• The Journal of the Acoustical Society of Korea
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• v.21 no.1
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• pp.11-18
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• 2002

In this paper, a gain-adapted speech recognition method in noise is developed in the time domain. Noise is assumed to be colored. To cope with the notable nonstationary nature of speech signals such as fricative, glides, liquids, and transition region between phones, the nonstationary autoregressive (NAR) hidden Markov model (HMM) is used. The nonstationary AR process is represented by using polynomial functions with a linear combination of M known basis functions. When only noisy signals are available, the estimation problem of noise inevitably arises. By using multiple Kalman filters, the estimation of noise model and gain contour of speech is performed. Noise estimation of the proposed method can eliminate noise from noisy speech to get an enhanced speech signal. Compared to the conventional ARHMM with noise estimation, our proposed NAR-HMM with noise estimation improves the recognition performance about 2-3%.

• Journal of Korea Water Resources Association
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• v.52 no.11
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• pp.895-904
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• 2019

It has been well recognized that extreme rainfall process often features a nonstationary behavior, which may not be effectively modeled within a stationary frequency modeling framework. Moreover, extreme rainfall events are often described by a two (or more)-component mixture distribution which can be attributed to the distinct rainfall patterns associated with summer monsoons and tropical cyclones. In this perspective, this study explores a Mixture Distribution based Nonstationary Frequency (MDNF) model in a changing rainfall patterns within a Bayesian framework. Subsequently, the MDNF model can effectively account for the time-varying moments (e.g. location parameter) of the Gumbel distribution in a two (or more)-component mixture distribution. The performance of the MDNF model was evaluated by various statistical measures, compared with frequency model based on both stationary and nonstationary mixture distributions. A comparison of the results highlighted that the MDNF model substantially improved the overall performance, confirming the assumption that the extreme rainfall patterns might have a distinct nonstationarity.

• The Journal of the Acoustical Society of Korea
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• v.27 no.7
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• pp.386-393
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• 2008

This paper proposes a new approach for speech enhancement in highly nonstationary noisy environments. The spectral subtraction (SS) is a well known technique for speech enhancement in stationary noisy environments. However, in real world, noise is mostly nonstationary. The proposed method uses an auto control parameter for an adaptive threshold to work well in highly nonstationary noisy environments. Especially, the auto control parameter is affected by a linear function associated with an a posteriori signal to noise ratio (SNR) according to the increase or the decrease of the noise level. The proposed algorithm is combined with spectral subtraction (SS) using a hangover scheme (HO) for speech enhancement. The performances of the proposed method are evaluated ITU-T P.835 signal distortion (SIG) and the segment signal to-noise ratio (SNR) in various and highly nonstationary noisy environments and is superior to that of conventional spectral subtraction (SS) using a hangover (HO) and SS using a minimum statistics (MS) methods.

• The Journal of Fisheries Business Administration
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• v.29 no.2
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• pp.97-110
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• 1998

This paper tries to estimate Korea's import demand function for fisheries using cointegration analysis. The estimation function consists of one dependent variable-import quantity of fisheries(FTIW) and two independent variables-relative price(RP) between importable and domestic products and real income(GDP). As it has been empirically found out that almost all of time series of macro-variables such as GDP, price index are nonstationary, existing studies which ignore this fact need to be reexamined. Conventional econometric method can not analyze nonstationary time series in level. To perform the analysis, time series should be differenciated until stationarity is guaranteed. Unfortunately, the difference method removes the long run element of data, and so leads to difficulties of interpretation. But according to new developed econometric theory, cointegration approach could solve these problems. Therefore this paper proceeds the estimation on the basis of cointegration analysis, because the quartly variables from 1988 to 1997 used in the model is found out to be nonstationary. The estimation results show that all of the variables are statistically significant. Therefore Korea's import demand for fisheries has been strongly affected by the variation of real income and the relative price.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2519-2521
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• 2005

