• Journal of Biomedical Engineering Research
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• v.35 no.6
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• pp.211-218
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• 2014

The objective of this research is to develop an automatic algorithm based on electrocardiogram (ECG) to estimate slow-wave sleep (SWS). An algorithm is based on 7 indices extracted from heart rate on ECG which simultaneously recorded with standard full night polysomnography from 31 subjects. Those 7 indices were then applied to independent component analysis to extract a feature that discriminates SWS and other sleep stages. Overall Cohen's kappa, accuracy, sensitivity and specificity of the algorithm to detect 30s epochs of SWS were 0.52, 0.87, 0.70 and 0.90, respectively. The automatic SWS detection algorithm could be useful combining with existing REM and wake estimation technique on unattended home-based sleep monitoring.

• Phonetics and Speech Sciences
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• v.5 no.4
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• pp.157-169
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• 2013

Using a target detection task, this study compared the processing automaticity of phonemes and features in spoken syllable stimuli to determine the primitive representation in speech perception, phoneme or distinctive feature. For this, we modified the visual search task(Treisman et al., 1992) developed to investigate the processing of visual features(ex. color, shape or their conjunction) for auditory stimuli. In our task, the distinctive features(ex. aspiration or coronal) corresponded to visual primitive features(ex. color and shape), and the phonemes(ex. /$t^h$/) to visual conjunctive features(ex. colored shapes). The automaticity is measured by the set size effect that was the increasing amount of reaction time when the number of distracters increased. Three experiments were conducted. The laryngeal features(experiment 1), the manner features(experiment 2), and the place features(experiment 3) were compared with phonemes. The results showed that the distinctive features are consistently processed faster and automatically than the phonemes. Additionally there were differences in the processing automaticity among the classes of distinctive features. The laryngeal features are the most automatic, the manner features are moderately automatic and the place features are the least automatic. These results are consistent with the previous studies(Bae et al., 2002; Bae, 2010) that showed the perceptual hierarchy of distinctive features.

• Fire Science and Engineering
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• v.22 no.4
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• pp.42-49
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• 2008

This research is to provide a scheme for an automatic fire alarm system with higher reliability through solving problems of malfunctioning (false or missing fire alarm) and power interruption (result from frequently unwanted activation, etc) of an automatic fire alarm system. A digital control system with microprocessor-based is proposed to reduce the possibility of malfunctioning through a combinational use of heat, smoke and CO sensors. Higher reliability could be achieved by these multiple sensors based fire detection system and fire distinction algorithm. In this research, we implemented actual fire detection system and conducted fire test to verify improvement on reliability.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.6
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• pp.523-531
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• 2001

This paper describes and automatic mirror adjustment system that rotates a pair of side mirrors and the room mirror of a car to the optimal position for a driver by using the locating of the driver\`s pupils. A stereo vision system measures 3D coordinates of a pair pupils by analyzing the input images of stereo B/W CCD cameras mounted on the instrument panel. this system determines the position angle of each mir-ror on the basis of information about the location of the pupils and rotates each mirror to the appropriate po-sition by mirror actuators. The vision system can detect the driver\`s pupils regardless of whether it is day-time or nighttime by virtue of an infrared light source. information about the pair of nostrils in used to im- prove the correctness of pupil detection. This system can adjust side mirrors and the room mirror automati- cally and rapidly by a simple interface regardless of driver replacement of driver\`s posture. Experiment has shown this to be a new mirror adjustment system that can make up for the weak points of previous mirror adjustment systems.

