• Transactions on Electrical and Electronic Materials
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• v.18 no.4
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• pp.221-224
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• 2017

This paper dealt with the extraction of series arc signals based on wavelet transform in order to improve the accuracy of arc detection in indoor wiring systems. Three types of arc sources including a cord-cord, a terminal-cord, and an outlet-plug were fabricated to simulate typical arc defects. An arc generator fabricated according to UL 1699 was used to generate arcs. The optimal mother wavelet was selected as bior1.5 by calculating the correlation coefficients between the detected single current pulse and the wavelet. The detected arc current signals were then decomposed into eight levels using the discrete wavelet transform that implements the multi-resolution analysis method. By analyzing the decomposed components, the detail components D6, D7, and D8 were associated with arc signals, which were used for signal reconstruction. From the result, it was verified that the proposed method can be used for the extraction of the series arc signal from the AC mains, which is expected to be applied to further analysis of arc signals in indoor wiring systems.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.23-29
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• 2019

In this paper, a deep learning-based detection and classification using multi-band frequency signals is presented for detecting earthquakes prevalent in Korea. Based on an analysis of the previous earthquakes in Korea, it is observed that multi-band signals are appropriate for classifying earthquake signals. Therefore, in this paper, we propose a deep CNN (Convolutional Neural Network) using multi-band signals as training data. The proposed algorithm extracts the multi-band signals (Low/Medium/High frequency) by applying band pass filters to mel-spectrum of earthquake signals. Then, we construct three CNN architecture pipelines for extracting features and classifying the earthquake signals by a late fusion of the three CNNs. We validate effectiveness of the proposed method by performing various experiments for classifying the domestic earthquake signals detected in 2018.

• Korean Journal of Clinical Pharmacy
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• v.31 no.4
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• pp.324-331
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• 2021

To investigate signals of adverse drug reactions of finasteride by using the Korea Adverse Events Reporting System (KAERS) database. This pharmacovigilance was based on the database of the drug-related adverse reactions reported spontaneously to the KAERS from 2013 to 2017. This study was conducted by disproportionality analysis. Data mining analysis was performed to detect signals of finasteride. The signal was defined by three criteria as proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC). The signals of finasteride were compared with those of the other drugs; dutasteride (similar mechanism of action), minoxidil (different mechanism but similar indications for alopecia), silodosin (different mechanism but similar indications for BPH). It was examined whether the detected signals exist in drug labels in Korea. The total number of adverse event-drug pairs was reported 2,665,429 from 2013 to 2017, of which 1,426 were associated with finasteride. The number of investigated signals of finasteride was 42. The signals that did not include in the drug label were 29 signals, including mouth dry, hypotension, dysuria etc. The signal of finasteride was similar to that of dutasteride and silodosin but was different to that of minoxidil. Early detection of signals through pharmacovigilance is important to patient safety. We investigated 29 signals of finasteride that do not exist in drug labels in Korea. Further pharmacoepidemiological studies should be needed to evaluate the signal causality with finasteride.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.455-458
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• 1997

We are exposed to the various external stimuli input from the environment, which cause emotional changes based on the characteristics of the stimuli. Unfortunately, there are no quantitative results on relationship between human sensibility and the characteristics of physiological signals. The objective of this study was to quantify EEG signals evoked by visual stimulation based on the assumption that the analysis of the variability on the characteristics of the EEG waveform may provide the significant information regarding changes in psychological states of the subject. Seven university students were participated in this study. The experiment was devised with eleven experimental conditions, which are control and ten different types of visual stimulation based on IAPS (International Affective Picture Systems). Wavelet transformation was employed to analyze the EEG signals. Most positive and negative emotional response were compared in pairs. The results showed that the reconstructed signals at the decomposition level revealed the different energy value on the EEG signals. Also, general patterns of EEG signals in rest state compare with positive and negative stimulus were found. This study could be extended to establish an algorithm which distinguishes psychophysiological states of the subjects exposed to the visual stimulation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.14 no.5
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• pp.12-18
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• 2000

This paper describes the analysis of ultrasonic signal and partial discharge(PD) signal patterns for each location of particle in GIS. The characteristics of the ultrasonic signal and PD signal patterns for each location were as follows; in case of particle on the electrode, large ultrasonic signals and large PD signals occurred in positive half-cycle periodically; in case of particle on the spacer, ultrasonic signals and large PD signals occurred in negative half-cycle periodically; in case of the free moving particle, ultrasonic signals and large PD signals occurred by collision non-periodically. As a result, it was confirmed that particle in GIS could be located by the comparison and analysis of ultrasonic signal and PD signal patterns.

• Journal of the Society of Naval Architects of Korea
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• v.49 no.6
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• pp.498-503
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• 2012

Independent component analysis (ICA) technique based on statistical independency of the signals is known as suitable to identify the source signals by measuring and separating mixed signals through transfer paths and has successfully applied in the field of medical care, communications and so forth. In this study, the ICA technique is introduced for the identification of excitation sources from measured vibration signals of structures, which can be done by evaluating negentropy of centered and whitened vibration signals and correlation of separated signals. To validate the method, numerical analyses are carried out for a plate and a cylinder structure. The results show that the method can be applied efficiently to source identification of complex structures. Nevertheless, additional studies would be required to complement problems of occasional inaccuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.5
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• pp.595-600
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• 2014

Nowadays our society is rapidly transforming into an aging society. A better understanding of dementia is a high priority in the aging society. Therefore our study is basically aimed at understanding characteristics of EEG signals from dementia patients. Firstly, we analyzed spontaneous EEG signals from normal persons and dementia patients to distinguish their characteristics. The EEG signals are recorded with 16 electrodes and we classified the EEG signals form the signals according to frequency band. To obtain the clean EEG signals, we used cross correlation function between two channels. From the analysis results, we can observe that the EEG characteristics from dementia patients are distinctly different from that from normal persons.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.3 no.1
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• pp.107-114
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• 1999

In many applications (e,g., identification of non-destructive testing signal and biomedical signal and multiscale analysis of image), it is of interest to analyze and identify phenomena occurring at the different scales. The recently introduced wave let transforms provide a time-scale decomposition of signals that offers the possibility of such signals. However, there is no corresponding statistical properties to development of multiscale statistical signal processing. In this paper, we derive such properties of random signals in wavelet domain.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.86.1-86
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• 2002

Polarity correlation is a simplified version of the usual, having a possibility of real-time processing without any reduction of precision. In this paper, its applicability in deterministic signals is first explored generally, by theoretical or numerical analysis of four kinds of the typical signals. Then, based on the results, its two applications are proposed, that are detection and demodulation of FSK signals in digital communication, and time delay estimasion in ultrasonic A-mode measurement. The effectiveness of the proposed applications is confirmed by its superior implementation ability and the results of numerical experiments under practical circumstances.

• Proceedings of the IEEK Conference
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• 2002.06e
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• pp.9-12
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• 2002

The problem of sub-Nyquist nonuniform sampling for the perfect reconstruction of signals with time-varying spectral contents is studied. The signals are assumed to have a known instantaneous bandwidth in time-frequency domain. As the function of time, the nonuniform sampling pattern of a given signal, that is, the instantaneous sampling frequency is determined by the observation of instantaneous bandwidth based on time-frequency analysis. The proposed sampling pattern guarantees the perfect reconstruction of nonuniform sampled signals under Nyquist-sampling rate in average.