• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.1
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• pp.154-164
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• 2019

A rotor dynamic analysis is mandatory for stability and design optimization of submerged propellers and turbines. An accurate simulation requires a proper consideration of fluid-induced reaction forces. This paper presents a bi-directional coupling of a bond graph method solver and an unsteady vortex lattice method solver where the former is used to model the rotor dynamics of the power train and the latter is used to predict transient hydrodynamic forces. Due to solver coupling, determination of hydrodynamic coefficients is obsolete and added mass effects are considered automatically. Additionally, power grid and structural faults like grid fluctuations, eccentricity or failure could be investigated using the same model. In this research work a fast, time resolved dynamic simulation of the complete power train is conducted. As an example, the rotor dynamics of a tidal stream turbine is investigated under two inflow conditions: I - shear flow, II - shear flow + water waves.

• Steel and Composite Structures
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• v.38 no.4
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• pp.365-380
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• 2021

Although traditional steel-concrete composite beams have excellent structural characteristics, it cannot meet the requirement of quick assembly and repair in the engineering. This paper presents a study on static behavior of bolt connected steel-concrete composite beam without post-cast zone. A three-dimensional finite element model was developed with its accuracy and reliability validated by available experimental results. The analysis results show that in the normal service stage, the bolt is basically in the state of unidirectional stress with the loss of pretightening can be ignored. Parametric studies are presented to quantify the effects of the post-cast zone, size and position of splicing gap on the behavior of the beam. Based on the studies, suggested size of gap and installation order were proposed. It is also confirmed that optimized concrete slab in mid-span can reduce the requirement of construction accuracy.

• Genomics & Informatics
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• v.20 no.1
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• pp.8.1-8.14
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• 2022

Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/ environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings.

• Journal of Ocean Engineering and Technology
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• v.38 no.4
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• pp.149-163
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• 2024

This paper employs the third-generation simulating waves nearshore (SWAN) ocean wave model to estimate and analyze storm waves induced by Typhoon Bolaven, focusing on its impact along the west coast and Jeju Island of Korea. Utilizing reanalyzed meteorological data from the Japan Meteorological Agency meso scale model (JMA-MSM), the study simulated storm waves from Typhoon Bolaven, which maintained its intensity up to high latitudes as it approached the Korean Peninsula in 2012. Validation of the SWAN model against observed wave data demonstrated a strong correlation, particularly in regions where wind speeds exceeded 20 m/s and wave heights surpassed 5 m. Results indicate significant storm wave heights across Jeju Island and Korea's west and southwest seas, with coastal grid points near islands recording storm wave heights exceeding 90% of the 50-year return period design wave heights. Notably, specific grid points near islands in the northern West Sea and southwest Jeju Island estimated storm wave heights at 90.22% and 91.48% of the design values, respectively. The paper highlights the increased uncertainty and vulnerability in coastal disaster predictions due to event-driven typhoons and emphasizes the need for enhanced accuracy and speed in typhoon wave predictions amid the escalating climate crisis.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1458-1469
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• 2018

This paper proposes a novel common-mode voltage (CMV) elimination (CMV-EL) method based on model predictive control (MPC) to eliminate CMV for three-level T-type inverters (3LT2Is). In the proposed MPC method, only six medium and one zero voltage vectors (VVs) (6MV1Z) that generate zero CMV are considered as candidates to perform the MPC. Moreover, the influence of dead-time effects on the CMV of the MPC-based 6MV1Z method is investigated, and the candidate VVs are redesigned by pre-excluding the VVs that will cause CMV fluctuations during the dead time from 6MV1Z. Only three or five VVs are included to perform optimization in every control period, which can significantly reduce the computational complexity. Thus, a small control period can be implemented in the practical applications to achieve improved grid current performance. With the proposed CMV-EL method, the CMV of the $3LT^2Is$ can be effectively eliminated. In addition, the proposed CMV-EL method can balance the neutral point potentials (NPPs) and yield satisfactory performance for grid current tracking in steady and dynamic states. Simulation and experimental results are presented to verify the effectiveness of the proposed method.

