• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.61-66
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• 2002

A fuzzy control strategy is described which is utilized to control the joint angle and tip deflection in single flexible manipulator. In this paper, an existing model for a single flexible manipulator is used for the initial development of an FLC. One FLC is designed to govern the joint angle of the manipulator as it is rotated from one position to another, and the second FLC is designed to attenuate the tip deflection which result from joint angle body motion. Reference Trajectory Command is an important method to reduce vibration in flexible beam. This paper presents a very simple command control shaping which eliminates multiple mode residual vibration in a flexible beam combined parallel fuzzy controller. The effectiveness of proposed scheme is demonstrated through computer simulation.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.469-472
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• 2008

In general, structures in service gradually lose original performance according to time due to initial defects in design and construction, or exposure to unfavorable external conditions such as repeated loading or deteriorating environment, and in extreme cases, may collapse in large disaster. Therefore, in order to maintain the serviceability of structures at optimal level, advanced structure measuring system which can inform optimal time point and method of maintenance is required in addition to accurate prediction of residual life the structure by periodic inspection. To guarantee the safety level of bridge structure and to prevent from disaster, the integration of safety network for bridge structures are needed. Therefore in this study, to enhance the effectiveness of safety network for bridge, the connection methodologies between safety network and pre-installed bridge monitoring system are investigated.

• Smart Media Journal
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• v.8 no.4
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• pp.64-71
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• 2019

The performance of an OMR (Optical Music Recognition) system is usually determined by the characterizing features of the input music score images. Low resolution is one of the main factors leading to degraded image quality. In this paper, we handle the low-resolution problem using the super-resolution technique. We propose the use of a deep neural network with instance normalization to improve the quality of music score images. We apply instance normalization which has proven to be beneficial in single image enhancement. It works better than batch normalization, which shows the effectiveness of shifting the mean and variance of deep features at the instance level. The proposed method provides an end-to-end mapping technique between the high and low-resolution images respectively. New images are then created, in which the resolution is four times higher than the resolution of the original images. Our model has been evaluated with the dataset "DeepScores" and shows that it outperforms other existing methods.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.8
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• pp.23-31
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• 2015

This paper proposes an advanced distance relaying based on the DC offset removal filter to minimize the effects of DC offset on a double circuit transmission line. The proposed DC offset removal filter uses only one cycle of data for phasor extraction computation, which does not need to preset the time constant of the DC offset component. This proposed distance relaying uses not only the residual current of the faulted circuit but also mutual current of the healthy adjacent circuit. A series of off-line test results using ATP simulation data show the effectiveness of the an advanced distance relaying.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1724-1725
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• 2007

This paper presents a fault detection and isolation(FDI) scheme for a nonlinear systems with a actuator and sensor faults. A residual generator based on the observer model generate the information for a fault detection. The proposed fault estimators are activated for a fault isolation and applied to estimate the time-varying lumped faults(model uncertainty + fault). but a fault estimator error dose not converge to zero since the derivative of lumped fault is not zero. Then the fuzzy neural network(FNN) is used to estimate the fault estimator error. Simulation results are presented to illustrate the effectiveness and the applicability of the approaches proposed.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.03a
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• pp.481-490
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• 2003

This paper presents an efficient eigensolution method for non-proportionally damped systems. The proposed method is obtained by applying the accelerated Newton-Raphson technique and the orthonormal condition of the eigenvectors to the linearized form of the quadratic eigenproblem. A step length and a selective scheme are introduced to increase the convergence of the solution. The step length can be evaluated by minimizing the norm of the residual vector using the least square method. While the singularity may occur during factorizing process in other iteration methods such as the inverse iteration method and the subspace iteration method if the shift value is close to an exact eigenvalue, the proposed method guarantees the nonsingularity by introducing the orthonormal condition of the eigenvectors, which can be proved analytically. A numerical example is presented to demonstrate the effectiveness of the proposed method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.59-61
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• 2018

Highly compressed images typically not only have low resolution, but are also affected by compression artifacts. Performing image super-resolution (SR) directly on highly compressed image would simultaneously magnify the blocking artifacts. In this paper, a SR method based on deep learning is proposed. The method is an end-to-end trainable deep convolutional neural network which performs SR on compressed images so as to reduce compression artifacts and improve image resolution. The proposed network is divided into compression artifacts removal (CAR) part and SR reconstruction part, and the network is trained by three-step training method to optimize training procedure. Experiments on JPEG compressed images with quality factors of 10, 20, and 30 demonstrate the effectiveness of the proposed method on commonly used test images and image sets.

• Proceedings of the Korean Geotechical Society Conference
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• 1992.12a
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• pp.19-32
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• 1992

Objectives of this study are : 1) to utilize capillary theory and obtain pore-size distribution profiles from moisture-suction relationships using Laplace theory. 2) to investigate the behavior of Light Non-Aqueous Phase Liquids(LNAPLs) in the subsurface environment and to develop several predictive relationships which can be used to assess the effectiveness of various LNAPLs remediation technologies. The relationship to predict pore-size distribution function expressed in differencial equation is found by using capillary theory. Also, experiments are conducted to : the various LNAPLs subjected to vadose zone drainage, groundwater table drainage, waterflooding with surfactants. The experiments are performed with #2 heating oil, jet fuel. and kerosene. Several relationships have been derived describing the effect of various properties and process parameters on the LNAPL residual saturation.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.37.2-37
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• 2001

This paper addresses the robust adaptive output feedback tracking for nonlinear systems under disturbances whose bounds are unknown. A new algorithm is proposed for estimation of unknown bounds and adaptive control of the uncertain nonlinear systems. The State estimation is solved using K-filters, together with the construction of a bound of an error in the state estimation due to the perturbation of the disturbance. Tuning functions are used to estimate unknown system parameters without overparametrization. The proposed control algorithm ensures that the out put tracking error converges to a residual set which can be arbitrarily small, while maintaining the boundedness of all other variables. A simulation shows the effectiveness of the proposed approach

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1234-1257
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• 2023

In this paper, we propose a framework for multi-focus image fusion called PATN. In our approach, by aggregating deep features extracted based on the U-type Transformer mechanism and shallow features extracted using the PSA module, we make PATN feed both long-range image texture information and focus on local detail information of the image. Meanwhile, the edge-preserving information value of the fused image is enhanced using a dense residual block containing the Sobel gradient operator, and three loss functions are introduced to retain more source image texture information. PATN is compared with 17 more advanced MFIF methods on three datasets to verify the effectiveness and robustness of PATN.