• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.4
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• pp.193-204
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• 2022

This study analyzed the degree of influence of measurement and improvement of customer satisfaction level targeting kiosk users. In modern times, due to the development of technology and the improvement of the online environment, the probability that simple labor tasks will disappear after 10 years is close to 90%. Even in domestic research, it is predicted that 'simple labor jobs' will disappear due to the influence of advanced technology with a probability of about 36%. there is. In particular, as the demand for non-face-to-face services increases due to the Corona 19 virus, which is recently spreading globally, the trend of introducing kiosks has accelerated, and the global market will grow to 83.5 billion won in 2021, showing an average annual growth rate of 8.9%. there is. However, due to the unmanned nature of these kiosks, some consumers still have difficulties in using them, and consumers who are not familiar with the use of these technologies have a negative attitude towards service co-producers due to rejection of non-face-to-face services and anxiety about service errors. Lack of understanding leads to role conflicts between sales clerks and consumers, or inequality is being created in terms of service provision and generations accustomed to using technology. In addition, since kiosk is a representative technology-based self-service industry, if the user feels uncomfortable or requires additional labor, the overall service value decreases and the growth of the kiosk industry itself can be suppressed. It is important. Therefore, interviews were conducted on the main points of direct use with actual users centered on display color scheme, text size, device design, device size, internal UI (interface), amount of information, recognition sensor (barcode, NFC, etc.), Display brightness, self-event, and reaction speed items were extracted. Afterwards, using the questionnaire, the Kano model quality attribute classification of each expected evaluation item was carried out, and Timko's customer satisfaction coefficient, which can be calculated with accurate numerical values The PCSI Index analysis was additionally performed to determine the improvement priorities by finally classifying the improvement impact of the kiosk expected evaluation items through research. As a result, the impact of improvement appears in the order of internal UI (interface), text size, recognition sensor (barcode, NFC, etc.), reaction speed, self-event, display brightness, amount of information, device size, device design, and display color scheme. Through this, we intend to contribute to a comprehensive comparison of kiosk-based research in each field and to set the direction for improvement in the venture industry.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.21-30
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• 2014

Lately, new influentials have secured a large number of volunteers on social networks due to vitalization of various social media. There has been considerable research on these influential people in social networks but the research has limitations on location information of Location Based Social Network Service(LBSNS). Therefore, the purpose of this study is to propose a spatial detection methodology and application plan for influentials who make comments about diverse social and cultural issues in LBSNS using spatial statistical analysis methods. Twitter was used to collect analysis object data and 168,040 Twitter messages were collected in Seoul over a month-long period. In addition, 'politics,' 'economy,' and 'IT' were set as categories and hot issue keywords as given categories. Therefore, it was possible to come up with an exposure index for searching influentials in respect to hot issue keywords, and exposure index by administrative units of Seoul was calculated through a spatial joint operation. Moreover, an influential index that considers the spatial dependence of the exposure index was drawn to extract information on the influential areas at the top 5% of the influential index and analyze the spatial distribution characteristics and spatial correlation. The experimental results demonstrated that spatial correlation coefficient was relatively high at more than 0.3 in same categories, and correlation coefficient between politics category and economy category was also more than 0.3. On the other hand, correlation coefficient between politics category and IT category was very low at 0.18, and between economy category and IT category was also very weak at 0.15. This study has a significance for materialization of influentials from spatial information perspective, and can be usefully utilized in the field of gCRM in the future.

