• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.539-544
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• 2003

This paper describes how to reduce the effect of an occupation threshold by that the transform of mixture components of HMM parameters is controlled in hierarchical tree structure to prevent from over-adaptation. To reduce correlations between data elements and to remove elements with less variance, we employ PCA (Principal component analysis) and ICA (independent component analysis) that would give as good a representation as possible, and decline the effect of over-adaptation. When we set lower occupation threshold and increase the number of transformation function, ordinary MLLR adaptation algorithm represents lower recognition rate than SI models, whereas the proposed MLLR adaptation algorithm represents the improvement of over 2% for the word recognition rate as compared to performance of SI models.

• Journal of Korea Multimedia Society
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• v.14 no.8
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• pp.1010-1019
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• 2011

This was a preliminary research for the goal of understanding between internal structure of Osteogenesis Imperfecta Murine (OIM) bone and its fragility. 54 hyperspectral bone data sets were captured by using JASCO 2000 Raman spectrometer at UMKC-CRISP (University of Missouri-Kansas City Center for Research on Interfacial Structure and Properties). Each data set consists of 1,091 data points from 9 OIM bones. The original captured hyperspectral data sets were noisy and base-lined ones. We removed the noise and corrected the base-lined data for the final efficient classification. High dimensional Raman hyperspectral data on OIM bones was reduced by Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) and efficiently classified for the first time. We confirmed OIM bones could be classified such as strong, middle and weak one by using the coefficients of their PCA or LDA. Through experiment, we investigated the efficiency of classification on the reduced OIM bone data by the Bayesian classifier and K -Nearest Neighbor (K-NN) classifier. As the experimental result, the case of LDA reduction showed higher classification performance than that of PCA reduction in the two classifiers. K-NN classifier represented better classification rate, compared with Bayesian classifier. The classification performance of K-NN was about 92.6% in case of LDA.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.491-499
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• 2001

In this paper, the scheme of an efficient fuzzy rule generation and fuzzy system construction using GA(genetic algorithm) and FCM(fuzzy c-means) clustering algorithm is proposed for TSK(Takagi-Sugeno-Kang) type fuzzy system. In the structure identification, input data is transformed by PCA(Principal Component Analysis) to reduce the correlation among input data components. And then, a set fuzzy rules are generated for a given criterion by FCM clustering algorithm . In the parameter identification premise parameters are optimally searched by GA. On the other hand, the consequent parameters are estimated by RLSE(Recursive Least Square Estimate) to reduce the search space. From this one can systematically obtain the valid number of fuzzy rules which shows satisfying performance for the given problem. Finally, we applied the proposed method to the Box-Jenkins data and rice taste data modeling problems and obtained a better performance than previous works.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.3
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• pp.311-317
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• 2015

Phenotypic characterization and body biometric in 13 traits (height at withers, body length, chest girth, paunch girth, ear length, tail length, length of tail up to switch, face length, face width, horn length, circumference of horn at base, distances between pin bone and hip bone) were recorded in 233 adult Gojri buffaloes from Punjab and Himachal Pradesh states of India. Traits were analysed by using varimax rotated principal component analysis (PCA) with Kaiser Normalization to explain body conformation. PCA revealed four components which explained about 70.9% of the total variation. First component described the general body conformation and explained 31.5% of total variation. It was represented by significant positive high loading of height at wither, body length, heart girth, face length and face width. The communality ranged from 0.83 (hip bone distance) to 0.45 (horn length) and unique factors ranged from 0.16 to 0.55 for all these 13 different biometric traits. Present study suggests that first principal component can be used in the evaluation and comparison of body conformation in buffaloes and thus provides an opportunity to distinguish between early and late maturing to adult, based on a small group of biometric traits to explain body conformation in adult buffaloes.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.4
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• pp.15-27
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• 2015

The objectives of this study are to analyze the quantitative ecological principal components of Korean Peninsula using the multivariate spatial environmental datasets and to compare the ecological difference between South and North Korea. Ecological maps with GIS(Geographical Information System) are constructed by PCA(Principal Component Analysis) based on seventeen raster(cell based) variables at 1km resolution. Ecological differences between South and North Korea are extracted by Factor Analysis using ecosystem maps masked from Korean ones. Spatial data include SRTM(Shuttle Radar Topography Mission), Temperature, Precipitation, SWC(Soil Water Content), fPAR(Fraction of Photosynthetically Active Radiation) representing for a productivity, and SR(Solar Radiation), which all cover Korean peninsula. When it performed PCA, the first three scores were assigned to red, green, and blue color. This color triplet indicates the relative mixture of the seventeen environmental conditions inside each ecological region. The first red one represents for 'physiographic conditions' worked by high elevation and solar radiation and low temperature. The second green one stands for 'seasonality' caused by seasonal variations of temperature, precipitation, and productivity. The third blue one means 'wetness condition' worked by high value such as precipitation and soil water contents. FA extraction shows that South Korea has relatively warm and humid ecosystem affected by high temperature, precipitation, and soil water contents whereas North Korea has relatively cold and dry ecosystem due to the high elevation, low temperature and precipitation. Results would be useful at environmental planning on inaccessible land of North Korea.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.247-250
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• 2005

In numerous occasions, there is need to reconstruct partially occluded facial image. Typical examples include the recognition of criminals whose facial images are captured by surveillance cameras- ln such cases a significant part of the face is occluded making the process of identification extremely difficult, both for automatic face recognition systems and human observers. To overcome these difficulties, we consider the application of Kernel PCA-based denoising method to partially occluded facial image in this paper.

