• Phonetics and Speech Sciences
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• v.3 no.1
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• pp.87-94
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• 2011

The decision directed (DD) approach is widely used to determine a priori SNR from noisy speech signals. In conventional speech enhancement systems with a DD approach, a priori SNR is estimated by using only the magnitude components and consequently follows a posteriori SNR with one frame delay. We propose a phase-dependent two-step a priori SNR estimator based on the minimum mean square error (MMSE) in the log-mel spectral domain so that we can consider both magnitude and phase information, and it can overcome the performance degradation caused by one frame delay. From the experimental results, the proposed estimator is shown to improve the output SNR of enhanced speech signals by 2.3 dB compared to the conventional DD approach-based system.

• Spatial Information Research
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• v.19 no.2
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• pp.29-36
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• 2011

This study performed quantitative assessment of the performance of adjustment models by a-priori analysis of the statistics of the surface parameter estimates against modeling factors. Lidar, airborne imagery, and SAR imagery have been used to acquire the earth surface elevation, where the shape properties of the surface need to be determined through neighboring observations around target location. In this study, parameters which are selected to be estimated are elevation, slope, second order coefficient. In this study, several factors which are needed to be specified to compose adjustment models are classified into three types: mathematical functions, kernel sizes, and weighting types. Accordingly, a-priori standard deviations of the parameters are computed for varying adjustment models. Then their corresponding confidence regions for both the standard deviation of the estimate and the estimate itself are calculated in association with probability distributions. Thereafter, the resulting confidence regions are compared to each other against the factors constituting the adjustment models and the quantitative performance of adjustment models are ascertained.

• ETRI Journal
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• v.36 no.5
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• pp.721-729
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• 2014

We propose a novel phase-based method for single-channel speech enhancement to extract and enhance the desired signals in noisy environments by utilizing the phase information. In the method, a phase-dependent a priori signal-to-noise ratio (SNR) is estimated in the log-mel spectral domain to utilize both the magnitude and phase information of input speech signals. The phase-dependent estimator is incorporated into the conventional magnitude-based decision-directed approach that recursively computes the a priori SNR from noisy speech. Additionally, we reduce the performance degradation owing to the one-frame delay of the estimated phase-dependent a priori SNR by using a minimum mean square error (MMSE)-based and maximum a posteriori (MAP)-based estimator. In our speech enhancement experiments, the proposed phase-dependent a priori SNR estimator is shown to improve the output SNR by 2.6 dB for both the MMSE-based and MAP-based estimator cases as compared to a conventional magnitude-based estimator.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.4
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• pp.94-100
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• 2002

The conventional sliding mode control (SMC) technique requires a priori knowledge of the upperbounds of disturbances and/or modeling uncertainties to assure robustness. This, however, may not be easy to obtain in practical situation. This paper presents a new methodology, a sliding mode control with disturbance estimator (SMCDE), which offers a robust control performance without a priori knowledge about the disturbance. The proposed technique is featured by an average value of the imposed disturbance over a certain period. A nonlinear spring-mass-damper system and a two-link robot system are adopted as illustrative application examples. Control performances such as estimation error and tracking error are compared between the proposed methodology and conventional scheme.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.1
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• pp.165-171
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• 2002

The conventional sliding mode control(SMC) technique requires a priori knowledge of the upperbounds of disturbances or/and modeling uncertainties to assure robustness. This, however, may not to be easy to obtain in practical situation. This paper presents a new methodology, sliding mode control with disturbance estimator(SMCDE), which offers a robust control performance without a priori knowledge about the disturbance. The proposed technique is featured by an average value of the imposed disturbance over a certain period. A nonlinear spring-mass-damper system is adopted as an illustrative example, and a comparative work between the conventional technique and the present one is undertaken.

