의 위험한 행동에 있어서, 인간의 인지 실패/오류, 충동성, 시간관, 그리고 확률적/수학-논리적 판단 능력이, 차지하는 비중을 분석하고자 하였다. 연구 결과는, 그 비중이 아주 크지는 않지만, 존재함을 발견하였고, 인간 생명은 매우 소중하므로, 보행자의 사고에서 인간의 그러한 심리적 요인들을 추가적으로 고려한다면, 보행자의 사고를 예방하고 그 불행을 줄이는 데에, 더 나아가 사고와 관련된 경제적 손실과 보험 지출을 줄이는 데에, 기여할 것이라고 사료된다.