• Journal of the Korean Geographical Society
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• 제45권5호
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• pp.647-668
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• 2010

As well as "Sangyeongpyo," "Gogyeong-Jeongripyo," table of national road transportation system is important to comprehend identity of national geography in Joseon era even if it had not been researched yet. The aim of this study is to divide type of these tables and find its genealogy through mainly analyzing the road network and land names. As the result of this research, "Yeojigo," topographical researches of Korea, edited by Shin Gyeong-Jun as a palt of "Dongguk-Munheonbigo" official book in natural history of the Joseon Dynasty published in 1770, might be identified as the origin for all copy of "Gogyeong-Jeongripyo." Then "Gogyeong-Jeongripyo," can be divided into at least three major types; almost direct descent of "Yeoji go" as 'type1', minor modification as 'type2', and hybrid edition(type3) with second type that quoted many land names as route information from "Dorogo," another topography specialized for road transportation. Since "Dorogo" was also composed by Shin, after all, all genealogy of "Gogyeong-Jeongripyo" came from him.

• Journal of the Korea Society of Computer and Information
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• 제15권3호
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• pp.81-89
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• 2010

The research on peer-to-peer streaming schemes has largely focused on tree-push and mesh-pull structures. However, the tree-push structure has a defect that the tree restructuring time is long, and the mesh-pull structure has long startup delay and lag time from source servers. In this paper, we propose a new peer-to-peer live streaming scheme based on a push-mesh structure that takes advantages of tree-push and mesh-pull structure simultaneously. This structure basically provides the mesh-pull mechanism for data transmission and utilizes peers with high network upload capacity. It also supports the push mechanism along with paths from a source server, super peers, and selected general peers. By NS-2 simulation experiments, we finally show that our proposed scheme can achieve shorter startup delay than the mesh-pull structure, similar lag time to tree-push structure and best playback continuity among the three schemes.

• Journal of Intelligence and Information Systems
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• 제12권2호
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• pp.167-183
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• 2006

Since data mining attempts to find unknown facts or rules by dealing with also vaguely-known data sets, it always suffers from high error rate. In order to reduce the error rate, many researchers have employed multiple models in solving a problem. In this research, we present a new type of multiple models, called DyMoS, whose unique feature is that it classifies the input data and applies the different model developed appropriately for each class of data. In order to evaluate the performance of DyMoS, we applied it to a real customer churn problem of an automobile insurance company, The result shows that the DyMoS outperformed any model which employed only one data mining technique such as artificial neural network, decision tree and case-based reasoning.

• Transactions of the Korean Society of Mechanical Engineers A
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• 제38권2호
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• pp.205-210
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• 2014

Recently, pattern recognition methods have been widely used by researchers for fault diagnoses of mechanical systems. A pattern recognition method determines the soundness of a mechanical system by detecting variations in the system's vibration characteristics. Hidden Markov models (HMMs) and artificial neural networks (ANNs) have recently been used as pattern recognition methods in various fields. In this study, a HMM-ANN hybrid method for the fault diagnosis of a mechanical system is introduced, and a rotating wind turbine blade with a crack is selected for fault diagnosis. The existence, location, and depth of said crack are identified in this research. For improving the diagnostic accuracy of the method in spite of the presence of noise, a moment with a few specific frequencies is applied to the structure.

• Journal of the Korea Society for Simulation
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• 제30권1호
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• pp.75-85
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• 2021

In this paper, we present a deep neural network-based prediction model that processes and analyzes the corporate credit and personal credit information of individual business owners as a new method to predict the default rate of individual business more accurately. In modeling research in various fields, feature selection techniques have been actively studied as a method for improving performance, especially in predictive models including many features. In this paper, after statistical verification of macroeconomic indicators (macro variables) and credit information (micro variables), which are input variables used in the default rate prediction model, additionally, through the credit information feature selection method, the final feature set that improves prediction performance was identified. The proposed credit information feature selection method as an iterative & hybrid method that combines the filter-based and wrapper-based method builds submodels, constructs subsets by extracting important variables of the maximum performance submodels, and determines the final feature set through prediction performance analysis of the subset and the subset combined set.

• Journal of Navigation and Port Research
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• 제28권3호
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• pp.213-219
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• 2004

In this paper, a LNA(Low Noise Amplifier) has been developed, which is operating at L-band i.e., 1452∼1492 MHz for satellite DAB(Digital Audio Brcadcasting) receiver. The LNA is designed to improve input and output reflection coefficient and VSWR(Voltage Standing Wave Ratio) by balanced amplifier. The LNA consists of low noise amplification stage and gain amplification stage, which make a using of GaAs FET ATF-10136 and VNA-25 respectively, and is fabricated by hybrid method. To supply most suitable voltage and current, active bias circuit is designed Active biasing offers the advantage that variations in $V_P$ and $I_{DSS}$ will not necessitate a change in either the source or drain resistor value for a given bias condition. The active bias network automatically sets $V_{gs}$ for the desired drain voltage and drain current. The LNA is fabricated on FR-4 substrate with RF circuit and bias circuit, and integrated in aluminum housing. As a reults, the characteristics of the LNA implemented more than 32 dB in gain. 0.2 dB in gain flatness. lower than 0.95 dB in noise figure, 1.28 and 1.43 each input and output VSWR, and -13 dBm in $P_{1dB}$.

