• Journal of Korean Society of Coastal and Ocean Engineers
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• v.35 no.6
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• pp.109-120
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• 2023

The High-Frequency Radar (HFR) is an equipment designed to measure real-time surface ocean currents in broad maritime areas.It emits radio waves at a specific frequency (HF) towards the sea surface and analyzes the backscattered waves to measure surface current vectors (Crombie, 1955; Barrick, 1972).The Seasonde HF Radar from Codar, utilized in this study, determines the speed and location of radial currents by analyzing the Bragg peak intensity of transmitted and received waves from an omnidirectional antenna and employing the Multiple Signal Classification (MUSIC) algorithm. The generated currents are initially considered ideal patterns without taking into account the characteristics of the observed electromagnetic wave propagation environment. To correct this, Antenna Pattern Measurement (APM) is performed, measuring the strength of signals at various positions received by the antenna and calculating the corrected measured vector to radial currents.The APM principle involves modifying the position and phase information of the currents based on the measured signal strength at each location. Typically, experiments are conducted by installing an antenna on a ship (Kim et al., 2022). However, using a ship introduces various environmental constraints, such as weather conditions and maritime situations. To reduce dependence on maritime conditions and enhance economic efficiency, this study explores the possibility of using unmanned aerial vehicles (drones) for APM. The research conducted APM experiments using a high-frequency radar installed at Dangsa Lighthouse in Dangsa-ri, Wando County, Jeollanam-do. The study compared and analyzed the results of APM experiments using ships and drones, utilizing the calculated radial currents and surface current fields obtained from each experiment.

• Journal of Advanced Navigation Technology
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• v.20 no.3
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• pp.232-237
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• 2016

As a possible standardization of wireless local area network (WLAN), IEEE 802.11 LRLP is under discussion in order to support long range and low power (LRLP) communication for internet of things (IoT) including drones and many other IoT devices. In this paper, an efficient adaptive resource unit allocation scheme for uplink multiuser transmission in IEEE 802.11 LRLP networks is proposed. In the proposed scheme, which adopts OFDMA random access based transmission scheme of IEEE 802.11ax, in order to enhance the efficiency of the slotted OFDMA random access, access point (AP) traces the history of the sizes of successfully transmitted uplink data, and adjusts the sizes of resource units for the next uplink multiuser transmission adaptively. Our simulation results corroborate that the proposed scheme significantly improves the system throughput.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.842-849
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• 2022

With the development of deep learning technology, there are many cases of using DNNs in embedded systems such as unmanned vehicles, drones, and robotics. Typically, in the case of an autonomous driving system, it is crucial to run several DNNs which have high accuracy results and large computation amount at the same time. However, running multiple DNNs simultaneously in an embedded system with relatively low performance increases the time required for the inference. This phenomenon may cause a problem of performing an abnormal function because the operation according to the inference result is not performed in time. To solve this problem, the solution proposed in this paper first reduces the computation by applying the Tucker decomposition to DNN models with big computation amount, and then, make DNN models run in parallel as much as possible in the unit of hidden layer inside the GPU. The experimental result shows that the DNN inference time decreases by up to 75.6% compared to the case before applying the proposed technique.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.1
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• pp.97-110
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• 2021

Vegetation index information is an important figure that is used in many fields such as landscape architecture, urban planning, and environment. Vegetation may vary slightly in vegetation vitality depending on photosynthesis and chlorophyll content. In this study, a range of vegetation worth preserving in the Taehwa River water system was determined, and hyperspectral images of drones were acquired (August, October), and the results were presented through DVI(Normalized Defference Vegetation Index), EVI(Enhanced Vegetation Index), PRI(Photochemical Reflectance Index), ARI (Anthocyanin Reflectance Index) index analysis. In addition, field spectral data and VRS-GPS(Virtual Reference System-GPS) surveys were performed to ensure the quality and location accuracy of the spectral band. As a result of the analysis, NDVI and EVI showed low vegetation vitality in October, -0.165 and -0.085, respectively, and PRI and ARI increased to 0.011 and 7.588 in October, respectively. For general vegetation vitality, it was suggested that NDVI and EVI analysis were effectively performed, and PRI and ARI were thought to be effective in analyzing detailed characteristics of plants by spectral band. It is expected that it can be widely used for park design and landscape information modeling by using drone image information construction and vegetation information.

• Journal of Sensor Science and Technology
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• v.32 no.4
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• pp.246-251
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• 2023

Internet of Things (IoT) systems process signals from various sensors using signal processing algorithms suitable for the signal characteristics. To analyze complex signals, these systems usually use signal processing algorithms in the frequency domain, such as fast Fourier transform (FFT), filtering, and short-time Fourier transform (STFT). In this study, we propose a multi-mode sensor signal processor (SSP) accelerator with an FFT-based hardware design. The FFT processor in the proposed SSP is designed with a radix-2 single-path delay feedback (R2SDF) pipeline architecture for high-speed operation. Moreover, based on this FFT processor, the proposed SSP can perform filtering and STFT operation. The proposed SSP is implemented on a field-programmable gate array (FPGA). By sharing the FFT processor for each algorithm, the required hardware resources are significantly reduced. The proposed SSP is implemented and verified on Xilinxh's Zynq Ultrascale+ MPSoC ZCU104 with 53,591 look-up tables (LUTs), 71,451 flip-flops (FFs), and 44 digital signal processors (DSPs). The FFT, filtering, and STFT algorithm implementations on the proposed SSP achieve 185x average acceleration.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.279-286
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• 2021

