• Journal of the Korea institute for structural maintenance and inspection
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• v.4 no.4
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• pp.101-107
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• 2000

In this study, the Ground Penetrating Radar(GPR) was tested to evaluate the condition of concrete decks. Test results obtained by CPR were compared with values measured from drilled cores and damage mapping by the visual survey. It is shown that GPR can provide highly accurate measurements of layer properties of concrete decks and can map areas of deterioration in bridge decks by dielectric constants. The deck condition can be grouped into categories of "good" or "distressed". The ground penetrating radar data shows promise for producing rapid and accurate condition assessment for bridge decks. And these data can be used to evaluate highway bridge condition and make cost-effective bridge deck rehabilitation by accurately estimating the quantity of deteriorated concrete.

• The Research Journal of the Costume Culture
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• v.7 no.4
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• pp.155-167
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• 1999

The present study investigated various sales training programs used by apparel companies and compared each other in order to provide an important information for developing effective training programs for professional salesperson. Sixty eight companies were used and grouped into four categories based on brand characteristics : domestic national brand(DNB), casual brand(CB), foreign brand(FB) and domestic designer brand(DDB). Data were collected from the managers in charge or training salesperson by both questionnaires and personal and telephone interviews. Data were collected during July in 1998, and analyzed by using ANOVA, Duncan\`s multiple range test, and Chi-square test. Since the sample size was small, Yates\` correction formula was used to maximize statistical validity in non-parametric procedure of Chi-square test. The main purpose of sales training indicated by the companies were to satisfy customers and to maximize the profit. Significant differences were found among the groups in the importance level of training contents such as knowledge, and customer relation, training methods, place, and duration/frequency of training at training center.

• IE interfaces
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• v.12 no.1
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• pp.49-55
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• 1999

There are many types of holes to be machined on dies manufactured in the car industry. In this paper we introduce a CAM software developed for hole machining of press dies. The CAM software automatically generates NC files for machining holes from CAD data modeled in the CATIA system. A procedure generating NC files consists of three steps. First, the geometric information such as types of holes, hole positions, hole diameters, and hole depths is extracted from CATIA models. And then tools to be used and operation orders are standardized to establish a data base. Finally, NC files are generated by considering the machining conditions such as feedrate and rpm. It is efficient that holes with the same type and the same size should be grouped and machined by a tool to reduce the tool change time. The optimal tool path for machining the holes in a group can be determined by applying an algorithm solving the traveling salesman problem.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3336-3346
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• 2022

In general, a number of severe accident scenarios derived from Level 2 probabilistic safety assessment (PSA) are typically grouped into several categories to efficiently evaluate their potential impacts on the public with the assumption that scenarios within the same group have similar source term characteristics. To date, however, grouping by similar source terms has been completely reliant on qualitative methods such as logical trees or expert judgements. Recently, an exhaustive simulation approach has been developed to provide quantitative information on the source terms of a large number of severe accident scenarios. With this motivation, this paper proposes a machine learning-based categorization method based on exhaustive simulation for grouping scenarios with similar accident consequences. The proposed method employs clustering with an autoencoder for grouping unlabeled scenarios after dimensionality reductions and feature extractions from the source term data. To validate the suggested method, source term data for 658 severe accident scenarios were used. Results confirmed that the proposed method successfully characterized the severe accident scenarios with similar behavior more precisely than the conventional grouping method.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.122-129
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• 2022

This study is a phenomenological qualitative study that confirms the structure of college students' thoughts on artificial abortion. The data collection period was from 5 March to 10 April 2022. To this end, a total of three interviews were conducted on seven college students aged 20 to 25. Interview data were conducted through analysis and interpretation using the phenomenological research method, the Giorgi method, and as a result, 40 semantic units were derived, grouped into six sub-components, and divided into three categories. As a result of the analysis, college students' thoughts on artificial abortion consisted of fetal rights, respect for women's rights, and choices for a healthy life. Based on the above meaning, college students' thoughts on artificial abortion were, in conclusion, that considering the happiness of the baby and the quality of life of the woman, consideration for non-marriage mothers was more urgent than legal sanctions, and that abortion was not irresponsible. Accordingly, this study suggests that understanding and consideration for pregnant women should be prioritized over legal sanctions.

