• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.23 no.1
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• pp.76-85
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• 2010

In general, anesthetic depth is evaluated by experience of anesthesiologist based on the changes of blood pressure and pulse rate. So it is difficult to guarantee the accuracy in evaluation of anesthetic depth. The efforts to develop the objective index for evaluation of anesthetic depth were continued but there was few progression in this area. Heart rate variability provides much information of autonomic activity of cardiovascular system and almost all anesthetics depress the autonomic activity. Novel monitoring system which can simply and exactly analyze the autonomic activity of cardiovascular system will provide important information for evaluation of anesthetic depth. We investigated the anesthetic depth as following 7 stages. These are pre-anesthesia, induction, skin incision, before extubation, after extubation, Post-anesthesia. In this study, temporal, frequency and chaos analysis method were used to analyze the HRV time series from electrocardiogram signal. There were NN10-NN50, mean, SDNN and RMS parameter in the temporal method. In the frequency method, there are LF and HF and LF/HF ratio, 1/f noise, alphal and alpha2 of DFA analysis parameter. In the chaos analysis, there are CD, entropy and LPE. Chaos analysis method was valuable to estimate the anesthetic depth compared with temporal and frequency method. Because human body was involved the choastic character.

• Journal of the Korean Society of Radiology
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• v.12 no.4
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• pp.491-496
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• 2018

The purpose of this study is to analyze the noise spectra in DWI (diffusion-weighted imaging) pulse sequences of 1.5 Tesla and 3.0 Tesla MRI, The ACR (American College of Radiology) phantom and noise spectrum were analyzed by FFT (fast Fourier transform) and TFFT (temporal frequency analysis) using WavePad sound editor version 8.13 (NCH software, Greenwood Village, CO, USA). Noise spectra, FFT and TFFT were analyzed for laboratory 1.5Tesla and 3.0Tesla DWI MR pulse sequences. The noise threshold of the frequency amplitude in the FFT and TFFT at 3.0Tesla compared to 1.5Tesla was between 1.5Tesla and -6 dB, and between 3.0Tesla and 0 dB, the DWI pulse sequence for the patient's noise reduction was appropriately MR examination needs to be applied.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.2
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• pp.188-195
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• 2018

Time series analysis was conducted to identify the factors affecting short-term variation of water temperature in Wando. Spectrum analysis showed that air temperature peaks at diurnal period, while water temperature and tide level peak at both semi-diurnal and diurnal periods. Coherence between water temperature and the tide level presented 0.92 at semi-diurnal period. Numerical experiment were carried out to understand the spatio-temporal distribution of water temperature in the study area. Average water temperature difference between maximum ebb and flood was $0.3^{\circ}C$ in spring tide, but $0.13^{\circ}C$ in neap tide. The reason for the large difference in spring tide is that relatively cold water entered with strong tidal currents at flood tide and flowed out at ebb tide. Water temperature on coasts was higher than out at sea. This is because the depth in the coast is shallower than at sea, and water temperature increases rapidly due to solar radiation.

• Journal of IKEEE
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• v.26 no.1
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• pp.62-66
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• 2022

Recently, there have been many research efforts based on data-based deep learning technologies to deal with the interference problem between heterogeneous wireless communication devices in unlicensed frequency bands. However, existing approaches are commonly based on the use of complex neural network models, which require high computational power, limiting their efficiency in resource-constrained network interfaces and Internet of Things (IoT) devices. In this study, we address the problem of classifying heterogeneous wireless technologies including Wi-Fi and ZigBee in unlicensed spectrum bands. We focus on a data-driven approach that employs a supervised-learning method that uses received signal strength indicator (RSSI) data to train Deep Convolutional Neural Networks (CNNs). We propose a simple measurement methodology for collecting RSSI training data which preserves temporal and spectral properties of the target signal. Real experimental results using an open-source 2.4 GHz wireless development platform Ubertooth show that the proposed sampling method maintains the same accuracy with only a 10% level of sampling data for the same neural network architecture.

