• Title/Summary/Keyword: ocean image

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Development of online drone control management information platform (온라인 드론방제 관리 정보 플랫폼 개발)

  • Lim, Jin-Taek;Lee, Sang-Beom
    • Journal of the Institute of Convergence Signal Processing
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    • v.22 no.4
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    • pp.193-198
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    • 2021
  • Recently, interests in the 4th industry have increased the level of demand for pest control by farmers in the field of rice farming, and the interests and use of agricultural pest control drones. Therefore, the diversification of agricultural control drones that spray high-concentration pesticides and the increase of agricultural exterminators due to the acquisition of national drone certifications are rapidly developing the agricultural sector in the drone industry. In addition, as detailed projects, an effective platform is required to construct large-scale big data due to pesticide management, exterminator management, precise spraying, pest control work volume classification, settlement, soil management, prediction and monitoring of damages by pests, etc. and to process the data. However, studies in South Korea and other countries on development of models and programs to integrate and process the big data such as data analysis algorithms, image analysis algorithms, growth management algorithms, AI algorithms, etc. are insufficient. This paper proposed an online drone pest control management information platform to meet the needs of managers and farmers in the agricultural field and to realize precise AI pest control based on the agricultural drone pest control processor using drones and presented foundation for development of a comprehensive management system through empirical experiments.

A Comparative Study on the Object Detection of Deposited Marine Debris (DMD) Using YOLOv5 and YOLOv7 Models (YOLOv5와 YOLOv7 모델을 이용한 해양침적쓰레기 객체탐지 비교평가)

  • Park, Ganghyun;Youn, Youjeong;Kang, Jonggu;Kim, Geunah;Choi, Soyeon;Jang, Seonwoong;Bak, Suho;Gong, Shinwoo;Kwak, Jiwoo;Lee, Yangwon
    • Korean Journal of Remote Sensing
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    • v.38 no.6_2
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    • pp.1643-1652
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    • 2022
  • Deposited Marine Debris(DMD) can negatively affect marine ecosystems, fishery resources, and maritime safety and is mainly detected by sonar sensors, lifting frames, and divers. Considering the limitation of cost and time, recent efforts are being made by integrating underwater images and artificial intelligence (AI). We conducted a comparative study of You Only Look Once Version 5 (YOLOv5) and You Only Look Once Version 7 (YOLOv7) models to detect DMD from underwater images for more accurate and efficient management of DMD. For the detection of the DMD objects such as glass, metal, fish traps, tires, wood, and plastic, the two models showed a performance of over 0.85 in terms of Mean Average Precision (mAP@0.5). A more objective evaluation and an improvement of the models are expected with the construction of an extensive image database.

Semantic Segmentation of the Habitats of Ecklonia Cava and Sargassum in Undersea Images Using HRNet-OCR and Swin-L Models (HRNet-OCR과 Swin-L 모델을 이용한 조식동물 서식지 수중영상의 의미론적 분할)

  • Kim, Hyungwoo;Jang, Seonwoong;Bak, Suho;Gong, Shinwoo;Kwak, Jiwoo;Kim, Jinsoo;Lee, Yangwon
    • Korean Journal of Remote Sensing
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    • v.38 no.5_3
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    • pp.913-924
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    • 2022
  • In this paper, we presented a database construction of undersea images for the Habitats of Ecklonia cava and Sargassum and conducted an experiment for semantic segmentation using state-of-the-art (SOTA) models such as High Resolution Network-Object Contextual Representation (HRNet-OCR) and Shifted Windows-L (Swin-L). The result showed that our segmentation models were superior to the existing experiments in terms of the 29% increased mean intersection over union (mIOU). Swin-L model produced better performance for every class. In particular, the information of the Ecklonia cava class that had small data were also appropriately extracted by Swin-L model. Target objects and the backgrounds were well distinguished owing to the Transformer backbone better than the legacy models. A bigger database under construction will ensure more accuracy improvement and can be utilized as deep learning database for undersea images.