• Journal of Korea Technology Innovation Society
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• v.9 no.2
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• pp.304-324
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• 2006

There were considerable researches by finance people trying to find out business ratios as predictors of corporate bankruptcy. However, such financial ratios usually lack theoretical justification to predict bankruptcy for technology-oriented small sized venture firms. This study proposes a bankruptcy predictive discriminant model using technology evaluation data instead of financial data, evaluates the model fit by the correct classification rate, cross-validation method and M-P-P method. The results indicate that linear discriminant model was found to be more appropriate model than the logistic discriminant model and 69% of original grouped data were correctly classified while 67% of future data were expected to be classified correctly.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1465-1475
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• 2013

The chi-square type test statistic is the most commonly used test in terms of measuring testing goodness-of-fit for multinomial logistic regression model, which has its grouped data (binomial data) and ungrouped (binary) data classified by a covariate pattern. Chi-square type statistic is not a satisfactory gauge, however, because the ungrouped Pearson chi-square statistic does not adhere well to the chi-square statistic and the ungrouped Pearson chi-square statistic is also not a satisfactory form of measurement in itself. Currently, goodness-of-fit in the ordinal setting is often assessed using the Pearson chi-square statistic and deviance tests. These tests involve creating a contingency table in which rows consist of all possible cross-classifications of the model covariates, and columns consist of the levels of the ordinal response. I examined goodness-of-fit tests for a proportional odds logistic regression model-the most commonly used regression model for an ordinal response variable. Using a simulation study, I investigated the distribution and power properties of this test and compared these with those of three other goodness-of-fit tests. The new test had lower power than the existing tests; however, it was able to detect a greater number of the different types of lack of fit considered in this study. I illustrated the ability of the tests to detect lack of fit using a study of aftercare decisions for psychiatrically hospitalized adolescents.

• Journal of Korean Society for Geospatial Information Science
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• v.14 no.2 s.36
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• pp.55-62
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• 2006

Droughts have been an important factor in disaster management in Korea because she has been grouped into nations of lack of water. Satellite imagery can be applied to droughts monitoring because it can provide periodic data for large area for long time. This study aims to present a process to analyze droughts in large area using satellite imagery. We estimated evapotranspiration in large area using NDVI data acquired from satellite imagery. For satellite imagery, we dealt with MODIS data operated by NASA. The evapotranspiration estimated from satellite imagery was combined with precipitation data and potential evapotranspiration data to estimate water balances. Using water balances we could analyze droughts effectively in our object area. As the result of this study, we could increase the usability of satellite imagery, especially in droughts analysis.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.242-242
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• 2002

The Earth's magnetic field acquired from KOMPSAT-1's TAM (Three-Axis Magnetometer) between June 19th and 21st 2000 was analyzed. The TAM, one of the KOMPSAT-1's Attitude and Orbit Control Subsystems, plays an important role in determining and controlling the satellite's attitude. This also can provide new insight on the Earth's magnetic field. By transforming the satellite coordinate from ECI to ECEF, spherical coordinate of total magnetic field was achieved. These data were grouped into dusk (ascending) and dawn (descending) data sets, based on their local magnetic times. This partitioning is essential for performing 1-D WCA (Wavenumber Correlation Analysis). Also, this enhances the perception of external fields in the Kompsat-1's TAM magnetic maps that were compiled according to different local. The dusk and dawn data are processed independently and then merged to produce a total field magnetic anomaly map. To extract static and dynamic components, the 1-D and 2-D WCAs were applied to the sub-parallel neighboring tracks and dawn-dusk data sets. The static components were compared with the IGRF, the global spherical harmonic magnetic field model. The static and dynamic components were analyzed in terms of corefield, external, and crustal signals based on their origins.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4443-4462
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• 2020

This paper proposes a new framework of reversible data hiding scheme for absolute moment truncation coding (AMBTC) compressed images. AMBTC-based RDH can be applied to optical remote sensing (ORS) image transmission, which achieves target region preservation and image compression simultaneously. Existing methods can be concluded as two types. In type I schemes, stego codes mimic the original AMBTC format where no file bloat occurs, yet the carried secret data is limited. Type II schemes utilize predication errors to recode quantity levels of AMBTC codes which achieves significant increase in embedding capacity. However, such recoding causes bloat inside stego format, which is not appropriate in mentioned ORS transmission. The proposed method is a novel type I RDH method which prevents bloat inside AMBTC stego codes with significant improvement in embedding capacity. The AMBTC compressed trios are grouped into two categories according to a given threshold. In smooth trio, the modified low quantity level is constructed by concatenating Huffman codes and secret bits. The reversible contrast mapping (RCM) is performed to complex trios for data embedment. Experiments show that the proposed scheme provides highest payload compared with existing type I methods. Meanwhile, no expansion inside stego codes is caused.