• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.5
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• pp.812-816
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• 2015

Recently, various monitoring systems have been proposed to detect interaction performance between trains and infrastructure, as well as, various techniques to improve the accuracy and performance of such inspection equipment in high speeds. Especially, it is important to predict electric noise of high speed trains due to its effect on detection system accuracy. In this paper, we analyze various types of electrical noise in electric vehicles to improve the accuracy of the detection module of the inspection car. In detail, analysis of electric noise of high speed railway is performed as a function of speed based on field tests that were carried out by HEMU-430X (Highspeed Eletric Multiple Unit - 430 km/h eXperiment).

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.47-54
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• 2022

For safe driving of autonomous vehicles, road damage detection is very important to lower the potential risk. In order to ensure safety while an autonomous vehicle is driving on the road, technology that can cope with various obstacles is required. Among them, technology that recognizes static obstacles such as poor road conditions as well as dynamic obstacles that may be encountered while driving, such as crosswalks, manholes, hollows, and speed bumps, is a priority. In this paper, we propose a method to extract similarity of images and find damaged road images using OpenCV image processing and CNN algorithm. To implement this, we trained a CNN model using 280 training datasheets and 70 test datasheets out of 350 image data. As a result of training, the object recognition processing speed and recognition speed of 100 images were tested, and the average processing speed was 45.9 ms, the average recognition speed was 66.78 ms, and the average object accuracy was 92%. In the future, it is expected that the driving safety of autonomous vehicles will be improved by using technology that detects road obstacles encountered while driving.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.722-730
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• 2012

Recently, eye image information has been widely used for iris recognition or gaze detection in biometrics or human computer interaction. According as long distance camera-based system is increasing for user's convenience, the noises such as eyebrow, forehead and skin areas which can degrade the accuracy of eye detection are included in the captured image. And fast processing speed is also required in this system in addition to the high accuracy of eye detection. So, we compared the most widely used algorithms for eye detection such as AdaBoost eye detection algorithm, adaptive template matching+AdaBoost algorithm, CAMShift+AdaBoost algorithm and rapid eye detection method. And these methods were compared with images including light changes, naive eye and the cases wearing contact lens or eyeglasses in terms of accuracy and processing speed.

• The KIPS Transactions:PartC
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• v.10C no.6
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• pp.717-726
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• 2003

In this paper, we analyze the associated rule based deductive algorithm which creates the rules automatically for intrusion detection from the vast packet data. Based on the result, we also suggest the deductive algorithm which creates the rules of intrusion pattern fast in order to apply the intrusion detection systems. The deductive algorithm proposed is designed suitable to the concept of clustering which classifies and deletes the large data. This algorithm has direct relation with the method of pattern generation and analyzing module of the intrusion detection system. This can also extend the appication range and increase the detection speed of exiting intrusion detection system as the rule database is constructed for the pattern management of the intrusion detection system. The proposed pattern generation technique of the deductive algorithm is used to the algorithm is used to the algorithm which can be changed by the supporting rate of the data created from the intrusion detection system. Fanally, we analyze the possibility of the speed improvement of the rule generation with the algorithm simulation.

• The KIPS Transactions:PartD
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• v.12D no.6 s.102
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• pp.915-920
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• 2005

In this paper we expanded a existing web based B2B system to support users in low speed network. To guarantee shared dat a consistency between clients and a server, we proposed a method of data change detection by using a time stamp tree and the performance analysis of the proposed method was proved by a simulation. Under the worst condition that leaf nodes of a times tamp tree were changed uniform distribution, the simulation result showed that the proposed method was more efficient than a sequential detection until the percentage of changed nodes were below $15\%$. According to our observation, the monthly average of data change was below $7\%$ on a web-based construction MRO B2B system or a company A from April 2004 to August 2004. Therefore the Proposed method improved performance of data change detection in practice. The proposed method also reduced storage consumption in a server because it didn't require a server to store replicated data for every client.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.9
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• pp.1324-1328
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• 2013

The reason of train location detection is generally the train interval control between the railway stations and the train path control in the railway station yard in order to avoid train collision. It is very important to know the train location for shortening the train headway, and improving the efficiency of railway maintenance as well as the safe operation of trains. Therefore, the accurate detection of train location is the prerequisite technology in railway signalling system. The track circuit and the wheel sensor of rolling stock have been used to detect the train location widely in urban railway as well as high speed train. The track circuit is continuously monitored by electrical equipment to detect the absence of the train and the tachometer and encoder is used for the wheel sensor to measure the train speed. But speed sensor failures are frequent due to the extremely harsh operating conditions encountered in rail vehicle. The CBTC(Communication based Train Control) system has been used in urban railway system recently. But the installation of CBTC system is very high and the modification of design is difficult. This paper deals with the feasibility of new train location detection method using magnetic sensors.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1330-1339
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• 2022

3D object detection generally aims to detect relatively large data such as automobiles, buses, persons, furniture, etc, so it is vulnerable to small object detection. In addition, in an environment with limited resources such as embedded devices, it is difficult to apply the model because of the huge amount of computation. In this paper, the accuracy of small object detection was improved by focusing on local features using only one layer, and the inference speed was improved through the proposed knowledge distillation method from large pre-trained network to small network and adaptive quantization method according to the parameter size. The proposed model was evaluated using SUN RGB-D Val and self-made apple tree data set. Finally, it achieved the accuracy performance of 62.04% at mAP@0.25 and 47.1% at mAP@0.5, and the inference speed was 120.5 scenes per sec, showing a fast real-time processing speed.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.113-118
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• 2010

The split spectrum processing algorithm of an ultrasonic wave on the tire was studied for the damage detection of a driving vehicle's tire. The processing results of normal and damaged tire was compared using the split spectrum algorithm to estimate the maximum value. The result that used Auto-correlation in case of damaged tire, the damage feature point is detected during 81ms intervals at a speed of 100km/h and during 162ms periodicity at a speed of 50km/h. This results was meaned the possibility for the tire's damage decision by damaging material with using periodicity feature point of tire damage according to vehicle speed.

• Journal of Industrial Convergence
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• v.18 no.3
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• pp.53-61
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• 2020

Travel speed is an important parameter for measuring road traffic and incident detection system. In this paper I suggests a model developed for estimating reliable and accurate average roadway link travel speeds using image processing sensor. This method extracts the vehicles from the video image from CCTV, tracks the moving vehicles using deep neural network, and extracts traffic information such as link travel speeds and volume. The algorithm estimates link travel speeds using a robust data-fusion procedure to provide accurate link travel speeds and traffic information to the public. In the field tests, the new model performed better than existing methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.452-455
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• 2005

The cutting characteristics of hardened steel by a PCBN tool is investigated with respect to workpiece surface roughness, cutting force and tool flank wear of the vision system. Backpropagation neural networks (BPNs) were used for detection of tool wear. The neural network consisted of three layers: input, hidden and output. The input vectors comprised of spindle rotational speed, feed rates, vision flank wear, and thrust force signals. The output was the tool wear state which was either usable or failure. Hard turning experiments with various spindle rotational speed and feed rates were carried out. The learning process was performed effectively by utilizing backpropagation. The detection of the abnormal states using BPNs achieved 96.4% reliability even when the spindle rotational speed and feedrate were changed.