Analysis of Influence Factors on the Satisfaction of Viewers on China's CCTV-9 Channel (중국 CCTV-9 채널 시청자의 프로그램 관람 만족도 결정요인 분석)

  • Guo, Yuan;Wang, Zhifeng
    • Journal of Korea Entertainment Industry Association
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    • v.15 no.8
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    • pp.107-116
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    • 2021
  • In recent years, the research on audience satisfaction after watching programs has been carried out in various fields. However, there is no precedent for the study of simply analyzing the influencing factors of audience satisfaction with the newly established CCTV-9 channel. For CCTV-9, how to explore the strategy of industrial development based on the needs of the audience in the era of big data is a very important part. This article exploratively focuses on the influencing factors related to CCTV-9 audience satisfaction. Using questionnaires, 101 samples of the satisfaction with the channel of men and women of different ages, education backgrounds, majors, and incomes were collected to test, and 9 hypotheses were tentatively proposed as relevant influencing factors of channel satisfaction. Through empirical analysis, this research searches for the determinants. The reliability and validity of the measurement were properly analyzed, and all hypotheses were statistically tested. The empirical results show that: subject matter, program format, program scheduling, program broadcast time, channel advertising, simulcast series of documentaries, diversified communication platforms, brand image packaging and audience satisfaction are significantly positively correlated.

Evaluation of Debonding Defects in Railway Concrete Slabs Using Shear Wave Tomography (전단파 토모그래피를 활용한 철도 콘크리트 궤도 슬래브 층분리 결함 평가)

  • Lee, Jin-Wook;Kee, Seong-Hoon;Lee, Kang Seok
    • Journal of the Korea institute for structural maintenance and inspection
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    • v.26 no.3
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    • pp.11-20
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    • 2022
  • The main purpose of this study is to investigate the applicability of the shear wave tomography technology as a non-destructive testing method to evaluate the debonding between the track concrete layer (TCL) and the hydraulically stabilized based course (HSB) of concrete slab tracks for the Korea high-speed railway system. A commercially available multi-channel shear wave measurement device (MIRA) is used to evaluate debonding defects in full-scaled mock-up test specimen that was designed and constructed according to the Rheda 200 system. A part of the mock-up specimen includes two artificial debonding defects with a length and a width of 400mm and thicknesses of 5mm and 10mm, respectively. The tomography images obtained by a MIRA on the surface of the concrete specimens are effective for visualizing the debonding defects in concrete. In this study, a simple image processing method is proposed to suppress the noisy signals reflected from the embedded items (reinforcing steel, precast sleeper, insert, etc.) in TCL, which significantly improves the readability of debonding defects in shear wave tomography images. Results show that debonding maps constructed in this study are effective for visualizing the spatial distribution and the depths of the debondiing defects in the railway concrete slab specimen.

A Study on the Design Improvement of Street Facilities in Jeollabuk-do Province (전라북도 가로시설물의 디자인 제고를 위한 연구)

  • Kim, Sang Hyun;Kim, Hong Bae
    • Journal of the Korean Institute of Rural Architecture
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    • v.25 no.2
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    • pp.1-8
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    • 2023
  • This study reveals the absence of standard design guidelines by region through investigation and analysis centered on public design cases by region in Jeollabuk-do and design elements that can reflect the integration, identity, and diversity of public design in each region by five regions. Through this, the following conclusions could be obtained. First, to improve the quality of street facilities in Jeollabuk-do, the design elements (design motif, color, pattern) applicable to the standard design were analyzed by dividing them into five regions. As a design motif, it was possible to extract patterns containing straight lines, sophistication, dignity, and smartness. In the Northeast region, it is comfortable with the motif of the mountain ridge reflecting geographical characteristics, and it can be extracted elements that contain warm and natural colors. In the southeastern region, patterns that reflect design elements were extracted by applying safe, lively, and peaceful colors with the design motif of curves that blend nature and agriculture. In the southwestern region, design pattern elements that highlight nature, history, and culture were extracted with various cultural assets and natural greenery as motifs. Lastly, in the Saemangeum region, the ocean flow and greenery could be used as a design motif to reflect a positive, clear, future-oriented image in the design spot zones by region. Second, based on the standard design elements (design motive, color, pattern) by region extracted for the standard design development of street facilities in each region in Jeollabuk-do, an integrated zone(Form, structure, material, color, functional element) to which regional design guidelines can be applied. Third, an integrated zone (form, structure, material, color, functional elements) was composed. In addition, design spot zones (patterns and colors in city and county units) that can contain the diversity and identity of each region were designated. By designating design spot zones (patterns and colors in city and county units) that can contain the diversity and identity of each region, standard design development plans (integrated pillars, jaywalking prevention fences, roundabouts (urban type, rural type), street trees) Eight standard designs, including protective covers, street planters, flat benches, light benches, visual media for user guidance, and parking zones for personal mobile devices) were presented.

