• Journal of IKEEE
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• v.5 no.1 s.8
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• pp.1-7
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• 2001

In this paper, we have investigated the CDMA(Code Division Multiple Access) Cellular System with non-linear equalizer in reverse link channel. In general, due to unknown characteristics of channel in the wireless communication, the distribution of the observables cannot be specified by a finite set of parameters; instead, we partitioned the m-dimensional sample space Into a finite number of disjointed regions by using quantiles and a vector quantizer based on training samples. The algorithm proposed is based on a piecewise approximation to regression function based on quantiles and conditional partition moments which are estimated by Robbins Monro Stochastic Approximation (RMSA) algorithm. The resulting equalizers and detectors are robust in the sense that they are insensitive to variations in noise distributions. The main idea is that the robust equalizers and robust partition detectors yield better performance in equiprobably partitioned subspace of observations than the conventional equalizer in unpartitioned observation space under any condition. And also, we apply this idea to the CDMA system and analyze the BER performance.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.171-174
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• 1998

A class of non-parametric detectors based on quantized m-dimensional noise sample space is introduced. Due to assuming the nongaussian noise as a channel model, it is not easy to design the detector through estimating the unknown functional form of noise; instead equiprobably partitioning m-dimensional noise into a finite number of regions, using a VQ and quantiles obtained by RMSA algorithm is used in this paper to design detectors. To show the comparison of performance between single sample detector and system suggested here, Monte-Carlo simulations were used. The effect of signal pulse shape on the receiver performance is analyzed too.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1343-1345
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• 2002

In this paper, we introduce the MC-CDMA (Multi-Carrier CDMA) system with MD (multi-detector). Due to unknown functional form of noise in wireless channel environments, it is not easy to design the detector through estimating the functional form of noise. Instead, we design the MD, which is constructed based on DGT (Data Grouping Technique) and quantiles estimated through RMSA (Robbins-Monro Stochastic Approximation) algorithm.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.106-109
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• 1998

Wireless communication systems, in particular, must operate in a crowded electro-magnetic environmnet where in-band undesired signals are treated as noise by the receiver. These interfering signals are often random but not Gaussian Due to nongaussian noise, the distribution of the observables cannot be specified by a finite set of parameters; instead r-dimensioal sample space (pure noise samples) is equiprobably partitioned into a finite number of disjointed regions using quantiles and a vector quantizer based on training samples. If we assume that the detected symbols are correct, then we can observe the pure noise samples during the training and transmitting mode. The algorithm proposed is based on a piecewise approximation to a regression function based on quantities and conditional partition moments which are estimated by a RMSA (Robbins-Monro Stochastic Approximation) algorithm. In this paper, we develop a diversity combiner with modified detector, called Non-Linear Detector, and the receiver has a differential phase detector in each diversity branch and at the combiner each detector output is proportional to the second power of the envelope of branches. Monte-Carlo simulations were used as means of generating the system performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.6A
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• pp.881-887
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• 1999

In this paper, were introduce two types of equalizers, called equalizer-a and equalizer-b, applying to wireless communications having unknown channel characteristics. The equalizer-a, which has the single sample detector with equalizer system, is developed while the equalizer-b has the partition detectors with the same system used in equalizer-a. The methodologiy we adopt for designing the equalizers is that the sample space is partitioned into finite number of regions by using quantiles, which are estimated by robbins-monro stochastic approximation (RMSA) algorithm, and the coefficients of equalizers are calculated based on nonlinear minimum mean, square error (MMSE) algorithm. Through the computer simulation, the equalizers show much better performance in equiprobably partitioned sample subspaces of observations than the single sample detector and the detector, which has the conventional equalizer, in unquantized observation space under various noise environments.

• Journal of Korean Academy of Nursing
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• v.40 no.4
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• pp.533-541
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• 2010

Purpose: This study was designed to test structural equation modeling of the quality of life of stroke survivors in order to provide guidelines for development of interventions and strategies to improve their quality of life. Methods: The participants in the study were patients who visited the neurology outpatient department of a tertiary hospital in Seoul between June 25 and October 15, 2009. Data collection was carried out through one-on-one interviews. Demographic factors, functional independence, social support, nutritional status, post-stroke biobehavioral changes and quality of life were investigated. Results: The final analysis included 215 patients. Fitness of the hypothetical model was appropriate (${\chi}^2$=111.5, p=.000, GFI=.926, AGFI=.880, RMSA=.068, NFI=.911, CFI=.953). Functional dependency, social support and post-stroke biobehavioral changes were found to be significant explaining variance in quality of life. Post-stroke biobehavioral changes had the strongest direct influence on quality of life. Nutritional status had an indirect effect on the quality of life. Conclusion: To improve the quality of life of stroke survivors, comprehensive interventions are necessary to manage post-stroke biobehavioral changes, and strengthening social support networks that can contribute to enhancing the quality of life of stroke survivors.

• Earthquakes and Structures
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• v.23 no.3
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• pp.259-269
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• 2022

In this study, we introduce a canonical correlation analysis method to accurately assess the tunnel damage potential of ground motion. The proposed method can retain information relating to the initial variables. A total of 100 ground motion records are used as seismic inputs to analyze the dynamic response of three different profiles of tunnels under deep and shallow burial conditions. Nine commonly used ground motion parameters were selected to form the canonical variables of ground motion parameters (GMP_CCA). Five structural dynamic response parameters were selected to form canonical variables of structural dynamic response parameters (DRP_CCA). Canonical correlation analysis is used to maximize the correlation coefficients between GMP_CCA and DRP_CCA to obtain multivariate ground motion parameters that can be used to comprehensively assess the tunnel damage potential. The results indicate that the multivariate ground motion parameters used in this study exhibit good stability, making them suitable for evaluating the tunnel damage potential induced by ground motion. Among the nine selected ground motion parameters, peck ground acceleration (PGA), peck ground velocity (PGV), root-mean-square acceleration (RMSA), and spectral acceleration (Sa) have the highest contribution rates to GMP_CCA and DRP_CCA and the highest importance in assessing the tunnel damage potential. In contrast to univariate ground motion parameters, multivariate ground motion parameters exhibit a higher correlation with tunnel dynamic response parameters and enable accurate assessment of tunnel damage potential.