• Title/Summary/Keyword: Marine air drone

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Evaluation of Korea Coast Guard Districts Using F-AHP & ARAS Method for Deployment Marine Air Drones (F-AHP법 및 ARAS법을 이용한 해양항공드론 배치를 위한 해양경찰서 관할구역 평가)

  • Jang, Woon-Jae
    • Journal of the Korean Society of Marine Environment & Safety
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    • v.26 no.5
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    • pp.466-473
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    • 2020
  • A marine air drone is a new device that can be used to respond to and prevent marine casualties. Determining the districts where marine air drones can be deployed helps the government decision makers identify efficient policy. The aim of this study is to develop a model using the fuzzy-analytic hierarchy process (F-AHP) and additive ratio assessment (ARAS) method to evaluate appropriate districts for deploying marine air drones. To verify the applicability of the proposed model, a case study was performed with respect to the Korea coast guard (KCG) districts. Since the deployed marine air drones are characterized by a high degree of overlap between the evaluation attributes. the F-AHP is used to determine the weights of identified criteria. The results of this study, show that missing people from the shore was the most important criterion for deployment of the drone. For ranking the local districts of the KCG, the ARAS is applied in the case study with the single goal of 50% reduction in marine casualties. Consequently, the highest priority district was identified as Mokpo, followed by Incheon, Seogwipo, Taean, Wando, Yeosu, Pohang, Tongyeong, Gunsan, Bolyeong, Jeju, Buan, Donghae, Sokcho, Ulsan, Uljin, Busan, Changwon, and Pyeongtaeg.

Artificial Intelligence-Based Harmful Birds Detection Control System (인공지능 기반 유해조류 탐지 관제 시스템)

  • Sim, Hyun
    • The Journal of the Korea institute of electronic communication sciences
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    • v.16 no.1
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    • pp.175-182
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    • 2021
  • The purpose of this paper is to develop a machine learning-based marine drone to prevent the farming from harmful birds such as ducks. Existing drones have been developed as marine drones to solve the problem of being lost if they collide with birds in the air or are in the sea. We designed a CNN-based learning algorithm to judge harmful birds that appear on the sea by maritime drones operating by autonomous driving. It is designed to transmit video to the control PC by connecting the Raspberry Pi to the camera for location recognition and tracking of harmful birds. After creating a map linked with the location GPS coordinates in advance at the mobile-based control center, the GPS location value for the location of the harmful bird is received and provided, so that a marine drone is dispatched to combat the harmful bird. A bird fighting drone system was designed and implemented.

Design, Development and Testing of the Modular Unmanned Surface Vehicle Platform for Marine Waste Detection

  • Vasilj, Josip;Stancic, Ivo;Grujic, Tamara;Music, Josip
    • Journal of Multimedia Information System
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    • v.4 no.4
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    • pp.195-204
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    • 2017
  • Mobile robots are used for years as a valuable research and educational tool in form of available open-platform designs and Do-It-Yourself kits. Rapid development and costs reduction of Unmanned Air Vehicles (UAV) and ground based mobile robots in recent years allowed researchers to utilize them as an affordable research platform. Despite of recent developments in the area of ground and airborne robotics, only few examples of Unmanned Surface Vehicle (USV) platforms targeted for research purposes can be found. Aim of this paper is to present the development of open-design USV drone with integrated multi-level control hardware architecture. Proposed catamaran - type water surface drone enables direct control over wireless radio link, separate development of algorithms for optimal propulsion control, navigation and communication with the ground-based control station. Whole design is highly modular, where each component can be replaced or modified according to desired task, payload or environmental conditions. Developed USV is planned to be utilized as a part of the system for detection and identification of marine and lake waste. Cameras mounted to the USV would record sea or lake surfaces, and recorded video sequences and images would be processed by state-of-the-art computer vision and machine learning algorithms in order to identify and classify marine and lake waste.

Vertical Measurement and Analysis of Meteorological Factors Over Boseong Region Using Meteorological Drones (기상드론을 이용한 보성 지역 기상 인자의 연직 측정 및 분석)

  • Chong, Jihyo;Shin, Seungsook;Hwang, Sung Eun;Lee, Seungho;Lee, Seung-Hyeop;Kim, Baek-Jo;Kim, Seungbum
    • Journal of the Korean earth science society
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    • v.41 no.6
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    • pp.575-587
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    • 2020
  • Meteorological phenomena are observed by the Korea Meteorological Administration in a variety of ways (e.g., surface, upper-air, marine, ocean, and aviation). However, there are limits to the meteorological observation of the planetary boundary layer (PBL) that greatly affects human life. In particular, observations using a sonde or aircraft require significant observational costs in economic terms. Therefore, the goal of this study was to measure and analyze the meteorological factors of the vertical distribution of the see-land breeze among local meteorological phenomena using meteorological drones. To investigate the spatial distribution of the see-land breeze, a same integrated meteorological sensor was mounted on each drone at three different points (seaside, bottom of mountain, and mountainside), including the Boseong tall tower (BTT) at the Boseong Standard Weather Observatory (BSWO) in the Boseong region. Vertical profile observations for air temperature, relative humidity, wind direction, wind speed, and air pressure were conducted up to 400 m every 30 minutes from 1100 LST to 1800 LST on August 4, 2018. The spatial characteristics of meteorological phenomena for temperature, relative humidity, and atmospheric pressure were not shown at the four points. Strong winds (~8 m s-1) were observed from the midpoint (~100 m) at strong solar radiation hour, and in the afternoon the wind direction changed from the upper layer at the inland area to the west wind. It is expected that the analysis results of the lower atmospheric layer observed using the meteorological drone may help to improve the weather forecast more accurately.