• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.4
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• pp.442-450
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• 2008

In this paper, the phase diverisity in a direct-conversion receiver for a UHF RFID reader is analyzed and the optimal I/Q signal combining methods is presented with respect to tag modulation. At first, fading characteristics of a single channel receiver is shown to prove the importance of phase diversity due to the phase relationship between the backscattered signal and the local oscillator. And the optimal signal combining methods are presented in order to overcome the signal power reduction due to phase diversity. In case of ASK, the power combining method is presented for the optimal I/Q combining. And the arctangent and principal component combining methods using covariance matrix of I and Q channels are presented for the optimal I/Q combining in case of PSK. In order to analyze the performance of suggested methods, the selection diversity and the optimal combining methods are compared. According to analysis and simulation results, the optimal combining methods have a maximum 3 dB SNR enhancement than selection diversity.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.3
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• pp.352-357
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• 2008

We present a space-time trellis coded OFDM system in slow fading channels. Generalized principal ratio combining (GPRC) is also analyzed theoretically in frequency domain. The analysis shows that the decoding metric of GPRC includes the metrics of maximum likelihood(ML) and PRC. The computer simulations with M-PSK modulation are obtained in frequency flat and frequency selective fading channels. The decoding complexity and simulation running times are also evaluated among the decoding schemes.

• Journal of Sericultural and Entomological Science
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• v.32 no.1
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• pp.17-30
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• 1990

A 6${\times}$4 top-cross set was made from the ten silkworm parental lines selected by the first principal component scores. They were also analysed for the relationship between the combining ability and the first principal component score. The highest general combining ability effects were detected in the parental lines of Japanese, N39 and chinese, C46, for the most quantitative characters in the study. The first principal component score of factors related to silk productivity in the parents was significantly and positively correlated to the general combining ability of the twelve characters such as cocoon yield, cocoon weight, cocoon shell weight, cocoon shell percentage, duration of the 5th instar larvae, total larval period, length of a bave, weight of a have, non-breaking length of a bave, non-breaking weight of a have, raw silk percentage, and neatness. Similarity distance (D$^2$) was related to the specific combining ability of the characters such as cocoon yield, non-breaking length of a bave, non-breaking weight of a have, non-breaking ratio of a bave, raw silk percentage, neatness. From the results, it is possible to predict the general combining ability effects by the principal component scores for the 12 characters of the parents related to silk productivity.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.51-61
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• 2007

Ridge regression (RR), principal component regression (PCR) and partial least squares regression (PLS) are among popular regression methods for collinear data. While RR adds a small quantity called ridge constant to the diagonal of X'X to stabilize the matrix inversion and regression coefficients, PCR and PLS use latent variables derived from original variables to circumvent the collinearity problem. One problem of PCR and PLS is that they are very sensitive to overfitting. A new regression method is presented by combining RR and PCR and PLS, respectively, in a unified manner. It is intended to provide better predictive ability and improved stability for regression models. A real-world data from NIR spectroscopy is used to investigate the performance of the newly developed regression method.

• Nuclear Engineering and Technology
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• v.32 no.5
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• pp.485-494
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• 2000

In this work, a fuzzy neural network is used to estimate the relevant sensor signal using other sensor signals. Noise components in input signals into the fuzzy neural network are removed through the wavelet denoising technique . Principal component analysis (PCA) is used to reduce the dimension of an input space without losing a significant amount of information. A lower dimensional input space will also usually reduce the time necessary to train a fuzzy-neural network. Also, the principal component analysis makes easy the selection of the input signals into the fuzzy neural network. The fuzzy neural network parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the fuzzy neural network and a least-squares algorithm is used to solve the consequent parameters. The proposed algorithm was verified through the application to the pressurizer water level and the hot-leg flowrate measurements in pressurized water reactors.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.12
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• pp.28-33
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• 2007

We present a space-time trellis coded OFDM system in flow fading channels. Generalized principal ratio combining (GPRC) is also analyzed theoretically in frequency domain. The analysis show that the decoding metric of GPRC include the metrics of maximum likelihood (ML) and PRC. The computer simulations with M-PSK modulation are obtained in frequency flat and frequency selective lading channels. The decoding complexity and simulation running times are also evaluated among the decoding schemes.

