• Analytical Science and Technology
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• v.15 no.5
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• pp.399-409
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• 2002

A system software on-line spectral analysis of atomic emission spectrometer. The system program consisted of a control part for the optical instruments and the spectrum analysis part the artificial intelligence method to reduce nonlinear error of the wavelengths. McPHERSON 207 Monochromator controlled GPIB communication protocol, and the detector signal was measured from PMT by using A/D Amplifier that was made by Photon_Tek. co.. HNN(Hybrid Neural Network) of artificial intelligence technique was applied to the qualitative analysis of P, Cu, Fe, Cr, and that was accurately applied to the quantitative analysis of Cd with 10 ppb level better than the conventional methods.

• ETRI Journal
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• v.31 no.2
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• pp.209-214
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• 2009

In this paper, we propose a novel test methodology for the detection of catastrophic and parametric faults present in analog very large scale integration circuits. An automatic test pattern generation algorithm is proposed to generate piece-wise linear (PWL) stimulus using wavelets and a genetic algorithm. The PWL stimulus generated by the test algorithm is used as a test stimulus to the circuit under test. Faults are injected to the circuit under test and the wavelet coefficients obtained from the output response of the circuit. These coefficients are used to train the neural network for fault detection. The proposed method is validated with two IEEE benchmark circuits, namely, an operational amplifier and a state variable filter. This method gives 100% fault coverage for both catastrophic and parametric faults in these circuits.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.8
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• pp.2109-2118
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• 1996

The neural convolutional decoder(NCD) was proposed as a method of decoding convolutional codes. In this paper, simulation results are presented for coherent BPSK in memoryless AWGN channels and coherent QPSK in the satellite channels. The NCD can learn the nonlinear distortion caused by the charactersitics of the satellite channel including the filtering effects and the nonlinear effects of the travling wave tube amplifier(TWTA). Thus, as compared with the AWGN channel, the performance difference in the satellite channel between the NCD for the systematic code and the Viterbi decoder for the nonsystematic code is reduced.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.113-117
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• 1997

In this paper we detected the thrombosis formation by spectral analysis and neural network. Using microphone and amplifier, we measured the sound from the mechanical valve which is attached to the pneumatic ventricular assist device. The sound was sampled by A/D converter and the periodogram is the main algorithm or obtaining spectrum. We made the valvular thrombosis models using pellethane and silicon and they are thrombosis model on the disk, around the sewing ring and fibrous tissue growth across the orifice of valve. The spectrum of normal and 5 kinds of thrombotic valve were obtained and primary and secondary peak appeared in each spectrum waveform. So to distinguish the secondary peak of normal and thrombotic valve quantatively, 3 layer back propagation neural network.

• Journal of Biomedical Engineering Research
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• v.21 no.5
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• pp.477-485
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• 2000

본 논문에서는 신경신호기록을 위한 반도체 미세전극용 전치증폭기의 잡음특성을 설계하기 위한 체계적인 방법을 제시한다. 세포외기록(extracellular recording)에 의하여 측정된 신경신호와 전형적인 CMOS소자의 저주파 잡음특성을 함계 고려하여 전체 신호대잡음비를 계산하였다. 2단 CMOS 차동증폭기에 대한 해석과 함께 신호대잡음비에 중요한 영향을 끼치는 요소들에 대하여 설명하였다. 출력잡음전력에 대한 해석적인식을 유도하였으며 이로부터 회로설계자가 조절할 수 있는 주파수응답과 소자 파라미터들을 결정하였다. 입력소자의 크기와 트랜스컨덕턴스의 비가 최적영역으로부터 약간 벗어날 경우에 신호대잡음비가 크게 저하됨을 보였다. 이와 함께 만족스런 잡음특성을 위한 증폭이의 설계 변수 값들도 제시하였다.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.41.3-41
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• 2001

In this paper, we present a decentralized adaptive neural net(NN) controller for the transient stability and voltage regulation of a multimachine power system. First, an adaptively input-output linearizing controller using NN is designed to eliminate the nonlinearities and interactions between generators. Then, a robust control term which bounds terminal voltage to a neighborhood of the operating point within the desired value is introduced using only local information. In addition, we consider input saturation which exists in the SCR amplifier and prove that the stability of the overall closed-loop system is maintained regardless of the input saturation. The design procedure is tested on a two machine infinite bus power system.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.86-89
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• 1995

