• The Journal of Bigdata
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• v.8 no.2
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• pp.45-54
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• 2023

Seoul's public bicycle system, 'Ttareungyi,' introduced in 2015, has achieved an annual ridership of 40 million in 2022. Similarly, electric scooters, a type of personal mobility device, surpassed one million riders in 2020 due to various sharing platforms. However, the major roadways for these new transportation, bicycle lanes, are notably insufficient compared to other forms of transport. Hence, this study proposes an optimal location selection method for bicycle lanes in Seoul to prevent accidents and enhance bicycle ride safety. The location selection process prioritizes road safety concerning bicycle accident risk. Using regression models, high-risk areas for bicycle accidents are identified. Cluster analysis categorizes these areas into six clusters, each suggesting suitable types of bicycle lanes based on cluster-specific characteristics. We hope that this study will contribute to the improvement of Seoul's transportation environment, including the expansion of dedicated bicycle lanes and lanes for personal mobility devices.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1367-1377
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• 2020

The purpose of this study is to assess the seismic vulnerability of buildings in Gyeongju city starting with the earthquake that occurred in the city on September 12, 2016, and produce a seismic vulnerability map. 11 influence factors related to geotechnical, physical, and structural indicators were selected to assess the seismic vulnerability, and these were applied as independent variables. For a dependent variable, location data of the buildings that were actually damaged in the 9.12 Gyeongju Earthquake was used. The assessment model was constructed based on random forest (RF) as a mechanic study method and support vector machine (SVM), and the training and test dataset were randomly selected with a ratio of 70:30. For accuracy verification, the receiver operating characteristic (ROC) curve was used to select an optimum model, and the accuracy of each model appeared to be 1.000 for RF and 0.998 for SVM, respectively. In addition, the prediction accuracy was shown as 0.947 and 0.926 for RF and SVM, respectively. The prediction values of the entire buildings in Gyeongju were derived on the basis of the RF model, and these were graded and used to produce the seismic vulnerability map. As a result of reviewing the distribution of building classes as an administrative unit, Hwangnam, Wolseong, Seondo, and Naenam turned out to be highly vulnerable regions, and Yangbuk, Gangdong, Yangnam, and Gampo turned out to be relatively safer regions.

• Journal of Preventive Medicine and Public Health
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• v.29 no.3 s.54
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• pp.539-553
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• 1996

The study was conducted from May to September in 1994 to investigate applicability of the Hearing Handicap Inventory for the Elderly-Screening version(HHIE-S) in parallel with the pure-tone audiometer to the initial screening test of noise-induced hearing loss(NIHL) in some noise-exposed workers. Subjects were selected by systemic sampling that took every 10th person from 6, 700 workers taking the annual occupational health examination by the department of Health Maintenance of Dongsan Hospital Keimyung University in Taegu. The authors administered the pure-tone audiometric test and self-reported questionnaire of HHIE-S including items of sociodemographic and job-related variables concurrently. The final subjects analysed were 1,019(488 males and 531 females) excluding fourteen persons who had many missing values in their questionnaires. The reliability coefficients of HHIE-S scale by Cronbach's alpha were 0.84. In the univariate analysis of hearing handicap measured by the HHIE-S, work duration, military service and the hearing threshold loss at 1KHz and 4KHz by the initial audiometer were significant in males while age, work duration and hearing threshold loss at 1KHz and 4KHz were significant in females. In the stepwise linear regression analysis, hearing threshold loss at 1KHz and 4KHz, was the only selected variable explaining the hearing handicap in males and hearing threshold loss at 1KHz and 4KHz, age, and work duration were selected in females. In ROC curves for HHIE-S scores against NIHL as gold standard which was defined by the follow-up audiogram as more than 30dB of the average of 0.5/1/2KHz and 50dB at 4KHz, the optimal cutoff for the parallel HHIE-S appeared to be 8. The results suggest that HHIE-S appeared to have some reliability and validity in this data and might be used in screening NIHL in parallel with pure-tone audiometer in noise-exposed workers.

