• Smart Media Journal
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• v.13 no.2
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• pp.52-61
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• 2024

In the shipyard, aerial images are acquired at regular intervals using Unmanned Aerial Vehicles (UAVs) for the management of external storage yards. These images are then investigated by humans to manage the status of the storage yards. This method requires a significant amount of time and manpower especially for large areas. In this paper, we propose an automated management technology based on a semantic segmentation foundation model to address these challenges and accurately assess the status of external storage yards. In addition, as there is insufficient publicly available dataset for external storage yards, we collected a small-scale dataset for external storage yards objects and equipment. Using this dataset, we fine-tune an object detector and extract initial object candidates. They are utilized as prompts for the Segment Anything Model(SAM) to obtain precise semantic segmentation results. Furthermore, to facilitate continuous storage yards dataset collection, we propose a training data generation pipeline using SAM. Our proposed method has achieved 4.00%p higher performance compared to those of previous semantic segmentation methods on average. Specifically, our method has achieved 5.08% higher performance than that of SegFormer.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.400-407
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• 2024

The role of unmanned aerial vehicles (UAVs) in modern warfare is increasingly significant, making their capacity for autonomous missions essential. Accordingly, autonomous target detection/identification based on captured images is crucial, yet the effectiveness of AI models depends on image sharpness. Therefore, this study describes how to determine the field of view (FOV) of the camera and the flight position of the UAV considering the required spatial resolution. Firstly, the calculation of the size of the acquisition area is discussed in relation to the relative position of the UAV and the FOV of the camera. Through this, this paper first calculates the area that can satisfy the spatial resolution and then calculates the relative position of the UAV and the FOV of the camera that can satisfy it. Furthermore, this paper propose a method for calculating the effective range of the UAV's position that can satisfy the required spatial resolution, centred on the coordinate to be photographed. This is then processed into a tabular format, which can be used for mission planning.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.1-16
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• 2015

Traffic accident is one of the major cause of death worldwide for the last several decades. According to the statistics of world health organization, approximately 1.24 million deaths occurred on the world's roads in 2010. In order to reduce future traffic accident, multipronged approaches have been adopted including traffic regulations, injury-reducing technologies, driving training program and so on. Records on traffic accidents are generated and maintained for this purpose. To make these records meaningful and effective, it is necessary to analyze relationship between traffic accident and related factors including vehicle design, road design, weather, driver behavior etc. Insight derived from these analysis can be used for accident prevention approaches. Traffic accident data mining is an activity to find useful knowledges about such relationship that is not well-known and user may interested in it. Many studies about mining accident data have been reported over the past two decades. Most of studies mainly focused on predict risk of accident using accident related factors. Supervised learning methods like decision tree, logistic regression, k-nearest neighbor, neural network are used for these prediction. However, derived prediction model from these algorithms are too complex to understand for human itself because the main purpose of these algorithms are prediction, not explanation of the data. Some of studies use unsupervised clustering algorithm to dividing the data into several groups, but derived group itself is still not easy to understand for human, so it is necessary to do some additional analytic works. Rule based learning methods are adequate when we want to derive comprehensive form of knowledge about the target domain. It derives a set of if-then rules that represent relationship between the target feature with other features. Rules are fairly easy for human to understand its meaning therefore it can help provide insight and comprehensible results for human. Association rule learning methods and subgroup discovery methods are representing rule based learning methods for descriptive task. These two algorithms have been used in a wide range of area from transaction analysis, accident data analysis, detection of statistically significant patient risk groups, discovering key person in social communities and so on. We use both the association rule learning method and the subgroup discovery method to discover useful patterns from a traffic accident dataset consisting of many features including profile of driver, location of accident, types of accident, information of vehicle, violation of regulation and so on. The association rule learning method, which is one of the unsupervised learning methods, searches for frequent item sets from the data and translates them into rules. In contrast, the subgroup discovery method is a kind of supervised learning method that discovers rules of user specified concepts satisfying certain degree of generality and unusualness. Depending on what aspect of the data we are focusing our attention to, we may combine different multiple relevant features of interest to make a synthetic target feature, and give it to the rule learning algorithms. After a set of rules is derived, some postprocessing steps are taken to make the ruleset more compact and easier to understand by removing some uninteresting or redundant rules. We conducted a set of experiments of mining our traffic accident data in both unsupervised mode and supervised mode for comparison of these rule based learning algorithms. Experiments with the traffic accident data reveals that the association rule learning, in its pure unsupervised mode, can discover some hidden relationship among the features. Under supervised learning setting with combinatorial target feature, however, the subgroup discovery method finds good rules much more easily than the association rule learning method that requires a lot of efforts to tune the parameters.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.6
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• pp.245-253
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• 2021

