• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2000.04a
• /
• pp.478-483
• /
• 2000

In this study to cope with the decreasing product's life-cycle a virtual simulator to realize the simulation environment similar to a real manufacturing line is developed. The developed simulator plays a role in reducing the product conversion time by alternating manufacturing components and work plans on the simulation as manufacturing lines change and actuating a virtual manufacturing lines change and actuating a virtual manufacturing line before a real production. The developed simulator realized a virtual manufacturing line on the simulation using various manipulators and work cells as manufacturing components. Also It can be shown that the simulator can cope with rapid change of a manufacturing line by developing a interface that a separate process is managed for each manufacturing module and a manipulator component and a work cell are changed for a user to become convenient to teach tasks of each work module. using Microsoft Visual C++ 6.0 and OpenGL of Silicon Graphics for libraries to realize 3-dimensional graphic and constructing a database system, a hybrid type of hierachical and relational model to develop a progra that has efficiency and standardization.

• Computers and Concrete
• /
• v.21 no.5
• /
• pp.505-511
• /
• 2018

This paper investigates the potential of a hybrid model which combines the least squares support vector machine (LSSVM) and an improved particle swarm optimization (IMPSO) techniques for prediction of concrete compressive strength. A modified PSO algorithm is employed in determining the optimal values of LSSVM parameters to improve the forecasting accuracy. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed IMPSO-LSSVM model. Further, predictions from five models (the IMPSO-LSSVM, PSO-LSSVM, genetic algorithm (GA) based LSSVM, back propagation (BP) neural network, and a statistical model) were compared with the experimental data. The results show that the proposed IMPSO-LSSVM model is a feasible and efficient tool for predicting the concrete compressive strength with high accuracy.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.11 no.2
• /
• pp.1-6
• /
• 2012

Recent ultra-precision machining systems have nano-scale resolution, and can machine various shapes of complex structures using five-axis driven modules. These systems are also multi-functional, which can perform various processes such as planing, milling, turning et al. in one system. Micro machining technology using these systems is being developed for machining fine patterns, hybrid patterns and high aspect-ratio patterns on large-area molds with high productivity. These technology is and will be applied continuously to the fields of optics, display, energy, bio, communications and et al. Domestic and foreign trends of micro machining technologies for flat molds were investigated in this study. Especially, we focused on the types and the characteristics of ultra-precision machining systems and application fields of micro patterns machined by the machining system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2006.11a
• /
• pp.408-411
• /
• 2006

Currently, the nano economy threatens the mass economy. This is based on the internet business models. In the nano business models based on internet, the diversely personalized services are needed. Many researches of the personalization on the web have been studied. The web usage mining using click stream data is a tool for personalization model. In this paper, we propose an internet business model using evolutionary support vector machine and web response survey as a web usage mining. After analyzing click stream data for web usage mining, a personalized service model is constructed in our work. Also, using an approach of web response survey, we improve the performance of the customers' satisfaction. From the experimental results, we verify the performance of proposed model using two data sets from KDD Cup 2000 and our web server.

• Journal of the Korean Society of Systems Engineering
• /
• v.18 no.2
• /
• pp.94-107
• /
• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.35 no.9
• /
• pp.1085-1098
• /
• 2011

In the $21^{st}$ century, technology is a tool for the expansion of the five senses and physical ability that works as an element for posthuman identity. This study analyzes and theorizes on the characteristics of the posthuman body in fashion illustration. The method of this study analyzes documentaries about posthuman and fashion illustration. The results are as follow. Posthuman body types are classed as hybrid body, plastic surgery body, and digital body. The characteristics of the posthuman body are categorized as ultra- functional prosthetic, mythical undifferentiated, radical plastic surgery type and post-physical digitization type. The ultra-functional prosthetic type shows a restored body and upgraded functional body through a machine hybrid, cyborg suit and mannequin hybrid. It is a break from classical gender identity to form a nerve sense extension that displays physical and abstract power. The mythical undifferentiated type shows a therianthropic form, parts of an animal body, radical skin and gender bending. It represents the return to an undifferentiated world, the desire of a powerful being and the possibility of radical transformation. The radical plastic surgery type shows a photomontage of an ideal body, transgendered body, grotesque body marking, absence of partial or overall face organ and the expansion of abnormal body organs. It represents the expression of narcissism, unconscious desire, fantasy, fear and suggests an alternative ideality, sexual attachment and ambiguous gender identity. The post-physical digitization type shows an imperfect form or duplicated ego image through the omission of the body silhouette or detailed form, fragmented image using net, representative self like optical illusion using typography, an imperfect vague silhouette and immaterial body outline through the use of virtual light. It represents the lack of desire, narcissism, fluidity in a virtual space, the continued creation of a new self, ambiguous gender identity and the liberation of environment, sex, and race. Likewise, the posthuman in fashion illustration shows the absence of a species boundary, destruction of classical gender identity, a new personality and virtual self image.

• Smart Structures and Systems
• /
• v.25 no.5
• /
• pp.605-617
• /
• 2020

The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.10 no.6
• /
• pp.48-56
• /
• 2001

In this paper, this study aims at the evaluation on the characteristics of CFRP laminate plates using a falling weight impact tester. The experiment was conducted on several laminates of different orientation. A system was built far measur- ing the impact strength of CFRP laminates in consideration of stress wave propagation theory using a falling weight impact tester. Delamination areas of impacted specimens for the different ply orientation were measured with ultrasonic C- scanner to find correlation between impact energy and delamination area. Absorbed energy of quasi-isotropic specimen having flour interfaces was higher than that of orthotropic laminates with two interfaces. The more interfaces, the greater the energy absorbed. The absorbed energy oft hybrid specimen containing a GFRP layer was higher than that of normal specimens.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.18 no.1
• /
• pp.90-102
• /
• 2009

In this paper, a tracking control problem for a mechanical servo system with nonlinear dynamic friction is treated. The nonlinear friction model contains directly immeasurable friction state and the uncertainty caused by incomplete modeling and variations of its parameter. In order to provide the efficient solution to these control problems, we propose a hybrid control scheme, which consists of a robust friction state observer, a RFNN estimator and an approximation error estimator with sliding mode control. A sliding mode controller and a robust friction state observer is firstly designed to estimate the unknown infernal state of the LuGre friction model. Next, a RFNN estimator is introduced to approximate the unknown lumped friction uncertainty. Finally, an adaptive approximation error estimator is designed to compensate the approximation error of the RFNN estimator. Some simulations and experiments on the mechanical servo system composed of ball-screw and DC servo motor are presented. Results demonstrate the remarkable performance of the proposed control scheme.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.4
• /
• pp.657-667
• /
• 2020

The Android framework allows apps to take full advantage of personal information through granting single permission, and does not determine whether the data being leaked is actual personal information. To solve these problems, we propose a tool with static/dynamic analysis. The tool analyzes the Source and Sink used by the target app, to provide users with information on what personal information it used. To achieve this, we extracted the Source and Sink through Control Flow Graph and make sure that it leaks the user's privacy when there is a Source-to-Sink flow. We also used the sensitive permission information provided by Google to obtain information from the sensitive API corresponding to Source and Sink. Finally, our dynamic analysis tool runs the app and hooks information from each sensitive API. In the hooked data, we got information about whether user's personal information is leaked through this app, and delivered to user. In this process, an automated Source/Sink classification model was applied to collect latest Source/Sink information, and the we categorized latest release version of Android(9.0) with 88.5% accuracy. We evaluated our tool on 2,802 APKs, and found 850 APKs that leak personal information.