• Spatial Information Research
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• v.8 no.1
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• pp.15-29
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• 2000

In the Recent rapidly changing technology environment the computerization of administration business which is driven or will be driven to give improved information services to people by local government or central government with a huge budget. The possibility of applying GIS application component to the computerization of administration business is investigated to prevent local government from investing redundant money and to reuse the existing investment at this point of time. Land civil service application component was developed at the $\ulcorner Development of Open GIS Component S/W \lrcorner$ project which was managed by Ministry of Information and Communication . GIS application component was based on Open GIS OLE/COM specification for development of standard interface and USD(Unified System Development ) for development method and UML (Unified Modeling Language) for system design and Visual C++ for component implementation. Implemented components were Process Control, Map, Print, Statistics component and were verified by using Visual Basic and Delhi. tis study shows that the development of component is very useful at the GIS application development for local governments. But the standard of business and data and system is the essential prerequisite to maximize business application.

• The Journal of the Korea Contents Association
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• v.5 no.2
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• pp.147-155
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• 2005

In this paper, we present a FAES(a Facial Animation with Emotion and Speech) system for speech-driven face animation with emotions. We animate face cartoons not only from input speech, but also based on emotions derived from speech signal. And also our system can ensure smooth transitions and exact representation in animation. To do this, after collecting the training data, we have made the database using SVM(Support Vector Machine) to recognize four different categories of emotions: neutral, dislike, fear and surprise. So that, we can make the system for speech-driven animation with emotions. Also, we trained on Korean young person and focused on only Korean emotional face expressions. Experimental results of our system demonstrate that more emotional areas expanded and the accuracies of the emotional recognition and the continuous speech recognition are respectively increased 7% and 5% more compared with the previous method.

• Journal of the Korea Society for Simulation
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• v.20 no.3
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• pp.89-100
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• 2011

The MAGLEV (Magnetically Levitated Vehicle) system, which is under commercialization as a new transportation system in Korea, is operated by means of unmanned automatic control system. Therefore the plan of train operation should be carefully established and validated in advance. In general, when making the train operation plan, the statistically predicted traffic data is used. However, traffic wave can occur when real train service is operated, and the demand-driven simulation technology is required to review train operation plans and service qualities considering traffic wave. This paper presents a method and model to simulate the MAGLEV's operation considering continuous demand changes. For this purpose, we employed the discrete event model which is suitable for modeling the behavior of railway passenger transportation, and modeled the system hierarchically using DEVS (Discrete Event System Specification) formalism. In addition, through the implementation and experiment using DEVSim++ simulation environment, we tested the feasibility of the proposed model and it is also verified that our demand-driven simulation technology could be used for the prior review of the train operation plans and strategies.

• Journal of Fashion Business
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• v.27 no.4
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• pp.141-162
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• 2023

With the prospect that integrating creative AI in the fashion design field will become more visible, this study considered the case of creative fashion design development through Human-AI Co-creation (HAIC). Methodologically, this research encompasses a literature review and empirical investigations. In the literature review, the fashion design and creative HAIC processes, and the possibilities of integrating AI in fashion design were considered. In the empirical study, based on the case analysis of generating fashion design through HAIC, the HAIC type according to the role and interaction method, and characteristics of humans and AI was considered, and the HAIC process for fashion design was derived. The results of this study are summarized as follows. First, HAIC types in fashion design are divided into four types: AI-driven passive HAIC, human-driven passive HAIC, flexible interaction-based HAIC, and integrated interaction-based value creation HAIC. Second, the stages of the HAIC process for creative fashion design can be broadly divided into semantic data integration, visual ideation, design creation and expansion, design presentation, and design/manufacturing solution and UX platform creation. Third, in fashion design, HAIC contributes to human ability, enhancement of creativity, achievement of efficient workflow, and creation of new values. This research suggests that HAIC has the potential to revolutionize the fashion design industry by facilitating collaboration between humans and AI; consequently, enhancing creativity, and improving the efficiency of the design process. It also offers a framework for understanding the different types of HAIC and the stages involved in the creative fashion design process.

• Journal of Korean Society for Quality Management
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• v.51 no.1
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• pp.67-78
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• 2023

Purpose: The purpose of this study is to investigate the COVID-19 pandemic-driven effect changes of quality factors on customer satisfaction in Korean Police Civil Affairs Service. Methods: This study fitted a regression model to the data collected by Korean National Police Agency from 2019 (before COVID-19 pandemic) to 2020 (during COVID-19 pandemic). In order to simultaneously estimate the effects of major seven quality factors on customer satisfaction for 'before the pandemic' and for 'during the pandemic', the regression model included not only customer satisfaction as the dependent variable, but also the fourteen independent variables consisting of the seven quality factors and their seven interaction terms. The interaction terms were defined by multiplying each quality factor by a dummy variable indicating either before or during the pandemic. Therefore, the coefficient estimates of the interaction terms indicate the changes of their corresponding quality factor effects on customer satisfaction between before and during the pandemic. The double bootstrap method was applied to test the significance of coefficient estimates. Results: Both before and during the pandemic, all quality factors had positive effects on customer satisfaction. However, these effects changed differently from before to during the pandemic: (increased) supportability, sincerity, and convenience; (decreased) integrity, professionalism, and fairness; (unchanged) promptness. Conclusion: This study found that the pandemic caused significant effect changes of quality factors on customer satisfaction in Korean Police Civil Affairs Service. This finding suggests the necessity of carefully monitoring such effect changes to effectively and efficiently improve customer satisfaction. This study also identified that from before to during the pandemic, supportability, sincerity, and convenience become more important and hence, need to be better managed.

