• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.24 no.2
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• pp.107-111
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• 2013

Objectives : We developed two-dimensional (2D) scanning videokyomography to evaluate the mucosal wave of whole vocal cords in real time to overcome the limit of preexisting stroboscopy and line scanning videokymography which could not evaluate it. Methods : We implemented a continuous light source with high brightness, a high-definition CMOS camera, and capture board for saving the data. We created the software program to analyze the image data from the system. The test of the functionality of the 2D scanning videokymography camera was performed in one of the authors (P.H.J 32 years old male). Vocal cord images were obtained during normal phonation and falsetto phonation. Images were obtained also during cough, diplophonia. Results : The system made it possible to measure objective parameters, including fundamental frequency, amplitude, regularity, mucosal wave, and phase difference, medial and lateral peak, opening versus closing duration related to vocal fold vibration. Simultaneously, it enabled analysis of the whole mucosal wave of the entire vocal fold in real time. 2D scanning videokymography was also effective for evaluating the dynamic status of the vocal fold when the subject phonated aperiodic voice. Conclusion : In conclusion, 2D scanning videokymography can support the analysis of the whole mucosal wave of the entire vocal cord with objective vocal parameters, overcoming the limitations of stroboscopy and previous line scanning videokymography techniques.

• Journal of Conservation Science
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• v.36 no.5
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• pp.405-420
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• 2020

A convergence research was conducted on glass beads excavated at the ruins of Hapgang-ri, Sejong, using the archeology and conservation approach. The period of appearance of Jugu Towangmyo in the ruins of Hapgang-ri can be estimated to begin in the late 2^nd century; for Jugu Towangmyo No. 15, the period spans from the late 2^nd to early 3^rd century. Form, color, and manufacturing techniques of complete glass beads were examined, and the cross-sections and chemical composition of 16 samples were observed. Based on these analyses, the glass beads were divided into blue, purple, and red colors. The blue-colored beads could be further sub-divided based on their gloss and brightness; in contrast, the red-colored beads were highly uniform with regard to these parameters. Based on the stripe and bubble arrangement on the surface of the glass beads, their drawing technique was identified. Traces of heat treatment or polishing were observed at the ends of the beads. According to their chemical composition, the 16 samples were classified into 3 potash glass and 13 soda glass groups; in the latter, the properties of the stabilizers were divided according to the blue and red bead colors. The stabilizers of the red beads are unique in that they allow the distinction among beads excavated in other areas in South Korea owing to their compositional differences. Colorants in blue- and red-colored beads are cobalt (containing MnO), and copper and iron, respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.1-8
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• 2008

The belief propagation method that has been studied recently yields good performance in disparity extraction. The method in which a target function is modeled as an energy function based on Markov random field(MRF), solves the stereo matching problem by finding the disparity to minimize the energy function. MRF models provide robust and unified framework for vision problem such as stereo and image restoration. the belief propagation method produces quite correct results, but it has difficulty in real time implementation because of higher computational complexity than other stereo methods. To relieve this problem, in this paper, we propose a fast algorithm of the belief propagation method. Energy function consists of a data term and a smoothness tern. The data term usually corresponds to the difference in brightness between correspondences, and smoothness term indicates the continuity of adjacent pixels. Smoothness information is created from messages, which are assigned using four different message arrays for the pixel positions adjacent in four directions. The processing time for four message arrays dominates 80 percent of the whole program execution time. In the proposed method, we propose an algorithm that dramatically reduces the processing time require in message calculation, since the message.; are not produced in four arrays but in a single array. Tn the last step of disparity extraction process, the messages are called in the single integrated array and this algorithm requires 1/4 computational complexity of the conventional method. Our method is evaluated by comparing the disparity error rates of our method and the conventional method. Experimental results show that the proposed method remarkably reduces the execution time while it rarely increases disparity error.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.39 no.1 s.119
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• pp.38-47
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• 2007

