• 제어로봇시스템학회:학술대회논문집
• /
• 1991.10a
• /
• pp.131-135
• /
• 1991

A quantization error model is suggested for analog to frequency(A/F) converter in strapdown inertial navigation system(SDINS),which is characterized by some white noise exciting the state variables. Also, effects on the performance of SDINS by analog to digital(A/D) converter and A/F converter are analyzed and compared via covariance simulation. As a result, A/F converter turns out to be superior to the A/D converter with respect to the induced navigation error and the difficulty in circuit realization. The quantization error model developed in this paper appears to be useful for optimal filter design.

• Earthquakes and Structures
• /
• v.24 no.1
• /
• pp.21-36
• /
• 2023

Structural vibrations generated by earthquakes and wind loads can be controlled by varying the structural parameters such as mass, stiffness, damping ratio, and geometry and providing a certain amount of passive or active reaction forces. A Double-Tuned Mass Dampers (DTMDs) system, which is simple and more effective than the conventional single tuned mass damper (TMD) system for vibration mitigation is presented. Two TMDs tuned to the first two natural frequencies were used to control vibrations. Experimental investigations were carried out on a three degrees-of-freedom frame model to investigate the effectiveness of DTMDs systems in controlling displacements, accelerations, and base shear. Numerical models were developed and validated against the experimental results. The validation showed a good match between the experimental and numerical results. The validated model was employed to investigate the behavior of a five degrees-of-freedom shear building structure, wherein mass dampers with different mass ratios were considered. The effectiveness of the DTMDs system was investigated for harmonic, seismic, and white noise base excitations. The proposed system was capable of significantly reducing the story displacements, accelerations, and base shears at the first and second natural frequencies, as compared to conventional single TMD.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.4
• /
• pp.29-33
• /
• 2022

In this paper, we propose the deep learning based pre interference cancellation scheme algorithm for power line communication (PLC) systems in smart grid. The proposed scheme estimates the channel noise information by applying a deep learning model at the transmitter. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the power line communication performance is effectively removed through interference cancellation technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the theoretical model based on additive white Gaussian noise. As a result, the proposed interference cancellation with deep learning improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC for smart grid and general communication systems.

• Journal of Korean Society of Steel Construction
• /
• v.13 no.5
• /
• pp.525-533
• /
• 2001

The wind resistant design of long-span bridges has urged a special attention to the prevention of the flutter occurrence Therefore calculation of flutter derivatives is indispensable to this prediction. A used system identification method must identify all the flutter derivatives from noisy experimental data In this paper MITD(Modified Ibrahim Tim Domain) method and AKF (Adaptive Kalman Filter) method are applied to extract flutter derivatives from section-model tests. The robustness and reliability of proposal SI methods under a high signal-to-noise ratio is demonstrated through numerical simulation for windtunnel test.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.2
• /
• pp.41-45
• /
• 2019

In this paper, we propose the interference cancellation scheme of end-to-end method algorithm for power line communication (PLC) systems in smart grid. The proposed scheme estimates the channel noise information of receiver by applying a deep learning model at the receiver. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the power line communication performance is effectively removed through interference cancellation technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the theoretical model based on additive white Gaussian noise. As a result, the proposed interference cancellation with deep learning improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC for smart grid and general communication systems.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2009.01a
• /
• pp.246-249
• /
• 2009

Due to the additive white Gaussian noise (AWGN), images are often corrupted. In recent days, Bayesian estimation techniques to recover noisy images in the wavelet domain have been studied. The probability density function (PDF) of an image in wavelet domain can be described using highly-sharp head and long-tailed shapes. If a priori probability density function having the above properties would be applied well adaptively, better results could be obtained. There were some frequently proposed PDFs such as Gaussian, Laplace distributions, and so on. These functions model the wavelet coefficients satisfactorily and have its own of characteristics. In this paper, mixture distributions of Gaussian and Laplace distribution are proposed, which attempt to corporate these distributions' merits. Such mixture model will be used to remove the noise in images by adopting Maximum a Posteriori (MAP) estimation method. With respect to visual quality, numerical performance and computational complexity, the proposed technique gained better results.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2003.11a
• /
• pp.72-77
• /
• 2003

In this paper, an experimental verification of system identification technique for constructing finite element model is conducted for a three-story test structure equipped with an active mass driver (AMD). Twenty Gaussian white noises were used as the input for AMD, and the corresponding accelerations of each floors are measured. Then, the complex frequency response function (FRF) for the input, the force induced by the AMD, was obtained and subsequently, the Markov parameters and system matrices were estimated. The magnitudes as well as phase of experimentally obtained FRFs match well with those of analytically obtained FRFs.

• Journal of Information Processing Systems
• /
• v.19 no.4
• /
• pp.465-473
• /
• 2023

An image enhancement algorithm and image defogging method are studied in this paper. The formation of fog and the characteristics of fog image are analyzed, and the fog image is preprocessed by histogram equalization method; then the additive white noise is removed by foggy image attenuation model, the atmospheric scattering physical model is constructed, the image detail characteristics are enhanced by image enhancement method, and the visual effect of defogging image is enhanced by guided filtering method. The proposed method has a good defogging effect on the image. When the number of training iterations is 3,000, the peak signal-to-noise ratio of the proposed method is 43.29 dB and the image structure similarity is 0.9616, indicating excellent image defogging effect.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2003.05a
• /
• pp.59-64
• /
• 2003

The parameter modification of the initial FEM model to match it with the experimental results needs the modal information and the modal sensitivity matrix to the parameter change. There are two cases this methodology is ill-equip to deal with; the deficiency of the necessary modal information and the ill-conditioning of the sensitivity matrix. In this research, a novel concept of the feedback exciter that uses the summation of the white noise and the signals from the measurement sensors multiplied with feedback gains as the reference signal is proposed. There are 2 advantages using this external feedback excitation. First, we can use the change of the system response such as modal data by the active energy Path from the sensor to the exciter. This change of the system response can be additional clues to the system dynamics that we want to know. Secondly, the external energy Path alternates the offset of the Parameter change to the system response. That means the modal sensitivity of the parameters becomes different from the original sensitivities by the feedback excitation. Through the feedback loop, we can change the similar modal sensitivities of some updating parameters and consequently discriminate the parameters using the closed-loop modal data. To demonstrate the discrimination performance, the parameter estimation of an indeterminate structure by use of the feedback method is introduced.

• Journal of the Semiconductor & Display Technology
• /
• v.23 no.2
• /
• pp.23-27
• /
• 2024

Underwater images are typically degraded due to color distortion, light absorption, scattering, and noise from artificial light sources. Restoration of these images is an essential task in many underwater applications. In this paper, we propose a two-phase deep learning-based method, Underwater Deep Curve Estimation (UWDCE), designed to effectively enhance the quality of underwater images. The first phase involves a white balancing and color correction technique to compensate for color imbalances. The second phase introduces a novel deep learning model, UWDCE, to learn the mapping between the color-corrected image and its best-fitting curve parameter maps. The model operates iteratively, applying light-enhancement curves to achieve better contrast and maintain pixel values within a normalized range. The results demonstrate the effectiveness of our method, producing higher-quality images compared to state-of-the-art methods.