• Honam Mathematical Journal
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• v.25 no.1
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• pp.95-115
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• 2003

Consider the problem of estimating a $p{\times}1$ mean vector ${\theta}(p{\geq}4)$ under the quadratic loss, based on a sample $X_1,\;{\cdots}X_n$. We find an optimal decision rule within the class of Lindley type decision rules which shrink the usual one toward the mean of observations when the underlying distribution is that of a variance mixture of normals and when the norm $||{\theta}-{\bar{\theta}}1||$ is known, where ${\bar{\theta}}=(1/p)\sum_{i=1}^p{\theta}_i$ and 1 is the column vector of ones. When the norm is restricted to a known interval, typically no optimal Lindley type rule exists but we characterize a minimal complete class within the class of Lindley type decision rules. We also characterize the subclass of Lindley type decision rules that dominate the sample mean.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.7A
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• pp.635-640
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• 2002

We investigate optimal and suboptimal decision rules for parallel code acquisition in chip asynchronous direct-sequence spread-spectrum systems. The conventional decision rule for parallel acquisition is to choose the largest correlator output of a receiver. However, such a scheme is optimum only for chip synchronous models. In this paper, an optimal decision rule is derived based on the maximum-likehood criterion for chip asynchronous models. A simpler suboptimal decision rule is also discussed. The performance of the optimum and suboptimum decision rules is compared to that of the conventional decision rule. Numerical results show that, for chip asynchronous models, both the optimal and suboptimal decision rules outperform the conventional decision rule.

• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.13-27
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• 1996

The management of blood inventory is very important within the medical care system. The efficient management of blood supplies and demands for transfusion is of great economic and social importance to both hospitals and patients. Fro any blood type, there is a complex interaction among the optimal inventory level, daily demand level , daily supply level, transfusion to crossmatch ratio, crossmatch release period, issuing policy and the age of arriving units that determine the shortage and outdate rate. In this paper, we develop an efficient decision rule for blood inventory management in a hospital blood bank which can support efficient hospital blood inventory management using simulation, The primary use of the efficient decision rule will be to establish minimum cost function which consists of inventory levels , period in inventory, outdate and shortage rate for whole blood and various component inventories for a hospital blood bank or a transfusion service, If the adminstrator compute the mean daily demand for each blood type, the mean daily supply for each blood type, the length of the crossmatch release period and the average transfusion to crossmatch ratio , then it is possible to apply the efficient decision rule to compute the optimal inventory level, inventory period , outdate and shortage rate. This rule can also be used as a decision support system that allows the blood bank adminstrator to do sensitivity analysis related to controlled blood inventory parameters.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.22 no.8
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• pp.761-767
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• 2011

In this paper, to improve performance of spectrum sensing, we propose the method which can find optimal threshold based on power of PU(Primary User) signal. To find optimal threshold value, we will use mathematical method, and find threshold which can has lowest error probability. Each SU(Secondary User) use this threshold and All Su makes local decision. All Su Send local decision to FC(Fusion Center). In this paper we consider K-out-of-n rule to combining local decision. To make global decision value, FC find optimal n. In the FC. FC received local decision which has lowest error probability and using optimal n and these vaule. FC make global decision value. In this paper, to analysis performance proposed scheme, we simulate proposed scheme using matlab and compare with traditional OR Rule. As a result of simulation, we can know that preposed scheme can get a better performance than traditional OR rule.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.37-45
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• 2000

Consider the problem of estimating a $p{\times}1$ mean vector ${\underline{\theta}}(p{\geq}4)$ under the quadratic loss, based on a sample ${\underline{x}_{1}},\;{\cdots}{\underline{x}_{n}}$. We find an optimal decision rule within the class of Lindley type decision rules which shrink the usual one toward the mean of observations when the underlying distribution is that of a variance mixture of normals and when the norm ${\parallel}\;{\underline{\theta}}\;-\;{\bar{\theta}}{\underline{1}}\;{\parallel}$ is known, where ${\bar{\theta}}=(1/p){\sum_{i=1}^p}{\theta}_i$ and $\underline{1}$ is the column vector of ones.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.97-98
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• 2006

