• Journal of Korean Society of Coastal and Ocean Engineers
• /
• v.4 no.4
• /
• pp.201-207
• /
• 1992

The averaged non-linear term in the turbulence equations, suggested by Yeo (1987), is analyzed theoretically and experimentally. It was formulated by applying the filtering concepts to the convolution integral average definition with the Gaussian response function. This filtering approach seems to be superior to the conventional averaging methods in which all four terms at the doubly average vol must be defined separately, and it also gives a very useful tool in understanding the turbulence structures. By theoretically analyzing the newly derived description for the averaged non-linear terms, it is found that the vortex stretching can be explicitly accounted for. Furthermore, comparisons of the correlation coefficients based on the experimental data show that the vortex stretching acts most significantly on the turbulence residual stress. Thus, it strongly supports the claim that the vortex stretching is essential in the transfer of turbulence. In addition. a general form of turbulent energy models in LES is derived, by which it is recognized that the Smagorinsky, the vorticity and the SGS energy models are not distinctive.

• The Journal of the Acoustical Society of Korea
• /
• v.35 no.2
• /
• pp.134-142
• /
• 2016

In this paper, a method synthesizing speech signal using the 40 kHz ultrasonic signals reflected from the articulatory muscles was introduced and performance was evaluated. When the ultrasound signals are radiated to articulating face, the Doppler effects caused by movements of lips, jaw, and chin observed. The signals that have different frequencies from that of the transmitted signals are found in the received signals. These ADS (Acoustic-Doppler Signals) were used for estimating of the speech parameters in this study. Prior to synthesizing speech signal, a quantitative correlation analysis between ADS and speech signals was carried out on each frequency bin. According to the results, the feasibility of the ADS-based speech synthesis was validated. ADS-to-speech transformation was achieved by the joint Gaussian mixture model-based conversion rules. The experimental results from the 5 subjects showed that filter bank energy and LPC (Linear Predictive Coefficient) cepstrum coefficients are the optimal features for ADS, and speech, respectively. In the subjective evaluation where synthesized speech signals were obtained using the excitation sources extracted from original speech signals, it was confirmed that the ADS-to-speech conversion method yielded 72.2 % average recognition rates.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.7 no.3
• /
• pp.132-136
• /
• 2014

We introduce a new architecture of TSK-based fuzzy inference system. The proposed model used fuzzy c-means clustering method(FCM) for efficient disposal of data. The premise part of fuzzy rules don't assume any membership function such as triangular, gaussian, ellipsoidal because we construct the premise part of fuzzy rules using FCM. As a result, we can reduce to architecture of model. In this paper, we are able to use four types of polynomials as consequence part of fuzzy rules such as simplified, linear, quadratic, modified quadratic. Weighed Least Square Estimator are used to estimates the coefficients of polynomial. The proposed model is evaluated with the use of Boston housing data called Machine Learning dataset.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.9 no.1
• /
• pp.59-69
• /
• 2001

The rotor blade is modeled using a composite box beam with arbitrary wall. The active constrained damping layers are bonded to the upper and lower surfaces of the box beam to provide active and passive damping. A finite element model, based on a hybrid displacement theory, is used in the structural analysis. The theory is capable of accurately capturing the transverse shear effects in the composite primary structure, the viscoelastic and the piezoelectric layers within the ACLs. A reduced order model is derived based on the Hankel singular value. A linear quadratic Gaussian (LQG) controller is designed based on the reduced order model and the available measurement output. However, the LQG control system fails to stabilize the perturbed system although it shows good control performance at the nominal operating condition. To improve the robust stability of LQG controller, the loop transfer recovery (LTR) method is applied. Numerical results show that the proposed controller significantly improves rotor aeromechanical stability and suppresses rotor response over large variations in rotating speed by increasing lead-lag modal damping in the coupled rotor-body system.

• Journal of KIISE:Software and Applications
• /
• v.32 no.8
• /
• pp.781-788
• /
• 2005

LDA (Linear Discriminant Analysis) is a data discrimination technique that seeks transformation to maximize the ratio of the between-class scatter and the within-class scatter While it has been successfully applied to several applications, it has two limitations, both concerning the underfitting problem. First, it fails to discriminate data with complex distributions since all data in each class are assumed to be distributed in the Gaussian manner; and second, it can lose class-wise information, since it produces only one transformation over the entire range of classes. We propose three extensions of LDA to overcome the above problems. The first extension overcomes the first problem by modeling the within-class scatter using a PCA mixture model that can represent more complex distribution. The second extension overcomes the second problem by taking different transformation for each class in order to provide class-wise features. The third extension combines these two modifications by representing each class in terms of the PCA mixture model and taking different transformation for each mixture component. It is shown that all our proposed extensions of LDA outperform LDA concerning classification errors for handwritten digit recognition and alphabet recognition.