• Journal of Computing Science and Engineering
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• v.8 no.2
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• pp.119-128
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• 2014

The appearance of retinal blood vessels is an important diagnostic indicator of serious disease, such as hypertension, diabetes, cardiovascular disease, and stroke. Automatic segmentation of the retinal vasculature is a primary step towards automatic assessment of the retinal blood vessel features. This paper presents an automated method for the enhancement and segmentation of blood vessels in fundus images. To decrease the influence of the optic disk, and emphasize the vessels for each retinal image, a multidirectional morphological top-hat transform with rotating structuring elements is first applied to the background homogenized retinal image. Then, an improved multiscale line detector is presented to produce a vessel response image, and yield the retinal blood vessel tree for each retinal image. Since different line detectors at varying scales have different line responses in the multiscale detector, the line detectors with longer length produce more vessel responses than the ones with shorter length; the improved multiscale detector combines all the responses at different scales by setting different weights for each scale. The methodology is evaluated on two publicly available databases, DRIVE and STARE. Experimental results demonstrate an excellent performance that approximates the average accuracy of a human observer. Moreover, the method is simple, fast, and robust to noise, so it is suitable for being integrated into a computer-assisted diagnostic system for ophthalmic disorders.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.267-272
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• 2009

Video digests provide an effective way of confirming a video content rapidly due to their very compact form. By watching a digest, users can easily check whether a specific content is worth seeing in full. The impression created by the digest greatly influences the user's choice in selecting video contents. We propose a novel method of automatic digest creation that evokes a joyful impression through the created digest by exploiting smile/laughter facial expressions as emotional cues of joy from video. We assume that a digest presenting smiling/laughing faces appeals to the user since he/she is assured that the smile/laughter expression is caused by joyful events inside the video. For detecting smile/laughter faces we have developed a neural network based method for classifying facial expressions. Video segmentation is performed by automatic shot detection. For creating joyful digests, appropriate shots are automatically selected by shot ranking based on the smile/laughter detection result. We report the results of user trials conducted for assessing the visual impression with automatically created 'joyful' digests produced by our system. The results show that users tend to prefer emotional digests containing laughter faces. This result suggests that the attractiveness of automatically created video digests can be improved by extracting emotional cues of the contents through automatic facial expression analysis as proposed in this paper.

• Korean Journal of Remote Sensing
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• v.24 no.4
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• pp.341-349
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• 2008

This study proposes new automatic thresholding method, which is important step for detecting binary change/non-change information using satellite images. Result value through pixel-based similarity measurement is calculated cumulatively with regular interval, and thresholding is pointed at the steep slope position. The proposed method is assessed in comparison with expectation-maximization algorithm and coner method using synthetic images, ALI images, and Hyperion images. Throughout the results, we validated that our method can guarantee the similar accuracy with previous algorithms. It is simpler than EM algorithm, and can be applied to the binormal histogram unlike the coner method.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.700-703
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• 2021

Infrared cameras are widely used in recent research for automatic monitoring the abnormal behaviors of the pig. However, when deployed in real pig farms, infrared cameras always get polluted due to the harsh environment of pig farms which negatively affects the performance of pig monitoring. In this paper, we propose a real-time noise-robust infrared camera-based pig automatic monitoring system to improve the robustness of pigs' automatic monitoring in real pig farms. The proposed system first uses a preprocessor with a U-Net architecture that was trained as a GAN generator to transform the noisy images into clean images, then uses a YOLOv5-based detector to detect pigs. The experimental results show that with adding the preprocessing step, the average pig detection precision improved greatly from 0.639 to 0.759.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.2
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• pp.113-120
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• 2017

In this paper, we present a night-time vehicle detection method using CNN (Convolutional Neural Network) classification. The camera based night-time vehicle detection plays an important role on various advanced driver assistance systems (ADAS) such as automatic head-lamp control system. The method consists mainly of thresholding, labeling and classification steps. The classification step is implemented by existing CIFAR-10 model CNN. Through the simulations tested on real road video, we show that CNN classification is a good alternative for night-time vehicle detection.

• Journal of Korea Multimedia Society
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• v.19 no.3
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• pp.633-641
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• 2016

Automatic defect inspection system is composed of the step in the pre-processing, defect candidate detection, and classification. Polarizing films containing various defects should be minimized over-detection for classifying defect blobs. In this paper, we propose a defect detection algorithm using a skewness of histogram for minimizing over-detection. In order to detect up defects with similar to background pixel, we are used the characteristics of the local region. And the real defect pixels are distinguished from the noise using the probability density function. Experimental results demonstrated the minimized over-detection by utilizing the artificial images and real polarizing film images.