• Information Systems Review
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• v.25 no.3
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• pp.139-162
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• 2023

With the emergence of new digital technologies into a supply chain, it is essential for companies to incorporate these technologies in managing their supply chains. However, various challenges have been identified in digital supply chain management, especially when it comes to its assessment. There are no universally agreed measurements for the performance of digital supply chain management within the research community so far. This paper explores an option of using user experience as one of possible measurements. Therefore, three different focus-group discussions were held and later analyzed with a qualitative content analysis. The subscription-based video on demand service, Netflix was used as an example in those discussions. Due to the fact that Netflix provides a digital product as a streamline service, user experience is critical for the company. Especially, user experience with a recommender system and related privacy issues have become significant for a company to retain existing customers and attract new customers in many fields. Since the recommender system and related privacy issues are parts of a digital supply chain, user experience can be one of appropriate measurements for digital supply chain management. This study opens a new perspective for research on performance measurements of digital supply chain management.

• Journal of the Korean Applied Science and Technology
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• v.40 no.6
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• pp.1521-1532
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• 2023

This study was done to identify the face-to-face and non-face-to-face student counseling experiences of nursing students during the COVID-19 period. Data were collected through interviews with 10 students at S Women's University in Gyeonggi-do from December 2022 to April 2023. All recorded data were analyzed using an inductive content analysis method. As a result of the study, two themes, four categories, and eight subcategories were found. Themes were 'factors promoting counseling' and 'Non-facilitating factors in counseling'. The first theme category was 'adaptation to new counseling methods' and 'pursuing convenience as the MZ generation', and the second theme category was 'resistance to counselling' and 'discrepancy from the desire for mutual relationships'. Through this study, it was helpful to understand that student counseling is essential for nursing students and what the most appropriate and useful counseling method is. Additionally, this study provided important evidence to lay the foundation for comparative research on face-to-face and non-face-to-face counseling among research participants according to gender and major. Further suggests research to develop a student counseling program for nursing students and confirm its effectiveness.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.240-252
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• 2023

Purpose To assess the effect of deep learning image reconstruction (DLIR) for head CT in pediatric patients. Materials and Methods We collected 126 pediatric head CT images, which were reconstructed using filtered back projection, iterative reconstruction using adaptive statistical iterative reconstruction (ASiR)-V, and all three levels of DLIR (TrueFidelity; GE Healthcare). Each image set group was divided into four subgroups according to the patients' ages. Clinical and dose-related data were reviewed. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and qualitative parameters, including noise, gray matter-white matter (GM-WM) differentiation, sharpness, artifact, acceptability, and unfamiliar texture change were evaluated and compared. Results The SNR and CNR of each level in each age group increased among strength levels of DLIR. High-level DLIR showed a significantly improved SNR and CNR (p < 0.05). Sequential reduction of noise, improvement of GM-WM differentiation, and improvement of sharpness was noted among strength levels of DLIR. Those of high-level DLIR showed a similar value as that with ASiR-V. Artifact and acceptability did not show a significant difference among the adapted levels of DLIR. Conclusion Adaptation of high-level DLIR for the pediatric head CT can significantly reduce image noise. Modification is needed while processing artifacts.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.645-661
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• 2024

Today, the development of new technologies due to the 4th industrial revolution requires work performance methods such as non-face-to-face collaboration. In response to this, various VR collaboration tools are emerging, but VR collaboration tools for brainstorming, which are used in collaboration or design development work, are not provided. Therefore, despite the advantages and possibilities of VR for non-face-to-face collaboration, there are limitations in practical use. Accordingly, the development of VR collaboration tools in a digitalized work environment is necessary, and research on UI design development for this is required. The purpose of this study is to propose a VR collaboration tool prototype by developing an interaction UI design that applies user hand behavior elements that appear during collaboration sessions through user research. This study was a qualitative study. The research method was to conduct user research through observation and in-depth interviews, and as a result of analyzing the data obtained from this, five types of user hand behavior elements were derived. In this study, an interaction UI design was developed that reflects hand gestures as behavioral elements. And using Unity and the Oculus Integration SDK Kit, we created a prototype VR collaboration tool that can be used without a controller. As a result of conducting a user evaluation of the prototype produced in this study, it was found that users had difficulty making hand gestures accurately, and it was possible to find areas for improvement in UI design. It is expected that this study will help develop interaction UI design for VR collaboration tools that can increase work efficiency.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• Archives of design research
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• v.17 no.1
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• pp.89-98
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• 2004