• Journal of the Korean Society of Tobacco Science
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• v.30 no.2
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• pp.94-102
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• 2008

In this paper, we describe our study on the characterization of tobacco leaves by their pyrolysis patterns. Two kinds of tobacco leaves were pyrolyzed and analyzed by Double-Shot Pyrolysis-Gas Chromatography/Mass Spectroscopy (Py-GC/MS) methods. Three grades of Korean flue-cured tobacco leafsuch as B1O, AB3O, CD3L and burley tobacco leaves such as B1T, AB3T, CD3W were pyrolyzed with six discrete but stepwise heating temperature ranges, those are from 100$^{\circ}C$ to 150$^{\circ}C$, 150$^{\circ}C$ to 200$^{\circ}C$, 200$^{\circ}C$ to 250$^{\circ}C$, 250$^{\circ}C$ to 300$^{\circ}C$, 300$^{\circ}C$ to 350$^{\circ}C$ and finally from 350$^{\circ}C$ to 400$^{\circ}C$. Using the resultant 52 pyrolytic components identified in the programs as components, principal component analysis (PCA) showed statistical classification between flue-cured and burley tobacco lamina. Among six pyrolysis temperature ranges, the best discrimination was achieved at the temperature range from 250$^{\circ}C$ to 300$^{\circ}C$ and from 300$^{\circ}C$ to 350$^{\circ}C$.

• The Journal of the Korea Contents Association
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• v.6 no.12
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• pp.105-112
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• 2006

In this study, we propose a simple preprocessing method for classification of basal cell carcinoma (BCC), which is one of the most common skin cancer. The preprocessing step consists of data clipping with a half Hanning window and dimension reduction with principal components analysis (PCA). The application of the half Hanning window deemphasizes the peak near $1650cm^{-1}$ and improves classification performance by lowering the false negative ratio. Classification results with various classifiers are presented to show the effectiveness of the proposed method. The classifiers include maximum a posteriori probability (MAP), k-nearest neighbor (KNN), probabilistic neural network (PNN), multilayer perceptron(MLP), support vector machine (SVM) and minimum squared error (MSE) classification. Classification results with KNN involving 216 spectra preprocessed with the proposed method gave 97.3% sensitivity, which is very promising results for automatic BCC detection.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.4 s.316
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• pp.36-44
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• 2007

In this paper, we implement the speech & face recognition system to support various ubiquitous sensor network application services such as switch control, authentication, etc. using wireless audio and image interface. The proposed system is consist of the H/W with audio and image sensor and S/W such as speech recognition algorithm using psychoacoustic model, face recognition algorithm using PCA (Principal Components Analysis) and LDPC (Low Density Parity Check). The proposed speech and face recognition systems are inserted in a HOST PC to use the sensor energy effectively. And improve the accuracy of speech and face recognition, we implement a FEC (Forward Error Correction) system Also, we optimized the simulation coefficient and test environment to effectively remove the wireless channel noises and correcting wireless channel errors. As a result, when the distance that between audio sensor and the source of voice is less then 1.5m FAR and FRR are 0.126% and 7.5% respectively. The face recognition algorithm step is limited 2 times, GAR and FAR are 98.5% and 0.036%.

• Smart Media Journal
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• v.6 no.2
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• pp.33-41
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• 2017

Railway point machines act as actuators that provide different routes to trains by driving switchblades from the current position to the opposite one. Since point failure caused by the aging effect can significantly affect railway operations with potentially disastrous consequences, replacement detection of point machine at an appropriate time is critical. In this paper, we propose a replacement condition detection method of point machine in railway condition monitoring systems using electrical current signals, after analyzing and relabeling domestic in-field replacement data by means of OLAP(On-Line Analytical Processing) operations in the multidimensional data cube into "does-not-need-to-be replaced" and "needs-to-be-replaced" data. The system enables extracting suitable feature vectors from the incoming electrical current signals by DWT(Discrete Wavelet Transform) with reduced feature dimensions using PCA(Principal Components Analysis), and employs SVM(Support Vector Machine) for the real-time replacement detection of point machine. Experimental results with in-field replacement data including points anomalies show that the system could detect the replacement conditions of railway point machines with accuracy exceeding 98%.