• 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.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.674-679
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• 1994

Acoustic signal such as speech and scattered sound, are generally a nonstationary process whose frequency contents vary at any instant of time. For time-varying signal, whether a nonstationary or a deterministic transient signal, a traditional frequency domain representation does not reveal the contents of signal characteristics and may lead to erroneous results such as the loss of desired characteristics features or the mis-interpretation for a wrong conclusion. A time-frequency domain representation is needed to characterize such signatures. Pseudo Wigner-Ville distribution (PWVD) is ideally suited for portraying nonstationary signal time-frequency domain and carried out by adapting the fast Fourier transform algorithm. In this paper, the important properties of PWVD were investigated using both stationary and nonstationry signatures by numerical examples PWVD was applied to acoustic sigtnatures to demonstrate its application for time-ferquency domain analysis.

• 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.

• Structural Engineering and Mechanics
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• v.20 no.5
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• pp.527-542
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• 2005

The main objective of this study is to examine the accuracy of the complete quadratic combination (CQC) rule with the modal responses defined by the ordinates of the uniform hazard spectra (UHS) to evaluate the peak responses of the multi-degree-of-freedom (MDOF) systems subjected to nonstationary seismic excitations. For the probabilistic analysis of the peak responses, it is considered that the seismic excitations can be modeled using evolutionary power spectra density functions with uncertain model parameters. More specifically, a seismological model and the Kanai-Tajimi model with the boxcar or the exponential modulating functions were used to define the evolutionary power spectral density functions in this study. A set of UHS was obtained based on the probabilistic analysis of transient responses of single-degree-of-freedom systems subjected to the seismic excitations. The results of probabilistic analysis of the peak responses of MDOF systems were obtained, and compared with the peak responses calculated by using the CQC rule with the modal responses given by the UHS. The comparison seemed to indicate that the use of the CQC rule with the commonly employed correlation coefficient and the peak modal responses from the UHS could lead to significant under- or over-estimation when contributions from each of the modes are similarly significant.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.441-445
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• 2004

In this paper, we deal with an inventory model of a multi-stage, serial supply chain system where a single product type and nonstationary customer demand pattern are considered. The retailer and suppliers place their orders according to an echelon-stock based replenishment control policy. We assume that the suppliers can access online information on the demand history and use this information when making their replenishment decisions. Using a reinforcement learning technique, the inventory control parameters are designed to adaptively change as the customer demand pattern is altered, in order to maintain a given target service level. Through a simulation based experiment, we verified that our approach is good for maintaining the target service level.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.1
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• pp.8-20
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• 1999

In this paper, a design and estimation procedure for the stochastically dependent nonstationary multiple acceptance sampling plans is developed. At first, the rough-cut acceptance and rejection numbers are given as an initial solution from the corresponding sequential sampling plan. A Monte-Carlo algorithm is used to find the acceptance and rejection probabilities of a lot. The conditional probability formula for a sample path is found. The acceptance and rejection probabilities are found when a decision boundary is given. Several decision criteria and the design procedure to select optimal plans are suggested. The formula for measuring performance of these sampling plans is developed. Type I and II error probabilities are also estimated. As a special case, by setting the stage size as 1 in a dependent sampling plan, a sequential sampling plan satisfying type I and II error probabilities is more accurate and a smaller average sample number can be found. In a numerical example, a Polya dependent process is examined. The sampling performances are shown to compare the selection scheme and the effect of the change of the dependency factor.

• Proceedings of the Korea Water Resources Association Conference
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• 1998.05b
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• pp.3-3
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• 1998

In this pressentation it is argued that the heterogeneity of a hydrologic attribute which may seem to be nonstationary at one scale, may become stationary at a larger scale. The fundamental reason for transformation from nonstationarity to stationarity whith the increase in scale is the phenomenon of coarse-graining of the hydrologic processes with increasing scale. Due to the phenomenon of aliasing, a particular scale hydrologic process heterogeneity which is observed as a nonstationary process at that scale, may be observed as a stationary process at a higher(larger) scale whose size is bigger than the stationary extent of the lower scale heterogeneity. As one goes through a hierarchical sequence of larger and larger scales for observations, one would eliminate nonstationarities which emerge at some lower scales at the expense of losing information on the high frequency fluctuations of the lower scale heterogeneities which will no longer be observed at the larger sampling scales. We call this phenimenon as the "coarse-graining in hydrologic observations". In this presentation, it is also argued that by the coarse-graining of hydrologic processes due to the averaging and aliasing operations at increasing scales, the conservation laws corresponging to these scales may still be quite parsimonious, and need not be more complicated as the scales get larger. It is shown that shen a higher(larger) scale process is formed by averaging a lower(smaller) scale process in time or space, the high frequency components of the lower scale process will be eliminated by the averaging operation. Thereby, the resuliiting average hydrologic dynamics, free from the effects of the high frequency components of the lower scale process, can still be quite simple in form. This is demonstrated by means of some recent upscaling work on the solute teansport conservation equation for hetergeneous aquifers. By means of this solute transport example, it is also shown that for the ensemble average form of a hydrologic conservation equation to be equivalent to its volume-average form at any scale, the parameter functions of that conservation equation at the immediately lower scale must be ergodic.

• International Journal of Aeronautical and Space Sciences
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• v.12 no.4
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• pp.305-317
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• 2011

This paper presents an overview on flutter boundary prediction in tests which is principally based on a system stability measure, named Jury's stability criterion, defined in the discrete-time domain, accompanied with the use of autoregressive moving-average (AR-MA) representation of a sampled sequence of wing responses excited by continuous air turbulences. Stability parameters applicable to two-, three- and multi-mode systems, that is, the flutter margin for discrete-time systems derived from Jury's criterion are also described. Actual applications of these measures to flutter tests performed in subsonic, transonic and supersonic wind tunnels, not only stationary flutter tests but also a nonstationary one in which the dynamic pressure increased in a fixed rate, are presented. An extension of the concept of nonstationary process approach to an analysis of flutter prediction of a morphing wing for which the instability takes place during the process of structural morphing will also be mentioned. Another extension of analytical approach to a multi-mode aeroelastic system is presented, too. Comparisons between the prediction based on the digital techniques mentioned above and the traditional damping method are given. A future possible application of the system stability approach to flight test will be finally discussed.