• 제어로봇시스템학회논문지
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• 제17권12호
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• pp.1210-1218
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• 2011

GA (Genetic Algorithms) are efficient for searching for global optima but may have some problems such as premature convergence, convergence to local extremum and divergence. These phenomena are related to the evolutionary operators. As population diversity converges to low value, the search ability of a GA decreases and premature convergence or converging to local extremum may occur but population diversity converges to high value, then genetic algorithm may diverge. To guarantee that genetic algorithms converge to the global optima, the genetic operators should be chosen properly. In this paper, we analyze the effects of the selection operator, crossover operator, and mutation operator on convergence properties, and propose the sweeping method of mutation probability and elitist propagation rate to maintain the diversity of the GA's population for getting out of the premature convergence. Results of simulation studies verify the feasibility of using these sweeping operators to avoid premature convergence and convergence to local extrema.

• 전기학회논문지
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• 제57권10호
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• pp.1847-1853
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• 2008

This paper presents stochastic methodology based fault detection algorithm for induction motor systems. We measure current of healthy induction motors by means of hall sensor systems and then establish its probability distribution. We propose online probability density estimation which is effective in real-time implementation due to its simplicity and low computational burden. In addition, we accomplish theoretical analysis to demonstrate convergence property of the proposed estimation by using statistical convergence and system stability theory. We apply our fault diagnosis approach to three-phase induction motors and achieve real-time experiment for evaluating its reliability and practicability in industrial fields.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.382-384
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• 2004

In this parer, we propose a clustering that perform algorithm using new convergence properties. For detection and optimization of cluster, we use to similarity measure with cumulative probability and to inference the its parameters with MLE. A merits of using the cumulative probability in our method is very effectiveness that robust to noise or unnecessary data for inference the parameters. And we adopt similarity threshold to converge the number of cluster that is enable to past convergence and delete the other influence for this learning algorithm. In the simulation, we show effectiveness of our algorithm for convergence and optimization of cluster in riven data set.

• 중소기업융합학회논문지
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• 제6권4호
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• pp.79-84
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• 2016

본 논문에서는 스마트 헬스케어 서비스 시스템의 바이오 데이터 분석 과정을 프로세스로 해석하기 위하여, 온톨로지 기반 통계학적 개인 맞춤형 질병예측 기법인 PCADP(Personalized Computer Aided Diagnosis Probability)를 제안하였다. 또한 이러한 개인 맞춤형 질병예측 기법을 바탕으로 스마트 헬스케어 데이터 및 헬스케어 서비스 명세의 의미 있는 표현을 위하여 헬스케어 온톨로지 프레임워크를 시맨틱스형으로 모델링하였다. PCADP 기법은 스마트 헬스케어 환경에서 개인 맞춤형 판별 기법이 갖추어야 할 조건인 실시간 처리, 유연한 구조, 판별과정의 모니터링, 지속적인 개선 등에 부합하는 통계학적 질병예측 기법임을 확인하였다.

• 한국지능시스템학회논문지
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• 제18권6호
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• pp.837-841
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• 2008

In this paper, we consider interval probability as a unifying concept for uncertainty and Choquet integrals with resect to a capacity functional. By using interval probability, we will define an interval-valued capacity functional and Choquet integral with respect to an interval-valued capacity functional. Furthermore, we investigate Choquet weak convergence of interval-valued capacity functionals of random sets.

• Journal of information and communication convergence engineering
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• 제5권2호
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• pp.93-97
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• 2007

In this paper, centralized access control and slot allocation algorithm is proposed for wireless networks. The proposed algorithm is characterized by the contention-based reservation. In order to reduce the collision probability of reservation request, the base station calculates and broadcasts the transmission probability of reservation requests, and the wireless terminal transmits its reservation request with the received transmission probability. The scheduler allocates the uplink data slots based on the successful reservation requests. Simulation results show that the proposed algorithms can provide high channel utilization, and furthermore, maintains constant delay performance in the heavy traffic environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권1호
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• pp.186-205
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• 2019

Intercell interference coordination (ICIC) is considered as a promising technique to increase the spectral efficiency of OFDMA cellular systems. The soft frequency reuse (SFR) and fractional frequency reuse (FFR) are representative and efficient management techniques for ICIC. Herein, to enhance the performance of the SFR scheme, we propose a call admission (CAC) scheme. In this CAC scheme, called Spectrum handoff-SFR(S-SFR), the spectrum handoff technique is applied to the user equipment (UE) located near the cell center. We derive the traffic analysis model to describe the S-SFR. In addition, a two-dimensional (2-D) Markov chain and an outage analysis are used in our analytical model. From the traffic analysis, the significant performance measures are the outage probability, call blocking probability, system throughput and resource utilization. Based on those, the outage probability and system throughput are obtained using resource utilization as an interference pattern. The analytical results are verified with computer simulation results. Finally, we compare our proposed scheme with other ICI schemes.

• 대한수학회보
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• 제57권5호
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• pp.1115-1126
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• 2020

The main purpose of this paper is to establish the rates of convergence in weak limit theorems for normalized geometric sums of independent identically distributed random variables via Zolotarev's probability metric.

• 한국통신학회논문지
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• 제34권2A호
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• pp.85-90
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• 2009

In this paper, as an alternative to constant modulus algorithm based on MSE, maximization of the probability that equalizer output power is equal to the constant modulus of the transmitted symbols is introduced. The proposed algorithm using the gradient ascent method to the maximum probability criterion has superior convergence and steady-state MSE performance, and the error samples of the proposed algorithm exhibit more concentrated density functions in blind equalization environments. Simulation results indicate that the proposed training has a potential advantage versus MSE training for the constant modulus approach to blind equalization.

• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.273-280
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• 1995

This paper gives a generalization of an $L_1$-convergence theorem for dependent processes due to Andrews (1988) and also a probability convergence theorem.