• Journal of the Ergonomics Society of Korea
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• v.29 no.4
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• pp.575-584
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• 2010

The aim of study is to suggest the design procedure of automobile headlamp by considering driver's experience in regard of the visibility and glare during nighttime driving. The characteristics of driver were investigated in terms of the drivers' cognitive ability and reaction time, headlamp specification and visibility, light source and glare. And, the degree of visual discomfort was categorized and recognized as a tool to represent the subjective user experience. The UX point of view was stated when the existing results were seemingly lacking of it. The visual comfort and safety of elderly drivers were also discussed by reviewing the studies of ageing regarding the visibility and driving responses. Finally, this study suggested how to reduce the negative effect of nighttime driving due to the height of headlamp, angle of lighting, color spectrum, discomfort glare, source of light by using the UX perspective and methodology.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.3
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• pp.129-136
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• 2020

In this paper, we propose a thermal imagery-based object detection algorithm for low-light level nighttime surveillance system. Many features selected by Haar-like feature selection algorithm and existing Adaboost algorithm are often vulnerable to noise and problems with similar or overlapping feature set for learning samples. It also removes noise from the feature set from the surveillance image of the low-light night environment, and implements it using the lightweight extended Haar feature and adaboost learning algorithm to enable fast and efficient real-time feature selection. Experiments use extended Haar feature points to recognize non-predictive objects with motion in nighttime low-light environments. The Adaboost learning algorithm with video frame 800*600 thermal image as input is implemented with CUDA 9.0 platform for simulation. As a result, the results of object detection confirmed that the success rate was about 90% or more, and the processing speed was about 30% faster than the computational results obtained through histogram equalization operations in general images.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.13-24
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• 2018

Recently, light pollution has become a serious environment issue caused by excessive uses of artificial light. Central and local governments have made efforts to manage light pollution and mitigate light pollution damages. Developing methods to diagnose light pollution is critical to effectively monitor light pollution conditions in Seoul. This study develops a methodology to create a map that presents the status of light pollution in Seoul, using International Space Station(ISS) night-time images. Through the map, we evaluated the areas that show high levels of light intensity and found out local characteristics of light intensity; Commercial area, office building concentrated area, and large sports facilities. The result of study provides basic understanding to present a new way for monitoring light pollution in urban sites.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.269-275
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• 2017

Automatic vehicle detection is a very important function for autonomous vehicles. Conventional vehicle detection approaches are based on visible-light images obtained from cameras mounted on a vehicle in the daytime. However, unlike daytime, a visible-light image is generally dark at night, and the contrast is low, which makes it difficult to recognize a vehicle. As a feature point that can be used even in the low light conditions of nighttime, the rear lamp is virtually unique. However, conventional rear lamp-based detection methods seldom cope with atypical lamps, such as LED lamps, or flashing turn signals. In this paper, we detect atypical lamps by blurring the lamp area with a low pass filter (LPF) to make out the lamp shape. We also propose to detect flickering of the turn signal lamp in a manner such that the lamp area is vertically projected, and the maximum difference of two paired lamps is examined. Experimental results show that the proposed algorithm has a higher F-measure value of 0.24 than the conventional lamp pairing-based detection methods, on average. In addition, the proposed algorithm shows a fast processing time of 6.4 ms per frame, which verifies real-time performance of the proposed algorithm.

• IEMEK Journal of Embedded Systems and Applications
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• v.6 no.1
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• pp.8-15
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• 2011

This paper presents an effective method for detecting vehicles in front of the camera-assisted car during nighttime driving. The proposed method detects vehicles based on detecting vehicle headlights and taillights using techniques of image segmentation and clustering. First, in order to effectively extract spotlight of interest, a pre-signal-processing process based on camera lens filter and labeling method is applied on road-scene images. Second, to spatial clustering vehicle of detecting lamps, a grouping process use light tracking method and locating vehicle lighting patterns. For simulation, we are implemented through Da-vinci 7437 DSP board with visible light mono-camera and tested it in urban and rural roads. Through the test, classification performances are above 89% of precision rate and 94% of recall rate evaluated on real-time environment.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1997.10a
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• pp.7-10
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• 1997

