• Journal of the Korea Institute of Building Construction
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• v.19 no.3
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• pp.239-246
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• 2019

According to the survey on the status of aged buildings in Korea, A number of concrete buildings deterioration such as houses and apartment buildings has been increased rapidly. To solve this problem, the research related to the facility management, that is one of the importance factor, for monitoring buildings has been increased. The research is divided into Survey-based and Technique-based. However, the problem is that Survey-based research is required a lot of time, money and manpower for management. Also, safety cannot be guaranteed in the case of high-rise buildings. Technique-based research has limitations to applying to the current facility maintenance system, as detailed information of deteriorated facilities is difficult to grasp and errors in accuracy are feared. Therefore, this paper contribute to improve the environment of facility management by 4D maps using UAV, camera and Pix4D mapper program to make 3D model. In addition, it is expected to suggest that residents will be offered easy verification to their buildings deterioration.

• Journal of Advanced Navigation Technology
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• v.25 no.4
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• pp.273-279
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• 2021

A flight control computer(FLCC) applied to an unmanned aerial vehicle(UAV) is a safety-critical item, and which is designed in a multiple structure to increase the reliability of operation by securing fault tolerance. These FLCC of multiple structure should be designed so that each independent processing/control components can perform the same operation at the same time. And for this reason, a synchronization algorithm for synchronizing the operation between FLCCs should be included in an operational flight program. In this paper, we propose a software design method for synchronization between dual FLCCs applied to UAVs. The proposed synchronization method is designed to synchronize using only the minimum hardware resources to reduce a failure rate. In addition, the proposed synchronization method is designed to minimized synchronization errors due to a timer operation by designing in consideration of operation characteristics of the hardware timer used for the synchronization.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.6
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• pp.473-480
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• 2021

In this paper, We analyze and present improvements to problems in software quality through Static Analysis for Open Source, which is widely used as the Flight Controller software for small unmanned aerial vehicle drones. MISRA coding rules, which are widely applied based on software quality, have been selected. Static analysis tools were used by LDRA tools certified international tools used in all industries, including automobiles, railways, nuclear power and healthcare, as well as aviation. We have identified some safety-threatening problems across the quality of the software, such as structure of open source modules, analysis of usage data, compliance with coding rules, and quality indicators (complexity and testability), and have presented improvements.

• Journal of Advanced Navigation Technology
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• v.28 no.2
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• pp.238-245
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• 2024

In this paper, we propose a framework for improving the performance of semantic segmentation of agricultural multispectral image using feature fusion techniques. Most of the semantic segmentation models being studied in the field of smart farms are trained on RGB images and focus on increasing the depth and complexity of the model to improve performance. In this study, we go beyond the conventional approach and optimize and design a model with multispectral and attention mechanisms. The proposed method fuses features from multiple channels collected from a UAV along with a single RGB image to increase feature extraction performance and recognize complementary features to increase the learning effect. We study the model structure to focus on feature fusion and compare its performance with other models by experimenting with favorable channels and combinations for crop images. The experimental results show that the model combining RGB and NDVI performs better than combinations with other channels.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.90-103
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• 2017

This study analyzed the surface temperature characteristics of urban green spaces under high summer temperatures to clarify the functions of green spaces in reducing urban temperatures. We obtained accurate surface temperature data using highresolution unmanned aerial vehicle images of the survey site, which was an isolated green space in the city. We analyzed differences in the surface temperature by land cover type, vegetation type, species type, and the relationship between surface temperature and vegetation volume. Based on the results, among the land cover types, wetlands and forests had low temperatures and paving areas had very high temperatures. Regarding vegetation type, broad-leaved trees had lower temperatures than coniferous trees in forests. However, in planted areas, coniferous trees had lower temperatures than broad-leaved trees. The temperature of long grass was higher than that of short grass, which suggested that the volume of grass affected the temperature. Regarding forest species type, the temperature of broad-leaved Robinia pseudoacacia forest and mixed broad-leaved forest was lower than coniferous Pinus densiflora forest. There was a slight difference in temperature between R. pseudoacacia forest and mixed broad-leaved forest. The analysis of the relationship between vegetation volume and temperature by forest species type indicated a negative correlation, where the temperature decreased with increasing vegetation volume, similar to the results of previous studies. However, we found a weak positive correlation in R. pseudoacacia forest; therefore, an increase in volume may not reduce the surface temperature depending on the dominant species.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.4
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• pp.296-303
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• 2015

