• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 I
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• pp.481-484
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• 2003

OFDM(Orthogonall Frequency Division Multiplexing) is an efficient transmission techniques for the frequency selective fading wireless channel where conventional modulations suffer from severe performance degradation. Recently, more efficient techniques for the OFDM are paid much attention such as adaptive modulation for each subcarrier, since more bit rate has been required for the wireless data network [1-3]. The proposed system employs the adaptive modulation between transmitter and receiver in each subcarrier, where the bit and power is properly allocated. Also multiple antenna system called MIMO is considered, which result in robustness in channel delay, improved power efficiency and improved bit SNR for the given BER.

• 전자공학회논문지C
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• 제35C권1호
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• pp.55-63
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• 1998

This paper presents a new maching algorithm by Hopfield Neural Network for 3-D object recognition. In the proposed method, a model object is represented by a set of polygons in a single coordinate. And each polygon is described by a set of features; feature attributes. In case of 3-D object recognition, the scale and poses of the object are important factors. So we propose a strategy for 3-D object recognition independently to its scale and poses. In this strategy, the respective features of the input or the model objects are changed to the startegical constants when they are compared with one another. Finally, we show that the proposed method has a robustness through the results of experiments which included the classification of the input objects and the matching sequence to its 3-D rotation and scale.

• 대한화학회지
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• 제58권6호
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• pp.543-552
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• 2014

A quantitative structure activity relationship (QSAR) study is performed for modeling and prediction of oral bioavailabilities of 216 diverse set of drugs. After calculation and screening of molecular descriptors, linear and nonlinear models were developed by using multiple linear regression (MLR), artificial neural network (ANN), support vector machine (SVM) and random forest (RF) techniques. Comparison between statistical parameters of these models indicates the suitability of SVM over other models. The root mean square errors of SVM model were 5.933 and 4.934 for training and test sets, respectively. Robustness and reliability of the developed SVM model was evaluated by performing of leave many out cross validation test, which produces the statistic of $Q^2_{SVM}=0.603$ and SPRESS = 7.902. Moreover, the chemical applicability domains of model were determined via leverage approach. The results of this study revealed the applicability of QSAR approach by using SVM in prediction of oral bioavailability of drugs.

• 한국통신학회논문지
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• 제15권6호
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• pp.475-484
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• 1990

Direct Sequence 스펙트럼 확산방식에서 전원동기를 이용한 전원선 통신장치의 구성을 제안한 것이다. 전원선은 주파수대역폭이 일반적으로 10KHz~450KHz로 제한되어 있고, 특히 동기회로가 복잡해져서 제작가격이 높아지며 또한 시스템 규모가 커지며 다중통신하기가 어렵다. 동기회로를 이루기 위해서 교류전원에 동기하는 전원동기 Clock 발생회로를 제안하고 어드레스 설정기를 두어서 다중통신을 가능하게 하였다.

• 대한전기학회논문지:전력기술부문A
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• 제51권6호
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• pp.296-301
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• 2002

Modern power systems are very complex and to model them completely is impractical for electromagnetic transient studies. Therefore areas outside the immediate area of interest must be represented by some form of Frequency Dependent Network Equivalent (FDNE). In this paper a method for developing Frequency Dependent AC system Equivalent (FDACSE) using Z-domain rational Function Fitting is presented and demonstrated. The FDACSE is generated by Linearized Least Squares Fitting(LSF) of the frequency response of a Z-domain formulation. This 1 & 2 port FDACSE have been applied to the New Zealand South Island AC power system. The electromagnetic transient package PSCAD/EMTDC is used to assess the transient response of the 1 & 2 port FDACSE developed under different condition (linear load, fault and nonlinear loading). The study results have indicated the robustness and accuracy of 1 & 2 port FDACSE for electromagnetic transient studies.

• 디지털산업정보학회논문지
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• 제13권3호
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• pp.43-51
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• 2017

MANET consists of only wireless nodes having limited processing capability. It processes routing and data transmission through cooperation among each other. And it is exposed to many attack threats due to the dynamic topology by movement of nodes and multi-hop communication. Therefore, the reliability of transmitted data between nodes must be improved and security of integrity must be high. In this paper, we propose a method to increase the reliability of transmitted data by providing a secure cryptography protocol. The proposed method used a hierarchical structure to provide smooth cryptographic services. The cluster authentication node issues the cluster authentication key pair and unique key to the nodes. The nodes performs the encryption through two steps of encryption using cluster public key and block encryption using unique key. Because of this, the robustness against data forgery attacks was heightened. The superior performance of the proposed method can be confirmed through comparative experiment with the existing security routing method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권7호
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• pp.3050-3065
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• 2016

Neural networks (NNs) are extensively used in applications requiring signal classification and regression analysis. In this paper, a NN based threshold selection algorithm for 60 GHz millimeter wave (MMW) time of arrival (TOA) estimation using an energy detector (ED) is proposed which is based on the skewness, kurtosis, and curl of the received energy block values. The best normalized threshold for a given signal-to-noise ratio (SNR) is determined, and the influence of the integration period and channel on the performance is investigated. Results are presented which show that the proposed NN based algorithm provides superior precision and better robustness than other ED based algorithms over a wide range of SNR values. Further, it is independent of the integration period and channel model.

• 한국컴퓨터정보학회논문지
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• 제20권7호
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• pp.49-56
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• 2015

In this paper, we introduce recent cloud system security technologies categorizing them according to Reliability, Availability, Serviceability, Integrity, and Security (RASIS), terms that evaluate robustness of the computer system. Then we describe examples of security attacks and corresponding security technologies for each of them. We introduce security technologies based on Software Defined Network (SDN) for Reliability, security technologies based on hypervisor and virtualization for Availability, disaster restoration systems for Serviceability, authorization and access control technologies for Integrity, and encryption algorithms for Security. We believe that this paper provide wise view and necessary information for recent cloud system security technologies.

• ETRI Journal
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• 제41권6호
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• pp.750-759
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• 2019

Presently, the extraction of hand-crafted features is still the dominant method in radar emitter recognition. To solve the complicated problems of selection and updation of empirical features, we present a novel automatic feature extraction structure based on deep learning. In particular, a convolutional neural network (CNN) is adopted to extract high-level abstract representations from the time-frequency images of emitter signals. Thus, the redundant process of designing discriminative features can be avoided. Furthermore, to address the performance degradation of a single platform, we propose the construction of an ensemble learning-based architecture for multi-platform fusion recognition. Experimental results indicate that the proposed algorithms are feasible and effective, and they outperform other typical feature extraction and fusion recognition methods in terms of accuracy. Moreover, the proposed structure could be extended to other prevalent ensemble learning alternatives.

• Journal of Power Electronics
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• 제11권6호
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• pp.922-930
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• 2011

In this paper, two radial basis function neural networks (RBFNNs) are used to dynamically identify harmonics content in converter waveforms based on the p-q (real power-imaginary power) theory. The converter waveforms are analyzed and the types of harmonic content are identified over a wide operating range. Constant power and sinusoidal current compensation strategies are investigated in this paper. The RBFNN filtering training algorithm is based on a systematic and computationally efficient training method called the hybrid learning method. In this new methodology, the RBFNN is combined with the p-q theory to extract the harmonics content in converter waveforms. The small size and the robustness of the resulting network models reflect the effectiveness of the algorithm. The analysis is verified using MATLAB simulations.