• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.167-174
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• 2021

The purpose of this study is to classify TIFF images, PNG images, and JPEG images using deep learning, and to compare the accuracy by verifying the classification performance. The TIFF, PNG, and JPEG images converted from chest X-ray DICOM images were applied to five deep neural network models performed in image recognition and classification to compare classification performance. The data consisted of a total of 4,000 X-ray images, which were converted from DICOM images into 16-bit TIFF images and 8-bit PNG and JPEG images. The learning models are CNN models - VGG16, ResNet50, InceptionV3, DenseNet121, and EfficientNetB0. The accuracy of the five convolutional neural network models of TIFF images is 99.86%, 99.86%, 99.99%, 100%, and 99.89%. The accuracy of PNG images is 99.88%, 100%, 99.97%, 99.87%, and 100%. The accuracy of JPEG images is 100%, 100%, 99.96%, 99.89%, and 100%. Validation of classification performance using test data showed 100% in accuracy, precision, recall and F1 score. Our classification results show that when DICOM images are converted to TIFF, PNG, and JPEG images and learned through preprocessing, the learning works well in all formats. In medical imaging research using deep learning, the classification performance is not affected by converting DICOM images into any format.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.12
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• pp.857-866
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• 2022

Fixed-wing UAVs have long endurance and range capabilities compared to other aerial platforms. These advantages led fixed-wing UAVs to become a popular platform for reconnaissance missions in the military. In this research, we modeled fixed-wing UAVs, including the landing gear model and developed a guidance and control system for flight control computers to construct a HILS environment. We also developed an autopilot system that includes automated take-off, cruise, and landing control for UAVs. We also retrived the Aerodynamic coefficients an UAV using Datcom and AVL software and used them for 6 degrees of freedom modeling. The Flight control computer calculates guidance commands using the Carrot chasing guidance law after distinguishing the condition of the UAV based on 16 pre-defined flight modes and calculates control inputs using Nonlinear Dynamic Inversion (NDI) control scheme. We used RTNngine to integrate the Simulink model and flight control computer for HILS environment formulation.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.5
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• pp.455-463
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• 2012

In this study, we calculate dynamic constrained force of tower top and blade root of a floating offshore wind turbine. The floating offshore wind turbine is multibody system which consists of a floating platform, a tower, a nacelle, and a hub and three blades. All of these parts are regarded as a rigid body with six degree-of-freedom(DOF). The platform and the tower are connected with fixed joint, and the tower, the nacelle, and the hub are successively connected with revolute joint. The hub and three blades are connected with fixed joint. The recursive formulation is adopted for constructing the equations of motion for the floating wind turbine. The non-linear hydrostatic force, the linear hydrodynamic force, the aerodynamic force, the mooring force, and gravitational forces are considered as external forces. The dynamic load at the tower top, rotor shaft, and blade root of the floating wind turbine are simulated in time domain by solving the equations of motion numerically. From the simulation results, the mutual effects of the dynamic response between the each part of the floating wind turbine are discussed and can be used as input data for the structural analysis of the floating offshore wind turbine.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.6034-6039
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• 2012

In this paper, this may appear to exacerbate it met slopes of the mobile robot moves to overcome this by driving can occur if the mobile robot system has its own sleep problems driving progress in until you hit the target and solvedriving straight driving safer model for adaptive fuzzy control method of mobile robot based control algorithm is proposed. First, we propose a model based adaptive fuzzy controller, if possible, the dynamics model of the mobile robot, including model-based controller is designed to determine if you can check the condition of the mobile robot climbing and driving the mobile robot to overcome the slope and the to overcome driving control. Enough considering the ground friction forces and ensure the stability of the mobile robot system and the disturbance compensation, etc. In this case, the controller design will be possible. In addition, the nonlinear model, the dynamic characteristics of the mobile robot control method of adaptive fuzzy control techniques in the design that you want to fully reflect Non-holonomic system of mobile robots and solve sleep problems, and will be useful enough, it was verified through computer simulations.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.18 no.4
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• pp.269-282
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• 2006

