• Korean Journal of Applied Biomechanics
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• v.25 no.2
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• pp.157-165
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• 2015

Object : The goals of this research were to make Performance Enhanced Model(PE) taken the largest performance index (PI) through artificial variation of principle components calculated by principle component analysis for trial data, and to verify the effect through comparing kinematic factors between trial data (Raw) and PE. Method : Ten subjects (5 men, 5 women) were recruited and 80% of their maximal record was considered. The PI is a regression equation. In order to develop PE, we extracted Principle components from trial position data (by Principle Components Analysis (PCA)). Before PCA, we made 17 position data to 3 row matrix according to components. We calculated 3 eigen value (principle components) through PCA. And except Y (medial-lateral direction) component (because motion of Y component is small), principle components of X (anterior-posterior direction) and Z (vertical direction) components were changed as following. Changed principle components = principle components + principle components ${\times}$ k. After changing the each principle component, we reconstructed position data using the changed principle components and calculated performance index (PI). A Paired t-test was used to compare Raw data and Performance Enhanced Model data. The level of statistical significance was set at $p{\leq}0.05$. Result : The PI was significantly increased about 12.9kg at PE ($101.92{\pm}6.25$) when compared to the Raw data ($91.29{\pm}7.10$). It means that performance can be increased by optimizing 3D positions. The difference of kinematic factors as follows : the movement distance of the bar from start to lock out was significantly larger (about 1cm) for PE, the width of anterior-posterior bar position in full phase was significantly wider (about 1.3cm) for PE and the horizontal displacement toward the weightlifter after beginning of descent from maximal height was significantly greater (about 0.4cm) for PE. Additionally, the minimum knee angle in the 2-pull phase was significantly smaller (approximately 2.7cm) for the PE compared to that of the Raw. PE was decided at proximal position from the Raw (origin point (0,0)) of PC variation). Conclusion : PI was decided at proximal position from the Raw (origin point (0,0)) of PC variation). This means that Performance Enhanced Model was decided by similar motion to the Raw without a great change. Therefore, weightlifters could be accept Performance Enhanced Model easily, comfortably and without large stress. The Performance Enhance Model can provide training direction for athletes to improve their weightlifting records.

• Journal of Korean Society of Environmental Engineers
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• v.32 no.2
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• pp.113-120
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• 2010

Air-cathode microbial fuel cell consisted of 4 unit cells were operated under batch condition and electricity generation and microbial community structure variation were investigated, depending on separator types and cathode characteristics: A) PEM(Proton Exchange Membrane)-30% Wet proofing Carbon Cloth(WC), B) AEM(Anion Exchange Membrane-WC, C) CEM(Cation Exchange Membrane)-WC, D) PEM-No Wet proofing Carbon Cloth(NC). Maximum power densities of PEM-WC, AEM-WC and CEM-WC were 510.9, 522.1 and 504.8 $mW/m^2$, respectively. But PEM-NC showed relatively lower maximum power density of 218.3 $mW/m^2$. And PEM-WC, AEM-WC and CEM-WC showed similar internal resistances(20.0-28.2 ${\Omega}$). PCRDGGE, PCA and diversity indices showed that uncultured bacteria which reported in previous MFC studies were detected in suspended growth bacteria and attached growth bacteria would be affected not by separator type but by cathode characteristic. Thus, cathode characteristic can be one of the critical factors for power generation in air-cathode MFC using PEM, AEM, and CEM as separator.

• Journal of Microbiology and Biotechnology
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• v.29 no.1
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• pp.66-78
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• 2019