• The Journal of the Korea Contents Association
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• v.17 no.6
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• pp.504-513
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• 2017

This study investigated one of the controversial issues on debate or even controversial between policy makers at the government and corporate levels: To examine any financial determinants on the cash holdings of the firms in the advanced and emerging capital markets. Futhermore, it focused on the large representative firms headquartered in the U.S. and the Republic of Korea, taking into account scarcity of the previous literature concentrated on the comparative studies on this particular subject. Several legitimate, but robust econometric estimations such as static and dynamic panel data models and Tobit regression, were applied to investigate possible financial factors ono the cash liquidity. Given the continued debates or arguments on the excess cash reserves between interest partied at the government and corporate levels in the advanced and/or emerging capital markets, and more accelerated capital transfers among associated nations by engaging in the arrangements of the FTAs, the results of the study may provide a vision to search for the optimal level of corporate cash holdings for firms in the two nations.

• Korean Journal of Environment and Ecology
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• v.31 no.5
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• pp.433-443
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• 2017

Securing the species compositional integrity (typicalness and representativeness) is the essential prerequisite for an integrated management of vegetation resources using the phytosociological $relev\acute{e}s$ and plant communities of the Z.-M. school. This study is intended to develop a tool for qualitative and quantitative evaluation of species compositional homotoneity of a set of $relev\acute{e}s$ per syntaxon. The new homotoneities, actual homotoneity ($H_{act}$), and optimal homotoneity ($H_{opt}$) taking into account the heterogeneous factors of $relev\acute{e}s$ are proposed. The correlations between the floristic variables such as the vegetation type, the new homotoneities, and the previously studied homogeneous measures (e.g. Pfeiffer's homogeneity, basic homotoneity-coefficient, corrected homotoneity-coefficient, and mean floristic similarity) are analyzed by using Spearman's rank correlation coefficient. $H_{act}$ and $H_{opt}$ are effective in determining the difference of inter-synthesized units and of inter-$relev\acute{e}s$, respectively. $H_{act}$ is the homotoneity that is the most independent of the number of $relev\acute{e}s$. On actual vegetation with long-term human impact in the Korean Peninsula, $H_{opt}$ has become an aid to the more precise understanding of $H_{act}$ as substantive homogeneousness of species composition of syntaxa. It is expected that $H_{act}$ and $H_{opt}$ can be used for the selection of a sort of homogeneous vegetation data to build a phytosociological $relev\acute{e}$-database with consistency and objectiveness for national vegetation resources.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.43-50
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• 2021

A Leg Mating Unit (LMU) is a device utilized during the float-over installation of offshore structures that include hyperelastic pads and mating part. The hyperelastic pads absorb the loads, whereas the mating part works as guidance between topside and supporting structures during the mating sequence of float-over installation. In this study, the design optimization of an LMU for the float-over installation of floating-type offshore structures is conducted to enhance the performance and to satisfy the requirements defined by classification society regulations. The initial dimensions of the LMU are referred to the dimensions of those used in fixed-type float-over installation because only the location and the number of LMUs are known. The two-parameter Mooney-Rivlin model is adopted to describe the hyperelastic pads under given material parameters. Geometric variables, such as the thickness, height, and width of members, as well as configuration variables, such as the angle and number of members, are defined as design variables and are parameterized. A sampling-based design sensitivity analysis based on latin hypercube sampling method is performed to filter the important design variables. The design optimization problem is formulated to minimize the total mass of the LMU under maximum von Mises stress and reaction force constraints.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• Journal of Life Science
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• v.30 no.6
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• pp.522-531
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• 2020

