• Smart Structures and Systems
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• v.30 no.5
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• pp.501-511
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• 2022

Three-dimensional (3D) models have become crucial for improving civil infrastructure analysis, and they can be used for various purposes such as damage detection, risk estimation, resolving potential safety issues, alarm detection, and structural health monitoring. 3D point cloud data is used not only to make visual models but also to analyze the states of structures and to monitor them using semantic data. This study proposes automating the generation of high-quality 3D point cloud data and removing noise using deep learning algorithms. In this study, large-format aerial images of civilian infrastructure, such as cut slopes and dams, which were captured by drones, were used to develop a workflow for automatically generating a 3D point cloud model. Through image cropping, downscaling/upscaling, semantic segmentation, generation of segmentation masks, and implementation of region extraction algorithms, the generation of the point cloud was automated. Compared with the method wherein the point cloud model is generated from raw images, our method could effectively improve the quality of the model, remove noise, and reduce the processing time. The results showed that the size of the 3D point cloud model created using the proposed method was significantly reduced; the number of points was reduced by 20-50%, and distant points were recognized as noise. This method can be applied to the automatic generation of high-quality 3D point cloud models of civil infrastructures using aerial imagery.

• Korean Journal of Remote Sensing
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• v.40 no.3
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• pp.257-268
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• 2024

Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

• Journal of KIBIM
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• v.10 no.4
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• pp.32-39
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• 2020

In recent years, the construction industry is getting bigger and more complex, so it is becoming difficult to acquire point cloud data for construction equipments and workers. Point cloud data is measured using a drone and MMS(Mobile Mapping System), and the collected point cloud data is used to create a 3D digital map. In particular, the construction site is located at outdoors and there are many irregular terrains, making it difficult to collect point cloud data. For these reasons, adopting a noise reduction algorithm suitable for the characteristics of the construction industry can affect the improvement of the analysis accuracy of digital maps. This is related to various environments and variables of the construction site. Therefore, this study reviewed and analyzed the existing research and techniques on the noise reduction algorithm. And based on the results of literature review, performance evaluation of major noise reduction algorithms was conducted for digital maps of construction sites. As a result of the performance evaluation in this study, the voxel grid algorithm showed relatively less execution time than the statistical outlier removal algorithm. In addition, analysis results in slope, space, and earth walls of the construction site digital map showed that the voxel grid algorithm was relatively superior to the statistical outlier removal algorithm and that the noise removal performance of voxel grid algorithm was superior and the object preservation ability was also superior. In the future, based on the results reviewed through the performance evaluation of the noise reduction algorithm of this study, we will develop a noise reduction algorithm for 3D point cloud data that reflects the characteristics of the construction site.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.208-214
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• 2020

In this study, we have developed a drone magnetic exploration system (proto-type) using a fluxgate magnetic sensor. Hardware of the system consists of a fluxgate magnetometer, an inertial measurement unit (IMU), a GPS, and a communication module. And we have developed monitoring software, which enables it to transmit the measured data to the ground control system (GCS) in real time. The measured magnetic data are finally saved as 1 Hz data after passing through a notch filter and a band-pass filter. For verification of this system, a preliminary test was conducted to check the magnetic responses of a magnetic object first, then the field test was carried out in two iron mines. We tested the developed system on the field test in Pocheon, Gyeonggi and Jeongseon, Gangwon. The magnetic data from the developed drone system was very similar to those from unmanned airship system developed by Korea Institute of Geoscience and Mineral Resources (KIGAM). As a result, preliminary experiment and field test have demonstrated that this system is applicable for outdoor aeromagnetic exploration. It requires more studies to improve filter function and instrument performance to minimize noise in the future.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.195-201
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• 2020

This paper implemented quadcopter-type drone system and proposed the heuristic method for obtaining the optimum gain coefficients in order to minimize the settling time. Control system for quadcopter posture stabilization reads the posture data from accelerator and gyro sensor, revises the original posture data using Mahony filter, and drives 4 DC motors using PID controller. The first step of the proposed method is to obtain the gain coefficients using the Ziegler-Nichols method, and then determine the optimum gain coefficients using the heuristic method at the next 3 steps. The experimental result shows that the maximum overshoot decreases from 44.3 to 29.8 degrees and the settling time decreases from 2.6 to 1.7 seconds compared to the Ziegler-Nichols method. Therefore, we proved that the proposed method works well in quadcopter flight system with high motor noise while reducing trial and error to obtain the optimal PID gain coefficients.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.3
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• pp.15-23
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• 2021

