• Korean Security Journal
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• no.48
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• pp.177-206
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• 2016

The purpose of this study is to verify the factors that affect the fear of crime by applying the risk interpretation model. Especially, whereas previous studies have not proven micro individual factor that the risk interpretation model had presented, This study includes micro individual elements such as neighborhood factor, perceived risk of crime, fears of crime as main variables. This study utilized secondary data of the National Crime Victimization Survey 2012, conducted by the Korean Institute of Criminology. In this study, multiple regression analysis of two stages and Sobel Test were conducted for verifying the individual influence of each independent variables and identifying the causal relationship between the variables set out in the risk analysis model. As the result, it appeared that the higher level of perceived risk of crime, neighborhood factor, crime experience, education, income cause the higher degree of the fear of crime. On the other hand, the lower degree of age was found to induce the higher level of the fear of crime. In addition, female showed the higher degree of the fear of crime than man. The causal relationship between the variables set out in the risk interpretation model was presented significantly in all variables, except for education.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.231-236
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• 2000

• Proceedings of the Korean Magnetic Resonance Society Conference
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• 2002.01a
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• pp.1-1
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• 2002

• The Korea Genome Conference
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• 2003.09a
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• pp.53.2-53.2
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• 2003

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.297-306
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• 2004

It is usual that multivariate data analysis produces related (small number of) artificial variates for data reduction. Among them, refer to MDS(multidimensional scaling), MDPREF(multidimensional preference analysis), CDA(canonical discriminant analysis), CCA(canonical correlation analysis) and FA(factor analysis). Varimax rotation of artificial variables which is originally invented in FA for easy interpretations is applied to diverse multivariate techniques mentioned above. Real data analysisis is performed in order to manifest that rotation improves interpretations of artificial variables.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.149-160
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• 2006

Association rule mining searches for interesting relationships among items in a given large data set. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary threshold measures in association rule; support and confidence and lift. In the case of appling real world to association rules, we have some difficulties in data interpretation because we obtain many rules. In this paper, we develop the model of association rules using latent variables for environmental survey data.

• Korean Journal of Remote Sensing
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• v.26 no.3
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• pp.305-316
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• 2010

The MODerate Resolution Imaging Spectroradiometer (MODIS) sensor has 36 bands at 250m, 500m, 1km spatial resolution. However, 500m or 1km MODIS data exhibits a few limitations when low resolution data is applied at small areas that possess complex land cover types. In this study, we produce seven 250m spectral bands by fusing two MODIS 250m bands into five 500m bands. In order to recommend the best fusion method by which one acquires MODIS data, we compare seven fusion methods including the Brovey transform, principle components algorithm (PCA) fusion method, the Gram-Schmidt fusion method, the least mean and variance matching method, the least square fusion method, the discrete wavelet fusion method, and the wavelet-PCA fusion method. Results of the above fusion methods are compared using various evaluation indicators such as correlation, relative difference of mean, relative variation, deviation index, peak signal-to-noise ratio index and universal image quality index, as well as visual interpretation method. Among various fusion methods, the local mean and variance matching method provides the best fusion result for the visual interpretation and the evaluation indicators. The fusion algorithm of 250m MODIS data may be used to effectively improve the accuracy of various MODIS land products.

• The Mathematical Education
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• v.55 no.3
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• pp.317-334
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• 2016

Knowledge and data interpretation on statistical estimation was important to have statistical literacy that current curriculum was said not to satisfy. The author investigated mathematics teachers' MKT on statistical estimation concerning interpretation of confidence interval by using questionnaire and interview. SMK of teachers' confidence was limited to the area of textbooks to be difficult to interpret data of real life context. Most of teachers wrongly understood SMK of interpretation of confidence interval to have influence upon PCK making correction of students' wrong concept. SMK of samples and sampling distribution that were basic concept of reliability and confidence interval cognized representation of samples rather exactly not to understand importance and value of not only variability but also size of the sample exactly, and not to cognize appropriateness and needs of each stage from sampling to confidence interval estimation to have great difficulty at proper teaching of statistical estimation. PCK that had teaching method had problem of a lot of misconception. MKT of sample and sampling distribution that interpreted confidence interval had almost no relation with teachers' experience to require opportunity for development of teacher professionalism. Therefore, teachers were asked to estimate statistic and to get confidence interval and to understand concept of the sample and think much of not only relationship of each concept but also validity of estimated values, and to have knowledge enough to interpret data of real life contexts, and to think and discuss students' concepts. So, textbooks should introduce actual concepts at real life context to make use of exact orthography and to let teachers be reeducated for development of professionalism.

• Geophysics and Geophysical Exploration
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• v.20 no.3
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• pp.121-128
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• 2017

Interpretation of time-domain electromagnetic (TEM) data has been made mostly based on one-dimensional (1-D) inversion scheme in Korea. A proper interpretation of TEM data should employ 3-D TEM forward and inverse modeling algorithms. This study developed a 3-D TEM modeling algorithm using a finite difference time-domain (FDTD) method with staggered grid. In numerically solving Maxwell equations, fictitious displacement current is included based on an explicit FDTD method using a central difference approximation scheme. The developed modeling algorithm simulated a small-coil source configuration to be verified against analytic solutions for homogeneous half-space models. Further, TEM responses for a 3-D anomaly are modeled and analyzed. We expect that it will contribute greatly to the precise interpretation of TEM data.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.817-824
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• 2003

We employed a hierarchical document classification method to classify a massive collection of electronic medical records(EMR) written in both Korean and English. Our experimental system has been learned from 5,000 records of EMR text data and predicted a newly given set of EMR text data over 68% correctly. We expect the accuracy rate can be improved greatly provided a dictionary of medical terms or a suitable medical thesaurus. The classification system might play a key role in some clinical decision support systems and various interpretation systems for clinical data.