• Korean Journal of Remote Sensing
• /
• v.24 no.5
• /
• pp.483-496
• /
• 2008

Qualified ground control points (GCPs) are required to construct a digital elevation model (DEM) from a pushbroom stereo pair. An inverse geolocation algorithm for extracting GCPs from ERS SAR data and the SRTM DEM was recently developed. However, not all GCPs established by this method are accurate enough for direct application to the geometric correction of pushbroom images such as SPOT, IRS, etc, and thus a method for selecting and removing inaccurate points from the sets of GCPs is needed. In this study, we propose a method for evaluating GCP accuracy and winnowing sets of GCPs through orientation modeling of pushbroom image and validate performance of this method using SPOT stereo pair of Daejon City. It has been found that the statistical distribution of GCP positional errors is approximately Gaussian without bias, and that the residual errors estimated by orientation modeling have a linear relationship with the positional errors. Inaccurate GCPs have large positional errors and can be iteratively eliminated by thresholding the residual errors. Forty-one GCPs were initially extracted for the test, with mean the positional error values of 25.6m, 2.5m and -6.1m in the X-, Y- and Z-directions, respectively, and standard deviations of 62.4m, 37.6m and 15.0m. Twenty-one GCPs were eliminated by the proposed method, resulting in the standard deviations of the positional errors of the 20 final GCPs being reduced to 13.9m, 8.5m and 7.5m in the X-, Y- and Z-directions, respectively. Orientation modeling of the SPOT stereo pair was performed using the 20 GCPs, and the model was checked against 15 map-based points. The root mean square errors (RMSEs) of the model were 10.4m, 7.1m and 12.1m in X-, Y- and Z-directions, respectively. A SPOT DEM with a 20m ground resolution was successfully constructed using a automatic matching procedure.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.41 no.2
• /
• pp.123-131
• /
• 2021

The buffer is a key component of an engineered barrier system that safeguards the disposal of high-level radioactive waste. Buffers are located between disposal canisters and host rock, and they can restrain the release of radionuclides and protect canisters from the inflow of ground water. Since considerable heat is released from a disposal canister to the surrounding buffer, the thermal conductivity of the buffer is a very important parameter in the entire disposal safety. For this reason, a lot of research has been conducted on thermal conductivity prediction models that consider various factors. In this study, the thermal conductivity of a buffer is estimated using the machine learning methods of: linear regression, decision tree, support vector machine (SVM), ensemble, Gaussian process regression (GPR), neural network, deep belief network, and genetic programming. In the results, the machine learning methods such as ensemble, genetic programming, SVM with cubic parameter, and GPR showed better performance compared with the regression model, with the ensemble with XGBoost and Gaussian process regression models showing best performance.

• Journal of Communications and Networks
• /
• v.12 no.4
• /
• pp.289-307
• /
• 2010

The problem of model selection arises in a number of contexts, such as subset selection in linear regression, estimation of structures in graphical models, and signal denoising. This paper studies non-asymptotic model selection for the general case of arbitrary (random or deterministic) design matrices and arbitrary nonzero entries of the signal. In this regard, it generalizes the notion of incoherence in the existing literature on model selection and introduces two fundamental measures of coherence-termed as the worst-case coherence and the average coherence-among the columns of a design matrix. It utilizes these two measures of coherence to provide an in-depth analysis of a simple, model-order agnostic one-step thresholding (OST) algorithm for model selection and proves that OST is feasible for exact as well as partial model selection as long as the design matrix obeys an easily verifiable property, which is termed as the coherence property. One of the key insights offered by the ensuing analysis in this regard is that OST can successfully carry out model selection even when methods based on convex optimization such as the lasso fail due to the rank deficiency of the submatrices of the design matrix. In addition, the paper establishes that if the design matrix has reasonably small worst-case and average coherence then OST performs near-optimally when either (i) the energy of any nonzero entry of the signal is close to the average signal energy per nonzero entry or (ii) the signal-to-noise ratio in the measurement system is not too high. Finally, two other key contributions of the paper are that (i) it provides bounds on the average coherence of Gaussian matrices and Gabor frames, and (ii) it extends the results on model selection using OST to low-complexity, model-order agnostic recovery of sparse signals with arbitrary nonzero entries. In particular, this part of the analysis in the paper implies that an Alltop Gabor frame together with OST can successfully carry out model selection and recovery of sparse signals irrespective of the phases of the nonzero entries even if the number of nonzero entries scales almost linearly with the number of rows of the Alltop Gabor frame.

• The Journal of the Acoustical Society of Korea
• /
• v.38 no.5
• /
• pp.485-493
• /
• 2019

The parametric array phenomenon refers to the generation of a high directivity low frequency wave from a small size radiation plate using the nonlinearity of the medium. In order to improve the usability of parametric array, the beam steering method of low frequency wave is researched, and the beam steering phenomenon is predicted easily using the PD (product directivity) model. However, the PD model can only be applied to Gaussian sources under quasi-linear conditions. Also, the prediction accuracy of low frequency wave beam width is poor. In this paper, a method for predicting the beam steering characteristics of a parametric array that can overcome the limitation of the PD model is investigated. For this purpose, the numerical analysis algorithm of the KZK (Khokhlov-Zabolotskaya-Kuzentsov) equation widely used for parametric array phenomenon prediction is improved. Thus, the beam steering characteristics are calculated by applying the electrical beam steering condition and comparing experimental results. As a result, the numerical analysis using the modified KZK equation algorithm in this study confirms that the beam steering phenomenon can be predicted even in a parametric array source that does not correspond to the quasi-linear condition.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.46 no.3
• /
• pp.1-7
• /
• 2009

An approach for the classification of music genres using a Fuzzy c-Means(FcM) with divergence-based kernel is proposed and presented in this paper. The proposed model utilizes the mean and covariance information of feature vectors extracted from music data and modelled by Gaussian Probability Density Function (GPDF). Furthermore, since the classifier utilizes a kernel method that can convert a complicated nonlinear classification boundary to a simpler linear one, he classifier can improve its classification accuracy over conventional algorithms. Experiments and results on collected music data sets demonstrate hat the proposed classification scheme outperforms conventional algorithms including FcM and SOM 17.73%-21.84% on average in terms of classification accuracy.