• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.768-783
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• 2020

Typically, a Dynamic Positioning System (DPS) uses a PID feed-back system, and it often adopts a wind feed-forward system because of its easier implementation than a feed-forward system based on current or wave. But, because a ship's drifting motion is caused by wind, current, and wave drift loads, all three environmental loads should be considered. In this study, a motion predictive control for the PID feedback system of the DPS is proposed, which considers the three environmental loads by utilizing predicted drifted ship positions in the future since it contains information about the three environmental loads from the moment to the future. The prediction accuracy for the future drifted ship position is ensured by adopting deep learning algorithms and a replay buffer. Finally, it is shown that the proposed motion predictive system results in better station-keeping performance than the wind feed-forward system.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1149-1159
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• 2019

We present a novel deep learning architecture for obtaining a latent image from a single blurry image, which contains dynamic motion blurs through object/camera movements. The proposed architecture consists of two sub-modules: blur image restoration and optical flow estimation. The tasks are highly related in that object/camera movements make cause blurry artifacts, whereas they are estimated through optical flow. The ablation study demonstrates that training multi-task architecture simultaneously improves both tasks compared to handling them separately. Objective and subjective evaluations show that our method outperforms the state-of-the-arts deep learning based techniques.

• Journal of Sensor Science and Technology
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• v.30 no.5
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• pp.290-294
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• 2021

Experts have designed popular and successful model architectures, which, however, were not the optimal option for different scenarios. Despite the remarkable performances achieved by deep neural networks, manually designed networks for classification tasks are the backbone of object detection. One major challenge is the ImageNet pre-training of the search space representation; moreover, the searched network incurs huge computational cost. Therefore, to overcome the obstacle of the pre-training process, we introduce a network adaptation technique using a pre-trained backbone model tested on ImageNet. The adaptation method can efficiently adapt the manually designed network on ImageNet to the new object-detection task. Neural architecture search (NAS) is adopted to adapt the architecture of the network. The adaptation is conducted on the MobileNetV2 network. The proposed NAS is tested using SSDLite detector. The results demonstrate increased performance compared to existing network architecture in terms of search cost, total number of adder arithmetics (Madds), and mean Average Precision(mAP). The total computational cost of the proposed NAS is much less than that of the State Of The Art (SOTA) NAS method.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.839-846
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• 2022

Traditionally, the construction industry has shown low labor productivity and productivity growth. To improve labor productivity, it must first be accurately measured. The existing method uses work-sampling techniques through observation of workers' activities at certain time intervals on site. However, a disadvantage of this method is that the results may differ depending on the observer's judgment and may be inaccurate in the case of a large number of missed scenarios. Therefore, this study proposes a model to automate labor productivity measurement by monitoring workers' actions using a deep learning-based pose estimation method. The results are expected to contribute to productivity improvement on construction sites.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.173-187
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• 2018

This paper proposes a computer vision and deep learning-based technique for surveillance camera system for vehicle counting as one part of parking lot management system. We applied the You Only Look Once version 2 (YOLOv2) detector and come up with a deep convolutional neural network (CNN) based on YOLOv2 with a different architecture and two models. The effectiveness of the proposed architecture is illustrated using a publicly available Udacity's self-driving-car datasets. After training and testing, our proposed architecture with new models is able to obtain 64.30% mean average precision which is a better performance compare to the original architecture (YOLOv2) that achieved only 47.89% mean average precision on the detection of car, truck, and pedestrian.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.1
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• pp.13-21
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• 2016

