• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 1997.10a
• /
• pp.76-79
• /
• 1997

In this study, an elasto-plastic finite element code, ESFORM, was developed to analyze sheet stamping processes. A formulation of 4-node degenerated shell element was implemented in the code. Workpiece materials were assumed to have planar anisotropy, and governed by associated flow rule. Explicit time integration method was employed to save computation time and reduce the required computer memory. Penalty method was used to describe interface behavior between workpiece and rigid die. Deep drawing of square cup and front finder stamping processes were simulated by ESFORM>

• Transactions of Materials Processing
• /
• v.23 no.2
• /
• pp.75-81
• /
• 2014

Progressive dies are used for metal stamping during which multiple operations are performed in a sequence. Material is fed automatically from a coil into the press and advances from one die station to the next with each press stroke. Transfer dies are used in high-volume manufacturing for round, deep-drawn, and medium-to-large parts. Several different operations may be incorporated within a transfer die such as blanking, bending, piercing, trimming, and deep drawing. The main challenge in the current study is how to deform a seat cushion panel meeting the design specifications without any defects. A complex automation die manufacturing technology for the automotive seat cushion panel, mixing both semi-progressive die and transfer die for continuous production, was developed.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.301-301
• /
• 2000

It is well known, for many years, that deep drawability ,can be improved by applying variable blank holding force. To apply variable blank holding force during cup during, we set up pressure controlling system on experimental hydraulic press, and the pressure control system is often called NC(Numerically Controlled} cushion system. Using the NC cushion system we carry out pressure control experiment and the proposed structure shows good performance. And we compare drawability of square steel cups with NC cushion and that with conventional cushion. The results show drawability is greatly improved when the pressure control curve is designed in a S-shaped curve. This paper includes design details of the NC cushion system and experimental analysis of drawability with experimental NC cushion system.

• Transactions of Materials Processing
• /
• v.5 no.1
• /
• pp.8-17
• /
• 1996

In the present study SEAM (Shear Energy Augmented Membrane) elements have been devel-oped. Maintaining the numerical efficiency of conventional membrane elements the effect of out-of-plane deformation (transverse shear deformation) has been incorporated for the purpose of computational stabilization without introducing additional degrees of freedom. Computations are carried out for the deep drawings of square cup to check the validity and the effectiveness of proposed SEAM elements. The computational results are compared with both the existing results. And the effects of process variables like initial sheet thickness punch & die round and clearance are checked

• The Bulletin of The Korean Astronomical Society
• /
• v.46 no.1
• /
• pp.41.1-41.1
• /
• 2021

We generate solar magnetograms and EUV images from Galileo sunspot drawings using a deep learning model based on conditional generative adversarial networks. We train the model using pairs of sunspot drawing from Mount Wilson Observatory (MWO) and their corresponding magnetogram (or UV/EUV images) from 2011 to 2015 except for every June and December by the SDO (Solar Dynamic Observatory) satellite. We evaluate the model by comparing pairs of actual magnetogram (or UV/EUV images) and the corresponding AI-generated one in June and December. Our results show that bipolar structures of the AI-generated magnetograms are consistent with those of the original ones and their unsigned magnetic fluxes (or intensities) are well consistent with those of the original ones. Applying this model to the Galileo sunspot drawings in 1612, we generate HMI-like magnetograms and AIA-like EUV images of the sunspots. We hope that the EUV intensities can be used for estimating solar EUV irradiance at long-term historical times.

• International journal of advanced smart convergence
• /
• v.9 no.4
• /
• pp.173-178
• /
• 2020

As the fourth industrial revolution, in which people, objects, and information are connected as one, various fields such as smart energy, smart cities, artificial intelligence, the Internet of Things, unmanned cars, and robot industries are becoming the mainstream, drawing attention to big data. Among them, Smart Grid is a technology that maximizes energy efficiency by converging information and communication technologies into the power grid to establish a smart grid that can know electricity usage, supply volume, and power line conditions. Smart meters are equient that monitors and communicates power usage. We start with the goal of building a virtual smart grid and constructing a virtual environment in which real-time data is generated to accommodate large volumes of data that are small in capacity but regularly generated. A major role is given in creating a software/hardware architecture deployment environment suitable for the system for test operations. It is necessary to identify the advantages and disadvantages of the software according to the characteristics of the collected data and select sub-projects suitable for the purpose. The collected data was collected/loaded/processed/analyzed by the Hadoop ecosystem-based big data platform, and used to predict power demand through deep learning.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.12
• /
• pp.4625-4647
• /
• 2020

