• Proceedings of the KSR Conference
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• 2011.10a
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• pp.69-74
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• 2011

In the curving detection method by using an accelerometer, the ride comfort in the first car is worse than one in the others due to spend the time to calculate the tilting command and drive the tilting mechanism after entering in the curve. In order to enhance the ride comfort in the first car, the preditive curve detection method which predicts the distance from a train to the starting point of curve by using the GPS, Tachometer, Ground balise and position DB for track. In this study, we predicted and evaluated the ride comfort for predictive curve detection method in transient curves according to the shape and dimension of transient curve and the various driving speed. Also, we predicted the improvement of the ride comfort for predictive curve detection method by comparing with the result of the ride comfort for predictive curve detection method and for curve detection method using an accelerometer in the short transient curve.

• Smart Structures and Systems
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• v.3 no.3
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• pp.263-280
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• 2007

In this paper, an improved GA-based damage detection algorithm using a set of combined modal features is proposed. Firstly, a new GA-based damage detection algorithm is formulated for beam-type structures. A schematic of the GA-based damage detection algorithm is designed and objective functions using several modal features are selected for the algorithm. Secondly, experimental modal tests are performed on free-free beams. Modal features such as natural frequency, mode shape, and modal strain energy are experimentally measured before and after damage in the test beams. Finally, damage detection exercises are performed on the test beam to evaluate the feasibility of the proposed method. Experimental results show that the damage detection is the most accurate when frequency changes combined with modal strain-energy changes are used as the modal features for the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.637-639
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• 2021

As autonomous driving research is actively progressing, lane detection is an essential technology in ADAS (Advanced Driver Assistance System) to locate a vehicle and maintain a route. Lane detection is detected using an image processing algorithm such as Hough transform and RANSAC (Random Sample Consensus). This paper implements a linear shape detection algorithm using OpenCV on Raspberry Pi 3 B+. Thresholds were set through OpenCV Gaussian blur structure and Canny edge detection, and lane recognition was successful through linear detection algorithm.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.2 s.72
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• pp.125-138
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• 2006

The use of system identification approaches for damage detection has been expanded in recent years owing to the advancements in data acquisition system andinformation processing techniques. Soft computing techniques such as neural networks and genetic algorithm have been utilized increasingly for this end due to their excellent pattern recognition capability. In this study, damage detection of bridge structures using neural networks technique based on the modal properties is presented, which can effectively consider the modeling uncertainty in the analysis model from which the training patterns are to be generated. The differences or the ratios of the mode shape components between before and after damage are used as the input to the neural networks in this method, since they are found to be less sensitive to the modeling errors than the mode shapes themselves. Two numerical example analyses on a simple beam and a multi-girder bridge are presented to demonstrate the effectiveness and applicability of the proposed method.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.2
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• pp.100-105
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• 2022

This paper applies the arc detection algorithm to prevent the false detection in photo voltaic series arc detection circuit, which is required not only to detect the series arc quickly, but also not falsely detect the arc for the non-arc noise. For this purpose, this study proposes a rapid and preventive false detection method of single peak noise and short noise signals. First, to prevent false detection by single peak noise, Discrete wavelet transform (DWT)-based characteristic parameters are applied to determine the shape and the amplitude of the noise. In addition, arc fault detection within a few milliseconds is performed with the DWT iterative algorithm to quickly prevent false detection for short noise signals, considering the continuity of serial arc noise. Thus, the method operates not only to detect series arc, but also to avoid false arc detection for peak and short noises. The proposed algorithm is applied to real-time serial arc detection circuit based on the TMS320F28335 DSP. The serial arc detection and peak noise filtering performances are verified in the built simulated arc test facility. Furthermore, the filtering performance of short noise generated through DC switch operation is confirmed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.136-138
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• 2022

