• Journal of Institute of Control, Robotics and Systems
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• v.4 no.6
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• pp.765-771
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• 1998

An artificial neural network(NN) technique is developed for hardware redundant sensor validation. Since the measurement space is a continuous space with many operating regions, it is difficult to train a NN to correctly detect failure in an accurate measurement system. A conventional backpropagation NN is modified to include an additional preprocessing layer that extracts classification features from scalar measurements. This feature extraction means transform the measurement space to parity space. The NN is independent of the state variable being measured, the instrument range, and the signal tolerance. This NN resembles the parity space approach to signal validation, except that analytical parity equations are unneeded and the NN pattern recognition capability is utilized for decision making.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.895-896
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• 2006

This paper proposes a new method to measure the ECG signal from the driver. The ECG signal is often measured in the room. But it is mixed with many kinds of noise when we measure it during the vehicle moving. We classified noise occupied most many parts as the experimental among them. And we designed one suitable filter for each noise. It used ALE(Adaptive Line Enhancement) to remove the noise occurred to electromagnetic wave in vehicle. To remove the noise occurred to steering or vibration of vehicle, we used Wavelet transformation after ALE(preprocessing filter).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.1133-1136
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• 2005

The MPEG/audio standard results from three years of co-work by an international committe of high-fidelity audio compression experts in the Moving Picture Experts Group (MPEG/audio). The MPEG standard is rigid only where necessary to ensure interoperability. In this paper, a new approach of training and classification of acoustic signal is addressed. This is some what a fields of application aspects rather than technonical problems such as MPEG/codec, MIDI. In preprocessing, acoustic signal is transformmed using DWT so as to extract a feature parameters of sound such as loudness, pitch, bandwidth and harmonicity. these accoustic parameters are exploited to the input vector of neural perceptron. Experimental results showed that proposed approach can be used for tunning the dissonance chord.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.6
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• pp.1461-1470
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• 2016

In side channel analysis, signal processing techniques can be used as preprocessing to enhance the efficiency and performance of analysis by reducing the noise or compressing the dimension. As signal processing techiniques using singular value decomposition can increase the information of main signal and reduce the noise by using the variance and tendency of signal, it is a great help to improve the performance of analysis. Typical techniques of that are PCA(Principal Component Analysis), LDA(Linear Discriminant Analysis) and SSA(Singular Spectrum Analysis). PCA and LDA can compress the dimension with increasing the information of main signal, and SSA reduces the noise by decomposing the signal into main siganl and noise. When applying each one or combination of these techniques, it is necessary to compare the performance. Therefore, it needs to suggest methodology of that. In this paper, we compare the performance of the three technique and propose using Sinal-to-Noise Ratio(SNR) as the methodology. Through the proposed methodology and various experiments, we confirm the performance and efficiency of each technique. This will provide useful information to many researchers in the field of side channel analysis.

• Geophysics and Geophysical Exploration
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• v.14 no.3
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• pp.227-233
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• 2011

Attention has been focused on geothermal energy as an alternative energy because it is continuously operable without external supply. Most of geothermal anomalies in Korea are related to deep circulation of groundwater through a fracture system in granite area. Therefore it is very important to understand the distribution of the fracture system which is the main channel of ground water. In this research, we constructed the velocity models with a fracture system and the layered sediments, respectively, and generated synthetic data sets with them to verify the presented vertical seismic profiling (VSP) preprocessing scheme. We compared the results from conventional VSP preprocessing flow to those from VSP preprocessing flow considering fracture system. We noticed that the preprocessing flow considering fracture system retains more sufficient signal including down-going wave than conventional preprocessing. In addition, we applied 3D VSP prestack phase screen migration to the preprocessed reversed VSP (RVSP) data from Seokmo Island so that we were able to image fracture structure of the geothermal site in Seokmo Island.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.11
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• pp.114-117
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• 1990

EEG signal is analyzed by two methods, analysis by visual inspection of EEG recording sheets and analysis by quantative method. Generally visual inspection method is used in the clinical field. But this method has its limitation because EEG signal is random signal. Therefore it is necessary to analyze EEG signals quantatively to obtain more precise and objective information of neural and brain. In this paper, power spectrum of EEG signal was estimated by AR(AutoRegressive) model in the frequency domain. This process is useful as a preprocessing stage for tomographic brain mapping (TBM) at each frequency, band. As a method for estimating power spectral density of EEG signals, periodogram method, autocorrelation method. covariance method, modified covariance method, and Burg method are tested in this paper.

• Journal of Biomedical Engineering Research
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• v.35 no.3
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• pp.42-49
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• 2014

We propose an individual identification method using a single-lead electrocardiogram signal. In this paper, lead I ECG is measured from subjects in various physical and psychological states. We performed a noise reduction for lead I signal as a preprocessing stage and this signal is used to acquire the representative beat waveform for individuals by utilizing the ensemble average. From the P-QRS-T waves, features are extracted to identify individuals, 19 using the duration and amplitude information, and 16 from the QRS complex acquired by applying Pan-Tompkins algorithm to the ensemble averaged waveform. To analyze the effect of each feature and to improve efficiency while maintaining the performance, Relief-F algorithm is used to select features from the 35 features extracted. Some or all of these 35 features were used in the support vector machine (SVM) learning and tests. The classification accuracy using the entire feature set was 98.34%. Experimental results show that it is possible to identify a person by features extracted from limb lead I signal only.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.193-196
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• 2000

An stereophonic acoustic echo canceller cannot exactly estimate the echo path in the receiving room, because of the cross-correlation between stereo signals. In this paper, the new preprocessing filter is proposed to reduce the cross-correlation between the signals without influence on stereophonic sound. Two channel signals are linearly decorrelated by using orthogonality principles and the attenuated absolute values of the decorrelated signals are added to each channel input signals. Assuming that the power of each channel signal is larger than that of the cross-correlation, computational burden is reduced.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.718-723
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• 1993

In this paper, we propose a neural network with fuzzy preprocessor not only for improving the classification accuracy but also for being able to classify objects whose attribute values do not have clear boundaries. The fuzzy input signal representation scheme is included as a preprocessing module. It transforms imprecise input in linguistic form and precisely stated numerical input into multidimensional numerical values. The transformed input is processed in the postprocessing module. The experimental results indicate the superiority of the backpropagation network with fuzzy preprocessor in comparison to the conventional backpropagation network.

• The Journal of the Korea Contents Association
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• v.15 no.4
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• pp.1-8
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• 2015

This paper presents a Electrocardiogram(ECG) identification method using adaptive weight based on Least Mean Square Error(LMSE) optimization. With a preprocessing for noise suppression, we extracts the average ECG signal and its standard deviation at every time instant. Then the extracted information is stored in database. ECG identification is achieved by matching an input ECG signal with the information in database. In computing the matching scores, the standard deviation is used. The scores are computed by applying adaptive weights to the values of the input signal over all time instants. The adaptive weight consists of two terms. The first term is the inverse of the standard deviation of an input signal. The second term is the proportional one to the standard deviation between user SAECGs stored in the DB. Experimental results show up to 100% recognition rate for 32 registered people.