• Archives of Pharmacal Research
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• v.26 no.5
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• pp.421-425
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• 2003

The aim of the present study was to evaluate the effects of two phospholipid permeation enhancers, lysophosphatidylcholine (LPC) and didecanoylphosphatidylcholine (DDPC), along with a fusidic acid derivative, sodium taurodihydrofusidate (STDHF) and ethanol (EtOH) on the buccal transport of propranolol hydrochloride (PPL) using an ex vivo buccal diffusion model. The permeation rate of [$^3 H$]PPL as measured by steady-state fluxes increased with increasing EtOH concentration. A significant flux enhancement (P＜0.05) was achieved by EtOH at 20 and 30 %v/v concentrations. At a 0.5 %w/v permeation enhancer concentration, the buccal permeation of [$^3 H$]PPL was significantly enhanced by all the enhancers studied (i.e., LPC, DDPC and STDHF) compared to the control (phosphate-buffered saline pH 7.4, PBS). LPC and DDPC displayed a greater degree of permeation enhancement compared with STDHF and EtOH-PBS mixtures with an enhancement ratio of 3.2 and 2.9 for LPC and DDPC, respectively compared with 2.0 and 1.5 for STDHF and EtOH:PBS 30:70 %v/v mixture, respectively. There was no significant difference between LPC and DDPC for the flux values and apparent permeability coefficients of [$^3$H]PPL. These results suggest that phospholipids are suitable as permeation enhancers for the buccal delivery of drugs.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.11
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• pp.1009-1017
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• 2010

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying various techniques such as signal processing and pattern recognition. Recently, fault diagnosis systems using artificial neural network have been proposed. For effective fault diagnosis, this paper used MLP(multi-layer perceptron) network which is widely used in pattern classification. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes the decision method of the proper feature vectors about each fault signal for neural-network-based fault diagnosis system. We applied LPC coefficients, maximum magnitudes of each spectral section in FFT and RMS(root mean square) and variance of wavelet coefficients as feature vectors and selected appropriate feature vectors as comparing error ratios of fault diagnosis for sound, vibration and current fault signals. From experiment results, LPC coefficients and maximum magnitudes of each spectral section showed 100 % diagnosis ratios for each fault and the method using wavelet coefficients had noise-robust characteristic.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.628-633
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• 2014

This paper proposes a performance improvement method of the conventional double talk detection method which can operate before convergence of the echo canceller. The proposed method estimates the coefficients of the linear predictive coding(LPC) filter by using the primary input signal. The time-varying threshold for double talk detection is determined based on the LPC filter gain of the primary input signal level. The proposed method can reduce not only false detection rate which means wrong detection of single talk as double talk but also double talk detection delay. Computer simulation was performed using a long-term real speech signals. It is shown that the proposed method improves the conventional method in terms of lowering the false detection rate and shortening the detection delay.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.1-10
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• 1988

In this study, the recognition experiments for Korean vowel are performed using peripheral auditory model. In addition, for the purpose of objective comparison, the recognition experiments are performed by extracting LPC cepstrum coefficients for the same speech data. The results are as follows. 1) The time and the frequency responses of the auditory model show that important features of input signal are involved in the responses of inner ear and auditory nerve. 2) The recognition results for Korean vowel show that the recognition rate by auditory model output is higher than the recognition rate by LPC cepstrum coefficients. 3) The adaptation phenomenon of auditory nerve provides useful characteristics for the discrimination of vowel signal.

• The Journal of the Acoustical Society of Korea
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• v.1 no.1
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• pp.45-53
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• 1982

A robust linear predictive coding method that can be used in noisy as well as quiet environment has been studied. In this method, noise autocorrelation coeffieients are first obtained and updated during nonspeech periods. Then, the effect of additive noise in the input speech is removed by subtracting values of the noise autocorrelation coefficients of corrupted speech in the course of computation of linear prediction coefficients. When signal-to-noise ratio of the input speech ranges from 0 to 10 dB, a performance improvement of about 5 dB can be gained by using this method. The proposed method is computationally very efficient and requires a small storage area.

• Journal of the Korea Computer Industry Society
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• v.5 no.4
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• pp.491-502
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• 2004

In this paper, we propose a HMM model using FSVQ(First Section VQ), fuzzy theory and doubly spectral feature, as study on the isolated word recognition system of speaker-independent. In the proposed paper, LPC cepstrum coefficients and regression coefficients of LPC cepstrum as doubly spectral feature be used. And, training data are divided several section and first section is generated codebook of VQ, and then is obtained multi-observation sequences by order of large propabilistic values based on fuzzy nile from the codebook of the first section. Thereafter, this observation sequences of first section is trained and is recognized a word to be obtained highest probaility by same concept. Besides the speech recognition experiments of proposed method, we experiment the other methods under the equivalent environment of data and conditions. In the whole experiment, it is proved that the proposed method is superior to the others in recognition rate.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.11
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• pp.1559-1563
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• 2014

Recently, the prevention of crime using CCTV draws special in accordance with the higher crime incidence rate. Therefore security systems like a CCTV with audio capability are developing for giving an instant alarm. This paper proposes a scream detecting engine from various ambient noises in real environment for surveillance systems. The proposed engine detects scream signals among the various ambient noises using the features extracted in time/frequency domain. The experimental result shows the performance of our engine is very promising in comparison with the traditional engines using the model based features like LPC, LPCC and MFCC. The proposed method has a low computational complexity by using FFT and cross correlation coefficients instead of extracting complex features like LPC, LPCC and MFCC. Therefore the proposed engine can be efficient for audio-based surveillance systems with low SNRs in real field.

• The Journal of the Acoustical Society of Korea
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• v.26 no.6
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• pp.243-249
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• 2007

We propose an effective voice-based gender identification using group delay(GD) Generally, features for speech recognition are composed of magnitude information rather than phase information. In our approach, we address a difference between male and female for GD which is a derivative of the Fourier transform phase. Also, we propose a novel way to incorporate the features fusion scheme based on a combination of GD and magnitude information such as mel-frequency cepstral coefficients(MFCC), linear predictive coding (LPC) coefficients, reflection coefficients and formant. The experimental results indicate that GD is effective in discriminating gender and the performance is significantly improved when the proposed feature fusion technique is applied.

• Proceedings of the KOSOMBE Conference
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• v.1992 no.11
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• pp.79-82
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• 1992

Under the assumption that the EMG signal was used as the reference signal for driving a prosthetic arm, function discrimination of EMG signal from the biceps and triceps of subject was achived with LPC CEPSTRUM coefficients. By varying the number of coefficients, the types of windows, window size, and window overlaping rates, the best conditions for the function discrimination of EMG signal were obtained.

• Journal of the Institute of Convergence Signal Processing
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• v.18 no.2
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• pp.43-48
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• 2017

The underwater transients signals contain the characteristics of complexity, time varying, nonlinear, and short duration. So it is very hard to model for these signals with reference patterns. In this paper we separate the whole length of signals into some short duration of constant length with overlapping frame by frame. The 20th LPC(Linear Predictive Coding) coefficients are extracted from the original signals using Durbin algorithm and applied to neural network. The 65% of whole signals were learned and 35% of the signals were tested in the neural network with two hidden layers. The types of the whales for sound classification are Blue whale, Dulsae whale, Gray whale, Humpback whale, Minke whale, and Northern Right whale. Finally, we could obtain more than 83% of classification rate from the test signals.