This study was carried out to develop a new application field and obtain the basic data of citrus peel as waste in Jeju island and traditional Hanji for producing functional Hanji. The results measuring physical and optical properties, water vapor permeance and antibacterial activity are as follows. It was revealed that apparent density go as down but bulk raise up in the structural view of Hanji with increasing of the addition various Korean citrus peel (citrus unshiu, cheonggyun and hanrabong peel, and citrus unshiu peel powder) percentages, and that the density of Hanji added citrus unshiu peel was higher, but bulk was lower in compared with Hanji added other kinds of peel. Those Hanji added citrus unshiu peel, cheonggyun peel, hanrabong peel and citrus unshiu powder were very great not only in the strength (breaking length, burst index, tear index and folding endurance) but also in water vapor permeant rate in comparison with Hanji. The pHs of Hanji were neutrality (7 to 8). The brightness of the Hanji added various citrus peel percentages was low in compared to Hanji, and the 40% addition of hanrabong peel was the lowest. When 40% hanrabong peel was added to Hanji, it was very yellow in the color degree. When cheonggyun peel was added to Hanji manufacture, water vapor permeant rate was highly effective. It is known that vacant space of intrafiber was reduced by image analysis of Hanji and the additions of peel of citrus unshiu, cheonggyun and hanrabong were distributed equally in the interior of Hanji. The antibacterial activity of Hanji added citrus unshiu peel is more than 98%. After all, it would be able to increase utilization of Hanji, extensively. Namely, production of high quality Hanji added functional materials is expected for new valuable industry of citrus peel and Hanji.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.105-114
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• 2015

This paper proposes methods to remove background area and segment region of interest (ROI) in chest X-ray images. Conventional algorithms to improve detail or contrast of images normally utilize brightness and frequency information. If we apply such algorithms to the entire images, we cannot obtain reliable visual quality due to unnecessary information such as background area. So, we propose two effective algorithms to remove background and segment ROI from the input X-ray images. First, the background removal algorithm analyzes the histogram distribution of the input X-ray image. Next, the initial background is estimated by a proper thresholding on histogram domain, and it is removed. Finally, the body contour or background area is refined by using a popular guided filter. On the other hand, the ROI, i.e., lung segmentation algorithm first determines an initial bounding box using the lung's inherent location information. Next, the main intensity value of the lung is computed by vertical cumulative sum within the initial bounding box. Then, probable outliers are removed by using a specific labeling and the pre-determined background information. Finally, a bounding box including lung is obtained. Simulation results show that the proposed background removal and ROI segmentation algorithms outperform the previous works.

• Journal of IKEEE
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• v.25 no.3
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• pp.485-492
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• 2021

In this paper, we propose the development of a defect inspection system for polygonal containers. Embedded board consists of main part, communication part, input/output part, etc. The main unit is a main arithmetic unit, and the operating system that drives the embedded board is ported to control input/output for external communication, sensors and control. The input/output unit converts the electrical signals of the sensors installed in the field into digital and transmits them to the main module and plays the role of controlling the external stepper motor. The communication unit performs a role of setting an image capturing camera trigger and driving setting of the control device. The input/output unit converts the electrical signals of the control switches and sensors into digital and transmits them to the main module. In the input circuit for receiving the pulse input related to the operation mode, etc., a photocoupler is designed for each input port in order to minimize the interference of external noise. In order to objectively evaluate the accuracy of the development of the proposed polygonal container defect inspection system, comparison with other machine vision inspection systems is required, but it is impossible because there is currently no machine vision inspection system for polygonal containers. Therefore, by measuring the operation timing with an oscilloscope, it was confirmed that waveforms such as Test Time, One Angle Pulse Value, One Pulse Time, Camera Trigger Pulse, and BLU brightness control were accurately output.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.320-323
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• 2014

