• Journal of Korea Society of Industrial Information Systems
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• v.20 no.6
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• pp.37-46
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• 2015

The wireless electrocardiographic monitoring system(WDMS) is designed to be long term monitoring for the early detection of cardiac disorders. The current version of the WDMS can identify two types of cardiac rhythms in real-time, such as atrial fibrillation(AF) and normal sinus rhythm(NSR), which are very important to track cardiac-rhythm disorders. In this study, we proposed the analysis method to discriminate the characteristics statistically evaluated in both time and frequency domains between AF and NSR using various parameters in the heart rate variability(HRV). And we applied various ECG detection methods (e.g., difference operation method) and compared the results with those of the discrete wavelet transform(DWT) method. From the statistically results, we found that the parameters such as STD RR, STD HR, RMSSD, NN50, pNN50, RR Trian, and TNN(p<0.05) are significantly different between the AF and NSR patients in time domain. On the other hand, the frequency domain analysis results showed a significant difference in VLF power($ms^2$), LF power($ms^2$), HF power($ms^2$), VLF(%), LF(%), and HF(%). In particular, the parameters such as STD RR, RMSSD, NN50, pNN50, VLF power, LF power and HF power were considered as the most useful parameters in both AF and NSR patient groups. Our proposed method can be efficiently applied to early detection of abnormal conditions and prevent the such abnormals from becoming serious.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2671-2676
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• 2009

Empirical mode decomposition (EMD) is new time-frequency analysis method to decompose the signal adaptively and efficiently. The key idea of EMD is to decompose the signal into a set of functions defined by the signal itself, named Intrinsic Mode Functions (IMFs), which preserve the inherent properties of the original signal. Since the decomposition is based on the local time scale of the signal, it is not only applicable to nonlinear and non-stationary processes but also useful in biomedical signals like electrocardiogram (ECG). Traditional low-pass filter uses fourier transform to analysis signal in frequency domain, but EMD is filtered to maintain signal properties in time domain. This paper performed signal decomposition and filtering for noisy ECGs using EMD method. The proposed method is presented and compared with traditional low-pass filter by two performance indices. Our results show effectiveness for enhancement of the noisy ECG waveforms.

• International journal of steel structures
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• v.18 no.4
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• pp.1191-1199
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• 2018

Concepts of submerged floating tunnels (SFTs) for land connection have been continuously suggested and developed by several researchers and institutes. To maintain their predefined positions under various dynamic environmental loading conditions, the submerged floating tunnels should be effectively moored by reasonable mooring systems. With rational mooring systems, the design of SFTs should be confirmed to satisfy the structural safety, fatigue, and operability design criteria related to tunnel motion, internal forces, structural stresses, and the fatigue life of the main structural members. This paper presents a feasibility study of a submerged floating tunnel moored by an inclined tendon system. The basic structural concept was developed based on the concept of conventional cable-stayed bridges to minimize the seabed excavation, penetration, and anchoring work by applying tower-inclined tendon systems instead of conventional tendons with individual seabed anchors. To evaluate the structural performance of the new type of SFT, a hydrodynamic analysis was performed in the time domain using the commercial nonlinear finite element code ABAQUS-AQUA. For the main dynamic environmental loading condition, an irregular wave load was examined. A JONSWAP wave spectrum was used to generate a time-series wave-induced hydrodynamic load considering the specific significant wave height and peak period for predetermined wave conditions. By performing a time-domain hydrodynamic analysis on the submerged floating structure under irregular waves, the motional characteristics, structural stresses, and fatigue damage of the floating tunnel and mooring members were analyzed to evaluate the structural safety and fatigue performance. According to the analytical study, the suggested conceptual model for SFTs shows very good hydrodynamic structural performance. It can be concluded that the concept can be considered as a reasonable structural type of SFT.

• Composites Research
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• v.35 no.1
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• pp.8-12
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• 2022

Analysis of defect signals inside glass fiber reinforced polymer (GFRP) was conducted through deconvolution of terahertz (THz) wave. The GFRP specimen with internal defects was manufactured and the THz signal was measured through the reflection mode of the Terahertz Time-Domain Spectroscopy (THz-TDS) system. For deconvolution of the measured THz signal, the peak position of the THz signal was amplified through Normalized Cross Correlation (NCC) of the incident and detected THz signals. The position and intensity of the amplified peak were extracted as impulse, and the extracted signal of the impulse position was removed from the THz original signal. By repeating the process, the critical impulses, which represent boundary of the specimen, were derived. The deconvolution process was verified by confirming that the original THz signal without noise can be restored through the convolution of the critical impulses and the incident signal. From the derived critical impulses, the thickness of the internal defect in the GFRP was calculated through the detection time of impulses within 15 ㎛ accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5527-5545
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• 2019

