• Journal of Information Technology Services
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• v.6 no.3
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• pp.241-249
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• 2007

This paper describes a real-time face tracking system using effective detector and Kalman filter. In the proposed system, an image is separated into a background and an object using a real-time updated face color for effective face detection. The face features are extracted using the five types of simple Haar-like features. The extracted features are reinterpreted using Principal Component Analysis (PCA), and interpreted principal components are used for Support Vector Machine (SVM) that classifies the faces and non-faces. The moving face is traced with Kalman filter, which uses the static information of the detected faces and the dynamic information of changes between previous and current frames. The proposed system sets up an initial skin color and updates a region of a skin color through a moving skin color in a real time. It is possible to remove a background which has a similar color with a skin through updating a skin color in a real time. Also, as reducing a potential-face region using a skin color, the performance is increased up to 50% when comparing to the case of extracting features from a whole region.

• Applied Chemistry
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• v.15 no.1
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• pp.17-20
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• 2011

A disposable light exposure detector kit has been developed by UV curing of a hydrogel material. The devised light exposure detector kit consisted of light sensitive structures, bottom plate, character sheet and sticky back plate. A light exposure detector kit has a serial light sensitive structures that contain various light sensitive dyes such as rhodamine and fluorescein. The light sensitive structure composed of UV curable hydrogel polymer material as a supporing material and photosensitive dye in a certain concentration. The fabrication procedure of the ligh exposure detector kit is very simple and fast due to UV curing procedure of a photopolymerizable hydrogel material such as poly(ethylene glycol) methyl ether acrylate (PEGMEA) and poly(ethylene glycol) diacrylate (PEGDA) with a photosensitive dye. By the proposed fabrication method, various size and shape of a light exposure detector kit could be fabricated using a flexible elastomer mold. Due to a fast and inexpensive fabrication method, the light exposure detector kit could be use a single use for various industrial applications. According to light irradation, the light sensitive structure on a light exposure detector kit could be lose its color by decomposition of a photosensitive dye chemical in the structure. Thus the amount of the exposed light on a substrate could easily be recognised by changing color or transparency of the structure.

• Journal of Sensor Science and Technology
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• v.21 no.6
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• pp.429-433
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• 2012

In this paper, we reduced the optical noise interference in a visible light communication system using two color LEDs. In the transmitter, the original and the inverted signals of the transmitted data modulated a red LED and a blue LED, respectively. In the receiver, a differential detector which is composed of two photodetectors and an optical red filter detected the mixed signal radiated from the two LEDs. In an environment that the optical noise from a fluorescent lamp exists, the signal-to-noise ratio in this system was improved by about 20dB compared to that in the conventional system which uses a single LED and a single photodetector.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.6
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• pp.38-45
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• 1997

Skin color reappearance problem in color processing system is necessary to transform a specific color sand improve color reappearance quality as a reference color. The skin color has been situated as an important memory color not only in our lives but also in color application systems such as TV. Thus, skin color reappearance problem is more important than other color processing problem. In this paper, we propose a skin color reappearance algorithm for color enhancement in TV which use phase detector to detect the skin colors at real-time from 3.58 MHz color burst signal and color signal, comparators to discriminate the types of skin color, and micom to reprodece standard skin colors for races.

• Journal of the Korea Safety Management & Science
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• v.18 no.1
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• pp.65-73
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• 2016

In this paper which was performed to bring public attention to the need for developing an photoelectric smoke detector that detects smokes emitted from diverse fire sources, it was confirmed that the photoelectric smoke detector may not respond to certain smoke types depending on the size and color of smoke particles. The test was performed on a photoelectric smoke detector which is being used generally for specific fire targets specified in UL268, including paper fire, wood fire and flammable liquid fire, while the actual response performance of the fire detector, as well as respective smoke particles collected from different fire source which were photographed with a SEM(Scanning electron microscope) were analyzed in the test for this study.

• Journal of Industrial Technology
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• v.27 no.A
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• pp.177-184
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• 2007

This paper presents a miniaturized electromagnetic wave detector. The dimension is reduced down to $31{\times}18{\times}13.5mm^3$ by using common OP-AMPs for amplification and oscillation. The strength from wave radiators is indicated by a color LED; green, orange and red colors mean the low, the middle and the high level, respectively. An alarm bell rings as the wave strength becomes above high level. The detector uses 3V battery. The detector could be used for a cellular phone decoration, a necklace pendant and a key holder.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.47-60
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• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.

• Journal of The Korean Astronomical Society
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• v.30 no.1
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• pp.1-11
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• 1997

Near infrared JHK magnitudes are presented for two hundred two high proper motion stars. We have observed high proper motion stars in the near-infrared bands(JHK) using the COB detector on the Kitt Peak 1.3m, 2.1m and 4m telescopes. The observations and data reduction procedures are described. The infrared color magnitude diagram and color-color diagrams for the program stars are presented.

• Fire Science and Engineering
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• v.31 no.5
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• pp.44-52
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• 2017

This paper is based on a study of the response characteristics of photoelectric type smoke detector chambers according to dust color. Due to an amendment to the Fire Safety Codes to automatic fire alarm systems and visual alarm device, the installation of indoor smoke detectors has become mandatory, but in Korea there is still insufficient research on the non-operation or false alarms that could arise in indoor environments by indoor dust and other environmental conditions etc. In light of this, for this study, research was conducted on the indoor adaptability of smoke detector under various colors of fiber dust that were judged to occur most frequently in among the common indoor dust, photoelectric smoke detector with the lattice-type smoke detection chamber that the smoke detector which is most popular in the country was used, and four colors of fiber dust (brown, white, gray and black) were used the test dusts for carrying out dust and sensitivity testing. Also, the voltage of the photocell part of the smoke chamber was measured, and the scattering phenomenon in the chamber was observed. The result of the testing showed that all four dust types were suitable for dust and sensitivity testing under conditions of pollution A. Yet, there were occasions, at pollution B or C, where the brown, white and gray dust would cause fail alarm during operation testing. And black dust was confirmed to cause non-operation during operation testing. In the case of brown and white dust, the voltage measurement result of the photocell part of the smoke chamber confirmed that the voltage increases as the pollution level increases, and in the case of gray and black dust, the voltage decreases.

• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.257-264
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• 2009

This paper proposes a novel face detection method that finds tiny faces located at a long range even with low-resolution input images captured by a mobile robot. The proposed approach can locate extremely small-sized face regions of $12{\times}12$ pixels. We solve a tiny face detection problem by organizing a system that consists of multiple detectors including a mean-shift color tracker, short- and long-rage face detectors, and an omega shape detector. The proposed method adopts the long-range face detector that is well trained enough to detect tiny faces at a long range, and limiting its operation to only within a search region that is automatically determined by the mean-shift color tracker and the omega shape detector. By focusing on limiting the face search region as much as possible, the proposed method can accurately detect tiny faces at a long distance even with a low-resolution image, and decrease false positives sharply. According to the experimental results on realistic databases, the performance of the proposed approach is at a sufficiently practical level for various robot applications such as face recognition of non-cooperative users, human-following, and gesture recognition for long-range interaction.