Edge detection includes information such as the shape, position, size, and material of an object with respect to an image, and is a very important factor in analyzing the characteristics of the image. Existing edge detection methods include Sobel edge detection filter, Roberts edge detection filter, Prewitt edge detection filter, and LoG (Lapacian of Gaussian) using secondary differentials. However, these methods have a disadvantage in that the edge detection results are somewhat insufficient because a fixed weight mask is applied to the entire image area. Therefore, in this paper, we propose an edge detection algorithm that increases edge detection characteristics by considering the center pixel in the mask. In addition, in order to confirm the proposed edge detection performance, it was compared through simulation result images.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.1
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• pp.9-15
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• 2012

This paper shows an effective scanning trajectory for a mine detection device that is one of the mission equipments of unmanned ground vehicle. The mine detection device is composed of a mine-detection sensor, and a 4 DOF manipulator enabling sensor position control. There are three modes that manage the mine detection device: passive, semi-automatic, and automatic. The automatic mode is used the most. This paper suggests a scanning method that makes shape of 8. This method prevents missing target area and enhances scanning speed when the mine detection device scans the ground surface in automatic mode. The suggested method is verified by simulations and experiments.

• Journal of the Semiconductor & Display Technology
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• v.17 no.4
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• pp.105-108
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• 2018

Face Detection is an essential first step of the face recognition, and this is significant effects on face feature extraction and the effects of face recognition. Face detection has extensive research value and significance. In this paper, we present and analysis the principle, merits and demerits of the classic AdaBoost face detection and ASM algorithm based on point distribution model, which ASM solves the problems of face detection based on AdaBoost. First, the implemented scheme uses AdaBoost algorithm to detect original face from input images or video stream. Then, it uses ASM algorithm converges, which fit face region detected by AdaBoost to detect faces more accurately. Finally, it cuts out the specified size of the facial region on the basis of the positioning coordinates of eyes. The experimental result shows that the method can detect face rapidly and precisely, with a strong robustness.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.190-211
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• 2022

This study investigated the effect of the shape and height of road signs on detection performance when detecting road signs with LiDAR, which is recognized as an essential sensor for autonomous vehicles. For the study, four types of road signs with the same area and material and different shapes were produced, and a road driving test was performed by installing a 32Ch rotating LiDAR on the upper part of the vehicle. As a result of comparing the shape of the point cloud and the NPC according to the shape of the road sign, It is expected that a distance of less than 40m is required to recognize the overall shape of a road sign using 32Ch LiDAR, and shapes such as triangles and rectangles are more advantageous than squares in securing the maximum point cloud from a long distance. As a result of the study according to the height of the road sign, At short distances (within 20m), if the height of the sign is raised to more than 2m, it deviates from the vertical viewing angle of the LiDAR and cannot express the complete point cloud shape. However, it showed a negligible effect compared to the near-field height change. These research results are expected to be utilized in the development of road facilities dedicated to LiDAR for the commercialization of autonomous cooperative driving technology.

• Preventive Nutrition and Food Science
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• v.7 no.2
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• pp.188-194
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• 2002

This study was carried out to observe changes in several detection factors derived from thermoluminescence (TL) of minerals separated from irradiated Korean perilla and sesame seeds during storage under normal room and darkroom conditions. The TL intensities of the first glow curves increased from 0 to 5 kGy but only slightly increase from 5 to 10 kGy. Maximum TL temperatures of the first glow curves in all irradiated samples were around 20$0^{\circ}C$, ranging from 150 to 25$0^{\circ}C$. Since the control (0 day of storage) glow curve ratios of G3 and G4, calculated from re-irradiated (1 kGy) sample were over 0.5, detection of irradiation was possible. However, because Gl ratios were below 0.1, they were classified as non-irradiated. There was n unique first glow curve shape that could be clearly seen in all irradiated samples, regardless of storage conditions, that was never seen in non-irradiated samples. In all samples, the maximum TL temperatures and shape of the second glow curve was in a lower temperature range than that of the first glow curve. Therefore, detection of irradiated Korean perilla and sesame seeds was possible fur up to 3 months after irradiation, regardless of storage conditions, by examining several TL detection factors; including TL intensity, glow curve ratios maximum TL temperatures, and the shapes of glow curves.