• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.5
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• pp.139-146
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• 2008

Wireless sensor network-related application system can perform functions such as environmental pollution monitoring, building control, home automation in future. In this paper, we present wireless sensor network based system for subway station in order to reduce the damage of the people and the subway station due to fire. Sensor nodes in this system can sense temperature, illumination, smoke, and human body in real time and detect the accident in the subway station. These real-time sensing and wireless networking minimize casualties and damage to property.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1799-1802
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• 2008

More than 7 million people use the Seoul metropolitan subway network daily. This number tends to increase due to the increase of oil price. Indoor air quality of electrical multiple unit (EMU) is strongly affected by outdoor air quality, however, indoor thermal comfort is subjected to heating, ventilating, and air conditioning (HVAC) system of EMU. In general, air temperature, humidity, air velocity, surface temperature, and illumination are key parameters affecting thermal comfort of passenger. It is known that the well-designed HVAC system should improve the thermal comfort of passengers and should increase the energy efficiency of HVAC system also. In this study, we analyzed the thermal comfort of advanced EMU developed by Korea Railroad Research Institute by using the computational fluid dynamics (CFD) in order to find the optimum HVAC system which can improve thermal comfort of passengers with a minimal energy use.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.124-130
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• 2017

This study is aimed to find out whether there is difference in the physiological change of a human body according to the illumination and color of interior space or not and to specify the effect of the condition of illumination and color, respectively on the attention. In order to do so, White and Green were selected for colors and 4,000k, 5,000k, and 6000k were done for color temperature, and then attention was identified. Examining the results, the more color temperature increased, the more attention improved (P < 0.05), and in the case of EEG, ${\alpha}$ wave decreased while performing the task of attention (P < 0.01), and ${\beta}$ wave decreased more in Green than White in color condition, and it increased more in 4,000k than 5,000k and 6,000k (p < 0.05) in color temperature condition. To sum up, color condition didn't contribute to the attention much, in the case of color temperature, when it is 6,000k, it is judged that it helped to improve attention. It is considered that relaxation contributed to improving attention, as ${\beta}$ wave and sympathetic nerve decreased in 6,000k (p < 0.05). It is judged that the relaxation of tensions which happened due to a beta wave and the reduction of sympathetic nervous system activity in 6,000k, a condition of high color temperature, contributed to the improvement of concentration. In further researches, it is intended that a test will be conducted for the subjects of different ages, and the correlation between color temperature and color stimulation and the influence of them on human body would be observed in subdivided, various test conditions through various color temperature and color stimulation.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.8
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• pp.599-607
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• 2018

This paper presents a ground-to-air bistatic radar system and its implementation algorithm, which resembles an SAR(synthetic aperture radar) reconstruction algorithm. Via cooperative working between a standoff transmitting radar and an array of ground based receiving radars, it detects and images moving targets under clutter in the air. In the proposed system, the whole receiving antenna aperture is synthesized by physical ground based radars, and thus, unlike conventional SAR, it does not require long illumination time of the target area. The reconstruction algorithm uses planewave approximation based polar format processing, which alleviates the requirement of positioning the receiving radars, which can cause grating lobes if not chosen properly. We derive a reconstruction algorithm including clutter suppression and discuss implementation issues, such as the resolution of a reconstructed image and the method of compensation for the irregularity of the receiving radars' positions. A simulation that validates the proposed algorithm is also shown.

• Journal of Korea Spatial Information System Society
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• v.11 no.1
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• pp.137-144
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• 2009

Understanding lane markings in a live video captured from a moving vehicle is essential to build services for intelligent vehicles such as LDWS(Lane Departure Warning Systems), unmanned vehicles, video-based car navigation systems. In this paper, we present a novel approach to recognize the color of lane markings and the lane number that he/she is driving on. More specifically, we present a background-color removal approach to understand the color of lane markings for various illumination conditions, such as backlight, sunset, and so on. In addition, we present a probabilistic network approach to decide the lane number. According to our experimental results, the proposed idea shows promising results to detect lane number in a various illumination conditions and road environments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.806-816
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• 2009

