• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.5
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• pp.70-77
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• 2009

In an RFID network consisting of a single reader and many tags, a framed and slotted ALOHA, which provides a number of slots for the tags to respond, was introduced for arbitrating a collision among tags' responses. In a framed and slotted ALOHA, the number of slots in each frame should be optimized to attain the maximal efficiency in tag cognizance. While such an optimization necessitates the knowledge about the number of tags, the reader hardly knows it. In this paper, we propose a tag cognizance scheme based on framed and slotted ALOHA, which is characterized by directly taking a Bayes action on the number of slots without estimating the number of tags separately. Specifically, a Bayes action is yielded by solving a decision problem which incorporates the prior distribution the number of tags, the observation on the number of slots in which no tag responds and the loss function reflecting the cognizance rate. Also, a Bayes action in each frame is supported by an evolution of prior distribution for the number of tags. From the simulation results, we observe that the pair of evolving prior distribution and Bayes action forms a robust scheme which attains a certain level of cognizance rate in spite of a high discrepancy between the Due and initially believed numbers of tags. Also, the proposed scheme is confirmed to be able to achieve higher cognizance completion probability than a scheme using classical estimate of the number of tags separately.

• Journal of the Korean Statistical Society
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• v.31 no.1
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• pp.33-43
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• 2002

The empirical Bayes linear loss two-action problem is studied. An empirical Bayes test $\delta$$_{n}$ $^{*}$ is proposed. It is shown that $\delta$$_{n}$ $^{*}$ is asymptotically optimal in the sense that its regret converges to zero at a rate $n^{-1}$ over a class of priors and the rate $n^{-1}$ is the optimal rate of convergence of empirical Bayes tests.sts.

• Journal of applied mathematics & informatics
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• v.23 no.1_2
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• pp.153-165
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• 2007

This paper deals with the empirical Bayes two-action problem of testing $H_0\;:\;{\theta}{\leq}{\theta}_0$: versus $H_1\;:\;{\theta}>{\theta}_0$ using a linear error loss for some discrete nonexponential families having probability function either $$f_1(x{\mid}{\theta})=(x{\alpha}+1-{\theta}){\theta}^x\prod\limits_{j=0}^x\;(j{\alpha}+1)$$ or $$f_2(x{\mid}{\theta})=[{\theta}\prod\limits_{j=0}^{x-1}(j{\alpha}+1-{\theta})]/[\prod\limits_{j=0}^x\;(j{\alpha}+1)]$$. Two empirical Bayes tests ${\delta}_n^*\;and\;{\delta}_n^{**}$ are constructed. We have shown that both ${\delta}_n^*\;and\;{\delta}_n^{**}$ are asymptotically optimal, and their regrets converge to zero at an exponential decay rate O(exp(-cn)) for some c>0, where n is the number of historical data available when the present decision problem is considered.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.430-439
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• 2016

Estimation and analysis of traffic jams plays a vital role in an intelligent transportation system and advances safety in the transportation system as well as mobility and optimization of environmental impact. For these reasons, many researchers currently mainly focus on the brilliant machine learning-based prediction approaches for traffic prediction systems. This paper primarily addresses the analysis and comparison of prediction accuracy between two machine learning algorithms: Naïve Bayes and K-Nearest Neighbor (K-NN). Based on the fact that optimized estimation accuracy of these methods mainly depends on a large amount of recounted data and that they require much time to compute the same function heuristically for each action, we propose an approach that applies multi-threading to these heuristic methods. It is obvious that the greater the amount of historical data, the more processing time is necessary. For a real-time system, operational response time is vital, and the proposed system also focuses on the time complexity cost as well as computational complexity. It is experimentally confirmed that K-NN does much better than Naïve Bayes, not only in prediction accuracy but also in processing time. Multi-threading-based K-NN could compute four times faster than classical K-NN, whereas multi-threading-based Naïve Bayes could process only twice as fast as classical Bayes.

