• 대한의용생체공학회:의공학회지
• /
• 제35권6호
• /
• pp.211-218
• /
• 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.

• 말소리와 음성과학
• /
• 제5권4호
• /
• pp.157-169
• /
• 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.

• 한국화재소방학회논문지
• /
• 제22권4호
• /
• pp.42-49
• /
• 2008

본 연구에서는 기존의 자동화재탐지설비의 오동작(비화재보, 실보)과 빈번한 오동작으로 인한 전원의 차단 등의 문제점을 해결하고 보다 높은 신뢰성을 갖는 자동화재탐지설비를 구성하기 위한 방안을 제시한다. 제안된 방식은 다중센서를 이용한 마이크로프로세서-기반의 디지털 제어시스템으로서 화재 시 발생되는 여러 가지 연소생성물을 감지하기 위해서 열, 연기, CO 센서 등을 복합적으로 사용한다. 이와 같은 방식에 따라 자동화재탐지설비의 오동작 발생 가능성을 줄인 화재감지시스템을 구성하였고, 다중센서 화재감지장치의 화재 감지 및 판별 알고리즘에 의해서 화재발생의 여부를 디지털 제어시스템에 의해서 신뢰도 높게 판단함을 실제 시험을 통해 검증하였다. 실제 화재감지시스템을 구성하였고, 화재시험을 통해서 제안 된 방식의 향상된 신뢰도를 검증하였다.

• 제어로봇시스템학회논문지
• /
• 제7권6호
• /
• pp.523-531
• /
• 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
• /
• 제8권2호
• /
• pp.119-128
• /
• 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.

• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송공학회 2009년도 IWAIT
• /
• pp.267-272
• /
• 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.

• 대한원격탐사학회지
• /
• 제24권4호
• /
• pp.341-349
• /
• 2008

본 논문은 위성영상을 이용한 변화정보를 취득하는데 있어 중요한 과정인 임계값 결정에 관한 새로운 기법을 제안하고 있다. 화소간 유사도 측정을 통해 도출된 결과 값을 일정 간격으로 누적 계산하고, 급격하게 변하는 지점을 임계값으로 결정하였다. 의사영상을 통해 기대최대화 기법, 교점방법과 성능을 비교하였으며, 두 시기의 ALI 영상과 Hyperion 영상에 실제 적용하여 변화탐지 결과를 확인하였다. 제안된 기법은 기존의 기법과 비슷한 수준의 변화탐지 결과 정확도를 확보할 수 있었으며, 기대최대화 기법에 비해 간단하게 적용할 수 있고, 교점방법과 달리 최빈 값을 둘 이상 가지는 히스토그램에도 적용할 수 있는 장점이 있어 향후 변화유무 정보 취득에 효과적으로 사용할 수 있을 것으로 기대한다.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2021년도 추계학술발표대회
• /
• pp.700-703
• /
• 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.

• 대한임베디드공학회논문지
• /
• 제12권2호
• /
• pp.113-120
• /
• 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.

• 한국멀티미디어학회논문지
• /
• 제19권3호
• /
• pp.633-641
• /
• 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.