• Proceedings of the Korean Society of Applied Pharmacology
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• 1998.11a
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• pp.158-158
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• 1998

The non-mevalonate pathway is a newly discovered isoprenoid biosynthetic pathway in some bacteria, cyanobacteria, algae and plants. Because isoprenoid metabolites (ubiquinone, menaquinone, undecaprenol) are essential for bacterial growth, this pathway may represent a novel target for antibacterial agents. Antibiotics with a unique mechanism of action are needed to combat the risk of antibiotic resistance that is a current worldwide problem. In order to study this pathway as viable target, it was necessary to verify use of the pathway in our model system, the bacterium Bacillus subtilis. Incubation experiments with [6,6-$^2$H$_2$]-D-glucose and [l-$^2$H$_3$]-deoxy-D-xylulose were conducted to provide labeled menaquinone-7 (MK -7), the most abundant isoprenoid in B. subtilis. $^2$H-NMR analysis of the MK-7 revealed labeling patterns that strongly support utilization of the non-mevalonate pathway. Another approach to study the pathway is by structure activity relationships of proposed inhibitors of the pathway. Fosmidomycin is a phosphonic acid with antibacterial activity known to inhibit isoprenoid biosynthesis in susceptible bacteria and may act by inhibiting the non-mevalonate pathway. Fosmidomycin and an N-methyl analog were synthesized and tested for antibacterial activity. Fosmidomycin was active against Escherichia coli and B. subtilis, while N-formyl-N-methyl-3-amino-propylphosphonic acid was inactive.

• Journal of the Korea Society of Computer and Information
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• v.21 no.12
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• pp.35-41
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• 2016

In this paper, we propose a new real-time dead pixel detection method based on spatial compare filtering, which are usually used in the small target detection. Actually, the soft dead and the small target are cast in the same mold. Our proposed method detect and remove the dead pixels as applying the spatial compare filtering, into the pixel outputs of a detector after the non-uniformity correction. Therefore, we proposed method can effectively detect and replace the dead pixels regardless of the non-uniformity correction performance. In infrared camera, there are usually many dead detector pixels which produce abnormal output caused by manufactural process or operational environment. There are two kind of dead pixel. one is hard dead pixel which electronically generate abnormal outputs and other is soft dead pixel which changed and generated abnormal outputs by the planning process. Infrared camera have to perform non-uniformity correction because of structural and material properties of infrared detector. The hard dead pixels whose offset values obtained by non-uniformity correction are much larger or smaller than the average can be detected easily as dead pixels. However, some dead pixels(soft dead pixel) can remain, because of the difficulty of uncleared decision whether normal pixel or abnormal pixel.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2505-2511
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• 2013

Many studies in detection and classification of the targets in the underwater environments have been conducted for military purposes, as well as for non-military purpose. Due to the complicated characteristics of underwater acoustic signal reflecting multipath environments and spatio-temporal varying characteristics, active sonar target classification technique has been considered as a difficult technique. And it has difficulties in collecting actual underwater data. In this paper, we synthesized active target echoes based on ray tracing algorithm using target model having 3-dimensional highlight distribution. Then, Fractional Fourier transform was applied to synthesized target echoes to extract feature vector. Recognition experiment was performed using neural network classifier.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.6
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• pp.403-410
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• 2016

This paper proposes a robust image-based visual servoing scheme using a nonlinear observer for a monocular eye-in-hand manipulator. The proposed control method is divided into a range estimation phase and a target-tracking phase. In the range estimation phase, the range from the camera to the target is estimated under the non-moving target condition to solve the uncertainty of an interaction matrix. Then, in the target-tracking phase, the feature point uncertainty caused by the unknown motion of the target is estimated and feature point errors converge sufficiently near to zero through compensation for the feature point uncertainty.

• Journal of the Korean Vacuum Society
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• v.12 no.2
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• pp.136-143
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• 2003

In this paper, high purity RF sputter-type ion source for non-mass separated ion beam deposition was evaluated. The fundamental characteristics of the ion source which is composed of an RF Cu coil and a high purity Cu target (99.9999 %) was studied, and the practical application of Cu thin films for ULSI metallization was discussed. The relationship between the DC target current and the DC target voltage at various RF power and Ar gas pressures was measured, and then preparation conditions for Cu thin films was described. As a result, it was found that the deposition conditions of the target voltage, the target current and the Ar pressure were optimized at -300 V, 240 W and 9 Pa, respectively. The resistivity of Cu films deposited at a bias voltage of -50 V showed a minimum value of 1.8 $\pm$ 0.1 $mu\Omega$cm, which is close to that of Cu bulk (1.67 $mu\Omega$cm).

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.21-31
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• 2020

NCTR(Non-Cooperative Target Recognition) refers to the function of radar to identify target on its own without support from other systems such as ELINT(ELectronic INTelligence). ISAR(Inverse Synthetic Aperture Radar) image is one of the representative methods of NCTR, but it is difficult to automatically classify the target without an identification database due to the significant changes in the image depending on the target's maneuver and location. In this study, we discuss how to build an identification database using simulation and deep-learning technique even when actual images are insufficient. To simulate ISAR images changing with various radar operating environment, A model that generates and learns images through the process named 'Perfect scattering image,' 'Lost scattering image' and 'JEM noise added image' is proposed. And the learning outcomes of this model show that not only simulation images of similar shapes but also actual ISAR images that were first entered can be classified.

• Journal of applied mathematics & informatics
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• v.34 no.5_6
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• pp.509-517
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• 2016

In this paper, we consider the following functional equation with involution f(x + y) + f(x + σ(y)) = 2f(x) + f(y) + f(σ(y)) and prove the generalized Hyers-Ulam stability for it when the target space is a non-Archimedean Banach space.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1738-1739
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• 2011

In this paper, the range difference based the moving target tracking problem using multiple UAVs is solved within the new framework of linear robust state estimation. To do this, the relative kinematics is modeled as an uncertain linear system containing stochastic parametric uncertainties in its measurement matrix. Applying the non-conservative robust Kalman filter for the uncertain system, a quasi-optimal linear target tracking filter is designed. For its recursive linear filter structure, the proposed method can ensure the fast convergence and reliable target tracking performance. Moreover, it is suitable for real-time applications using multiple UAVs.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1261-1267
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• 2015

The problem of underwater target detection and classification has been attracted a substantial amount of attention and studied from many researchers for both military and non-military purposes. The difficulty is complicate due to various environmental conditions. In this paper, we study classifier ensemble methods for active sonar target classification to improve the classification performance. In general, classifier ensemble method is useful for classifiers whose variances relatively large such as decision trees and neural networks. Bagging, Random selection samples, Random subspace and Rotation forest are selected as classifier ensemble methods. Using the four ensemble methods based on 31 neural network classifiers, the classification tests were carried out and performances were compared.

• Journal of information and communication convergence engineering
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• v.6 no.3
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• pp.337-342
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• 2008

Controlling spillway gates of dams is a complex, nonlinear, non-stationary control process and is significantly affected by hydrological conditions which are not predictable beforehand. In this paper, control methods based on dynamic fuzzy control are proposed for the operation of spillway gates of dams during floods. The proposed methods are not only suitable for controlling spillway gates but also able to maintain target water level in order to prepare a draught. In the proposed methods, we use dynamic fuzzy control that the membership functions can be varied by changing environment conditions for keeping up the target water level, instead of conventional static fuzzy control. Simulation results demonstrate that the proposed methods based on dynamic fuzzy control produce an accurate and efficient solution for both of controlling spillway gates and maintaining target water level defined beforehand.