Fusion of decisions from multiple distributed sensor nodes is studied in this work. Based on the canonical parallel fusion model, we derive the optimal likelihood ratio based fusion rule with the assumptions of the generalized Gaussian noise model and the arbitrary fading channel. This optimal fusion rule, however, requires the complete knowledge of the channels and the detection performance of local sensor nodes. To mitigate these requirements and to provide near optimum performance, we derive suboptimum fusion rules by using high and low signal-to-noise ratio (SNR) approximations to the optimal fusion rule. Performance evaluation is conducted through simulations.

• IE interfaces
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• v.10 no.1
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• pp.119-134
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• 1997

The management of blood inventory is very important within the medical care system. The efficient management of blood supplies and demands for transfusions is of great economic and social importance to both hospitals and patients. For any blood type, there is a complex interaction among the optimal inventory level, daily demand level, daily supply level, transfusion to crossmatch ratio, crossmatch release period, issuing policy and the age of arriving units that determine the shortage and outdate rate. In this paper, we develop an efficient decision rule for blood inventory management in a hospital blood bank which can support efficient hospital blood inventory management using simulation. The primary use of the efficient decision rule will be to establish minimum cost function which consists of inventory levels, period in inventory, outdate and shortage rate for whole blood and various component inventories for a hospital blood bank or a transfusion service. If the administrator compute the mean daily demand for each blood type, the mean daily supply for each blood type, the length of the crossmatch release period and the average transfusion to crossmatch ratio, then it is possible to apply the efficient decision rule to compute the optimal inventory level, inventory period, outdate and shortage rate. This rule can also be used as a decision support system that allows the blood bank administrator to do sensitivity analysis related to controllable blood inventory parameters.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.127-135
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• 2011

Efficient ordering of decision variables is one of the methods that find solutions quickly in the depth first search using backtracking. At this time, development of variables ordering algorithms considering dynamic and static properties of the problems is very important. However, to exploit optimal variable ordering algorithms appropriate to the problems. In this paper, we propose a problem classifying rule which provides problem type based on variables' properties, and use this rule to predict optimal type of variable ordering algorithms. We choose frequency allocation problem as a DS-type whose decision variables have dynamic and static properties, and estimate optimal variable ordering algorithm. We also show the usefulness of problem classifying rule by applying base station problem as a special case whose problem type is not generated from the presented rule.

• Journal of the Korean Statistical Society
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• v.9 no.2
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• pp.135-144
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• 1980

A problem of choosing units for the series system and the 1-out-of-2 system from k available brands is treated from a decision-theoretic points of view. It is assumed that units from each brand have exponentially distributed life lengths, and that the loss functions are inversely proportional to the reliability of the system. For the series system the 'natural' rule is shown to be optimal. For the 1-out-of-2 system, the Bayes rule wrt the natural conjugate prior is derived and teh constants to implement the Bayes rule are given.

• Journal of Integrative Natural Science
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• v.5 no.3
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• pp.186-189
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• 2012

For the mean vector of a p-variate normal distribution ($p{\geq}3$), the optimal estimation within the class of James-Stein type decision rules under the quadratic loss are given when the underlying distribution is that of a variance mixture of normals and when the norm ${\parallel}\underline{{\theta}}{\parallel}$ in known. It also demonstrated that the optimal estimation within the class of Lindley type decision rules under the same loss when the underlying distribution is the previous type and the norm ${\parallel}{\theta}-\overline{\theta}\underline{1}{\parallel}$ with $\overline{\theta}=\frac{1}{p}\sum\limits_{i=1}^{n}{\theta}_i$ and $\underline{1}=(1,{\cdots},1)^{\prime}$ is known.