• Korean Journal of Remote Sensing
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• v.20 no.2
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• pp.91-102
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• 2004

This paper reflects the estimation of using the EOC(Electro-optical Camera) images supporting GIUH(geomorphological instantaneous unit hydrograph) approach. We have analyzed GIUH in its density and frequency distribution by creating a DEM(digital elevation model) for the sub basin produced from the EOC images and examined topographical and hydrological application possibility of the EOC images. In this process, we have topographical basin characteristic analysis that use the remote sensing technique analyzing the DEM creation process of the EOC stereo images by studying the basic topographical hydrology analysis about abstraction technique since it is flirty complex and is more time-consuming than other method. we executed statistical analysis of a basin size and river length using the frequency function after divided lattice spacing applied have to the sub river basin from the image data and the digital map into 10m intervals ranging from 10m to 100m. After comparing and examining the peak and time to peak of the GIUH, we proceeded with a comparative analysis by lattice concerning the topographical divergence rate, area ratio, length ratio. Accumulating the peak and time to peak of the GIUH is altered to non-linear form in accordance to lattice dimension as well as basin factor. It was proved that the lattice dimension is one of the important factors about the peak and time to peak of the GIUH.

• Geophysics and Geophysical Exploration
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• v.8 no.1
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• pp.104-111
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• 2005

Dmitri Donskoi, the Russian cruiser launched in 1883, is known to have sunk near Ulleung Island (East Sea, Korea) on May 29, 1905, while it was participating in the Russo-Japanese War. In order to find this ship, information about its possible location was obtained from Russian and Japanese maritime historical records. The supposed location of the ship was identified, and we conducted a five-year geophysical survey from 1999 to 2003. A reconnaissance three-dimensional topographic survey of the sea floor was carried out using multi-beam echo sounder, marine magnetometer, and side-scan sonar. An anomalous body identified through the initial reconnaissance survey was identified by a detailed survey using a remotely operated vehicle, deep-sea camera, and the mini-submarine Pathfinder. Interpretation of the acquired data showed that the ship is hanging on the side of a channel, at the bottom of the sea 400 m below sea level. The location is about 2 km from Port Jeodong, Uleung Island. We discovered 152 mm naval guns and other war materiel still attached to the hull of the ship. In addition, the remnants of the steering gear and other machinery that were burnt during the final action were found near the hull. Strong magnetic fields, resulting from the presence of volcanic rocks in the survey area, affected the resolution of the magnetic data gathered; as a result, we could not locate the ship reliably using the magnetic method. Severe sea floor topography in the gully around the hull gave rise to diffuse reflections in the side-scan sonar data, and this prevented us from identifying the anomalous body with the side-scan sonar technique. However, the sea-floor image obtained from the multi-bean echo sounder was very useful in verifying the location of the ship.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.279-286
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• 2021

Recently, We are carrying out a policy of physical distancing of at least 1m from each other to prevent the spreading of COVID-19 disease in public places. In this paper, we propose a method for measuring distances between people in real time and an automation system that recognizes objects that are within 1 meter of each other from stereo images acquired by drones or CCTVs according to the estimated distance. A problem with existing methods used to estimate distances between multiple objects is that they do not obtain three-dimensional information of objects using only one CCTV. his is because three-dimensional information is necessary to measure distances between people when they are right next to each other or overlap in two dimensional image. Furthermore, they use only the Bounding Box information to obtain the exact coordinates of human existence. Therefore, in this paper, to obtain the exact two-dimensional coordinate value in which a person exists, we extract a person's key point to detect the location, convert it to a three-dimensional coordinate value using Stereo Vision and Camera Calibration, and estimate the Euclidean distance between people. As a result of performing an experiment for estimating the accuracy of 3D coordinates and the distance between objects (persons), the average error within 0.098m was shown in the estimation of the distance between multiple people within 1m.

