• (The)Study of the Eastern Classic
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• no.54
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• pp.155-182
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• 2014

This study aimed to find the possibility of interactions between the Doctrine of the Mean and evolutionary biology. Between the two disciplines, there exists a huge gap such as "traditional era vs. modern times" and "humanities vs. natural science." However, this paper assumed that an analysis of their similarities and differences would allow us to find the possibility for them to interact and communicate with each other. For this purpose, the author proposed a three-step approach to studies of the following topics: human nature in step 1, validity of reasons to live in step 2 and biologically affinitive relations in step 3. The present study in step 1 pays attention to the similarities and differences between genes and in-ui-ye-ji (a set of four Confucian values: benevolence, righteousness, propriety and wisdom). This step discusses the issues of ri (principle) and ki (generative force) in Zhu Xi's theory vs. genes and vehicles in evolutionary biology, innate goodness vs. altruism of genes and in-ui-ye-ji vs. epigenetic rules. In step 2, attention is paid to the similarities and differences between natural selection and shi zhong (時中). They are discussed in terms of the upset of the law of nature vs. mutation, changes vs. evolutions and shi zhong vs. natural selection/adaptation. Step 3 focuses on the similarities and differences between species diversity and li-yi-fen-shu (one li and its many aspects). The discussion in this step addresses the issues of part or whole vs. li-yi-fen-shu, biological affinity vs. single energy and ecosystem vs. "the earth moves orderly, and everything thereon flourishes." If these studies are conducted as planned, a new direction can be set for Zhu Xi's neo-Confucianism. Further, the interaction between humanities and natural science will pave the way for us to overcome asymmetry between different disciplines.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.608-613
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• 2007

Prediction of corporate bankruptcies has long been an important topic and has been studied extensively in the finance and management literature because it is an essential basis for the risk management of financial institutions. Recently, support vector machines (SVMs) are becoming popular as a tool for bankruptcy prediction because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. In addition, they don't require huge training samples and have little possibility of overfitting. However. in order to Use SVM, a user should determine several factors such as the parameters ofa kernel function, appropriate feature subset, and proper instance subset by heuristics, which hinders accurate prediction results when using SVM In this study, we propose a novel hybrid SVM classifier with simultaneous optimization of feature subsets, instance subsets, and kernel parameters. This study introduces genetic algorithms (GAs) to optimize the feature selection, instance selection, and kernel parameters simultaneously. Our study applies the proposed model to the real-world case for bankruptcy prediction. Experimental results show that the prediction accuracy of conventional SVM may be improved significantly by using our model.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.73-76
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• 2007

Photodynamic diagnosis(PDD) is a method to diagnose the possibility of cancer, both by the principle that if a photosensitizer is injected into an organic tissue, it is accumulated in the tissue of a malignant tumor selectively after a specific period, and by a comparison of the intensity of the fluorescence of normal tissue with abnormal tissue after investigating the excitation light of a tissue with accumulated photosensitizer. Since the selection of the wavelength band of excitation light has an interrelation with fluorescence generation according to the selection of a photosencitizer, it plays an important role in POD. This study aims at designing and evaluating light source devices that can stably generate light with various kinds of wavelengths In order to make possible PDD using a photosensitizer and diagnosis using auto-fluorescence. The light source device was a Xenon lamp and filter wheel, composed of an optical output control through Iris and filters with several wavelength bands It also makes the inducement of auto-fluorescence possible because it is designed to generate a wavelength band of 380-400. The transmission part of the light source was, developed to enhance the efficiency of light transmission. To evaluate this light source device, the characteristics of the light output and wavelength band were verified. To validate the capability of this device as PDD the detection of auto-fluorescence using mouse was performed.

• International Journal of Contents
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• v.8 no.1
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• pp.23-29
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• 2012

Multiple expression levels of genes obtained using time series microarray experiments have been exploited effectively to enhance understanding of a wide range of biological phenomena. However, the unique nature of microarray data is usually in the form of large matrices of expression genes with high dimensions. Among the huge number of genes presented in microarrays, only a small number of genes are expected to be effective for performing a certain task. Hence, discounting the majority of unaffected genes is the crucial goal of gene selection to improve accuracy for disease diagnosis. In this paper, a non-Gaussian weight matrix obtained from an incremental model is proposed to extract useful features of multivariate time series microarrays. The proposed method can automatically identify a small number of significant features via discovering hidden variables from a huge number of features. An unsupervised hierarchical clustering representative is then taken to evaluate the effectiveness of the proposed methodology. The proposed method achieves promising results based on predictive accuracy of clustering compared to existing methods of analysis. Furthermore, the proposed method offers a robust approach with low memory and computation costs.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.93-107
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• 2020

Recently, artificial intelligence (AI) technologies has been widely used in various fields such as finance, and distribution. Accordingly, Korea has also announced its AI R&D strategy for the realization of i-Korea 4.0 in May 2018. However, Korea's AI technology is inferior to major competitors such as the US, Canada, and Japan Therefore, in order to cope with the 4th industrial revolution, it is necessary to allocate AI R&D budgets efficiently through selection and concentration so as to gain competitive advantage under a limited budget. In this study, the importance of each AI technology was evaluated in multi-dimensional way through the questionnaire of expert group using the evaluation index derived from the literature review From the results of this study, we draw the following implication. In order to successfully establish the AI technology development strategies, it is necessary to prioritize the cognitive computing technology that has great market growth potential, ripple effect of technology development, and the urgency of technology development according to the principle of selection and concentration. To this end, it is necessary to find creative ideas, manage assessments, converge multidisciplinary systems and strengthen core competencies. In addition, since AI technology has a large impact on socioeconomic development, it is necessary to comprehensively grasp and manage scientific and technological regulations in order to systematically promote AI technology development.

