• Journal of information and communication convergence engineering
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• 제12권2호
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• pp.97-103
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• 2014

In spectrum sensing, there is a tradeoff between the probability of missed detection and the probability of a false alarm according to the value of the sensing threshold. Therefore, it is important to determine the sensing threshold suitable to the environment of cognitive radio networks. In this study, we consider a cognitive radio-based ad hoc network where secondary users directly communicate by using the same frequency band as the primary system and control their transmit power on the basis of the distance between them. First, we investigate a condition in which the primary and the secondary users can share the same frequency band without harmful interference from each other, and then, propose an algorithm that controls the sensing threshold dynamically on the basis of the transmit power of the secondary user. The analysis and simulation results show that the proposed sensing threshold control algorithm has low probabilities of both missed detection and a false alarm and thus, enables optimized spectrum sharing between the primary and the secondary systems.

• Journal of Information Processing Systems
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• 제16권6호
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• pp.1261-1270
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• 2020

The physical security layer of industrial wireless sensor networks in the event of an eavesdropping attack has been investigated in this paper. An optimal sensor selection scheme based on the maximum channel capacity is proposed for transmission environments that experience Nakagami fading. Comparing the intercept probabilities of the traditional round robin (TRR) and optimal sensor selection schemes, the system secure performance is analyzed. Simulation results show that the change in the number of sensors and the eavesdropping ratio affect the convergence rate of the intercept probability. Additionally, the proposed optimal selection scheme has a faster convergence rate compared to the TRR scheduling scheme for the same eavesdropping ratio and number of sensors. This observation is also valid when the Nakagami channel is simplified to a Rayleigh channel.

• 디지털융복합연구
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• 제13권9호
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• pp.201-208
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• 2015

SNS와 페이스북과 같은 서비스가 대중화되면서 마이크로블로그와 같은 작은 크기의 빅 데이터 사용이 증대되고 있다. 그러나, 현재까지 작은 크기의 빅 데이터의 탐색 결과의 정확성과 계산비용은 미해결 상태로 남아있다. 본 논문에서는 빅 데이터 환경에서 마이크로블러그와 같은 작은 크기의 텍스트 정보의 탐색 속도를 향상시키기 위한 확률기반의 서브넷 선택 기법을 제안한다. 제안 기법은 데이터의 속성 정보에 확률값을 부여하여 서브넷을 구성하여 데이터 탐색 속도를 높였다. 또한, 제안 기법은 분산된 데이터를 손쉽게 접근하기 위해서 서브넷을 구성하는 데이터 의확률값 간 연계 정보를 쌍으로 처리함으로써 데이터의 접근성을 향상시켰다. 실험결과, 제안 기법은 CELF 알고리즘보다 평균 6.8% 높은 탐지율을 보였으며, 처리시간은 평균 8.2% 단축시켰다.

• Journal of the Korean Statistical Society
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• 제31권2호
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• pp.261-276
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• 2002

Probability density or its derivatives may have a discontinuity/change point at an unknown location. We propose a method of estimating the location and the jump size of the discontinuity point based on kernel type density or density derivatives estimators with one-sided equivalent kernels. The rates of convergence of the proposed estimators are derived, and the finite-sample performances of the methods are illustrated by simulated examples.

• 대한수학회논문집
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• 제22권1호
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• pp.137-143
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• 2007

In this paper, a Berstein-Hoeffding type inequality is established for linearly negative quadrant dependent random variables. A condition is given for almost sure convergence and the associated rate of convergence is specified.

• 한국통신학회논문지
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• 제23권8호
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• pp.1984-1989
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• 1998

Convergence properties of the stochastic gradient adaptive algorithm based on the least mean absolute third (LMAT) error criterion is presented.In particular, the performnce of the algorithmis examined and compared with least mena square (LMS) algorithm for several different probability densities of the measurement noisein a system identification mode. It is observedthat the LMAT algorithm outperforms the LMS algorithm for most of the noise probability densities, except for the case of the exponentially distributed noise.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권11호
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• pp.3967-3983
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• 2014

The performance of an partial relay selection on the decode-and-forward (DF) mode cognitive radio (CR) relay networks is studied, with some important factors, including the outage probability, the bit error ratio (BER), and the average channel capacity being analyzed. Different from the conventional relay selection schemes, the impact of spectrum sensing process as well as the spectrum utilization efficiency of primary users on the performance of DF-based CR relaying networks has been taken into consideration. In particular, the exact closed-form expressions for the figures of merit such as outage probability, BER, and average channel capacity over independent and identically distributed (i.i.d.) Rayleigh fading channels, have been derived in this paper. The validity of the proposed analysis is proven by simulation, which showed that the numerical results are consistent with the theoretical analysis in terms of the outage probability, the BER and the average channel capacity. It is also shown that the full spatial diversity order can always be obtained at the signal-to-noise ratio (SNR) range of [0dB, 15dB] in the presence of multiple potential relays.

• Journal of the Korean Data and Information Science Society
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• 제10권2호
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• pp.465-472
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• 1999

We considered a bootstrap method for constructing confidenc intervals for a one parameter model using multinomial sampling. The convergence rates or the proposed bootstrap method are calculated for model-based maximum likelihood estimators(MLE) using multinomial sampling. Monte Carlo simulation was used to compare the performance of bootstrap methods with normal approximations in terms of the average coverage probability criterion.

• 디지털융복합연구
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• 제12권12호
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• pp.293-301
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• 2014

본 논문에서는 데이터 마이닝에 필요한 클러스터링과정에서 불필요한 정보를 감축하기 위하여 베이지언 사후확률의 신뢰도를 이용한 새로운 척도를 제안한다. 데이터 감축을 위한 속성의 중요도가 클러스터링의 결과에 지배적이기 때문에 많은 속성의 변별력을 향상시키기 위하여 사후확률의 신뢰도에 정보 엔트로피를 적용하였다. 제안된 사후확률을 기반으로 한 러프 엔트로피 척도에 의한 속성의 신뢰도의 중복성은 엔트로피의 자연로그에 의하여 상당히 줄어든다. 따라서 제안된 척도에 의하여 생성된 군집화 알고리즘은 속성값의 변별력을 향상시켜 기존의 리덕트를 최소화하였고, 이는 분할의 효율성을 향상시킬 수 있었다. 제안된 알고리즘의 검증을 위해 패턴분류 문제에 적용되는 ACME 데이터에 대하여 속성간의 변별력, 분할결과에 따른 분할의 순정도를 기존의 알고리즘과 비교 분석하였다.

• Smart Structures and Systems
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• 제15권3호
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• pp.735-749
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• 2015

The major difficulty of using Bayesian probabilistic inference for system identification is to obtain the posterior probability density of parameters conditioned by the measured response. The posterior density of structural parameters indicates how plausible each model is when considering the uncertainty of prediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posterior inference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting (DREAM) algorithm boasts a population-based mechanism, which nms multiple different Markov chains simultaneously, and a global optimum exploration ability. This paper proposes an improved differential evolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density of structural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of the adaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robust solutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters with limited output data and noise polluted measurements.