• Journal of Korean Academy of Oral Health
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• v.41 no.1
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• pp.22-27
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• 2017

Objectives: Dental plaque is composed of 700 bacterial species. It is known that some oral microorganisms produce porphyrin, and thus, they emit red fluorescence when illuminated with blue light at a specific wavelength of <410 nm. Porphyromonas gingivalis belongs to the genus Porphyromonas, which is characterized by the production of porphyrin. The aim of this study was to evaluate red fluorescence emission of some oral microorganisms interacting with P. gingivalis. Methods: Five bacterial strains (P. gingivalis, Streptococcus mutans, Lactobacillus casei, Actinomyces naeslundii, and Fusobacterium nucleatum) were used for this study. Tryptic soy agar medium supplemented with hemin, vitamin K3, and sheep blood was used as a growth medium. The fluorescence emission of bacterial colonies was evaluated under 405 nm-wavelength blue light using a Quantitative Light-induced Fluorescence Digital (QLF-D) camera system. Each bacterium was cultured alone and co-cultured in close proximity with P. gingivalis. The red/green (R/G) ratio of fluorescence image was calculated and the differences of R/G ratio according to each growth condition were compared using the Mann-Whitney test (P<0.05). Results: Single cultured S. mutans, L. casei and A. naeslundii colonies emitted red fluorescence (R/G ratio=$2.15{\pm}0.06$, $4.31{\pm}0.17$, $5.52{\pm}1.29$, respectively). Fusobacterium nucleatum colonies emitted green fluorescence (R/G ratio=$1.36{\pm}0.06$). The R/G ratios of A. naeslundii and F. nucleatum were increased when P. gingivalis was co-cultured with each bacterium (P<0.05). In contrast, the R/G ratios of S. mutans and L. casei were decreased when P. gingivalis was co-cultured with each bacterium (P=0.002, 0.003). Conclusions: This study confirmed that P. gingivalis could affect the red fluorescence of other oral bacteria under 405 nm-wavelength blue light. Our findings concluded that P. gingivalis has an important role for red fluorescence emission of dental biofilm.

• 재활복지
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• v.21 no.3
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• pp.129-146
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• 2017

In an event of an emergency, such as fire in a building, visually impaired and blind people are prone to exposed to a level of danger that is greater than that of normal people, for they cannot be aware of it quickly. Current fire detection methods such as smoke detector is very slow and unreliable because it usually uses chemical sensor based technology to detect fire particles. But by using vision sensor instead, fire can be proven to be detected much faster as we show in our experiments. Previous studies have applied various image processing and machine learning techniques to detect fire, but they usually don't work very well because these techniques require hand-crafted features that do not generalize well to various scenarios. But with the help of recent advancement in the field of deep learning, this research can be conducted to help solve this problem by using deep learning-based object detector that can detect fire using images from security camera. Deep learning based approach can learn features automatically so they can usually generalize well to various scenes. In order to ensure maximum capacity, we applied the latest technologies in the field of computer vision such as YOLO detector in order to solve this task. Considering the trade-off between recall vs. complexity, we introduced two convolutional neural networks with slightly different model's complexity to detect fire at different recall rate. Both models can detect fire at 99% average precision, but one model has 76% recall at 30 FPS while another has 61% recall at 50 FPS. We also compare our model memory consumption with each other and show our models robustness by testing on various real-world scenarios.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.48 no.2
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• pp.87-95
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• 2022

This study is about the development of transparent cleansing water with one of the beta-hydroxy acids (BHA), salicylic acid, and capryloyl salicylic acid, which is one of the lipo-hydroxy acids (LHA). Transparent appearance was stabilized by increasing the solubility of lipophilic salicylic acid and capryloyl salicylic acid in water using ethanol, polyol, and sodium hydroxide, and supplementing suspension and deposition using a double micelle structure of two types of PEG surfactants. Cleansing water applied with this technology was developed, and makeup removing ability and skin texture improvement ability were confirmed using an optical camera and an image analyzer. This solubilization technology is proposed as a new approach of LHA, which has been difficult to apply due to its low solubility in water, and is expected to help in the development of new chemical peeling products.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1479-1488
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• 2022