RC slab bridges account for the largest portion of deteriorated bridges in Korea. However, most RC slabs are not included in the first and second classes of bridges, which are subject to bridge safety management and maintenance. The highest damaged components in highway bridges are the subsidiary facilities including expansion joints and bearings. In particular, leakage through expansion joints causes deterioration and cracks of concrete and exposure of reinforced bars. Therefore, this study analyzed the effect of adhesion damage at expansion joints on the response of the deck in RC slab bridges. When the spacing between the expansion joints at both ends was closely adhered, cracks occurred in the concrete at both ends of the deck due to the resistance rigidity at the expansion joints. Based on the response results, the correlation analysis between displacements in the longitudinal direction of the expansion joint and concrete stress at both ends of the deck for each damage scenario was performed to investigate the effect of the occurrence of damage on the bridge behavior. When expansion joint devices at both sides were damaged, the correlation between displacement and stress showed a low correlation of 0.18 when the vehicles proceeded along all the lanes. Compared with those in the intact state, the deflections of the deck in the damaged case at both sides showed a low correlation of 0.34 to 0.53 while the vehicle passed and 0.17 to 0.43 after the vehicle passed. This means that the occurrence of cracks in the ends of concrete changed the behavior of the deck. Therefore, data-deriven damage detection could be developed to manage the damage to expansion joints that cause damage and deterioration of the deck.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.295-304
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• 2022

The purpose of this study is to detect the sorghum panicle using YOLOv5 based on RGB images acquired by a unmanned aerial vehicle (UAV) system. The high-resolution images acquired using the RGB camera mounted in the UAV on September 2, 2022 were split into 512×512 size for YOLOv5 analysis. Sorghum panicles were labeled as bounding boxes in the split image. 2,000images of 512×512 size were divided at a ratio of 6:2:2 and used to train, validate, and test the YOLOv5 model, respectively. When learning with YOLOv5s, which has the fewest parameters among YOLOv5 models, sorghum panicles were detected with mAP@50=0.845. In YOLOv5m with more parameters, sorghum panicles could be detected with mAP@50=0.844. Although the performance of the two models is similar, YOLOv5s ( 4 hours 35 minutes) has a faster training time than YOLOv5m (5 hours 15 minutes). Therefore, in terms of time cost, developing the YOLOv5s model was considered more efficient for detecting sorghum panicles. As an important step in predicting sorghum yield, a technique for detecting sorghum panicles using high-resolution RGB images and the YOLOv5 model was presented.

• Radiation Oncology Journal
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• v.28 no.3
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• pp.141-146
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• 2010

