• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1299-1311
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• 2017

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines using the sounds emitted by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We present here an automatic fault diagnosis system of hand drills using discrete wavelet transform (DWT) and pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The diagnosis system consists of three steps. Because of the presence of many noisy patterns in our signals, we first conduct a filtering analysis based on DWT. Second, the wavelet coefficients of the filtered signals are extracted as our features for the pattern recognition part. Third, PCA is performed over the wavelet coefficients in order to reduce the dimensionality of the feature vectors. Finally, the very first principal components are used as the inputs of an ANN based classifier to detect the wear on the drills. The results show that the proposed DWT-PCA-ANN method can be used for the sounds based automated diagnosis system.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.698-701
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• 2002

Subspace analysis (which includes PCA) seeks for feature subspace (which corresponds to the eigenspace), given multivariate input data and has been widely used in computer vision and pattern recognition. Typically data space belongs to very high dimension, but only a few principal components need to be extracted. In this paper I present a fast sequential algorithm for subspace analysis or tracking. Useful behavior of the algorithm is confirmed by numerical experiments.

• Journal of the Korean Society of Food Culture
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• v.35 no.2
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• pp.193-200
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• 2020

This study was conducted to understand the sensory characteristics and consumer acceptance for the commercially available clear broth for noodles. Totally, eight different clear broth samples were evaluated in this study. Seven trained panelists developed and evaluated sensory characteristics in the descriptive analysis. Significant differences (p<0.05) were obtained for all 28 attributes evaluated. Descriptive data was obtained by performing multivariate analysis of variance to identify differences between samples. Principal component analysis (PCA) was performed on the mean values of descriptive attributes obtained in the descriptive analysis, and summarizes the sensory characteristics of clear broth for noodles. PCA of the clear broths revealed that the first two principal components are responsible for 80.66% variations. For sensory testing, 160 consumers were recruited, and their acceptance for each sample was assessed. Consumer data was obtained by applying partial least square-regression (PLSR) to establish the relationship between the descriptive data and the consumer acceptance data.

• Korean Journal of Soil Science and Fertilizer
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• v.48 no.4
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• pp.285-294
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• 2015

Ordination has been recognized useful method to analyze the effects of multiple environmental factors on dozens of species in vegetation ecology because of summarizing community data by producing a low-dimensional graphics. Main objective of this study was the application of ordination method, especially principal components analysis (PCA), to analyze the soil characterization on paddy treated by different green manure crops and tillage methods. Treatments included the three tillage treatments and two green manure crops as the following; (i) moldrotary + rotary tillage without green manure crop (Con), with (ii) hairy vetch (ConHv), and (iii) hairy vetch + green barely (ConHvGb), (iv) rotary tillage without green manure crop (Rot), with (v) hairy vetch (RotHv), and (vi) hairy vetch + green barly (RotHvGb), and (vii) no-tillage (Notill). Vectorial distance result from PCA of soil properties including physical, chemical, and microbial properties showed the two main difference. Firstly, soil properties among plots without green manure were strongly affected by tillage strength [Vectorial distance: Con-Notil (5.88) > Rot-Notill (4.58)] at PC1 (35.0%) axis. But it was difficult to find the fixed trend among plots when green manure crop was added in plot. Nevertheless, two groups were separated by adding green manure crop at PC2 (29.2%) axis. These results show that PCA ordination methods could be used the research for change of soil characterization.

• The korean journal of orthodontics
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• v.49 no.1
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• pp.3-11
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• 2019

Objective: The purpose of this study was to investigate the influence of heritability on the craniofacial soft tissue cephalometric characteristics of monozygotic (MZ) twins, dizygotic (DZ) twins, and their siblings (SIB). Methods: The samples comprised Korean adult twins and their siblings (mean age, 39.8 years; MZ group, n = 36 pairs; DZ group, n = 13 pairs of the same gender; and SIB group, n = 26 pairs of the same gender). Thirty cephalometric variables were measured to characterize facial profile, facial height, soft-tissue thickness, and projection of nose and lip. Falconer's method was used to calculate heritability (low heritability, $h^2$ < 0.2; high heritability, $h^2$ > 0.9). After principal components analysis (PCA) was performed to extract the models, we calculated the intraclass correlation coefficient (ICC) value and heritability of each component. Results: The MZ group exhibited higher ICC values for all cephalometric variables than DZ and SIB groups. Among cephalometric variables, the highest ${h^2}_{(MZ-DZ)}$ and ${h^2}_{(MZ-SIB)}$ values were observed for the nasolabial angle (NLA, 1.544 and 2.036), chin angle (1.342 and 1.112), soft tissue chin thickness (2.872 and 1.226), and upper lip thickness ratio (1.592 and 1.026). PCA derived eight components with 84.5% of a cumulative explanation. The components that exhibited higher values of ${h^2}_{(MZ-DZ)}$ and ${h^2}_{(MZ-SIB)}$ were PCA2, which includes facial convexity, NLA, and nose projection (1.026 and 0.972), and PCA7, which includes chin angle and soft tissue chin thickness (2.107 and 1.169). Conclusions: The nose and soft tissue chin were more influenced by genetic factors than other soft tissues.

