• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1463-1478
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• 2022

Maritime transport accounts for 99.7% of the exports and imports of the Republic of Korea; therefore, developing a vessel monitoring system for efficient operation is of significant interest. Several studies have focused on tracking and monitoring vessel movements based on automatic identification system (AIS) data; however, ships without AIS have limited monitoring and tracking ability. High-resolution optical satellite images can provide the missing layer of information in AIS-based monitoring systems because they can identify non-AIS vessels and small ships over a wide range. Therefore, it is necessary to investigate vessel monitoring and small vessel classification systems using high-resolution optical satellite images. This study examined the possibility of developing ship monitoring systems using Compact Advanced Satellite 500-1 (CAS500-1) satellite images by first training a deep learning model using satellite image data and then performing detection in other images. To determine the effectiveness of the proposed method, the learning data was acquired from ships in the Yellow Sea and its major ports, and the detection model was established using the You Only Look Once (YOLO) algorithm. The ship detection performance was evaluated for a domestic and an international port. The results obtained using the detection model in ships in the anchorage and berth areas were compared with the ship classification information obtained using AIS, and an accuracy of 85.5% and 70% was achieved using domestic and international classification models, respectively. The results indicate that high-resolution satellite images can be used in mooring ships for vessel monitoring. The developed approach can potentially be used in vessel tracking and monitoring systems at major ports around the world if the accuracy of the detection model is improved through continuous learning data construction.

• Korean Journal of Biological Psychiatry
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• v.19 no.3
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• pp.121-127
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• 2012

Objectives : The multimodal telepresence systems have been adopted in a variety of applications, such as telemedicine, space or underwater teleoperation and videoconference. Multimedia, one of the telepresence systems, has been used in various fields including entertainment, education and communication. The degree of subjective telepresence is defined as the probability that a person perceives to be physically in the remote place when he/she experiences a multisensory feedback from the multimedia. The current study aimed to explore the neural mechanism of telepresence related to multisensory feedback in patients with schizophrenia. Methods : Brain activity was measured using functional magnetic resonance imaging while fifteen healthy controls and fifteen patients with schizophrenia were experiencing filmed referential conversation at various distances (1 m, 5 m and 10 m). Correlations between the image contrast values and the telepresence scores were analyzed. Results : Subjective telepresence was not significantly different between the two groups. Some significant correlations of brain activities with the telepresence scores were found in the left postcentral gyrus, bilateral inferior frontal gyri, right fusiform gyrus, and left superior temporal sulcus. There were no main effects of group and distance. Conclusion : These results suggest that patients with schizophrenia experience telepresence as appropriately as healthy people do when exposed to multimedia. Therefore, patients with schizophrenia would have no difficulty in immersing themselves in multimedia which may be used in clinical training therapies.

• Korean Journal of Remote Sensing
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• v.11 no.3
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• pp.129-145
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• 1995

SeaBeam2000, a multibeam echo sounder, is a new generation seabed mapping system of which a single swath covers an angular range of -60.deg. to 60.deg. from the vertical direction with 121 beams. It provides high-density and high-quality bathymetric data along with sidescan acoustic data. The purpose of the research is to develop a system for processing multibeam underwater acoustic and bathymetric data using digital signal processing techniques. Recently obtained multibeam echo sounder data covering a survey area in the East Sea of Korea ($37{\circ}$.00'N to $37{\circ}$30'N and $129{\circ}$40'E to $130{\circ}$30'E) are preliminarily processed using the developed system and reproduced in the raster image format as well as three dimensionally visualized form.

• Journal of the Korean Society of Propulsion Engineers
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• v.19 no.3
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• pp.9-19
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• 2015

Experimental research on characteristics of particle-laden jet by using a coaxial injector was conducted in order to design fuel and oxidizer injectors of the supercavitation underwater vehicle. $1{\mu}m$ and $42{\mu}m$ particles was simultaneously injected to obtain particle and fluid velocity. Small particles($1{\mu}m$) and large particles represent fluid and fuel characteristics respectively. Small particles, which was processed using PIV algorithms, and one for the large particles processed using PTV algorithms. Fluid phase axial velocity increases according to particle loading ratio increases, and particles are located at the outside of the high vorticity region in a mixing layer of a coaxial injector.

• Journal of Ocean Engineering and Technology
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• v.33 no.3
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• pp.289-299
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• 2019

Various sizes of ROVs are being utilized in offshore industrial, scientific, and military applications all around the world. Because of innovative developments in science and technology, image acquisition devices such as sonar devices and cameras have been reduced in size and their performance has been improved. Thus, we can expect better accuracy and higher resolution even in the case of exploration using a small ROV. The purpose of this paper is to prepare a standard procedure for the identification of unidentified hazardous materials found during the National Oceanographic Survey. In this paper, we propose an IDEF (Integrated DEFinition) method modeling technique to identify unidentified targets using a small ROV. In accordance with the proposed procedure, an ROV survey was carried out on target No.16 with a four-ton-class fishing boat as a support vessel on September 18th of 2018 in the sea near Daebu Island. Unidentified targets, which were not known by the multi-beam data obtained from the ship, could be identified as concrete pipes by analyzing the HD camera and high-resolution sonar images acquired by the ROV. The whole proposed procedure could be verified, and the survey with the small ROV required about 10 days to identify the target in one place.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.208-216
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• 2023