Purpose: We examined the radioprotective effects of 5-androstendiol (5-AED), a natural hormone produced in the reticularis of the adrenal cortex, as a result of intestinal damage in gamma-irradiated C3H/HeN mice. Materials and Methods: Thirty mice (C3H/HeN) were divided into three groups; 1) non-irradiated control group, 2) irradiated group, and 3) 5-AED-treated group prior to irradiation. Next, 5-AED (50 mg/kg per body weight) was subcutaneously injected 24 hours before irradiation. The mice were whole-body irradiated with 10 Gy for the histological examination of jejunal crypt survival and the determination of the villus morphology including crypt depth, crypt size, number of villi, villus height, and length of basal lamina, as well as 5 Gy for the detection of apoptosis. Results: The 5-AED pre-treated group significantly increased the survival of the jejunal crypt, compared to irradiation controls (p<0.05 vs. irradiation controls at 3.5 days after 10 Gy). The evaluation of morphological changes revealed that the administration of 5-AED reduced the radiation-induced intestinal damages such as villus shortening and increased length of the basal lamina of enterocytes (p<0.05 vs irradiation controls on 3.5 day after 10 Gy, respectively). The administration of 5-AED decreased the radiation-induced apoptosis in the intestinal crypt, with no significant difference between the vehicle and 5-AED at 12 hours after 5 Gy. Conclusion: The results of this study suggest that the administration of 5-AED has a protective effect on intestinal damage induced by $\gamma$-irradiation. In turn, these results suggest that 5-AED could be a useful candidate for radioprotection against intestinal mucosal injury following irradiation.

• Korean Journal of Veterinary Research
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• v.39 no.5
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• pp.878-895
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• 1999

Ischemic preconditioing (IPC), i.e., a preliminary brief episode of ischemia and reperfusion, has been shown to reduce the cell damage induced by long ischemia and reperfusion. Superoxide radical which is produced during reperfusion after ischemia was recognized as a factor of the ischemic injury and it is dismutated into $H_2O_2$ and $O_2$ by two types of intracellular superoxide dismutase (SOD), Cu,Zn-SOD in cytoplasm and Mn-SOD in mitochondria. Recently oxygen free radicals are suggested to induce the apoptosis, however mechanism of the reduced apoptosis by ischemic preconditioing was unknown, while many studies performed in mammalian heart indicated that ATP-sensitive $K^+$ ($K_{APT}$) channel activation related with the protective effects. The aim of present study is to investigate 1) whether IP upregulate the Cu,Zn-SOD and Mn-SOD activities, and 2) whether ischemic preconditioning decreases apoptosis via $K_{APT}$ channel activation in timely reperfused skeletal muscle after long ishemia. The experimental animals, Sprague-Dawley rats weighing 250~300g, were divided into 8 groups; 1) control group, 2) ischemic preconditioning only groups, 3) pinacidil, a $K_{APT}$ channel opener, treatment only groups, 4) glibenclamide, a $K_{APT}$ channel blocker, treatment only groups, 5) ischemia groups, 6) ischemia after IPC groups, 7) ischemia and pinacidil treatment groups, and 8) IP and ischemia after glibenclamide pretreatment groups. Animals of the control group were administered with the vehicle (DMSO) alone. Pinacidil (1mg/kg) was administered intravenously 5 minutes after initiation of ischemia, and glibenclamide (0.5mg/kg) was injected intravenously 20 minutes before IPC. In rats that were ischemic preconditioned, the left common iliac artery was occluded for 5 minutes followed by 5 minutes of reperfusion by three times using vascular clamp. Ischemia was done by occlusion of the same artery for 4 hours. The specimens of left rectus femoris muscle were obtained immediately (0 hour), 12 hours, 24 hours after drug administrations, IP or ischemia and reperfusion. The immunoreactivities of SOD and its alterations were observed by use of sheep antihuman Cu,Zn-SOD and Mn-SOD antibodies on the $10{\mu}m$ cryosections. The incidencies of apoptosis were observed by TUNEL methods with in situ apoptosis detection kit on $6{\mu}m$ paraffine section. The results obtained were as follows : 1. After IPC, immunoreactivities of Cu,Zn-SOD mainly in the small-sized fibers were increased by 24 hours, that of Mn-SOD at 0 hour and 24 hours. 2. No significant changes in immunoreactivities of SOD was observed in the pinacidil and in the glibenclamide treatment only groups, and in the ischemia only groups. 3. The immunoreactivities of the Cu,Zn-SOD were increased in the ischemia after IPC groups and the ischemia and pinacidil treatment groups. 4. The immunoreactivities of the Cu,Zn-SOD in the IPC and ischemia after glibenclamide pretreatment groups were not increased except for the 12 hours reperfusion group. But, Mn-SOD immunoreactivities were increased in the 0 hours, 12 hours and 24 hours after reperfusion. 5. In the control group, the IPC only groups, and the pinacidil treatment only groups, negative or trace apoptotic reactions were observed, but the positive apoptotic reaction occured in the glibenclamide treatment groups. 6. Moderate or many number of apoptosis were revealed in the ischemia groups, and also the IPC and ischemia after glibenclamide pretreatment group except for 12 hours and 24 hours after reperfusion. However, the incidence of apoptosis was decreased in the ischemia after IPC groups and in the ischemia and pinacidil treatment groups. 7. There is a coincidence between the increase of Cu,Zn-SOD immunoreactivities and the decrease of apoptosis in the presence of ischemia and reperfusion. These results suggest that the protective effects of ishemic preconditioing may related to the SOD activation, and the ischemic preconditioning decreases the apoptosis partially via $K_{APT}$ channel activation in timely reperfused rat skeletal muscle. It is also suggested that inhibition of apoptosis by IPC may related with the SOD activation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.815-825
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• 2017