The aim of this study was to establish the optimal medium composition for enhancing L(+)-lactic acid (LLA) production using response surface methodology (RSM). Lactobacillus paracasei SRCM201474 was selected as the LLA producer by productivity analysis from nine candidates isolated from kimchi and identified by 16S rRNA gene sequencing. Plackett-Burman design was used to assess the effect of eleven media components on LLA production, including carbon (glucose, sucrose, molasses), nitrogen (yeast extract, peptone, tryptone, beef extract), and mineral (NaCl, K₂HPO₄, MgSO₄, MnSO₄) materials. Glucose, sucrose, molasses, and peptone were subsequently chosen as promising media for further optimization studies, and a hybrid design experiment was used to establish their optimal concentrations as glucose 15.48 g/l, sucrose 16.73 g/l, molasses 39.09 g/l, and peptone 34.91 g/l. The coefficient of determination of the equation derived from RSM regression for LLA production was mathematically reliable at 0.9969. At optimum parameters, 33.38 g/l of maximum LLA increased by 193% when compared with MRS broth as unoptimized medium (17.66 g/l). Our statistical model was confirmed by subsequent validation experiments. Increasing the performance of LLA-producing microorganisms and establishing an effective LLA fermentation process can be of particular benefit for bioplastic technologies and industrial applications.

• Proceedings of the Optical Society of Korea Conference
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• 2006.07a
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• pp.197-198
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• 2006

본 연구에서 개발하는 성형렌즈는 그림1과 같이 한쪽 면이 비구면인 평볼록 형상이다. Glass렌즈의 고온압축성형을 위해서는 초정밀 가공기술로 제작된 성형Mold가 필요하며, Mold재질에 따른 성형기술의 확립이 필수적이다. 또한, 성형Mold의 표면과 융착반응이 없는 Glass소재가 요구된다. 본 실험을 위한 성형Mold는 코발트(Co) 함량 0.5 %의 초경합금(WC; 일본, Everloy社, 002K)을 초정밀 연삭가공하여 제작하였다. Glass소재는 전이점(Transformation Point; Tg) $572\;^{\circ}C$,항복점(Yielding Point; At) $630\;^{\circ}C$의 열적 특성을 갖는 K-BK7(일본, Sumita社)을 사용하였으며, d선에서 굴절률 및 아베수는 각각 1.51633, 64.1이다. 비구면 Glass렌즈 성형은 GMP(Glass Molding Press; 일본, Sumitomo社, Nano Press-S)장비를 사용하여 성형온도 $625\;^{\circ}C$, 서냉온도 $550\;^{\circ}C$로 고정하고 성형압력를 200-800 N 범위에서 변화시켰다. 표 1에 성형변수로 사용한 서냉속도와 서냉전환온도 조건을 나타낸다. 표1과 같이 각 서냉조건별로5장의 렌즈를 성형 후 특성값이 평균치에 가까운 3장을 선별하여 그 특성을 비교하였다. 각 조건에 따른 성형렌즈의 형상정도(일본, Panasonic社, UA3P, 자유곡면형상측정기), 두께(일본, Mitutoyo社, MDC-25M, 마이크로메터), 굴절률(일본, Shimatus社, KPR-200, 정밀굴절률측정기) 및 MTF[해상도](독일, Trioptics社, Image Master HR, MTF-Field)를 측정하여 각각의 광학적 특성을 비교 평가하였다. 비구면 Glass렌즈 성형장비와 형상측정기를 그림 2, 3에 각각 나타낸다.

• Resources Recycling
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• v.11 no.3
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• pp.10-16
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• 2002

In the leaching of $LiCoO_2$with a strong acid such as sulfuric and nitric acid, an additional step was needed to recover cobalt and lithium separately from spent lithium ion batteries (LIBs). The leaching of $LiCoO_2$in an oxalic acid solution was investigated to recover cobalt selectively using a low solubility of cobalt oxalate at low pH. Leaching efficiency of 95% of lithium and less than 1% of cobalt were obtained when pure $LiCoO_2$powder was leached in 3M oxalic acid at $80^{\circ}C$ and 50 g/L pulpdensity. Under the above leaching conditions, complete dissolution of lithium was accomplished with mere 0.25% of cobalt in the solution when the cathodic active material collected from spent LIBs was employed. The lithium in the leaching solution can be recovered as a form of carbonate or hydroxide depending on the addition of $Na_2$$CO_3$or LiOH.