• Korean Security Journal
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• no.33
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• pp.197-228
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• 2012

The private security industry in Korea has rapidly proliferated. While the industry has grown quickly, though, private security officers have recently been implicated in incidents involving violence, demonstrating an urgent need for systematic reform and regulation of private security practices in Korea. Due to its quasi-public service character, the industry also risks losing the public's favor if it is not quickly disciplined and brought under legitimate government regulation: the industry needs professional standards for conduct and qualification for employment of security officers. This paper shares insights for the reform of the Korean private security industry through a study of the licensing and training requirements for private security businesses in the United States, mainly focusing on the Private Security Officer Employment Authorization Act (hereinafter the PSOEAA) and the California system. According to the PSOEAA, aspiring security officers shall submit to a criminal background check (a check of the applicants' criminal records). Applicants' criminal records should include not only felony convictions but also any other moral turpitude offenses (involving dishonesty, false statement, and information on pending cases). The PSOEAA also allows businesses to do background checks of their employees every twelve months, enabling the employers to make sure that their employees remain qualified for their security jobs during their employment. It also must be mentioned that the state of California, for effective management of its private security sector, has established a professional government authority, the Bureau of Security and Investigative Services, a tacit recognition that the private security industry needs to be thoroughly, professionally, and actively managed by a professional government authority. The American system provides a workable model for the Korean private security industry. First, this paper argues that the Korean private security industry should implement a more strict criminal background check system similar to that required by the PSOEAA. Second, it recommends that an independent professional government authority be established to oversee and enforce regulation of Korea's private security industry. Finally, this article suggests that education and training course be implemented to provide both diverse training as well as specialization and phasing.

• Progress in Medical Physics
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• v.10 no.2
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• pp.95-101
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• 1999

The aims of this report are to classify the incorrect data of patients and the errors of dose and dose distribution observed in QA activities on teleradiotherapy chart, and to analyze their frequency. In our department, radiation physicists check several sheets of patient chart to reduce numeric errors before starting radiation therapy and at least once a week, which include history, port diagram, MU calculation or treatment planning summary and daily treatment sheet. The observed errors are classified as followings. 1) Identity of patient, 2) Omitted or unrecorded history sheet even though not including the item related to dose, 3) Omission of port diagram, or omitted or erroneous data, 4) Erroneous calculation of MU and point dose, and important causes, 5) Loss of summary sheet of treatment planning, and erroneous data of patient in the sheet, 6) Erroneous record of radiation therapy, and errors of daily dose, port setup, MU and accumulated dose in the daily treatment sheet, 7) Errors leading inexact dose or dose distribution, errors not administerd even though its possibility, and simply recorded errors, 8) Omission of sign. Number of errors was counted rather than the number of patients. In radiotherapy chart QA from Jun 17, 1996 to Jul 31, 1999, no error of patient identity had been observed. 431 Errors in 399 patient charts had been observed and there were 405 physical errors, 9 cases of omitted or unrecorded history sheet, and 17 unsigned. There were 23 cases (5.7%) of omitted port diagram, 21 cases (5.2%) of omitted data and 73 cases (18.0 %) of erroneous data in port diagram, 13 cases (3.2 %) treated without MU calculation, 68 cases (16.3 %) of erroneous MU, 8 cases (2.0%) of erroneous point dose, 1 case (0.2 %) of omitted treatment planning summary, 11 cases (2.7%) of erroneous input of patient data, 13 cases (3.2%) of uncorrected record of treatment, 20 cases (4.9%) of discordant daily doses in MU calculation sheet and daily treatment sheet, 33 cases (8.1%) of erroneous setup, 52 cases (12.8%) of MU setting error, 61 cases (15.1%) of erroneous accumulated dose. Cases of error leading inexact dose or dose distribution were 239 (59.0 %), cases of error not administered even though its possibility were 142 (35.1 %), and cases of simply recorded error were 24 (5.9 %). The numeric errors observed in radiotherapy chart ranged over various items. Because errors observed can actually contribute to erroneous dose or dose distribution, or have the possibility to lead such errors, thorough QA activity in physical aspects of radiotherapy charts is required.

