• Journal of KIISE:Computing Practices and Letters
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• v.16 no.5
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• pp.596-600
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• 2010

This paper proposes hough transform-based semi-automatic vertex detection algorithm for object modeling on a mobile phone supporting touch-screens. The proposed algorithm shows fast processing time by searching the limited range of parameters for computing hough transform with a small range of ROI image. Moreover, the proposed algorithm removes bad candidates among the detected lines by selecting the two closest candidate lines from the position of user's input. After that, it accurately detects an interesting vertex without additionally required interactions by detecting an intersection point of the two lines. As a result, we believe that the proposed algorithm shows a 1.4 pixel distance error on average as a vertex detection accuracy under such conditions as a 5.7 pixel distance error on average as an inaccurate input.

• The KIPS Transactions:PartB
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• v.15B no.6
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• pp.543-552
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• 2008

ATIS(Advanced Traveler Information System) is the system to offer a real-time traffic information or traffic situation for the benefit of the client. One of traffic information collection methods for ATIS research is the method of image analysis. The method is divided into two : one is the method to set two loop detectors at the area and the other is the method detecting the vehicle through an image analysis. In this paper, we propose a real-time traffic information collection system to mix two methods. The system installs multiple virtual detection lines and traces the location of the vehicle. Use of multiple virtual detection lines supplements the defect of the method of loop detectors. And we drew a representative pixels in the detecting area and used it for image analysis. This is to solve the problem of time delay which increases as the image size increases. We gathered traffic images and experimented using the system and got 92.32% of detection accuracy.

• Proceedings of the KIEE Conference
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• 2006.10d
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• pp.238-240
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• 2006

For the method of train position detection, ground-based train position estimation mainly has been applied so far. Ground-based position detection is the way to detect train current positions by installing train position equipments on railroad lines. However, the ground-based methods should install detection equipments on each section, and can only be able to detect train positions from main command center. So this method has several disadvantages such as an discontinuous position detection, an increment in cost of installation and maintenance. To make possible continuous train position detection, and to minimize amount of the cost, the vehicle-based position detection method should be chosen to determine train positions by loading position equipments on vehicles. In this paper, to realize the vehicle-based train position detection method, configuration scheme of train position detection equipment is suggested by using GPS, inertial sensor, speed sensor and its performance is verified by simulations.

• Korean Journal of Breeding Science
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• v.43 no.4
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• pp.252-259
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• 2011

Conventional staining and fluorescence in situ hybridization (FISH) karyotypes of the non-genetically modified (GM) parental rice line, 'Nakdong' (Oryza sativa L. japonica), and its four GM rice lines, LS28 (event LS30-32-20-1), Cry1Ac1 (event C7-1-9-1), and LS28 ${\times}$ Cry1Ac1 (events L/C1-1-3-1 and L/C1-3-1-1) were analyzed using 5S and 45S rDNAs as probes. Both parental and transgenic lines were diploids (2n=24) with one satellite chromosome pair. The lengths of the prometaphase chromosomes ranged from 1.50 to $6.30{\mu}m$. Four submetacentric and eight metacentric pairs comprised the karyotype of 'Nakdong' and its four GM lines. One pair of 5S rDNA signals was detected near the centromeric region of chromosome g in both the parental and transgenic lines. The 45S rDNA signals were detected on the secondary constrictions of the satellite chromosome pair in both the parental and transgenic lines. There was no significant difference in chromosome size, length, and composition between 'Nakdong' and its four GM lines. This research was conducted as a preliminary study for chromosomal detection of transgenes in GM rice lines and would be useful for their breeding programs.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.1
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• pp.197-205
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• 2007

We have proposed a novel line detection method based on the estimated probability density function of gradient directions of edges. By estimating peaks of the density function, we determine groups of edges that have the same gradient direction. For edges in the same groups, we detect lines that correspond to peaks of the connectivity weighted distribution of the distances from the origin. In the experiments using the Data Matrix barcode images and LCD images, the proposed method showed better performance than conventional Methods in terms of the processing speed and accuracy.