Recently, We are carrying out a policy of physical distancing of at least 1m from each other to prevent the spreading of COVID-19 disease in public places. In this paper, we propose a method for measuring distances between people in real time and an automation system that recognizes objects that are within 1 meter of each other from stereo images acquired by drones or CCTVs according to the estimated distance. A problem with existing methods used to estimate distances between multiple objects is that they do not obtain three-dimensional information of objects using only one CCTV. his is because three-dimensional information is necessary to measure distances between people when they are right next to each other or overlap in two dimensional image. Furthermore, they use only the Bounding Box information to obtain the exact coordinates of human existence. Therefore, in this paper, to obtain the exact two-dimensional coordinate value in which a person exists, we extract a person's key point to detect the location, convert it to a three-dimensional coordinate value using Stereo Vision and Camera Calibration, and estimate the Euclidean distance between people. As a result of performing an experiment for estimating the accuracy of 3D coordinates and the distance between objects (persons), the average error within 0.098m was shown in the estimation of the distance between multiple people within 1m.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.469-476
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• 2021

An electronic attack (EA) system is an essential weapon system for performing electronic warfare missions that contain signal tracking and jamming against multiple threats using electromagnetic waves, such as air defense radars, wireless command and communication networks, and guided missiles. The combat effectiveness can be maximized, and the survivability of militarily protecting combat power can be enhanced through EA mission operations, such as disabling the functions of multiple threats. The EA system can be used as a radio frequency jamming system to respond to drone attacks on the core infrastructure, such as airports, power plants, and communication broadcasting systems, in the civilian field. This study examined the criteria for classification according to the electronic attack missions of foreign EA systems based on an aviation platform. The foreign R&D trends by those criteria were investigated. Moreover, by analyzing the R&D trends of domestic EA systems and future battlefields in the domestic security environments, this paper proposes technological development plans of EA systems suitable for the future battlefield environments compared to the foreign R&D trends.

• Tunnel and Underground Space
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• v.32 no.6
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• pp.451-463
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• 2022

Whenever a mineshaft accidentally collapses, speedy risk assessment is both required and crucial. But onsite safety diagnosis by humans is reportedly difficult considering the additional risk of collapse of the unstable mineshaft. Generally, drones equipped with high-speed lidar sensors can be used for such inspection. However, the drone technology is restrictively applicable at very shallow depth, failing in mineshafts with depths of hundreds of meters because of the limit of wireless communication and turbulence inside the mineshaft. In previous study, a three-dimensional (3D) scanning system with a single channel lidar was fabricated and operated using towed cable in a mineshaft to a depth of 200 m. The rotation and pendulum movement errors of the measuring unit were compensated for by applying the data of inertial measuring unit and comparing the similarity between the scan data of the adjacent depths (Kim et al., 2020). However, the errors grew with scan depth. In this paper, a multi-channel lidar sensor to obtain a continuous cross-sectional image of the mineshaft from a winch system pulled from bottom upward. In this new approach, within overlapped region viewed by the multi-channel lidar, rotation error was compensated for by comparing the similarity between the scan data at the same depth. The fabricated system was applied to scan 0-165 m depth of the mineshaft with 180 m depth. The reconstructed image was depicted in a 3D graph for interpretation.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.917-926
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• 2021

Lodging rice is one of critical agro-meteorological disasters. In this study, the UAV-based multispectral imageries before and after rice lodging in rice paddy field of Jeollanamdo agricultural research and extension servicesin 2020 was analyzed. The UAV imagery on 14th Aug. includesthe paddy rice without any damage. However, 4th and 19th Sep. showed the area of rice lodging. Multispectral camera of 10 bands from 444 nm to 842 nm was used. At the area of restoration work against lodging rice, the reflectance from 531 nm to 842 nm were decreased in comparison to un-lodging rice. At the area of lodging rice, the reflectance of around 668 nm had small increases. Further, the blue and NIR (Near-Infrared) wavelength had larger. However, according to the types of lodging, the change of reflectance was different. The NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge) shows dome sensitivities to lodging rice, but they were different to types of lodging. These results will be useful to make algorithm to detect the area of lodging rice using a UAV.

• Journal of Korean Society of Forest Science
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• v.112 no.3
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• pp.363-373
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• 2023

Forest biomass surveys are regularly conducted to assess and manage forests as carbon sinks. LiDAR (Light Detection and Ranging), a remote sensing technology, has attracted considerable attention, as it allows for objective acquisition of forest structure information with minimal labor. In this study, we propose a method for estimating overstory and understory biomass in forest stands using backpack laser scanning (BPLS) and unmanned aerial vehicle laser scanning (UAV-LS), and assessed its accuracy. For overstory biomass, we analyzed the accuracy of BPLS and UAV-LS in estimating diameter at breast height (DBH) and tree height. For understory biomass, we developed a multiple regression model for estimating understory biomass using the best combination of vertical structure metrics extracted from the BPLS data. The results indicated that BPLS provided accurate estimations of DBH (R² =0.92), but underestimated tree height (R² =0.63, bias=-5.56 m), whereas UAV-LS showed strong performance in estimating tree height (R² =0.91). For understory biomass, metrics representing the mean height of the points and the point density of the fourth layer were selected to develop the model. The cross-validation result of the understory biomass estimation model showed a coefficient of determination of 0.68. The study findings suggest that the proposed overstory and understory biomass survey methods using BPLS and UAV-LS can effectively replace traditional biomass survey methods.