Comprehensive analysis of deep learning-based target classifiers in small and imbalanced active sonar datasets (소량 및 불균형 능동소나 데이터세트에 대한 딥러닝 기반 표적식별기의 종합적인 분석)

  • Geunhwan Kim;Youngsang Hwang;Sungjin Shin;Juho Kim;Soobok Hwang;Youngmin Choo
    • The Journal of the Acoustical Society of Korea
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    • v.42 no.4
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    • pp.329-344
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    • 2023
  • In this study, we comprehensively analyze the generalization performance of various deep learning-based active sonar target classifiers when applied to small and imbalanced active sonar datasets. To generate the active sonar datasets, we use data from two different oceanic experiments conducted at different times and ocean. Each sample in the active sonar datasets is a time-frequency domain image, which is extracted from audio signal of contact after the detection process. For the comprehensive analysis, we utilize 22 Convolutional Neural Networks (CNN) models. Two datasets are used as train/validation datasets and test datasets, alternatively. To calculate the variance in the output of the target classifiers, the train/validation/test datasets are repeated 10 times. Hyperparameters for training are optimized using Bayesian optimization. The results demonstrate that shallow CNN models show superior robustness and generalization performance compared to most of deep CNN models. The results from this paper can serve as a valuable reference for future research directions in deep learning-based active sonar target classification.

Development of a Program for Calculating Typhoon Wind Speed and Data Visualization Based on Satellite RGB Images for Secondary-School Textbooks (인공위성 RGB 영상 기반 중등학교 교과서 태풍 풍속 산출 및 데이터 시각화 프로그램 개발)

  • Chae-Young Lim;Kyung-Ae Park
    • Journal of the Korean earth science society
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    • v.45 no.3
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    • pp.173-191
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    • 2024
  • Typhoons are significant meteorological phenomena that cause interactions among the ocean, atmosphere, and land within Earth's system. In particular, wind speed, a key characteristic of typhoons, is influenced by various factors such as central pressure, trajectory, and sea surface temperature. Therefore, a comprehensive understanding based on actual observational data is essential. In the 2015 revised secondary school textbooks, typhoon wind speed is presented through text and illustrations; hence, exploratory activities that promote a deeper understanding of wind speed are necessary. In this study, we developed a data visualization program with a graphical user interface (GUI) to facilitate the understanding of typhoon wind speeds with simple operations during the teaching-learning process. The program utilizes red-green-blue (RGB) image data of Typhoons Mawar, Guchol, and Bolaven -which occurred in 2023- from the Korean geostationary satellite GEO-KOMPSAT-2A (GK-2A) as the input data. The program is designed to calculate typhoon wind speeds by inputting cloud movement coordinates around the typhoon and visualizes the wind speed distribution by inputting parameters such as central pressure, storm radius, and maximum wind speed. The GUI-based program developed in this study can be applied to typhoons observed by GK-2A without errors and enables scientific exploration based on actual observations beyond the limitations of textbooks. This allows students and teachers to collect, process, analyze, and visualize real observational data without needing a paid program or professional coding knowledge. This approach is expected to foster digital literacy, an essential competency for the future.