The concentration of solute in a $NaBH_4$ solution is limited due to the low solubility of $NaBO_2$. The performance of a hydrogen generation system was evaluated using various concentrations of $NaBH_4$ solution. First, a self-hydrolysis test and a hydrogen generation test for 30 min were performed. The composition of $NaBH_4$ solution was selected to be 1 wt% NaOH + 25 wt% $NaBH_4$+74wt% $H_2O$ by considering the amount of hydrogen loss, stability of hydrogen generation, $NaBO_2$ precipitation, conversion efficiency, and the purpose of its application. A hydrogen generation system for a 200 W fuel cell was evaluated for 3 h. Although hydrogen generation rate decreased with time due to $NaBO_2$ precipitation, hydrogen was produced for 3 h (conversion efficiency: 87.4%). The energy density of the 200 W fuel cell system was 263 Wh/kg. A small unmanned aerial vehicle with this fuel cell system can achieve 1.5 times longer flight time than one flying on batteries.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.367-378
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• 2021

Field compost is a representative non-point pollution source for livestock. If the field compost flows into the water system due to rainfall, nutrients such as phosphorus and nitrogen contained in the field compost can adversely affect the water quality of the river. In this paper, we propose a method for detecting field compost using unmanned aerial vehicle images and deep learning-based semantic segmentation. Based on 39 ortho images acquired in the study area, about 30,000 data were obtained through data augmentation. Then, the accuracy was evaluated by applying the semantic segmentation algorithm developed based on U-net and the filtering technique of Open CV. As a result of the accuracy evaluation, the pixel accuracy was 99.97%, the precision was 83.80%, the recall rate was 60.95%, and the F1-Score was 70.57%. The low recall compared to precision is due to the underestimation of compost pixels when there is a small proportion of compost pixels at the edges of the image. After, It seems that accuracy can be improved by combining additional data sets with additional bands other than the RGB band.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.199-213
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• 2022

The unsupervised domain adaptation can solve the impractical issue of repeatedly collecting high-quality training data every year for annual crop classification. This study evaluates the applicability of deep learning-based unsupervised domain adaptation models for crop classification. Three unsupervised domain adaptation models including a deep adaptation network (DAN), a deep reconstruction-classification network, and a domain adversarial neural network (DANN) are quantitatively compared via a crop classification experiment using unmanned aerial vehicle images in Hapcheon-gun and Changnyeong-gun, the major garlic and onion cultivation areas in Korea. As source baseline and target baseline models, convolutional neural networks (CNNs) are additionally applied to evaluate the classification performance of the unsupervised domain adaptation models. The three unsupervised domain adaptation models outperformed the source baseline CNN, but the different classification performances were observed depending on the degree of inconsistency between data distributions in source and target images. The classification accuracy of DAN was higher than that of the other two models when the inconsistency between source and target images was low, whereas DANN has the best classification performance when the inconsistency between source and target images was high. Therefore, the extent to which data distributions of the source and target images match should be considered to select the best unsupervised domain adaptation model to generate reliable classification results.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.3
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• pp.282-287
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• 2015

In modern warfare, the importance of SEAD(Suppression of Enemy Air Defense) mission is being emphasized. However, this mission runs the risk of hull damage or casualties of our friendly air forces. Because of these risks, research on the way of minimizing damages and enhancing mission capability is under active discussion. As a part of this research, SEAD mission planning with multiple UAVs has been covered using vector field guidance. This guidance method not only applies to various forms of flight path but also requires less computational power than other guidance methods. Therefore, in this paper, planning methods of SEAD mission for multiple UAVs using vector field guidance and experimental data from flight experiments regarding designed mission has been covered.