• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.12
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• pp.2479-2488
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• 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 applied mathematics & informatics
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• v.41 no.1
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• pp.181-192
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• 2023

This work deals with the numerical modeling of dam-break flow over erodible bed. The mathematical model consists of the shallow water equations, the transport diffusion and the bed morphology change equations. The system is solved by central upwind scheme. The obtained results of the resolution of dam-beak problem is presented in order to show the performance of the numerical scheme. Also a comparison of central upwind and Roe schemes is presented.

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

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

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.6
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• pp.1522-1545
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• 2012

Call admission control (CAC) plays an important role in mobile cellular network to guarantee the quality of service (QoS). In this paper, a dynamic hybrid CAC scheme with integrated cutoff priority and handoff queue for mobile cellular network is proposed and some performance metrics are derived. The unique characteristic of the proposed CAC scheme is that it can support any number of service types and that the cutoff thresholds for handoff calls are dynamically adjusted according to the number of service types and service priority index. Moreover, timeouts of handoff calls in queues are also considered in our scheme. By modeling the proposed CAC scheme with a one-dimensional Markov chain (1DMC), some performance metrics are derived, which include new call blocking probability ($P_{nb}$), forced termination probability (PF), average queue length, average waiting time in queue, offered traffic utilization, wireless channel utilization and system performance which is defined as the ratio of channel utilization to Grade of Service (GoS) cost function. In order to validate the correctness of the derived analytical performance metrics, simulation is performed. It is shown that simulation results match closely with the derived analytic results in terms of $P_{nb}$ and PF. And then, to show the advantage of 1DMC modeling for the performance analysis of our proposed CAC scheme, the computing complexity of multi-dimensional Markov chain (MDMC) modeling in performance analysis is analyzed in detail. It is indicated that state-space cardinality, which reflects the computing complexity of MDMC, increases exponentially with the number of service types and total channels in a cell. However, the state-space cardinality of our 1DMC model for performance analysis is unrelated to the number of service types and is determined by total number of channels and queue capacity of the highest priority service in a cell. At last, the performance comparison between our CAC scheme and Mahmoud ASH's scheme is carried out. The results show that our CAC scheme performs well to some extend.

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

• Korean Journal of Computational Design and Engineering
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• v.19 no.1
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• pp.41-49
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• 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 Communications and Networks
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• v.11 no.4
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• pp.327-336
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• 2009

Geographic routing has been considered as an attractive approach in wireless sensor networks, since it routes data packets by using location information rather than global topology information. In geographic routing schemes, packets are usually sent along the boundary of a hole by face routing to detour the hole. As result, all data flows which need to detour the hole are concentrated on the boundary of the hole. This hole detour scheme results in much more energy consumption for nodes at the hole boundary, and the energy exhaustion of hole boundary nodes enlarges the holes. This is referred to as a hole diffusion problem. The perimeter mode may also lead to data collisions on the hole boundary nodes if multiple data flows need to bypass a hole simultaneously. In this paper, we propose a hole modeling and detour scheme for geographic routing in wire-less sensor networks. Our hole modeling and detour scheme can efficiently prevent hole diffusion, avoid the local minimum problem faced by geographic routing protocols, and reduce data collisions on the hole boundary nodes. Simulation results show that the proposed scheme is superior to the other protocols in terms of control overhead, average delivery delay and energy consumption.

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

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.3 s.96
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• pp.290-296
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• 2005

This paper proposes the modeling scheme of the Miniature Universal Testing Machine (MUTM) composed of 36 thin-beam-type bimorph PZTs and the control algorithm to minimize the residual vibration of the MUTM in the dynamic testing of specimens. In the operation of the MUTM, hysteresis, residual displacement and vibration of it are major issues. From the analysis of the MUTM behaviors, the hysteresis is described by the curving fitting scheme with the function of an input voltage. The dynamic characteristics of the MUTM are identified by the frequency domain modeling technique based on the experimental data. The interested bandwidth is focused on 125-315HZ for effective modeling and control. For the robust vibration control of the MUTM, the sliding mode control and the Kalman filter as observer are proposed. The paper also proposes the best input signal type to operate the MUTM effectively. The feasibility of the proposed modeling scheme and control algorithm are tested and verified experimentally.