We numerically studied tsunami propagation characteristics through Korean Straits based on nonlinear shallow water equation, a robust wave driver of the near field tsunamis. Tsunamis are presumed to be generated by the earthquake in Tsuhima-Koto fault line. The magnitude of earthquake is chosen to be 7.5 on Richter scale, which corresponds to most plausible one around Korean peninsula. It turns out that it takes only 60 minutes for leading waves to cross Korean straits, which supports recently raised concerns at warning system might be malfunctioned due to the lack of evacuation time. We also numerically obtained the probability of tsunami inundation of various levels, usually referred as tsunami hazard, along southern coastal area of Korean Peninsula based on simple seismological and Kajiura (1963)'s hydrodynamic model due to tsunami-generative earthquake in Tsuhima-Koto fault line. Using observed data at Akita and Fukaura during Okushiri tsunami in 1993, we verified probabilistic model of tsunami height proposed in this study. We believe this inundation probability of various levels to give valuable information for the amendment of current building code of coastal disaster prevention system to tame tsunami attack.

• Journal of the Korea Society for Simulation
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• v.22 no.3
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• pp.1-6
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• 2013

The ecosystem is the complex system consisting of various biotic and abiotic factors and the factors interact with each other in the hierarchical predator-prey relationship. Since the competitive relation spatiotemporally occurs, the initial state of population density and species distribution are likely to play an important role in the stability of the ecosystem. In the present study, we constructed a lattice model to simulate the three-trophic ecosystem (predatorprey- plant) and using the model, explored how the ecosystem stability is affected by the initial density. The size of lattice space was $L{\times}L$, (L=100) with periodic boundary condition. The initial density of the plant was arbitrarily set as the value of 0.2. The simulation result showed that predator and prey coexist when the density of predator is less than or equal to 0.4 and the density of prey is less than or equal to 0.5. On the other hand, when the predator density is more than or equal to 0.5 and the density of prey is more than or equal to 0.6, both of predator and prey were extinct. In addition, we found that the strong nonlinearity in the interaction between species was observed in the border area between the coexistence and extinction in the species density space.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.455-461
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• 2021

Cardiomegaly is one of the most common diseases seen on chest X-rays, but if it is not detected early, it can cause serious complications. In view of this, in recent years, many researches on image analysis in which deep learning algorithms using artificial intelligence are applied to medical care have been conducted with the development of various science and technology fields. In this paper, we would like to evaluate whether the Inception V3 deep learning model is a useful model for the classification of Cardiomegaly using chest X-ray images. For the images used, a total of 1026 chest X-ray images of patients diagnosed with normal heart and those diagnosed with Cardiomegaly in Kyungpook National University Hospital were used. As a result of the experiment, the classification accuracy and loss of the Inception V3 deep learning model according to the presence or absence of Cardiomegaly were 96.0% and 0.22%, respectively. From the research results, it was found that the Inception V3 deep learning model is an excellent deep learning model for feature extraction and classification of chest image data. The Inception V3 deep learning model is considered to be a useful deep learning model for classification of chest diseases, and if such excellent research results are obtained by conducting research using a little more variety of medical image data, I think it will be great help for doctor's diagnosis in future.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.5
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• pp.335-347
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• 2013

In February 2008, high storm waves due to a developed atmospheric low pressure system propagating from the west off Hokkaido, Japan, to the south and southwest throughout the East Sea (ES) caused extensive damages along the central coast of Japan and along the east coast of Korea. This study consists of two parts. In the first part, we estimate extreme storm wave characteristics in the Toyama Bay where heavy coastal damages occurred, using a non-hydrostatic meteorological model and a spectral wave model by considering the extreme conditions for two factors for wind wave growth, such as wind intensity and duration. The estimated extreme significant wave height and corresponding wave period were 6.78 m and 18.28 sec, respectively, at the Fushiki Toyama. In the second part, we perform numerical experiments on wave-structure interaction in the Fushiki Port, Toyama Bay, where the long North-Breakwater was heavily damaged by the storm waves in February 2008. The experiments are conducted using a non-linear shallow-water equation model with adaptive mesh refinement (AMR) and wet-dry scheme. The estimated extreme storm waves of 6.78 m and 18.28 sec are used for incident wave profile. The results show that the Fushiki Port would be overtopped and flooded by extreme storm waves if the North-Breakwater does not function properly after being damaged. Also the storm waves would overtop seawalls and sidewalls of the Manyou Pier behind the North-Breakwater. The results also depict that refined meshes by AMR method with wet-dry scheme applied capture the coastline and coastal structure well while keeping the computational load efficiently.