• Horticultural Science & Technology
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• v.17 no.1
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• pp.23-28
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• 1999

To examine taxonomic relationships among 53 plums derived from Prunus cerasifera, P. domestica, and P. salicina, principal component analysis (PCA) and cluster analysis on 27 morphological characters were conducted. Of 27 characters, leaf size, leaf shape, and leaf hair were useful characters for plum identification and understanding of taxonomic relationships among them. Leaf length, petiole length, number of leaf nectaries, leaf shape, leaf base, and date of full blooming showed the clear differences between P. salicina group and P. domestica group. Results of cluster analysis using scores of the first three principal components indicated that 53 plums could be grouped into P. salicina-P. cerasifera, P. domestica, and P. spinosa phenon at 1.0 of average distance in UPGMA. Although PCA was useful for rough classification of plums, much more characters were needed for the exact classification.

• Journal of Environmental Health Sciences
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• v.41 no.2
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• pp.90-101
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• 2015

Objective: This study was conducted to develop a chemical oxygen demand (COD) regression model using water quality monitoring data (January, 2014) obtained from the Han River auto-monitoring stations. Methods: Surface water quality data at 198 sampling stations along the six major areas were assembled and analyzed to determine the spatial distribution and clustering of monitoring stations based on 18 WQPs and regression modeling using selected parameters. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR), cluster analysis (CA) and principal component analysis (PCA) were used to build a COD model using water quality data. Results: A best GA-MLR model facilitated computing the WQPs for a 5-descriptor COD model with satisfactory statistical results ($r^2=92.64$,$Q{^2}_{LOO}=91.45$,$Q{^2}_{Ext}=88.17$). This approach includes variable selection of the WQPs in order to find the most important factors affecting water quality. Additionally, ordination techniques like PCA and CA were used to classify monitoring stations. The biplot based on the first two principal components (PCs) of the PCA model identified three distinct groups of stations, but also differs with respect to the correlation with WQPs, which enables better interpretation of the water quality characteristics at particular stations as of January 2014. Conclusion: This data analysis procedure appears to provide an efficient means of modelling water quality by interpreting and defining its most essential variables, such as TOC and BOD. The water parameters selected in a COD model as most important in contributing to environmental health and water pollution can be utilized for the application of water quality management strategies. At present, the river is under threat of anthropogenic disturbances during festival periods, especially at upstream areas.

• Journal of Applied Biological Chemistry
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• v.64 no.2
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• pp.127-131
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• 2021

Perilla frutescens var. crispa (Pfc), a herb belonging to the mint family (Lamiaceae), has been used for medicinal and aromatic purposes. In the present study, we analyzed the variation in the chemical composition of essential oils (EOs) obtained from five different genotypes of Pfc collected from different regions. Based on principal component analysis (PCA) and hierarchical cluster analysis (HCA), we identified three groups: PA type containing perillaldehyde, PP type containing dillapiole, and 2-acetylfuran type. To assess the correlation between EO components and antimicrobial activities, we compared classification results generated by PCA and HCA based on antimicrobial activity values. The findings suggested that the major compounds obtained from EOs of Pfc are responsible for their antimicrobial activities. Chemotypes of Pfc plants are essentially qualitative traits that are important for breeders. The present findings provide potential information for breeding Pfc as an antimicrobial agent.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.499-503
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• 2008

In this paper, a novel human activity recognition method is proposed which utilizes independent components of activity shape information from image sequences and Hidden Markov Model (HMM) for recognition. Activities are represented by feature vectors from Independent Component Analysis (ICA) on video images, and based on these features; recognition is achieved by trained HMMs of activities. Our recognition performance has been compared to the conventional method where Principle Component Analysis (PCA) is typically used to derive activity shape features. Our results show that superior recognition is achieved with our proposed method especially for activities (e.g., skipping) that cannot be easily recognized by the conventional method.

• The Journal of the Acoustical Society of Korea
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• v.22 no.8
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• pp.711-716
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• 2003

This paper proposes the method to select the number of optimal mixtures in each state in Continuous Density HMM (Hidden Markov Models), Previously, researchers used the same number of mixture components in each state of HMM regardless spectral characteristic of speaker, To model each speaker as accurately as possible, we propose to use a different number of mixture components for each state, Selection of mixture components considered the probability value of mixture by each state that affects much parameter estimation of continuous density HMM, Also, we use PCA (principal component analysis) to reduce the correlation and obtain the system' stability when it is reduced the number of mixture components, We experiment it when the proposed method used average 10% small mixture components than the conventional HMM, When experiment result is only applied selection of mixture components, the proposed method could get the similar performance, When we used principal component analysis, the feature vector of the 16 order could get the performance decrease of average 0,35% and the 25 order performance improvement of average 0.65%.