Accidents in which oil spills occur intermittently in the ocean due to ship collisions and sinkings. In order to prepare prompt countermeasures when such an accident occurs, it is necessary to accurately identify the current status of spilled oil. To this end, the Coast Guard patrols the target area with a fixed-wing airplane or helicopter and checks it with the naked eye or video, but it was difficult to determine the area contaminated by the spilled oil and its exact location on the map. Accordingly, this study develops a technology for direct ortho-rectification by automatically geo-referencing aerial images collected by the Coast Guard without individual ground reference points to identify the current status of spilled oil. First, meta information required for georeferencing is extracted from a visualized screen of sensor information such as video by optical character recognition (OCR). Based on the extracted information, the external orientation parameters of the image are determined. Images are individually orthorectified using the determined the external orientation parameters. The accuracy of individual orthoimages generated through this method was evaluated to be about tens of meters up to 100 m. The accuracy level was reasonably acceptable considering the inherent errors of the position and attitude sensors, the inaccuracies in the internal orientation parameters such as camera focal length, without using no ground control points. It is judged to be an appropriate level for identifying the current status of spilled oil contaminated areas in the sea. In the future, if real-time transmission of images captured during flight becomes possible, individual orthoimages can be generated in real time through the proposed individual orthorectification technology. Based on this, it can be effectively used to quickly identify the current status of spilled oil contamination and establish countermeasures.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1219-1230
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• 2022

Multi-spectral drones in agricultural observation require quantitative and reliable data based on physical quantities such as radiance or reflectance in crop yield analysis. In the case of remote sensing data for crop monitoring, images taken in the same area over time-series are required. In particular, biophysical data such as leaf area index or chlorophyll are analyzed through time-series data under the same reference, it can be directly analyzed. So, comparable reflectance data are required. Orthoimagery using drone images, the entire image pixel values are distorted or there is a difference in pixel values at the junction boundary, which limits accurate physical quantity estimation. In this study, reflectance and vegetation index based on drone images were calculated according to the correction method of drone images for time-series crop monitoring. comparing the drone reflectance and ground measured data for spectral characteristics analysis.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.121-127
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• 2022

Smart mirror is an IoT device that attaches a display and an embedded computer to the mirror and provides various information to the useer along with the mirror function. This paper went beyond the form of dealing with smart mirrors only stand alone device the provide information to users, and constructed a network in which smart mirrors are connected, and proposed and implemented a CoMirror system that allows users to talk and share information with other smart mirror users. The CoMirror system has a structure in which several CoMirror clients are connected on one CoMirror server. The CoMirror client consists of Raspberry Pi, a mirror film, a touch pad, a display device, an web camera, etc. The server has functions such as face learning and recognition, user management, a relay role for exchanging messages between clients, and setting up for video call. Users can communicate with other CoMirror users via the server, such as text, image, and audio messages, as well as 1:1 video call.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.57-64
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• 2022

If the inside of a building collapses due to a disaster such as fire, collapse, or natural disaster, the physical security inside the building is likely to become ineffective. Here, physical security is needed to minimize the human casualties and physical damages in the collapsed building. Therefore, this paper proposes an algorithm to minimize the damage in a disaster situation by fusing existing research that detects obstacles and collapsed areas in the building and a deep learning-based object detection algorithm that minimizes human casualties. The existing research uses a single camera to determine whether the corridor environment in which the robot is currently located has collapsed and detects obstacles that interfere with the search and rescue operation. Here, objects inside the collapsed building have irregular shapes due to the debris or collapse of the building, and they are classified and detected as obstacles. We also propose a method to detect rescue requesters-the most important resource in the disaster situation-and minimize human casualties. To this end, we collected open-source disaster images and image data of disaster situations and calculated the accuracy of detecting rescue requesters in disaster situations through various deep learning-based object detection algorithms. In this study, as a result of analyzing the algorithms that detect rescue requesters in disaster situations, we have found that the YOLOv4 algorithm has an accuracy of 0.94, proving that it is most suitable for use in actual disaster situations. This paper will be helpful for performing efficient search and rescue in disaster situations and achieving a high level of physical security, even in collapsed buildings.