• Korean Journal of Head & Neck Oncology
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• v.19 no.2
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• pp.137-141
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• 2003

Introduction: A routine superficial parotidectomy with facial nerve dissection in parotid tumor surgery often results in facial dysfunction, Frey syndrome and defect in operation site. Formal facial nerve dissection has been a recommended procedure, because pleomorphic adenoma is a commonly recurrent tumor in case of inadequate surgical management, however it can not be always reasonable in aspect of postoperative sequelae. Patients and Methods: Through retrospective review of 245 cases parotidectomies and follow up for more than three years, clinicophathologic factors influencing to the selection of surgical procedure were considered to be age, sex, and preoperative pathology confirmed by preoperative MRI and FNA. Results: Five categories were established as follow for surgical decision in parotid tumor surgery. Category 1. Superficial lobe adenoma -- Superficial parotidectomy -- 124 Category 2. Deep lobe adenoma -- Deep parotidectomy -- 39 Category 3. Non pleomorphic adenoma -- Tumorectomy 1.5cm adenoma in young female -- Tumorectomy -- 25 Category 4. Recurrent multicentric tumor -- Parotidectomy+RT -- 9 Category 5. Parotid cancer; Parotidectomy + UND (RND) + RT -- 48 ; CORE (Composite Regional Ear Resection) -- 2 Conclusion: Surgical morbidity and recurrence rate could be minimized by individualizing the surgical procedure according to the category principle based on the clincopathologic features.

• Current Optics and Photonics
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• v.1 no.4
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• pp.402-411
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• 2017

Combined LIBS-Raman spectroscopy has been widely studied, due to its complementary capabilities as an elemental analyzer that can acquire signals of atoms, ions, and molecules. In this study, the classification of polymorphs was performed by laser-induced breakdown spectroscopy (LIBS) to overcome the limitation in molecular analysis; the results were verified by Raman spectroscopy. LIBS signals of the $CaCO_3$ polymorphs calcite and aragonite, and $CaSO_4{\cdot}2H_2O$ (gypsum) and $CaSO_4$ (anhydrite), were acquired using a Nd:YAG laser (532 nm, 6 ns). While the molecular study was performed using Raman spectroscopy, LIBS could also provide sufficient key data for classifying samples containing different molecular densities and structures, using the peculiar signal ratio of $5s{\rightarrow}4p$ for the orbital transition of two polymorphs that contain Ca. The basic principle was analyzed by electronic motion in plasma and electronic transition in atoms or ions. The key factors for the classification of polymorphs were the different electron quantities in the unit-cell volume of each sample, and the selection rule in electric-dipole transitions. The present work has extended the capabilities of LIBS in molecular analysis, as well as in atomic and ionic analysis.

• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.143-154
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• 2017

A variable selection method based on probabilistic principal component analysis (PCA) using penalized likelihood method is proposed. The proposed method is a two-step variable reduction method. The first step is based on the probabilistic principal component idea to identify principle components. The penalty function is used to identify important variables in each component. We then build a model on the original data space instead of building on the rotated data space through latent variables (principal components) because the proposed method achieves the goal of dimension reduction through identifying important observed variables. Consequently, the proposed method is of more practical use. The proposed estimators perform as the oracle procedure and are root-n consistent with a proper choice of regularization parameters. The proposed method can be successfully applied to high-dimensional PCA problems with a relatively large portion of irrelevant variables included in the data set. It is straightforward to extend our likelihood method in handling problems with missing observations using EM algorithms. Further, it could be effectively applied in cases where some data vectors exhibit one or more missing values at random.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2B
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• pp.107-116
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• 2006

In this paper, we propose a Sensing Resolution-based Grouping(SRG) protocol for wireless sensor networks. SRG is intended for meeting the application's sensing objectives, where sensor nodes are densely deployed and have the determinate accuracy requirement. The primary contribution of this paper is active group header node selection and round-robin procedure, which increase the sensing accuracy and evenly distribute the node energy consumption. The second contribution is use of energy efficient intermediate node selection by considering group size and energy consumption. We present the design principle of SRG and provide simulation results.

• Korean Journal of Acupuncture
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• v.37 no.3
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• pp.203-208
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• 2020

Objectives : Since there are complex associations between diseases/symptoms and acupoints, one-to-one correspondence may not be the proper approach. Pattern identification has been being used as a clinical framework to make treatment decisions by extracting and synthesizing clinical data including patients' signs and symptoms. In this article, we propose two different models explaining the relationships between diseases and acupoints based on the branch treatment [Zhibiaofa] and the root treatment [Zhibenfa]. Methods : We explained the relationships between diseases/symptoms and acupoints from the example data from our previous study on traditional acupuncture point selection patterns for pain control. Diseases include low back pain, migraine, irritable bowel syndrome, osteoarthritis, ankle sprain, carpal tunnel syndrome, and dysmenorrhea, and acupoints included LI4, BL23, BL25, SP6, BL60, TE5, and CV4. Results : The relationships between diseases/symptoms and acupoints can be explained directly based on the branch treatment, and also can be explained indirectly through pattern identification based on the root treatment. Pattern identifications included both meridian-based pattern identification based on the spatial information of diseases and visceral organ-based pattern identification based on the characteristics of diseases. Conclusions : In the East Asian traditional medicine, Korean medicine doctors choose the most appropriate acupoints based either on the diseases/symptoms (i.e., branch treatment) or on the results of pattern identifications (i.e., root treatment). It is necessary to understand the two different approaches to choose specific acupoints for the targeted diseases.