• Journal of Nutrition and Health
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• v.51 no.4
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• pp.340-356
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• 2018

Purpose: This study was conducted to develop a nutrition quotient (NQ) to assess overall dietary quality and food behaviors of Korean adults. Methods: The NQ was developed in three steps: item generation, item reduction, and validation. Candidate items of the NQ checklist were derived from a systematic literature review, expert in-depth interviews, statistical analyses of the Korea National Health and Nutrition Examination Survey (2010 ~ 2013) data, and national nutrition policies and recommendations. A total of 368 adults (19 ~ 64 years) participated in a one-day dietary record survey and responded to 43 items in the food behavior checklist. Pearson's correlation coefficients between responses to the checklist items and nutritional intake status of the adults were calculated. Item reduction was performed, and 24 items were selected for a nationwide survey. A total of 1,053 nationwide adult subjects completed the checklist questionnaires. Exploratory and confirmatory factor analyses were performed to develop a final NQ model. Results: The 21 checklist items were used as final items for NQ. Checklist items were composed of four factors: nutrition balance (seven items), food diversity (three items), moderation for the amount of food intake (six items), and dietary behavior (five items). The four-factor structure accounted for 41.8% of the total variance. Indicator tests of the NQ model suggested an adequate model fit (GRI = 0.9693, adjusted GFI = 0.9617, RMR = 0.0054, SRMR = 0.0897, p < 0.05), and item loadings were significant for all subscales. Standardized path coefficients were used as weights of the items. The NQ and four-factor scores were calculated according to the obtained weights of the questionnaire items. Conclusion: NQ for adults would be a useful tool for assessing adult dietary quality and food behavior. Further investigations of adult NQ are needed to reflect changes in their food behavior, environment, and prevalence of chronic diseases.

• Economic and Environmental Geology
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• v.55 no.6
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• pp.737-760
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• 2022

For the geological disposal of high-level radioactive wastes (HLW), an understanding of deep subsurface environment is essential through geological, hydrogeological, geochemical, and geotechnical investigations. Although South Korea plans the geological disposal of HLW, only a few studies have been conducted for characterizing the geochemistry of deep subsurface environment. To guide the hydrogeochemical research for selecting suitable repository sites, this study overviewed the status and trends in hydrogeochemical characterization of deep groundwater for the deep geological disposal of HLW in developed countries. As a result of examining the selection process of geological disposal sites in 8 countries including USA, Canada, Finland, Sweden, France, Japan, Germany, and Switzerland, the following geochemical parameters were needed for the geochemical characterization of deep subsurface environment: major and minor elements and isotopes (e.g., ³⁴S and ¹⁸O of SO₄^2-, ¹³C and ¹⁴C of DIC, ²H and ¹⁸O of water) of both groundwater and pore water (in aquitard), fracture-filling minerals, organic materials, colloids, and oxidation-reduction indicators (e.g., Eh, Fe²⁺/Fe³⁺, H₂S/SO₄^2-, NH₄⁺/NO₃^-). A suitable repository was selected based on the integrated interpretation of these geochemical data from deep subsurface. In South Korea, hydrochemical types and evolutionary patterns of deep groundwater were identified using artificial neural networks (e.g., Self-Organizing Map), and the impact of shallow groundwater mixing was evaluated based on multivariate statistics (e.g., M3 modeling). The relationship between fracture-filling minerals and groundwater chemistry also has been investigated through a reaction-path modeling. However, these previous studies in South Korea had been conducted without some important geochemical data including isotopes, oxidationreduction indicators and DOC, mainly due to the lack of available data. Therefore, a detailed geochemical investigation is required over the country to collect these hydrochemical data to select a geological disposal site based on scientific evidence.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of agricultural medicine and community health
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• v.25 no.2
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• pp.231-244
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• 2000

