• 음성과학
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• 제9권4호
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• pp.253-263
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• 2002

In this paper, we propose new algorithm of speaker recognition which identifies the speaker using the information obtained by the intensive speech feature analysis such as pitch, intensity, duration, and formant, which are crucial parameters of individual voice, for candidates of high percentage of wrong recognition in the existing speaker recognition algorithm. For testing the power of discrimination of individual parameter, DTW (Dynamic Time Warping) is used. We newly set the range of threshold which affects the power of discrimination in speech verification such that the candidates in the new range of threshold are finally discriminated in the next stage of sound parameter analysis. In the speaker verification test by using voice DB which consists of secret words of 25 males and 25 females of 8 kHz 16 bit, the algorithm we propose shows about 1% of performance improvement to the existing algorithm.

• 공학논문집
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• 제2권1호
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• pp.13-21
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• 1997

본 연구는 연속음성을 인식하기 위하여 특징 Parameter의 변동성을 Fuzzy 변수로 취하여 Membership 함수로 표현한 후, Fuzzy 추론으로 연속음성을 인식하는 연구이다. 특징 Parameter로는 Formant 주파수, Pitch, Log Energy, Zero Crossing Rate등을 사용한다. 연속음성의 Data로서는 한국어의 연속음성을 대상으로 하여 음성인식 system을 구현한다음, 인식실험을 통하여 본 연구의 유교성을 확인한다.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권12호
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• pp.702-707
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• 2003

This study describes a new technique for human sensibility evaluation using electroencephalogram(EEG). Production of EEG is assumed to be linear. The linear predictor coefficients and the linear cepstral coefficients of EEG are used as the feature parameters of sensibility and pattern classification performances of them are compared. Using the better parameter, a human sensibility evaluation algorithm is designed. The obtained results are as follows. The linear predictor coefficients showed the better performance in pattern classification than the linear cepstral coefficients. Then, using the linear predictor coefficients as the feature parameter, a human sensibility evaluation algorithm is developed at the base of a multi-layer neural network. This algorithm showed 90% of accuracy in comfortableness evaluation in spite of fluctuations in statistics of EEG signal.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 G
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• pp.2448-2450
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• 1998

For the exact classification of the arm motion this paper proposes EMG pattern recognition method with neural network. For this autoregressive coefficient, linear cepstrum coefficient, and adaptive cepstrum coefficient are selected for the feature parameter of EMG signal, and they are extracted from time series EMG signal. For the function recognition of the feature parameter a radial basis function network, a field of neural network is designed. For the improvement of recognition rate, a number of radial basis function network are combined in parallel, comparing with a backpropagation neural network an existing method.

• 한국음향학회:학술대회논문집
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• 한국음향학회 2002년도 하계학술발표대회 논문집 제21권 1호
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• pp.25-28
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• 2002

Voice activity detection is important Problem in the speech recognition and communication. This paper introduces feature parameter which is reconstructed by the spectral entropy of information theory for the robust voice activity detection in the noise environment, analyzes and compares it with the energy method of voice activity detection and performance. In experiment, we confirmed that the spectral entropy is more feature parameter than the energy method for the robust voice activity detection in the various noise environment.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권6호
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• pp.417-422
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• 2005

This paper presents an EMG pattern classification method to identify motion commands for the control of the artificial arm by SOM-TVC(self organizing map - tracking Voronoi cell) based on neural network with a feature parameter. The eigenvalue is extracted as a feature parameter from the EMG signals and Voronoi cells is used to define each pattern boundary in the pattern recognition space. And a TVC algorithm is designed to track the movement of the Voronoi cell varying as the condition of additive noise. Results are presented to support the efficiency of the proposed SOM-TVC algorithm for EMG pattern recognition and compared with the conventional EDM and BPNN methods.

• 융합정보논문지
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• 제11권10호
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• pp.10-19
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• 2021

본 논문은 뇌파 측정 및 분석을 통해 정신적 피로를 반영하는 특징 파라미터를 도출하고자 하였다. 이를 위해 30분간 눈을 감은 편안한 안정 상태와 뺄셈연산을 암산으로 수행하는 작업을 통해 정신적 피로를 유도하였다. 5명의 피험자가 실험에 참가하였으며, 피험자들은 모두 대학 재학 중인 오른손잡이 남학생들이며 평균 나이는 25.5세이다. 정신적 피로를 반영하는 특징 파라미터 도출을 위해 실험 처음과 마지막에서 수집된 뇌파에 대해 스펙트럼분석을 수행하였다. 분석 결과, 정신적으로 피로할수록 후두엽 및 측두엽 위치에서 알파대역의 절대파워는 증가한 반면 상대파워는 감소하였다. 또한 안정 상태와 작업 상태간 파워 차이는 절대파워에 비해 상대파워가 크게 나타났다. 이 결과는 후두엽 및 측두엽 위치에서의 알파 상대파워가 정신적 피로를 반영하는 특징 파라미터임을 나타낸다. 본 연구 결과는 운전 중 피로 및 졸음 판단과 같은 정신적 피로 판별을 위한 자동화시스템 개발을 위한 특징 파라미터로 활용될 수 있다.

• 전기학회논문지
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• 제61권4호
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• pp.542-548
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• 2012

This study presents a new method of system modeling by using the Discrete Fourier Transform for the position control system of DC Motor. And the proposed method is similar to the method of System Identification by analysis of correlation of the measured input-output data. The measured output signals are transformed to the frequency domain using DFT. The Fourier Spectrum of the transformed signals is used for knowing to the feature of having an important effect on the system. And transfer function of the second order system is estimated by the dominant parameter which is computed in the magnitude and the phase of Fourier spectrum of the transformed signals. In addition, the output signal includes the unique feature of system. So, although the basic parameter of the system is unknown for us, the proposed method has an advantage to system modeling. And the controller is easily designed by the estimated transfer function. Thus, in this paper, the proposed method is applied to the system modeling for the position control system of DC Motor and the PD-controller is designed by the estimated model. And the efficiency and the reliability of the proposed method are verified by the experimental result.

• Bulletin of the Korean Chemical Society
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• 제8권5호
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• pp.398-403
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• 1987

A general theory of polymer adsorption at a semi-permeable oil-water interface of the biphasic solution is presented. The configurational factor of the solution in the presence of the semi-open boundary at the interface is evaluated by the quasicrystalline lattice model. The present theory gives the feature of the bulk concentration equilibria between oil-water subsystems and the surface excesses of ${\Gamma}^{\alpha}$ and ${\Gamma}^\{beta}$ of the polymer segments as a function of the degree of polymerization $\gamma$, the Flory-Huggins parameter in $\beta$-phase $x_{\rho}^{{\beta}_{\rho}}$, the differential adsorption energy parameter in $\beta$-phase $x_{\sigma}^{{\beta}_{\rho}}$, the differential interaction energy parameter ${\Delta}x_{\rho}$ and the bulk concentration of the polymer in ${\beta}-phase ${\varphi}_2^{{\beta(*)}_2}$. From our numerical results, the characteristics of ${\Gamma}^{\alpha}$ are shown to be significantly different from those of ${\Gamma}^{\beta}$ in the case of high polymers, and this would be the most apparent feature of the adsorption behavior of the polymer at a semi-permeable oil-water interface, which is sensitively dependent on ${\Delta}x_{\rho}$ and r.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.150-154
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• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.