• Proceedings of the Korean Statistical Society Conference
• /
• 2004.11a
• /
• pp.67-72
• /
• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval linear and nonlinear regression models combining the possibility and necessity estimation formulation with the principle of SVM. For data sets with crisp inputs and interval outputs, the possibility and necessity models have been recently utilized, which are based on quadratic programming approach giving more diverse spread coefficients than a linear programming one. SVM also uses quadratic programming approach whose another advantage in interval regression analysis is to be able to integrate both the property of central tendency in least squares and the possibilistic property In fuzzy regression. However this is not a computationally expensive way. SVM allows us to perform interval nonlinear regression analysis by constructing an interval linear regression function in a high dimensional feature space. In particular, SVM is a very attractive approach to model nonlinear interval data. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function for interval nonlinear regression model with crisp inputs and interval output. Experimental results are then presented which indicate the performance of this algorithm.

• Journal of Christian Education in Korea
• /
• v.66
• /
• pp.11-48
• /
• 2021

This paper is a thesis that explored the necessity and possibility of spiritual teaching that forms the inner side of human beings in the age of AI where objective knowledge is prevalent, focusing on "contemplative pedagogy". For this it first examined the characteristics of objective epistemology of AI and the direction of school education in the AI and explored the necessity and character of spirituality and spiritual teaching as a request for the AI era, and also explores the possibility of realization of spiritual teaching in general school setting through contemplative pedagogy, which actually puts this into practice. As a result of the study, it found that spiritual teaching is not exclusive to a specific area such as religious studies or theology, but is a teaching that should be embodied in all schools and educational fields in today's era where third person knowledge is widespread. It also found that in addition to contemplative teaching, various spiritual teaching models need to be developed and put into practice for this purpose.

• Journal of Physiology & Pathology in Korean Medicine
• /
• v.38 no.2
• /
• pp.49-58
• /
• 2024

The rapid advancement of generative large language models has revolutionized various real-life domains, emphasizing the importance of exploring their applications in healthcare. This study aims to examine how generative large language models are implemented in the medical domain, with the specific objective of searching for the possibility and potential of integration between generative large language models and East Asian medicine. Through a comprehensive current state analysis, we identified limitations in the deployment of generative large language models within East Asian medicine and proposed directions for future research. Our findings highlight the essential need for accumulating and generating structured data to improve the capabilities of generative large language models in East Asian medicine. Additionally, we tackle the issue of hallucination and the necessity for a robust model evaluation framework. Despite these challenges, the application of generative large language models in East Asian medicine has demonstrated promising results. Techniques such as model augmentation, multimodal structures, and knowledge distillation have the potential to significantly enhance accuracy, efficiency, and accessibility. In conclusion, we expect generative large language models to play a pivotal role in facilitating precise diagnostics, personalized treatment in clinical fields, and fostering innovation in education and research within East Asian medicine.

• Fashion & Textile Research Journal
• /
• v.10 no.3
• /
• pp.364-370
• /
• 2008

This research aims to recognize necessity of modern expression of traditional pattern, understand the matter which is expressed when developing design using traditional pattern, and find the solutions. As the solutions, this research presents design of traditional pattern using symmetry concept and works on the possibility of symmetry as the pattern design. So this research carried out in-depth interview to textile designer working at Gyeongsangnam-do, analyzed the substances. The main results are as follows. First, the presented problems of developing common pattern design and designing using traditional pattern show the necessities for adequate harmony among conception of creative idea, traditional pattern and present pattern. As the solution, the efficient design principles are required. Second, the seven traditional figures can present design applying symmetry, also draw the 8 mapping models for the visuality and utilization. Third, the symmetry-applied traditional pattern design makes it possible for the traditional figure to be represented with the creative and modern sense and provides easier way to the design development by complementing the pattern design formation. As the result, symmetry-utilized traditional pattern design improvement shows the expectation that it will increase the design development ability and ease the figure drawing. In addition, the pattern development which can be applied to any figure presents the time efficiency as well as possibility of the high added value textile industry.

