• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.9
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• pp.1480-1490
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• 1997

A method by which the system parameter matrices can be estimated from measured time data of excitation force and acceleration has been studied. The acceleration data are integrated numerically to obtain the velocities and displacements, and the systm parameters are estimated from these data by solving equations of motion. The characteristics of the method have been investigated through its application to simulated data of 1 DOF and 2 DOF systems and experimental data measured from a simple structure. It was found that the method is very sensitive to measurement noise and the accuracy of the estimated parameters can be improved by averaging the repeatedly measured data and removing the noise. One of the main advantages of the parameter estimation method is that no a priori information about the system under test is required. The method can be easily extended to non-linear parameter estimation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.59-64
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• 2003

The parameter modification of the initial FEM model to match it with the experimental results needs the modal information and the modal sensitivity matrix to the parameter change. There are two cases this methodology is ill-equip to deal with; the deficiency of the necessary modal information and the ill-conditioning of the sensitivity matrix. In this research, a novel concept of the feedback exciter that uses the summation of the white noise and the signals from the measurement sensors multiplied with feedback gains as the reference signal is proposed. There are 2 advantages using this external feedback excitation. First, we can use the change of the system response such as modal data by the active energy Path from the sensor to the exciter. This change of the system response can be additional clues to the system dynamics that we want to know. Secondly, the external energy Path alternates the offset of the Parameter change to the system response. That means the modal sensitivity of the parameters becomes different from the original sensitivities by the feedback excitation. Through the feedback loop, we can change the similar modal sensitivities of some updating parameters and consequently discriminate the parameters using the closed-loop modal data. To demonstrate the discrimination performance, the parameter estimation of an indeterminate structure by use of the feedback method is introduced.

• The Journal of the Acoustical Society of Korea
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• v.29 no.3
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• pp.200-208
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• 2010

In this work, the minimum statistics (MS) algorithm is combined with the codebook driven short-term predictor parameter estimation (CDSTP) to design a speech enhancement algorithm that is robust against various background noise environments. The MS algorithm functions well for the stationary noise but relatively not for the non-stationary noise. The CDSTP works efficiently for the non-stationary noise, but not for the noise that was not considered in the training stage. Thus, we propose to combine CDSTP and MS. Compared with the single use of MS and CDSTP, the proposed method produces better perceptual evaluation of speech quality (PESQ) score, and especially works excellent for the mixed background noise between stationary and non-stationary noises.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.2
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• pp.772-778
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• 2012

In this paper, the states and the parameters in the dynamic system are simultaneously estimated by applying the UKF(Unscented Kalman Filter), which is widely used for estimating the state of non-linear systems. Estimating the parameter is very important in various fields, such as system control, modeling, analysis of performance, and prediction. Most of the dynamic systems which are dealt with in engineering have non-linearity as well as some noise. Therefore, the parameter estimation is difficult. This paper estimates the states and the parameters applying to the UKF, which is a non-linear filter and has strong noise. The augmented equation is used by including the addition of the parameter factors to the original state equation of the system. Moreover, it is simulated by applying to a 2-DOF(Degree of Freedom) dynamic system composed of the pendulum and the slide. The measurement noise of the dynamic equation is assumed to be a Gaussian distribution. As the simulation results show, the proposed parameter estimation performs better than the LSM(Least Square Method). Furthermore, the estimation errors and convergence time are within three percent and 0.1 second, respectively. Consequentially, the UKF is able to estimate the system states and the parameters for the system, despite having measurement data with noise.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1263-1272
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• 2016

Product array approach which is used in the Taguchi parameter design has a number of advantages by considering the noise factor. However, a disadvantage of this method is that it requires an excessively large number of experiments. So combined array approach have been proposed to reduce the number of experiments. Taguchi has used the signal-to-noise ratio to find the optimum conditions in the Taguchi parameter design. In analyzing the data from the parameter design various problems tends occur by using an SN. In this paper, we propose an alternative solution for reducing the number of experiments without depending on the signal-to-noise ratio to overcome the shortcomings of the parameter design. Two examples illustrate this procedure in the two different experimental design (product array, combined array) approaches.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.130-131
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• 2012

