• Ocean Systems Engineering
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• v.10 no.1
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• pp.25-47
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• 2020

A study of the wave conditions for the Asabo offshore location at the Qua Iboe oil field in Eastern Nigeria has been carried out. Statistical analysis was applied to three (3) years of data comprising spectral periods, Tp and significant wave heights, H_s. The data was divided into two (2); data from October to April represents one set of data and data from May to September represents another set of data. The results were compared with similar studies at other locations offshore of West Africa. It was found that there is an absence of direct swellwaves from the Southern Ocean reaching the location under study (the Asabo site). This work suggests that the wave system is largely emanating from the North Atlantic storms. The presence of numerous islands near the Asabo location shields the site from effects of storms from south west and therefore swells from the Southern Ocean. It is noted that the local wind has little or no contribution. An H_s maximum of 2 m is noted at the Asabo offshore location. It is found that the Weibull distribution best describes the wave distribution at Asabo. Thus, the Weibull distribution is suggested to be adequate for long term prediction of extreme waves needed for offshore design and operations at this location.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.27 no.4
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• pp.274-279
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• 2015

Wave data acquired over eleven years near Sokcho Harbor located in the central area of the east coast were analyzed using spectral method and wave-by-wave analysis method and its major wave characteristics were examined. Significant wave heights were found to be high in winter and low in summer, and peak periods were also found to be long in winter and short in summer. The maximum significant wave height observed was 8.95 m caused by the East Sea twister. The distributional pattern of the significant wave heights and peak periods were both fitted better by Kernel distribution function than by Generalized Gamma distribution function and Generalized Extreme Value distribution function. The wave data were compiled to subdivide the wave height into intervals for each month, and the cumulative occurrence rates of wave heights were calculated to be utilized for the design and construction works in nearby construction works.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.27 no.2
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• pp.142-147
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• 2015

Wave data acquired over seven years near Daejin Harbor located in the north central area of the east coast was analyzed using spectral method and wave-by-wave analysis method and its major wave characteristics were examined. Significant wave heights were found to be high in winter and low in summer, and peak periods were also found to be long in winter and short in summer. The maximum significant wave height observed was 6.59 m and was caused by Typhoon No. 1216, SANBA. The distributional pattern of the significant wave heights and peak periods were both reproduced better by Kernel distribution function than by Generalized Gamma distribution function and Generalized Extreme Value distribution function. Meanwhile, the wave data was subdivided by month and wave height level and the cumulative appearance rate was proposed to aid designing and constructing works in nearby coastal areas.

• New & Renewable Energy
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• v.20 no.2
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• pp.2-16
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• 2024

Various loads induced by marine environmental conditions, such as waves, currents, and wind, are crucial for the operation and viability of offshore wind power (OWP) systems. In particular, waves have a significant impact on the stress and fatigue load of offshore structures, and highly reliable design parameters should be derived through extreme value analysis (EVA) techniques. In this study, extreme wave analyses were conducted with various Weibull distribution models to determine the reliable design parameters of an OWP system suitable for the Ulsan area. Forty-three years of long-term hindcast data generated by a numerical wave model were adopted as the analyses data, and the least-squares method was used to estimate the parameters of the distribution function for EVA. The inverse first-order reliability method was employed as the EVA technique. The obtained results were compared among themselves under the assumption that the marginal probability distributions were 2p, 3p, and exponentiated Weibull distributions.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.14 no.1
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• pp.51-64
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• 2002

To investigate long-period wave responses in Sokcho Harbor and Cheongcho lagoon, field measurements were made for long-and short-period waves and current velocities using a Directional Waverider, a ultrasonic-type wave gauge, four pressure-type wave gauges, and a current meter. From the data analysis, it was found that the Helmholtz resonant periods of Sokcho Harbor and Cheongcho lagoon are about 13.6 and 54.5 minutes, respectively, and the dominant period of wave induced current in the passage between Sokcho Harbor and Cheongcho lagoon is about 55.2 minutes which depends on Helmholtz resonant condition of the Cheongcho lagoon. It was also found that the energy level of the far-infra-gravity waves during storm conditions is very high compared with that during calm sea conditions. To investigate relationships between far-infra-gravity waves and short-period waves at offshore station, regression analyses were carried out especially for 1) heights, 2) periods, 3) direction and height, 4) height and period between short-and far-infra-gravity waves, respectively. The results showed that the long-period wave height is highly correlated with the short-period wave height. However, no special trend was found for the other relations. In the future far-infra-gravity wave heights on return period around Sokcho Harbor region can be suggested by using extreme value analyses of long term measured data.

