• Proceedings of the KOSOMBE Conference
• /
• v.1998 no.11
• /
• pp.257-258
• /
• 1998

To reveal the interconnected brain regions involved in human working memory, their functional connectivity was analyzed using principal component analysis (PCA). rCBF PET scans were peformed on 5 normal volunteers during the verbal and visual working memory tasks and PCA was applied. PCA produced the first principal components related with the increase of the difficulty and the second one which demonstrate the dissociation of verbal and visual memory system.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.6
• /
• pp.2388-2398
• /
• 2017

In this study, we propose a robust face recognition system to pose variations based on automatic pose estimation. Radial basis function neural network is applied as one of the functional components of the overall face recognition system. The proposed system consists of preprocessing and recognition modules to provide a solution to pose variation and high-dimensional pattern recognition problems. In the preprocessing part, principal component analysis (PCA) and 2-dimensional 2-directional PCA ($(2D)^2$ PCA) are applied. These functional modules are useful in reducing dimensionality of the feature space. The proposed RBFNNs architecture consists of three functional modules such as condition, conclusion and inference phase realized in terms of fuzzy "if-then" rules. In the condition phase of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by the Fuzzy C-Means (FCM) algorithm. In conclusion phase of rules, the connections (weights) are realized through four types of polynomials such as constant, linear, quadratic and modified quadratic. The coefficients of the RBFNNs model are obtained by fuzzy inference method constituting the inference phase of fuzzy rules. The essential design parameters (such as the number of nodes, and fuzzification coefficient) of the networks are optimized with the aid of Particle Swarm Optimization (PSO). Experimental results completed on standard face database -Honda/UCSD, Cambridge Head pose, and IC&CI databases demonstrate the effectiveness and efficiency of face recognition system compared with other studies.

• International Journal of Computer Science & Network Security
• /
• v.21 no.3
• /
• pp.31-36
• /
• 2021

RNA sequencing (RNA-Seq) is a technology that facilitates transcriptome analysis using next-generation sequencing (NSG) tools. Information on the quantity and sequences of RNA is vital to relate our genomes to functional protein expression. RNA-Seq data are characterized as being high-dimensional in that the number of variables (i.e., transcripts) far exceeds the number of observations (e.g., experiments). Given the wide range of dimensionality reduction techniques, it is not clear which is best for RNA-Seq data analysis. In this paper, we study the effect of three dimensionality reduction techniques to improve the classification of the RNA-Seq dataset. In particular, we use PCA, SVD, and SOM to obtain a reduced feature space. We built nine classification models for a cancer dataset and compared their performance. Our experimental results indicate that better classification performance is obtained with PCA and SOM. Overall, the combinations PCA+KNN, SOM+RF, and SOM+KNN produce preferred results.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.1
• /
• pp.225-231
• /
• 2013

In this study, we propose the design of optimized pRBFNNs-based night vision face recognition system using PCA algorithm. It is difficalt to obtain images using CCD camera due to low brightness under surround condition without lighting. The quality of the images distorted by low illuminance is improved by using night vision camera and histogram equalization. Ada-Boost algorithm also is used for the detection of face image between face and non-face image area. The dimension of the obtained image data is reduced to low dimension using PCA method. Also we introduce the pRBFNNs as recognition module. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned by using Fuzzy C-Means clustering. In the conclusion part of rules, the connection weights of pRBFNNs is represented as three kinds of polynomials such as linear, quadratic, and modified quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution.

• Journal of Korean Society of Rural Planning
• /
• v.1 no.2
• /
• pp.33-42
• /
• 1995

For formulation of the rational land us2 plan in regional base, it is a basic and prior condition to categorize total planning area into some functional subregions by purposely-selected indicators. As one of quantitive approaches to the areal categorization in rural area, Principal Component Analysis(PCA) was introduced and testified its applicability through a case study on Sunheungdistrict(called as myun in Korea) area, Youngpoong-county, Kyungbuk-province, Korea. Areal analysis by PCA was carried out on rurality and urbanity of parish-level area(ri in Korea) respectively. By use of PCA analysis results, classifying matrix was made through categorization of both index scores. Among 18 ri's of the case study area, 12 was classified as rural-dominated areas, 2 as urban- dominated areas, and reamaining 3 as intermediate areas.

