• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.240-247
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• 2006

Piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SYM) classifier is discussed for damage identification of a railroad track. An active sensing system composed of two PZT patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage of the railroad track (one is a hole damage of 0.5cm in diameter at web section and the other is a transverse cut damage of 7.5cm in length and 0.5cm in depth at head section). Two damage-sensitive features were extracted one by one from each method; a) feature I: root mean square deviations (RMSD) of impedance signatures and b) feature II: wavelet coefficients for $A_0$ mode of guided waves. By defining damage indices from those damage-sensitive features, a two-dimensional damage feature (2-D DF) space was made. In order to minimize a false-positive indication of the current active sensing system, a two-step SYM classifier was applied to the 2-D DF space. As a result, optimal separable hyper-planes were successfully established by the two-step SYM classifier: Damage detection was accomplished by the first step-SYM, and damage classification was also carried out by the second step-SYM. Finally, the applicability of the proposed two-step SYM classifier has been verified by thirty test patterns.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.2
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• pp.82-89
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• 2000

Edge detection is the first step and very important step in image analysis. In this paper, proposed edge detection operators based on informations of edge types and it is different from other classical edge detection operators such as gradient and surface fitting operators. The first, we defined characteristics of edge types such as localization, thinness, length. The second, we defined valid edge types and ideal edge pixel positions in $3\times3$window based on edge characteristics of edge types. And we proposed edge detection algorithm and twelve windows based on valid edge types. In specially, proposed algorithm was shown better performence of edge detection than other operators such as gradient operator and the LoG(Laplacian of Gaussian) operator of zero crossings.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.2
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• pp.371-384
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• 2001

In these days, it is continuously increased to the intrusion of system in internet environment. The methods of intrusion detection can be largely classified into anomaly detection and misuse detection. The former uses statistical methods, features selection method in order to detect intrusion, the latter uses conditional probability, expert system, state transition analysis, pattern matching. The existing studies for IDS(intrusion detection system) use combined methods. In this paper, we propose a new intrusion detection algorithm combined both state transition analysis and association mining techniques. For the intrusion detection, the first step is generated state table for transmitted commands through the network. This method is similar to the existing state transition analysis. The next step is decided yes or no for intrusion using the association mining technique. According to this processing steps, we present the automated generation algorithm of the penetration scenarios.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.83-85
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• 2012

Gait analysis through wearable sensors is becoming a key research topic in mobile. In gait analysis, step detection is one of the most important processes that will lay down the foundation for future implementation. In this paper, we will propose a simpler algorithm to determine and analyze the steps using accelerometer sensor built-in mobile phone that physically placed into the trouser pocket. This is the location where most of mobile devices are. With 5 volunteers walking in 160 seconds, the accuracy of this method is approximately 98.5%.

• Smart Media Journal
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• v.2 no.4
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• pp.20-26
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• 2013

This paper presents an approach to Mongolian car plate recognition using artificial neural network. Our proposed method consists of two steps: detection and recognition. In detection step, we implement Flood fill algorithm. In recognition step we proceed to segment the plate for each Cyrillic character, and use an Artificial Neural Network (ANN) machine - learning algorithm to recognize the character. We have learned the theory of ANN and implemented it without using any library. A total of 150 vehicles images obtained from community entrance gates have been tested. The recognition algorithm shows an accuracy rate of 89.75%.

• Journal of Physiology & Pathology in Korean Medicine
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• v.18 no.3
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• pp.874-879
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• 2004

Alakaline phosphatase (ALP)- or horseradish peroxidase (HRP)-antibody conjugate was used frequently on the immunological detection methods such as enzyme-linked immunosobent assay (ELISA), immunobolt, immunohistochemistry. The classical enzyme-antibody coupling method by one-step (direction) injection of glutaraldehyde bring into being disadvantage such as low sensitivity of antigen detection because of homopolymers. This study was modified with the dialysis glutaraldehyde method to provide simple coupling through E-amino residues present in most protein. The dialysis glutaraldehyde coupling effects were better than the classical one-step glutaraldehyde injection in antigen detection of ELISA and immunobolt. Optimal dose of the dialysis glutaraldehyde solution was 0.10-0.25 %. This results suggest that the dialysis glutaraldehyde coupling method can readily applied to antigen detection of in vitro and in vivo.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.10
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• pp.1508-1515
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• 2003

In this study, the image processing program for deducing parameters of the elliptic shape of the partially overlapped liquid droplets was developed using the randomized Hough transform and the parameter decomposition. The procedure for the shape detection consists of three steps. For the first step, the candidate centers of ellipses are determined by the geometric property of the ellipse. Next, the rest parameters are estimated by the randomized Hough transform. In the final step for the post-processing, optimally approximated parameters of ellipses are determined. The developed program was applied to the simulated overlapped ellipses, real overlapped droplets, and real spray droplets. The shape detection was very excellent unless there existed inherent problems in original images. Moreover, this method can be used as an effective separating method for the overlapped small particles.

• Proceedings of the KSME Conference
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• 2003.04a
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• pp.1783-1788
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• 2003

In this study, the image processing program for deducing parameters of the elliptic shape of the partially overlapped liquid droplets was developed using the randomized Hough transform and the parameter decomposition. The procedure for the shape detection consists of three steps. For the first step, the candidate centers of ellipses are determined by the geometric property of the ellipse. Next, the rest parameters are estimated by the randomized Hough transform. In the final step for the post-processing, optimally approximated parameters of ellipses are determined. The developed program was applied to the simulated overlapped ellipses, real overlapped droplets, and real spray droplets. The shape detection was very excellent unless there existed inherent problems in original images. Moreover, this method can be used as an effective separating method for the overlapped small particles.

• The Journal of Information Systems
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• v.14 no.3
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• pp.117-122
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• 2005

This paper presents a method for detecting scene changes in video sequences, in which the $chi^{2}$-test is modified by imposing weights according to NTSC standard. To automatically determine threshold values for scene change detection, the proposed method utilizes the frame differences that are obtained by the weighted $chi^{2}$-test. In the first step, the mean and the standard deviation of the difference values are calculated, and then, we subtract the mean difference value from each difference value. In the next step, the same process is performed on the remained difference values, mean-subtracted frame differences, until the stopping criterion is satisfied. Finally, the threshold value for scene change detection is determined by the proposed automatic threshold estimation algorithm. The proposed method is tested on various video sources and, in the experimental results, it is shown that the proposed method is reliably estimates the thresholds and detects scene changes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.896-897
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• 2014

Multi-lane detection is useful for the smart navigation system. In this paper, a novel multi-lane detection method is presented. The proposed three point Laplacian filter (TPLF) can complement the weak points of the previous box filter and step filter. The experimental results validate the feasibility of the proposed multi-lane detection method.