In this paper, modeling of the communication channel occupancy characteristics at the base station in the wide area radio trunking syste has been performed. Using the results, mathematical analysis for the switching capacity at the network switch has also been done. Specificatlly, we propose a G/M/m queueing model for the single service area modeling, and analyze changes in call blockingprobabilities accoridng to changes in the burstness characteristics of group paging calls. As a result, we have observe dthat the channel occupancy variations become larger as the burstness characteristics become more apparent, to make the call blocking probability higher. Next, based on the single service area analysis, we have anayzed mathematically the average switching capacity required to serve mobile subscribers for a wide area radio trunking sytem, where multiple switching port assignments are required when the people in the same group are distributed over several base stations. Accordingly, we have observed that the average and the variance of switching channel occupancy are closely related to the mobility characteristics of subscribers, and that we need a netowrk switch having bigger capacity as subscribers show wider distribution. Especially, with the call dropping probability within 0.2%, a switch having about 5 to 6 times bigger can be required, compared with the one when the mobility of subscribers is mostly restricte dto a single service area.

An intelligent steering control system is designed for the steering control of a 4 wheel drive (4WD) electric vehicles without steering mechanism, where the vehicle is assumed to have 3 degree of freedom and input-output feedback linearization is employed. Especially, a fuzzy-rule-based side force estimator is suggested to avoid uncertain highlynonlinearexpression sof relations between side forces and their factors. Also, aneural-network-based predictive compensator is additionally utilized for the vehicle model to be correctly controlled with unstructured uncertainties. The proposed overall control system is numerically shown to be robust against drastic change of the external environments.

In this paper, we propose an adaptive BTC algorithm using the characteristics of the error signals. The BTC algorithm has a avantage that it is low computational complexity, but a disadvantage that it produces the ragged edges in the reconstructed images for th esloping regions beause of coding the input with 2-level signals. Firstly, proposed methods classify the input into low, medium, and high activity blocks based on the variance of th einput. Using 1-level quantizer for low activity block, 2-level for medium, and 4-level for high, it is adaptive methods that reduce bit rates and the inherent quantization noises in the 2-level quantizer. Also, in case of processing high activity block, we propose a new quantization level allocation algorithm using the characteristics of the error signals between the original signals and the reconstructed signals used by 2-level quantizer, in oder that reduce bit rates superior to the conventional 4-level quantizer. Especially, considering the characteristics of input block, we reduce the bit rates without incurrng the visual noises.

Korean unvoiced phonemes consist of nonstationary parts comparing that the vowels and nasal consonants consist of quasi-stationary part. And some phonemes, which have smae point of articulation but differnt manner of articulation, has similar characteristics, so it makes to be hard to distinguish each other. A new method usin gchanges and characteristics of acoustic properties of these phonemes to improve recognition rate are proposed. And because these changes and cahracteristics evidently occur in continuous speech except some unvoiced consonants are articulated as voiced phoneme in case to be used as an midial between voiced phonemes, this method can be applied easily. The features of the frames extracted to represent each phonemes are used asinputs to the hierarchical neural network. And with these results final decision for phoneme recognition is made thorugh post processing which the new method is applied to. Through the experimental recognition results for 9 unvoiced consonants which belong to bilabial, alveolar, and velar phoneme series, 89.4% recognition rate to distinguish in same phoneme series is obtained, and 85.6% recognition rate is obtained in case of including cistinguishing phoneme series.

A conventional adaptive noise canceller (ANC) using LMS algorithm suffers from the misadjustment of adaptive filter weights due to the gradient-estimate noise by input speech signal at steady state. In this paper, an ANC is proposed which uses the combination of VSLMS (variable step size LMS) and SA (sign algorithm) to improve steady state performance and convergence rate. SA algorithm is applied in speech region to prevent the weights from perturbing by output speech of ANC and VSLMS algorithm is applied to improve convergence rate and channel tracking ability in silence region and adaptive transient region. In compute rsimulation, the performance of the proposed VSLMS-SA combination algorithm is much better than LMS algorithm and the algorithm, recently proposed by greenberg, with adaptation step-size parameter determine dby sum method in convergence rate, channel tracking and steady state performance.

In this paper, efficient quantization methods of line spectrum pairs (LSP) which has good performances and low complexity and memory are proosed for vocoder of mobile communication system. The adaptive quantization method utilizing the ordering property of LSP parameters is used in a scalar quantizer and a vector-scalar hybrid quantizer. The proposed scalar quantization algorithm needs 31 bits/frame to maintain the transparent quality of speech. The improved vector-scalar quantizer achieves an average spectral distortion of 1dB using 26 bits/frame. The proposed methods are evaluated in the channel errors and changed the predictor structure to maintain the robustness to channel errors.

The purpose of this paper is to propose the HMM (hidden markov model) based on multi-observation sequence for the isolated word recognition. The proosed model generates the codebook of MSVQ by dividing each word into several sections followed by dividing training data into several sections. Then, we are to obtain the sequential value of multi-observation per each section by weighting the vectors of distance form lower values to higher ones. Thereafter, this the sequential with high probability value while in recognition. 146 DDD area names are selected as the vocabularies for the target recognition, and 10LPC cepstrum coefficients are used as the feature parameters. Besides the speech recognition experiments by way of the proposed model, for the comparison with it, the experiments by DP, MSVQ, and genral HMM are made with the same data under the same condition. The experiment results have shown that HMM based on multi-observation sequence proposed in this paper is proved superior to any other methods such as the ones using DP, MSVQ and general HMM models in recognition rate and time.

Recently, high-developed domestic industry and environmental issues demand high-voltage short-pulse power supply(power, voltage, pulse width and repetition rate of 100KW, 150KV, 500ns and 500Hz, respectively) for electron beam process and puolse corona process. In such power supplies magnetic compression circuit can be one of the effectives solutions. In ths study design and operation principle of a pulse-power supply with a three-stage magnetic-compression circuit are described. A good agreement between simulation and experimental results verifies the validity of thescheme. The system delivers energy of 10[J/pulse] at the efficiency of 82%[%].