• International Journal of Aeronautical and Space Sciences
• /
• 제10권2호
• /
• pp.140-147
• /
• 2009

The paper presents the design, development and testing activities of the multi-rotorcraft-based unmanned aerial vehicle at the Center for Unmanned System Studies, Institut Teknologi Bandung (ITB), Indonesia. The multi-rotor system was selected as the design stepping stone for future development of personal aerial vehicle prototypes. A step-by-step design program is conducted to study the technology building blocks and critical issues associated with the design, development and operation of personal aerial vehicles. A number of multi-rotor configurations have been investigated providing basic guidelines for developing a stable unmanned aerial platform. The benefit of the presently selected configuration is highlighted and some preliminary testing results are presented.

• 대한토목학회논문집
• /
• 제32권2D호
• /
• pp.129-136
• /
• 2012

무인항공기(UAV: Unmanned Aerial Vehicle), 헬리콥터, 항공기를 이용하여 실시간 교통자료를 수집하는 항공 기반 차량 검지시스템(ADS: Aerial-Based Vehicle Detection System)에 관한 연구가 미국, 일본, 독일에서 이루어져 왔다. 따라서 본 연구에서는 ADS의 교통자료 수집 시스템으로 활용 가능성을 검토하기 위하여 먼저 ADS에 의하여 수집된 자료가 이미지프로세싱 등 자료추출 기법을 거쳐 통행속도 등 교통정보를 산출할 수 있는 지를 확인하였다. 다음으로는 ADS에 의하여 수집된 자료의 신뢰성 정도가 교통정보 제공에 적합한 지를 확인하였다. 그 결과 ADS는 기존에 상시적으로 실시간 교통정보 제공을 하기 위하여 사용되고 있는 VDS 등을 대체하기에는 기술적 비용적 측면에서 어려움이 있을 것으로 파악되었다. 하지만 재해 발생 등 비반복적 교통상황이 장시간 발생할 경우 비상교통관리대책 등을 세우기 위한 보완적 방안으로 활용할 수 있을 것이다.

• 한국멀티미디어학회논문지
• /
• 제19권8호
• /
• pp.1281-1287
• /
• 2016

This paper presents a method of single image dehazing and feature matching for aerial remote sensing images. In the case of a aerial image, transferring the information of the original image is difficult as the contrast leans by the haze. This also causes that the image contrast decreases. Therefore, a refined transmission map based on a hidden Markov random field. Moreover, the proposed algorithm enhances the accuracy of image matching surface-based features in an aerial remote sensing image. The performance of the proposed algorithm is confirmed using a variety of aerial images captured by a Worldview-2 satellite.

• ETRI Journal
• /
• 제42권5호
• /
• pp.700-711
• /
• 2020

Because of the rapid increase of mobile traffic, flexible broadband supportive unmanned aerial vehicle (UAV)-based 6G mobile networks using free space optical (FSO) links have been recently proposed. Considering the advancements made in UAVs, big data processing, and artificial intelligence precision control technologies, the formation of an additional wireless network based on UAV aerial platforms to assist the existing fixed base stations of the mobile radio access network is considered a highly viable option in the near future. In this paper, a combined time bound optimization scheme is proposed that can adaptively satisfy the control and communication time constraints as well as the processing time constraints in FSO-based 6G UAV aerial networks. The proposed scheme controls the relation between the number of data flows, input data rate, number of worker nodes considering the time bounds, and the errors that occur during communication and data processing. The simulation results show that the proposed scheme is very effective in satisfying the time constraints for UAV control and radio access network services, even when errors in communication and data processing may occur.

• 한국측량학회지
• /
• 제27권2호
• /
• pp.177-186
• /
• 2009

최근 지식기반정보화 사회로 진입하면서 정보의 수집, 처리, 서비스가 디지털 기반에서 이루어지고 있다. 측량분야 역시 전통적인 아날로그 기반에서 디지털 기반으로 전환하고 있으며, 항공사진측량분야에서도 아날로그항공사진측량에서 디지털항공사진측량으로 변화하고 있다. 이에 본 연구에서는 전통적인 아날로그항공사진측량과 UltraCamX를 이용한 디지털항공사진측량의 사진기준점측량 및 블록조정 후 잔차 특성에 대한 비교분석을 실시하였다. 분석 결과, 사진기준점측량에서는 GPS/INS를 탑재한 디지털항공사진측량의 번들조정법이 아날로그항공사진측량의 전통적인 독립모델법보다 우수하였으며 최소의 기준점만을 사용하여도 우수한 결과값을 가짐을 알 수 있었다. 또한 블록조정 후 잔차 특성 분석에서도 디지털항공사진측량이 우수하였다.

