• KEPCO Journal on Electric Power and Energy
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• v.1 no.1
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• pp.157-162
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• 2015

Large scale superconducting magnetic energy storage (SMES) system requires very high magnetic energy density in its superconducting coils to enhance the energy capacity and efficiency of the system. The recent high temperature superconducting (HTS) conductors, so called 2G conductors, show very good performance under very high magnetic field so that they seem to be perfect materials for the large scale SMES coils. A general shape of the coil system with the 2G HTS conductor has been a tor oid, because the magnetic field applied perpendicularly to the surface of the 2G HTS conductor could be minimized in this shape of coil. However, a toroid coil requires a 3-dimensional computation to acquire the characteristics of its critical current density - magnetic field relations which needs very complicated numerical calculation, very high computer specification, and long calculation time. In this paper, we suggested an analytic and statistical calculation method to acquire the maximum magnetic flux density applied perpendicularly to the surface of the 2G HTS conductor and the stored energy in the toroid coil system. Although the result with this method includes some errors but we could reduce these errors within 5 percent to get a reasonable estimation of the important parameters for design process of the HTS toroid coil system. As a result, the calculation time by the suggested method could be reduced to 0.1 percent of that by the 3-dimensional numerical calculation.

• Journal of KIISE:Information Networking
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• v.30 no.1
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• pp.75-81
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• 2003

User authentication, including confidentiality, integrity over untrusted networks, is an important part of security for systems that allow remote access. Using human-memorable Password for remote user authentication is not easy due to the low entropy of the password, which constrained by the memory of the user. This paper presents a new password authentication and key agreement protocol suitable for authenticating users and exchanging keys over an insecure channel. The new protocol resists the dictionary attack and offers perfect forward secrecy, which means that revealing the password to an attacher does not help him obtain the session keys of past sessions against future compromises. Additionally user passwords are stored in a form that is not plaintext-equivalent to the password itself, so an attacker who captures the password database cannot use it directly to compromise security and gain immediate access to the server. It does not have to resort to a PKI or trusted third party such as a key server or arbitrator So no keys and certificates stored on the users computer. Further desirable properties are to minimize setup time by keeping the number of flows and the computation time. This is very useful in application which secure password authentication is required such as home banking through web, SSL, SET, IPSEC, telnet, ftp, and user mobile situation.

• The Journal of Korean Institute of Information Technology
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• v.15 no.12
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• pp.157-164
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• 2017

Mobile ECG signal measurement is a technique to measure small signals of several mV, and many studies have been conducted to remove noise including wandering scheme. Removal of the equipotential line noise caused by shaking or movement of the electrode cable is one of the core research contents for the electrocardiogram measurement. In this study, we proposed a modified step-size of combined NLMS(normalized least squares) and DLMS(delayed least squares) adaptive filter to eliminate baseline noise from ECG signals. The proposed method mainly adjusts initial filter step-size to reduce distortion of original ECG signals characteristic after eliminating baseline noise. The modified filter step-size is scaled by filter order size and distortion minimization factor. This method is suitable for portable ECG device with a small processor and less power consumption. This technique also decreases computation time which is essential for real-time filtering. The proposed filter also increase the signal to noise ratio (SNR) compared to conventional NLMS filter.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.88-95
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• 2018

Maintainability indicates how easily a system can be restored to the normal state when a system failure occurs. Systems developed to have high maintainability can be competitive due to reduced maintenance time, workforce and resources. Quantification of the maintainability is possible in many ways, but only after prototype production or with historical data. As such, the graph theory and 3D model data have been used, but there are limitations in management efficiency and early use. To solve this problem, we studied the maintainability index of weapon systems using SysML-based modeling and simulation technique. A SysML structure diagram was generated to simultaneously model the system design and maintainability of system components by reflecting the maintainability attributes acquired from the system engineering tool. Then, a SysML parametric diagram was created to quantify the maintainability through simulation linked with MATLAB. As a result, an integrated model to account for system design and maintainability simultaneously has been presented. The model can be used from early design stages to identify components with low maintainability index. The design of such components can be changed to improve maintainability and thus to reduce the risks of cost overruns and time delays due to belated design changes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.523-533
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• 2019

The convergence of engineering and IT technology has brought many changes to the industry as well as academic research. In particular, computer simulation technology has evolved to a level that can accurately simulate actual physical phenomena and analyze them in real time. In this paper, we describe the CFD technology, which is mainly used in industry, and the post processor that uses the augmented reality which is emerging as the post-processing. Research on the visualization of fluid simulation results using AR technology is actively being carried out. However, due to the large size of the result data, it is limited to researches that are published in a desktop environment. Therefore, it is limitation that needs to be reviewed in actual space. In this paper, we discuss how to solve these problems. We analyze the fluid analysis results in the post-processing, and then perform optimizing data (more than 70%)to support operation in the mobile environment. In the visualization, lightweight data is used to perform real-time tracking using cloud computing, The analysis result is matched to the screen and visualized. This allows the user to review and analyze the fluid analysis results in an efficient and immersive manner in the various spaces where the simulation is performed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.2
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• pp.23-36
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• 2005

