• Health Policy and Management
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• v.23 no.3
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• pp.260-265
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• 2013

Background: The aim of this study was to examine the seasonal variation of death from intentional self-harm by hanging, strangulation and suffocation (HSS: Korean Standard Classification of Diseases-6 code: X70) using the 2011 death registry data. Methods: The analysis was based on data of 8,359 HSS deaths from 2011 national vital statistics in Korea. Daily, weekly, and monthly death pattern on HSS were used to examine the relationship seasonal variation and HSS deaths. Results: A total of 8,359 HSS deaths occurred in 2011, with a mean age of 50.6 years. The HSS death rate (per 100,000) was 25.5 in male and 10.8 in female. In one day 17.6 males and 8.0 females occurred HSS death on average. The number of HSS death per day was the highest on 8th June (45 deaths), and lowest on 1st February (7 deaths) during the period. The variations of daily HSS death showed wide fluctuation from a peak of 34 to 45 deaths (29th May to 9th June) to a trough of 17-26 deaths (10th-13th September: the Korean thank-giving consecutive holidays), 13-20 deaths (2nd-5th February: the new year's day by the lunar calendar) and 8-9 deaths (24th-25th December: Christmas holidays). There were no significant difference between gender and seasonal variation (month, season, and week). Conclusion: The mean number of HSS death per day was highest in June (30.6 deaths), and months with the lowest number of deaths was January and December (range, 19.4 to 19.6 deaths). HSS death were more prevalent during summer and spring and were less likely to occur during winter. On Saturdays (21.0 deaths), the number of HSS death per day was the lowest, and Monday (27.9 deaths) was the highest. HSS death was less likely to occur on holidays (21.4 deaths). There was significant seasonal variation in HSS death by weekly and monthly (p<0.01).

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.11
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• pp.95-104
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• 2018

Healthcare technology has been growing and fostering cooperation between industry, university and hospitals as growth engines in korea. So, the medibio research institutes in hospital have been constructed to promote research and industrialization centering on healthcare technology. The purpose of this study is to investigate the cases of research institutes in hospitals, and search the characteristics of building organization of medibio research laboratory facilities. Case study is investigated by floor plan, homepage and site visits about five research institutes selected in research-driven hospitals. The facility title and size of research laboratory is originated from site area and research building location. The building function include not only the research lab and business office reflecting on the development platform, and but assembly and meeting room in the ground level. Laboratory floor plans have three types, rectangular, rectangular+linear and linear type, one is traditional and efficient, the others are people and friendly. And building core types are correlated with lab space unit modules, single and double side core are shown in rectangular type. All the laboratories are open lab, composed with laboratory bench and research note writing desk facing the lab service and enclosed lab-support area. And they have communication space looking as warm and cozy common area for the innovation, convergence and collaboration. As the high risk of contamination and high standard for safety and security, equipment and facilities are well managed with biological environment including BSC, fume hood, PCR classification, eye washing and emergency shower.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.99-122
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• 2019