A systemic approach to behavior on the basis of human psychology is needed for behavior-centered space design. Also, the recognition that human and environment, in all, have complementarity is needed- human and space shall be understood as a general phenomenon, supposing interaction. Design of behavior-oriented space means configuration and coordination of physical subjects as well as understanding, analysis and reflection of psychological and behavioral phenomena. It is analysis of a private individual as well as understanding of interaction between human groups, as well. In respect of space recognition, analysis not on material movement but on energy circulation and variable is important. It means that the understanding of user's behavior and psychology does not orient reasonable purpose just for convenience. That is, such understanding intends to understand behavioral patterns and psychological phenomena between space and human beyond the decomposition of structure of human and space into physical elements and the design based on standardized data. Thereby, more human-oriented space design might be implemented by the understanding of behavioral essence. Also, a user-centered design process from another viewpoint might be created, and the general amenity among man, space and environment - better environmental quality - might be produced. For this, the consciousness of human activity that is, activity system shall be ahead of it, and the approaches for design shall be implemented into a process not in predictive ideas but in semi-scientific system. On the basis of the above view, this study was attempted to investigate the orientation of design to recognize space as another life, and explore a process where it is drawn into a design language on the basis of human behavior. If the essence of space behavior and the activity system are analyzed through user observation and it is reflected upon a space design program and then developed into a formative language, a new design process on human and environment might be produced. In conclusion, the reflection of user's behavior and psychology into design, contrary to existing public space design based on physical data, can orient quality improvement of human life and ultimately be helpful to the proposition, 'humanization of space'.

• Economic Analysis
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• v.26 no.1
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• pp.1-33
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• 2020

Recent discussions about a minimum wage increase (MWI) and its influence on the economy have mainly focused on the quantitative aspects, such as labor costs and employment. However, concerning the qualitative aspects, an MWI could have positive effects by enhancing firm productivity and crowding out marginal firms from the market. These positive effects of an MWI can offset, to some extent, its potential negative effects - increasing labor costs and decreasing employment, among others. In this regard we empirically examine the impact of an MWI on firm productivity (total factor productivity). Using firm level panel data from the manufacturing industry in Korea, we calculate the influence rates of a minimum wage by sector and by firm size (number of workers), and analyze its effects on firm productivity. In particular, the production functions of the firms are estimated by taking into account endogeneity among the input factors, in order to resolve the drawbacks of existing studies - underestimating the capital factor coefficient and overestimating the labor factor coefficient. This study finds that the influences of an MWI on wages, employment, and productivity are substantially different across sectors and firm sizes. While an MWI has shown to have positive influences on productivity growth in the manufacturing industry as a whole, each sector demonstrates a different direction of effect, and the degree of productivity change also varies by sector. The impacts of an MWI on firm productivity are generally estimated to be more negative for smaller firms, but in some sectors the effects are found to be positive. In addition, the wage increases resulting from an MWI seem to cause a productivity enhancement across all sectors in the manufacturing industry. The policy implications of this study are as follows. Considering the empirical findings that an MWI causes an increase in productivity in many sectors of the manufacturing industry, it would be desirable to take into consideration not only the negative side effects but also the positive effects of an MWI when designing any future minimum wage policy. Moreover, in spite of there being a uniform minimum wage, this study finds that the diverse influence rates of a minimum wage across firms have different impacts on wages, employment, and productivity across sectors or firm size. This finding could be conducive to discussions about differentiation among minimum wage schemes by sector or firm size.

