• The Journal of the Acoustical Society of Korea
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• v.19 no.8
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• pp.78-83
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• 2000

Passive sonar system is designed to classify the underwater targets by analyzing and comparing the various acoustic characteristics such as signal strength, bandwidth, number of tonals and relationship of tonals from the extracted tonals and frequency lines. First of all the precise detection and extraction of signal frequency lines is of particular importance for enhancing the reliability of target classification. But, the narrowband frequency lines which are the line formed in spectrogram by a tonal of constant frequency in each frame can be detected weakly or discontinuously because of the variation of signal strength and transmission loss in the sea. Also, it is very difficult to detect and extract precisely the signal frequency lines by the complexity of impulsive ambient noise and signal components. In this paper, the automatic detection and extraction method that can detect and extract the signal components of frequency tines precisely are proposed. The proposed method can be applied under the bad conditions with weak signal strength and high ambient noise. It is confirmed by the simulation using real underwater target data.

• Journal of Biomedical Engineering Research
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• v.13 no.2
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• pp.115-124
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• 1992

An algorithm has been proposed for the automatic detection of optimal epiand endocardial left ventricular borders from 2-D short axis echocardiogram which is degraded by noise and echo drop out. For the implementation of the algorithm, we modified Ballard's Generalized Hough Transform which can be applicable only for deterministic object border, and newly proposed Fuzzy Hough Transform method. The algorithm presented here allows detection of object whose exact shapes are unknown. The algorithm only requires an approximate model of target object based on anatomical data. To detect the approximate epicardial contour of left ventricle, Fuzzy Hough Transform was applied to the echocardiogram. The optimal epicardial contour was founded by using graph searching method which contains cost function analysis process. Using this optimal epicardial contour and average thickness imformation of left ventricular wall, the approximate endocardial line was founded, and graph searching method was also used to detect optimal endocardial contour.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1224-1227
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• 2002

In this paper, we propose a composition method of the template image whose detection performance does not have incorrect detection and improves also on the tough photography conditions of the outdoors, rainy weather and night. This research was done to measure a relative position between a spreader and a target container with image processing to realize full-automatic quayside gantry cranes. By the proposal method, we confirmed that the template image for object detection has a contour image more effective than a gray image.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.393-395
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• 2003

The changes of DEMs(Digital Elevation Models) will be detected to correct the information of DEMs and/or to get the information about themselves. This study suggests the evaluation of DEM using correlation coefficient value between the target and the reference DEMs for detect the changes.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.45 no.3
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• pp.177-187
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• 2009

This paper describes a automatic video surveillance system(AVSS) with long range and 360$^{\circ}$ coverage that is automatically rotated in an elevation over azimuth mode in response to the TTM(tracked target message) signal of vessels tracked by ARPA(automatic radar plotting aids) radar. This AVSS that is a video security and tracking system supported by ARPA radar, CCTV(closed-circuit television) camera system and other sensors to automatically identify and track, detect the potential dangerous situations such as collision accidents at sea and berthing/deberthing accidents in harbor, can be used in monitoring the illegal fishing vessels in inshore and offshore fishing ground, and in more improving the security and safety of domestic fishing vessels in EEZ(exclusive economic zone) area. The movement of the target vessel chosen by the ARPA radar operator in the AVSS can be automatically tracked by a CCTV camera system interfaced to the ECDIS(electronic chart display and information system) with the special functions such as graphic presentation of CCTV image, camera position, camera azimuth and angle of view on the ENC, automatic and manual controls of pan and tilt angles for CCTV system, and the capability that can replay and record continuously all information of a selected target. The test results showed that the AVSS developed experimentally in this study can be used as an extra navigation aid for the operator on the bridge under the confusing traffic situations, to improve the detection efficiency of small targets in sea clutter, to enhance greatly an operator s ability to identify visually vessels tracked by ARPA radar and to provide a recorded history for reference or evidentiary purposes in EEZ area.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1384-1390
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• 2019

