• Journal of Advanced Navigation Technology
• /
• v.20 no.6
• /
• pp.604-611
• /
• 2016

Drone, which refers to unmanned aerial vehicles (UAV), industries are improving rapidly and exceeding existing level of remote controlled aircraft models. Also, they are applying automation and cloud network technology. Recently, the ability of drones can bring serious threats to public safety such as explosives and unmanned aircraft carrying hazardous materials. On the purpose of reducing these kinds of threats, it is necessary to detect these illegal drones, using acoustic feature extraction and classifying technology. In this paper, we introduce sound feature vector extraction method by harmonic feature extraction method (HLA). Feature vector extraction method based on HLA make it possible to distinguish drone sound, extracting features of sound data. In order to assess the performance of distinguishing sounds which exists in outdoor environment, we analyzed various sounds of things and real drones, and classified sounds of drone and others as simulation of each sound source.

• KEPCO Journal on Electric Power and Energy
• /
• v.2 no.4
• /
• pp.543-548
• /
• 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
• /
• v.34 no.3
• /
• pp.486-492
• /
• 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%.

• Proceedings of the Korean Institute of Building Construction Conference
• /
• 2023.11a
• /
• pp.261-262
• /
• 2023

Drone is a suitable equipment for capturing images of cracks at construction sites based on its efficient mobility and high-resolution image acquisition capabilities. In this study, drone was used to acquire indoor construction sites framework images and deep learning technology was applied to detect cracks and measure width, and size. Finally, the usability of the process was verified based on the indoor crack monitoring system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.33 no.6
• /
• pp.605-614
• /
• 2015

Drone imaging, which is more cost-effective and controllable compared to airborne LiDAR, requires a low-cost camera and is used for capturing color images. From the overlapped color images, we produced two high-resolution digital surface models over different test areas. After segmentation, we performed tree identification according to the method proposed by , and computed the tree height and the canopy crown size. Compared with the field measurements, the computed results for the tree height in test area 1 (coniferous trees) were found to be accurate, while the results in test area 2 (deciduous coniferous trees) were found to be underestimated. The RMSE of the tree height was 0.84 m, and the width of the canopy crown was 1.51 m in test area 1. Further, the RMSE of the tree height was 2.45 m, and the width of the canopy crown was 1.53 m in test area 2. The experiment results validated the use of drone images for the extraction of a tree structure.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.8
• /
• pp.3862-3879
• /
• 2019

More and more problems for public security have occurred due to the limited solutions for drone detection especially for micro-drone in long range conditions. This paper aims at dealing with drones detection using a radar system. The radio frequency (RF) signals emitted by a controller can be acquired using the radar, which are usually too weak to extract. To detect the drone successfully, the static clutters and linear trend terms are suppressed based on the background estimation algorithm and linear trend suppression. The principal component analysis technique is used to classify the noises and effective RF signals. The automatic gain control technique is used to enhance the signal to noise ratios (SNR) of RF signals. Meanwhile, the empirical mode decomposition (EMD) based wavelet transform (WT) is developed to decrease the influences of the Gaussian white noises. Then, both the azimuth information between the drone and radar and the bandwidth of the RF signals are acquired based on the statistical analysis algorithm developed in this paper. Meanwhile, the proposed accumulation algorithm can also provide the bandwidth estimation, which can be used to make a decision accurately whether there are drones or not in the detection environments based on the probability theory. The detection performance is validated with several experiments conducted outdoors with strong interferences.

• Aerospace Engineering and Technology
• /
• v.13 no.1
• /
• pp.166-173
• /
• 2014

Recently, small quadcopters have been widely used in various areas ranging from military to entertainment applications because interest in the quadcopter increases. Especially, the research on swarm flight which control quadcopters simultaneously without any collision can increase success probability of a important mission. In addition the swarm flight can be applied for demonstrating choreographed aerial maneuvers such as dancing and playing musical instruments. In this paper, we introduce multiple AR.Drone control system based on motion capture for indoor environment in which quadcopters can recognize current position each other and perform scenario based mission.

• International Journal of Aeronautical and Space Sciences
• /
• v.13 no.4
• /
• pp.468-473
• /
• 2012

Cost is an important aspect in designing a target drone, however the poor performance of low cost IMU, GPS, and microcontrollers prevents the use of complex algorithms, such as ARS, or INS/GPS to estimate attitude angles. We propose an autopilot which uses rate gyro and GPS only for a target drone to follow a prescribed path for anti-aircraft training. The autopilot consists of an altitude hold, roll hold, and path following controller. The altitude hold controller uses vertical speed output from a GPS to improve phugoid damping. The roll hold controller feeds back yaw rate after filtering the dutch roll oscillation to estimate the roll angle. The path following controller operates as an outer loop of the altitude and roll hold controllers. A 6-DOF simulation showed that the proposed autopilot guides the target drone to follow a prescribed path well from the view point of anti-aircraft gun training.

• Journal of Sensor Science and Technology
• /
• v.27 no.4
• /
• pp.209-215
• /
• 2018

Recent research in animal behavior has shown that gradient information plays an important role in finding food and home. It is also important in optimization of performance because it indicates how the inputs should be adjusted for maximization/minimization of a performance index. We introduce perturbation as an additional input to obtain gradient information. Unlike the typical approach of calculating the gradient from the derivative, the proposed processing is very robust to noise since it is performed as a summation. Experimental results prove the validity of the process of spatial gradient acquisition. Quantitative indices for measuring the effect of the amplitude and the frequency are developed based on linear regression analysis. Drones are very useful for environmental monitoring and an autonomous path planning is required for unstructured environment. Guiding the drone for finding the origin of the interested physical property is done by estimating the gradient of the sensed value and generating the drone trajectories in the direction which maximizes the sensed value. Simulation results show that the proposed method can be successfully applied to identify the source of the physical quantity of interest by utilizing it for path planning of an autonomous drone in 3D environment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.2
• /
• pp.73-79
• /
• 2021

Recently there are growing interests on the energy conservation and emission reduction. In the fields of architecture and civil engineering, the energy monitoring of structures is required to response the energy issues. In perspective of thermal monitoring, thermal images gains popularity for their rich visual information. With the rapid development of the drone platform, aerial thermal images acquired using drone can be used to monitor not only a part of structure, but wider coverage. In addition, the stereo photogrammetric process is expected to generate 3D point cloud with thermal information. However thermal images show very poor in resolution with narrow field of view that limit the use of drone-based thermal photogrammety. In the study, we aimed to generate 3D thermal point cloud using visible and thermal images. The visible images show high spatial resolution being able to generate precise and dense point clouds. Then we extract thermal information from thermal images to assign them onto the point clouds by precisely establishing photogrammetric collinearity between the point clouds and thermal images. From the experiment, we successfully generate dense 3D thermal point cloud showing 3D thermal distribution over the building structure.