• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1951-1956
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• 2004

The transient nature and complex flow geometries of two-phase gas-liquid flows cause fundamental difficulties when measuring flow velocity using an electromagnetic flowmeter. Recently, a current-sensing flowmeter was introduced to obtain measurements with high temporal resolution (Ahn et $al.^{(1)}$). In this study, current-sensing flowmeter theory was applied to measure the fast velocity transients in slug flows. To do this, the velocity fields of axisymmetric gas-liquid slug flow in a vertical pipe were obtained using Volume-of-Fluid (VOF) method and the virtual potential distributions for the electrodes of finite size were also computed using the finite volume method for the simulated slug flow. The output signal prediction for slug flow was carried out from the velocity and virtual potential (or weight function) fields. The flowmeter was numerically calibrated to obtain the cross-sectional liquid mean velocity at an electrode plane from the predicted output signal. Two calibration parameters are required for this procedure: a flow pattern coefficient and a localization parameter. The flow pattern coefficient was defined by the ratio of the liquid resistance between the electrodes for two-phase flow with respect to that for single-phase flow, and the localization parameter was introduced to avoid errors in the flowmeter readings caused by liquid acceleration or deceleration around the electrodes. These parameters were also calculated from the computed velocity and virtual potential fields. The results can be used to obtain the liquid mean velocity from the slug flow signal measured by a current-sensing flowmeter.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.1
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• pp.69-74
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• 1998

In order to monitor the tool wear in milling operation, cutting force is measured as the tool wear increased. The digital signal processing methods are used to detect the tool wear . As AR parameter extract the feature of tool wear , it can be used as input parameter of pattern classifier. The FFT monitor the tool wear exactly , but it can not do real time signal processing. The band energy method can be used to real time monitoring of tool wear ,but int can degrade the exact monitoring.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.91-93
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• 1993

This paper introduced an advanced PWM method to drive a variable speed AC motor. With this technique, a switching pattern is determined to minimize the error between a reference signal and feedback signal. In addition to its simplicity of implementation, the proposed technique has the advantage of control led constant voltage per frequency operation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04a
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• pp.208-212
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• 1993

Third order correlation function is to be advantageous to detect unique features embedded in various random signals that are undtectable with second order correlation or power spectrum. In order paper, we will introduce a real time third order correlator which consists of a laser light source, an acousto-optical modulator with signal controlling circuitry, and a CCD camera connected to computer for image processing. With this correlator, the third order correlation pattern of a signal can be immediately obtained at the camera due to parallelnature of optical computing.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.205-208
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• 2001

본 논문은 Biometrics분야 중 다른 생체학적 특징보다도 정확도면에서 가장 뛰어난 특징인 안구의 홍채 패턴을 이용하여 사용자를 인증 하는 시스템에 관한 연구이다. 입력된 안구 영상으로부터 전처리과정을 거쳐 극좌표 변환을 통해 홍채 패턴을 추출한 후 웨이블릿 변환을 이용하여 특징패턴을 압축하였으며, PCA(Principal Component Analysis:주성분 해석)의 한 종류인 GHA(Generalized Hebbian Algorithm)를 사용하여 등록된 사용자의 패턴 DB 에서 Basis 배열을 추출하고, 구축된 Basis 배열과 입력 영상 패턴과의 비교 Matching을 통하여 사용자를 인증하는 시스템을 제안한다.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.377-380
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• 2002

In this paper, speaker and context independent emotion recognition using speech signal is studied. For this purpose, a corpus of emotional speech data recorded and classified according to the emotion using the subjective evaluation were used to make statical feature vectors such as average, standard deviation and maximum value of pitch and energy and to evaluate the performance of the conventional pattern matching algorithms. The vector quantization based emotion recognition system is proposed for speaker and context independent emotion recognition. Experimental results showed that vector quantization based emotion recognizer using MFCC parameters showed better performance than that using the Pitch and energy Parameters.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.233-236
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• 2000

본 연구에서는 패턴 인식용 다층 퍼셉트론 신경망을 순수 디지털 논리회로 모델로 전환 구현할 수 있도록 새로운 논리뉴런의 구조, 디지털 정형 다층논리신경망 구조, 그리고 패턴인식의 응용을 위한 다단 다층논리 신경망 구조를 제안하고, 또한 제안된 구조는 매우 단순하면서도 효과적인 증가적인 가법적(Incremental Additive) 학습알고리즘이 존재함을 보였다.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.682-685
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• 2004

This paper investigated a method for classifying emotional states by using pulse wave signal. It focused on finding effective features for emotional state classification. The emptional states considered here consisted of interest and neutral. Classification experiments utilized 65 and 60 samples of interest and neutral states respectively. We have investigated 19 features derived from pulse wave signals by using both time domain and frequency domain analysis methods with 2 classifiers of minimum distance (normalized Euclidean distanece) and ${\kappa}$-Nearest Neighbour. The Leave-one-out cross validation was used as an evaluation mehtod. Based on experimental results, the most efficient features were a combination of 4 features consisting of (i) the mean of the first differences of the smoothed pulse rate time series signal, (ii) the mean of absolute values of the second differences of thel normalized interbeat intervals, (iii) the root mean square successive difference, and (iv) the power in high frequency range in normalized unit, which provided 80.8% average accuracy with ${\kappa}$-Nearest Neighbour classifier.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.433-441
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• 1992

This paper describes an algorithm that can efficiently analyze the ECG signal using frame knowledge representation technique. Input to the analysis process is a set of significant points which have been extracted from an original sampled signal(lead II) by the syntactic peak recognition algorithm. The hierarchical property of ECG signal is represented by hierarchical AND/OR graph. The semantic information and constraints of the ECG signal are desctibed by frame. As the control mechanism for labeling points, the search mechanism with the mixed paradigms of data-driven and model driven hypothesis formation, scoring function, hypothesis modification network and instance inheritance are used. We used the CSE database in order to evaluate the performance of the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.55.4-55
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• 2001

Analyzing the ECG signal, we can find heart disease. Myocardial ischemia is a disorder of cardiac function caused by insufficient blood flow to the muscle tissue of the heart. Myocardial ischemia is inscribed on ST-segment of the ECG during and after patient takes exercise or is under stress, but after long time past, the ECG pattern is return to steady state. Therefore, it is necessary to monitor and analyze the ECG signal continuously for patient or aged people. Our primary purpose is the detection of temporary change of the ST-segment of ECG automatically. In the signal processing, the wavelet transform decomposes the ECG signal into high and low frequency components using wavelet function. Recomposing the high frequency bands including QRS complex, we can detect QRS complex more easily ...