Development of a Prototype System for Aquaculture Facility Auto Detection Using KOMPSAT-3 Satellite Imagery (KOMPSAT-3 위성영상 기반 양식시설물 자동 검출 프로토타입 시스템 개발)

  • KIM, Do-Ryeong;KIM, Hyeong-Hun;KIM, Woo-Hyeon;RYU, Dong-Ha;GANG, Su-Myung;CHOUNG, Yun-Jae
    • Journal of the Korean Association of Geographic Information Studies
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    • v.19 no.4
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    • pp.63-75
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    • 2016
  • Aquaculture has historically delivered marine products because the country is surrounded by ocean on three sides. Surveys on production have been conducted recently to systematically manage aquaculture facilities. Based on survey results, pricing controls on marine products has been implemented to stabilize local fishery resources and to ensure minimum income for fishermen. Such surveys on aquaculture facilities depend on manual digitization of aerial photographs each year. These surveys that incorporate manual digitization using high-resolution aerial photographs can accurately evaluate aquaculture with the knowledge of experts, who are aware of each aquaculture facility's characteristics and deployment of those facilities. However, using aerial photographs has monetary and time limitations for monitoring aquaculture resources with different life cycles, and also requires a number of experts. Therefore, in this study, we investigated an automatic prototype system for detecting boundary information and monitoring aquaculture facilities based on satellite images. KOMPSAT-3 (13 Scene), a local high-resolution satellite provided the satellite imagery collected between October and April, a time period in which many aquaculture facilities were operating. The ANN classification method was used for automatic detecting such as cage, longline and buoy type. Furthermore, shape files were generated using a digitizing image processing method that incorporates polygon generation techniques. In this study, our newly developed prototype method detected aquaculture facilities at a rate of 93%. The suggested method overcomes the limits of existing monitoring method using aerial photographs, but also assists experts in detecting aquaculture facilities. Aquaculture facility detection systems must be developed in the future through application of image processing techniques and classification of aquaculture facilities. Such systems will assist in related decision-making through aquaculture facility monitoring.

Study on sea fog detection near Korea peninsula by using GMS-5 Satellite Data (GMS-5 위성자료를 이용한 한반도 주변 해무탐지 연구)

  • 윤홍주
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.4 no.4
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    • pp.875-884
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    • 2000
  • Sea fog/stratus is very difficult to detect because of the characteristics of air-sea interaction and locality ,and the scantiness of the observed data from the oceans such as ships or ocean buoys. The aim of our study develops new algorism for sea fog detection by using Geostational Meteorological Satellite-5(GMS-5) and suggests the technics of its continuous detection. In this study, atmospheric synoptic patterns on sea fog day of May, 1999 are classified; cold air advection type(OOUTC, May 10, 1999) and warm air advection type(OOUTC, May 12, 1999), respectively, and we collected two case days in order to analyze variations of water vapor at Osan observation station during May 9-10, 1999.So as to detect daytime sea fog/stratus(OOUTC, May 10, 1999), composite image, visible accumulated histogram method and surface albedo method are used. The characteristic value during day showed A(min) .20% and DA < 10% when visible accumulated histogram method was applied. And the sea fog region which is detected is similar in composite image analysis and surface albedo method. Inland observation which visibility and relative humidity is beneath 1Km and 80%, respectively, at OOUTC, May 10,1999; Poryoung for visble accumulated histogram method and Poryoung, Mokp'o and Kangnung for surface albedo method. In case of nighttime sea fog(18UTC, May 10, 1999), IR accumulated histogram method and Maximum brightness temperature method are used, respectively. Maxium brightness temperature method dectected sea fog better than IR accumulated histogram method with the charateristic value that is T_max < T_max_trs, and then T_max is beneath 700hPa temperature of GDAPS(Global Data Assimilation and Prediction System). Sea fog region which is detected by Maxium brighness temperature method was similar to the result of National Oceanic and Atmosheric Administratio/Advanced Very High Resolution Radiometer (NOAA/AVHRR) DCD(Dual Channel Difference), but usually visibility and relative humidity are not agreed well in inland.

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