The main obstacle hindering the wide deployment of identity-based cryptosystem is that the entity responsible for creating the private key has too much power. As a result, private keys are no longer private. One obvious solution to this problem is to apply the threshold technique. However, this increases the authentication computation, and communication cost during the key issuing phase. In this paper, we propose a new effi ient model for issuing multiple private keys in identity-based encryption schemes based on the Weil pairing that also alleviates the key escrow problem. In our system, the private key of a user is divided into two components, KGK (Key Description Key) and KUD(Key Usage Desscriptor), which are issued separately by different parties. The KGK is issued in a threshold manner by KIC (Key Issuing Center), whereas the KW is issued by a single authority called KUM (Key Usage Manager). Changing KW results in a different private key. As a result, a user can efficiently obtain a new private key by interacting with KUM. We can also adapt Gentry's time-slot based private key revocation approach to our scheme more efficiently than others. We also show the security of the system and its efficiency by analyzing the existing systems.

• Journal of IKEEE
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• v.25 no.2
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• pp.243-251
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• 2021

In this paper, we propose a measurement technique of indoor location based on markerless applicable to AR. The proposed technique has the following originality. The first is to extract feature points and use them to generate local patches to enable faster computation by learning and using only local patches that are more useful than the surroundings without learning the entire image. Second, learning is performed through deep learning using the convolution neural network structure to improve accuracy by reducing the error rate. Third, unlike the existing feature point matching technique, it enables indoor location measurement including left and right movement. Fourth, since the indoor location is newly measured every frame, errors occurring in the front side during movement are prevented from accumulating. Therefore, it has the advantage that the error between the final arrival point and the predicted indoor location does not increase even if the moving distance increases. As a result of the experiment conducted to evaluate the time required and accuracy of the measurement technique of indoor location based on markerless applicable to AR proposed in this paper, the difference between the actual indoor location and the measured indoor location is an average of 12.8cm and a maximum of 21.2cm. As measured, the indoor location measurement accuracy was better than that of the existing IEEE paper. In addition, it was determined that it was possible to measure the user's indoor location in real time by displaying the measured result at 20 frames per second.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.378-390
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• 2022

With the recent development of deep composite multiplication neural network learning, deep learning techniques applied to single-image super-resolution have shown good results, and the strong expression ability of deep networks has enabled complex nonlinear mapping between low-resolution and high-resolution images. However, there are limitations in applying it to real-time or low-power devices with increasing parameters and computational amounts due to excessive use of composite multiplication neural networks. This paper uses blocks that extract hierarchical characteristics little by little using information distillation and suggests the Recursive Distillation Super Resolution Network (RDSRN), a lightweight network that improves performance by making more accurate high frequency components through high frequency residual purification blocks. It was confirmed that the proposed network restores images of similar quality compared to RDN, restores images 3.5 times faster with about 32 times fewer parameters and about 10 times less computation, and produces 0.16 dB better performance with about 2.2 times less parameters and 1.8 times faster processing time than the existing lightweight network CARN.

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.365-372
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• 2022

Compared to sensors mainly used in human activity recognition (HAR) systems, inertial measurement unit (IMU) sensors are small and light, so can achieve lightweight system at low cost. Therefore, in this paper, we propose a binary neural network (BNN) based gait pattern analysis system using IMU sensor, and present the design and implementation results of an FPGA-based accelerator for computational acceleration. Six signals for gait are measured through IMU sensor, and a spectrogram is extracted using a short-time Fourier transform. In order to have a lightweight system with high accuracy, a BNN-based structure was used for gait pattern classification. It is designed as a hardware accelerator structure using FPGA for computation acceleration of binary neural network. The proposed gait pattern analysis system was implemented using 24,158 logics, 14,669 registers, and 13.687 KB of block memory, and it was confirmed that the operation was completed within 1.5 ms at the maximum operating frequency of 62.35 MHz and real-time operation was possible.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.429-440
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• 2021

With the recent development of deep convolutional neural network learning, deep learning techniques applied to single image super-resolution are showing good results. One of the existing deep learning-based super-resolution techniques is RDN(Residual Dense Network), in which the initial feature information is transmitted to the last layer using residual dense blocks, and subsequent layers are restored using input information of previous layers. However, if all hierarchical features are connected and learned and a large number of residual dense blocks are stacked, despite good performance, a large number of parameters and huge computational load are needed, so it takes a lot of time to learn a network and a slow processing speed, and it is not applicable to a mobile system. In this paper, we use the residual dense structure, which is a continuous memory structure that reuses previous information, and the residual dense channel attention block using the channel attention method that determines the importance according to the feature map of the image. We propose a method that can increase the depth to obtain a large receptive field and maintain a concise model at the same time. As a result of the experiment, the proposed network obtained PSNR as low as 0.205dB on average at 4× magnification compared to RDN, but about 1.8 times faster processing speed, about 10 times less number of parameters and about 1.74 times less computation.