• Information and Communications Magazine
• /
• v.32 no.5
• /
• pp.73-86
• /
• 2015

본고에서는 먼저 서비스 측면에서 사용자 중심인 모바일인터넷과 기기 간 통신인 사물인터넷 서비스, 단말 측면에서 안경단말기, HMD(Head Mounted Display), 드론, 스마트센서, Connected Car가 어떻게 5G 통신의 견인차 역할을 하는 지 알아보고, 5G서비스와 단말을 수용하는 코어와 무선액세스 네트워크 구조 및 중요 기술에 대해 설명한다. 또한, 5G 주파수 소요량 분석 및 선정을 위한 ITU-R 동향에 대해서도 간략히 알아본다.

• TTA Journal
• /
• s.170
• /
• pp.66-67
• /
• 2017

4차 산업혁명 시대가 빠르게 다가오고 있다. 사물인터넷(IoT)과 인공지능(AI), 자율주행 자동차, 로봇, 드론, 스마트 홈 등이 그것. 4차 산업혁명의 핵심 기술 중 하나는 지능형 첨단센서다. 이 '똑똑한' 센서들 없이는 인공지능도 사물인터넷도 불가능했을 것이다. 첨단 센서 기술이 4차 산업혁명의 기폭제 역할을 하는 셈이다.

• Journal of Internet Computing and Services
• /
• v.20 no.6
• /
• pp.47-54
• /
• 2019

Social dredging of various river facilities, such as dams and agricultural reservoirs currently being constructed, should be done to ensure stable reservoirs. However, it is difficult to find a system that tells the exact amount of water in real-time in a reservoir or dam. These measurements require an automated system to collect and analyze highly accurate data in real time. In this study, we propose a method to measure the amount of water and soil of reservoir in real time through multi-division volume calculation using a drone, and this method can detect sediment conditions in real time and determine the exact timing and scale of dredging.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.4
• /
• pp.639-644
• /
• 2024

Recent developments in the use of drones on battlefields, extending beyond reconnaissance to firepower support, have greatly increased the importance of technologies for early automatic drone identification. In this study, to identify an effective image classification model that can distinguish drones from other aerial targets of similar size and appearance, such as birds and balloons, we utilized a dataset of 3,600 images collected from the internet. We adopted a transfer learning approach that combines the feature extraction capabilities of three pre-trained convolutional neural network models (VGG16, ResNet50, InceptionV3) with an additional classifier. Specifically, we conducted a comparative analysis of the performance of these three pre-trained models to determine the most effective one. The results showed that the InceptionV3 model achieved the highest accuracy at 99.66%. This research represents a new endeavor in utilizing existing convolutional neural network models and transfer learning for drone identification, which is expected to make a significant contribution to the advancement of drone identification technologies.

• Land and Housing Review
• /
• v.14 no.2
• /
• pp.125-135
• /
• 2023

Drones are used in various fields, including land survey, transportation, forestry/agriculture, marine, environment, disaster prevention, water resources, cultural assets, and construction, as their industrial importance and market size have increased. In this study, image data for deep learning was collected using a mavic3 drone capturing images at a shooting altitude was 20 m with ×7 magnification. Swin Transformer and UperNet were employed as the backbone and architecture of the deep learning model. About 800 sheets of labeled data were augmented to increase the amount of data. The learning process encompassed three rounds. The Cross-Entropy loss function was used in the first and second learning; the Tversky loss function was used in the third learning. In the future, when the crack detection model is advanced through convergence with the Internet of Things (IoT) through additional research, it will be possible to detect patching or potholes. In addition, it is expected that real-time detection tasks of drones can quickly secure the detection of pavement maintenance sections.

