• Journal of the Korean Ceramic Society
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• v.40 no.11
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• pp.1047-1057
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• 2003

A sub-nanometer resolution Scanning Nonlinear Dielectric Microscope (SNDM) was developed for observing ferroelectric polarization. We also demonstrate that the resolution of SNDM is higher than that of a conventional piezo-response imaging. Secondly, we report new SNDM technique detecting higher nonlinear dielectric constants $\varepsilon$$\_$3333/ and $\varepsilon$$\_$33333/. Higher order nonlinear dielectric imaging provides higher lateral and depth resolution. Finally, the formation of artificial small inverted domain is reported to demonstrate that SNDM system is very useful as a nano-domain engineering tool. The nano-size domain dots were successfully formed in LiTaO$_3$ single crystal. This means that we can obtain a very high density ferroelectric data storage with the density above 1T-bits/inch$^2$.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.305-316
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• 1994

All physical data in the real world are nonstationary signals that have the time varying statistical characteristics. Although few algorithms suitable to process the nonstationary signals have ever been suggested, these are treated the nonstationary signals under the assumption that the nonstationary signal is a piece-wise stationary signal. Recently, statistical analysis algorithms for the nonstationary signal have concentrated so much interest. In this paper, nonstationary EMG signals are mapped onto the orthogonal wavelet transform domain so that the eigenvalue spread of its autocorrelation matrix could be more smaller than that in the time domain. Then the model in the wavelet transform domain and an algorithm to estimate the model parameters are suggested. Also, an test signal generated by a white gaussian noise and the EMG signal are identified, and the algorithm performance is considered in the sense of the mean square error and the evaluation parameters.

• Journal of Information Processing Systems
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• v.13 no.1
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• pp.57-67
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• 2017

In this paper, Atanassov's intuitionistic fuzzy set theory is used to handle the uncertainty of students' knowledgeon domain concepts in an E-learning system. Their knowledge on these domain concepts has been collected from tests that were conducted during their learning phase. Atanassov's intuitionistic fuzzy user model is proposed to deal with vagueness in the user's knowledge description in domain concepts. The user model uses Atanassov's intuitionistic fuzzy sets for knowledge representation and linguistic rules for updating the user model. The scores obtained by each student were collected in this model and the decision about the students' knowledge acquisition for each concept whether completely learned, completely known, partially known or completely unknown were placed into the information table. Finally, it has been found that the proposed scheme is more appropriate than the fuzzy scheme.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.10a
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• pp.29-35
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• 1997

This research presented a frequency analysis method to analyze NRRO in a computer hard disk drive. RRO was proved to be the harmonics of rotational frequency. The frequency components of NRRO is the subtraction of the harmonics from TIR in frequency domain, so that NRRO in time domain can be obtained by Fourier inverse transformation of NRRO in frequency domain. This method can make the experiments simple without the index signal indispensable to time domain analysis. This research also shows that NRRO is caused by the defect frequencies of ball bearing. Even though the excitation force of ball bearing is independent of the rotational speed, the amplitude of NRRO is magnified near the resonance frequencies of the spindle motor. NRRO in axial direction is almost twice bigger than that in radial direction, because the spindle motor has smaller stiffness in axial direction.

• Journal of Biomedical Engineering Research
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• v.14 no.2
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• pp.137-145
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• 1993

Time domain analysis as well as frequency domain analysis of signal conditioning filters is very useful for practical applications. Time domain analysis of digital filters for noise cancelling in ECG signals is presented. Several band pass and band reject filters are designed for the analysis. Computer simulations are performed to compare the distortions of the Butterworth type filters and linear phase optimal FIR filters which are widely used for ECG signal processing. Band reject filters are applied to power line interference cancelling in ECG signals.

• Journal of the Optical Society of Korea
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• v.15 no.1
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• pp.103-108
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• 2011

Recently reported asynchronous-optical-sampling terahertz (THz) time-domain spectroscopy enables high-resolution spectroscopy due to a long time-delay window. However, a long-lasting tail signal following the main pulse is often measured in a time-domain waveform, resulting in spectral fluctuation above a background noise level on a high-resolution THz amplitude spectrum. Here, we adopt the wavelet power spectrum estimation technique (WPSET) to effectively remove the spectral fluctuation without sacrificing spectral features. Effectiveness of the WPSET is verified by investigating a transmission spectrum of water vapor.

• Korean Journal of Optics and Photonics
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• v.22 no.3
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• pp.134-140
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• 2011

In this paper, we developed a frequency-domain diffuse optical tomography(DOT) system for non-invasively imaging in vivo. The system uses near-infrared(NIR) light sources and detectors for which the photon propagation in human tissue is dominated by scattering rather than by absorption. We present the experimental reconstruction images of absorption and scattering coefficients using a liquid tissue phantom, and we obtain the location and shape of an anomaly which has different optical properties than the phantom.

• Journal of the Optical Society of Korea
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• v.17 no.6
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• pp.467-470
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• 2013

We propose a blind feedforward phase noise mitigation method in the time-domain for a coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. By exploiting the redundancy of the cyclic prefix (CP), the proposed scheme acquires the overall phase noise difference information during an OFDM block and attempts to mitigate the phase noise in the time domain using a linear approximation. The proposed algorithm mitigates common phase error (CPE) and inter-carrier-interference (ICI) due to phase noise simultaneously, improving the system performance, especially when decision-directed equalizers are used. The simulation results demonstrate the effectiveness of the proposed feedforward phase noise mitigation approach in time domain.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1319-1325
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• 2021

It is important to remove fog for accurate object recognition and detection during preprocessing because images taken in foggy adverse weather suffer from poor quality of images due to scattering and absorption of light, resulting in poor performance of various vision-based applications. This paper proposes an end-to-end deep learning-based single image de-fogging method using U-Net architecture. The loss function used in the algorithm is a loss function based on Mahalanobis distance with fog features, which solves the problem of domain shifts, and demonstrates superior performance by comparing qualitative and quantitative numerical evaluations with conventional methods. We also design it to generate fog through the VGG19 loss function and use it as the next training dataset.

• Structural Engineering and Mechanics
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• v.78 no.3
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• pp.369-378
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• 2021

This paper reviewed a few output-only system identification algorithms and identified the shortcomings of those popular blind source separation methods. To address the issues such as less sensors than the targeted modal modes (under-determinate problem), repeated natural frequencies as well as systems with complex mode shapes, this paper proposed a complex wavelet modified second order blind identification method (CWMSOBI) by transforming the time domain problem into time-frequency domain. The wavelet coefficients with different dominant frequencies can be used to address the under-determinate problem, while complex mode shapes are addressed by introducing the complex wavelet transformation. Numerical simulations with both high and low signal-to-noise ratios validate that CWMSOBI can overcome the above-mentioned issues while obtaining more accurate identified results than other blind identification methods.