• Proceedings of the Korean Society of Crop Science Conference
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• 2022.04a
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• pp.122-122
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• 2022

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1812-1824
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• 2016

The neural dynamics underlying the causal network during motor planning or imagery in the human brain are not well understood. The lack of signal processing tools suitable for the analysis of nonlinear and nonstationary electroencephalographic (EEG) hinders such analyses. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) is used to estimate the causal inference in the frequency domain, i.e., partial directed coherence (PDC). Natural and intrinsic oscillations corresponding to the motor imagery tasks can be extracted due to the data-driven approach of NA-MEMD, which does not employ predefined basis functions. Simulations based on synthetic data with a time delay between two signals demonstrated that NA-MEMD was the optimal method for estimating the delay between two signals. Furthermore, classification analysis of the motor imagery responses of 29 subjects revealed that NA-MEMD is a prerequisite process for estimating the causal network across multichannel EEG data during mental tasks.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.189-195
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• 2003

This paper represents an identification procedure for leading mechanism of a car A/V system which is composed of a DC motor and a set of plastic gears. In addition, we studied dominant noise source of rattle noise generated by external forced vibration as a car drives. we made a dynamometer to produce stationary operation on loading mechanism of A/V system because noise generated by actual loading mechanism is non-stationary signal. operating the dynamometer setup at various motor speeds, sound pressure spectra are measured and the results are analyzed. its dominant noise source is also identified by using a sound Intensity technique. we made use of multi-dimensional spectral analysis to rind a dominant rattle noise. this method is so useful to eliminate coherence between vibration sources and helps us obtain coherent output spectrum of individual vibration source which make a rattle noise.

• Journal of IKEEE
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• v.22 no.4
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• pp.1109-1114
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• 2018

Electroencephalogram (EEG) is a tool used to study brain activity caused by external stimuli. In this process, artifacts are mixed and it is easy to distort the signal, so post-processing is necessary to remove it. Independent Component Analysis (ICA) is a widely used method for removing artifact. This method has a disadvantage in that it has excellent performance but some loss of brain wave information. In this paper, we propose a method to reduce EEG information loss by restricting the filter coverage using eye blink information obtained from Eyetracker. We then compared the results of the proposed method with the conventional method using quantization methods such as Signal to Noise Ratio (SNR) and Spectral Coherence (SC).

• Wind and Structures
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• v.38 no.4
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• pp.277-293
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• 2024

The recorded thunderstorm winds at a point contain tri-directional components. The probabilistic characteristics of such recorded winds in terms of instantaneous mean wind speed and direction, and the probability distribution and the time-frequency dependent crossed and non-crossed power spectral density functions for the high-frequency fluctuating wind components are unclear. In the present study, we analyze the recorded tri-directional thunderstorm wind components by separating the recorded winds in terms of low-frequency time-varying mean wind speed and high-frequency fluctuating wind components in the alongwind direction and two orthogonal crosswind directions. We determine the time-varying mean wind speed and direction defined by azimuth and elevation angles, and analyze the spectra of high-frequency wind components in three orthogonal directions using continuous wavelet transforms. Additionally, we evaluate the coherence between each pair of fluctuating winds. Based on the analysis results, we develop empirical spectral models and lagged coherence models for the tri-directional fluctuating wind components, and we indicate that the fluctuating wind components can be treated as Gaussian. We show how they can be used to generate time histories of the tri-directional thunderstorm winds.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.531-542
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• 2021

Open-pit mines require constant monitoring as they can cause surface changes and environmental disturbances. In open-pit mines, there is little vegetation at the mining site and can be monitored using InSAR (Interferometric Synthetic Aperture Radar) coherence imageries. In this study, activities occurring in mine were analyzed by applying the recently developed InSAR coherence-based NDAI (Normalized Difference Activity Index). The March 5 Youth Mine is a North Korean mine whose development has been expanded since 2008. NDAI analysis was performed with InSAR coherence imageries obtained using Sentinel-1 SAR images taken at 12-day intervals in the March 5 Youth Mine. First, the area where the elevation decreased by about 75.24 m and increased by about 9.85 m over the 14 years from 2000 was defined as the mining site and the tailings piles. Then, the NDAI images were used for time series analysis at various time intervals. Over the entire period (2017-2019), average mining activity was relatively active at the center of the mining area. In order to find out more detailed changes in the surface activity of the mine, the time interval was reduced and the activity was observed over a 1-year period. In 2017, we analyzed changes in mining operations before and after artificial earthquakes based on seismic data and NDAI images. After the large-scale blasting that occurred on 30 April 2017, activity was detected west of the mining area. It is estimated that the size of the mining area was enlarged by two blasts on 30 September 2017. The time-averaged NDAI images used to perform detailed time-series analysis were generated over a period of 1 year and 4 months, and then composited into RGB images. Annual analysis of activity confirmed an active region in the northeast of the mining area in 2018 and found the characteristic activity of the expansion of tailings piles in 2019. Time series analysis using NDAI was able to detect random surface changes in open-pit mines that are difficult to identify with optical images. Especially in areas where in situ data is not available, remote sensing can effectively perform mining activity analysis.