• Journal of the Institute of Electronics Engineers of Korea TC
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• 제47권3호
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• pp.8-15
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• 2010

Due to the increasing demand of network convergence, in future, hybrid/integrated satellite and terrestrial systems will play an important role. In that case, compatibilities between the satellite and terrestrial systems are very important for efficiency of the systems. 3GPP Long Term Evolution (LTE) is one of the most powerful candidates of the 4G system Therefore, in this paper, we introduce the design of interleaver for mobile satellite system based 3GPP LTE specification. The 4G system including the LTE specification adopted adaptive modulation and coding (AMC) schemes for efficient usage of resources, and the updating interval of resource allocation is an order of msec. However, because of the long round trip delay of satellite systems, we cannot employ the same AMC scheme specified for the terrestrial system, and thus it cannot effectively counteract to short term fadings. Therefore, in order to overcome these problems, we propose an interleaver scheme combined with AMC. We present the interleaγer design results considering mobile satellite system based on the LTE and analyze the simulation results.

• Corrosion Science and Technology
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• 제9권4호
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• pp.147-152
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• 2010

Conventional paints for conversion coating applications in steel production derived mainly from water-based polymer dispersions containing several additives actually show good general performance, but suffer from poor scratch and abrasion resistance during use. The reason for this is because the relatively soft organic binder matrix dominates the mechanical surface properties. In order to maintain the high quality and decorative function of coated steel sheets, the mechanical performance of the surface needs to be improved significantly. In fact the wear resistance should be enhanced without affecting the optical appearance of the coatings by using appropriate nanoparticulate additives. In this direction, nanocomposite coating compositions (Nanomer$^{(R)}$) have been derived from water-based polymer dispersions with an increasing amount of surface-modified nanoparticles in aqueous dispersion in order to monitor the effect of degree of filling with rigid nanoparticles. The surface of nanoparticles has been modified for optimum compatibility with the polymer matrix in order to achieve homogeneous nanoparticle dispersion over the matrix. This approach has been extended in such a way that a more expanded hybrid network has been condensed on the nanoparticle surface by a hydrolytic condensation reaction in addition to the quasi-monolayer type small molecular surface modification. It was expected that this additional modification will lead to more intensive cross-linking in coating systems resulting in further improved scratch-resistance compared to simple addition of nanoparticles with quasi-monolayer surface modification. The resulting compositions have been coated on zinc-galvanized steel and cured. The wear resistance and the corrosion protection of the modified coating systems have been tested in dependence on the compositional change, the type of surface modification as well as the mixing conditions with different shear forces. It has been found out that for loading levels up to 50 wt.-% nanoparticles, the mechanical wear resistance remains almost unaffected compared to the unmodified resin. In addition, the corrosion resistance remained unaffected even after $180^{\circ}$ bending test showing that the flexibility of coating was not decreased by nanoparticle addition. Electron microscopy showed that the inorganic nanoparticles do not penetrate into the organic resin droplets during the mixing process but rather formed agglomerates outside the polymer droplet phase resulting in quite moderate cross linking while curing, because of viscosity. The proposed mechanisms of composite formation and cross linking could explain the poor effect regarding improvement of mechanical wear resistance and help to set up new synthesis strategies for improved nanocomposite morphologies, which should provide increased wear resistance.

• Journal of Internet Computing and Services
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• 제23권5호
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• pp.127-134
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• 2022

Steganography is a hidden technique in which secret messages are hidden in various multimedia files, and it is widely exploited for cyber crime and attacks because it is very difficult for third parties other than senders and receivers to identify the presence of hidden information in communication messages. Botnet typically consists of botmasters, bots, and C&C (Command & Control) servers, and is a botmasters-controlled network with various structures such as centralized, distributed (P2P), and hybrid. Recently, in order to enhance the concealment of botnets, research on Stego Botnet, which uses SNS platforms instead of C&C servers and performs C&C communication by applying steganography techniques, has been actively conducted, but image or video media-oriented stego botnet techniques have been studied. On the other hand, audio files such as various sound sources and recording files are also actively shared on SNS, so research on stego botnet based on audio steganography is needed. Therefore, in this study, we present the results of comparative analysis on hidden capacity by file type and tool through experiments, using a stego botnet that performs C&C hidden communication using audio files as a cover medium in Telegram Messenger.

• Journal of Intelligence and Information Systems
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• 제21권1호
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.