• Tribology and Lubricants
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• v.11 no.5
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• pp.128-135
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• 1995

Atomic force microscopy/friction force microscopy (AFM/FFM) techniques are increasingly used for tribological studies of engineering surfaces at scales, ranging from atomic and molecular to microscales. These techniques have been used to study surface roughness, adhesion, friction, scratching/wear, indentation, detection of material transfer, and boundary lubrication and for nanofabrication/nanomachining purposes. Micro/nanotribological studies of single-crystal silicon, natural diamond, magnetic media (magnetic tapes and disks) and magnetic heads have been conducted. Commonly measured roughness parameters are found to be scale dependent, requiring the need of scale-independent fractal parameters to characterize surface roughness. Measurements of atomic-scale friction of a freshly-cleaved highly-oriented pyrolytic graphite exhibited the same periodicity as that of corresponding topography. However, the peaks in friction and those in corresponding topography were displaced relative to each other. Variations in atomic-scale friction and the observed displacement has been explained by the variations in interatomic forces in the normal and lateral directions. Local variation in microscale friction is found to correspond to the local slope suggesting that a ratchet mechanism is responsible for this variation. Directionality in the friction is observed on both micro- and macro scales which results from the surface preparation and anisotropy in surface roughness. Microscale friction is generally found to be smaller than the macrofriction as there is less ploughing contribution in microscale measurements. Microscale friction is load dependent and friction values increase with an increase in the normal load approaching to the macrofriction at contact stresses higher than the hardness of the softer material. Wear rate for single-crystal silicon is approximately constant for various loads and test durations. However, for magnetic disks with a multilayered thin-film structure, the wear of the diamond like carbon overcoat is catastrophic. Breakdown of thin films can be detected with AFM. Evolution of the wear has also been studied using AFM. Wear is found to be initiated at nono scratches. AFM has been modified to obtain load-displacement curves and for nanoindentation hardness measurements with depth of indentation as low as 1 mm. Scratching and indentation on nanoscales are the powerful ways to screen for adhesion and resistance to deformation of ultrathin fdms. Detection of material transfer on a nanoscale is possible with AFM. Boundary lubrication studies and measurement of lubricant-film thichness with a lateral resolution on a nanoscale have been conducted using AFM. Self-assembled monolyers and chemically-bonded lubricant films with a mobile fraction are superior in wear resistance. Finally, AFM has also shown to be useful for nanofabrication/nanomachining. Friction and wear on micro-and nanoscales have been found to be generally smaller compared to that at macroscales. Therefore, micro/nanotribological studies may help def'me the regimes for ultra-low friction and near zero wear.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.5
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• pp.177-188
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• 2018

The scale of graph data has been increased rapidly because of the growth of mobile Internet applications and the proliferation of social network services. This brings upon the imminent necessity of efficient distributed and parallel graph processing approach since the size of these large-scale graphs are easily over a capacity of a single machine. Currently, there are two popular parallel graph processing approaches, vertex-centric graph processing and block centric processing. While a vertex-centric graph processing approach can easily be applied to the parallel processing system, a block-centric graph processing approach is proposed to compensate the drawbacks of the vertex-centric approach. In these systems, the initial quality of graph partition affects to the overall performance significantly. However, it is a very difficult problem to divide the graph into optimal states at the initial phase. Thus, several dynamic load balancing techniques have been studied that suggest the progressive partitioning during the graph processing time. In this paper, we present a load balancing algorithms for the block-centric graph processing approach where most of dynamic load balancing techniques are focused on vertex-centric systems. Our proposed algorithm focus on an improvement of the graph partition quality by dynamically reassigning blocks in runtime, and suggests block split strategy for escaping local optimum solution.

• Journal of the Korea Convergence Society
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• v.11 no.5
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• pp.19-25
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• 2020

As the number of mobile device users increases, research on various user authentication methods has been actively conducted to protect sensitive personal information. Knowledge-based techniques have the disadvantage that security is deteriorated due to easy exposure of authentication means, and proprietary-based techniques have a problem of increasing construction cost and low user convenience to use the service. In order to solve this problem, a FIDO authentication system, which is a user authentication method using a smart device, has been proposed. Since the FIDO authentication system performs authentication based on the biometric information of the user, the risk of the authentication means being leaked is low, and since the authentication information is stored in the user's smart device, the user information due to server hacking is solved. Through this, it is possible to select and utilize user authentication technology suitable for the security level of the service. In this paper, we introduce the FIDO authentication system, explain the main parts required for FIDO UAF client-server development, and show examples of implementation using UAF open source provided by ebay.

