• Journal of the Korean Mathematical Society
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• v.55 no.1
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• pp.59-71
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• 2018

In the present paper, we study and classify factorable surfaces in a 3-dimensional isotropic space with constant isotropic Gaussian (K) and mean curvature (H). We provide a non-existence result relating to such surfaces satisfying ${\frac{H}{K}}=const$. Several examples are also illustrated.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1987.04a
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• pp.223-229
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• 1987

The error rate eqations of digital modulated signals transmitted through the fading cdannel have been derived in the Gaussian/Impulsive noise environments Whing the derived equations for the error drobadillties of ASK, QAM, FSK, MSK, PSK, and DPSK signais, the error tate performance of digital modulation systems have been evaluated and represented in the graghes as parameters of carrier to \ulcornernoise power ratio (CNR) and fading figures The results show that in the fading environenet the error is occurred more frequently by gaussian noise in the deep fading Howerer in the shallow fading lmpulsive noise is more domiant than gaussian nosie in occurring the error

• Journal of Soil and Groundwater Environment
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• v.21 no.6
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• pp.67-79
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• 2016

Gaussian process regression (GPR) is proposed as a tool of long-term groundwater quality predictions. The major advantage of GPR is that both prediction and the prediction related uncertainty are provided simultaneously. To demonstrate the applicability of the proposed tool, GPR and a conventional non-parametric trend analysis tool are comparatively applied to synthetic examples. From the application, it has been found that GPR shows better performance compared to the conventional method, especially when the groundwater quality data shows typical non-linear trend. The GPR model is further employed to the long-term groundwater quality predictions based on the data from two domestically operated groundwater monitoring stations. From the applications, it has been shown that the model can make reasonable predictions for the majority of the linear trend cases with a few exceptions of severely non-Gaussian data. Furthermore, for the data shows non-linear trend, GPR with mean of second order equation is successfully applied.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.1C
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• pp.48-56
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• 2011

Cognitive radio (CR) systems have received significant interest as a promising solution to the spectral shortage problem through efficient use of the frequency spectrum by opportunistically exploiting unlicensed frequency bands. Orthogonal frequency division multiplexing (OFDM) is widely regarded as a highly promising candidate for CR systems. However, the frequency bands used by CR systems are expected to suffer from non-Gaussian noise, which considerably degrades the performance of the conventional OFDM carrier frequency offset (CFO) estimation schemes. In this paper, robust CFO estimation schemes for OFDM-based CR systems in non-Gaussian channels are proposed. Simulation results demonstrate that the proposed estimators offer robustness and substantial performance improvement over the conventional estimator.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2002.09a
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• pp.181-188
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• 2002

It is well known that double integration of measured acceleration records are very difficult, particularly in the case of measurements on civil engineering structures. The measured accelerations on civil structures usually contain non-gaussian and low-frequency noises as well as acceleration records are non-stationary. For this type of signals, wavelet transform can be useful because of its inherent processing abilities for non-stationary signals. In this paper, the do-noising algorithm using the wavelet transform are slightly extended to process non-gaussian and low frequency noises, using median filter concepts. The example studies show that the integration can be improved using proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3480-3500
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• 2017

Accurate locating for the mobile target remains a challenge in various applications of wireless sensor networks (WSNs). Unfortunately, most of the typical localization algorithms perform well only in the WSN with densely distributed sensor nodes. The non-localizable problem is prone to happening when a target moves into the WSN with sparsely distributed sensor nodes. To solve this problem, we propose a collaborative and predictive localization algorithm (CPLA). The Gaussian mixture model (GMM) is introduced to predict the posterior trajectory for a mobile target by training its prior trajectory. In addition, the collaborative and predictive schemes are designed to solve the non-localizable problems in the two-anchor nodes locating, one-anchor node locating and non-anchor node locating situations. Simulation results prove that the CPLA exhibits higher localization accuracy than other tested predictive localization algorithms either in the WSN with sparsely distributed sensor nodes or in the WSN with densely distributed sensor nodes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.7A
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• pp.738-745
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• 2007

The non-linearity of HPA(high power amplifier) which is an important component in modern communications systems introduces AM/AM and AM/PM distortion so that the transmitted signal is deteriorated. And, the I/Q unbalances and phase error which are generated by non-ideal components are inevitable physical phenomena and lead to performance degradation when we implement a practical two-dimensional (2-D) modulation system. In this paper, we provide an exact and general expression involving the 2-D Gaussian Q-function for the error probabilities of arbitrary 2-D signaling with I/Q amplitude and phase unbalances in nonlinear additive white Gaussian noise (AWGN) channels by using the coordinate rotation and shifting technique.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.3
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• pp.1416-1421
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• 2013

In this paper, the Euclidean distance between the probability density functions (PDFs) for biased errors and a Dirac-delta function located at zero on the error axis is proposed as a new performance criterion for adaptive systems in non-Gaussian noise environments. Also, based on the proposed performance criterion, a supervised adaptive algorithm is derived and applied to adaptive equalization in the shallow-water communication channel distorted by severe multipath fading, impulsive and DC-bias noise. The simulation results compared with the performance of the existing MEDE algorithm show that the proposed algorithm yields over 5 dB of MSE enhancement and the capability of relocating the mean of the error PDF to zero on the error axis.

• Structural Engineering and Mechanics
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• v.28 no.4
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• pp.373-386
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• 2008

In this study stochastic analysis of non-linear dynamical systems under ${\alpha}$-stable, multiplicative white noise has been conducted. The analysis has dealt with a special class of ${\alpha}$-stable stochastic processes namely sub-Gaussian white noises. In this setting the governing equation either of the probability density function or of the characteristic function of the dynamical response may be obtained considering the dynamical system forced by a Gaussian white noise with an uncertain factor with ${\alpha}/2$- stable distribution. This consideration yields the probability density function or the characteristic function of the response by means of a simple integral involving the probability density function of the system under Gaussian white noise and the probability density function of the ${\alpha}/2$-stable random parameter. Some numerical applications have been reported assessing the reliability of the proposed formulation. Moreover a proper way to perform digital simulation of the sub-Gaussian ${\alpha}$-stable random process preventing dynamical systems from numerical overflows has been reported and discussed in detail.

• International Journal of Control, Automation, and Systems
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• v.5 no.3
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• pp.251-268
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• 2007

A Gaussian sum filter (GSF) is proposed in this paper on simultaneous localization and mapping (SLAM) for mobile robot navigation. In particular, the SLAM problem is tackled here for cases when only bearing measurements are available. Within the stochastic mapping framework using an extended Kalman filter (EKF), a Gaussian probability density function (pdf) is assumed to describe the range-and-bearing sensor noise. In the case of a bearing-only sensor, a sum of weighted Gaussians is used to represent the non-Gaussian robot-landmark range uncertainty, resulting in a bank of EKFs for estimation of the robot and landmark locations. In our approach, the Gaussian parameters are designed on the basis of minimizing the representation error. The computational complexity of the GSF is reduced by applying the sequential probability ratio test (SPRT) to remove under-performing EKFs. Extensive experimental results are included to demonstrate the effectiveness and efficiency of the proposed techniques.