• Structural Engineering and Mechanics
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• v.44 no.2
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• pp.179-196
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• 2012

An eigenspace projection clustering method is proposed for structural damage detection by combining projection algorithm and fuzzy clustering technique. The integrated procedure includes data selection, data normalization, projection, damage feature extraction, and clustering algorithm to structural damage assessment. The frequency response functions (FRFs) of the healthy and the damaged structure are used as initial data, median values of the projections are considered as damage features, and the fuzzy c-means (FCM) algorithm are used to categorize these features. The performance of the proposed method has been validated using a three-story frame structure built and tested by Los Alamos National Laboratory, USA. Two projection algorithms, namely principal component analysis (PCA) and kernel principal component analysis (KPCA), are compared for better extraction of damage features, further six kinds of distances adopted in FCM process are studied and discussed. The illustrated results reveal that the distance selection depends on the distribution of features. For the optimal choice of projections, it is recommended that the Cosine distance is used for the PCA while the Seuclidean distance and the Cityblock distance suitably used for the KPCA. The PCA method is recommended when a large amount of data need to be processed due to its higher correct decisions and less computational costs.

• Physical Therapy Korea
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• v.15 no.2
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• pp.44-53
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• 2008

The purpose of this preliminary study was to develop a measurement for assessing risk factors for falling in community-dwelling elderly persons. Rasch analysis and principal component analysis were performed to examine whether items on the Activities-Specific Balance Confidence (ABC), assessing self-efficacy, and items on the Berg Balance Scale (BBS), assessing balance function, contribute jointly to a unidimensional construct in the elderly. A total of 35 elderly persons (4 men, 31 women) participated. In this study, each item of ABC (16 items) and BBS (14 items) was scored on a 5-point ordinal rating scale from 0 to 4. The initial Rasch and principal component analysis indicated that 3 of the ABC items and 2 of the BBS items were misfit for this study. These 5 items were excluded from further study. After combining ABC and BBS, Rasch and principal component analyses were examined and finally 23 items selected; 12 items from ABC, 11 items from BBS. The 23 combined ABC-BBC items were arranged in order of difficulty. The hardest item was 'walk outside on icy sidewalks' and the easiest item was 'pivot transfer'. Although structural calibration of each 5 rating scale categories was not ordered, the other three essential criteria of Linacre's optimal rating scale were satisfied. Overall, the ABC-BBS showed sound item psychometric properties. Each of the 5 rating scale categories appeared to distinctly identify subjects at different ability levels. The findings of this study support that the new ABC-BBS scale measure balance function and self-efficacy. It will be a clinically useful assessment of risk factors for falling in the elderly. However, the number of subjects was too small to generalize our results. Further study is needed to develop a new assessment considering more risk factors of falling in elderly.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.549-557
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• 2016

In this paper we propose multiclassification method for large data sets by ensembling least squares support vector machines (LS-SVM) with principal components instead of raw input vector. We use the revised one-vs-all method for multiclassification, which is one of voting scheme based on combining several binary classifications. The revised one-vs-all method is performed by using the hat matrix of LS-SVM ensemble, which is obtained by ensembling LS-SVMs trained using each random sample from the whole large training data. The leave-one-out cross validation (CV) function is used for the optimal values of hyper-parameters which affect the performance of multiclass LS-SVM ensemble. We present the generalized cross validation function to reduce computational burden of leave-one-out CV functions. Experimental results from real data sets are then obtained to illustrate the performance of the proposed multiclass LS-SVM ensemble.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.995-1006
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• 2011

A time series can be decomposed into simple components with a multiscale method. Empirical mode decomposition(EMD) is a recently invented multiscale method in Huang et al. (1998). It is natural to apply a classical prediction method such a vector autoregressive(AR) model to the obtained simple components instead of the original time series; in addition, a prediction procedure combining a classical prediction model to EMD and Hilbert spectrum is proposed in Kim et al. (2008). In this paper, we suggest to adopt principal component analysis(PCA) to the prediction procedure that enables the efficient selection of input variables among obtained components by EMD. We discuss the utility of adopting PCA in the prediction procedure based on EMD and Hilbert spectrum and analyze the daily worm account data by the proposed PCA adopted prediction method.