Classification and research trends were studied to analyze a total of 240 original articles that have been published in 34 volumes of The Korean Journal of Rural Medicine from 1976 to 1999. The results were as follows: 1. A total of 337 articles were published. Among them, 240(71.2%) articles were classified as original articles. This number has been increasing significantly over the years as the number of the articles was 13 in the 1970s, 73 in the 1980s, and 154 in the 1990s. 2. There were 10 authors in the original articles and 55(22.9%) of them were written by 3 of them. There were five research institutions involved in the articles, and 106(44.2%) of the articles were done by one research group. 3. In the original articles. 24(10.0%) were noted to be done using research funds, and only 6(2.5%) were written in English. 4. In the view of the research styles of the original articles, 115(47.9%) used analytical study, 92(38.3%) used technical study, 21(9.2%) used experimental study, and 6(2.5%) used case reports. In the 1970s, 13(100.0%) articles used technical study, and in the 1980s, 47(64.4%) used technical studies and 19(26.0%) used analytical studies. However, in the 1990s, 96(62.8%) articles used analytical studies and 32(20.9%) used technical studies. The statistical methods most commonly used in the articles were technical statistics, the ${\chi}^2$-test, and the t-test respectively. 5. On the classification into three different research fields, 105(43.8%) articles were classified as health management, 96(40.0%) as disease epidemiology, and 39(16.3%) as rural environment and rural occupational disorders. In the 1970s, 12 (92.3 %) of the articles were on disease epidemiology and 1(7.7%) on health management were published. In the 1980s, 33(45.2%) articles on disease epidemiology, 29(39.7%) on health control, and 11(15.1%) on rural environment and rural occupational disorders were recorded. In the 1990s, however, 75(48.7%) articles were on health control, 51(33.1%) on disease control, and 28(18.2%) on the rural environment and rural occupational disorders. 6. According to the research subjects in each research field, the 39 articles in rural environment and rural occupational disorders were composed of 8(20.5%) articles on pesticide intoxication, 7(17,9%) on farmer's diseases, 7(17.9%) on vinyl-house diseases, and 6(15.4%) on accidents. From a total of 96 articles in disease epidemiology 56(58.3%) articles were on parasites, 16(16.7%) on non-infectious diseases, 12(12.5) on infectious diseases. From 105 articles in health control 25(23.8%) articles were on medical care utilization patterns, 18(17.1%) on the health care delivery system, and 13(12.4%) on maternal and child health. In the analysis of the 10 most prevalent subjects dealt in the above articles, 6(46.2%) articles were on parasites and 4(30.8%) on non-infectious diseases were recorded in the 1970s. In the 1980s, 28(38.4%) were on parasites. 9(12.3%) on the health care system, 7(9.6%) on medical care utilization patterns, 5(6.8%) on maternal and child health, and 4(5.5%) were on pesticide intoxication. In the 1990s, 22(14.3%) articles were on parasites. 18(11.7%) on medical care utilization patterns, 16(10.4%) on senile health, 14(9.1%) on the health care system, 10(6.5%) on infectious diseases, arid 10(6.5%) were on non-infectious diseases. In conclusion, the research activity on rural health has been strengthened in this country because the original articles in The Korean Journal of Rural Medicine have significantly increased in the past 24 years. In the 1970s and 1980s, research on disease epidemiology was most prevalent, but in the 1990s papers on health care were most popular. In addition, the articles on parasites were most frequently published in the 1970s, 1980s, and 1990s, showing that parasitic problem was the main theme in those eras. However, in the 1990s, it was evident that the articles on parasites were decreasing and articles on the subject of medical care utilization patterns and senile health increased. Hereafter it was expected that research on health care would be more common in rural health in Korea.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.117-140
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• 2019