A clinically oriented 32 channel Electroencephalogram (EEG) and evoked potential (EP) mapping system has been developed. The EEG and EP signals acquired from 32-channel electrodes are amplified by the pre-amplifier located near patient and are then tither amplified by main amplifier. An automatic artifact rejection scheme is employed using a neural network by which examination time is reduced substantially. Auditary and visual stimuli are used for the evoked potential mapping. A user-friendly graphical interface based on the Microsoft Window 3.1 is developed for the operation of the system. Statistical databases for the poop and individual comparisons are also included to support statistically based diagnosis.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.1-5
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• 2013

The two major problems related to the blood flow in replaced prosthetic heart valve are thrombus formation and hemolysis. Reliability of prosthetic valve is very important because its failure means the death of patient. There are many factors affecting the valvular failures and their representatives are mechanical failure and thrombosis, so early noninvasive detection is essentially required. The purpose of this study is to detect the various thromboses formation by using acoustic signal acquisition and its spectral analysis on the frequency domain. We made the thrombosis models using Polydimethylsiloxane (PDMS) and they are thrombosis model on the disc, around the sewing ring and fibrous tissue growth across the orifice of valve. Using microphone and amplifier, we measured the acoustic signal from the prosthetic valve, which is attached to the pulsatile mock circulation system. A/D converter sampled the acoustic signal and the spectral analysis is the main algorithm for obtaining spectrum. Then the spectrum of normal and 5 different kinds of abnormal valve were obtained. Each spectrum waveform shows a primary and secondary peak. The secondary peak changes according to the thrombus model. To quantitatively distinguish the frequency peak of the normal valve from that of the thrombosed valves, analysis using a neural network was employed. Acoustic measurement has been used as a noninvasive diagnostic tool and is thought to be a good method for detecting possible mechanical failure or thrombus.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.429-438
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• 1997

Mechanical valve is one of the most widely used implantable artificial organs of which the reliability is so important that its failure means the death of patient. Therefore early noninvasive detection is essentially required, though mechanical valve failure with thrombosis is the most common. The objective of this paper is to detect the thrombosis formation by spectral analysis and neural network. Using microphone and amplifier, we measured the sound from the mechanical valve which is attached to the pneumatic ventricular assist device. The sound was sampled by A/D converter(DaqBook 100) and the periodogram is the main algorithm for obtaining spectrum. We made the thrombosis models using pellethane and silicon and they are thrombosis model on the valvular disk, around the sewing ring and fibrous tissue growth across the orifice of valve. The performance of the measurment system was tested firstly using 1 KHz sinusoidal wave. The measurement system detected well 1KHz spectrum as expected. The spectrum of normal and 5 kinds of thrombotic valve were obtained and primary and secondary peak appeared in each spectrum waveform. We find that the secondary peak changes according to the thrombosis model. So to distinguish the secondary peak of normal and thrombotic valve quantatively, 3 layer back propagation neural network, which contains 7, 000 input node, 20 hidden layer and 1 output was employed The trained neural network can distinguish normal and valve with more than 90% probability. As a conclusion, the noninvasive monitoring of implanted mechanical valve is possible by analysing the acoustical spectrum using neural network algorithm and this method will be applied to the performance evaluation of other implantable artificial organs.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.147-156
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• 2023

Efforts to employ smart home sensors to monitor the indoor activities of elderly single residents have been made to assess the feasibility of a safe and healthy lifestyle. However, the bathroom remains an area of blind spot. In this study, we have developed and evaluated a new edge computer device that can automatically detect water usage activities in the bathroom and record the activity log on a cloud server. Three kinds of sound as flushing, showering, and washing using wash basin generated during water usage were recorded and cut into 1-second scenes. These sound clips were then converted into a 2-dimensional image using MEL-spectrogram. Sound data augmentation techniques were adopted to obtain better learning effect from smaller number of data sets. These techniques, some of which are applied in time domain and others in frequency domain, increased the number of training data set by 30 times. A deep learning model, called CRNN, combining Convolutional Neural Network and Recurrent Neural Network was employed. The edge device was implemented using Raspberry Pi 4 and was equipped with a condenser microphone and amplifier to run the pre-trained model in real-time. The detected activities were recorded as text-based activity logs on a Firebase server. Performance was evaluated in two bathrooms for the three water usage activities, resulting in an accuracy of 96.1% and 88.2%, and F1 Score of 96.1% and 87.8%, respectively. Most of the classification errors were observed in the water sound from washing. In conclusion, this system demonstrates the potential for use in recording the activities as a lifelog of elderly single residents to a cloud server over the long-term.