• 한국멀티미디어학회논문지
• /
• 제20권8호
• /
• pp.1299-1311
• /
• 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.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2002년도 ITC-CSCC -1
• /
• pp.698-701
• /
• 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.

• 한국식생활문화학회지
• /
• 제35권2호
• /
• pp.193-200
• /
• 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.

• 한국토양비료학회지
• /
• 제48권4호
• /
• pp.285-294
• /
• 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.

• 대한치과교정학회지
• /
• 제49권1호
• /
• pp.3-11
• /
• 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.

• 원예과학기술지
• /
• 제17권1호
• /
• pp.23-28
• /
• 1999

Prunus cerasifera, P. domestica 및 P. salicina에서 기원된 자두 53 종 또는 품종으로 부터 27 형태형질의 특성을 조사하여 품종 동정에 필요한 기초자료를 제공하고, 그들간의 분류적 관계를 알아보기 위하여 주성분분석 및 집괴분석을 실시하였다. 조사된 형태형질 중 잎의 크기, 잎의 형태 및 엽내 털 발생정도 등이 자 두아속 식물의 동정 및 분류적 관계 해석에 있어서 상당히 유용하게 이용될 수 있을 것으로 단되었으며, 잎의 길이, 엽병 길이, 밀선수, 형, 엽저, 만개기 등은 P. domestica계 자두와 P. salicina계 자두간에 명확한 차이를 나타내었다. 또한 주성분분석 득점치를 이용한 비가 평균결합법에 의한 집괴분석 결과 53개 분류 군들은 평균거리 1.0을 기준으로 할 때 P. alicina-P. cerasifera 표현군, P. domestica 표현군 및 P. pinosa 표현군 등 3개의 표현군으로 분류될 수 있어, 분류군들의 대략적인 분류적 관계 분석이 가능하였다. 그러나 정확한 종군 분류를 위해서는 보다 많은 형태형질의 용이 필요할 것으로 판단되었다.

• 한국환경보건학회지
• /
• 제41권2호
• /
• pp.90-101
• /
• 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
• /
• 제64권2호
• /
• pp.127-131
• /
• 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
• /
• 한국HCI학회 2008년도 학술대회 1부
• /
• pp.499-503
• /
• 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.

• 한국음향학회지
• /
• 제22권8호
• /
• pp.711-716
• /
• 2003

본 논문은 연속밀도 은닉마코프모델에서 각 상태별 혼합성분 개수를 결정하는 방법을 제안한다. 지금까지의 대부분의 연구가 연속밀도 은닉마코프모델에서 화자의 스펙트럼 특성에 상관없이 각 상태별 동일한 혼합성분 개수를 적용하였다. 이런 접근방법은 많은 계산량을 요구할 뿐만 아니라, 각 상태의 특성을 무시하고 있기 때문에 각 상태별 음성신호의 정확한 모델링을 할 수 없다. 따라서 본 논문에서 제안한 연속밀도 은닉마코프모델의 파라미터 추정은 각 상태별 혼합성분에 대한 발생 확률값에 따라서 결정하였다. 또한 혼합성분의 개수를 줄이는 과정에서 신호의 상관성을 줄이고 시스템의 전체적인 안정성을 얻기 위해서 주성분 분석을 이용하였다. 제안한 방법은 기존의 은닉마코프모델에 비해서 평균 10% 작은 혼합성분 개수를 이용했을 때를 기준으로 실험하였다. 실험결과에서 혼합성분 결정만을 적용했을 때 거의 비슷한 성능을 얻을 수 있었다. 그리고 주성분 분석을 이용했을 때, 특정벡터가 16 차일 때 평균 0.35%의 성능감소가 일어났지만, 25 차에서는 평균 0.65%의 성능개선을 얻을 수 있었다.