• 한국데이터정보과학회:학술대회논문집
• /
• 2004.04a
• /
• pp.91-101
• /
• 2004

The turbulent flow is of fundamental interest because the conservation equations for thermodynamics, mass and momentum are linked together. This turbulent flow consists of some coherent time- and space-organized vortical structures. Research has already shown that some dynamic systems and experimental models still cannot provide a good nonlinear analysis of turbulent time series. In the real turbulent flow, very complicated nonlinear behaviors, which are affected by many vague factors are present. In this paper, a kernel-based machine for fuzzy nonlinear regression analysis is proposed to predict the nonlinear time series of turbulent flows. In order to show the practicality and usefulness of this model, we present an example of predicting the near-wall turbulence time series as a verifiable model and compare with fuzzy piecewise regression. The results of practical applications show that the proposed method is appropriate and appears to be useful in nonlinear analysis and in fuzzy environments to predict the turbulence time series.

• Proceedings of the KIEE Conference
• /
• 1992.07b
• /
• pp.925-927
• /
• 1992

Since that the portion of failures caused by mis-installation of accessories is about 50% of whole failures in power transmission lines, the necessity of assembly type joint boxes, which have excellent installation capability and which can be electrically tested before installation, is gradually increasing. In this paper, we presented the results from the study from the point of design and the results of experiments using models. With the results of study, we could make it clear that the possibility of practical use of assembly type joint boxes instead of conventional molding type joint boxes is very high.

• Journal of Gifted/Talented Education
• /
• v.19 no.1
• /
• pp.119-140
• /
• 2009

This paper examined its possibility and made its new definition by finding relevant bases in order to make a close inquiry into its Identity and direction at this point when cyber-based gifted education academy is established and operated again by its necessity And 4 models which can be used in special education for the gifted were developed making a link with special education for the gifted by collecting and re-classifying cyber educational methods developed by basic research as priority of the educational method which is considered to be the most urgent issue in practical cyber learning. It is a project-type cooperation education model, an information collection-type research education model, a community-type discussion education model, and a problem focus-type e-PBL education model. To apply developed leaching-learning models to reality, students at gifted education academy in Gyeonggi Cyber Gifted Province were imputed models in different ways respectively for 4 months. As a result of analysis and statistical data of activity level and satisfaction level of students who participated in learning activity, it appeared that high level of satisfaction and active activity level were induced compared to the previous method based on tasks. It is expected that this paper will provide the bases when each cyber-based gifted education academy plans operation plan later on, and it will provide proper methods when cyber guidance teachers plan class activities.

• The Journal of the Korea Contents Association
• /
• v.9 no.8
• /
• pp.309-323
• /
• 2009

Business simulation learning is a business education method of the software applications that applied for the university's management training. Its interest is increasing as management training tools to simulate the virtual enterprise and to learn the business management knowledge, management skills, experience and problem-solving. This research was done for the feasibility analysis and management education model design of the business simulation learning. As a prior research to the introduction and operation of the university's business simulation learning curriculum, the need and literature research on the possibility recognition of classes for college students and corporate executives, a demand survey research and analysis was done and discussed the necessity and possibility of the business simulation learning for the industry and universities education area. In addition, we designed a curriculum of the business simulation learning and presented some of education models of management for the university's management training purpose by using the results of the survey research and literature analysis.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.9
• /
• pp.518-527
• /
• 2020

The establishment of a green remodeling strategy is focused on technology, so the necessity of establishing a customized strategy considering the field situation has emerged. This paper examined the technology strategy through sensitivity analysis as a methodology for guiding strategy. For a 90-square-meter detached house, nine models of the construction standards of pre-1980s, 1984, and 2010 in Seoul, Daejeon, and Busan were assessed using the optimization method that combines the energy plus engine and the ModeFrontier. Sensitivity analysis was performed, and the remodeling strategy priority was derived. For pre-1980 models, the strategy for enhancing the roof insulation performance had a significant priority. The SHGC values of the windows were found to have the next highest priority regardless of the region and the time of completion, showing that the performance standard, including the SHGC, needs to be expanded. The possibility of remodeling while maintaining the existing geometry was confirmed because the adjustment of the window wall ratio accompanying large-scale demolition works has low priority. The priorities of technology strategies in each case showed very different patterns, suggesting the possibility of establishing a remodeling strategy by a comprehensive evaluation along with economics and constructability analysis.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_1
• /
• pp.999-1009
• /
• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.