• Journal of Internet Computing and Services
• /
• v.25 no.1
• /
• pp.147-155
• /
• 2024

Currently, the 4th Industrial Revolution, like other revolutions, is bringing great change and new life to humanity, and in particular, the demand for and use of drones, which can be applied by combining various technologies such as big data, artificial intelligence, and information and communications technology, is increasing. Recently, it has been widely used to carry out dangerous military operations and missions, such as the Russia-Ukraine war and North Korea's reconnaissance against South Korea, and as the demand for and use of drones increases, concerns about the safety and security of drones are growing. Currently, a variety of research is being conducted, such as detection of wireless communication abnormalities and sensor data abnormalities related to drones, but research on real-time detection of threats using radio frequency characteristic data is insufficient. Therefore, in this paper, we conduct a study to determine whether the characteristic data is normal or abnormal signal data by collecting radio frequency signal characteristic data generated while the drone communicates with the ground control system while performing a mission in a HITL(Hardware In The Loop) simulation environment similar to the real environment. proceeded. In addition, we propose an unsupervised learning-based threat detection system and optimal threshold that can detect threat signals in real time while a drone is performing a mission.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.1
• /
• pp.11-17
• /
• 2021

Due to the recent development of the Internet of Things (IoT) and the increase in services using drones, research on IoD is actively underway. Drones have limited computational power and storage size, and when communicating between drones, data is exchanged after proper authentication between entities. Drones must be secure from traceability because they contain sensitive information such as location and travel path. In this paper, we point out a fatal security vulnerability that can be caused by the use of pseudonyms and certificates in existing IoD research and propose a solution.

• KIPS Transactions on Computer and Communication Systems
• /
• v.8 no.12
• /
• pp.277-286
• /
• 2019

The Unmanned Aerial Vehicle (UAV) is one of the most precious inventions of Internet of things (IOT). UAV faces the necessity to charge battery or replace battery from the charging stations during or between services. We propose scheduling algorithms for drone power charging (SADPC). The basic idea of algorithm is considering both a deadline (for increasing deadline miss ratio) and a charging time (for decreasing waiting time) to decide priority on charging station among drones. Our simulation results show that our power charging algorithm for drones are efficient in terms of the deadline miss ratio as well as the waiting time in general in compare to other conventional algorithms (EDF or SJF). Also, we can choose proper algorithms for battery charge scheduling and charge ready battery allocation according to system parameters and user requirements based on our simulation.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.3
• /
• pp.143-149
• /
• 2018

Recently, due to the large-scale damage of natural disasters caused by global climate change, a monitoring system applying remote sensing technology is being constructed in disaster areas. Among remote sensing platforms, the drone has been actively used in the private sector due to recent technological developments, and has been applied in the disaster areas owing to advantages such as timeliness and economical efficiency. This paper deals with the development of a preprocessing system that can map the drone image data in a near-real time manner as a basis for constructing the disaster monitoring system using the drones. For the research purpose, our system is based on the SURF algorithm which is one of the computer vision technologies. This system aims to performs the desired correction through the feature point matching technique between reference images and shot images. The study area is selected as the lower part of the Gahwa River and the Daecheong dam basin. The former area has many characteristic points for matching whereas the latter area has a relatively low number of difference, so it is possible to effectively test whether the system can be applied in various environments. The results show that the accuracy of the geometric correction is 0.6m and 1.7m respectively, in both areas, and the processing time is about 30 seconds per 1 scene. This indicates that the applicability of this study may be high in disaster areas requiring timeliness. However, in case of no reference image or low-level accuracy, the results entail the limit of the decreased calibration.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.18 no.1
• /
• pp.143-149
• /
• 2018

This paper presents an implementation of a streaming system which can forward live video stream to multiple users from a Phantom4, which is a drone made by DJI. We constructed the streaming server on Raspberry Pi 3 board for high mobility. Also We implemented the system so that the video stream can be played on any devices if the HTML5 standard web browser is utilized. We compiled C codes of FFmpeg open sources and installed in the Raspberry Pi3 as the streaming server and developed a Java application to execute as the integrated server that controls the other softwares on the streaming server. Also we developed an Android application which receives the live video stream from the drone and sends the streaming server continuously. The implemented system in this paper can successfully stream the live video on 24 frames per second at the resolution of 148x112 in considering the low hardware throughput of the streaming server.