• Journal of the Optical Society of Korea
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• v.7 no.3
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• pp.139-144
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• 2003

We have characterized the optical parametric chirped-pulse amplification of femtosecond Ti:sapphire laser pulses by using a BBO crystal. It is numerically verified that a high gain and a broad gain bandwidth can be obtained with a 532-nm pump laser. The dependence of the gain profile of OPA on phase matching angles, pump intensity, and crystal length is numerically investigated. Experimental results shows that the temporal fluctuation of a pump laser causes the modulation of an amplified spectrum in OPCPA.

• Journal of the Korean Vacuum Society
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• v.14 no.2
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• pp.69-77
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• 2005

Pumping performance of large cryosorption pumps of different types installed on the 60 $m^3$ test stand for developing and testing ion sources and beam line components of the NBI system was investigated. Hydrogen adsorption and desorption characteristics of the cryosorption panels were analyzed using the temporal change of the hydrogen spectrum obtained with short introduction of the hydrogen gas as cooling the panel, and simulations on the mutual influence between related parameters were also carried out.

• Journal of Mechanical Science and Technology
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• v.17 no.11
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• pp.1599-1607
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• 2003

The characteristics of elastic waves emanated from crack initiation in 6061 aluminum alloy subjected to fatigue loading are investigated through experiments. The objective of the study is to determine the differences in the properties of the signals generated from fatigue test and also to examine if the sources of the waves could be identified from the temporal and spectral characteristics of the acoustic emission (AE) waveforms. The signals are recorded using nonresonant, flat, broadband transducers attached to the surface of the alloy specimens. The time dependence and power spectra of the signals recorded during the tests were examined and classified according to their special features. Six distinct types of signals were observed. The waveforms and their power spectra were found to be dependent on the crack propagation stage and the type of fracture associated with the signals. The potential application of the approach in health monitoring of structural components using a network of surface mounted broadband sensors is discussed.

• Journal of Korea Multimedia Society
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• v.20 no.10
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• pp.1637-1645
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• 2017

Target/Nontarget classification can be divided into the study of shape estimation of the target analysing reflected echo signal and of type classification of the target using acoustical features. In active sonar system, the feature vectors are extracted from the signal reflected from the target, and an classification algorithm is applied to determine whether the received signal is a target or not. However, received sonar signals can be distorted in the underwater environments, and the spatio-temporal characteristics of active sonar signals change according to the aspect of the target. In addition, it is very difficult to collect real sea-trial data for research. In this paper, target/non-target classification were performed using real sea-trial data. Feature vectors are extracted using MFCC(Mel-Frequency Cepstral Coefficients), filterbank energy in the Fourier spectrum and wavelet domain. For the performance verification, classification experiments were performed using backpropagation neural network classifiers.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.7
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• pp.327-333
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• 2002

In this study, we developed a snoring detection algorithm that detects snores automatically. It consists of preprocessing and snoring detection part. The preprocessing part is composed of a noise removal part using spectrum subtraction, and segmentation part, and computation part of temporal and spectral features. And the snoring detection part decides whether detected blocks are snores with BPNN(Back-Propagation Neural Network). BPNN with one hidden layer and one output layer, is trained with data of 7 subjects and tested with data of 11 subjects of total 18 subjects. The proposed algorithm showed a Sensitivity of 90.41% and a Predictive Positive Value of 84.95%.

• The Bulletin of The Korean Astronomical Society
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• v.35 no.1
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• pp.28.1-28.1
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• 2010

Ground-based solar observations have several merits such as wider field of view and higher time cadence than those of satellite observations. The Domeless Solar Telescope of Hida Observatory is designed to acquire solar surface images at the highest possible spatial resolution using two types of spectrographs: a vertical spectrograph with the highest wavelength resolution in the world, and a horizontal spectrograph that can take images of the sun in multi-wavelength over the entire visible solar spectrum. The temporal variation of fine features such as chromospheric grains in the supergranulation cells and facular points in the network region has been obtained using DST Ca II K lines compared with Hinode Ca II H lines. This analysis is expected to provide a fundamental tool for research of diverse phenomena on the solar surface.