• 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.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.247-253
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• 2023

As climate change problem has recently become serious, studies are being conducted to identify carbon dioxide (CO₂) emission dynamics based on satellite data to reduce emissions. It is also very important to analyze spatial patterns by estimating and mapping CO₂ emissions dynamic. Therefore, in this study, CO₂ emissions in the Korean Peninsula from 2013 to 2020 were estimated and mapped. To spatially estimate and map emissions, we use the enhanced vegetation index adjusted nighttime light index, an index that combines nighttime light (NTL) and vegetation index, to map both areas where NTL is observed and areas where NTL is not observed. In order to spatially estimate and map CO₂ emissions, the total annual emissions of the Korean Peninsula were calculated, resulting in an increase of 11% from 2013 to 2017 and a decrease of 13% from 2017 to 2020. As a result of the mapping, it was confirmed that the spatial pattern of CO₂ emissions in the Korean Peninsula were concentrated in urban areas. After being divided into 17 regions, which included the downtown area, the metropolitan area accounted for roughly 40% of CO₂ emissions in the Korean Peninsula. The region that exhibited the most significant change from 2013 to 2020 was Sejong City, showing a 96% increase.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1647-1656
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• 2021

In order to reduce the spread of infection due to COVID-19, South Korea has established a four-step social distancing standard and implemented it by changing the steps based on the rate of confirmed cases. The implementation of social distancing brought about a change in the amount of activity of citizens by limiting social contact such as movement and gathering of people. One of the data that can intuitively confirm this is Night Time Light (NTL). NTL is a variable that can measure the size of the national economy measured using lights captured by satellites, and can be used to understand people's social activities during the night. The NTL visible data is obtained via the Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. 1023 of Suomi data from 1 January 2019 until 26 October 2021 were collected to generate time series of NTL radiance change over Seoul to analyze the correlation with social distancing policy. The results show that implementing the level of social distancing generally decreased the NTL radiance both in spatial disparities and temporal patterns. The higher level of policy, limiting human activities combined with the low number of people who have been vaccinated and the closure of various facilities. Because of social distancing, the differences in human activities affected the nighttime light during the COVID-19 pandemic, especially in Seoul, South Korea. Therefore, this study can be used as a reference for the government in evaluating and improving policies related to efforts reducing the transmission of COVID-19.

• Korean Journal of Environment and Ecology
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• v.34 no.1
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• pp.63-71
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• 2020

This study characterized the spatial and seasonal patterns of light pollution in the Deogyusan National Park and examined the potential effects of light pollution on ecosystems in the park using light intensities derived from VIIRS (Visible Infrared Imaging Radiometer Suite) DNB (Day and Night Band) nightlight images collected in January and August 2018. Results showed that the Muju Deogyusan resort had the greatest light intensity than other sources of light pollution in the park, and light intensity of the resort was much higher in January than in August, suggesting that artificial lights in ski slopes and facilities were the major source of light pollution in the park. An analysis of an urban-natural light pollution gradient along a neighboring urban area through the inside of the park indicated that light radiated from a light pollution source permeated for up to 1km into the adjacent area and contaminated the edge area of the park. Of the legally protected species whose distributions were reported in literature, four mammals (Martes flavigula, Mustela nivalis, Prionailurus bengalensis, Pteromys volans aluco), two birds (Falco subbuteo, Falco tinnunculus), and nine amphibians and reptiles (Onychodactylus koreanus, Hynobius leechii, Karsenia koreana, Rana dybowskii, Rana huanrenensis, Elaphe dione, Rhabdophis tigrinus, Gloydius ussuriensis, Gloydius saxatilis) inhabited light-polluted areas. Of those species inhabiting light-polluted areas, nocturnal species, such as Prionailurus bengalensis and Pteromys volans aluco, in particular, were vulnerable to light pollution. These results implied that protecting ecosystems from light pollution in national parks requires managing nighttime light in the parks and surrounding areas and making a plan to manage nighttime light pollution by taking into account ecological characteristics of wild animals in the parks.