• Smart Structures and Systems
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• v.23 no.1
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• pp.45-60
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• 2019

This paper is devoted to proposing a new approach for damage detection of structures. In this technique, the biconjugate gradient method (BCG) is employed. To remedy the noise effects, a new preconditioning algorithm is applied. The proposed preconditioner matrix significantly reduces the condition number of the system. Moreover, based on the characteristics of the damage vector, a new direct search algorithm is employed to increase the efficiency of the suggested damage detection scheme by reducing the number of unknowns. To corroborate the high efficiency and capability of the presented strategy, it is applied for estimating the severity and location of damage in the well-known 31-member and 52-member trusses. For damage detection of these trusses, the time history responses are measured by a limited number of sensors. The results of numerical examples reveal high accuracy and robustness of the proposed method.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.2
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• pp.44-54
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• 1985

The high resolution of positron emission tomography, in particular, requires the use of detector crystals of narrow width but still with sufficiently high detection efficiency. If the crystal width is reduced to several millimeters, degradation of detection efficiency and leakage coefficient becomes significant, particularly in case of obliquely incident photons. Alleviation of such a problem can be made possible by modification of the detector shape from the conventional rectangular type to a wed농e type. The Proposed wedge shape makes the absorption length longer for obliquely incident photons, thus increasing the detection efficiency and suppressing leakage coefficient. For the BGO detectors of 4-8mm width, the computer simulation result of the system using wedge detectors reveals resolution improvement to the system using conventional detectors. For the system composed of 200 BGO detectors of 8mm width with 2 point sampling motion, the simulation resolution system using conventional detectors. For the very high resolution system of 3-7mm FWHM, the characteristics of the detector shape and size is studied by computer simulation.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.9-11
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• 2002

We improve the free-space optical interconnection efficiency by using circular aperture computer-generated hologram (CGH). In free-space optical interconnection system using CGH, the single CGH is composed of sub-CGHs, which can change the direction of input beams to desired output positions, by Fourier transform. Each sub-CGH is rectangular shape, so the input beams through each sub-CGH are transformed to sinc functions in output plane. The side lobes of each sinc function are superimposed in output plane and they result in detection error in output plane, so the detection efficiency is low. We use the circular shaped sub-CGHs in order to reduce the side lobe value in output plane instead of rectangular shaped sub-CGHs. The each input beam is transformed to first-order Bessel functions through circular shaped sub-CGHs in output plane. The side lobes of first-order Bessel functions us low values compared with side lobes of sinc function, so we can improve the detection efficiency in output plane. We use binary phase modulated CGH, and confirm this improvement results by simulation.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.491-500
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• 2002

In this paper, we propose a real-time face detection and recognition system by using skin color informations, geometrical feature vectors of face, and facial angle informations from color face image. The proposed algorithm improved face region extraction efficiency by using skin color informations on the HSI color coordinate and face edge information. And also, it improved face recognition efficiency by using geometrical feature vectors of face and facial angles from the extracted face region image. In the experiment, the proposed algorithm shows more improved recognition efficiency as well as face region extraction efficiency than conventional methods.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.240-241
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• 2008

We are presenting the efficient and robust simplified module based self-collision detection of humanoid robot simulator. For safe and reliable operations of humanoid robot, the self-collision detection is essential and extremely important. The main methods of self-collision detection are inverse X-Y-Z fixed angles and module distance filtering (MDF). According to experiments on the humanoid robot simulator with the self-collision detection, we could have a confidence about the efficiency of the self-collision.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.963-968
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• 2007

In this paper introduce Standard 3D object(Bounding-Volume). In 3D game very efficient control algorithm Using collision detection which controls the convenient of a game based on Standard 3D object specially collision-detection. This algorithm is designed LOD(Level-of-Detail) using Dynamic-Hierarchies of collision detection efficiency improvement in 3D object.

• Journal of the Optical Society of Korea
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• v.20 no.6
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• pp.745-751
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• 2016

A detection algorithm, based on the combined local-global (CLG) optical-flow model and Gaussian pyramid for a moving target appearing against a dynamic background, can compensate for the inadaptability of the classic Horn-Schunck algorithm to illumination changes and reduce the number of needed calculations. Incorporating the hypothesis of gradient conservation into the traditional CLG optical-flow model and combining structure and texture decomposition enable this algorithm to minimize the impact of illumination changes on optical-flow estimates. Further, calculating optical-flow with the Gaussian pyramid by layers and computing optical-flow at other points using an optical-flow iterative with higher gray-level points together reduce the number of calculations required to improve detection efficiency. Finally, this proposed method achieves the detection of a moving target against a dynamic background, according to the background motion vector determined by the displacement and magnitude of the optical-flow. Simulation results indicate that this algorithm, in comparison to the traditional Horn-Schunck optical-flow algorithm, accurately detects a moving target undergoing illumination changes against a dynamic background and simultaneously demonstrates a significant reduction in the number of computations needed to improve detection efficiency.

• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.593-607
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• 2022

To solve the problem of detecting structural damage, a two-stage method using the Kalman filter and Particle Swarm Optimization (PSO) is proposed. In this method, the first PSO population is enhanced using the Kalman filter method based on dynamic responses. Due to noise in the sensor responses and errors in the damage detection process, the accuracy of the damage detection process is reduced. This method proposes a novel approach for solve this problem by integrating the Kalman filter and sensitivity analysis. In the Kalman filter, an approximate damage equation is considered as the equation of state and the damage detection equation based on sensitivity analysis is considered as the observation equation. The first population of PSO are the random damage scenarios. These damage scenarios are estimated using a step of the Kalman filter. The results of this stage are then used to detect the exact location of the damage and its severity with the PSO algorithm. The efficiency of the proposed method is investigated using three numerical examples: a 31-element planer truss, a 52-element space dome, and a 56-element space truss. In these examples, damage is detected for several scenarios in two states: using the no noise responses and using the noisy responses. The results show that the precision and efficiency of the proposed method are appropriate in structural damage detection.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.154-157
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• 2022

In the existing Tensorflow CNN environment, the object detection method is a method of performing object labeling and detection by Tensorflow itself. However, with the advent of YOLO, the efficiency of image object detection has increased. As a result, more deep layers can be built than existing neural networks, and the image object recognition rate can be increased. Therefore, in this paper, the detection ability and speed were compared and analyzed by designing an object detection system based on Darknet and YOLO and performing multi-layer construction and learning based on the existing convolutional neural network. For this reason, in this paper, a neural network methodology that efficiently uses Darknet's learning is presented.

• Convergence Security Journal
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• v.1 no.1
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• pp.37-45
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• 2001

In this paper, separated and optimized pattern database model is proposed. In order to improve efficiency of Network-based IDS, pattern database is classified by proper basis. Classification basis is decided by the specific Intrusions validity on specific target. Using this model, IDS searches only valid patterns in pattern database on each captured packets. In result, IDS can reduce system resources for searching pattern database. So, IDS can analyze more packets on the network. In this paper, proper classification basis is proposed and pattern database classified by that basis is formed. And its performance is verified by experimental results.