• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1998.11a
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• pp.112-115
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• 1998

We investigate the qualities of organic materials by which can manufacture organic thin films for solar cells and make thin films for insulation layers of an insulated cable. We give pressure stimulation into organic thin films and detect the induced displacement current. In processing of a device manufacture, We can see the process is good from the change of a surface pressure for organic thin films and transfer ratio of area per molecule. The structure of manufactured device is Au/organic thin films(polyimide)/Au and I-V characteristic of the device is measured from 0[V] to +5[V]. The maximum value of measured current is increased as the number of accumulated layers are decreased. The resistance for the number of accumulated layers, the energy density for an input voltage show desired results, and the insulation of a thin film is better as the interval between electrodes is larger.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.98-102
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• 2005

In the QR Decomposition-based (QRD) decoding class, the system performance is sensitive to the error propagation. Thus, it is critical to correctly decode the previous layers. One approach to desensitize the error propagation is to propose the optimal decoding order of layers. In this work, we propose a new extended-list Sorted QRD-base (SQRD) decoding approach. In the proposed decoding scheme, the solution of the few first layers is extended as the list of promising possible solutions. By doing so, the diversity of the lowest layer is increased. As a result, the system performance is less sensitive to the error propagation than its counterparts. The proposed approach is verified by the computer simulation results.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.87-93
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• 2019

These days, as interest in the recommendation system with deep learning is increasing, a number of related studies to develop a performance for collaborative filtering through autoencoder, a state-of-the-art deep learning neural network architecture has advanced considerably. The purpose of this study is to propose autoencoder which is used by the recommendation system to predict ratings, and we added more hidden layers to the original architecture of autoencoder so that we implemented deep autoencoder with 3 to 5 hidden layers for much deeper architecture. In this paper, therefore we make a comparison between the performance of them. In this research, we use 2-dimensional arrays and 3-dimensional tensor as the input dataset. As a result, we found a correlation between matrix entry of the 3-dimensional dataset such as item-time and user-time and also figured out that deep autoencoder with extra hidden layers generalized even better performance than autoencoder.

• International journal of advanced smart convergence
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• v.10 no.3
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• pp.51-58
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• 2021

In this paper, we propose a deep learning classifier for estimating the number of layers in the Earth's structure. When installing a grounding system, knowledge of the subsurface in the area is absolutely necessary. The subsurface structure can be modeled by the earth parameters. Knowing the exact number of layers can significantly reduce the amount of computation to estimate these parameters. The classifier consists of a feedforward neural network. Apparent resistivity curves were used to train the deep learning classifier. The apparent resistivity at 20 equally spaced log points in each curve are used as the features for the input of the deep learning classifier. Apparent resistivity curve data sets are collected either by theoretical calculations or by Wenner's measurement method. Deep learning classifiers are coded by Keras, an open source neural network library written in Python. This model has been shown to converge with close to 100% accuracy.

• Journal of Welding and Joining
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• v.34 no.3
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• pp.69-76
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• 2016

Characteristics of dissimilar metal welds between ASTM Type 316L and carbon steel ASTM A516 Gr.70 made with FCAW were evaluated in terms of microstructure, ferrite content, EDS analysis, hardness, tensile strength, impact toughness and corrosion resistance. Three heat inputs of 10.4, 16.9, 23.4kJ/cm were employed to make joints of dissimilar metals with E309LMoT1-1 wire. Microstructure of dissimilar weld metals consisted of mostly vermicular type of ${\delta}$-ferrite and some lathy type of ${\delta}$-ferrite, and ${\delta}$-ferrite was transformed into globular type in reheated zone. In all conditions, weld metals were solidified on FA solidification mode. Based on the EDS analysis of weld metals, All Creq/Nieq values were in the range of FA solidification mode, and it was decreased with increasing heat inputs whereas it was increased with increasing layers. The amount of ${\delta}$-ferrite was decreased with increasing heat input due to the difference of cooling rate, and it was increased with increasing layers. Accordingly, hardness and tensile strength of dissimilar metals weld joints was decreased with increasing heat input while impact energy was increased with increasing heat input. Corrosion test of dissimilar metals weld joints showed that weight gain rate of heat input 10.4kJ/cm was the greatest, and that of three heat inputs became constant after certain time.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.1
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• pp.137-146
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• 2002

