• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.19 no.12
• /
• pp.2479-2488
• /
• 1994

In this paper, a new dynamic excitation modeling scheme is proposed. Based upon the proposed excitation modeling scheme, two variable bit rate homomorphic vocoders are designed, whose average bit rates are 3.8 Kbps and 4.4 Kbps. The performance of the proposed excitation modeling scheme is then evaluated through the subjective listening tests. In the tests, the performances of two speech coders designed in this paper ate compared with the one of 4.8 Kbps homomorphic vocoder designed by Chung and Schafer, in which conventional static excitation modeling scheme applied. The subjective listening tests show that proposed dynamic excitation modeling scheme improves synthesized speech quality while lowering the average bit rate of speech coders.

• Journal of Computing Science and Engineering
• /
• v.6 no.3
• /
• pp.219-226
• /
• 2012

Recently, shape analysis of human organs has achieved much attention, owing to its potential to localize structural abnormalities. For a group-wise shape analysis, it is important to accurately restore the shape of a target structure in each subject and to build the inter-subject shape correspondences. To accomplish this, we propose a shape modeling method based on the Laplacian deformation framework. We deform a template model of a target structure in the segmented images while restoring subject-specific shape features by using Laplacian surface representation. In order to build the inter-subject shape correspondences, we implemented the progressive weighting scheme for adaptively controlling the rigidity parameter of the deformable model. This weighting scheme helps to preserve the relative distance between each point in the template model as much as possible during model deformation. This area-preserving deformation allows each point of the template model to be located at an anatomically consistent position in the target structure. Another advantage of our method is its application to human organs of non-spherical topology. We present the experiments for evaluating the robustness of shape modeling against large variations in shape and size with the synthetic sets of the second cervical vertebrae (C2), which has a complex shape with holes.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.21 no.11
• /
• pp.1220-1228
• /
• 2010

In this paper, we suggest a scheme to develop an efficient discrete nonlinear model based on polynomial structure for a RF power amplifier(PA). We describe a procedure to extract a discrete nonlinear model such as Taylor series or memory polynomial by sampling the input and output signal of RF PA. The performance of the model is analyzed varying the model parameters such as sample rate, nonlinear order, and memory depth. The results show that the relative error of the model is converged if the parameters are larger than specific values. We suggest an efficient modeling scheme considering complexity of the discrete model depending on the values of the model parameters. Modeling efficiency index(MEI) is defined, and it is used to extract optimum values for the model parameters. The suggested scheme is applied to discrete modeling of various RF PAs with various input signals such as WCDMA, WiBro, etc. The suggested scheme can be applied to the efficient design of digital predistorter for the wideband transmitter.

• Korean Journal of Computational Design and Engineering
• /
• v.19 no.1
• /
• pp.41-49
• /
• 2014

For the reuse of the existing 3D block model of a ship, we analyze the hull modeling process using AVEVA Marine which is a representative CAD (Computer-Aided Design) system for the shipbuilding. In the AVEVA Marine environment where the design engineer makes 3D model on the 2D view that is so-called 2.5D, it cannot be possible to copy to reuse the block model just simply copying the 3D feature model itself like in the general mechanical CAD system or Smart Marine 3D which are on the basis of the 3D model representation. In this paper, we analyze the scheme file where the 3D model is defined in AVEVA Marine so that we develop the program for the block copy and the translation using this scheme file. It is significant that this program can be immediately available as a real-world application on the AVEVA Marine environment.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.6
• /
• pp.577-582
• /
• 2002

Neural network can be trained to approximate an arbitrary nonlinear function of multivariate data like the mini-mill crown values in Automatic Shape Control. The trained weights of neural network can evaluate or generalize the process data outside the training vectors. Sometimes, the blind modeling of the process data is necessary to compare with the scattered analytical model of mini-mill process in isolated electro-mechanical forms. To come up with a viable model, we propose the blind neural-based range-division domain-clustering piecewise-linear modeling scheme. The basic ideas are: 1) dividing the range of target data, 2) clustering the corresponding input space vectors, 3)training the neural network with clustered prototypes to smooth out the convergence and 4) solving the resulting matrix equations with a pseudo-inverse to alleviate the ill-conditioning problem. The simulation results support the effectiveness of the proposed scheme and it opens a new way to the data analysis technique. By the comparison with the statistical regression, it is evident that the proposed scheme obtains better modeling error uniformity and reduces the magnitudes of errors considerably. Approximatly 10-fold better performance results.