In this study, strain KNU17Pc1 was tested for its antifungal activity against Rhizoctonia solani AG-1(IA), which causes banded leaf and sheath blight (BLSB) of maize. KNU17Pc1 was tested further for its broad-spectrum antifungal activity and in vitro plant growth promoting (PGP) traits. In addition, the in vivo effects of KNU17Pc1 on reduction of BLSB severity and seedling growth promotion of two maize cultivars under greenhouse conditions were investigated. On the basis of multilocus sequence analysis (MLSA), KNU17Pc1 was confirmed as P. chlororaphis subsp. aurantiaca. The study revealed that KNU17Pc1 had strong in vitro antifungal activity and was effective toward all in vitro PGP traits except phosphate solubilization. In this study, for the first time, a strain of P. chlororaphis against Colletotrichum dematium, Colletotrichum gloeosporioides, Fusarium oxysporum f.sp. melonis, Fusarium subglutinans and Stemphylium lycopersici has been reported. Further biochemical studies showed that KNU17Pc1 was able to produce both types of phenazine derivatives, PCA and 2-OH-PCA. In addition, solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) analysis identified 13 volatile organic compounds (VOCs) in the TSB culture of KNU17Pc1, 1-undecene being the most abundant volatile. Moreover, for the first time, Octamethylcyclotetrasiloxan (D4), dimethyl disulfide, 2-methyl-1,3-butadiene and 1-undecene were detected in P. chlororaphis. Furthermore, this study reported for the first time the effectiveness of P. chlororaphis to control BLSB of maize. Hence, further studies are necessary to test the effectiveness of KNU17Pc1 under different environmental conditions so that it can be exploited further for biocontrol and plant growth promotion.

• The Journal of Bigdata
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• v.6 no.2
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• pp.29-38
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• 2021

Deviation of route in aviation safety management is a dangerous factor that can lead to serious accidents. In this study, the anomaly score is calculated by classifying the tracks through clustering and calculating the distance from the cluster center. The study was conducted by extracting tracks within 100 km of the airport from the ADS-B track data received for one year. The wake was vectorized using linear interpolation. Latitude, longitude, and altitude 3D coordinates were used. Through PCA, the dimension was reduced to an axis representing more than 90% of the overall data distribution, and k-means clustering, hierarchical clustering, and PAM techniques were applied. The number of clusters was selected using the silhouette measure, and an abnormality score was calculated by calculating the distance from the cluster center. In this study, we compare the number of clusters for each cluster technique, and evaluate the clustering result through the silhouette measure.

• Annual Conference of KIPS
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• 2011.11a
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• pp.384-387
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• 2011

본 논문에서 제안된 모델은 기존의 얼굴 인식 및 지명수배자 얼굴 인식 시스템보다 효과적인 인식률 향상을 위해 전역적 특징을 사용하는 PCA(Principal Component Analysis) 알고리즘과 지역적 특징을 사용하는 2D-HMM(Hidden Markov Model) 알고리즘을 결합한 지명수배자 얼굴인식 시스템이다. 입력된 영상을 전역적 얼굴인식 알고리즘을 통해 얼굴 탐지 및 인식을 수행하고, 탐지 및 인식에 실패한 영상은 지역적 얼굴인식 알고리즘을 통해 2차 인식 과정을 수행한다. 실험과 분석을 통해 제안된 방법을 효율성을 증명하였다.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.2053-2058
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• 2013

Background: Formation of new blood vessels is necessary for the development and spread of neoplasms more than 1 mm3 in volume, angiogenesis being responsible for formation of new from pre-existing blood vessels. Vascular endothelial growth factor (VEGF) is pivotal and the best studied angiogenic factor in all human cancers. Therefore we designed this study to investigate the role of VEGF-A and VEGF-C in prostate cancer in comparison with BPH controls in a north Indian population. Methods: In this case-control study a total of 100 subjects were included on the basis of confirmed histopathological reports, out of which 50 were prostate cancer patients and the other 50 were BPH patients with PSA levels >2 ng/ml and abnormal digital rectal examination (DRE) findings during September 2009 to August 2011 from the Department of Urology, KGMU, Lucknow, India. Plasma levels of VEGF were determined using quantitative immunoassay (ELISA-enzyme linked immunosorbent assay). Statistical analysis was carried out using SPSS 15.0 version. Results: The mean age of prostate cancer ($67.6{\pm}5.72$) patients was significantly higher (p=0.005) than BPH ($63.6{\pm}7.92$) patients. Expression of VEGF-A was not significantly higher in disease stage C1 than D1 or D2 and A or B (p=0.13) while the level of VEGF-A was significantly higher (p=0.04) in prostate cancer as compared to BPH subjects (PCa=13.0 pg/ml, BPH=6.8 pg/ml). Levels of VEGF-C were similar in both groups (PCa=832.6 pg/ml, BPH=823.7 pg/ml). In ROC curve, the area under curve (AUC) was 0.70 (95%CI: 0.60-0.80) and the cut-off value for which a higher proportion of patients was correctly classified (20%) was 26.0 pg/mL. Conclusion: Although VEGF-A is increased in cancer prostate patients a statistically significant correlation could not be established in this study. VEGF-C was not found to be a useful biomarker.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.3
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• pp.173-178
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• 2024