The predictions of the loading characteristics was performed by the neural networks which use the results through structural analysis. The momentum backperpagtion which can be modified the teaming rate and momentum coefficient, was developed. Input patterns of the neural networks are the 9 strains which positioned at the side of the shell and output layers is the loading characteristics. Hidden layers were increased from 1 layers to 3 layers. Developed program which were trained by 9 strains predict the loading characteristics under 0.5%. Inverse engineering can be applicable to the composite laminated cylindrical shells with developed neural networks.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.4
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• pp.145-151
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• 2021

In this paper, we propose a novel visualization technique to explain the predictions of deep neural networks. We use knowledge distillation (KD) to identify the interior of a black-box model for which we know only inputs and outputs. The information of the black box model will be transferred to a white box model that we aim to create through the KD. The white box model will learn the representation of the black-box model. Second, the white-box model generates attention maps for each of its layers using Grad-CAM. Then we combine the attention maps of different layers using the pixel-wise summation to generate a final saliency map that contains information from all layers of the model. The experiments show that the proposed technique found important layers and explained which part of the input is important. Saliency maps generated by the proposed technique performed better than those of Grad-CAM in deletion game.

• KIEE International Transactions on Electrophysics and Applications
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• v.4C no.4
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• pp.137-141
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• 2004

In this paper, a thermopneumatic PMDS (polydimethlysiloxane) micropump with nozzle/diffuser elements is presented. The micropump is composed of nozzle/diffuser elements as dynamic valves, an actuator consisting of a circular PDMS diaphragm and a Cr/Au heater on a glass substrate. Four PDMS layers are used for fabrication of an actuator chamber, actuator diaphragm by a spin coating process, spacer layer, and nozzle/diffuser by the SU-8 molding process. The radius and thickness of the actuator diaphragm is 2 mm and 30 ${\mu}{\textrm}{m}$, respectively. The length and the conical angle of the nozzle/diffuser elements are 3.5 mm and 20$^{\circ}$, respectively. The actuator diaphragm is driven by the air cavity pressure variation caused by ohmic heating and natural cooling. The flow rate of the micropump in the frequency domain is measured for various duty cycles of the square wave input voltage. When the square wave input voltage of 5 V DC is applied to the heater, the maximum flow rate of the micropump is 44.6 ${mu}ell$/min at 100 Hz with a duty ratio of 80% under the zero pressure difference.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.52-55
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• 2004

VMS (virtual manufacturing system) may be defined as a transparent interface/control mechanism to support human decision-making via simulation and monitoring of real operating situation through modeling of all activities in RMS (real manufacturing system). The three main layers in VMS are business process layer, manufacturing execution layer, and facility operation layer, and each layer is represented by a specific software system having its own input modeler module. The current version of these input modelers has been implemented based on its own 'local' framework, and as a result, there are no information sharing mechanism, nor a common user view among them. Proposed in this paper is a unified modeler framework covering the three VMS layers, in which the concept of PPR (product-process-resource) model is employed as a common semantics framework and a 2D graphic network model is used as a syntax framework. For this purpose, abstract class PPRObject and GraphicObject are defined and then a subclass is inherited from the abstract class for each application layer. This feature would make it easier to develop and maintain the individual software systems. For information sharing, XML is used as a common data format.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4214-4230
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• 2020

This paper introduces a pilot allocation scheme for massive MIMO systems based on deep convolutional neural network (CNN) learning. This work is an extension of a prior work on the basic deep learning framework of the pilot assignment problem, the application of which to a high-user density nature is difficult owing to the factorial increase in both input features and output layers. To solve this problem, by adopting the advantages of CNN in learning image data, we design input features that represent users' locations in all the cells as image data with a two-dimensional fixed-size matrix. Furthermore, using a sorting mechanism for applying proper rule, we construct output layers with a linear space complexity according to the number of users. We also develop a theoretical framework for the network capacity model of the massive MIMO systems and apply it to the training process. Finally, we implement the proposed deep CNN-based pilot assignment scheme using a commercial vanilla CNN, which takes into account shift invariant characteristics. Through extensive simulation, we demonstrate that the proposed work realizes about a 98% theoretical upper-bound performance and an elapsed time of 0.842 ms with low complexity in the case of a high-user-density condition.