• 한국지구물리탐사학회:학술대회논문집
• /
• 2011.10a
• /
• pp.151-152
• /
• 2011

• 한국지구물리탐사학회:학술대회논문집
• /
• 2011.10a
• /
• pp.161-162
• /
• 2011

• Korean Journal of Remote Sensing
• /
• v.35 no.6_1
• /
• pp.999-1009
• /
• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.27 no.5
• /
• pp.535-543
• /
• 2024

Studies on estimating the underwater sound source localization using acoustic signal characteristics have mainly been conducted in shallow waters. Recently, technologies for stably and efficiently estimating the underwater sound sources localization using the underwater sound propagation characteristics of the Reliable Acoustic Path(RAP) in deep water areas are being studied. Underwater surveillance technology in deep sea areas is known to have the advantage of having low detection performance variability due to time-varying underwater environments and having a small shadow zone, making it easy to stably detect underwater sound sources and estimate location even from relatively long distance. In this study, we analyzed the sound propagation characteristics based on the actual marine environment in the deep sea of the Korean Peninsula and conducted a study to analyze the estimation performance of sound source depth using the broadband interference pattern of direct wave and sea surface reflected waves radiating from underwater sound sources.

• Journal of Environmental Science International
• /
• v.4 no.2
• /
• pp.121-139
• /
• 1995

Based on the Results of Marine Meteorological and Oceanographical Observations (1966 -1987), the phenomenon of chimney is found as a candidate for the formation of the Japan Sea Proper Water (JSPW). The chimney phenomenon occurs twelve times Inuring 1966∼ 1987. The water types in the chimney denoting the deep convection are similar to those of the JSPW 0∼ 1℃ in potential temperature, 34.0∼34.1 ‰ in salinity and 68∼80 cl/t in potential thermosteric anomaly from the sea surface to the deep layer. The static stabilities in the chimney stations are unstable or neutral. This indicates that the winter time convection occurs. The JSPW sunken from the surface layer of chimney in winter spreads out under the Tsushima Warm Current area, following the isosteric surface of about 76 cl/t in Potential thermosteric anomaly. The formation of the deep water of the JSPW is mainly affected by the cooling of the sea surface than the evaporation of winds because the temperature and the salinity on the isoteric surface of about 76 cl/t in potential thermosteric anomaly ate cold and low The phenomenon of chimney occurred in here and there of the area in the north of 40" 30'N, west of 138" E. This suggests that the deep water of the JSPW is formed not in a limited area but probably in the overall region of the northern open ocean.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
• /
• 2003.10a
• /
• pp.24-29
• /
• 2003

Deep sea water has cold temperature, abundant nutrients and minerals, and good water quality that is pathogen-free and stable. Compared with surface water, deep sea water contains more nutrition salt, such as nitrogen and phosphor. Moreover, if has the good balance of minerals. Because of the ability of the spray drying process to produce a free-flowing power considering of spherical particles with a well-defined size distribution and the rapid drying times for heat-sensitive material, spray drying is attractive for a wide range of applications spray drying is a unique unit operation in which powders are produced from a liquid feed in a single processing step. Key component of the process are atomizer, spray chamber. Design of spray chamber should be based on the atomizer type, the production rate, and the particle size required. Because of the complex processes taking place during spray drying, traditional design method are based on pilot-plant tests and empirical scale-up rules. Modern technique such as CFD have a role to play in design and troubleshooting.

• Ocean and Polar Research
• /
• v.26 no.2
• /
• pp.265-272
• /
• 2004

This study was conducted to investigate the community structure and distributional pattern of meiobenthos in the deep-sea bottom of the Clarion-Clipperton Fracture Zone of northeastern Pacific during July 2001. Examination of sediment samples collected on the eight survey station showed that there were 10 different types of meiobenthos. The most abundant meiobenthic animals were nematodes in all stations. Sarcomastigophorans, benthic harpacticoids were next abundant meiobenthos. Vertical distribution of meiobenthic animals showed the highest individual numbers in the surface sediment layers of 0-1 cm depth and showed more steep decreasing trend as sediment gets deeper on the stations of high latitude located in $16-17^{\circ}N$. Horizontal distribution of meiobenthic animal in the study area within CCFZ showed high densities of meiobenthos at the stations had few manganese nodules on their sediment surface in the site of low latitude. For size distribution analyses showed that animals which fit into the sieve mesh size of 0.063 mm were abundant.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
• /
• 2003.05a
• /
• pp.83-88
• /
• 2003

Recently, deep ocean water (DOW), which is plentiful in the East sea, has been recognized a global resources for 21st century. To develop DOW resource of 300m deep at Gosung sea, the pipeline of about 4 km long is essentially required to establish land based model complex of DOWA techno-park at coastal zone. This study aims to establish design procedure of DOW supplying and utilizing systems, and to complete basic design of every major facilities. To design, various numerical analysis and engineering consideration have been studied by cooperative works for practical use.

• Journal of the Society of Naval Architects of Korea
• /
• v.56 no.3
• /
• pp.211-216
• /
• 2019

In order to utilize the marine environment in various fields such as renewable energy and offshore plant, it is necessary to utilize the far and deep ocean. However, there is still a limit to overcome and utilize the extreme deep-sea environment. Currently, the mooring system, which is the representative position control method of floating structure, has a structural and economic limit to expand the installation range to extreme deep-sea environment. Research has been conducted to utilize wave energy by developing floater using flapping foil as an alternative for station keeping in the deep sea by University of Ulsan. Based on the research, a model test was conducted for application to actual structures. In this study, we investigate how the floating body with passive flapping foils move in regular waves with different periods and study the condition of the model that can maintain its position within a certain range by overcoming the movement.

• Ocean Systems Engineering
• /
• v.10 no.4
• /
• pp.415-434
• /
• 2020

Detecting objects is important for the safe operation of ships, and enables collision avoidance, risk detection, and autonomous sailing. This study proposes a ship detection method from images and videos taken at sea using one of the state-of-the-art deep neural network-based object detection algorithms. A deep learning model is trained using a public maritime dataset, and results show it can detect all types of floating objects and classify them into ten specific classes that include a ship, speedboat, and buoy. The proposed deep learning model is compared to a universal trained model that detects and classifies objects into general classes, such as a person, dog, car, and boat, and results show that the proposed model outperforms the other in the detection of maritime objects. Different deep neural network structures are then compared to obtain the best detection performance. The proposed model also shows a real-time detection speed of approximately 30 frames per second. Hence, it is expected that the proposed model can be used to detect maritime objects and reduce risks while at sea.