• Korean Journal of Food Science and Technology
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• v.44 no.5
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• pp.648-652
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• 2012

The sensory characteristics of nine commercially distilled soju samples were determined by sensory descriptive analysis. Eight aroma attributes, as well as four flavor/taste attributes, and six mouth-feel related attributes were evaluated by 9 judges. The descriptive data set was initially analyzed for a significant overall product effect by employing a three-way mixed model analysis of variance (judges, samples, and replications) as well as two-way interactions, with judges treated as random. In addition, correlations between mean attribute ratings were calculated, and a principal component analysis (PCA) of the mean attribute ratings employing the covariance matrix was conducted. Based on the PCA, distilled soju samples were primarily separated along the first principal component, which accounted for 66% of the total variance between the samples, with high intensities of 'alcohol taste' and 'alcohol aroma' versus 'yeast aroma'. The second principal component accounted for 14% of the total variance. Soju containing high alcohol showed stronger intensities of 'bitterness', 'alcohol taste', 'alcohol aroma', as well as all mouth-feel attributes.

• Korean Chemical Engineering Research
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• v.60 no.2
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• pp.223-228
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• 2022

In chemical processes, unintended faults can make serious accidents. To tackle them, proper fault diagnosis models should be designed to identify the root cause of faults. To design a fault diagnosis model, a process and its data should be analyzed. However, most previous researches in the field of fault diagnosis just handle the data set of benchmark processes simulated on commercial programs. It indicates that it is really hard to get fresh data sets on real processes. In this study, real faulty conditions of an industrial polystyrene process are tested. In this process, a runaway reaction occurred and this caused a large loss since operators were late aware of the occurrence of this accident. To design a proper fault diagnosis model, we analyzed this process and a real accident data set. At first, a mode classification model based on support vector machine (SVM) was trained and principal component analysis (PCA) model for each mode was constructed under normal operation conditions. The results show that a proposed model can quickly diagnose the occurrence of a fault and they indicate that this model is able to reduce the potential loss.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.1
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• pp.55-60
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• 2012

Recent botnets are widely using the DNS services at the connection of C&C server in order to evade botnet's detection. It is necessary to study on DNS analysis in order to counteract anomaly-based technique using the DNS. This paper studies collection of DNS traffic for experimental data and supervised learning for DNS traffic-based malicious domain classification such as query of domain name corresponding to C&C server from zombies. Especially, this paper would aim to determine significant features of DNS-based classification system for malicious domain extraction by the Principal Component Analysis(PCA).

• 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.

• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.629-633
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• 2001

In this paper, we improve noise tolerance in image classification by combining ICA(Independent Component Analysis) with Normalization. When we add noise to the raw image data the degree of noise tolerance becomes N(0, 0.4) for PCA and N(0, 0.53) for ICA. However, when we use the preprocessing approach the degree of noise tolerance after Normalization becomes N(0, 0.75), which shows the improvement of noise tolerance in classification.

• Journal of the Korean Society for Precision Engineering
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• v.32 no.5
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• pp.463-470
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• 2015

Solar cell is typical representative of renewable green energy. Silicon wafer contributes about 66 percent to its cost structure. In its manufacturing, micro-cracks are often occurred due to manufacturing process such as wire sawing, grinding and cleaning. Their detection and classification are important to process feedback information. In this paper, a classification method of micro-cracks is proposed, based on the fusion of principal component analysis(PCA) and neural network. The proposed method shows that it gives higher results than single application of two methods, in terms of shape and size classification of micro-cracks.

• ETRI Journal
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• v.40 no.5
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• pp.634-642
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• 2018

Projection spectral analysis is investigated and refined in this paper, in order to unify principal component analysis and independent component analysis. Singular value decomposition and spectral theorems are applied to nonsymmetric correlation or covariance matrices with multiplicities or singularities, where projections and nilpotents are obtained. Therefore, the suggested approach not only utilizes a sum-product of orthogonal projection operators and real distinct eigenvalues for squared singular values, but also reduces the dimension of correlation or covariance if there are multiple zero eigenvalues. Moreover, incremental learning strategies of projection spectral analysis are also suggested to improve the performance.

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.188-195
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• 1999

The fMRI signals are composed of many various signals. It is very difficult to find the accurate parameter for the model of fMRI signal containing only neural activity, though we may estimating the signal patterns by the modeling of several signal components. Besides the nose by the physiologic motion, the motion of object and noise of MR instruments make it more difficult to analyze signals of fMRI. Therefore, it is not easy to select an accurate reference data that can accurately reflect neural activity, and the method of an analysis of various signal patterns containing the information of neural activity is an issue of the post-processing methods for fMRI. In the present study, fMRI data was analyzed with the Independent Component Analysis(ICA) method that doesn't need a priori-knowledge or reference data. ICA can be more effective over the analytic method using cross-correlation analysis and can separate the signal patterns of the signals with delayed response or motion related components. The Principal component Analysis (PCA) threshold, wavelet spatial filtering and analysis of a part of whole images can be used for the reduction of the freedom of data before ICA analysis, and these preceding analyses may be useful for a more effective analysis. As a result, ICA method will be effective for the degree of freedom of the data.

• Analytical Science and Technology
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• v.10 no.5
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• pp.362-369
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• 1997

Chemometric technique was applied to the sugar composition in five honeys of known botanical or geographical origin following HPLC. Fructose and glucose were predominant carbohydrates in honeys, and small amount of sucrose was also detected in one sample. Sugar contents in honeys samples were compared by the geographical or botanical origin. Fructose/glucose ratio ranged from 0.99 to 1.55 was obtained and these results are in good agreement with the ratio of literature. The plot of principal components analysis(PCA) showed that different honey samples grouped into distinct cluster by the geographical or botanical origin. Increasing the first or second principal component score, higher amount of sugar or less fructose/glucose ratio was observed in PCA plot. Chemometric approach was very useful to provide pattern recognition of sugar profile or quality indices of honey sample and to detect adulteration.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1408-1412
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• 2004

This paper presents a method that a mobile robot finds location of doors in complex environments and safely traverses the door. A robot must be able to find the door in order that it achieves the behavior that is scheduled after traversing a door. PCA(Principal Component Analysis) algorithm using the vision sensor is used for a robot to find the location of door. Fuzzy controller using sonar data is used for a robot to avoid an obstacle and traverse the doors.