• Journal of Korea Water Resources Association
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• v.39 no.10 s.171
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• pp.845-854
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• 2006

Establishment of snowmelt factors is necessary to simulate stream flow using snowmelt models during snowmelt periods. The few observed data related snowmelt was the major cause of difficulty in extracting snowmelt factors such as snow cover area, snow depth and depletion curve. The objective of this study was to extract snowmelt factors using RS, GIS technique and meteorological data. Snow cover maps were derived from NOAA/AVHRR images for the winter seasons from 1997 to 2003. Distributed snow depth was mapped by overlapping between snow cover maps and interpolated snowfall maps from 69 meteorological observation station. Depletion curves of snowmelt area were described from the linear regression equations of each year between the average temperature and snow cover area in Soyanggang-dam and chungju-dam watershed.

• Journal of KIISE:Software and Applications
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• v.33 no.10
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• pp.851-861
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• 2006

The paper proposes a novel unsupervised video object segmentation algorithm for image sequences with low depth-of-field (DOF), which is a popular photographic technique enabling to represent the intention of photographer by giving a clear focus only on an object-of-interest (OOI). The proposed algorithm largely consists of two modules. The first module automatically extracts OOIs from the first frame by separating sharply focused OOIs from other out-of-focused foreground or background objects. The second module tracks OOIs for the rest of the video sequence, aimed at running the system in real-time, or at least, semi-real-time. The experimental results indicate that the proposed algorithm provides an effective tool, which can be a basis of applications, such as video analysis for virtual reality, immersive video system, photo-realistic video scene generation and video indexing systems.

• Solar Energy
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• v.7 no.2
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• pp.22-29
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• 1987

This paper dealed with thickness variation of bottom heat sotrage zone due to salinity and flow rate of extration hot brine in small test solar pond (0.5m wide, 0.5m high, 1.0m long). Testing apparatus and situation were follows: 7.1 cm of height of suction diffuser and 1.8cm of height of discharge diffuser above the test pond respectively, 0.3cm of slot size of suction diffuser, 1.0cm of slot size of discharge diffuser, 47cm of length of the slot; heating of hot water ($75^{\circ}C$) through separated hot water tank, discharge of the brine into storage zone through discharge diffuser, the extration of the brine through suction diffuser, circulation of the extracted brine through a heat exchanger (cooler). Following results were obtained through the experiments. 1. In small test solar pond, the typical three zone which showed up in real solar pond were established. 2. Richardson Number was used more effectively to confirm hydrodynamic stability of the stratified flow. 3. The thickness of non convective layer had a great effect on the heat storage of the bottom convective layer, then the temperature of bottom convective layer had a relation to that of upper convective layer. 4. Optimum operating condition in the test pond was on 10%-15% of salt concentration and $0.05m^3/hr$ of flow rate of extraction hot brine. 5. Following thickness of 3 zones were available to obtain under optimum operation condition: o bottom storage zone: $30%{\pm}10%$ of total pond depth o non-convective zone: $40%{\pm}10%$ of total pond depth o Upper surface zone: $20%{\pm}10%$ of total pond depth.

• Journal of Periodontal and Implant Science
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• v.40 no.5
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• pp.220-226
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• 2010

Purpose: This study was performed to evaluate the periodontal wound healing effect of particulate equine bone mineral on canine alveolar bone defects. Methods: Twelve adult male beagle dogs were used as study subjects. The mandibular second and fourth premolars were extracted prior to the experimental surgery, and the extraction sites were allowed to heal for 8 weeks. After periodontal probing, two-walled defects were created at the mesial and distal sides of the mandibular third premolars bilaterally, and the defects were filled with equine particulate bone with collagen membrane or bovine particulate bone with collagen membrane, or collagen membrane alone. The defects without any treatment served as negative controls. After probing depth measurement, animals were sacrificed at 10, 16, and 24 post-surgery weeks for micro-computed tomographic and histomorphometric analysis. Results: The equine particulate bone-inserted group showed significantly decreased values of probing depth and first bone contact compared to the negative control and collagen membrane alone groups at weeks 10, 16, and 24 (P<0.05). There were no significant differences in the new cementum length, newly-formed bone area, or newly-formed bone volume between equine particulate bone- and bovine particulate bone-inserted groups, both of which showed significantly increased values compared to the negative control and collagen membrane alone groups (P<0.05). Conclusions: Equine particulate bone showed significant differences in probing depth, first bone contact, new cementum length, newly formed bone area, and bone volume fraction values when compared to the negative control and collagen membrane alone groups. There were no significant differences between equine and bovine particulate bone substitutes in these parameters; therefore, we can conclude that equine particulate bone is equivalent to bovine bone for periodontal regeneration.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.341-350
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• 2022

In this paper, we propose a deep learning based on-device augmented reality (AR) system in which multiple input images are used to implement the correct occlusion in a real environment. The proposed system is composed of three technical steps; camera pose estimation, depth estimation, and object augmentation. Each step employs various mobile frameworks to optimize the processing on the on-device environment. Firstly, in the camera pose estimation stage, the massive computation involved in feature extraction is parallelized using OpenCL which is the GPU parallelization framework. Next, in depth estimation, monocular and multiple image-based depth image inference is accelerated using the mobile deep learning framework, i.e. TensorFlow Lite. Finally, object augmentation and occlusion handling are performed on the OpenGL ES mobile graphics framework. The proposed augmented reality system is implemented as an application in the Android environment. We evaluate the performance of the proposed system in terms of augmentation accuracy and the processing time in the mobile as well as PC environments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.256-257
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• 2022