This study presents a technique for assessing the dimensional quality of assembly parts in Prefabricated Steel Structures (PSS) using a stereo vision sensor. The stereo vision system captures images and point cloud data of the assembly area, followed by applying image processing algorithms such as fuzzy-based edge detection and Hough transform-based circular bolt hole detection to identify bolt hole locations. The 3D center positions of each bolt hole are determined by correlating 3D real-world position information from depth images with the extracted bolt hole positions. Principal Component Analysis (PCA) is then employed to calculate coordinate axes for precise measurement of distances between bolt holes, even when the sensor and structure orientations differ. Bolt holes are sorted based on their 2D positions, and the distances between sorted bolt holes are calculated to assess the assembly part's dimensional quality. Comparison with actual drawing data confirms measurement accuracy with an absolute error of 1mm and a relative error within 4% based on median criteria.

• The Korean Journal of Quaternary Research
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• v.17 no.2
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• pp.157-159
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• 2003

Much of northern Asia is lacking in high-resolution palaeoclimatic data coverage. This vast region thus represents a sizeable gap in data sets used to reconstruct hemispheric-scale temperature trends for the past millennium. To improve coverage, we present a regional-scale composite of four tree-ring width records of Siberian pine and Siberian larch from temperature-sensitive alpine timber-line sites in Mongolia. The chronologies load closely in principal components analysis (PCA) with the first eigenvector accounting for over 53% of the variance from ad 1450 to 1998. The 20-year interval from 1974 to 1993 is the highest such growth period in this composite record, and 17 of the 20 highest growth years have occurred since 1946. Thus these trees, unlike those recently described at some northern sites, do not appear to have lost their temperature sensitivity, and suggest that recent decades have been some of the warmest in the past 500 years for this region. There are, however, comparable periods of inferred, local warmth for individual sites, e.g., in 1520-1580 and 1760-1790. The percent common variance between chronologies has increased through time and is highest (66.1%) in the present century. Although there are obvious differences among the individual chronologies, this result suggests a coherent signal which we consider to be related to temperature. The PCA scores show trends which strongly resemble those seen in recent temperature reconstructions for the Northern Hemisphere, very few of which included representation from Eurasia east of the Ural Mountains. The Mongolia series therefore provides independent corroboration for these reconstructions and their indications of unusual wanning during the twentieth century.

• Korean Journal of Optics and Photonics
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• v.19 no.4
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• pp.270-275
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• 2008

This paper addresses occluded object reconstruction and recognition with computational integral imaging (II). Integral imaging acquires and reconstructs target information in the three-dimensional (3D) space. The reconstruction is performed by averaging the intensities of the corresponding pixels. The distance to the object is estimated by minimizing the sum of the standard deviation of the pixels. We adopt principal component analysis (PCA) to classify occluded objects in the reconstruction space. The Euclidean distance is employed as a metric for decision making. Experimental and simulation results show that occluded targets are successfully classified by the proposed method.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1997.04a
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• pp.99-102
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• 1997

An electronic nose system is an instrument designed far mimicking human olfactory system. It consists generally of gas (odor) sensor array corresponding to olfactory receptors of human nose and artificial neural network pattern recognition technique based on human biological odor sensing mechanism. Considerable attempts to develop the electronic nose system have been made far applications in the fields of floods, drinks, cosmetics, environment monitoring, etc. A portable electronic nose system has been fabricated by using oxide semiconductor gas sensor array and pattern recognition technique such as principal component analysis (PCA) and back propagation artificial neural network The sensor array consists of six thick film gas sensors whose sensing layers are Pd-doped WO$_3$ Pt-doped SnO$_2$ TiO$_2$-Sb$_2$O$_3$-Pd-doped SnO$_2$ TiO$_2$-Sb$_2$O$_{5}$-Pd-doped SnO$_2$+Pd filter layer, A1$_2$O$_3$-doped ZnO and PdCl$_2$-doped SnO$_2$. As an application the system has been used to identify CO/HC car exhausting gases and the identification has been successfully demonstrated.d.