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.32-41
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• 1999

In this paper, an inversion method using chirp signals and two near field receivers is proposed. Inversion problems can be formulated into the probabilistic models composed of signals, a forward model and noise. Forward model to simulate chirp signals is chosen to be the source-wavelet-convolution planewave modeling method. The solution of the inversion problem is defined by a posteriori pdf. The wavelet matching technique, using weighted least-squares fitting, estimates the sediment sound-speed and thickness on which determination of the ranges for a priori uniform distribution is based. The genetic algorithm can be applied to a global optimization problem to find a maximum a posteriori solution for determined a priori search space. Here the object function is defined by an L₂norm of the difference between measured and modeled signals. The observed signals can be separated into a set of two signals reflected from the upper and lower boundaries of a sediment. The separation of signals and successive applications of the genetic algorithm optimization process reduce the search space, therefore improving the inversion results. Not only the marginal pdf but also the statistics are calculated by numerical evaluation of integrals using the samples selected during importance sampling process of the genetic algorithm. The examples applied here show that, for synthetic data with noise, it is possible to carry out an inversion for sedimentary layers using the proposed inversion method.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.401-411
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• 2003

Tube expanding process is for combining a heat-sink plate with hair-pins (Cu-tube) through plastic deformation. The two parts, i. e. , heat-sink plate and hair-pins are they components of a heat-exchanger for an air conditioner. This paper presents the analysis and dynamic modeling of a high-speed tube expander which integrates transfer of parts, fixing of parts, and tube expanding into one process. The 3-dimensional modeling of all the parts for the tube-expander was constructed using CATIA$\circledR$. then the CATIA$\circledR$ models are transferred into visuaINastran$\circledR$ to execute the 3-dimensional animation for checking prescribed cycle-time. The technique presented in this paper has been shown to be effective as a priori tool for verifying the design of a high-speed tube expander.

• The Transactions of the Korean Institute of Power Electronics
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• v.21 no.3
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• pp.207-214
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• 2016

Magnetic circuit is a physical modeling method that is useful in designing and analyzing power transformers, especially for a priori evaluation of leakage and magnetizing inductance before actual production. In this study, a novel modeling approach that uses PSIM magnetic elements adopting gyrator and permeance-capacitances is investigated. A formula to determine the permeance-capacitors in the core and leakage path are established, and a simulation jig is devised to link the physical model and the electrical terminal characteristics with an automated parameter determination process. The derived formula is verified by measurement results of the prototype transformer samples. Given its accuracy and simplicity, this approach is suitable for analyzing and designing LLC resonant transformers whose leakage and magnetizing inductance are very critical to circuit operation.

• The Journal of Korean Physical Therapy
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• v.22 no.1
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• pp.75-81
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• 2010

Purpose: The purpose of this study was to apply association rule mining to explore the labyrinthine network of cerebral infarction comorbidity and basic data supply to develop cutting-edge physical therapy protocols for cerebral infarction with comorbidity Methods: From clinic records of enrollees of A Hospital in D city, patients over 18 years of age with cerebral infarction and cerebral infarction comorbidity were recruited as a case group. All diagnoses of that hospital were categorized according to the "International Classification of Disease (ICD)" diagnosis system. We extracted code I63 from the "Korea Classification of Disease (KCD)-4". Associated rule mining was done with a priori modeling and Web nodes to examine the strengths of associations among those diagnoses. The support and confidence values of associated rule mining results were examined. Results: The subjects of this study were 2,267 cerebral infarction patients. E11 (Non-insulin-dependent diabetes mellitus), E78 (Disorders of lipoprotein metabolism and other lipidaemias), G81 (Hemiplegia), I10 (Essential hypertension), and K29 (Gastritis and duodenitis) were high frequency diagnoses, being found in 10% or more of total diagnoses of cerebral infarction from frequency analysis results. The highest frequency diagnosis was 1,042 (46.0%) for I10. The second most frequent diagnosis was for E11(21.5%) while the third most frequent diagnosis was E78 (20.2%). Results from a priori modeling and Web nodes indicated that cerebral infarction has a strong association withessential hypertension, non-insulin-dependent diabetes mellitus, disorders of lipoprotein metabolism and other lipidaemias. Conclusion: Cerebral infarction is associated with hypertension, diabetes mellitus, and disorders of lipoprotein metabolism and other lipidaemias. The result of this study will be helpful to clinicians treating patients with cerebral infarction.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.159.1-159
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• 2001

A new time-domain FIR parameter estimation called the receding horizon least square estimation (RHLSE) is suggested for stochastic systems by combining the well known least square estimation with the receding horizon strategy. It can be always obtained without the requirement of any \textit{a priori} information about the horizon initial parameter. A fast algorithm for the suggested estimation is also presented which is remarkable in the view of computational advantage and simple implementation. It is shown that the proposed estimation is robust against temporary modeling uncertainties due to their FIR structure through simulation studies.