Today is going with the advancement of intelligent transportation systems and traffic environment and helping to provide safe and convenient service through a mobile device work with the popularization of the vehicle black box. The traffic flow by a variety of causes is constantly changing, it is often unable to prepare the driver, depending on external factors can not be controlled by the power of the public, leading to a major accident. The system needs to pass the real-time weather data in the inter-operator to prevent this. The proposed detection algorithm weather information delivered real-time weather information for this paper. The weather condition is detected by using the contrast between the histogram of the motion of the wiper and the clear day algorithm. In general, the wiper is worked in extreme weather conditions that will have a value different contrast due to rain or snow. Situation was considered clear, snowy conditions, such as using it on a rainy situation. First, designated as ROI (Region Of Interest) of the minimum area that can be detected in order to reduce the amount of calculation for the wiper, the wiper, which was detected through the operation of the threshold Thresholding the brightness of the vehicle wiper. In addition, we distinguish the value of each meteorological situation by using contrast. Results was obtained to 80% for the snow conditions, a rainy situation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.393-399
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• 2021

In this study, experiments were conducted to estimate the PM concentrations by learning the nighttime CCTV images of various PM concentrations environments. In the case of daytime images, there have been many related studies, and the various texture and brightness information of images is well expressed, so the information affecting learning is clear. However, nighttime images contain less information than daytime images, and studies using only nighttime images are rare. Therefore, we conducted an experiment combining nighttime images with non-uniform characteristics due to light sources such as vehicles and streetlights and building roofs, building walls, and streetlights with relatively constant light sources as an ROI (Region of Interest). After that, the correlation was analyzed compared to the daytime experiment to see if deep learning-based PM concentrations estimation was possible with nighttime images. As a result of the experiment, the result of roof ROI learning was the highest, and the combined learning model with the entire image showed more improved results. Overall, R² exceeded 0.9, indicating that PM estimation is possible from nighttime CCTV images, and it was calculated that additional combined learning of weather data did not significantly affect the experimental results.

• Journal of Platform Technology
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• v.10 no.3
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• pp.60-68
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• 2022

As AI-technology develops, interest in the safety of autonomous driving is increasing. Recently, autonomous vehicles have been increasing, but efforts to solve side effects have been sluggish. In particular, night autonomous vehicles have more problems. This is because the probability of accidents is higher in the night driving environment than in the day environment. There are more factors to consider for self-driving at night. Among these factors, reflection of light or reflected light of lighting may be a fundamental cause of night accidents. Therefore, this study proposes method to reduce accidents and improve safety by reducing reflected light generated by the headlights of opposite vehicles or various surrounding light that appear as an important problem in night autonomous vehicles. Therefore, first, in an image obtained by a sensor of a night autonomous vehicle, illumination reflected light is extracted using reflected light characteristic information, and a color of each pixel using a reflection coefficient is found to reduce a special area generated by geometric characteristics. In addition, we find a new area using only the brightness component of the specular area, define it as Illuminated Reflection Light (IRL), and finally present a method to reduce it. Although the illumination reflection light could not be completely reduce, generally satisfactory results could be obtained. Therefore, it is believed that the proposed study can reduce casualties by solving the problems of night autonomous driving and improving safety.

• Journal of IKEEE
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• v.27 no.3
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• pp.340-344
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• 2023

In this paper, we propose a study on the development of deep learning structure to secure visibility of outdoor LED display board according to weather change. The proposed technique secures the visibility of the outdoor LED display board by automatically adjusting the LED luminance according to the weather change using deep learning using an imaging device. In order to automatically adjust the LED luminance according to weather changes, a deep learning model that can classify the weather is created by learning it using a convolutional network after first going through a preprocessing process for the flattened background part image data. The applied deep learning network reduces the difference between the input value and the output value using the Residual learning function, inducing learning while taking the characteristics of the initial input value. Next, by using a controller that recognizes the weather and adjusts the luminance of the outdoor LED display board according to the weather change, the luminance is changed so that the luminance increases when the surrounding environment becomes bright, so that it can be seen clearly. In addition, when the surrounding environment becomes dark, the visibility is reduced due to scattering of light, so the brightness of the electronic display board is lowered so that it can be seen clearly. By applying the method proposed in this paper, the result of the certified measurement test of the luminance measurement according to the weather change of the LED sign board confirmed that the visibility of the outdoor LED sign board was secured according to the weather change.