• 한국노년학
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• v.39 no.2
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• pp.199-211
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• 2019

The purpose of this study is to analyze leisure satisfaction and leisure performance according to whether elderly people are performing their preferred leisure activities. For the analysis, we used sample from the 6th (2015) panal data as Korean Retirement and Income Study(KReIS). The results of this study were as follows. First, the total data of 4,197 elderly (2,212 young-old and 1,985 old-old) were analyzed. As a result, weekday and weekend leisure time of the old-old (7.64 hours / 7.81 hours) than the young-old (6.83 hour / 7.39 hour) was increased and resting activites (over 70% of watching TV and listening to the radio) accounted for more than 80% of the both elderly leisure activities. Leisure performance were higher in old-old who did not perform preferred leisure activities during weekdays. Leisure performance on weekends was higher in old-old regardless of whether they had preferred leisure time. Average of leisure performance was high in both groups and they responded leisure satisfaction was moderate. In the case of need for leisure change, young-old was higher than oid-old regardless of preference leisure performance and day of the week. However, the responses of the both groups are closed to those that do not want to change. Based on the results of this study, it should be practiced such as develomenting program and introduction of health management system considering leisure constraints to improve leisure satisfaction and continuance of leisure activities for young-old and old-old. We also emphasize the need for a systematic survey scale that takes into account the qualitative aspects of leisure activities as well as the subjective factors influencing leisure participation.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.67-72
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• 2010

Purpose: In Asan Medical Center we perform myocardial perfusion SPECT to evaluate cardiac event risk level for non-cardiac surgery patients. In case of patients with cancer, we check tumor metastasis using whole body bone scan and whole body PET scan and then perform myocardial perfusion SPECT to reduce unnecessary exam. In case of short term in patients, we perform $^{201}Tl$ myocardial perfusion SPECT after whole body bone scan a minimum 16 hours in order to reduce hospitalization period but it is still the actual condition in which the evaluation about the affect of the crosstalk contamination due to the each other dissimilar isotope administration doesn't properly realize. So in our experiments, we try to evaluate crosstalk contamination influence on $^{201}Tl$ myocardial perfusion SPECT using anthropomorphic torso phantom and patient's data. Materials and Methods: From 2009 August to September, we analyzed 87 patients with $^{201}Tl$ myocardial perfusion SPECT. According to $^{201}Tl$ myocardial perfusion SPECT yesterday whole body bone scan possibility of carrying out, a patient was classified. The image data are obtained by using the dual energy window in $^{201}Tl$ myocardial perfusion SPECT. We analyzed $^{201}Tl$ and $^{99m}Tc$ counts ratio in each patients groups obtained image data. We utilized anthropomorphic torso phantom in our experiment and administrated $^{201}Tl$ 14.8 MBq (0.4 mCi) at myocardium and $^{99m}Tc$ 44.4 MBq (1.2 mCi) at extracardiac region. We obtained image by $^{201}Tl$ myocardial perfusion SPECT without gate method application and analyzed spatial resolution using Xeleris ver 2.0551. Results: In case of $^{201}Tl$ window and the counts rate comparison result yesterday whole body bone scan of being counted in $^{99m}Tc$ window, the difference in which a rate to 24 hours exponential-functionally notes in 1:0.114 with Ventri (GE Healthcare, Wisconsin, USA), 1:0.249 after the bone tracer injection in 12 hours in 1:0.411 with 1:0.79 with Infinia (GE healthcare, Wisconsin, USA) according to a reduction a time-out was shown (Ventri p=0.001, Infinia p=0.001). Moreover, the rate of the case in which it doesn't perform the whole body bone scan showed up as the average 1:$0.067{\pm}0.6$ of Ventri, and 1:$0.063{\pm}0.7$ of Infinia. According to the phantom after experiment spatial resolution measurement result, and an addition or no and time-out of $^{99m}Tc$ administrated, it doesn't note any change of FWHM (p=0.134). Conclusion: Through the experiments using anthropomorphic torso phantom and patients data, we found that $^{201}Tl$ myocardium perfusion SPECT image later carried out after the bone tracer injection with 16 hours this confirmed that it doesn't receive notable influence in spatial resolution by $^{99m}Tc$. But this investigation is only aimed to image quality, so it needs more investigation in patient's radiation dose and exam accuracy and precision. The exact guideline presentation about the exam interval should be made of the validation test which is exact and in which it is standardized about the affect of the crosstalk contamination according to the isotope use in which it is different later on.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.