Instantaneous frequency(IF) has important physical meaning for nonstationary signal. Motor current is well known that to be a nonstationary signal whose properties vary with respect to the time-varying normal operating conditions of the motor, particularly with load. Time-frequency methods can overcome the shortcomings of the traditional spectral analysis techniques, nonstationary signal analysis approaches have been introduced. We examine the concept of IF as a potential candidate for condition monitoring of motors.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.103-112
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• 1994

An asymptotic property of the estimated eigenvalues for multivariate AR(p) process which consists of vector of nonstationary process and vector of stationary process is developed. All components of the nonstationary process are assumed to reveal random walk behavior. The asymptotic property is helpful in understanding multiple unit roots. In this paper we show the stationay part in multivariate AR(p) process does not affect the limiting distribution of estimated eigenvalues associated with the nonstationary process. A test statistic based on the ordinary least squares estimator for testing a certain number of multiple unit roots is suggested.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.08a
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• pp.225-230
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• 1998

In this paper, a Kalman filter approach for enhancing speech signals degraded by statistically independent additive nonstationary noise is developed. The autoregressive hidden markov model is used for modeling the statistical characteristics of both the clean speech signal and the nonstationary noise process. In this case, the speech enhancement comprises a weighted sum of conditional mean estimators for the composite states of the models for the speech and noise, where the weights equal to the posterior probabilities of the composite states, given the noisy speech. The conditional mean estimators use a smoothing spproach based on two Kalmean filters with Markovian switching coefficients, where one of the filters propagates in the forward-time direction with one frame. The proposed method is tested against the noisy speech signals degraded by Gaussian colored noise or nonstationary noise at various input signal-to-noise ratios. An app개ximate improvement of 4.7-5.2 dB is SNR is achieved at input SNR 10 and 15 dB. Also, in a comparison of conventional and the proposed methods, an improvement of the about 0.3 dB in SNR is obtained with our proposed method.

• The Journal of the Acoustical Society of Korea
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• v.15 no.3E
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• pp.13-18
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• 1996

A real-time identifier for the nonstationary time-varying signals and systems was implemented using a low cost DSP (digital signal processing) chip. The identifier is comprised of I/O units, a central processing unit, a control unit and its supporting software. In order t estimate the system accurately and to reduce quantization error during arithmetic operation, the firmware was programmed with 64-bit extended precision arithmetic. The performance of the identifier was verified by comparing with the simulation results. The implemented real-time identifier has negligible quantization errors and its real-time processing capability crresponds to 0.6kHz for the nonstationary AR (autoregressive) model with n=4 and m=1.

• Journal of the military operations research society of Korea
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• v.24 no.2
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• pp.146-161
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• 1998

This article develops model of the nonstationary state behavior of the multiechelon spare parts provisioning systems. This study is concerned with a problem of determining the near optimal requirements level of the spare parts, especially Concurrent Spare Parts(CSP). CSP is supplied with the procurement of new equipment system, and is used to sustain the equipment without resupply during the initial coverage period. We consider this situation as a multiechelon inventory model with several bases and one depot. And we assume an equipment system which consists of many types of parts would grounded if one of the parts fail. Also this multiechelon CSP problem is considering the nonstationary poisson failure process and nonstationary exponential repair process in a dynamic environment. We develop an efficient computational procedure to find the near optimal number of spare parts minimizing the total expected cost, while achieving the required system availability. Finally we present a simple example of suggested method.

• International Journal of Control, Automation, and Systems
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• v.6 no.1
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• pp.109-118
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• 2008

We present two estimators for discrete non-Gaussian and nonstationary probability density estimation based on a dynamic Bayesian network (DBN). The first estimator is for off line computation and consists of a DBN whose transition distribution is represented in terms of kernel functions. The estimator parameters are the weights and shifts of the kernel functions. The parameters are determined through a recursive learning algorithm using maximum likelihood (ML) estimation. The second estimator is a DBN whose parameters form the transition probabilities. We use an asymptotically convergent, recursive, on-line algorithm to update the parameters using observation data. The DBN calculates the state probabilities using the estimated parameters. We provide examples that demonstrate the usefulness and simplicity of the two proposed estimators.