• Journal of the Korea Society for Simulation
• /
• v.11 no.1
• /
• pp.71-82
• /
• 2002

One of the most important things in the research for MIN-based switch operation the management scheme of network cycle. In the traditional MIN, when the receving buffer module is empty, the sell has to move forward the front-most buffer position by the characteristic of the conventional FIFO queue. However, most of buffer modules are almost always full for practical amount of input loads. The long network cycle of the traditional scheme is thus a substantial waste of bandwidth. In this paper, we propose the modeling method for the input and multi-buffered MIN with unit step buffering scheme, In spite of simplicity, simulation results show that the proposed model is very accurate comparing to previous modeling approaches in terms of throughput and the trend of delay.

• Journal of Environmental Science International
• /
• v.19 no.2
• /
• pp.137-148
• /
• 2010

This paper investigates the characteristics of turbulence schemes. Turbulence closures are fundamental for modeling the atmospheric diffusion, transport and dispersion in the boundary layer. In particular, in non-homogeneous conditions, a proper description of turbulent transport in planetary boundary layer is fundamental aspect. This study is based on the Regional Atmospheric Modeling System (RAMS) and combines four different turbulence schemes to assess if the different schemes have a impact on simulation results of vertical profiles. Two of these schemes are Isotropc Deformation scheme (I.Def) and Anisotropic deformation scheme (A.Def) that are simple local scheme based on Smagorinsky scheme. The other two are Mellor-Yamada scheme (MY2.5) and Deardorff TKE scheme (D.TKE) that are more complex non-local schemes that include a prognostic equation for turbulence kinetic energy. The simulated potential temperature, wind speed and mixing ratio are compared against radiosonde observations from the study region. MY2.5 shows consistently reasonable vertical profile and closet to observation. D.TKE shows good results under relatively strong synoptic condition especially, mixing ratio simulation. Validation results show that all schemes consistently underestimated wind speed and mixing ratio but, potential temperature was somewhat overestimated.

• Korean Journal of Computational Design and Engineering
• /
• v.11 no.4
• /
• pp.235-245
• /
• 2006

This paper proposes a CSG-based representation scheme for heterogeneous objects including multi-material objects and Functionally Graded Materials (FGMs). In particular, this scheme focuses on the construction of complicated heterogeneous objects guaranteeing desired material continuities at all the interfaces. In order to create various types of heterogeneous primitives, we first describe methods for specifying material composition functions such as geometry-independent, geometry-dependent functions. Constructive Material Composition (CMC) and corresponding heterogeneous Boolean Operators (e.g. material union, difference, intersection. and partition) are then proposed to illustrate how material continuities are dealt with. Finally, we describe the model hierarchy and data structure for computer representation. Even though the proposed scheme alone is sufficient for modeling all sorts of heterogeneous objects, the proposed scheme adopts a hybrid representation between CSG and decomposition. That is because hybrid representation can avoid the unnecessary growth of binary trees.

• Structural Engineering and Mechanics
• /
• v.16 no.6
• /
• pp.643-654
• /
• 2003

Two real-time modeling prediction (RMP) schemes are presented in this paper for analyzing the behavior of deep excavations during construction. The first RMP scheme is developed from the traditional AR(p) model. The second is based on the simplified Elman-style recurrent neural networks. An on-line learning algorithm is introduced to describe the dynamic behavior of deep excavations. As a case study, in-situ measurements of an excavation were recorded and the measured data were used to verify the reliability of the two schemes. They proved to be both effective and convenient for predicting the behavior of deep excavations during construction. It is shown through the case study that the RMP scheme based on the neural network is more accurate than that based on the traditional AR(p) model.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.43 no.5
• /
• pp.838-847
• /
• 1994

Although there is an analytical proof of modeling capability of the neural network, the convergency error in nonlinearity modeling is inevitable, since the steepest descent based practical larning algorithms do not guarantee the convergency of modeling error. Therefore, it is difficult to apply the neural network to control system in critical environments under an on-line learning scheme. Although the convergency of modeling error of a neural network is not guatranteed in the practical learning algorithms, the convergency, or boundedness of tracking error of the control system can be achieved if a proper feedback control law is combined with the neural network model to solve the problem of modeling error. In this paper, the neural network is introduced for compensating a system uncertainty to control a nonlinear dynamic system. And for suppressing inevitable modeling error of the neural network, an iterative neural network learning control algorithm is proposed as a virtual on-line realization of the Adaptive Variable Structure Controller. The efficiency of the proposed control scheme is verified from computer simulation on dynamics control of a 2 link robot manipulator.