• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.19 no.2
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• pp.131-146
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• 2014

High-resolution seismic profiles coupled with sediment sampling were analyzed to investigate the acoustic characters and distribution patterns of the late Holocene sediments in Gamak Bay of the South Sea, Korea. The mean grain size of surficial sediment lies around $6.3{\sim}9.7{\Phi}$. Sediments in the bay consist of silt and clay with progressive decrease toward the inner bay. The seismic sedimentary sequence overlying the acoustic basement can be divided into two sedimentary units (GB I and II) by a prominent mid-reflector (Maximum Flooding Surface; MFS). The acoustic basement occurs at the depth between 20 m and 40 m below the sea-level and deepens gradually southward. The GB I, mostly occupying the channel-fill, is characterized by reflection-free seismic facies. It can be formed as late Transgressive System Tract (TST), interpreted tidal environment deposits. MFS appears at the depth of about 15~28 m below the sea-level and is well defined by even and continuous reflectors on the seismic profile. The GB II overlying MFS is composed of acoustically transparent to semitransparent and parallel internal reflectors. GB II is interpreted as the Highstand System Tract (HST) probably deposited during the last 6,000 yrs when the sea level was close to the present level. Especially, it is though that the GB II was subdivided into two layers (GB II-a and II-b) by a HST-reflector and this was classified by wind, sea water flux, and tidal current.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.3
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• pp.147-153
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• 2013

There exist various types of the WEC (Wave Energy Converter), and among them, the point absorber is the most popularly investigated type. However, it is difficult to find examples of systematically measured data analysis for the design of the point absorber type of power buoy in the world. The study investigates the wave load acting on the point absorber type resonance power buoy wave energy extraction system proposed by Kweon et al. (2010). This study analyzes the time series spectra with respect to the three-year wave data (2002.05.01~2005.03.29) measured using the pressure type wave gage at the seaside of north breakwater of Hupo harbor located in the east coast of the Korean peninsula. From the analysis results, it could be deduced that monthly wave period and wave height variations were apparent and that monthly wave powers were unevenly distributed annually. The average wave steepness of the usual wave was 0.01, lower than that of the wind wave range of 0.02-0.04. The mode of the average wave period has the value of 5.31 sec, while mode of the wave height of the applicable period has the value of 0.29 m. The occurrence probability of the peak period is a bi-modal type, with a mode value between 4.47 sec and 6.78 sec. The design wave period can be selected from the above four values of 0.01, 5.31, 4.47, 6.78. About 95% of measured wave heights are below 1 m. Through this study, it was found that a resonance power buoy system is necessary in coastal areas with low wave energy and that the optimal design for overcoming the uneven monthly distribution of wave power is a major task in the development of a WEF (Wave Energy Farm). Finding it impossible to express the average spectrum of the usual wave in terms of the standard spectrum equation, this study proposes a new spectrum equation with three parameters, with which basic data for the prediction of the power production using wave power buoy and the fatigue analysis of the system can be given.

• 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.

• Economic and Environmental Geology
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• v.45 no.6
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• pp.661-672
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• 2012

Lab scale experiments to investigate the dissolution reaction among supercritical $CO_2$-sandstone-groundwater by using sandstones from Gyeongsang basin were performed. High pressurized cell system (100 bar and $50^{\circ}C$) was designed to create supercritical $CO_2$ in the cell, simulating the sub-surface $CO_2$ storage site. The first-order dissolution coefficient ($k_d$) of the sandstone was calculated by measuring the change of the weight of thin section or the concentration of ions dissolved in groundwater at the reaction time intervals. For 30 days of the supercritical $CO_2$-sandstone-groundwater reaction, physical properties of sandstone cores in Gyeongsang basin were measured to investigate the effect of supercritical $CO_2$ on the sandstone. The weight change of sandstone cores was also measured to calculate the dissolution coefficient and the dissolution time of 1 g per unit area (1 $cm^2$) of each sandstone was quantitatively predicted. For the experiment using thin sections, mass of $Ca^{2+}$ and $Na^+$ dissolved in groundwater increased, suggesting that plagioclase and calcite of the sandstone would be significantly dissolved when it contacts with supercritical $CO_2$ and groundwater at $CO_2$ sequestration sites. 0.66% of the original thin sec-tion mass for the sandstone were dissolved after 30 days reaction. The average porosity for C sandstones was 8.183% and it increased to 8.789% after 30 days of the reaction. The average dry density, seismic velocity, and 1-D compression strength of sandstones decreased and these results were dependent on the porosity increase by the dissolution during the reaction. By using the first-order dissolution coefficient, the average time to dissolve 1 g of B and C sandstones per unit area (1 $cm^2$) was calculated as 1,532 years and 329 years, respectively. From results, it was investigated that the physical property change of sandstones at Gyeongsang basin would rapidly occur when the supercritical $CO_2$ was injected into $CO_2$ sequestration sites.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.