• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.32 no.3
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• pp.213-220
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• 2014

This is the study to conduct the topographical analysis using the orthophotographic data from the waypoint flight using the UAV and constructed the system required for the automatic waypoint flight using the multicopter.. The results of the waypoint photographing are as follows. First, result of the waypoint flight over the area of 9.3ha, take time photogrammetry took 40 minutes in total. The multicopter have maintained the certain flight altitude and a constant speed that the accurate photographing was conducted over the waypoint determined by the ground station. Then, the effect of the photogrammetry was checked. Second, attached a digital camera to the multicopter which is lightweight and low in cost compared to the general photogrammetric unmanned airplane and then used it to check its mobility and economy. In addition, the matching of the photo data, and production of DEM and DXF files made it possible to analyze the topography. Third, produced the high resolution orthophoto(2cm) for the inside of the river and found out that the analysis is possible for the changes in vegetation and topography around the river. Fourth, It would be used for the more in-depth research on landscape analysis such as terrain analysis and visibility analysis. This method may be widely used to analyze the various terrains in cities and rivers. It can also be used for the landscape control such as cultural remains and tourist sites as well as the control of the cultural and historical resources such as the visibility analysis for the construction of DSM.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.79-94
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• 2013

Recently, the high value added business is steadily growing in the culture and art area. To generated high value from a performance, the satisfaction of audience is necessary. The flow in a critical factor for satisfaction, and it should be induced from audience and measures. To evaluate interest and emotion of audience on contents, producers or investors need a kind of index for the measurement of the flow. But it is neither easy to define the flow quantitatively, nor to collect audience's reaction immediately. The previous studies of the group flow were evaluated by the sum of the average value of each person's reaction. The flow or "good feeling" from each audience was extracted from his face, especially, the change of his (or her) expression and body movement. But it was not easy to handle the large amount of real-time data from each sensor signals. And also it was difficult to set experimental devices, in terms of economic and environmental problems. Because, all participants should have their own personal sensor to check their physical signal. Also each camera should be located in front of their head to catch their looks. Therefore we need more simple system to analyze group flow. This study provides the method for measurement of audiences flow with group synchronization at same time and place. To measure the synchronization, we made real-time processing system using the Differential Image and Group Emotion Analysis (GEA) system. Differential Image was obtained from camera and by the previous frame was subtracted from present frame. So the movement variation on audience's reaction was obtained. And then we developed a program, GEX(Group Emotion Analysis), for flow judgment model. After the measurement of the audience's reaction, the synchronization is divided as Dynamic State Synchronization and Static State Synchronization. The Dynamic State Synchronization accompanies audience's active reaction, while the Static State Synchronization means to movement of audience. The Dynamic State Synchronization can be caused by the audience's surprise action such as scary, creepy or reversal scene. And the Static State Synchronization was triggered by impressed or sad scene. Therefore we showed them several short movies containing various scenes mentioned previously. And these kind of scenes made them sad, clap, and creepy, etc. To check the movement of audience, we defined the critical point, ${\alpha}$and ${\beta}$. Dynamic State Synchronization was meaningful when the movement value was over critical point ${\beta}$, while Static State Synchronization was effective under critical point ${\alpha}$. ${\beta}$ is made by audience' clapping movement of 10 teams in stead of using average number of movement. After checking the reactive movement of audience, the percentage(%) ratio was calculated from the division of "people having reaction" by "total people". Total 37 teams were made in "2012 Seoul DMC Culture Open" and they involved the experiments. First, they followed induction to clap by staff. Second, basic scene for neutralize emotion of audience. Third, flow scene was displayed to audience. Forth, the reversal scene was introduced. And then 24 teams of them were provided with amuse and creepy scenes. And the other 10 teams were exposed with the sad scene. There were clapping and laughing action of audience on the amuse scene with shaking their head or hid with closing eyes. And also the sad or touching scene made them silent. If the results were over about 80%, the group could be judged as the synchronization and the flow were achieved. As a result, the audience showed similar reactions about similar stimulation at same time and place. Once we get an additional normalization and experiment, we can obtain find the flow factor through the synchronization on a much bigger group and this should be useful for planning contents.

• Journal of Korea Multimedia Society
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• v.14 no.10
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• pp.1348-1364
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• 2011

As the existing edutainment contents for children are mostly comprised of educational tools which unilaterally induce educatees to passively respond to them, the content-creating methodologies in terms of which active and voluntary learning is made possible is urgently needed. In this paper, we present the implementation of the tangible 'electronic dice' interface as an interactive tool for behavior-based edutainment contents, and propose a methodology for developing edutainment contents for children by utilizing the recognition technique of hand movement based on depth-image information. Also proposed in the paper are an authoring and management tool of learning quizzes that allows educators to set up and manage their learning courseware, and a log analysis system of learning achievement for real-time monitoring of educational progress. The behavior-based tangible interface and edutainment contents that we propose provide the easy-to-operate interaction with a real object, which augments educatees' interest in learning, thus leading to their active and voluntary attitude toward learning. Furthermore, The authoring and management tool and log analysis system allow us to construct learning programs by children's achievement level and to monitor in real-time the learning development of children educatees by understanding the situation and behavior of their learning development from the analytic results obtained by observing the processes of educatees' solving problems for themselves, and utilizing them for evaluation materials for lesson plans.

• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.689-699
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• 2016

As the demand for LCD increases, the importance of inspection equipment for improving the efficiency of LCD production is continuously emphasized. The pattern inspection apparatus is one that detects minute defects of pattern quickly using optical equipment such as line scan camera. This pattern inspection apparatus makes a decision on whether a pixel is a defect or not using a single threshold value in order to meet constraint of real time inspection. However, a method that uses an adaptive thresholding scheme with different threshold values according to characteristics of each region in a pattern can greatly improve the performance of defect detection. To apply this adaptive thresholding scheme it has to be known that a certain pixel to be inspected belongs to which region. Therefore, this paper proposes a region matching algorithm that recognizes the region of each pixel to be inspected. The proposed algorithm is based on the pattern matching scheme with the consideration of real time constraint of machine vision and implemented through GPGPU in order to be applied to a practical system. Simulation results show that the proposed method not only satisfies the requirement for processing time of practical system but also improves the performance of defect detection.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.545-550
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• 2013

In pattern recognition field, data classification is an essential process for extracting meaningful information from data. Adaptive boosting algorithm, known as AdaBoost algorithm, is a kind of improved boosting algorithm for applying to real data analysis. It consists of weak classifiers, such as random guessing or random forest, which performance is slightly more than 50% and weights for combining the classifiers. And a strong classifier is created with the weak classifiers and the weights. In this paper, a research is performed using AdaBoost algorithm for detecting chaff echo which has similar characteristics to precipitation echo and interrupts weather forecasting. The entire process for implementing chaff echo classifier starts spatial and temporal clustering based on similarity with weather radar data. With them, learning data set is prepared that separated chaff echo and non-chaff echo, and the AdaBoost classifier is generated as a result. For verifying the classifier, actual chaff echo appearance case is applied, and it is confirmed that the classifier can distinguish chaff echo efficiently.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.10
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• pp.95-104
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• 2004

This paper propose a motion estimator architecture to reduce the power consumption of the most-power-consuming motion estimation method when designing multimedia SoC with deep submicron technologies below 0.13${\mu}{\textrm}{m}$. The proposed architecture considers both dynamic and static power consumption so that it is suitable for large leakage process technologies, while conventional architectures consider only dynamic power consumption. Consequently, it is suitable for mobile information terminals such as mobile videophone where efficient power management is essential. It exploits full search method for simple hardware implementation. It also exploits early break-off method to reduce dynamic power consumption. To reduce static power consumption, megablock shutdown method considering power line noise is also employed. To evaluate the proposed architecture when applied multimedia SoC, system-level control flow and low-power control algorithm are developed and the power consumption was calculated based on thor From the simulation results, power consumption was reduced to about 60%. Considering the line width reduction and increased leakage current due to heat dissipation in chip core, the proposed architecture shows steady power reduction while it goes worse in conventional architectures.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.4
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• pp.449-454
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• 2010

Lately, airborne digital camera and airborne laser scanner in field of airborne surveying are used to build geography information such as digital ortho photo map and DEM(Digital Elevation Model). In this study, 3D position accuracy is compared medium format CCD camera RCD105 with airborne digital camera DMC. For this, test area was decided for aerial photograph. And using 1/1,000 scale digital map, ground control points were selected for aerial triangulation and check points were selected for horizontal/vertical accuracy analysis using softcopy stereoplotter. Accuracy of RCD105 and DMC was estimated by result of aerial triangulation and result of check points measurement of using softcopy stereoplotter. In result of aerial triangulation, RMSE(Root Mean Square Error) X, Y, Z of RCD105 is 2.1, 2.2, 1.3 times larger than DMC. In result of check point measurement using softcopy stereoplotter, horizontal/ vertical RMSE of RCD105 is 2.5, 4.3 times larger than DMC. Even though accuracy of RCD105 is lower than DMC, it is maybe possible to make digital map and ortho photo using RCD105.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1273-1283
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• 2022

Recently, forest fires have frequently occurred due to climate change, leading to human and property damage every year. The forest fire monitoring technique using remote sensing can obtain quick and large-scale information of fire-damaged areas. In this study, the Gangneung and Donghae forest fires that occurred in March 2022 were analyzed using the spectral band of Sentinel-2, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) to classify the affected areas of forest fires. The U-net based convolutional neural networks (CNNs) model was simulated for the fire-damaged areas. The accuracy of forest fire classification in Donghae and Gangneung classification was high at 97.3% (f₁=0.486, IoU=0.946). The same model used in Donghae and Gangneung was applied to Uljin and Samcheok areas to get rid of the possibility of overfitting often happen in machine learning. As a result, the portion of overlap with the forest fire damage area reported by the National Institute of Forest Science (NIFoS) was 74.4%, confirming a high level of accuracy even considering the uncertainty of the model. This study suggests that it is possible to quantitatively evaluate the classification of forest fire-damaged area using a spectral band and indices similar to that of the Compact Advanced Satellite 500 (CAS500-4) in the Sentinel-2.

• Land and Housing Review
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• v.14 no.2
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• pp.125-135
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• 2023

Drones are used in various fields, including land survey, transportation, forestry/agriculture, marine, environment, disaster prevention, water resources, cultural assets, and construction, as their industrial importance and market size have increased. In this study, image data for deep learning was collected using a mavic3 drone capturing images at a shooting altitude was 20 m with ×7 magnification. Swin Transformer and UperNet were employed as the backbone and architecture of the deep learning model. About 800 sheets of labeled data were augmented to increase the amount of data. The learning process encompassed three rounds. The Cross-Entropy loss function was used in the first and second learning; the Tversky loss function was used in the third learning. In the future, when the crack detection model is advanced through convergence with the Internet of Things (IoT) through additional research, it will be possible to detect patching or potholes. In addition, it is expected that real-time detection tasks of drones can quickly secure the detection of pavement maintenance sections.