This paper proposes a technological solution for improving the recognition direction of target objects for automatic vision inspection by machine vision. This paper proposes a technological solution for improving the recognition direction of target objects for automatic vision inspection by machine vision. This enables the automatic machine vision inspection to detect the image of the inspection object regardless of the position and orientation of the object, eliminating the need for a separate inspection jig and improving the automation level of the inspection process. This study develops the technology and method that can be applied to the wire harness manufacturing process as the inspection object and present the result of real system. The results of the system implementation was evaluated by the accredited institution. This includes successful measurement in the accuracy, detection recognition, reproducibility and positioning success rate, and achievement the goal in ten kinds of color discrimination ability, inspection time within one second and four automatic mode setting, etc.

• The Journal of Korea Robotics Society
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• v.15 no.4
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• pp.350-356
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• 2020

Workers have been replaced by mobile manipulators for factory automation in recent years. One of the typical tasks for automation is that a mobile manipulator moves to a target location and picks and places an object on the worktable. However, due to the pose estimation error of the mobile platform, the robot cannot reach the exact target position, which prevents the manipulator from being able to accurately pick and place the object on the worktable. In this study, we developed an automatic alignment system using a low-cost camera mounted on the end-effector of a collaborative robot. Camera calibration and pose estimation methods were also proposed for the automatic alignment system. This algorithm uses a markerboard composed of markers to calibrate the camera and then precisely estimate the camera pose. Experimental results demonstrate that the mobile manipulator can perform successful pick and place tasks on various conditions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.10
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• pp.1508-1515
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• 2017

This paper proposes the defect detection method for automatic inspection machines in printed circuit boards (PCBs) manufacturing system. The defects of PCB such as open, short, pin hole and scratch can be detected by comparing the standard image and the target image. The standard image is obtained from CAD file such as ODB++ format, and the target image is obtained by arranging, filtering and binarization of captured PCB image. Since the PCB size is too large and image resolution is too high, the image processing requires a lot of memory and computational time. The wavelet transform is applied to compress the standard and target images, which results in reducing the memory and computational time. To increase the inspection accuracy, we utilize the he HH-domain as well as LL-domain of the transformed images. Experimental results are finally presented to show the performance improvement of the proposed method.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.242-248
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• 2022

The article presents the analysis of modern methods of automatic comparison of original and unoriginal text to detect textual plagiarism. The study covers two types of plagiarism - literal, when plagiarists directly make exact copying of the text without changing anything, and intelligent, using more sophisticated techniques, which are harder to detect due to the text manipulation, like words and signs replacement. Standard techniques related to extrinsic detection are string-based, vector space and semantic-based. The first, most common and most successful target models for detecting literal plagiarism - N-gram and Vector Space are analyzed, and their advantages and disadvantages are evaluated. The most effective target models that allow detecting intelligent plagiarism, particularly identifying paraphrases by measuring the semantic similarity of short components of the text, are investigated. Models using neural network architecture and based on natural language sentence matching approaches such as Densely Interactive Inference Network (DIIN), Bilateral Multi-Perspective Matching (BiMPM) and Bidirectional Encoder Representations from Transformers (BERT) and its family of models are considered. The progress in improving plagiarism detection systems, techniques and related models is summarized. Relevant and urgent problems that remain unresolved in detecting intelligent plagiarism - effective recognition of unoriginal ideas and qualitatively paraphrased text - are outlined.

• Journal of Korea Multimedia Society
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• v.21 no.9
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• pp.1062-1067
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• 2018

Conventional active sonar technology has relied heavily on the hearing of sonar operator, but recently, many techniques for automatic detection and classification have been studied. In this paper, we extract the image data from the spectrogram of the active sonar signal and classify the extracted data using CNN(convolutional neural networks), which has recently presented excellent performance improvement in the field of pattern recognition. First, we divided entire data set into eight classes depending on the ratio containing the target. Then, experiments were conducted to classify the eight classes data using proposed CNN structure, and the results were analyzed.