• Journal of the Korean Data and Information Science Society
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• v.12 no.1
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• pp.11-18
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• 2001

Taguchi parameter design is an approach to reducing performance variation of quality characteristic value in products and processes. Taguchi has used signal-to-noise(SN) ratio to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. Many statisticians criticize the Taguchi techniques of analysis, particularly those based on SN ratio. In this paper we propose a substantially simpler optimization procedure for parameter design without resorting to SN ratio.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.2
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• pp.1-7
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• 2007

재료의 중량과 강도는 기계부품 특히 항공기의 부품에 중요한 요소가 되므로 가볍고 강인한 열처리 강화 알루미늄이나 티타늄 등이 많이 사용된다. 그러나 알루미늄은 용융점이 낮기 때문에 기계 가공 시 발생되는 열에 의해 부품이 얇고 길수록 쉽게 변형된다. 본 연구는 end milling 가공에서 최적의 절삭 parameter를 선정하여 열 변형을 최소화한다. 밀링 가공의 절삭속도, 이송속도, 절삭 깊이를 실험 인자로 정하여 다구찌 방법으로 실험을 계획하고 얇은 시편을 절삭하여 특성을 측정한다. 결과를 분산분석 (ANOVA) 과 signal to noise 비를 (SNR) 분석하여 최소 열 변형의 절삭 parameter를 찾는다. 실험의 data를 SQL database 프로그램화하여 다양한 절삭 환경에서 최소 열 변형과 최소 표면거칠기의 parameter를 찾을 수 있도록 하였다.

• The Journal of the Acoustical Society of Korea
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• v.16 no.5
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• pp.112-121
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• 1997

In this paper, we study a model parameter compensation method for noise-robust speech recognition. We study model parameter compensation on a sentence by sentence and no other informations are used. Parallel model combination(PMC), well known as a model parameter compensation algorithm, is implemented and used for a reference of performance comparision. We also propose a modified PMC method which tunes model parameter with an association factor that controls average variability of gaussian mixtures and variability of single gaussian mixture per state for more robust modeling. We obtain a re-estimation solution of environmental variables based on the expectation-maximization(EM) algorithm in the cepstral domain. To evaluate the performance of the model compensation methods, we perform experiments on speaker-independent isolated word recognition. Noise sources used are white gaussian and driving car noise. To get corrupted speech we added noise to clean speech at various signal-to-noise ratio(SNR). We use noise mean and variance modeled by 3 frame noise data. Experimental result of the VTS approach is superior to other methods. The scheme of the zero order VTS approach is similar to the modified PMC method in adapting mean vector only. But, the recognition rate of the Zero order VTS approach is higher than PMC and modified PMC method based on log-normal approximation.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.818-827
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• 2008

In this paper, we propose a new noise estimation and reduction algorithm for stationary and nonstationary noisy environments. This approach uses an algorithm that classifies the speech and noise signal contributions in time-frequency bins. It relies on the ratio of the normalized standard deviation of the noisy power spectrum in time-frequency bins to its average. If the ratio is greater than an adaptive estimator, speech is considered to be present. The propose method uses an auto control parameter for an adaptive estimator to work well in highly nonstationary noisy environments. The auto control parameter is controlled by a linear function using a posteriori signal to noise ratio(SNR) according to the increase or the decrease of the noise level. The estimated clean speech power spectrum is obtained by a modified gain function and the updated noisy power spectrum of the time-frequency bin. This new algorithm has the advantages of much more simplicity and light computational load for estimating the stationary and nonstationary noise environments. The proposed algorithm is superior to conventional methods. To evaluate the algorithm's performance, we test it using the NOIZEUS database, and use the segment signal-to-noise ratio(SNR) and ITU-T P.835 as evaluation criteria.