Visibility of lane marking is impaired at night or in the rain, which thereby threatens traffic safety. Recently, various studies and technologies have been developed to improve lane marking visibility, such as the extension of lane marking life expectancy (up to 1.5 times), improvement of lane marking equipment productivity, improvement of lane marking visibility by applying phosphorescent material mixed paint. Cost-benefit analysis was performed with considering various benefit items that can be expected. About 45% of traffic accidents would be prevented by improving lane marking visibility. Additionally, accident reduction benefit and traffic congestion reduction benefit were calculated as much as 246 billion KRW per year and 12 billion KRW per year, respectively, by reducing repaint cycle due to enhanced durability. 45 billion KRW per year is expected to reduced with improved lane detection performance of autonomous vehicle. Meanwhile, total increased cost when introducing phosphorescent material mixed paint to 91,195km of nationwide road is identified as 1922 billion KRW per year. However, economic feasibility could not be secured with 0.16 of cost-benefit ratio when applied to the road network as a whole. In case of "Accident Hot Spot" analyzing section window (400m), one or more fatality or two or more injured (one or more injured in case of less than 2 lanes per direction) per year were caused by pavement marking related accident, economic feasibility was secured. In detail, 3.91 of cost-benefit ratio is estimated with comparison of the installation cost for 5,697 of accident hot spot and accident reduction benefit. Some limitations and future research agenda have also been discussed.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

• Journal of Cadastre & Land InformatiX
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• v.52 no.2
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• pp.171-187
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• 2022

Recently, the demand and development for non-face-to-face services are rapidly progressing due to the pandemic caused by the COVID-19, and attention is focused on the metaverse at the center. Entering the era of the 4th industrial revolution, Metaverse, which means a world beyond virtual and reality, combines various sensing technologies and 3D reconstruction technologies to provide various information and services to users easily and quickly. In particular, due to the miniaturization and economic increase of convergence sensors such as unmanned aerial vehicle(UAV) capable of high-resolution imaging and high-precision LiDAR(Light Detection and Ranging) sensors, research on digital-Twin is actively underway to create and simulate real-life twins. In addition, Game engines in the field of computer graphics are developing into metaverse engines by expanding strong 3D graphics reconstuction and simulation based on dynamic operations. This study constructed a mirror-world type metaverse that reflects real-world coordinate-based reality using Unreal Engine 5, a recently announced metaverse engine, with accurate 3D spatial information data of convergence sensors based on unmanned aerial system(UAS) and LiDAR. and then, spatial information contents and simulations for users were produced based on various public data to verify the accuracy of reconstruction, and through this, it was possible to confirm the construction of a more realistic and highly utilizable metaverse. In addition, when constructing a metaverse that users can intuitively and easily access through the unreal engine, various contents utilization and effectiveness could be confirmed through coordinate-based 3D spatial information with high reproducibility.