According to the recent IDC (International Data Corporation) report, as from 2025, the total volume of data is estimated to reach ten times higher than that of 2016, corresponding to 163 zettabytes. then the main body of generating information is moving more toward corporations than consumers. So-called "the wave of Big-data" is arriving, and the following aftermath affects entire industries and firms, respectively and collectively. Therefore, effective management of vast amounts of data is more important than ever in terms of the firm. However, there have been no previous studies that measure the effects of big data investment, even though there are number of previous studies that quantitatively the effects of IT investment. Therefore, we quantitatively analyze the Big-data investment effects, which assists firm's investment decision making. This study applied the Event Study Methodology, which is based on the efficient market hypothesis as the theoretical basis, to measure the effect of the big data investment of firms on the response of market investors. In addition, five sub-variables were set to analyze this effect in more depth: the contents are firm size classification, industry classification (finance and ICT), investment completion classification, and vendor existence classification. To measure the impact of Big data investment announcements, Data from 91 announcements from 2010 to 2017 were used as data, and the effect of investment was more empirically observed by observing changes in corporate value immediately after the disclosure. This study collected data on Big Data Investment related to Naver 's' News' category, the largest portal site in Korea. In addition, when selecting the target companies, we extracted the disclosures of listed companies in the KOSPI and KOSDAQ market. During the collection process, the search keywords were searched through the keywords 'Big data construction', 'Big data introduction', 'Big data investment', 'Big data order', and 'Big data development'. The results of the empirically proved analysis are as follows. First, we found that the market value of 91 publicly listed firms, who announced Big-data investment, increased by 0.92%. In particular, we can see that the market value of finance firms, non-ICT firms, small-cap firms are significantly increased. This result can be interpreted as the market investors perceive positively the big data investment of the enterprise, allowing market investors to better understand the company's big data investment. Second, statistical demonstration that the market value of financial firms and non - ICT firms increases after Big data investment announcement is proved statistically. Third, this study measured the effect of big data investment by dividing by company size and classified it into the top 30% and the bottom 30% of company size standard (market capitalization) without measuring the median value. To maximize the difference. The analysis showed that the investment effect of small sample companies was greater, and the difference between the two groups was also clear. Fourth, one of the most significant features of this study is that the Big Data Investment announcements are classified and structured according to vendor status. We have shown that the investment effect of a group with vendor involvement (with or without a vendor) is very large, indicating that market investors are very positive about the involvement of big data specialist vendors. Lastly but not least, it is also interesting that market investors are evaluating investment more positively at the time of the Big data Investment announcement, which is scheduled to be built rather than completed. Applying this to the industry, it would be effective for a company to make a disclosure when it decided to invest in big data in terms of increasing the market value. Our study has an academic implication, as prior research looked for the impact of Big-data investment has been nonexistent. This study also has a practical implication in that it can be a practical reference material for business decision makers considering big data investment.

• Journal of the Ergonomics Society of Korea
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• v.31 no.3
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• pp.445-453
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• 2012

The aim of this paper was to prove that if the risk level in combined tasks was improved through evaluation of postural load of liquid weight measurement process, the workload level and ratio of exposure time would be changed, and the time of process would be seen concurrently. Background: According to results of epidemiological studies conducted by Korea Occupational Safety & Health Agency, 122 musculoskeletal disorders occurred during 1992 to 2008, in which manufacturing industry covers 96(78.7%) of total. However, this is an insufficient level and only occupies 39% based on the South Korea's manufacturing standard industrial classification(246 industries). Method: Firstly, the number of batches weighed on one day(460min) was investigated based on the work performed and Weight measured weekly. VCR recording was taken based on the level of liquid ingredients prescribed for 1batch using the Camcorder. After dividing a 356 sec video into 1 sec using the screen capture function in Gom player, the job classification was performed by analyzing the change of working postures, which revealed 148 working postures. Time measurement was decided by time of the postures was being maintained. Then, the REBA analysis was performed for the working postures. The ratio of Exposure time was calculated based on the measurement time and REBA Score. In addition, the recommendations were designed and implementation was carried out for the working postures with REBA Score higher than 3. Finally, after the intervention, REBA measurement, time measurement, and ratio of exposure time were calculated for the comparison of works before and after improvement. Results: The number of work elements was decreased by 30.4% from 148 to 103 after improvement. The results of time measurement showed that the time was reduced by 46.3% from 356 sec to 191 sec. And the ratio of exposure time was also improved by 52.1% from 0% to 52.1% after improvement. Conclusion: The reduction of time was found to improve the productivity of management. Furthermore, because the reduction of ratio of exposure time and the improvement of workload level are the improvement of discomfort, it would contribute to the improvement of the worker's psychological working posture. Application: These results would contribute to musculoskeletal disease prevention and management performance. Further studies for other industries would be needed based on this case study.