• Journal of Climate Change Research
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• v.9 no.3
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• pp.209-221
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• 2018

The characteristics of heat wave events in Seoul are analyzed using weather station data from Korea Meteorological Administration (KMA) and European Centre for Medium-Range Weather Forecast (ECMWF) ERA-Interim reanalysis data from 1979 to 2016. Heat waves are defined as events in the upper 10th percentile of the daily maximum temperatures. The associated synoptic weather patterns are then classified into six clusters through Self-Organizing Map (SOM) analysis for sea-level pressure anomalies in East Asia. Cluster 1 shows an anti-cyclonic circulation and weak troughs in southeast and west of Korea, respectively. This synoptic pattern leads to southeasterly winds that advect warm and moist air to the Korean Peninsula. Both clusters 2 and 3 are associated with southerly winds formed by an anti-cyclonic circulation over the east of Korea and cyclonic circulation over the west of Korea. Cluster 4 shows a stagnant weather pattern with weak winds and strong insolation. Clusters 5 and 6 are associated with F?hn wind resulting from an anti-cyclonic circulation in the north of the Korean Peninsula. In terms of long-term variations, event frequencies of clusters 4 and 5 show increasing and decreasing trends, respectively. However, other clusters do not show any long-term trends, indicating that the mechanisms that drive heat wave events in Seoul have remained constant over the last four decades.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.5
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• pp.505-511
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• 2023

In the present study, a deep learning model was established to predict the motion response of small fishing vessels. Hydrodynamic performances were evaluated for two small fishing vessels for the dataset of deep learning model. The deep learning model of the Long Short-Term Memory (LSTM) which is one of the recurrent neural network was utilized. The input data of LSTM model consisted of time series of six(6) degrees of freedom motions and wave height and the output label was selected as the time series data of six(6) degrees of freedom motions. The hyperparameter and input window length studies were performed to optimize LSTM model. The time series motion response according to different wave direction was predicted by establised LSTM. The predicted time series motion response showed good overall agreement with the analysis results. As the length of the time series increased, differences between the predicted values and analysis results were increased, which is due to the reduced influence of long-term data in the training process. The overall error of the predicted data indicated that more than 85% of the data showed an error within 10%. The established LSTM model is expected to be utilized in monitoring and alarm systems for small fishing vessels.

• International Journal of Aerospace System Engineering
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• v.2 no.2
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• pp.58-61
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• 2015

Combustion instability in solid rocket motors is a long-term open problem since the first rockets were used. Based on the numerous previous studies, it is known that the limit cycle amplitude is one of the key characteristics of the nonlinear combustion instability in solid rocket motors. Flandro's extended energy balance corollary, aims to predict the limit cycle amplitude of complex, nonlinear pressure oscillations for rockets or air-breathing engines, and leads to a precise assessment of nonlinear combustion instability in solid rocket motors. However, based on the comparison with experimental data, it is revealed that the Flandro's method cannot accurately describe such a complex oscillatory pressure. Thus in this work we make modifications of the nonlinear term in the nonlinear wave equations which represents the interaction of different modes. Through this modified method, a numerical simulation of the cylindrical solid rocket has been carried out, and the simulated result consists well with the experimental data. It means that the added coefficient makes the nonlinear wave growth equations describe the experimental data better.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.7
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• pp.728-736
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• 2014

Due to the recent development of mobile wireless telecommunication, RRHs(Remote Radio Heads) are widely being utilized to expand the cell coverage. Wire-link, an existing connection between DU(Digital Unit) and RRH through fiber cables, is considered to have limitations in relation to cost for a wide range of applications. This paper focuses on replacing the wire-link by wireless transmission using millimeter-wave, and implementing cellular network system through RRH from a remote location. After an influence element analysis on millimeter-wave wireless link transmissions, a comparative analysis between wireless link and an existing fiber link method will be made by utilizing currently commercialized LTE(Long Term Evolution), to prove the compatibility of millimeter-wave wireless link RRH method in replacing fiber link method without a significant decline in data transmission performance.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.307-314
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• 2022

Recently, researches using deep learning-based models are being actively conducted to replace statistical-based time series forecast techniques to predict electric power demand. The result of analyzing the researches shows that the performance of the LSTM-based prediction model is acceptable, but it is not sufficient for long-term regional-wide power demand prediction. In this paper, we propose a WaveNet deep learning model to predict electric power demand 24-hour-ahead with temperature data in order to achieve the prediction accuracy better than MAPE value of 2% which statistical-based time series forecast techniques can present. First of all, we illustrate a delated causal one-dimensional convolutional neural network architecture of WaveNet and the preprocessing mechanism of the input data of electric power demand and temperature. Second, we present the training process and walk forward validation with the modified WaveNet. The performance comparison results show that the prediction model with temperature data achieves MAPE value of 1.33%, which is better than MAPE Value (2.33%) of the same model without temperature data.