• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.83-86
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• 2020

본 논문에서는 해군의 향후 군사용 드론 활용 가능방안을 제안한다. AI, 자율주행 등의 4차 산업혁명 기술들과 함께 상용분야에서는 이미 다양한 드론 활용방안들이 제시되고 있으며, 육군은 이에 발맞춰 2018년 10월 드론봇 전투단을 창설하여 운용 중에 있다. 하지만 아직 해군의 군사용 드론 운용 및 활용방안 등에 관한 연구는 미진하며, 따라서 현재 해군의 군용 드론 활용현황을 살펴보고 객체인식, 자율주행 등의 최신기술과 상용활용 사례 등을 군에 접목시켜 앞으로의 활용 가능방안에 대해서 제안하고자 한다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.69-78
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• 2024

Recently, the use of CCTV to prevent crowd accidents has been promoted, but research is needed to compensate for the spatial limitations of CCTV. In this study, pedestrian density was measured using drone footage, and based on a review of existing literature, a threshold of 6.7 people/m² was selected as the cutoff risk level for crowd accidents. In addition, we conducted a preliminary study to determine drone parameters and found that the pedestrian recognition rate was high at a drone altitude of 20 meters and an angle of 60°. Based on a previous study, we selected a target area with a high concentration of pedestrians and measured pedestrian density, which was found to be 0.27~0.30 per m². The study shows it is possible to measure risk levels by determining pedestrian densities in target areas using drone images. We believe drone surveillance will be utilized for crowd safety management in the near future.

• Journal of the Institute of Convergence Signal Processing
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• v.25 no.1
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• pp.39-45
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• 2024

Recently, the paradigm of the aircraft industry, not only domestically but also globally, has been changing significantly starting with the era of the Fourth Industrial Revolution. With the convergence of new technologies such as ICT and AI, the drone market, centered around the military, is expanding its overall services to include the civilian market. Additionally, drones operate by being equipped with batteries, and for product lines that use batteries, lightening the product is one of the critical factors. This is because the lighter the aircraft, the less battery consumption and maximum efficiency. Therefore, recently, composite materials have been used to reduce the weight of the aircraft. To not only reduce weight but also achieve high functionality, it is being applied to most areas such as propellers, airframes, interior materials, floor plates, driving devices, and battery housings, and is emerging as a core technology. In this paper will utilize ceramic fiber composite materials, which have recently emerged for lightweight. It aims to improve noise and strength by targeting propellers, one of the most important factors in drones. In addition, the performance of the propeller developed through the low-noise design will be verified.

• Journal of the Korean Institute of Gas
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• v.25 no.5
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• pp.88-96
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• 2021

Advanced Internet of Things (IoT) technology is being used in various ways for the safety of the energy industry. At the center of safety measures, drones play various roles on behalf of humans. Drones are playing a role in reaching places that are difficult to reach due to large-scale facilities and space restrictions that are difficult for humans to inspect. In this study, the accuracy and completeness of movement of dangerous facilities were tested using drone images, and it was confirmed that the movement recognition accuracy was 100%, the average data analysis accuracy was 95.8699%, and the average completeness was 100%. Based on the experimental results, a future-oriented facility risk analysis system combined with ICT technology was implemented and presented. Additional experiments with diversified conditions are required in the future, and ICT convergence analysis system implementation is required.

• Journal of Advanced Navigation Technology
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• v.28 no.5
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• pp.601-609
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• 2024

In this paper, we present the design and implementation results of a system that classifies drones from other objects using an FMCW (frequency-modulated continuous wave) radar sensor. The proposed system detects various objects through a four-stage signal processing procedure, consisting of FFT, CFAR, clustering, and tracking, using signals received from the radar sensor. Subsequently, a deep learning process is conducted to classify the detected objects as either drones or other objects. To mitigate the high computational demands and extensive memory requirements of deep learning, a BNN (binary neural network) structure was applied, binarizing the CNN (convolutional neural network) operations. The performance evaluation and verification results demonstrated a drone classification accuracy of 89.33%, with a total execution time of 4 ms, confirming the feasibility of real-time operation.

