• Korean Journal of Child Studies
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• v.32 no.1
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• pp.51-69
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• 2011

This study examined how young children understand representational relations between referents and their representational objects. Ninety-four children aged 2- to 4.5-years of age were individually tested; firstly in the scale-model tasks, and then in the scale-map tasks. Data were analyzed both by means of Chi-Square test and by a more descriptive, micro analysis. According to the results, there were significant age differences in the understanding of representational relations, regardless of the type of representational objects. In the descriptive, micro analysis, it was found that before 3 years of age, young children have a great deal of difficulties in understanding representational relations. More importantly, young children under three seemed unable to understand representational relations, especially when the similarities as well as the differences between the representational object and the referent were very high. These results suggest that teachers of very young children need to select representational materials carefully, taking into consideration children's understanding of representational relations.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.283-286
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• 2010

안전중요 소프트웨어 코드의 검증은 1%의 잠재적 가능성을 가진 오류조차 허용하지 않는 철저한 검증방식을 요구한다. 이러한 요구에 부응하여 최근 수학적 모델을 사용한 정형검증 기법이 코드검증에 활발하게 적용되고 있으나, 코드의 복잡도와 크기의 증가에 따른 검증비용의 기하급수적 증가가 해결과제로 부각되어왔다. 본 연구에서는 검증하고자 하는 특성을 중심으로 검증대상 코드를 추출, 정형검증의 대상을 자동으로 축소하는 코드추출기를 개발하였다. 개발된 코드추출기는 자동차 전장용 운영체제의 검증에 보조적으로 활용되어 검증비용을 90% 이상 절감하고 검증 사용성을 높이는데 기여하였다.

• Convergence Security Journal
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• v.12 no.5
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• pp.23-32
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• 2012

The police is responsible for protecting nation's property and life. Protecting nation from crime among the core duties is the most important activity of the police. But the big problem on reported crime system of the police was founded in the recent Suwon incident. Unfortunately, the unprofessional response made a toll of human sacrifice. Taking this opportunity, we need to consider closely the problems of the reported crime and system of the police and the solutions on the problems. This study analyzes the reported crime system of the police from the law and institutional and try to seek the solutions. This study searches the management status of the police system and arranges the problems in legal and institutional terms. And then, it arranges the solutions on the problems.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1239-1247
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• 2009

Study the gene about economical characteristic of human disease or domestic animal is a matter of grave interest, preserve and elevation of gene of Korea cattle is key subject. Studies have been done on the gene of Korea cattle using EST based SNP map, but it is based on statistical model, therefore there are difference between real position and statistical position. These problems are solved using both EST_based SNP map and Gene on sequence by Lee et al. (2009b). We have used multifactor dimensionality reduction(MDR) method to study interaction effect of statistical model in general. But MDR method cannot be applied in all cases. It can be applied to the only case-control data. So, method is suggested E-MDR method using CART algorithm. Also we identified interaction effects of single nucleotide polymorphisms(SNPs) responsible for average daily gain(ADG) and marbling score(MS) using E-MDR method.

• KIPS Transactions on Software and Data Engineering
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• v.8 no.10
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• pp.397-402
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• 2019

Accurately predicting cancer prognosis to provide appropriate treatment strategies for patients is one of the critical challenges in bioinformatics. Many researches have suggested machine learning models to predict patients' outcomes based on their gene expression data. Gene expression data is high-dimensional numerical data containing about 17,000 genes, so traditional researches used feature selection or dimensionality reduction approaches to elevate the performance of prognostic prediction models. These approaches, however, have an issue of making it difficult for the predictive models to grasp any biological interaction between the selected genes because feature selection and model training stages are performed independently. In this paper, we propose a novel two-dimensional image formatting approach for gene expression data to achieve feature selection and prognostic prediction effectively. Node2Vec is exploited to integrate biological interaction network and gene expression data and a convolutional neural network learns the integrated two-dimensional gene expression image data and predicts cancer prognosis. We evaluated our proposed model through double cross-validation and confirmed superior prognostic prediction accuracy to traditional machine learning models based on raw gene expression data. As our proposed approach is able to improve prediction models without loss of information caused by feature selection steps, we expect this will contribute to development of personalized medicine.