The purpose of our study is to design datasets for Artificial Intelligence learning for cold sea fish farming. Salmon is considered one of the most popular fish species among men and women of all ages, but most supplies depend on imports. Recently, salmon farming, which is rapidly emerging as a specialized industry in Gangwon-do, has attracted attention. Therefore, in order to successfully develop salmon farming, the need to systematically build data related to salmon and salmon farming and use it to develop aquaculture techniques is raised. Meanwhile, the catch of pollack continues to decrease. Efforts should be made to improve the major factors affecting pollack survival based on data, as well as increasing the discharge volume for resource recovery. To this end, it is necessary to systematically collect and analyze data related to pollack catch and ecology to prepare a sustainable resource management strategy. Image data was obtained using CCTV and underwater cameras to establish an intelligent aquaculture strategy for salmon and pollock, which are considered representative fish species in Gangwon-do. Using these data, we built learning data suitable for AI analysis and prediction. Such data construction can be used to develop models for predicting the growth of salmon and pollack, and to develop algorithms for AI services that can predict water temperature, one of the key variables that determine the survival rate of pollack. This in turn will enable intelligent aquaculture and resource management taking into account the ecological characteristics of fish species. These studies look forward to achievements on an important level for sustainable fisheries and fisheries resource management.

• Economic and Environmental Geology
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• v.54 no.6
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• pp.717-731
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• 2021

We analyzed the characteristics of the habitat environment for the Seonam study area in Ulsan, the southern shore of the East Sea using bathymetry and seafloor environment data. The depth of the study area ranges from about 0 m to 23 m. In the west of the study area, the water depth is shallow with a gentle slope, and the water depth becomes deeper with a steep slope in the east. Due to the right-lateral strike-slip faults located in the continental margin of the East Sea, the fracture surfaces of the seabed rocks are mainly in the N-S direction, which is similar to the direction of the strike faults. Three seafloor types (conglomeratic-grained sandy, coasre-graiend sandy, fine-grained sandy) and rocky bottom area have been classified according to the analyses of the bathymerty, seafloor image, and surface sediment data. The rocky bottom areas are mainly distributed around Seaoam and in the northern and southern coastal area. But the intermediate zone between Seonam and coastal area has no rocky bottom. This intermediate area is expected to have active sedimentation as seawater way. The sandy sediments are widely distributed throughout the study area. Underwater images and UAV images show that Cnidarians, Brachiopods, Mollusks are mostly dominant in the shallow habitat and various Nacellidae, Mytilidae live on the intertidal zone around Seonam. Annelida and Arthropod are dominant in the sandy sediments. The distribution of marine organism in the study area might be greatly influenced by the seafloor type, the composition and particle size distribution of the seafloor sediments. The analysis of habitat environment mapping with bathymetry, seafloor data and underwater images is supposed to contribute to the study of the structure and function of marine ecosystem.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.552-560
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• 2021

Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1343-1356
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• 2022

Clouds or shadows are the most problematic when monitoring crops using optical satellite images. To reduce this effect, a composite algorithm was used to select the maximum Normalized Difference Vegetation Index (NDVI) for a certain period. This Maximum NDVI Composite (MNC) method reduces the influence of clouds, but since only the maximum NDVI value is used for a certain period, it is difficult to show the phenomenon immediately when the NDVI decreases. As a way to maintain the spectral information of crop as much as possible while minimizing the influence of clouds, a Score-Based Composite (SBC) algorithm was proposed, which is a method of selecting the most suitable pixels by defining various environmental factors and assigning scores to them when compositing. In this study, the Sentinel-2A/B Level 2A reflectance image and cloud, shadow, Aerosol Optical Thickness(AOT), obtainging date, sensor zenith angle provided as additional information were used for the SBC algorithm. As a result of applying the SBC algorithm with a 15-day and a monthly period for Dangjin rice fields and Taebaek highland cabbage fields in 2021, the 15-day period composited data showed faster detailed changes in NDVI than the monthly composited results, except for the rainy season affected by clouds. In certain images, a spatially heterogeneous part is seen due to partial date-by-date differences in the composited NDVI image, which is considered to be due to the inaccuracy of the cloud and shadow information used. In the future, we plan to improve the accuracy of input information and perform quantitative comparison with MNC-based composite algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.597-606
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• 2020

Because the USV(Unmanned Surface Vehicle) is capable of remote control or autonomous navigation at sea, it can secure the superiority of combat power while minimizing human losses in a future combat environment. To plan the technology for the development of USV, the trend analysis of related technology and the selection of promising technology should be preceded, but there has been little research in this area. The purpose of this paper was to measure and evaluate the technology trends quantitatively. For this purpose, this study analyzed the technology trends and selected promising/declining technologies using topic modeling of papers and patent data. As a result of topic modeling, promising technologies include control and navigation, verification/validation, autonomous level, mission module, and application technology, and declining technologies include underwater communication and image processing technology. This study also identified new technology areas that were not included in the existing technology classification, e.g., technology related to research and development of USV, artificial intelligence, launch/recovery, and operation, such as cooperation with manned and unmanned systems. The technology trends and new technology areas identified through this study may be used to derive key technologies related to the development of the USV and establish appropriate R&D policies.