• Journal of Advanced Navigation Technology
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• v.18 no.2
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• pp.107-113
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• 2014

In this paper, controller Propose to prevent compressor surge and improve the transient response of the fuel flow control system of turbojet engine. Turbojet engine controller is designed by applying Artificial Neural Network PID control algorithm and make an inference by applying Artificial Neural Network Error Back Propagation Algorithm. To prevent any surge or a flame out event during the engine acceleration or deceleration, the ANN PID controller effectively controls the fuel flow input of the control system. ANN PID results are used as the fuel flow control inputs to prevent compressor surge and flame-out for turbo-jet engine and the controller is designed to converge to the desired speed quickly and safely. Using MATLAB to perform computer simulations verified the performance of the proposed controller. Response characteristics pursuant to the gain were analyzed by simulation.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1525-1535
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• 2008

High Altitude Platform (HAP) based networks deploy network infrastructures of Mobile Base Station (MBS) in a form of Unmanned Aerial Vehicle (UAV) at stratosphere in order to build network configuration. The ultimate goal of HAP based network is a wireless network service for wide area by deploying multiple MBS for such area. In this paper we assume multiple UAVs over designated area and solve the MBS placement and coverage problem by clustering the mobile ground nodes. For this study we assumed area around Cheju island and nearby naval area where multiple mobile and fixed nodes are deployed and requires HAP based networking service. By simulation, visual results of stratospheric MBS placement have been presented. These results include clustering, MBS placement and coverage as well as dynamic reclustering according to the movement of mobile ground nodes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.33-39
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• 2023

Recently, reinforcement learning has been used to improve the communication performance of flying ad-hoc networks (FANETs) and to design mobility models. Mobility model is a key factor for predicting and controlling the movement of unmmaned aerial vehicle (UAVs). In this paper, we designed and analyzed the performance of Q-learning with fourier basis function approximation and Deep-Q Network (DQN) models for optimal path finding in a three-dimensional virtual environment where UAVs operate. The experimental results show that the DQN model is more suitable for optimal path finding than the Q-learning model in a three-dimensional virtual environment.

• Electronics and Telecommunications Trends
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• v.33 no.3
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• pp.70-77
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• 2018

Considering the increased demand for unmanned aircraft systems (UASs) in various commercial and public sectors, it is necessary to integrate a UAS into a national airspace program for manned aircraft operations. For the safe operation of a UAS in a national airspace program, in addition to the detection and avoidance capability at a similar level of "see and avoid" by pilots of manned aircraft, a highly reliable control and non-payload communication (CNPC) link is needed for unmanned aircraft vehicle (UAV) control at a similar level as aircraft control by manned aircraft pilots. In this paper, we analyze the trends in domestic and international standardization activities on the UAS CNPC network technology for the safe integration of UAS into a national airspace program.

• Proceedings of the Korean Institute of Industrial Safety Conference
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• 2003.10a
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• pp.3-16
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• 2003

After a not-widely-known fact is revealed that Japan is a leading country in production and use of industrial unmanned helicopters, a kind of UAV. The voice command system and the autonomous flight control system with a variety of control algorithms including neural network, robust and adaptive control that have been developed in collaboration between Kyoto University and Yamaha Motor Co., and funded by the Ministry of Education and Science of Japan are described in some detail. Both already-proven and promising future applications of the autonomous unmanned helicopters are given.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.3
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• pp.139-148
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• 2020

This paper is concerned with the H₂ attitude controller design for 3 degree of freedom (DOF) Hover systems with an event-triggered mechanism. The 3 DOF Hover system is an embedded platform for unmanned aerial vehicle (UAV) provided by Quanser. The mathematical model of this system is obtained by a linearization around operating points and it is represented as a linear parameter-varying (LPV) model. To save communication network resources, the event-triggered mechanism (ETM) is considered and the performance of the system is guaranteed by the H₂ controller. The stabilization condition is obtained by using Lyapunov-Krasovskii functionals (LKFs) and some useful lemmas. The effectiveness of the proposed method is shown by simulation and experimental results.

• 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.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.71-80
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• 2024

In this study, a proposed ensemble learning technique aims to enhance the semantic segmentation performance of images captured by Unmanned Aerial Vehicles (UAVs). With the increasing use of UAVs in fields such as urban planning, there has been active development of techniques utilizing deep learning segmentation methods for land cover segmentation. The study suggests a method that utilizes prominent segmentation models, namely U-Net, DeepLabV3, and Fully Convolutional Network (FCN), to improve segmentation prediction performance. The proposed approach integrates training loss, validation accuracy, and class score of the three segmentation models to enhance overall prediction performance. The method was applied and evaluated on a land cover segmentation problem involving seven classes: buildings,roads, parking lots, fields, trees, empty spaces, and areas with unspecified labels, using images captured by UAVs. The performance of the ensemble model was evaluated by mean Intersection over Union (mIoU), and the results of comparing the proposed ensemble model with the three existing segmentation methods showed that mIoU performance was improved. Consequently, the study confirms that the proposed technique can enhance the performance of semantic segmentation models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.693-698
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• 2018

This paper proposes a vehicle detection method for parking areas using unmanned aerial vehicles (UAVs) and using YOLOv2, which is a recent, known, fast, object-detection real-time algorithm. The YOLOv2 convolutional network algorithm can calculate the probability of each class in an entire image with a one-pass evaluation, and can also predict the location of bounding boxes. It has the advantage of very fast, easy, and optimized-at-detection performance, because the object detection process has a single network. The sliding windows methods and region-based convolutional neural network series detection algorithms use a lot of region proposals and take too much calculation time for each class. So these algorithms have a disadvantage in real-time applications. This research uses the YOLOv2 algorithm to overcome the disadvantage that previous algorithms have in real-time processing problems. Using Darknet, OpenCV, and the Compute Unified Device Architecture as open sources for object detection. a deep learning server is used for the learning and detecting process with each car. In the experiment results, the algorithm could detect cars in a dense area using UAVs, and reduced overhead for object detection. It could be applied in real time.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.7
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• pp.15-21
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• 2015

Due to the appearance of various mobile terminals like smartphone, smartpad, and smartwatch and tremendous development of WiFi technology, data utilization rate on WiFi network is significantly increasing. As a result, users are wanting to use WiFi network using only a simple identification at a visited place as if they are at their home institute. In this paper, we review the domestic status of eduroam service which supports global extension of wireless network access environment and present the future development perspective of the service in Korea. Besides, we shed light on the current status of WiFi sharing service between domestic universities and propose some methods to facilitate the join of domestic universities in eduroam service.