• Journal of Korea Ship Safrty Technology Authority
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• s.38
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• pp.69-77
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• 2015

If we don't know the intention of altering course of a target ship when being in a head-on or a crossing situation, we may be confused about our decision making to change our course for collision avoidance and be in a danger of collision. In order to solve these problems, we need to develop an automatic detection system on altering course of a target ship for efficient collision avoidance. In this paper, we proposed an early detection system on altering course of a target ship using the steering wheel signal. This system will contribute to the reduction of collision accidents and also be used to the VTS system and the analysis of marine accidents.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.835-838
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• 2008

In this paper, we present automatic face detection and tracking which is robustness in rotation and translation. Detecting a face image, we used Haar-like feature, which is fast detect facial image. Also tracking, we applied Lucas-Kanade feature tracker and KLT algorithm, which has robustness for rotated facial image. In experiment result, we confirmed that face detection and tracking which is robustness in rotation and translation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.268-271
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• 1998

this paper presents the potential application of fuzzy logic to the automatic incident detection system. While the conventional incident detection algorithms are based on a binary decision process, the algorithm using fuzzy logic can incorporate ambiguity which occurs in determining incidents. Since collecting good amount of data to construct data base for incidents is pretty expensive, a traffic simulator called FRESIM is used to simulate traffic condition in a freeway. Incident data are obtained by changing input parameters of the simulator and the fuzzy algorithm generates fuzzy rule for determining normal and incident traffic conditions. In this paper, various steps are described to test the algorithm and its results are summarized.

• Journal of Sensor Science and Technology
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• v.27 no.2
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• pp.137-141
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• 2018

Detecting the ego-lane of a vehicle (the lane on which the vehicle is currently running) is one of the basic techniques for a smart car. Vision sensing is a widely-used method for the ego-lane detection. Existing studies usually find road lane lines by detecting edge pixels in the image from a vehicle camera, and then connecting the edge pixels using Hough Transform. However, this approach takes rather long processing time, and too many straight lines are often detected resulting in false detections in various road conditions. In this paper, we find the lane lines by scanning only a limited number of horizontal lines within a small image region of interest. The horizontal image line scan replaces the edge detection process of existing methods. Automatic thresholding and spatiotemporal filtering procedures are also proposed in order to make our method reliable. In the experiments using real road images of different conditions, the proposed method resulted in high success rate.

• MALSORI
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• no.66
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• pp.117-130
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• 2008

This paper presents a method for automatically detecting Korean prosodic boundaries using both acoustic and grammatical information for the performance improvement of speech information processing systems. While most of previous works are solely based on grammatical information, our method utilizes not only grammatical information constructed by a Maximum-Entropy-based grammar model using 10 grammatical features, but also acoustical information constructed by a GMM-based acoustic model using 14 acoustic features. Given that Korean prosodic structure has two intonationally defined prosodic units, intonation phrase (IP) and accentual phrase (AP), experimental results show that the detection rate of AP boundaries is 82.6%, which is higher than the labeler agreement rate in hand transcribing, and that the detection rate of IP boundaries is 88.7%, which is slightly lower than the labeler agreement rate.

• Korean Journal of Veterinary Service
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• v.39 no.1
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• pp.35-39
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• 2016

The purpose of this study was to survey on infection status of pathogens of diarrhea from Korean indigenous goat. A total of 800 fecal samples was collected from 50 farms from January to October 2015 and was tested by automatic biochemical machine and polymerase chain reaction (PCR). The overall detection ratio of bacterial pathogens was 22.4% and viral pathogens was 16.3%, respectively. The detection ratio of Escherichia coli (E. coli), Salmonella spp., bovine viral diarrhea virus (BVDV), rotavirus and coronavirus were 21.5%, 0.9%, 7.6%, 5.6% and 3.0%, respectively. In the rates of mixed detection, single was 78.2%, double 8.4%, triple 11.6% and quadruple 1.8% in each sample and 38%, 12%, 16%, 20% in each farm, respectively.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.1
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• pp.86-93
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• 2017

In this paper, we propose a novel defect detection method using component tree representations of scanning electron microscopy (SEM) images. The component tree contains rich information about the topological structure of images such as the stiffness of intensity changes, area, and volume of the lobes. This information can be used effectively in detecting suspicious defect areas. A quasi-linear algorithm is available for constructing the component tree and computing these attributes. In this paper, we modify the original component tree algorithm to be suitable for our defect detection application. First, we exclude pixels that are near the ground level during the initial stage of component tree construction. Next, we detect significant lobes based on multiple attributes and edge information. Our experiments performed with actual SEM wafer images show promising results. For a $1000{\times}1000$ image, the proposed algorithm performed the whole process in 1.36 seconds.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2E
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• pp.72-78
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• 2009

Heart sound segmentation into its components, S1, systole, S2 and diastole is the first step of analysis and the most important part in the automatic diagnosis of heart sounds. Conventionally, the Shannon energy envelope peak detection method has been popularly used due to its superior performance in locating S1 and S2. Recently, the HMM has been shown to be quite suitable in modeling the heart sound signal and its use in segmenting the heart sound signal has been suggested with some success. In this paper, we compared the two methods for heart sound segmentation using a common database. Experimental tests carried out on the 4 different types of heart sound signals showed that the segmentation accuracy relative to the manual segmentation was 97.4% in the HMM based method which was larger than 91.5% in the peak detection method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3151-3168
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• 2015

Automatic detection of estrus in cows is important in cattle management. This paper proposes a method of estrus detection by automatically checking cattle mounting. We use a side-view video camera and apply computer vision techniques to detect mounting behavior. In particular, we extract motion information to select a potential mount-up and mount-down motion and then verify the true mounting behavior by considering the direction, magnitude, and history of the mount motion. From experimental results using video data obtained from a Korean native cattle farm, we believe that the proposed method based on the abrupt change of a mounting cow's height and motion history information can be utilized for detecting mounting behavior automatically, even in the case of fence occlusion.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.621-632
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• 2013

The most important things for a forest fire detection system are the exact extraction of the smoke from image and being able to clearly distinguish the smoke from those with similar qualities, such as clouds and fog. This research presents an intelligent forest fire detection algorithm via image processing by using the Gaussian Mixture model (GMM), which can be applied to detect smoke at the earliest time possible in a forest. GMMs are usually addressed by making the model adaptive so that its parameters can track changing illuminations and by making the model more complex so that it can represent multimodal backgrounds more accurately for smoke plume segmentation in the forest. Also, in this paper, we suggest a way to classify the smoke plumes via a feature extraction using HSL(Hue, Saturation and Lightness or Luminanace) color space analysis.