• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1452-1459
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• 2016

Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing in and under Nyquist rate representation. Generally, the measurement prediction usually works well with a small block while the quality of recovery is known to be better with a large block. In order to overcome this dilemma, conventional research works use a structural measurement matrix with which compressed sensing is done in a small block size but recovery is performed in a large block size. In this way, both prediction and recovery are made to be improved at same time. However, the conventional researches did not compare the performances of the structural measurement matrix, affected by the block size. In this paper, by expanding a structural measurement matrix of conventional works, their performances are compared with different block sizes. Experimental results show that a structural measurement matrix with $4{\times}4$ Hadamard transform matrix provides superior performance in block size 4.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.270-270
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• 2000

Input delay is frequently encountered in the practical systems since measurement delay and computational delay can be represented by input delay. In this viewpoint, this paper deals with the robust control problem of input delayed systems with structured uncertainty. Robust stability conditions are provided in terms of linear matrix inequalities(LMIs) and it is shown that the proposed conditions can give less conservative maximum bound of input delay guaranteeing robust stability.

• Journal of Biosystems Engineering
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• v.24 no.2
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• pp.159-166
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• 1999

A system to extract continuously the real 3-D geometric fearture information from 2-D image of an object, which is fed randomly via conveyor has been developed. Two sets of structured laser lightings were utilized. And the laser structured light projection image was acquired using the camera from the signal of the photo-sensor mounted on the conveyor. Camera coordinate calibration matrix was obtained, which transforms 2-D image coordinate information into 3-D world space coordinate using known 6 points. The maximum error after calibration showed 1.5 mm within the height range of 103mm. The correlation equation between the shift amount of the laser light and the height was generated. Height information estimated after correlation showed the maximum error of 0.4mm within the height range of 103mm. An interactive 3-D geometric feature extracting software was developed using Microsoft Visual C++ 4.0 under Windows system environment. Extracted 3-D geometric feature information was reconstructed into 3-D surface using MATLAB.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.3
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• pp.21-28
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• 2016

Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing in an under-sampled (i.e., under Nyquist rate) representation. Specially, a block compressed sensing with Smoothed Projected Landweber (BCS-SPL) framework is one of the most widely used schemes. Currently, a variety of BCS-SPL schemes have been actively studied. However, when restoring, block sizes have effects on the reconstructed visual qualities, and in this paper, both a basic scheme of BCS-SPL and several modified schemes of BCS-SPL with structured measurement matrix are analyzed for the effects of the block sizes on the performances of reconstructed image qualities. Through several experiments, it is shown that a basic scheme of BCS-SPL provides superior performance in block size 4.

• Journal of the Korea Safety Management & Science
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• v.8 no.3
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• pp.171-187
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• 2006

Modern management considers customer satisfaction as a baseline standard of performance and a possible standard of excellence for any business organization including the college. Quality function deployment(QFD) is a structured approach to seek out voice of customers, understanding their needs, and ensure that their needs are met. The strategy value proposed by Chien et al. combines importance, satisfaction, performance, and ability to enhance decision making effectiveness. But in their model, the correlation among the strategic alternatives isn't considered the decision chain and is therefore eliminated. This paper proposes how to calculate the new weight of columns to consider various strength levels of correlations matrix, representing the correlation among the strategic alternatives, using normalization procedure. The aim of this paper is to present and original customer satisfaction survey conducted in the college. Thus, this paper presents an original customer satisfaction survey in the college and provides to demonstrate the practical usage of the design model to compare this model with Chien's model.

• Korean Journal of Materials Research
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• v.32 no.11
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• pp.458-465
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• 2022

Interest in the use of semiconductor-based photocatalyst materials for the degradation of organic pollutants in a liquid phase has grown, due to their excellent performance and response to the light source. Herein, we fabricated a NiO-SiC-TiO₂ ternary structured photocatalyst which had reduced bandgap energy, with strong activation under UV-light irradiation. The synthesized samples were examined using XRD, SEM, EDX, TEM, DRS, EIS techniques and photocurrent measurement. The results confirmed that the two types of metal oxides were well bonded to the SiC fiber surface. The junction of the new photocatalyst exhibited a large number of photoexcited electrons and holes. The holes tended to oxidize the water and form a hydroxyl radical, which promoted the decomposition of methylene blue. The close contact between the 2D SiC fiber and metal oxide semiconductors expanded the scope of absorption wavelength, and enhanced the usability of the ternary photocatalyst for the degradation of methylene blue. Among three synthesized samples, the NiO-SiC-TiO₂ showed the best photocatalytic effect, and was considered to have excellent photoelectron transfer due to the synergy effect between the metal oxide and SiC.

• Journal of the Optical Society of Korea
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• v.18 no.4
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• pp.317-329
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• 2014

In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.