Character recognition system consists of four step; text localization, text segmentation, character segmentation, and recognition. The character segmentation is very important and difficult because of noise, illumination, and so on. For high recognition rates of the system, it is necessary to take good performance of character segmentation algorithm. Many algorithms for character segmentation have been developed up to now, and many people have been recently making researches in segmentation of touching or overlapping character. Most of algorithms cannot apply to the text regions of management number marked on the slab in steel image, because the text regions are irregular such as touching character by strong illumination and by trouble of nozzle in marking machine, and loss of character. It is difficult to gain high success rate in various cases. This paper describes a new algorithm of character segmentation to recognize slab management number marked on the slab in the steel image. It is very important that pre-processing step is to convert gray image to binary image without loss of character and touching character. In this binary image, non-touching characters are simply separated by using vertical projection profile. For separating touching characters, after we use combined profile to find candidate points of boundary, decide real character boundary by using method based on recognition. In recognition step, we remove noise of character images, then recognize respective character images. In this paper, the proposed algorithm is effective for character segmentation and recognition of various text regions on the slab in steel image.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.6 no.1
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• pp.1-5
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• 2013

The number of pedestrians is considered essential information which can be used to control a person who makes a entrance or a exit into a building. The number of pedestrians, also, can be used to help to manage pedestrian traffic and the volume of pedestrian flow within the building. Due to the fact there is incorrect detection by occluded, shadows, and illumination, however, difficulty can arise in existing system which is for detection and counts of a person who makes a entrance or a exit into a building. In this paper, it is minimized that the change of illumination and the effect of shadow through the transmitted image from camera which is created and processed with great adaptability. The accuracy of the calculations can be increase as well by using Kalman Filter and Mean-Shift Algorithm in order to avoid overlapped counts. As a result of the test, it is proved that the count method that shows the accuracy of 95.4% should be effective for detection and counts.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.11
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• pp.1029-1035
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• 2013

This paper proposes a real time lane detection algorithm using LRF (Laser Range Finder) for autonomous navigation of a mobile robot. There are many technologies for safety of the vehicles such as airbags, ABS, EPS etc. The real time lane detection is a fundamental requirement for an automobile system that utilizes outside information of automobiles. Representative methods of lane recognition are vision-based and LRF-based systems. By the vision-based system, recognition of environment for three dimensional space becomes excellent only in good conditions for capturing images. However there are so many unexpected barriers such as bad illumination, occlusions, and vibrations that the vision cannot be used for satisfying the fundamental requirement. In this paper, we introduce a three dimensional lane detection algorithm using LRF, which is very robust against the illumination. For the three dimensional lane detections, the laser reflection difference between the asphalt and lane according to the color and distance has been utilized with the extraction of feature points. Also a stable tracking algorithm is introduced empirically in this research. The performance of the proposed algorithm of lane detection and tracking has been verified through the real experiments.

• Journal of the Korean Institute of Landscape Architecture
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• v.41 no.6
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• pp.140-146
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• 2013

This experiment was conducted in order to study the physiological characteristics of Sedum takevimense in different moisture conditions. The photosynthetic rate, water use efficiency and the respiratory rate were determined by using a photable photosynthesis system. According to the results, the best illumination range and moisture range were explicitly selected. The highest photosynthetic rate was at $600{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$, and after this value, the trend showed a reduction. When the moisture was 11.31%, the photosynthetic capacity and water use efficiency reached maximum value, but the respiratory rate reached maximum value at 7.91%. According to the measured values, the best illumination range was $600{\sim}1,200{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$ and the best moisture range was 7.09~11.31%.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.6-29
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• 2020

Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics. In this paper, we present a novel multimodal recognition system that trains a deep learning network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and non-redundant features. The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Moreover, the proposed technique has proven robust when some of the above modalities were missing during the testing. The proposed system has three main components that are responsible for detection, which consists of modality specific detectors to automatically detect images of different modalities present in facial video clips; feature selection, which uses supervised denoising sparse auto-encoders network to capture discriminative representations that are robust to the illumination and pose variations; and classification, which consists of a set of modality specific sparse representation classifiers for unimodal recognition, followed by score level fusion of the recognition results of the available modalities. Experiments conducted on the constrained facial video dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% Rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips even in the situation of missing modalities.