• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.71-91
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• 2018

In recent years, application of UAV(Unmanned Aerial Vehicle) to seed sowing and pest control has been actively carried out in the field of agriculture. In this study, UAS(Unmanned Aerial System) is constructed by combining image sensor of various wavelength band and SfM((Structure from Motion) based image analysis technique in UAV. Utilization of UAS based vegetation survey was investigated and the applicability of precision farming was examined. For this purposes, a UAS consisting of a combination of a VIS_RGB(Visible Red, Green, and Blue) image sensor, a modified BG_NIR(Blue Green_Near Infrared Red) image sensor, and a TIR(Thermal Infrared Red) sensor with a wide bandwidth of $7.5{\mu}m$ to $13.5{\mu}m$ was constructed for a low cost UAV. In addition, a total of ten vegetation indices were selected to investigate the chlorophyll, nitrogen and water contents of plants with visible, near infrared, and infrared wavelength's image sensors. The images of each wavelength band for the test area were analyzed and the correlation between the distribution of vegetation index and the vegetation index were compared with status of the previously surveyed vegetation and ground cover. The ability to perform vegetation state detection using images obtained by mounting multiple image sensors on low cost UAV was investigated. As the utility of UAS equipped with VIS_RGB, BG_NIR and TIR image sensors on the low cost UAV has proven to be more economical and efficient than previous vegetation survey methods that depend on satellites and aerial images, is expected to be used in areas such as precision agriculture, water and forest research.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.6
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• pp.790-798
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• 2018

Discrete noise signals from a small scale tactical unmanned aerial vehicle(UAV) propeller were predicted numerically using time domain approach. Two-bladed 29 inch propeller in diameter and 150 kgf in gross weight were used for main parameters of the UAV based on the actual size of the similar scale vehicle. Panel method and Farassat formula A1 were adopted for aerodynamic and aeroacoustic analysis respectively. Time domain signals of both thickness and loading noises were transformed into frequency domain to analyze the discrete noise characteristics. Directivity pattern in a plane perpendicular to the rotating disc plane and attenuation of noise intensity according to double distance were also presented.

• Journal of Cadastre & Land InformatiX
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• v.47 no.1
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• pp.179-190
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• 2017

A terrestrial LiDAR survey was conducted and unmanned aerial vehicle(UAV) images were taken for target cultural properties to present the utilization measures of terrestrial LiDAR and UAV in three-dimensional modeling of cultural properties for the identification of the status and restoration of cultural properties. Then the accuracy of the point clouds generated through this process was compared, an overlap analysis of the 3D model was conducted, and a convergence model was created. According to the results, the modeling with terrestrial LiDAR is more appropriate for precise survey because 3D modeling for the detection of displacement and deformation of cultural properties requires an accuracy of mm units. And UAV model has limitation as the impossibility of detailed expression of parts with sharp unevenness such as cracks of bricks. However, it is found that the UAV model has a wide range of modeling and has the advantage of modeling of real cultural properties. Finally, the convergence model created in this study using the advantages of the terrestrial LiDAR model and the UAV model could be efficiently utilized for the basic data development of cultural properties.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.5
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• pp.675-683
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• 2018

In this paper, we have proposed a design and verification methods of electrical system and power loads for unmaned aeriel vehicles(UAVs) through electrical load analysis. In order to meet a UAV system requirement and electrical system specifications, we have designed an electrical power system for efficient power supply and distribution and have theoretically analyzed the power loads according to the power consumption and power bus design of UAV. Using electrical system rig, the designed electrical power system has been experimentally verified. Also, we have performed several flight tests to verify the UAV electrical system and power loads. It is concluded that the proposed design and verification method of electrical system for UAV system.

• Journal of Information Processing Systems
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• v.13 no.6
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• pp.1640-1651
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• 2017

To make an unmanned aerial vehicle (UAVs) fly in indoor environments, the indoor locations of the UAV are required. One of the approaches to calculate the locations of an UAV in indoor environments is enhanced trilateration using one Bluetooth-based beacon and three or more access points (APs). However, the locations of the UAV calculated by the common chord-based trilateration has errors due to the distance errors of the beacon measured at the multiple APs. This paper proposes a method that corrects the errors that occur in the process of applying the common chord-based trilateration to calculate the locations of an UAV. In the experiments, the results of measuring the locations using the proposed method in an indoor environment was compared and verified against the result of measuring the locations using the common chord-based trilateration. The proposed method improved the accuracy of location measurement by 81.2% compared to the common chord-based trilateration.