• 국제학술발표논문집
• /
• The 6th International Conference on Construction Engineering and Project Management
• /
• pp.608-611
• /
• 2015

The objectives of this research are 1) control of schedule or improvement of management for aerial environment, 2) distribution of responsibility to the parties concerned (factory, material company, construction company, design and engineering, occupancy). The results show the relative priority of the four major items in wall-based apartment buildings and in column-based apartment buildings. An analysis of the parties responsible for improvement based on the IAQ results shows more efforts to improve IAQ are needed in material factories and engineering/design companies.

• Agricultural and Biosystems Engineering
• /
• 제3권1호
• /
• pp.18-28
• /
• 2002

Neural network based models were developed and evaluated for predicting corn yield from aerial images based on 1998 and 1994 image data. The model used images in multi-spectral bands such as R, G, B, and IR (Red, Green, Blue and Infrared). The inputs to the neural network consisted of mean and standard deviation of multispectral bands of the aerial images. Performances of several neural network architectures using back-propagation with momentum were compared. The maximum yield prediction accuracy obtained was 97.81%. The BPNN model prediction accuracy could be enhanced by using more number of observations to the model, other data transformation techniques, or by performing optical calibration of the aerial image.

• 반도체디스플레이기술학회지
• /
• 제17권1호
• /
• pp.88-92
• /
• 2018

Currently drone industry has become one of the fast growing markets and the technology for unmanned aerial vehicles are expected to continue to develop at a rapid rate. Especially small unmanned aerial vehicle systems have been designed and utilized for the various field with their own specific purposes. In these fields the path planning problem to find the shortest path between two oriented points is important. In this paper we introduce a path planning strategy for an autonomous flight of unmanned aerial vehicles through reinforcement learning with self-positioning technique. We perform Q-learning algorithm, a kind of reinforcement learning algorithm. At the same time, multi sensors of acceleraion sensor, gyro sensor, and magnetic are used to estimate the position. For the functional evaluation, the proposed method was simulated with virtual UAV environment and visualized the results. The flight history was based on a PX4 based drones system equipped with a smartphone.

• 한국측량학회지
• /
• 제34권4호
• /
• pp.349-356
• /
• 2016

This study investigates two camera self-calibration approaches, on-site self-calibration and laboratorial self-calibration, both of which are based on self-calibration theory and implemented by using a commercial photogrammetric solution, Agisoft PhotoScan. On-site self-calibration implements camera self-calibration and aerial triangulation by using the same aerial photos. Laboratorial self-calibration implements camera self-calibration by using photos captured onto a patterned target displayed on a digital panel, then conducts aerial triangulation by using the aerial photos. Aerial photos are captured by an unmanned aerial vehicle, and target photos are captured onto a 27in LCD monitor and a 47in LCD TV in two experiments. Calibration parameters are estimated by the two approaches and errors of aerial triangulation are analyzed. Results reveal that on-site self-calibration excels laboratorial self-calibration in terms of vertical accuracy. By contrast, laboratorial self-calibration obtains better horizontal accuracy if photos are captured at a greater distance from the target by using a larger display panel.

• 대한원격탐사학회지
• /
• 제37권4호
• /
• pp.747-761
• /
• 2021

With the increasing application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become an essential engineering technology and has academic research significance. In this paper, a vehicle detection method for aerial images based on the YOLOv4 deep learning algorithm is presented. At present, the most known datasets are VOC (The PASCAL Visual Object Classes Challenge), ImageNet, and COCO (Microsoft Common Objects in Context), which comply with the vehicle detection from UAV. An integrated dataset not only reflects its quantity and photo quality but also its diversity which affects the detection accuracy. The method integrates three public aerial image datasets VAID, UAVD, DOTA suitable for YOLOv4. The training model presents good test results especially for small objects, rotating objects, as well as compact and dense objects, and meets the real-time detection requirements. For future work, we will integrate one more aerial image dataset acquired by our lab to increase the number and diversity of training samples, at the same time, while meeting the real-time requirements.