• Journal of IKEEE
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• v.26 no.1
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• pp.104-110
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• 2022

In this paper, we propose a new dataset for AI diagnosis and BLDC motor diagnosis in UAV. In the diagnosis of BLDC motor, PVA(Park's Vector Approach) is difficult to apply due to many ripples of frequency components. However, since the components of ripples are the third harmonics, we propose a method to utilize PVA as circle fitting by applying Savitzky-Golay filter which is excellent for the third harmonics. On the other hand, PVA, a technique to convert from three-phase to two-phase, is always based on the origin during the transformation process. This study demonstrates that the error of the origin and the measured center can be detected and diagnosed in the application process of Circle fitting, and that it can be used as a new data set of AI technology.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.115-122
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• 2024

Multi-object tracking (MOT) is a vital component in understanding the surrounding environments. Previous research has demonstrated that MOT can successfully detect and track surrounding objects. Nonetheless, inaccurate classification of the tracking objects remains a challenge that needs to be solved. When an object approaching from a distance is recognized, not only detection and tracking but also classification to determine the level of risk must be performed. However, considering the erroneous classification results obtained from the detection as the track class can lead to performance degradation problems. In this paper, we discuss the limitations of classification in tracking under the classification uncertainty of the detector. To address this problem, a class update module is proposed, which leverages the class uncertainty estimation of the detector to mitigate the classification error of the tracker. We evaluated our approach on the VisDrone-MOT2021 dataset,which includes multi-class and uncertain far-distance object tracking. We show that our method has low certainty at a distant object, and quickly classifies the class as the object approaches and the level of certainty increases.In this manner, our method outperforms previous approaches across different detectors. In particular, the You Only Look Once (YOLO)v8 detector shows a notable enhancement of 4.33 multi-object tracking accuracy (MOTA) in comparison to the previous state-of-the-art method. This intuitive insight improves MOT to track approaching objects from a distance and quickly classify them.

• Journal of Convergence for Information Technology
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• v.11 no.4
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• pp.83-101
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• 2021

This study is to investigate, using AHP, what technologies should be applied first to Korean SMEs in order to respond to the 4th industrial revolution and change to a smart enterprise. To this end, technologies related to the 4th industrial revolution and smart factory are synthesized, and the classification criteria of Dae-Hoon Kim et al. (2019) are applied, but additional opinions of experts are collected and related technologies are converted to artificial intelligence (AI), Big Data, and Cloud Computing. As a base technology, mobile, Internet of Things (IoT), block chain as hyper-connected technology, unmanned transportation (autonomous driving), robot, 3D printing, drone as a convergence technology, smart manufacturing and logistics, smart healthcare, smart transportation and smart finance were classified as smart industrial technologies. As a result of confirming the priorities for technical use by AHP analysis and calculating the total weight, manufacturing companies have a high ranking in mobile, artificial intelligence (AI), big data, and robots, while service companies are in big data and robots, artificial intelligence (AI), and smart healthcare are ranked high, and in all companies, it is in the order of big data, artificial intelligence (AI), robot, and mobile. Through this study, it was clearly identified which technologies should be applied first in order to respond to the 4th industrial revolution and change to a smart company.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.121-128
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• 2023

To increase the lifetime of the network in wireless sensor networks, energy harvesting from the surrounding environment or wireless power transfer is being used. In addition, to reduce the energy imbalance and increase the amount of data gathered, a method using mobile sink nodes that visit sensor nodes to gather data has been used. In this paper, we propose a technique to reduce the load on the relay node and collect a lot of data evenly in this environment. In the proposed scheme, sensor nodes construct Minimum Depth Trees (MDTs) considering the network environment and energy, and allocate the data collection amount. Simulation results show that the proposed technique effectively suppresses energy depletion and collects more data compared to existing techniques.

• Annual Conference of KIPS
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• 2020.11a
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• pp.842-845
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• 2020

본 연구는 시각-관성 기반의 딥러닝 학습으로 자유분방하게 움직이는 드론의 주행기록을 정확하게 추정하는 것을 목표로 한다. 드론의 비행주행은 드론의 온보드 센서와 조정값을 이용하는 것이 일반적이다. 본 연구에서는 이 온보드 센서 데이터를 학습에 사용하여 비행주행의 위치추정을 실험하였다. 선행연구로써 DeepVO[1]룰 구현하여 KITTI[3] 데이터와 Midair[4] 데이터를 비교, 분석하였다. 3D 좌표면에서의 위치 추정에 선행연구 모델의 한계가 있음을 확인하고 IMU를 Feature로써 사용하였다. 본 모델은 FlowNet[2]을 모방한 CNN 네트워크로부터 Optical Flow Feature에 IMU 데이터를 더해 RNN으로 학습을 진행하였다. 본 연구를 통해 주행기록 예측을 다소 정확히 했다고 할 수 없지만, IMU Feature를 통해 주행기록의 예측이 가능함을 볼 수 있었다. 본 연구를 통해 시각-관성 분야에서 사람의 지식이나 조정이 들어가는 센서를 융합하는 기존의 방식에서 사람의 제어가 들어가지 않는 End-to-End 방식으로 인공지능을 학습했다. 또한, 시각과 관성 데이터를 통해 주행기록을 추정할 수 있었고 시각적으로 그래프를 그려 정답과 얼마나 차이 있는지 확인해보았다.