• Journal of Information Processing Systems
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• v.20 no.4
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• pp.442-457
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• 2024

The issues were studied of an open-source scaling drone imagery platform, called WebODM. It is known that processing drone images has a high demand for resources because of many preprocessing and post-processing steps involved in image loading, orthophoto, georeferencing, texturing, meshing, and other procedures. By default, WebODM allocates one node for processing. We explored methods to expand the platform's capability to handle many processing requests, which should be beneficial to platform designers. Our primary objective was to enhance WebODM's performance to support concurrent users through the use of container technology. We modified the original process to scale the task vertically and horizontally utilizing the Kubernetes cluster. The effectiveness of the scaling approaches enabled handling more concurrent users. The response time per active thread and the number of responses per second were measured. Compared to the original WebODM, our modified version sometimes had a longer response time by 1.9%. Nonetheless, the processing throughput was improved by up to 101% over the original WebODM's with some differences in the drone image processing results. Finally, we discussed the integration with the infrastructure as code to automate the scaling is discussed.

• Journal of Auto-vehicle Safety Association
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• v.16 no.2
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• pp.20-26
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• 2024

The delivery using drones has been attracting attention because it can innovatively reduce the delivery time from the time of order to completion of delivery compared to the current delivery system, and there have been pilot projects conducted for safe drone delivery. However, the current drone delivery system has the disadvantage of limiting the operational efficiency offered by fully autonomous delivery drones in that drones mainly deliver goods to pre-set landing sites or delivery bases, and the final delivery is still made by humans. In this paper, to overcome these limitations, we propose obstacle detection and landing site selection algorithm based on a vision sensor that enables safe drone landing at the delivery location of the product orderer, and experimentally prove the possibility of station-to-door delivery. The proposed algorithm forms a 3D map of point cloud based on simultaneous localization and mapping (SLAM) technology and presents a grid segmentation technique, allowing drones to stably find a landing site even in places without prior information. We aims to verify the performance of the proposed algorithm through streaming data received from the drone.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.600-602
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• 2022

This paper proposes an algorithm that cancels the jamming and interference in the 433 MHz band promoted by the Ministry of Science and ICT at the physical layer level so that the drone operation distance can be extended up to 20 km.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.4
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• pp.543-548
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• 2016

Live-line maintenance for electric power lines is very dangerous because of their ultra-high voltage environments and the risks of falling from heights. Recently, drone technology has been spotlighted due to its maneuverability and stable controllability, and has been being applied to maintenance works in the electric power industry. This paper presents a new type of drone that can be transformable by introducing biomimetics to its mechanism and can run on an overhead ground wire as well as it can fly. Its technical feasibility was confirmed through experiments.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.3
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• pp.486-492
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• 2018

Black carbon concentrations were measured along the altitude at various locations using a drone coupled with a small black carbon detector. The measurement locations are Eunseok Mountain, downtown, four places in KOREATECH campus, Byeongcheon, Cheonan, Chungcheongnam-do, and Chungbu Expressway in Ochang-eup, Cheongju, Chungcheongbuk-do. The average concentration of black carbon measured in Eunseok Mountain was $1.64{\mu}g/m^3$ and the average concentration near the Chungbu Expressway was measured to be $3.86{\mu}g/m^3$. The average concentrations of four places inside campus ranged from 1.37 to $2.67{\mu}g/m^3$. The concentration of black carbon at all places tended to be slightly decreased according to the altitude, but the influence of pollution source, geometry, wind speed, and wind direction are thought to be larger than the effect of altitude. Effect of air flow caused by drone flight on the measurement of black carbon were investigated and it resulted in that the measurement of BC concentration was affected by less than 5%.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.21-31
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• 2018

The result of examination of diagnostic method, which is composed of a combination of a thermal camera and a drone that visually shows the temperature of the object by detecting the infrared rays, for detecting the leakage of earth dam was driven in this research. The drone infrared thermography method was suggested to precise safety diagnosis through direct comparing the two method results of electrical resistivity survey and thermal image survey. The important advantage of the thermal leakage detection method was the simplicity of the application, the quickness of the results, and the effectiveness of the work in combination with the existing diagnosis method.

• Journal of Environmental Science International
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• v.26 no.5
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• pp.685-689
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• 2017

Aquatic plants serve the crucial function of helping to balance water reservoir ecosystem, as they filter and remove major minerals required for algal growth such as nitrogen, ammonia, and nitrates. Aquatic plants provide food, shade, and protection for the aquatic biome in and around the reservoir. Thus, it is important to accurately determine the existence and areal extent of the aquatic plants. In the present study drone-based facilities were used for this purpose. In the Muncheon water reservoir, Gyeongbuk, the Normalized Difference Vegetation Index (NDVI) and Surface Algal Bloom Index (SABI) were used to determine the existence status of the aquatic plants. The data so obtained exhibited reasonable accuracy; drone-based facilities can be used in future to identify the areal extent of aquatic plants.

• Electronics and Telecommunications Trends
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• v.35 no.4
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• pp.81-90
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• 2020

The use of machine learning technologies such as deep and reinforcement learning has proliferated in various domains with the advancement of deep neural network studies. To make the learning successful, both big data acquisition and fast processing are required. However, for some physical world applications such as autonomous drone flight, it is difficult to achieve efficient learning because learning with a premature A.I. is dangerous, cost-ineffective, and time-consuming. To solve these problems, simulation-based approaches can be considered. In this study, we analyze recent trends in drone simulation technologies and compare their features. Subsequently, we introduce Octopus, which is a highly precise and scalable drone simulator being developed by ETRI.

• 한국전산유체공학회:학술대회논문집
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• 2010.05a
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• pp.17-20
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• 2010

An aerial target system is used for the purpose of experimental test and fire training of missile that newly developed and in mass production. Since the target drones of aerial target systems are monopolized by several major countries so that they are selling at a high price. In this paper, we present the CFD simulation results on a new target drone that Kyungan co. ltd is developing with their own technologies. The presented CFD simulation was conducted in the same conditions of a wind tunnel tests and we could obtain the simulation results of the lift and drag values were in errors by less than 15 percent compared to the experiment. The simulation results were used to determine the modified shapes of new prototype target drone that could fly safely.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.168-170
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• 2016

Drone package delivery routing problem is realistic problem used to find efficient route of drone package delivery service. In this paper, we present an approach for solving drone routing problem for package delivery service using two different heuristics algorithms, genetic and nearest neighbor. We implement and analyze both heuristics algorithms for solving the problem efficiently with respect to cost and time. The respective experimental results show that for the range of customers 10 to 50 nearest neighbor and genetic algorithms can reduce the tour length on average by 34% and 40% respectively comparing to FIFO algorithm.