• Korean Journal of Soil Science and Fertilizer
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• v.49 no.6
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• pp.743-750
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• 2016

Recently, usage of UAV (Unmanned Aerial Vehicle) has increased in agricultural part. This study was conducted to classify onion and garlic using supervised classification of a fixed-wing UAV (Model : Ebee) images for evaluation of possibility about estimation of onion and garlic cultivation area using UAV images. Aerial images were obtained 11~12 times from study sites in Changryeng-gun and Hapcheon-gun during farming season from 2015 to 2016. The result for accuracy in onion and garlic image classification by R-G-B and R-G-NIR images showed highest Kappa coefficients for the maximum likelihood method. The result for accuracy in onion and garlic classification showed high Kappa coefficients of 0.75~0.97 from DOY 105 to DOY 141, implying that UAV images could be used to estimate onion and garlic cultivation area.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1506-1513
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• 2019

Recently, as the industrial scope of multi-rotor unmanned aerial vehicles(UAV) is greatly expanded, the demands for data collection, processing, and analysis using UAV are also increasing. However, the acoustic data collected by using the UAV is greatly corrupted by the UAV's motor noise and wind noise, which makes it difficult to process and analyze the acoustic data. Therefore, we have studied a method to enhance the target sound from the acoustic signal received through microphones connected to UAV. In this paper, we have extended the densely connected dilated convolutional network, one of the existing single channel acoustic enhancement technique, to consider the inter-channel characteristics of the acoustic signal. As a result, the extended model performed better than the existed model in all evaluation measures such as SDR, PESQ, and STOI.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.11-19
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• 2018

In the Unmanned Aerial Vehicle Line-Of-Sight datalink system, there is a possibility that the communication line is disconnected because line of sight can not be secured by one antenna due to changes in position and posture of the air vehicle. In order to prevent this, both top and bottom of air vehicle are equipped with antennas. At this time, if the signal can be transmitted and received by switching to an antenna advantageous for securing the line of sight, communication disconnection can be minimized. The legacy antenna switching method has disadvantages such that diffraction, fading due to the surface or obstacles, interference and reflection of the air vehicle are not considered, or antenna switching standard is not clear. In this paper, we propose an airborne antenna switching method for improving the performance of UAV LOS datalink system. In the antenna switching method, the performance of each of the upper and lower parts of the mounted antenna according to the position and attitude of the air vehicle is predicted by using the deep learning in an UAV LOS datalink system in which only the antenna except the receiver is duplicated. Simulation using flying test dataset shows that it is possible to switch antennas considering the position and attitude of unmanned aerial vehicle in the datalink system.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.2
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• pp.131-138
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• 2004

Automatic flight control system (AFCS) for a low-cost unmanned aerial vehicle is described in this paper. Development process and block diagram of the AFCS are introduced. The flight control law for longitudinal and lateral channel autopilot is designed using optimization process. In this procedure, the performance index is composed of desired location of closed loop system poles and H$_2$norm of the resultant flight control system. This procedure is applied to the autopilot design of an unmanned target vehicle. Performance of the AFCS is evaluated by processor-in-the-loop simulation test and flight test. These results show that the AFCS has acceptable performance fur low cost UAV.

• Journal of The Korean Society of Grassland and Forage Science
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• v.40 no.4
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• pp.196-202
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• 2020

Rye, whole-crop barley and Italian Ryegrass are major winter forage species in Korea, and yield monitoring of winter forage species is important to improve forage productivity by precision management of forage. Forage monitoring using Unmanned Aerial Vehicle (UAV) has offered cost effective and real-time applications for site-specific data collection. To monitor forage crop by multispectral camera with UAV, we tested four types of vegetation index (Normalized Difference Vegetation Index; NDVI, Green Normalized Difference Vegetation Index; GNDVI, Normalized Green Red Difference Index; NGRDI and Normalized Difference Red Edge Index; NDREI). Field measurements were conducted on paddy field at Naju City, Jeollanam-do, Korea between February to April 2019. Aerial photos were obtained by an UAV system and NDVI, GNDVI, NGRDI and NDREI were calculated from aerial photos. About rye, whole-crop barley and Italian Ryegrass, regression analysis showed that the correlation coefficients between dry matter and NDVI were 0.91~0.92, GNDVI were 0.92~0.94, NGRDI were 0.71~0.85 and NDREI were 0.84~0.91. Therefore, GNDVI were the best effective vegetation index to predict dry matter of rye, wholecrop barley and Italian Ryegrass by UAV system.