Through-wall ultra-wide band (UWB) radar has been considered as one of the preferred and non-contact technologies for the targets detection owing to the better time resolution and stronger penetration. The high time resolution is a result of a larger of bandwidth of the employed UWB pulses from the radar system, which is a useful tool to separate multiple targets in complex environment. The article emphasised on human subject localization and detection. Human subject usually can be detected via extracting the weak respiratory signals of human subjects remotely. Meanwhile, the range between the detection object and radar is also acquired from the 2D range-frequency matrix. However, it is a challenging task to extract human respiratory signals owing to the low signal to clutter ratio. To improve the feasibility of human respiratory signals detection, a new method is developed via analysing the standard deviation based kurtosis of the collected pulses, which are modulated by human respiratory movements in slow time. The range between radar and the detection target is estimated using joint time-frequency analysis (JTFA) of the analysed characteristics, which provides a novel preliminary signature for life detection. The breathing rates are obtained using the proposed accumulation method in time and frequency domain, respectively. The proposed method is validated and proved numerically and experimentally.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.951-960
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• 2016

Tower insulators in electric power transmission network play a crucial role in preserving the reliability of the system. Electrical utilities frequently face the problem of flashover of insulators due to pollution deposition on their surface. Several research works based on leakage current (LC) measurement has been already carried out in developing diagnostic techniques for these insulators. Since the LC signal is highly intermittent in nature, estimation of pollution severity based on LC signal measurement over a short period of time will not produce accurate results. Reports on the measurement and analysis of LC signals over a long period of time is scanty. This paper attempts to use Random Forest (RF) classifier, which produces accurate results on large data bases, to analyze the pollution severity of high voltage tower insulators. Leakage current characteristics over a long period of time were measured in the laboratory on porcelain insulator. Pollution experiments were conducted at 11 kV AC voltage. Time domain analysis and wavelet transform technique were used to extract both basic features and histogram features of the LC signal. RF model was trained and tested with a variety of LC signals measured over a lengthy period of time and it is noticed that the proposed RF model based pollution severity classifier is efficient and will be helpful to electrical utilities for real time implementation.

• Biomolecules & Therapeutics
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• v.8 no.1
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• pp.63-72
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• 2000

We intended to show some basic requirements for spectrum analysis of electroencephalogram (EEG) by visualizing the differences of the results according to different values of some parameters for analysis. Spectrum analysis is the most popular technique applied for the quantitative analysis of the electroen- cephalographic signals. Each step from signal acquisition through spectrum analysis to presentation of parameters was examined with providing some different values of parameters. The steps are:(1) signal acquisition; (2) spectrum analysis; (3) parameter extractions; and (4) presentation of results. In the step of signal acquisition, filtering and amplification of signal should be considered and sampling rate for analog-to-digital conversion is two-time faster than highest frequency component of signal. For the spectrum analysis, the length of signal or epoch size transformed to a function on frequency domain by courier transform is important. Win dowing method applied for the pre-processing before the analysis should be considered for reducing leakage problem. In the step of parameter extraction, data reduction has to be considered so that statistical comparison can be used in appropriate number of parameters. Generally, the log of power of all bands is derived from the spectrum. For good visualization and quantitative evaluation of time course of the parameters are presented in chronospectrogram.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• Journal of the Earthquake Engineering Society of Korea
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• v.27 no.2
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• pp.77-82
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• 2023

The structural analysis module is an essential part of any integrated structural system. Diverse integrated systems today require, from the analysis module, efficient real-time responses to real-time input such as earthquake signals, extreme weather-related forces, and man-made accidents. An integrated system may also be for the entire life span of a civil structure conceived during the initial conception, developed throughout various design stages, effectively used in construction, and utilized during usage and maintenance. All these integrated systems' essential part is the structural analysis module, which must be automated and computationally efficient so that responses may be almost immediate. The finite element method is often used for structural analysis, and for automation, many effective finite element meshes must be automatically generated for a given analysis. A computationally efficient finite element mesh generation scheme based on the r-h method of mesh refinement using strain deviations from the values at the Gauss points as error estimates from the previous mesh is described. Shape factors are used to sort out overly distorted elements. A standard cantilever beam analyzed by four-node plane stress elements is used as an example to show the effectiveness of the automated algorithm for a time-domain dynamic analysis. Although recent developments in computer hardware and software have made many new applications in integrated structural systems possible, structural analysis still needs to be executed efficiently in real-time. The algorithm applies to diverse integrated systems, including nonlinear analyses and general dynamic problems in earthquake engineering.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.178-201
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• 2019

The appropriate design of a mooring system to maintain the position of an offshore structure in deep sea under various environmental loads is important. Fatigue design of the mooring line considering OPB/IPB(out-of-plane bending/in-plane bending) became an essential factor after the incident of premature fatigue failure of the mooring chain due to OPB/IPB in the Girassol region in West Africa. In this study, mooring line fatigue analysis was performed considering the OPB/IPB of a spread moored FPSO in deep sea. The tension of the mooring line was derived by hydrodynamic analysis using the de-coupled analysis method. The floater motion time histories were calculated under the assumption that the mooring line behaves in quasi-static manner. Additional time domain analysis was carried out by prescribing the obtained motions on top of the selected critical mooring line, which was determined based on spectral fatigue analysis. In addition, nonlinear finite element analysis was performed considering the material nonlinearities, and both the interlink stiffness and stress concentration factors were derived. The fatigue damage to the chain surface was estimated by combining both the hydrodynamic and stress analysis results.