• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.10
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• pp.2256-2262
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• 2015

Recently, device-to-device (D2D) communication has been considered as key technology for future cellular system, because it can solve the problem of excessive data traffic increment and can also provide new communication services. Herein, we propose new neighbor discovery for D2D communication and examine its performance. Our proposed scheme is proximity beacon based discovery in which wireless resource for pilot transmission is assigned based on the spatial correlation of wireless channel and sensing period is adjusted according to target accuracy such that power consumption can be reduced. The performance of our propose scheme is analyzed mathematically and verified through computer simulations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.578-581
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• 2007

For tracing tag locations, a trajectories should be modeled and indexed in radio frequency identification (RFID) systems. The trajectory of a tag can be represented as a line that connects two spatiotemporal locations captured when the tag enters and leaves the vicinity of a reader. If a tag enters but does not leave a reader, its trajectory is represented only as a point captured at entry. Because the information that the tag stays in the reader is missing from the trajectory represented only as a point, we should extend the region of a query to find the tag that remains in a reader. In this paper, we propose an interval data model of tag's trajectory in order to solve the problem. Trajectories of tags are represented as two kinds of intervals; dynamic intervals which are time-dependent lines and static intervals which are fixed lines. We also show that the interval data model has better performance than others with a cost model

• Archives of design research
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• v.16 no.1
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• pp.15-24
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• 2003

In the latest date, vehicles are offered to the drivers, not only the skill for shifting but the pleasure for driving vehicles that are existing today can be a social problem because the amount of vehicles that are increasing give difficulty for the traffic facilities and parking expansion. these day 80% of four wheeled vehicle carriers single or double person the reducing car scale is an important thing about the financial good use resources of energy and the storage of environment. A solution for these problem is a new general idea vehicle development for one or two person to ride. For the sake of these reasons, first, the information is collected and analyzed about existing foreign countries production. Car external design is intended by mathematical modeling, simulation and model testing about frame system of new concept specially we would like to show three wheeled vehicle that has active tilt control(ATC) system. This car tilts actively by the center rotation wheel and frame when the vehicle turns.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.2
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• pp.78-83
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• 2019

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically abnormal sound on machines using the acoustic emission by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose here an automatic fault diagnosis method of hand drills using discrete wavelet transform(DWT) and pattern recognition techniques such as artificial neural networks(ANN). We first conduct a filtering analysis based on DWT. The power spectral density(PSD) is performed on the wavelet subband except for the highest and lowest low frequency subband. The PSD of the wavelet coefficients are extracted as our features for classifier based on ANN the pattern recognition part. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.19 no.1 s.71
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• pp.63-71
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• 2006

This study presents the effective stiffness-based optimal technique to control Quantitatively lateral drift for shear wall-frame structure system using sensitivity analysis. To this end, the element stiffness matrices are constituted to solve the compatibility problem of displacement degree of freedom between the frame and shear wall. Also, lateral drift constraint to introduce the approximation concept that can preserve the generality of the mathematical programming and can effectively solve the large scaled problems is established. And, the section property relationships for shear wall and frame members are considered in order to reduce the number of design variables and differentiate easily the stiffness matrices. Specifically, constant-shape assumption which is uniformly varying in size during optimal process is applied in frame structure. The thickness or length of shear wall can be changed depending on user's intent. Two types of 20 story shear wall-frame structure system are presented to illustrate the features of the stiffness-based optimal design technique.

• The KIPS Transactions:PartC
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• v.13C no.4 s.107
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• pp.461-470
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• 2006

Recently mobile Stream Control Transmission Protocol (mSCTP) has been proposed as a transport layer approach for supporting mobility. When a mobile terminal (MT) is not located in the home network. a terminal that wishes to communicate with the MT is not able to establish mSCTP association to the MT, since mSCTP does not include the location management mechanism. In order to solve this problem. an interworking approach using the Session Initiation Protocol (SIP) INVITE method has been proposed. However, this approach has shown subsequent delay in acquiring the current location information of the MT when initiating mSCTP association establishment. In this paper, we propose new SIP methods and an approach that minimizes the address acquisition delay (AAD) by utilizing those SIP methods. Mathematical analysis and simulation results show that the proposed approach is more efficient than the previous approach in terms of AAD in all kinds of SIP environments.

• Convergence Security Journal
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• v.21 no.1
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• pp.33-44
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• 2021

Data outsourcing utilizing the Cloud faces a problem of the third-party exposure, modulation, and reliability for the provided computational delegation results. In order to solve those problematic security issues, homomorphic encryption(HE) which executes calculation and analysis on encrypted data becomes popular. By extension, a new type of HE with a authentication functionality, homomorphic authenticated encryption(HAE) is suggested. However, a research on the HAE is on the initial stage. Furthermore, based on a message authenticated scheme with HE, the method and analysis to design is still absent. This paper aims to analyze an HAE, with a generic combination of a message authenticated scheme and a HE, known as "Encrypt with Authentication". Following a series of analysis, we show that by adopting a unforgeable message authenticated scheme, the generically constructed HAE demonstrated an unforgeability as well. Though, a strong unforgeability is not the case. This paper concludes that although indistinguishable HE can be applied to design the HAE, a security issue on the possibility of indistinguishability is still not satisfied.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.121-126
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• 2020

Sound-based machine fault diagnosis is the automatic detection of abnormal sound in the acoustic emission signals of the machines. Conventional methods of using mathematical models were difficult to diagnose machine failure due to the complexity of the industry machinery system and the existence of nonlinear factors such as noises. Therefore, we want to solve the problem of machine fault diagnosis as a deep learning-based image classification problem. In the paper, we propose a CNN-based automatic machine fault diagnosis method using Spectrogram images. The proposed method uses STFT to effectively extract feature vectors from frequencies generated by machine defects, and the feature vectors detected by STFT were converted into spectrogram images and classified by CNN by machine status. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.191-197
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• 2021

To calculate the induced charge of atoms in molecular dynamics, linear equations for the induced charges need to be solved. As induced charges are determined at each time step, the process involves considerable computational costs. Hence, an efficient method for calculating the induced charge distribution is required when analyzing large systems. This paper introduces the Uzawa method for solving saddle point problems, which occur in linear systems, for the solution of the Lagrange equation with constraints. We apply the accelerated Uzawa algorithm, which reduces computational costs noticeably using the Schur complement and preconditioned conjugate gradient methods, in order to overcome the drawback of the Uzawa parameter, which affects the convergence speed, and increase the efficiency of the matrix operation. Numerical models of molecular dynamics in which two gold nanoparticles are placed under external electric fields reveal that the proposed method provides improved results in terms of both convergence and efficiency. The computational cost was reduced by approximately 1/10 compared to that for the Gaussian elimination method, and fast convergence of the conjugate gradient, as compared to the basic Uzawa method, was verified.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.91-98
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• 2022

Lifelog data continuously collected through a wearable device may contain many outliers, so in order to improve data quality, it is necessary to find and remove outliers. In general, since the number of outliers is less than the number of normal data, a class imbalance problem occurs. To solve this imbalance problem, we propose a method that applies Variational AutoEncoder to outliers. After preprocessing the outlier data with proposed method, it is verified through a number of machine learning models(classification). As a result of verification using body weight data, it was confirmed that the performance was improved in all classification models. Based on the experimental results, when analyzing lifelog body weight data, we propose to apply the LightGBM model with the best performance after preprocessing the data using the outlier processing method proposed in this study.