Parametric pitch instability of a Deep Draft Semi-submersible platform (DDS) is investigated in irregular waves. Parametric pitch is a form of parametric instability, which occurs when parameters of a system vary with time and the variation satisfies a certain condition. In previous studies, analyzing of parametric instability is mainly limited to regular waves, whereas the realistic sea conditions are irregular waves. Besides, parametric instability also occurs in irregular waves in some experiments. This study predicts parametric pitch of a Deep Draft Semi-submersible platform in irregular waves. Heave motion of DDS is simulated by wave spectrum and response amplitude operator (RAO). Then Hill equation for DDS pitch motion in irregular waves is derived based on linear-wave theory. By using Bubnov-Galerkin approach to solve Hill equation, the corresponding stability chart is obtained. The differences between regular-waves stability chart and irregular-waves stability chart are compared. Then the sensitivity of wave parameters on DDS parametric pitch in irregular waves is discussed. Based on the discussion, some suggestions for the DDS design are proposed to avoid parametric pitch by choosing appropriate parameters. The results indicate that it's important and necessary to predict DDS parametric pitch in irregular waves during design process.

• Journal of the Society of Naval Architects of Korea
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• v.56 no.6
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• pp.473-479
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• 2019

Welding is one of the most popular joining methods and most welding quality estimation methods are executed using joined material. This paper propose welding quality estimation methods using dynamic current, voltage and resistance which are obtained during welding in real time. There are many kinds of welding method. Among them, we focused on the projection welding and gathered dynamic characteristics from two different types of projection welding. For image learning, graphs are drawn using obtained current, voltage and resistance, and the graphs are converted to images. The images are labeled with two sub-categories - normal and defect. For deep learning of images obtained from welding, Convolutional Neural Network (CNN) is applied, and Tensorflow was used as a framework for deep learning. With two resistance welding test datasets, we conclude that the Convolutional Neural Network helps in predicting the welding quality.

• Steel and Composite Structures
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• v.45 no.4
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• pp.563-577
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• 2022

This paper presents experimental and numerical investigations of a new seismic enhancement method for existing reinforced concrete (RC) frames by using an external sub-structure, the hybrid seismic retrofit method (HSRM) system. This retrofit system is an H-shaped frame bolt-connected to an existing RC frame with an infilled-concrete layer between their gaps. Two RC frames were built, one with and one without HSRM, and tested under cyclic loading. The experimental findings showed that the retrofitted RC frame was superior to the non-retrofitted specimen in terms of initial stiffness, peak load, and energy dissipation capacity. A numerical simulation using a commercial program was employed for verification with the experiments. The results obtained from the simulations were consistent with those from the experiments, indicating the finite element (FE) models can simulate the seismic behaviors of bare RC frame and retrofitted RC frame using HSRM.

• Journal of the Korean Society of Visualization
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• v.22 no.1
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• pp.49-60
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• 2024

The purpose of this study establishes the deep neural network (DNN) and Decoder models to predict the flow and thermal fields of three-dimensional wavy wings as a passive flow control. The wide ranges of the wavy geometric parameters of wave amplitude and wave number are considered for the various the angles of attack and the aspect ratios of a wing. The huge dataset for training and test of the deep learning models are generated using computational fluid dynamics (CFD). The DNN and Decoder models exhibit quantitatively accurate predictions for aerodynamic coefficients and Nusselt numbers, also qualitative pressure, limiting streamlines, and Nusselt number distributions on the surface. Particularly, Decoder model regenerates the important flow features of tiny vortices in the valleys, which makes a delay of the stall. Also, the spiral vortical formation is realized by the Decoder model, which enhances the lift.

• Journal of the Society of Naval Architects of Korea
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• v.45 no.4
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• pp.462-467
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• 2008

In this paper, we investigated the design method of mooring system in ultra deep sea and carried out the conceptual design for offshore West Africa oil field in ultra deep sea of 3000 meters. Recently, it was feasible to design and install the offshore floating structures in deep sea of up to 2000 meters. Due to the simplicity, two-point mooring design is fully utilized. Force-excursion curves are throughly examined to find out the feasibility of various combinations of mooring lines. Free length and pretension effects are discussed. It is found that composite materials including synthetic fiber rope may be good solution for ultra deep sea mooring design.