• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.6
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• pp.1004-1015
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• 1994

Modulation type classifier based on statistical moments has been successfully employed to classify PSK signals. Previously, the classifier developed utilizes the statistical moment of samples of the received signal phase, which may be difficult to extract from received signal. In this paper we propose a new moments-based classifier to classify PSK signals by using the moments of the demodulated signal for PSK. THe demodulated signal can be easily extracted from the conventional demodulation of PSK. The evaluation of the performance of the proposed classifier for PSK signals has been investigated in additive white Gaussian noise environment using the exact distribution of the demodulated signal. The performances of classifier in terms of probability of misclassification were evaluated. We found that the coherent system classifier gave 4dB improvement for BPSK and 3dB for QPSK over noncoherent system classifier, when the probability of misclassification is 10 and m equals to 4.

• Journal of the Korean GEO-environmental Society
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• v.10 no.7
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• pp.135-141
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• 2009

For more reliable estimation of soil-structure interaction and seismic source and attenuation properties, site amplification function should be considered. This study use the Nakamura's method (1989) for estimating site amplification though various methods for the same purpose have been proposed. This method was originally applied to the surface waves of background noise and therefore there are some limitation for applications to general wave energy. However, recently this method has been extended and applied to the S wave energy successfully. This study applied the method to the coda wave energy which is equivalent to the backscattered S wave energy. We used more than 60 observed ground motions from 5 earthquakes which occurred recently, with magnitude range from 3.6 to 5.1 Each station showed characteristic site amplification property in low-, high- and resonance frequency ranges. In the case of comparing these results to those from S wave energy, lots of information to the site classification work can be gained. Moreover, removal of site amplification can give us more reliable seismic source parameters.

• The Journal of Engineering Geology
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• v.22 no.4
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• pp.399-408
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• 2012

To estimate seismic source and soil-structure interaction more reliably, site amplification characteristics should be considered. Among the various estimation methods, we used Nakamura's method (1989) to estimate site amplification. This method was originally applied to background noise; however, it has recently been successfully applied to S-wave and Coda-wave energy, and is applied to S-waves in the present study. We used more than 180 observed ground motions from 23 macro-earthquakes and then analyzed site amplification characteristics at eight seismic stations. Each station showed characteristics of site amplification properties in the low-, high- and resonance-frequency ranges. Comparison of the present results with those of other studies provide successful information regarding the dynamic amplification of domestic site characteristics and site classification.

• Journal of Digital Convergence
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• v.14 no.4
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• pp.277-283
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• 2016

This study proposed new methods to classify normal and epileptic seizure signals from EEG signals using peaks extracted by wavelet transform(WT) and Hilbert transform(HT) based on a neural network with weighted fuzzy membership functions(NEWFM). This study has the following three steps for extracting inputs for NEWFM. In the first step, the WT was used to remove noise from EEG signals. In the second step, the HT was used to extract peaks from the wavelet coefficients. We also selected the peaks bigger than the average of peaks to extract big peaks. In the third step, statistical methods were used to extract 16 features used as inputs for NEWFM from peaks. The proposed methodology shows that accuracy, specificity, and sensitivity are 99.25%, 99.4%, 99% with 16 features, respectively. Improvement in feature selection method in view to enhancing the accuracy is planned as the future work for selecting good features from 16 features.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7C
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• pp.604-610
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• 2010

In this paper we proposed FSFN (Filter bank sub-band energy subtraction based CLSFN) method to improve the recognition performance of the existing CLSFN (Cepstral distance and Log-energy based Silence Feature Normalization). The proposed FSFN reduces the energy of noise components in filter bank sub-band domain when extracting the features from speech data. This leads to extract the enhanced cepstral features and thus improves the accuracy of speech/silence classification using the enhanced cepstral features. Therefore, it can be expected to get improved performance comparing with the existing CLSFN. Experimental results conducted on Aurora 2.0 DB showed that our proposed FSFN method improves the averaged word accuracy of 2% comparing with the conventional CLSFN method, and FSFN combined with CMVN (Cepstral Mean and Variance Normalization) also showed the best recognition performance comparing with others.