• Science of Emotion and Sensibility
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• v.17 no.4
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• pp.51-60
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• 2014

This double-blind study, as a human experiment of nonlocality, investigated the effects of senders' intention on the central nervous system of a distant human receiver and it explored the roles that motivation might have in modulating these effects. Whole brain activity was measured in the receiver whom was asked to relax in a distant room for 16 minutes; the sending person directed intention of oneness toward the receiver during repeated variable-second epochs separated by variable-second non-intention epochs. The total length of intention epochs and that of nonintention epochs were balanced. Eighteen sessions were conducted. In 9 of those sessions, the sender was the receiver's lover. In another 9 of those sessions, the sender was just acquainted with the receiver before the session. The receiver's whole brain activity recorded during the intention epochs were compared with the same measures recorded during the nonintention epochs used as controls. The statistical difference between the intentions versus controls across 18 sessions was examined by paired-t test. In addition, subgroup analysis for the 9 couple sessions and 9 non-couple sessions were additionally examined by the same test. The effect of distant intentionality decreased slow waves or increased EEG fast waves mainly in frontal regions, and increased EEG coherence during the intention epochs. The effects was not statistically significant after Bonferroni correction, but the couple sessions combined showed the largest effect followed by all sessions combined. Non-couple sessions combined showed the smallest effect. The changes in EEG power mean that receiver participants became more alert during the intention epochs and the change in EEG coherence might be evidence of coherent heart influence on EEG activity. Planned comparison with specific hypothesis testing for the suggested changes in this study have to be followed for an evidence of electroencephalographic changes in isolated subjects for the distant mental intention.

• Journal of Periodontal and Implant Science
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• v.48 no.2
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• pp.84-91
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• 2018

Purpose: The purpose of this study was to visualize and identify peri-implant bone defects in optical coherence tomography (OCT) images and to obtain quantitative measurements of the defect depth. Methods: Dehiscence defects were intentionally formed in porcine mandibles and implants were simultaneously placed without flap elevation. Only the threads of the fixture could be seen at the bone defect site in the OCT images, so the depth of the peri-implant bone defect could be measured through the length of the visible threads. To analyze the reliability of the OCT measurements, the flaps were elevated and the depth of the dehiscence defects was measured with a digital caliper. Results: The average defect depth measured by a digital caliper was $4.88{\pm}1.28mm$, and the corresponding OCT measurement was $5.11{\pm}1.33mm$. Very thin bone areas that were sufficiently transparent in the coronal portion were penetrated by the optical beam in OCT imaging and regarded as bone loss. The intraclass correlation coefficient between the 2 methods was high, with a 95% confidence interval (CI) close to 1. In the Bland-Altman analysis, most measured values were within the threshold of the 95% CI, suggesting close agreement of the OCT measurements with the caliper measurements. Conclusions: OCT images can be used to visualize the peri-implant bone level and to identify bone defects. The potential of quantitative non-invasive measurements of the amount of bone loss was also confirmed.

• Journal of the Korea Society for Simulation
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• v.19 no.2
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• pp.127-135
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• 2010

The validation of a simulation model is a key to demonstrate that the simulation model is reliable. However, among various validation methods have been introduced, it is very poor to research the specific techniques for the time series data. Therefore, this paper suggests the methodology to verify the simulation using the time series data by Wavelet Transform, Power Spectrum and Coherence. This method performs 2 steps as followed. Firstly, we get spectrum using the Wavelet transform available for non-periodic signal separation. Secondly, we compare 2 patterns of output data from simulation model and actual system by Coherence Analysis. As a result of comparing it with other validation techniques, the suggested way can judge simulation model accuracy more clearly. By this way, we can make it possible to perform the simulation validation test under various situations using detailed sectional validation method, which has been impossible using a single statistics for the whole model.

• Journal of The Korean Association For Science Education
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• v.28 no.8
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• pp.796-813
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• 2008

A good understanding of how gifted science students understand physics is important to developing and delivering effective curriculum for gifted science students. This dissertation reports on a systematic investigation of gifted science students' reasoning model in learning physics. An analysis of videotaped class work, written work and interviews indicate that I will discuss the framework to characterize student reasoning. There are three main groups of students. The first group of gifted science students holds several different understandings of a single concept and apply them inconsistently to the tasks related to that concept. Most of these students hold the Aristotelian Model about Newton's second law. In this case, I define this reasoning model as the manifold model. The second group of gifted science students hold a unitary understanding of a single concept and apply it consistently to several tasks. Most of these students hold a Newtonian Model about Newton's second law. In this case, I define this reasoning model as the coherence model. Finally, some gifted science students have a manifold model with several different perceptions of a single concept and apply them inconsistently to tasks related to the concept. Most of these students hold the Aristotelian Model about Newton's second law. In this case, I define this reasoning model as the coherence model.