• Korean Journal of Plant Resources
• /
• v.27 no.4
• /
• pp.271-278
• /
• 2014

Rubus coreanus has been used as a traditional medicine in Asia because of its various pharmacological properties. This study examined the effects of protocatechuic acid (PCA), one of phenolic compounds derived from R. coreanus on the lipid metabolism in high cholesterol diet-induced mice. A total of 30 male C57BL/6 mice were divided into 5 groups with 6 mice in each group as follows: (1) Control mice received normal diet (ND). (2) Mice received high-cholesterol diet (HCD) plus water, 10% sucrose solution and treated daily oral phosphate-buffered-saline (PBS) of equal volumes through gavage. (3) Mice received HCD and treated daily with 25 mg/kg b.w./day of PCA (4) with 50 mg/kg b.w./day or (5) with 10 mg/kg b.w./day of simvastatin via oral gavage for 12 weeks. Body weights were measured weekly for a period of experiment. After treatment, liver, thymus, spleen and kidney were harvested and weighed, and the lipid metabolite profiles (total cholesterol, triglyceride (TG), HDL-cholesterol (HDL-c), LDL-cholesterol (LDL-c) and liver-damaging markers (GOT and GPT) in serum were examined. PCA significantly reduced the total cholesterol, TG, LDL-c level and increased the HDL-c level. PCA administration also significantly reduced the levels of GOT and GPT. These results indicate that the PCA could be used as a functional material for lowering lipid and an adjuvant for the treatment of hyperlipemia.

• Journal of Korean Neurosurgical Society
• /
• v.49 no.2
• /
• pp.92-96
• /
• 2011

Objective : We retrospectively evaluated the prognostic factors that can influence long-term survival in patients who suffered acute large cerebral infarction. Methods : Between June 2003 and October 2008, a total of 178 patients were diagnosed with a large cerebral infarction, and, among them, 122 patients were alive one month after the onset of stroke. We investigated the multiple factors that might have influenced the life expectancies of these 122 patients. Results : The mean age of the patients was $70{\pm}13.4$ years and the mean survival was $41.7{\pm}2.8$ months. The mean survival of the poor functional outcome group ($mRS{\geq}4$) was $33.9{\pm}3.3$ months, whereas that of the good functional outcome group ($mRS{\leq}3$) was $58.6{\pm}2.6$ months (p value=0.000). The mean survival of the older patients (270 years) was $29.7{\pm}3.4$ months, whereas that of the younger patients (<70 years) was much better as $58.9{\pm}3.2$ months (p value=0.000). Involvement of ACA or PCA territory in MCA infarction is also a poor prognostic factor (p value=0.021). But, other factors that are also known as significant predictors of poor survival (male gender, hypertension, heart failure, atrial fibrillation, diabetes mellitus, a previous history of stroke, smoking, and dyslipidemia) did not significantly influence the mean survival time in the current study. Conclusion : Age (older versus younger than 70 years old) and functional outcome at one month could be critical prognostic factors for survival after acute large cerebral infarction. Involvement of ACA or PCA territory is also an important poor prognostic factor in patients with MCA territorial infarction.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.5
• /
• pp.744-752
• /
• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.25 no.3
• /
• pp.115-122
• /
• 2017

To achieve designed quality and reliability from R&D to force integration, an establishment of precise and distinct specifications and standards are required. However, the recent process of R&D standardization on defense acquisition system, has brought plenty of corrections on specifications and standards that may cause not only difficulties to production line and retardation to the military forces, but also a degradation of provided weapon systems. Therefore, a technical review should be performed by the developer, the producer, and the client, establishing the standard that include mass production requirements as well as clients' requirements. This paper propose a specified solution on process of R&D standardization, by applying a substantial FCA(Functional Configuration Audit) and PCA(Physical Configuration Audit) which implies participation of related agencies. By the improved PCA, 2,023 corrections on 74 types of QAR(Quality Assurance Requirement)s and 12,715 corrections on drawings are identified.

• Preventive Nutrition and Food Science
• /
• v.17 no.3
• /
• pp.184-191
• /
• 2012

The purpose of this study was to investigate the fatty acid profiles in 18 soybean cultivars grown in Korea. A total of eleven fatty acids were identified in the sample set, which was comprised of myristic (C14:0), palmitic (C16:0), palmitoleic (C16:1, ${\omega}7$), stearic (C18:0), oleic (C18:1, ${\omega}9$), linoleic (C18:2, ${\omega}6$), linolenic (C18:3, ${\omega}3$), arachidic (C20:0), gondoic (C20:1, ${\omega}9$), behenic (C22:0), and lignoceric (C24:0) acids by gas-liquid chromatography with flame ionization detector (GC-FID). Based on their color, yellow-, black-, brown-, and green-colored cultivars were denoted. Correlation coefficients (r) between the nine major fatty acids identified (two trace fatty acids, myristic and palmitoleic, were not included in the study) were generated and revealed an inverse association between oleic and linoleic acids (r=-0.94, p<0.05), while stearic acid was positively correlated to arachidic acid (r=0.72, p<0.05). Principal component analysis (PCA) of the fatty acid data yielded four significant principal components (PCs; i.e., eigenvalues>1), which together account for 81.49% of the total variance in the data set; with PC1 contributing 28.16% of the total. Eigen analysis of the correlation matrix loadings of the four significant PCs revealed that PC1 was mainly contributed to by oleic, linoleic, and gondoic acids, PC2 by stearic, linolenic and arachidic acids, PC3 by behenic and lignoceric acids, and PC4 by palmitic acid. The score plots generated between PC1-PC2 and PC3-PC4 segregated soybean cultivars based on fatty acid composition.