• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.668-673
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• 2007

We propose the design of the warfighting experiment process for software intensive systems using the intelligent maturity model and suggest the application results of the target searching capability in smart UAV. For this, we design the intelligent maturity model to evaluate the intelligent degree of the software intensive systems considering the domain and intelligent level. Then we classify the IS0/1EC-12207 process and CMMI process as LITO domain for designing the warfighting experiment process, map the classifed process to the five factors of the warfighting experiment and derive the process as warfighting experiment element and phase. Based on the derived process, we design the warfighting experiment process using the IDEF0. Finally we apply the proposed process to the target search capability and suggest the results which are required to develop and acquire the smart UAV.

• Journal of the Korea Institute of Military Science and Technology
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• v.4 no.2
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• pp.249-255
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• 2001

무인자율항체는 자동차, 선박, 잠수함과 같이 인간에 의해 직접 조종되는 유인항체에 인간의 역할을 대신할 수 있는 지능시스템을 배치하여 전체적 혹은 부분적으로 무인화한 이동체를 말한다. 무인자율항체에서 사용되는 소프트웨어는 인식, 사고, 행위와 같은 인간의 지적능력을 내포한 인공지능시스템이어야 한다. 자율무인잠수정, 자율운항선박과 같은 저속무인자율항체는 무인항공기나 무인차량과 같이 빠른 판단과 제어가 요구되는 지능제어시스템과는 다른 특성을 가진다. 저속무인자율항체에서 가장 주목되는 특성은 주위 환경 변화속도와 운항속도에 따른 긴박감의 차이이다. 고속자율항체에서는 제어시스템의 처리속도에, 저속자율항체에서는 제어시스템의 신뢰성에 비중을 둔다. 본 연구에서는 이와 같은 저속무인자율항체의 특성과 기능별 독립성 보장, 반응형 및 인식형 인공지능 기법의 융화 극대화에 촛점을 맞춘 RVC(Reactive Layer - Virtual World - Considerative Layer) 지능시스템 모델을 소개한다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.172-175
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• 2000

무인자율항체는 자동차, 선박, 잠수함과 같이 인간에 의해 직접 조종되는 유인항체에 인간의 역할을 대신할 수 있는 지능시스템을 배치하여 전체적 혹은 부분적으로 무인화한 이동체를 말한다. 무인자율항체에서 사용되는 소프트웨어는 인식, 사고, 행위와 같은 인간의 지적능력을 내포한 인공지능시스템이어야 한다. 자율무인잠수정, 자율운항선박과 같은 저속무인자율항체는 무인항공기나 무인차량과 같이 빠른 판단과 제어가 요구되는 지능제어시스템과는 다른 특성을 가진다. 저속무인자율항체에서 가장 주목되는 특성은 주위 환경 변화속도와 운항속도에 따른 긴박감의 차이이다. 고속자율항체에서는 제어시스템의 처리속도에, 저속자율항체에서는 제어시스템의 신뢰성에 비중을 둔다. 본 연구에서는 이와 같은 저속무인자율항체의 특성과 기능별 독립성 보장, 반응형 및 인식형 인공지능 기법의 융화 극대화에 촛점을 맞춘 RVC(Reactive Layer-Virtual World-Congnitive Layer) 지능시스템 모델을 제안한다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.5
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• pp.1-9
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• 2004

The performance analysis code has been developed for vertical take-off and landing(VTOL) UAV which can be utilized as a trade analysis tool in the pre-conceptual design phase. The UAV requires VTOL capability and high speed cruise performance. The main logic of this performance analysis code is to estimate performance parameters of each mission segment by mission fuel weight iteration. The reliability of this performance analysis code is discussed by comparing the data of existing dual flight mode VTOL UAVs such as Boeing CRW and Bell Tilt Rotor.

• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.35-40
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• 2022

In the agricultural environment, pesticide control requires a high risk of work and a high labor force for farmers. The effectiveness of pesticide control using unmanned aerial vehicles varies according to climate, land type, and characteristics of unmanned aerial vehicles. Therefore, an effective method for pesticide control by unmanned aerial vehicles considering the spraying conditions and environmental conditions is required. In this paper, we propose an efficient pesticide control system based on agricultural unmanned aerial vehicles considering the application conditions and environmental information for each crop. The effectiveness of the proposed model was demonstrated by measuring the drop uniformity of pesticides according to the change in altitude and speed after attaching the sensory paper and measuring the penetration rate of the drug inside the canopy according to the change in crop growth conditions. Experiment result, the closer the height of the UAV is to the ground, the more evenly the crops are sprayed, but for safety reasons, 2m more is suitable, and on average a speed of 2m/s is most suitable for control. The proposed control system is expected to help develop intelligent services based on the use of various unmanned aerial vehicles in agricultural environments.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.4
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• pp.61-68
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• 2010

Appling high technology of aerospace to automobile, so it is able to progress safety which is a goal of future automobile and to approach development of self-control automobile. This is realized dynamic model of airplane at DFCS(Digital Flight Control System). The DFCS calculates control values for self-control flight. If this high technology applies to automobile, then it is able to be maneuvered automobile like UAV's self-control flight. In this paper is reanalyzed automobile dynamic applied load control model used high-tech of airplane. It analyzes riding comfortable according to movement of automobile using the load control model, presents method of solution for improvement riding comfortable and presents example of self-control system used the load control model for self-control driving.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.967-982
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• 2011

This paper presents a FTNCS (Fault-Tolerant Networked Control System) that can tolerate control surface failure and packet delay/loss in an UAV (Unmanned Aerial Vehicle). The proposed method utilizes the benefits of self-diagnosis by smart actuators along with the control allocation technique. A smart actuator is an intelligent actuation system combined with microprocessors to perform self-diagnosis and bi-directional communications. In the event of failure, the smart actuator provides the system supervisor with a set of actuator condition data. The system supervisor then compensate for the effect of faulty actuators by re-allocating redundant control surfaces based on the provided actuator condition data. In addition to the compensation of faulty actuators, the proposed FTNCS also includes an efficient algorithm to deal with network induced delay/packet loss. The proposed algorithm is based on a Lagrange polynomial interpolation method without any mathematical model of the system. Computer simulations with an UAV show that the proposed FTNCS can achieve a fast and accurate tracking performance even in the presence of actuator faults and network induced delays.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.233-240
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• 2024

In this study, we introduce a system for real-time monitoring of field conditions within an industrial complex using a 5G network UAV (: Unmanned Aerial Vehicle). When a monitoring event occurs in a sensor mounted on a UAV (detection of fire, harmful gas, or industrial disaster type human accident), key information from the sensor is transmitted to the UAS (: Unmanned Aerial System) application server. As a result of this information transmission and processing, managers or operators of the Industrial Complex Corporation were able to secure legal basis data for fatal accidents, fires, and detection of harmful gases at sites within the Industrial Complex Corporation through trigger processing for each accident risk situation.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.770-775
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• 2023

Recently, as the amount of digital data increases exponentially, the importance of data processing systems is being emphasized. In this situation, the selection and construction of data processing systems are becoming more important. In this study, the performance of cloud computing (CC), edge computing (EC), and UAV-based intelligent edge computing (UEC) was compared as a way to solve this problem. The characteristics, strengths, and weaknesses of each method were analyzed. In particular, this study focused on real-time large-capacity data processing situations such as corridor monitoring. When conducting the experiment, a specific scenario was assumed and a penalty was given to the infrastructure. In this way, it was possible to evaluate performance in real situations more accurately. In addition, the effectiveness and limitations of each computing method were more clearly understood, and through this, the help was provided to enable more effective system selection.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.353-361
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• 2020

Due to the rapid urbanization, various traffic problems such as traffic jams during commute and regular traffic jams are occurring. In order to solve these traffic problems, it is necessary to quickly and accurately estimate and analyze traffic volume. ITS (Intelligent Transportation System) is a system that performs optimal traffic management by utilizing the latest ICT (Information and Communications Technology) technologies, and research has been conducted to analyze fast and accurate traffic volume through various techniques. In this study, we proposed a deep learning-based vehicle detection method using UAV (Unmanned Aerial Vehicle) video for real-time traffic analysis with high accuracy. The UAV was used to photograph orthogonal videos necessary for training and verification at intersections where various vehicles pass and trained vehicles by classifying them into sedan, truck, and bus. The experiment on UAV dataset was carried out using YOLOv3 (You Only Look Once V3), a deep learning-based object detection technique, and the experiments achieved the overall object detection rate of 90.21%, precision of 95.10% and the recall of 85.79%.