• 한국생물공학회:학술대회논문집
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• 2004.07a
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• pp.47-84
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• 2004

Principal Component Analysis나 Partial Least Squares와 같은 다변량 통계 기법은 변수간의 correlation structure로부터 공정의 variance를 설명할 수 있는 latent variable를 얻고 이를 이용하여 공정을 효과적으로 modeling할 수 있는 방법으로 최근 들어 많은 관심을 얻고 있다. 하지만 PLS는 공정이 stationary state에 있다고 가정하기 때문에, 생물학적 공정의 non-stationary and time-varying behavior를 설명하기에 부적절하다. 본 논문에서는 PLS 알고리즘의 혐기성 폐수처리 공정에의 적용에 있어, 이와 같은 문제를 해결하기 위해서 adaptive PLS 알고리즘을 사용함으로써 변화하는 공정의 특성에 대응하여 모델을 update하는 방법을 이용하였다. 하지만 실시간 데이터로부터 adaptive PLS 방법을 적용하는 데에는 많은 어려움이 존재하며, 특히 outlier나 abnormal disturbance에 모델이 부적절하게 adaptation하는 문제가 발생할 수 있다. 따라서 이의 해결을 위해 adaptive PLS를 적용하는데 있어 robustness를 향상시키기 위해 monitoring index를 이용하여 abnormal data에 weight를 주고 안정적인 모델의 update가 가능하게 하는 방법을 제안하였으며, 이를 적용하여 성공적으로 혐기성 폐수처리 공정의 Output을 예측하고 효과적으로 공정을 모니터링할 수 있었다. 만들어진 PLS 모델은 산업폐수를 처리하기 위한 industrial plan에서 측정된 실제 데이터에 적용하여 그 효용성을 입증하였으며, 그 결과는 mechanistic model을 적용하기 힘든 실공정에 비교적 쉽게 implementation할 수 있는 장점이 있다.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.201-203
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• 2007

The paper concerns the successive optimization for structure and parameters of fuzzy inference systems that is based on parallel Genetic Algorithms (PGA) and information data granulation (IG). PGA is multi, population based genetic algorithms, and it is used tu optimize structure and parameters of fuzzy model simultaneously, The granulation is realized with the aid of the C-means clustering. The concept of information granulation was applied to the fuzzy model in order to enhance the abilities of structural optimization. By doing that, we divide the input space to form the premise part of the fuzzy rules and the consequence part of each fuzzy rule is newly' organized based on center points of data group extracted by the C-Means clustering, It concerns the fuzzy model related parameters such as the number of input variables to be used in fuzzy model. a collection of specific subset of input variables, the number of membership functions according to used variables, and the polynomial type of the consequence part of fuzzy rules, The simultaneous optimization mechanism is explored. It can find optimal values related to structure and parameter of fuzzy model via PGA, the C-means clustering and standard least square method at once. A comparative analysis demonstrates that the Dnmosed algorithm is superior to the conventional methods.

• Journal of Korean Institute of Industrial Engineers
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• v.43 no.1
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• pp.62-71
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• 2017

In order to attract foreign tourists, it is important to understand what factors on domestic tour spots are critically considered and how they are evaluated after visit. However, most of the researches on tour business have collected information from tourists through survey on a small number of tourists, which leads to inaccurate and biased conclusion. In this paper, we suggest a data-driven methodology to figure out tourists' satisfaction factors and estimate sentiment scores on them. To do so, we collected review comments data from popular web site. Latent dirichlet allocation is employed to extract key factors and elastic net is used to estimate sentiment scores. Then, an aggregated evaluation score is generated by combining the factors and the sentiment scores per topics. Our proposed method can be used to recommend travel schedules with themes and discover new spots.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5464-5484
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• 2016

Few samples face recognition has become a highly challenging task due to the limitation of available labeled samples. As two popular paradigms in face image representation, sparse component analysis is highly robust while parts-based paradigm is particularly flexible. In this paper, we propose a probabilistic generative model to incorporate the strengths of the two paradigms for face representation. This model finds a common spatial partition for given images and simultaneously learns a sparse component analysis model for each part of the partition. The two procedures are built into a probabilistic generative model. Then we derive the score function (i.e. feature mapping) from the generative score space. A similarity measure is defined over the derived score function for few samples face recognition. This model is driven by data and specifically good at representing face images. The derived generative score function and similarity measure encode information hidden in the data distribution. To validate the effectiveness of the proposed method, we perform few samples face recognition on two face datasets. The results show its advantages.

• Journal of Korean Academy of Nursing Administration
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• v.8 no.3
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• pp.381-392
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• 2002

Purpose : The purpose of this study was to examine the degrees of turnover intention among Korean staff nurses(N=175) and to identify discriminating factors of their turnover intention. Method : The data were driven from a larger study and staff nurses who had worked more than 1 year as nurses were included in the analyses. The original data were collected from May 1999 to March 2000. Descriptive and discriminant analyses were utilized. Results : 87% of the participants reported turnover intention. Nurses were grouped into three group(GP)s depending on the frequencies of turnover intention: Never GP(N=23), Sometimes GP(N=107), Frequent GP(N=43). With three GPs, two functions were produced and only function 1 was significant that significantly discriminated Never and Frequent GPs. Additional discriminant analysis with only Never and Frequent GPs produced function classified 93% of the participants correctly into two GPs. Sub-dimensions of work satisfaction were significant discriminating factors. Nurses who are satisfied with doctor and nurse relationship, pay, and hospital administration tend to report no intention in turnover. Conclusion : Based on the findings of this study, possible managemental intervention for increasing interpersonal skills and assertiveness of nurses, inviting medical residents in ward team meeting, increasing incentives or baseline adjustment of annual income for registered nurses were suggested.