• Journal of KIISE:Software and Applications
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• v.33 no.6
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• pp.580-587
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• 2006

As PDA, IMT-2000, and e-Book are developed and popular in these days, the number of users who use these products has been increasing. However, available memory size of these machines is still smaller than that of desktop PCs. In these products, TrueType fonts have been increased in demand because the number of users who want to use good quality fonts has increased, and TrueType fonts are of great use in Windows CE products. However, TrueType fonts take a large portion of available device memory, considering the small memory sizes of mobile devices. Therefore, it is required to reduce the size of TrueType fonts. In this paper, two-phase compression techniques are presented for the purpose of reducing the sire of hangul bitmap data embedded in TrueType fonts. In the first step, each character in bitmap is divided into initial consonant, medial vowel, and final consonant, respectively, then the character is recomposed into the composite bitmap. In the second phase, if any two consonants or vowels are determined to be the same, one of them is removed. The TrueType embedded bitmaps in Hangeul Wanseong (pre-composed) and Hangul Johab (pre-combined) are used in compression. By using our compression techniques, the compression rates of embedded bitmap data for TrueType fonts can be reduced around 35% in Wanseong font, and 7% in Johab font. Consequently, the compression rate of total TrueType Wanseong font is about 9.26%.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.21-30
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• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.377-380
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• 2022

An application called an app can be downloaded and used on mobile devices. Among them, Android-based apps have the disadvantage of being implemented on an open source basis and can be exploited by anyone, but unlike iOS, which discloses only a small part of the source code, Android is implemented as an open source, so it can analyze the code. However, since anyone can participate in changing the source code of open source-based Android apps, the number of malicious codes increases and types are bound to vary. Malicious codes that increase exponentially in a short period of time are difficult for humans to detect one by one, so it is efficient to use a technique to detect malicious codes using AI. Most of the existing malicious app detection methods are to extract Features and detect malicious apps. Therefore, three ways to select the optimal feature to be used for learning after feature extraction are proposed. Finally, in the step of modeling with optimal features, ensemble techniques are used in addition to a single model. Ensemble techniques have already shown results beyond the performance of a single model, as has been shown in several studies. Therefore, this paper presents a plan to select the optimal feature and implement a learning model for Android app-based malicious code detection.

• Smart Media Journal
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• v.13 no.4
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• pp.33-48
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• 2024

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.585-594
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• 2003

Java card is one of promising smart card platform with java technology. Java card defines necessary packages and classes for Embedded device that have small memory such as smart card Jana card is compatible with EMV that is Industry specification standard and ISO-7816 that is international standard. However, Java card is not offers user authentication protocol. In this paper, We design and implement an user authentication protocol applicable wireless devices based on Java Card using standard 3GPP Specification (SMS), Java Card Specification (APDU), Cryptography and so on. Our Java Card user authentication techniques can possibly be applied to the area of M-Commerce, Wireless Security, E-Payment System, Mobile Internet, Global Position Service, Ubiquitous Computing and so on.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.5
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• pp.51-58
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• 2012

Accurate transmission and estimation of the channel statistics affected by high Doppler spread is one of the main issues of concern for the latest and future mobile communication systems. Therefore, it is important to research in novel channel estimation techniques that overcome the limitations of conventional methods. In this paper, we propose a novel channel estimation method that, after a simple estimation in the first OFDM symbol, uses Kalman filter to predict the channel in the rest of OFDM symbols with pilot subcarriers. Our method is designed considering the lattice-type arrangement of pilot subcarriers in LTE-Advanced, since most of the studies so far focus on block-type or comb-type pilot arrangements. In addition, to optimize the results, we use the filtering of channel impulse response and Wiener Filter for the estimation of the channel frequency response in the rest of the subcarriers.