The Generative Adversarial Networks, as one of the most creative deep learning models in recent years, has achieved great success in computer vision and natural language processing. It uses the game theory to generate the best sample in generator and discriminator. Recently, many deep learning models have been applied to the security field. Along with the idea of "generative" and "adversarial", researchers are trying to apply Generative Adversarial Networks to the security field. This paper presents the development of Generative Adversarial Networks. We review traditional generation models and typical Generative Adversarial Networks models, analyze the application of their models in natural language processing and computer vision. To emphasize that Generative Adversarial Networks models are feasible to be used in security, we separately review the contributions that their defenses in information security, cyber security and artificial intelligence security. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.

• Structural Engineering and Mechanics
• /
• v.84 no.3
• /
• pp.323-335
• /
• 2022

The main idea of the framework is to seamlessly combine a reasonably accurate and fast surrogate model with the importance sampling strategy. Developing a surrogate model for predicting structures' dynamic responses is challenging because it involves high-dimensional inputs and outputs. For this purpose, a novel surrogate model based on cutting-edge deep learning architectures specialized for capturing temporal relationships within time-series data, namely Long-Short term memory layer and Transformer layer, is designed. After being properly trained, the surrogate model could be utilized in place of the finite element method to evaluate structures' responses without requiring any specialized software. On the other hand, the importance sampling is adopted to reduce the number of calculations required when computing the failure probability by drawing more relevant samples near critical areas. Thanks to the portability of the trained surrogate model, one can integrate the latter with the Importance sampling in a straightforward fashion, forming an efficient framework called TTIS, which represents double advantages: less number of calculations is needed, and the computational time of each calculation is significantly reduced. The proposed approach's applicability and efficiency are demonstrated through three examples with increasing complexity, involving a 1D beam, a 2D frame, and a 3D building structure. The results show that compared to the conventional Monte Carlo simulation, the proposed method can provide highly similar reliability results with a reduction of up to four orders of magnitudes in time complexity.

• International journal of advanced smart convergence
• /
• v.12 no.4
• /
• pp.182-189
• /
• 2023

A recently research, object detection and segmentation have emerged as crucial technologies widely utilized in various fields such as autonomous driving systems, surveillance and image editing. This paper proposes a program that utilizes the QT framework to perform real-time object detection and precise instance segmentation by integrating YOLO(You Only Look Once) and Mask R CNN. This system provides users with a diverse image editing environment, offering features such as selecting specific modes, drawing masks, inspecting detailed image information and employing various image processing techniques, including those based on deep learning. The program advantage the efficiency of YOLO to enable fast and accurate object detection, providing information about bounding boxes. Additionally, it performs precise segmentation using the functionalities of Mask R CNN, allowing users to accurately distinguish and edit objects within images. The QT interface ensures an intuitive and user-friendly environment for program control and enhancing accessibility. Through experiments and evaluations, our proposed system has been demonstrated to be effective in various scenarios. This program provides convenience and powerful image processing and editing capabilities to both beginners and experts, smoothly integrating computer vision technology. This paper contributes to the growth of the computer vision application field and showing the potential to integrate various image processing algorithms on a user-friendly platform

• Journal of the Korean Society of Industry Convergence
• /
• v.27 no.1
• /
• pp.161-172
• /
• 2024

Approximately 89% of all capsizing accidents involve small vessels, and despite their relatively high accident rates, small vessels are not subject to ship stability regulations. Small vessels, where the provision of essential basic design documents for stability calculations is omitted, face challenges in directly calculating their stability. In this study, considering that the majority of domestic coastal small vessels are of the Chine-type design, the goal is to establish the major hull form characteristic data of vessels, which can be identified from design documents such as the general arrangement drawing, as input data. Through the application of a deep learning approach, specifically a multilayer neural network structure, we aim to infer hydrostatic curves, operational draft ranges, and more. The ultimate goal is to confirm the possibility of directly calculating the initial stability of small vessels.