In this paper, we introduce a method of extracting three-dimensional feature points through the movement of a single mobile device. Using a monocular camera, a 2D image is acquired according to the camera movement and a baseline is estimated. Perform stereo matching based on feature points. A feature point and a descriptor are acquired, and the feature point is matched. Using the matched feature points, the disparity is calculated and a depth value is generated. The 3D feature point is updated according to the camera movement. Finally, the feature point is reset at the time of scene change by using scene change detection. Through the above process, an average of 73.5% of additional storage space can be secured in the key point database. By applying the algorithm proposed to the depth ground truth value of the TUM Dataset and the RGB image, it was confirmed that the\re was an average distance difference of 26.88mm compared with the 3D feature point result.

• Journal of IKEEE
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• v.28 no.1
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• pp.65-71
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• 2024

This paper proposes a neural network that can reduce the number of parameters while reducing matching errors in occluded regions to increase the accuracy of depth maps in stereo matching. Stereo matching-based object recognition is utilized in many fields to more accurately recognize situations using images. When there are many objects in a complex image, an occluded area is generated due to overlap between objects and occlusion by background, thereby lowering the accuracy of the depth map. To solve this problem, existing research methods that create context information and combine it with the cost volume or RoIselect in the occluded area increase the complexity of neural networks, making it difficult to learn and expensive to implement. In this paper, we create a depthwise seperable neural network that enhances regional feature extraction before cost volume generation, reducing the number of parameters and proposing a neural network that is robust to occlusion errors. Compared to PSMNet, the proposed neural network reduced the number of parameters by 30%, improving 5.3% in color error and 3.6% in test loss.

• Journal of the Korean Society of Marine Environment & Safety
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• v.20 no.2
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• pp.143-156
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• 2014

We reviewed investigation status on turbidity plume in the statement of marine environmental survey(2008 to 2012) associated with marine sand extraction projects. The survey statement from seven marine sand extraction sites (extraction area of Southern EEZ, extraction area of Western EEZ, relocation zone in the Western EEZ, sea area under jurisdiction of Taean-gun, sea area under jurisdiction of Ansan City, and two discrete sea areas under jurisdiction of Ongjin-gun) in the nearshore and offshore of Korea showed that in situ observations were carried out for the dispersion and transport of suspended sediments on two areas (One is a extraction area in the EEZs, the other is an area of coastal sites). However, sampling station and range have not been selected considering physical, geographical factors (tide, wave, stratification, water depth, etc.) and weather conditions (wind direction and velocity, fetch, duration, etc). Especially turbidity plumes originating from three sources, which include suspended sediments in overflow(or overspill) discharged from spillways and reject chutes of dredging vessel, and resuspended sediments from draghead at the seabed, may be transported to a far greater distance outside the boundary of the extraction site and have undesirable impacts on the marine environment and ecosystem. We address that behaviour of environmental pollutants such as suspended solids, nutrients, and metals should be extensively monitored and diagnosed during the dispersion and transport of the plume. Finally we suggest the necessity to supplement the current system of the sea area utilization consultation and establish the combined guidelines on marine sand extraction to collect basic data, to monitor cumulative effects, and to minimize environmental damages incurred by the aftermath of sand extraction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.716-723
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• 2008

With rapid development of science and technology and recent widening of mankind's range of activities, development of coastal waters and the environment have emerged as global issues. In relation to this, to allow more extensive analyses, the use of satellite images has been on the increase. This study aims at utilizing hyperspectral satellite images in determining the depth of coastal waters more efficiently. For this purpose, a partial image of the research subject was first extracted from an EO-1 Hyperion satellite image, and atmospheric and geometric corrections were made. Minimum noise fraction (MNF) transformation was then performed to compress the bands, and the band most suitable for analyzing the characteristics of the water body was selected. Within the chosen band, the diffuse attenuation coefficient Kd was determined. By deciding the end-member of pixels with pure spectral properties and conducting mapping based on the linear spectral unmixing method, the depth of water at the coastal area in question was ultimately determined. The research findings showed the calculated depth of water differed by an average of 1.2 m from that given on the digital sea map; the errors grew larger when the water to be measured was deeper. If accuracy in atmospheric correction, end-member determination, and Kd calculation is enhanced in the future, it will likely be possible to determine water depths more economically and efficiently.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.130-139
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• 2021

In this study, size of the fruit of Japanese apricot (plum) was estimated through a plum recognition and size estimation program using 3D images in order to control the Eurytoma maslovskii that causes the most damage to plum in a timely manner. In 2018, night shooting was carried out using a Kinect 2.0 Camera. For night shooting in 2019, a RealSense Depth Camera D415 was used. Based on the acquired images, a plum recognition and estimation program consisting of four stages of image preprocessing, sizeable plum extraction, RGB and depth image matching and plum size estimation was implemented using MATLAB R2018a. The results obtained by running the program on 10 images produced an average plum recognition error rate of 61.9%, an average plum recognition error rate of 0.5% and an average size measurement error rate of 3.6%. The continued development of these plum recognition and size estimation programs is expected to enable accurate fruit size monitoring in the future and the development of timely control systems for Eurytoma maslovskii.