• Journal of the Korean Crystal Growth and Crystal Technology
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• v.9 no.4
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• pp.417-423
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• 1999

Detailed description of the crystal growth methods permitting one to obtain complicated shape crystals from the melt is given. The variable shaping technique provides the growth of crystals with a discrete altering cross-section configuration during crystallization. The dynamic local shaping technique enables one to grow items with a continuous alteration of the side surface profile by a preset program.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1999.11a
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• pp.121-126
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• 1999

In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. In order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment systems is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.187-187
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• 1999

in order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed. Also, the learning architecture to compute inverse kinematic coordinates transformations in the manipulator of auto-equipment system is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulation are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.12
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• pp.1251-1256
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• 2015

This study investigates the friction noise characteristic in relation to the corrosion of metal by using the frictional reciprocating and pin-on-disk system. From the experiments, it is found that the corrosion of metal advances the onset time and increases the magnitude of friction noise. Further, it is observed that the effect of corrosion on friction noise stems from the alteration of tribo-surface during repetitive frictional motion. The alteration of the corrosive contact surface induces a negative friction-velocity slope, by which the corrosion of metal can generate dynamic instability faster than non-corrosion of metal.

• The Journal of Korean Association of Computer Education
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• v.17 no.6
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• pp.103-113
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• 2014

This study designed and implemented a query model for consecutive analyses of dynamic multivariate graph data. First, the query model consists of two procedures; setting the discriminant function, and determining an alteration method. Second, the query model was implemented as a query system that consists of a query panel, a graph visualization panel, and a property panel. A Node-Link Diagram and the Force-Directed Graph Drawing algorithm were used for the visualization of the graph. The results of the queries are visually presented through the graph visualization panel. Finally, this study used the data of worldwide import & export data of small arms to verify our model. The significance of this research is in the fact that, through the model which is able to conduct consecutive analyses on dynamic graph data, it helps overcome the limitations of previous models which can only perform discrete analysis on dynamic data. This research is expected to contribute to future studies such as online decision making and complex network analysis, that use dynamic graph models.

• The Research Journal of the Costume Culture
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• v.19 no.5
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• pp.1019-1030
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• 2011

The aim of this study was to develop a pattern of flight attendant uniform shirts to provide better comfort for their work postures. Flight attendants' work postures were evaluated to determine the problems of clothing and mobility during their work. The pattern of the flight attendants' uniform shirt was altered by applying dynamic wearing ease(DWE). DWE was calculated from four standardized dynamic postures and a static posture. An experimental garment was made with the altered postures. The researcher redesigned the pattern of the uniform shirts, which minimizes physical limitations in movements. The fit and mobility of the shirts were evaluated. Results of this study are as follows. First, the five representative work postures were selected by "clothing stress" and "repetitiveness." These postures included raised arms, twisting midriff and shoulder postures. Five representative postures were selected by using the ergonomic posture assessment device index(OWAS). Second, the experimental garment was developed by applying DWE across the back and at armhole depth, back length, and side length. Third, the fit and mobility of the experimental garments and the original uniform shirts were compared at the flight working environment set and 5 dynamic body postures of raising arms. The experimental garments made with an altered pattern provided better fit and mobility than the original sample shirts.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1574-1580
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• 2008

In this paper, to bring under robust and accurate control of auto-equipment systems which disturbance, parameter alteration of system, uncertainty and so forth exist, neural network controller called dynamic neural processor(DNP) is designed In order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. Also, the learning architecture to compute inverse kinematic coordinates transformations in the Plants of auto-equipment systems is developed and the example that DNP can be used is explained. The architecture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simulations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.49-62
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• 2023

There has been a rapid increase in the creation and alteration of new malware samples which is a huge financial risk for many organizations. There is a huge demand for improvement in classification and detection mechanisms available today, as some of the old strategies like classification using mac learning algorithms were proved to be useful but cannot perform well in the scalable auto feature extraction scenario. To overcome this there must be a mechanism to automatically analyze malware based on the automatic feature extraction process. For this purpose, the dynamic analysis of real malware executable files has been done to extract useful features like API call sequence and opcode sequence. The use of different hashing techniques has been analyzed to further generate images and convert them into image representable form which will allow us to use more advanced classification approaches to classify huge amounts of images using deep learning approaches. The use of deep learning algorithms like convolutional neural networks enables the classification of malware by converting it into images. These images when fed into the CNN after being converted into the grayscale image will perform comparatively well in case of dynamic changes in malware code as image samples will be changed by few pixels when classified based on a greyscale image. In this work, we used VGG-16 architecture of CNN for experimentation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.2
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• pp.55-62
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• 1999

In order to perform a elaborate task like as assembly, manufacturing and so forth of components, tracking control on the trajectory of power coming in contact with a target as well as tracking control on the movement course trajectory of end-effector is indispensable. In this paper, to bring under robust ard accurate control of auto-equipnent systems which disturbance, parameter alteration of system, uncertainty ard so forth exist, neural network controller called dynamic neural processor(DNP) is designed. Also, the learning architecture to compute inverse kinematic coordinates transfonnations in the manirclator of auto-equipnent systems is developed ard the example that DNP can be used is explained The architocture and learning algorithm of the proposed dynamic neural network, the DNP, are described and computer simllations are provided to demonstrate the effectiveness of the proposed learning method using the DNP.he DNP.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.10
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• pp.1728-1737
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• 2003

The vertical lathe is a very popular machine tool in modem manufacturing industries because of its small area of installation. The small installation area is highly desirable for the flexibility improvement of the manufacturing systems such as FMS, CIM, and IMS. The vertical lathe, however, has much taller height compared to the traditional horizontal lathe, and consequently more severe vibrations. In this study, the structure of a vertical lathe is analyzed to investigate the dynamic properties and further to establish some ideas of the design alteration for the improved dynamic stability even at the higher operating speed. The simulation model is implemented to apply those design alterations for the optimization. The result of this study is not only prosperous but also ready to be applied to the optimum design of various machine tool structures for the improved stability.