• Fashion & Textile Research Journal
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• v.19 no.6
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• pp.804-812
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• 2017

This study builds a database that can be reflected in the production of dress form for fitting by typifying the upper body shape of a senior male. This study analyzed the 3D shape data of 405 persons of the 5th Size Korea. The age range is from 50s 210 persons and 60s 205 persons. Analysis items to identify upper body shape of senior males consisted of 51 items. 3D shape data were also measured using a Geomagic Design ${\times}$ program for the analysis of the upper body of the senior male required for the dress form of this study. The reference point was based on the Size Korea 2010 3D measurement standard and created points (Back-protrusion) on shape data. As a result of the senior men type, the senior men's body type was classified into four types:1. Overall, the upper body is a large body type and the most undistorted overall body type 2. Width / Thickness Flatness is the largest and vertical length factor is the smallest abdominal obesity type 3. Severe flexion of the back part type 4. The upper body is small and the scapular bending is severe. The elderly body type showed a high distribution ratio in the type with severe flexion. The development of a dress form that reflects the cause of the finery issue can improve the fit of ready-to-wear.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.145-151
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• 2011

This study assumed the competitive structure based on genre classification for Korean online game contents market, and carried out the analysis on the degree and characteristics of the competition that appear differently in each sub market classified according to the genre. First, to analyze the market power of the rank 1 and 2 game contents in each genre, using the play time share ratio and standard deviation statistics values in the genre, ANOVA analysis and Cluster analysis were carried out for each genre. According to ANOVA analysis result, in the rank 1 game share ratio in each genre, there was a relationship of 'FPS/Racing > RST/Sports > Poker > Go-stop > RPG > Arcade > Board', and in the play time total share ratio of rank 1 and 2 games, the relationship of 'RTS > FPS/Racing > Sports > RPG > Go-stop > Poker > Arcade > Board' was verified. And in Cluster analysis, the groups of the genres with the degree of market power tendency and the variability at similar level were classified and stated.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.155-162
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• 2001

Recently, in Korea, a few construction companies have been tried to develop a management system, which is able to integrate schedule and cost. In spite of these attempts, however, advanced management techniques can be hardly applied under the BoQ based management system. In order to improve these problems, many studies have been peformed, but yet could not overcome practical limitations. Besides, the application of historical data is below the level since it is so difficult to accumulate and feed-back historical data under the unique character of construction industry. Consequently, lots of time and effort have being wasted to establish control criteria. The newly generated Information is not systematically managed as well. Therefore, this study suggests Integrated Construction Management System complemented the existing practical problems.

• The Korean Journal of Applied Statistics
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• v.35 no.5
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• pp.593-601
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• 2022

We call "fruad" the cases that are not frequently occurring but cause significant losses. Fraud detection is commonly encountered in various applications, including wafer production in the semiconductor industry. It is not trivial to directly extend the standard binary classification methods to the fraud detection context because the misclassification cost is much higher than the normal class. In this article, we propose the functional fraud detection support vector machine (F²DSVM) that extends the fraud detection support vector machine (FDSVM) to handle functional covariates. The proposed method seeks a classifier for a function predictor that achieves optimal performance while achieving the desired sensitivity level. F²DSVM, like the conventional SVM, has piece-wise linear solution paths, allowing us to develop an efficient algorithm to recover entire solution paths, resulting in significantly improved computational efficiency. Finally, we apply the proposed F²DSVM to the defective wafer detection problem and assess its potential applicability.

• Journal of Korea Technology Innovation Society
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• v.17 no.3
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• pp.467-489
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• 2014

The purpose of this study is to explore the suitable creative class in korea as the core capital of creative urban growth under creative economy era. We are test to find it for two types of creative class. One is Richard Florida(2002)'s creative class, the other is Mcgranahan & Wojan(2007)'s recasting creative class. Data on 2010 for this paper are generated from Statistics Korea. As a result, we find that the economic geography of creative class is highly concentrated. Furthermore, the geography of creative class is strongly associated with innovation index and high-technology industry location. And Mcgranahan & Wojan(2007)'s creative class is more strong relationship between all dependent variables than Florida's. We also find that it has better power of explanation than Florida's with all of them in regression analysis. According to the results, this study suggests some solutions. First, this study can be provided to government and local policy makers as basis data and practical policy guide to attract creative class. Second, this paper presents standard about a diversity of definitions for creative class in Korea. Third, this research also facilitates follow-up studies about regional economic growth and creative climates.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.