• Journal of Environmental Health Sciences
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• v.30 no.3
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• pp.272-282
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• 2004

Stress is a primary health promotion issue in worksite research because psychological distress is closely related not only to workers　 health status but also to their job performance. This study identified the work environment and work condition factors affecting workers　 stress symptoms among the Korean manufacturing factory workers. A total of 7,818 factory workers employed in 1,562 manufacturing companies participated in the Korean nation-wide occupational health survey conducted by the Korean Occupational Safety and Health Agency in 2003. Participants were selected by the stratified proportional sampling process by standardized industry classification, company size, and locations. Trained interviewers visited the target companies and interviewed the factory workers randomly selected in each company. Work environments included physical work environments (temperature, noise, hazardous organic compounds, and so on) and psychological work environments (job demands, job control, and social support at work), and work conditions included daily working hour, rest time, and so on. Men were 71.5% and the mean age was 34.0 years old. The average working period in the present company was 6.9 years. The average stress score was 26.2 under the perfect score, 50, which means the moderate level of stress. Perceived stress had significant correlations with young age, poor physical work environment, high fatigue, bad perceived health status, and high job demands in Pearson's simple correlation analysis. Perceived health status and perceived fatigue explained 21% variance of stress symptoms and the work environment factor explained 4.8% of that; however, work condition did not have the sufficient effect. In particular, psychosocial work environment variables (job demand, job control, and social support at work) had a clear effect on stress symptoms rather than the physical work environments. Poor perceived health status, severe perceived fatigue, poor physical work environment, high job demands, low social support, heavy alcohol consumption and little exercise were significantly related to high stress symptoms in the Korean manufacturing workers.

• Journal of IKEEE
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• v.24 no.1
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• pp.120-126
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• 2020

We present a user recognition method using human body impulse response signals. The body compositions vary from person to person depending on the portion of water, muscle, and fat. In the body communication study, the body has been interpreted circuit models using capacitance and resistances, and its characteristics are determined by the body compositions. Therefore, the individual body channel is unique and can be used for user recognition. In this paper, we applied pseudo impulse signals to the left hand and recorded received signals from the right hand. The empirical mode decomposition (EMD) method removed noise from the received signals and 10 peak values are extracted. We set the differences between peak amplitudes as a key feature to identify individuals. We collected data from 6 subjects and achieved accuracy of 97.71% for the user recognition application.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.4C
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• pp.533-540
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• 2004

This paper proposes a new multispectral image data compression algorithm that can efficiently reduce spatial and spectral redundancies by applying classified prediction, a Karhunen-Loeve transform (KLT), and the three-dimensional set partitioning in hierarchical trees (3-D SPIHT) algorithm in the wavelet transform (WT) domain. The classification is performed in the WT domain to exploit the interband classified dependency, while the resulting class information is used for the interband prediction. The residual image data on the prediction errors between the original image data and the predicted image data is decorrelated by a KLT. Finally, the 3-D SPIHT algorithm is used to encode the transformed coefficients listed in a descending order spatially and spectrally as a result of the WT and KLT. Simulation results showed that the reconstructed images after using the proposed algorithm exhibited a better quality and higher compression ratio than those using conventional algorithms.

• Transactions of the KSME C: Technology and Education
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• v.3 no.1
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• pp.71-78
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• 2015

Although there are differences in the classification of category adopted by each country, small UAS is usually classified as the one less than 25 kg. UAS has been mainly used for military and public purposes, but in recent years, it has spread to the private sector for hobby, media, and so on. Especially, considering the nature of the operating region and applications, it is necessary to improve operating time, noise and vibration in small UAS to ensure the same level of safety with a manned aircraft. This is because the drone can pose health and safety hazard through collision with manned aircraft or crashing into the ground. In this paper, we investigated operational regulations in the United States and European countries. Based on the investigation, a domestic system development plan for small UAS operation is under development.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.3
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• pp.221-228
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• 2016

In this paper, we introduce our simulation software for EEG signal accuracy improvement. Users can check and train own EEG signal accuracy using our simulation software. Subjects were shown emotional imagination condition with landscape photography and logical imagination condition with a mathematical problem to subject. We use that EEG signal data, and apply Independent Component Analysis algorithm for noise removal. So we can have beta waves(${\beta}$, 14-30Hz) data through Band Pass Filter. We extract feature using Root Mean Square algorithm and That features are classified through Support Vector Machine. The classification result is 78.21% before EEG signal accuracy improvement training. but after successive training, the result is 91.67%. So user can improve own EEG signal accuracy using our simulation software. And we are expecting efficient use of BCI system based EEG signal.