• Journal of the korean society of oceanography
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• v.39 no.1
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• pp.72-95
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• 2004

The Yellow Sea is characterized by relatively shallow water depth, varying range of tidal action and very complex coastal geometry such as islands, bays, peninsulas, tidal flats, shoals etc. The dynamic system is controlled by tides, regional winds, river discharge, and interaction with the Kuroshio. The circulation, water mass properties and their variability in the Yellow Sea are very complicated and still far from clear understanding. In this study, an effort to improve our understanding the dynamic feature of the Yellow Sea system was conducted using numerical simulation with the ROMS model, applying climatologic forcing such as winds, heat flux and fresh water precipitation. The inter-annual variability of general circulation and thermohaline structure throughout the year has been obtained, which has been compared with observational data sets. The simulated horizontal distribution and vertical cross-sectional structures of temperature and salinity show a good agreement with the observational data indicating significantly the water masses such as Yellow Sea Warm Water, Yellow Sea Bottom Cold Water, Changjiang River Diluted Water and other sporadically observed coastal waters around the Yellow Sea. The tidal effects on circulation and dynamic features such as coastal tidal fronts and coastal mixing are predominant in the Yellow Sea. Hence the tidal effects on those dynamic features are dealt in the accompanying paper (Kim et at., 2004). The ROMS model adopts curvilinear grid with horizontal resolution of 35 km and 20 vertical grid spacing confirming to relatively realistic bottom topography. The model was initialized with the LEVITUS climatologic data and forced by the monthly mean air-sea fluxes of momentum, heat and fresh water derived from COADS. On the open boundaries, climatological temperature and salinity are nudged every 20 days for data assimilation to stabilize the modeling implementation. This study demonstrates a Yellow Sea version of Atlantic Basin experiment conducted by Haidvogel et al. (2000) experiment that the ROMS simulates the dynamic variability of temperature, salinity, and velocity fields in the ocean. However the present study has been improved to deal with the large river system, open boundary nudging process and further with combination of the tidal forcing that is a significant feature in the Yellow Sea.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.17 no.2
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• pp.97-104
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• 1999

Digital elevation models(DEMs) represent an important data base for orthophoto generation The quality of a DEM depends on the geometrical accuracy of the original point or line data. This study analyzes the effects of grid space and scanning resolution in DEM creation with image matching method. The less standard deviation of DEM error was introduced when we adopted small grid space, but no effects in scanning resolution. Based on the bias error analysis of the DEM, we found that the error of a large scale of aerial photograph was bigger than that of a small scale case, and that such error mainly came from the closed area in large scale photographs. In order to reduce the closed area, the experiment has been conducted using multi scale and different overlap of aerial photo images. The result shows that the size of closed area and the shaded area has been dramatically decreased due to the adoption of multi scale aerial images instead of a couple of stereo images.

• 유체기계공업학회:학술대회논문집
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• 2006.08a
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• pp.327-330
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• 2006

This paper represents the numerical analysis on effects of radius ratio in a concentric annulus with a rotating inner cylinder. The numerical model consisted of two cylinder which inner cylinder is rotating and outer cylinder is fix, and the axial direction is used the cyclic condition because of the length for axial direction is assumed infinite. The diameter of inner cylinder is assumed 86.8mm, the numerical parameters are angular velocity and radius ratio. Also, the whole walls of numerical model have no-slip and the working fluid is used water at $20^{\circ}C$. The numerical analysis is assumed the transient state to observe the flow variations by time and the 3-D cylindrical coordinate system. The calculation grid adopted a non-constant grid for dense arrangement near the wall side of cylinder, the standard $k-{\omega}$ high Reynolds number model to consider the effect of turbulence flow and wall, the fully implicit method for time term and the quick scheme for momentum equation. The numerical method is compared with the experimental results by Wereley and Lueptow, and the results are very good agreement. As the results, TVF isn't appeared when Re is small because of the initial flow instability is disappear by effect of the centrifugal force and viscosity. The vortex size is from 0.8 to 1.1 for TVF at various $\eta$, and the traveling distance for wavy vortex have the critical traveling distance for each case.

• The Transactions of the Korean Institute of Power Electronics
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• v.16 no.5
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• pp.511-520
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• 2011

Nowadays, industry of smart grid is important for practical use of the renewable energy. In this situation, it is important to use the energy storage to make more stable and efficient renewable energy sources. The power conditioning systems consist in a boost converter which makes renewable energy source connected with the grid-connected inverter and the charger/discharger which takes the energy transfer between the boost converter and an energy storage. The effects on the controller design of each converter must be investigated to avoid the instability of the entire system. small-signal modelling of the boost converter and charger/discharger have been done and a controller design example is also presented. In this paper, effects on the controller design of the boost converter and the charger/discharger are investigated according to the existence of the parasitic resistance of the boost converter. In conclusion, the parasitic resistance of the inductor should be considered from the aspect of both the frequency domain analysis and time domain simulation using both MATLAB and PSIM.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.