Personal Air Vehicle is expected to be a promising solution to relieve traffic congestion using urban airspace. The development of related technologies such as materials or batteries has been accelerated. In addition, commercial transportation services are being prepared. When fierce competition begins in the PAV market, even technologically superior products will disappear without choices by consumers. Therefore, demand factors should be reflected in PAV development to enhance competitiveness. In the paper, values were estimated for the major technological attributes of PAV. Stated preference data were collected through a survey, and the conjoint method and ordered probit model were adopted. Thereafter, it was confirmed that the value would be high in the order of dual mode, drone-type appearance, and noise reduction. Some R&D strategies were proposed based on this.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.10
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• pp.633-639
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• 2018

High-resolution images using remote sensing (RS) is importance to secure for spatial classification depending on the characteristics of the complex and various factors that make up the river environment. The purpose of this study is to evaluate the accuracy of the classification results and to suggest the possibility of applying the high resolution hyperspectral images obtained by using the drone to perform spatial classification. Hyperspectral images obtained from study area were reduced the dimensionality with PCA and MNF transformation to remove effects of noise. Spatial classification was performed by supervised classifications such as MLC(Maximum Likelihood Classification), SVM(Support Vector Machine) and SAM(Spectral Angle Mapping). In overall, the highest classification accuracy was showed when the MLC supervised classification was used by MNF transformed image. However, it was confirmed that the misclassification was mainly found in the boundary of some classes including water body and the shadowing area. The results of this study can be used as basic data for remote sensing using drone and hyperspectral sensor, and it is expected that it can be applied to a wider range of river environments through the development of additional algorithms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.319-325
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• 2021

A fine dust measurement using drones is becoming an increasingly common technology, and air pollutants can be identified through dust monitoring in partial industrial areas. A station for measuring fine dust provides information at large construction site offices. On the other hand, it was difficult to check the fine dust in the pollutant source accurately. Therefore, the drone took measurements directly after been placed at the site. While measuring fine dust, monitoring noise occurred due to the influence of the drone's down-wind during landing, but the measurements were similar to the numerical value of the grounded pollution source on the height of 30 m. The field applicability to the study area has limitations in periodic updates using satellite images because the terrain was constantly changing due to considerable flattening fieldwork. Therefore, this study implemented a system that can reflect real-time field information through GIS mapping using drones.

• Geophysics and Geophysical Exploration
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• v.25 no.1
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• pp.26-37
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• 2022

Recently, multiple methods using small aircraft for geophysical exploration have been suggested as a result of the development of information and communication technology. In this study, we introduce the hybrid unmanned aircraft electromagnetic system of the Korea Institute of Geosciences and Mineral resources, which is under development. Additionally, data processing and interpretation methods are suggested via the analysis of datasets obtained using the system under development to verify the system. Because the system uses a three-component receiver hanging from a drone, the effects of rotation on the obtained data are significant and were therefore corrected using a rotation matrix. During the survey, the heights of the source and the receiver and their offsets vary in real time and the measured data are contaminated with noise. The noise makes it difficult to interpret the data using the conventional method. Therefore, we developed a recurrent neural network (RNN) model to enable rapid predictions of the apparent resistivity using magnetic field data. Field data noise is included in the training datasets of the RNN model to improve its performance on noise-contaminated field data. Compared with the results of the electrical resistivity survey, the trained RNN model predicted similar apparent resistivities for the test field dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.595-609
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• 2020

In this study, we propose a decentralized mathematical model for predictive control of a system of multi-autonomous unmanned aerial vehicles (UAVs), also known as drones. Being decentralized and autonomous implies that all members make their own decisions and fly depending on the dynamic information received from other unmanned aircraft in the area. We consider a variety of realistic characteristics, including time delay and communication locality. For this flocking flight, we do not possess control for central data processing or control over each UAV, as each UAV runs its collision avoidance algorithm by itself. The main contribution of this work is a mathematical model for stable group flight even in adverse weather conditions (e.g., heavy wind, rain, etc.) by adding Gaussian noise. Two of our proposed variance control algorithms are presented in this work. One is based on a simple biological imitation from statistical physical modeling, which mimics animal group behavior; the other is an algorithm for cooperatively tracking an object, which aligns the velocities of neighboring agents corresponding to each other. We demonstrate the stability of the control algorithm and its applicability in autonomous multi-drone systems using numerical simulations.