• Journal of applied mathematics & informatics
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• v.25 no.1_2
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• pp.1-15
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• 2007

Consider a Rayleigh distribution with $$pdf\;p(x/{\theta})\;=\;2x{\theta}^{-1}\;{\exp}\;({-x^2}/{\theta})$$ and mean lifetime ${\mu}\;=\;\sqrt{\pi\theta}/2$. We study the two-action problem of testing the hypotheses $H_{0}\;:\;{\mu}{\leq}{\mu}_{0}$ against $H_{1}\;:\;{\mu}\;>\;{\mu}_{0}$ using a linear error loss of ${\mid}{\mu}\;-\;{\mu}_{0}{\mid}$ via the empirical Bayes approach. We construct a monotone empirical Bayes test ${\delta}^{*}_{n}$ and study its associated asymptotic optimality. It is shown that the regret of ${\delta}^{*}_{n}$ converges to zero at a rate $\frac{{\ln}^{2}n}{n}$, where n is the number of past data available when the present testing problem is considered.

• Journal of the Korean Statistical Society
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• v.20 no.1
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• pp.67-76
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• 1991

The empirical Bayes version involves ″independent″ repetitions(a sequence) of the component decision problem. With the varying sample size possible, these are not identical components. However, we impose the usual assumption that the parameters sequence $\theta$=($\theta$$_1$, $\theta$$_2$, …) consists of independent G-distributed parameters where G is unknown. We assume that G $\in$ g, a known family of distributions. The sample size $N_i$ and the decisin rule $d_i$ for component i of the sequence are determined in an evolutionary way. The sample size $N_1$ and the decision rule $d_1$$\in$ $D_{N1}$ used in the first component are fixed and chosen in advance. The sample size $N_2$and the decision rule $d_2$ are functions of *see full text($\underline{X}^1$equation omitted), the observations in the first component. In general, $N_i$ is an integer-valued function of *see full text(equation omitted) and, given $N_i$, $d_i$ is a $D_{Ni}$/-valued function of *see full text(equation omitted). The action chosen in the i-th component is *(equation omitted) which hides the display of dependence on *(equation omitted). We construct an empirical Bayes decision rule for estimating normal mean and show that it is asymptotically optimal.

• The Journal of Korea Robotics Society
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• v.4 no.1
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• pp.25-33
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• 2009

One of the main problems of topological localization in a real indoor environment is variations in the environment caused by dynamic objects and changes in illumination. Another problem arises from the sense of topological localization itself. Thus, a robot must be able to recognize observations at slightly different positions and angles within a certain topological location as identical in terms of topological localization. In this paper, a possible solution to these problems is addressed in the domain of global topological localization for mobile robots, in which environments are represented by their visual appearance. Our approach is formulated on the basis of a probabilistic model called the Bayes filter. Here, marginalization of dynamics in the environment, marginalization of viewpoint changes in a topological location, and fusion of multiple visual features are employed to measure observations reliably, and action-based view transition model and action-associated topological map are used to predict the next state. We performed experiments to demonstrate the validity of our proposed approach among several standard approaches in the field of topological localization. The results clearly demonstrated the value of our approach.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.10 no.2
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• pp.41-47
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• 2006

The Renewal process and the Non-homogeneous Poisson process (NHPP) process are probably the most popular models for describing the failure pattern of repairable systems. But both these models are based on too restrictive assumptions on the effect of the repair action. For these reasons, several authors have recently proposed point process models which incorporate both renewal type behavior and time trend. One of these models is the Modulated Power Law Process (MPLP). The Modulated Power Law Process is a suitable model for describing the failure pattern of repairable systems when both renewal-type behavior and time trend are present. In this paper we propose Bayes estimation of the next failure time after the system has experienced some failures, that is, Mean Time Between Failure for the MPLP model. Numerical examples illustrate the estimation procedure.