As the aging phenomenon accelerates and various social problems related to the elderly of the vulnerable are raised, the need for effective elderly care solutions to protect the health and safety of the elderly generation is growing. Recently, more and more people are using Smart Toys equipped with ICT technology for care for elderly. In particular, log data collected through smart toys is highly valuable to be used as a quantitative and objective indicator in areas such as policy-making and service planning. However, research related to smart toys is limited, such as the development of smart toys and the validation of smart toy effectiveness. In other words, there is a dearth of research to derive insights based on log data collected through smart toys and to use them for decision making. This study will analyze log data collected from smart toy and derive effective insights to improve the quality of life for elderly users. Specifically, the user profiling-based analysis and elicitation of a change in quality of life mechanism based on behavior were performed. First, in the user profiling analysis, two important dimensions of classifying the type of elderly group from five factors of elderly user's living management were derived: 'Routine Activities' and 'Work-out Activities'. Based on the dimensions derived, a hierarchical cluster analysis and K-Means clustering were performed to classify the entire elderly user into three groups. Through a profiling analysis, the demographic characteristics of each group of elderlies and the behavior of using smart toy were identified. Second, stepwise regression was performed in eliciting the mechanism of change in quality of life. The effects of interaction, content usage, and indoor activity have been identified on the improvement of depression and lifestyle for the elderly. In addition, it identified the role of user performance evaluation and satisfaction with smart toy as a parameter that mediated the relationship between usage behavior and quality of life change. Specific mechanisms are as follows. First, the interaction between smart toy and elderly was found to have an effect of improving the depression by mediating attitudes to smart toy. The 'Satisfaction toward Smart Toy,' a variable that affects the improvement of the elderly's depression, changes how users evaluate smart toy performance. At this time, it has been identified that it is the interaction with smart toy that has a positive effect on smart toy These results can be interpreted as an elderly with a desire to meet emotional stability interact actively with smart toy, and a positive assessment of smart toy, greatly appreciating the effectiveness of smart toy. Second, the content usage has been confirmed to have a direct effect on improving lifestyle without going through other variables. Elderly who use a lot of the content provided by smart toy have improved their lifestyle. However, this effect has occurred regardless of the attitude the user has toward smart toy. Third, log data show that a high degree of indoor activity improves both the lifestyle and depression of the elderly. The more indoor activity, the better the lifestyle of the elderly, and these effects occur regardless of the user's attitude toward smart toy. In addition, elderly with a high degree of indoor activity are satisfied with smart toys, which cause improvement in the elderly's depression. However, it can be interpreted that elderly who prefer outdoor activities than indoor activities, or those who are less active due to health problems, are hard to satisfied with smart toys, and are not able to get the effects of improving depression. In summary, based on the activities of the elderly, three groups of elderly were identified and the important characteristics of each type were identified. In addition, this study sought to identify the mechanism by which the behavior of the elderly on smart toy affects the lives of the actual elderly, and to derive user needs and insights.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.111-124
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• 2010

To monitor the environment of land surface change is considered as an important research field since those parameters are related with land use, climate change, meteorological study, agriculture modulation, surface energy balance, and surface environment system. For the change detection, many different methods have been presented for distributing more detailed information with various tools from ground based measurement to satellite multi-spectral sensor. Recently, using high resolution satellite data is considered the most efficient way to monitor extensive land environmental system especially for higher spatial and temporal resolution. In this study, we use two different spatial resolution satellites; the one is SPOT/VEGETATION with 1 km spatial resolution to detect coarse resolution of the area change and determine objective threshold. The other is Landsat satellite having high resolution to figure out detailed land environmental change. According to their spatial resolution, they show different observation characteristics such as repeat cycle, and the global coverage. By correlating two kinds of satellites, we can detect land surface change from mid resolution to high resolution. The K-mean clustering algorithm is applied to detect changed area with two different temporal images. When using solar spectral band, there are complicate surface reflectance scattering characteristics which make surface change detection difficult. That effect would be leading serious problems when interpreting surface characteristics. For example, in spite of constant their own surface reflectance value, it could be changed according to solar, and sensor relative observation location. To reduce those affects, in this study, long-term Normalized Difference Vegetation Index (NDVI) with solar spectral channels performed for atmospheric and bi-directional correction from SPOT/VEGETATION data are utilized to offer objective threshold value for detecting land surface change, since that NDVI has less sensitivity for solar geometry than solar channel. The surface change detection based on long-term NDVI shows improved results than when only using Landsat.