• Fisheries and Aquatic Sciences
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• v.8 no.3
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• pp.185-187
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• 2005

We tested the in vivo ability of a DNA chip to detect virus-specific genes from virus-infected olive flounder Paralichthys olivaceus and rainbow trout Oncorhynchus mykiss. Target cDNA was obtained from total RNA of virus infected cell lines by reverse transcription (RT) and was labeled with fluorescent dye (Cy5-dUTP). The results show the successful detection of infectious hematopoietic necrosis virus (IHNV) and viral hemorrhagic septicaemia virus (VHSV) genes in the virus-infected fishes.

• Journal of KIISE
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• v.43 no.3
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• pp.346-352
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• 2016

The fishing industry requires many workers to manually carry out the jobs of sorting and cutting fishes. There are therefore many dangerous situations in their working environment and the throughput is inefficiently low. This paper introduces an automatic fin cutting system based on RANSAC that is able to increase the throughput of fish processing jobs. The system proposed in this paper first detects the edges of a fish using a high-pass filter. The boundary lines between fin and body are then detected by adjusting parameters and the threshold of the noise filters. Finally, the optimal cutting lines are detected using RANSAC. Through an experiment with a sample of 50 fishes, this paper shows that the proposed system detects the cutting lines with about 90% accuracy.

• KIEE International Transactions on Power Engineering
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• v.3A no.4
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• pp.191-197
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• 2003

Accurate detection and classification of faults on transmission lines is vitally important. In this respect, many different types of faults occur, such as inter alia low impedance faults (LIF) and high impedance faults (HIF). The latter in particular pose difficulties for the commonly employed conventional overcurrent and distance relays, and if undetected, can cause damage to expensive equipment, threaten life and cause fire hazards. Although HIFs are far less common than LIFs, it is imperative that any protection device should be able to satisfactorily deal with both HIFs and LIFs. Because of the randomness and asymmetric characteristics of HIFs, their modeling is difficult and numerous papers relating to various HIF models have been published. In this paper, the model of HIFs in transmission lines is accomplished using the characteristics of a ZnO arrester, which is then implemented within the overall transmission system model based on the electromagnetic transients program (EMTP). This paper proposes an algorithm for fault detection and classification for both LIFs and HIFs using Adaptive Network-based Fuzzy Inference System (ANFIS). The inputs into ANFIS are current signals only based on Root-Mean-Square (RMS) values of 3-phase currents and zero sequence current. The performance of the proposed algorithm is tested on a typical 154 kV Korean transmission line system under various fault conditions. Test results demonstrate that the ANFIS can detect and classify faults including LIFs and HIFs accurately within half a cycle.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.2
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• pp.162-170
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• 2002

Detecting straight lines in an image is frequently required for various machine vision applications such as restoring CAD drawings from scanned images and object recognition. The standard Hough transform has been dominantly used to that purpose. However, massive storage requirement and low precision in estimating line parameters due to the quantization of parameter space are the major drawbacks of the Hough transform technique. In this paper, to overcome the drawbacks, an iterative algorithm based on a self-organizing map is presented. The self-organizing map can be adaptively learned such that image points are clustered by prominent lines. Through the procedure of the algorithm, a set of lines are sequentially detected one at a time. The algorithm can produce highly precised estimates of line parameters using very small amount of storage memory. Computational results for synthetically generated images are given. The promise of the algorithm is also demonstrated with its application to two natural images of inserts.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.421-424
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• 2022

Hough transform is a representative algorithm for detecting straight lines in an edge image. It corresponds the parameters of straight lines that may occur in the edge pixel into a parameter space, and detects valid parameters satisfying a given condition as straight lines. In general Hough transform, the parameters of the line are calculated with the image origin as the reference point. However, in this paper, the Hough transform based on the image center as a reference point is performed and its performance is compared and analyzed with the conventional Hough transform.