• Smart Media Journal
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• v.9 no.4
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• pp.102-108
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• 2020

In this paper, we propose a real-time method of detecting hand motions and extracting the signal frame induced by EF(Electric Field) sensors. The signal induced by hand motion includes not only noises caused by various environmental sources as well as sensor's physical placement, but also different initial off-set conditions. Thus, it has been considered as a challenging problem to detect the motion signal and extract the motion frame automatically in real-time. In this study, we remove the PLN(Power Line Noise) using LPF with 10Hz cut-off and successively apply MA(Moving Average) filter to obtain clean and smooth input motion signals. To sense a hand motion, we use two thresholds(positive and negative thresholds) with offset value to detect a starting as well as an ending moment of the motion. Using this approach, we can achieve the correct motion detection rate over 98%. Once the final motion frame is determined, the motion signals are normalized to be used in next process of classification or recognition stage such as LSTN deep neural networks. Our experiment and analysis show that our proposed methods produce better than 98% performance in correct motion detection rate as well as in frame-matching rate.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.1C
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• pp.45-51
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• 2010

In urban area, design and construction of civil engineering structures such as subway tunnel, underground space and deep excavation is impeded by unreliable site investigation. Variety of embedded objects, electric noises and traffic vibrations degrades the quality of site investigation, whatever the site-investigation technique would be. In this research, a preliminary research was performed to develop a dedicated site investigation technique for urban geotechnical sites, which can overcome the limitations of urban sites. HiRAS (Hybrid Integration of Surface Waves and Resistivity) technique which is the first outcome of the preliminary research was proposed in this paper. The technique combines surface wave as well as electrical resistivity. CapSASW method for surface-wave technique and PDC-R technique for electrical resistivity survey were incorporated to develop HiRAS technique. CapSASW method is a good method for evaluating material stiffness and PDC-R technique is a reliable method for determination of underground stratification even in a site with electrical noise. For the inversion analysis of HiRAS techniuqe, a site-specific relationship between stress-wave velocity and resistivity was employed. As for outgrowth of this research, the 2-D distribution of Poisson's ratio could be also determined.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.4A
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• pp.507-515
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• 2008

In this study, a method predicting the displacement response of structures from the measured dynamic strain signal is proposed by using mode decomposition technique. Evaluation of bridge stability is normally focused on the bridge completed. However, dynamic loadings including wind and seismic loadings could be exerted to the bridge under construction. In order to examine the bridge stability against these dynamic loadings, the prediction of displacement response is very important to evaluate bridge stability. Because it may be not easy for the displacement response to be acquired directly on site, an indirect method to predict the displacement response is needed. Thus, as an alternative for predicting the displacement response indirectly, the conversion of the measured strain signal into the displacement response is suggested, while the measured strain signal can be obtained using fiber optic Bragg-grating (FBG) sensors. As previous studies on the prediction of displacement response by using the FBG sensors, the static displacement has been mainly predicted. For predicting the dynamic displacement, it has been known that the measured strain signal includes higher modes and then the predicted dynamic displacement can be inherently contaminated by broad-band noises. To overcome such problem, a mode decomposition technique was used. Mode decomposition technique estimates the displacement response of each mode with mode shape estimated to use POD from strain signal and with the measured strain signal decomposed into mode by EMD. This is a method estimating the total displacement response combined with the each displacement response about the major mode of the structure. In order to examine the mode decomposition technique suggested in this study model experiment was performed.

• Economic and Environmental Geology
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• v.34 no.4
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• pp.375-383
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• 2001

We extracted sea surface heights(SSH) from the TopexJPoseidon(T/P) radar altimeter data to compare with fhe SSH estimated from in-situ lide gauges(T/G) at Ulleungdo, Pohang, and SockcholMucko sites. Selection criteria such as wet/dry troposphere, ionosphere, and ocean tide were used to estimate accurate SSH. For time series analysis, the one-hour interval tide gauge SSHs were resampled al lO-day interval of the satellite SSHs. The ocean tide model applied in the altimeter data processing showed periodic aliasings of 175.5 day, 87.8 day, 62J day, 58.5 day, 49.5 day and 46.0 day, and, hence, the ZOO-day filtering was applied to reduce these spectral noises. Wavenumber correlation analysis was also applied to extract common components between the two SSHs, resulting in enhancing the correlation coefficient(CC) dramatically. The original CCs between the satenite and tide gauge SSHs are 0.46. 0.26, and 0.]5, respectively. Ulleungdo shows the largest cc bec;luase the site is far from the coast resulting in the minimun error in the satellite observations. The CCs were then increased to 0.59, 030, and 0.30, respectively, after 200.day filtering, and to 0.69, 0.63. and 0.59 after removing inversely correlative components using wavenumber correlation analysis. The CCs were greatly increased by 87, 227, and 460% when the wavenumber correlation analysis was followed by 2oo-day filtering, resulting in the final CCs of 0.86, 0.85, 0.84, respectively. It was found that the best SSHs were estimated when the two methods were applied to the original data. The low-pass filtered TIP SSHs were found to be well correlated with the TIG SSHs from tide gauges, and the best correlation results were found when we applied both low-pass filtering and spectral correlation analysis to the original SSHs.