• 제목/요약/키워드: optimal stopping rule

검색결과 8건 처리시간 0.024초

Exploiting Multichannel Diversity in Spectrum Sharing Systems Using Optimal Stopping Rule

  • Xu, Yuhua;Wu, Qihui;Wang, Jinlong;Anpalagan, Alagan;Xu, Yitao
    • ETRI Journal
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    • 제34권2호
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    • pp.272-275
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    • 2012
  • This letter studies the problem of exploiting multichannel diversity in a spectrum sharing system, where the secondary user (SU) sequentially explores channel state information on the licensed channels with time consumption. To maximize the expected achievable throughput for the SU, we formulate this problem as an optimal stopping problem, whose objective is to choose the right channel to stop exploration based on the observed signal-to-noise ratio sequence. Moreover, we propose a myopic but optimal rule, called one-stage look-ahead rule, to solve the stopping problem.

감소(減少)하는 고장률(故障率)하에서 오류예측 및 테스트 시간(時間)의 최적화(最適化)에 관한 연구(硏究) (Error Forecasting & Optimal Stopping Rule under Decreasing Failure Rate)

  • 최명호;윤덕균
    • 품질경영학회지
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    • 제17권2호
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    • pp.17-26
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    • 1989
  • This paper is concerned with forecasting the existing number of errors in the computer software and optimizing the stopping time of the software test based upon the forecasted number of errors. The most commonly used models have assessed software reliability under the assumption that the software failure late is proportional to the current fault content of the software but invariant to time since software faults are independents of others and equally likely to cause a failure during testing. In practice, it has been observed that in many situations, the failure rate decrease. Hence, this paper proposes a mathematical model to describe testing situations where the failure rate of software limearly decreases proportional to testing time. The least square method is used to estimate parameters of the mathematical model. A cost model to optimize the software testing time is also proposed. In this cost mode two cost factors are considered. The first cost is to test execution cost directly proportional to test time and the second cost is the failure cost incurred after delivery of the software to user. The failure cost is assumed to be proportional to the number of errors remained in the software at the test stopping time. The optimal stopping time is determined to minimize the total cost, which is the sum of test execution cast and the failure cost. A numerical example is solved to illustrate the proposed procedure.

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A Batch Sequential Sampling Scheme for Estimating the Reliability of a Series/Parallel System

  • Enaya, T.;Rekab, L.;Tadj, L.
    • International Journal of Reliability and Applications
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    • 제11권1호
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    • pp.17-22
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    • 2010
  • It is desired to estimate the reliability of a system that has two subsystems connected in series where each subsystem has two components connected in parallel. A batch sequential sampling scheme is introduced. It is shown that the batch sequential sampling scheme is asymptotically optimal as the total number of units goes to infinity. Numerical comparisons indicate that the batch sequential sampling scheme performs better than the balanced sampling scheme and is nearly optimal.

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BUYING AND SELLING RULES FOR A SIMPLE TRANSACTION OF A MEAN-REVERTING ASSET

  • Shin, Dong-Hoon
    • 한국수학교육학회지시리즈B:순수및응용수학
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    • 제18권2호
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    • pp.129-139
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    • 2011
  • We consider an optimal trading rule in this paper. We assume that the underlying asset follows a mean-reverting process and the transaction consists of one buying and one selling. To maximize the profit, we find price levels to buy low and to sell high. Associated HJB equations are used to formulate the value function. A verification theorem is provided for sufficient conditions. We conclude the paper with a numerical example.

Prediction-Based Routing Methods in Opportunistic Networks

  • Zhang, Sanfeng;Huang, Di;Li, Yin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제9권10호
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    • pp.3851-3866
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    • 2015
  • The dynamic nature of opportunistic networks results in long delays, low rates of success for deliveries, etc. As such user experience is limited, and the further development of opportunistic networks is constrained. This paper proposes a prediction-based routing method for opportunistic networks (PB-OppNet). Firstly, using an ARIMA model, PB-OppNet describes the historical contact information between a node pair as a time series to predict the average encounter time interval of the node pair. Secondly, using an optimal stopping rule, PB-OppNet obtains a threshold for encounter time intervals as forwarding utility. Based on this threshold, a node can easily make decisions of stopping observing, or delivering messages when potential forwarding nodes enter its communication range. It can also report different encounter time intervals to the destination node. With the threshold, PB-OppNet can achieve a better compromise of forwarding utility and waiting delay, so that delivery delay is minimized. The simulation experiment result presented here shows that PB-OppNet is better than existing methods in prediction accuracy for links, delivery delays, delivery success rates, etc.

On the Bayesian Sequential Estiamtion Problem in k-Parameter Exponential Family

  • Yoon, Byoung-Chang;Kim, Jea-Joo
    • Journal of the Korean Statistical Society
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    • 제10권
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    • pp.128-139
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    • 1981
  • The Bayesian sequential estimation problem for k parameters exponential families is considered using loss related to the Fisher information. Tractable expressions for the Bayes estimator and the posterior expected loss are found, and the myopic or one-step-ahead stopping rule is defined. Sufficient conditions are given for optimality of the myopic procedure, and the myopic procedure is shown to be asymptotically optimal in all cases considered.

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On Bahadur Efficiency and Bartlett Adjustability of Quasi-LRT Statistics

  • Lee, Kwan-Jeh
    • Journal of the Korean Statistical Society
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    • 제27권3호
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    • pp.251-264
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    • 1998
  • When the LRT is not feasible, we define quasi-LRT(QLRT) as a modification of the LRT Under some appropriate conditions the QLRT shares Bahadur optimality and Bartlett Adjustability with the LRT. When we can find maximum likelihood estimator under the null parameter space but not under the unrestricted parameter space, our QLRT is Bahadur optimal as is the LRT We suggest the stopping rule of the Newton-Raphson iterations for constructing the QLRT statistics which are Bartlett adjustable.

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퍼터베이션 분석을 이용한 대기행렬 네트워크의 최적화 (Optimization of Queueing Network by Perturbation Analysis)

  • 권치명
    • 한국시뮬레이션학회논문지
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    • 제9권2호
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    • pp.89-102
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    • 2000
  • In this paper, we consider an optimal allocation of constant service efforts in queueing network to maximize the system throughput. For this purpose, using the perturbation analysis, we apply a stochastic optimization algorithm to two types of queueing systems. Our simulation results indicate that the estimates obtained from a stochastic optimization algorithm for a two-tandem queuing network are very accurate, and those for closed loop manufacturing system are a little apart from the known optimal allocation. We find that as simulation time increases for obtaining a new gradient (performance measure with respect to decision variables) by perturbation algorithm, the estimates tend to be more stable. Thus, we consider that it would be more desirable to have more accurate sensitivity of performance measure by enlarging simulation time rather than more searching steps with less accurate sensitivity. We realize that more experiments on various types of systems are needed to identify such a relationship with regards to stopping rule, the size of moving step, and updating period for sensitivity.

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