The Experts below are selected from a list of 68097 Experts worldwide ranked by ideXlab platform
Zhou Zhen-hong - One of the best experts on this subject based on the ideXlab platform.
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Optimal Threshold price for name-your-own-price retailer with limited marketing period
Journal of Computer Applications, 2011Co-Authors: Zhou Zhen-hongAbstract:Name-Your-Own-Price(NYOP),a new sales mode,has emerged in recent years and is different from traditional pricing mode.To solve the problem of Optimal pricing strategy of a name-your-own-price retailer when marketing period is limited and the retailers' inventory is limited,the online retailers' maximum expected revenue model was put forward based on optimization method.The relationship between the Optimal Threshold price and limited inventory and the selling time were obtained by numerical analysis of the model.The conclusion shows that the retailer should set the Optimal Threshold price based on sales period and inventory.
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ICEE - The Optimal Threshold Price for the Name-Your-Own-Price Retailer with Stochastic Demand
2010 International Conference on E-Business and E-Government, 2010Co-Authors: Zhou Zhen-hong, Huang Shen-zeAbstract:Name-Your-Own-Price (NYOP), a new sales mode, has emerged in recent years and is different from traditional pricing mode. Most previous papers study the NYOP channel from the auction perspective or predicting the retailers` inventory is unlimited. However, the NYOP channel differs substantially from the environment studied in the auction. The typical NYOP retailer such as priceline.com sells airline tickets, hotels et al that faces a supply constraint. The online retailers` stochastic revenue model are put forward and numerically analyzed when customers` stochastic demand and the probability distribution of pricing are known. The relationship between the Optimal Threshold price and limited inventory and the selling time are discussed.
Jongan Park - One of the best experts on this subject based on the ideXlab platform.
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automatic hepatic tumor segmentation using statistical Optimal Threshold
International Conference on Computational Science, 2005Co-Authors: Seungjin Park, Kyungsik Seo, Jongan ParkAbstract:This paper proposes an automatic hepatic tumor segmentation method of a computed tomography (CT) image using statistical Optimal Threshold. The liver structure is first segmented using histogram transformation, multi-modal Threshold, maximum a posteriori decision, and binary morphological filtering. Hepatic vessels are removed from the liver because hepatic vessels are not related to tumor segmentation. Statistical Optimal Threshold is calculated by a transformed mixture probability density and minimum total probability error. Then a hepatic tumor is segmented using the Optimal Threshold value. In order to test the proposed method, 262 slices from 10 patients were selected. Experimental results show that the proposed method is very useful for diagnosis of the normal and abnormal liver.
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International Conference on Computational Science (1) - Automatic hepatic tumor segmentation using statistical Optimal Threshold
Lecture Notes in Computer Science, 2005Co-Authors: Seungjin Park, Kyungsik Seo, Jongan ParkAbstract:This paper proposes an automatic hepatic tumor segmentation method of a computed tomography (CT) image using statistical Optimal Threshold. The liver structure is first segmented using histogram transformation, multi-modal Threshold, maximum a posteriori decision, and binary morphological filtering. Hepatic vessels are removed from the liver because hepatic vessels are not related to tumor segmentation. Statistical Optimal Threshold is calculated by a transformed mixture probability density and minimum total probability error. Then a hepatic tumor is segmented using the Optimal Threshold value. In order to test the proposed method, 262 slices from 10 patients were selected. Experimental results show that the proposed method is very useful for diagnosis of the normal and abnormal liver.
Houmin Yan - One of the best experts on this subject based on the ideXlab platform.
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Approximating Optimal Threshold values for unreliable manufacturing systems via stochastic optimization
[1992] Proceedings of the 31st IEEE Conference on Decision and Control, 1Co-Authors: Houmin Yan, G. Yin, X.c. LouAbstract:The algorithms proposed utilize perturbation analysis to carry out gradient estimation and stochastic approximation to find the Optimal Threshold values for unreliable one- and two-machine systems. The perturbation analysis techniques initiated by Y.C. Ho and X. Cao (1991) are used to deduce a simple gradient estimate, and the stochastic optimization techniques are employed to develop iterative algorithms for approximating the Optimal Threshold values. The formulation for the one-machine problem is given and the iterative algorithm is also developed. An example for the one-machine case is included. The result from the numerical study is compared with existing analytical results. The extension to multimachine systems is explained. >
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Further results on approximation of the Optimal Threshold values for manufacturing systems
Proceedings of 1994 33rd IEEE Conference on Decision and Control, 1Co-Authors: George Yin, Houmin YanAbstract:The main goal in this work is to develop numerical algorithms for approximating the Optimal Threshold values of manufacturing systems. The key idea is to construct gradient estimates of the objective function with respect to the Threshold values, and to use stochastic recursive algorithms for locating the Optimal value. Motivated by designing more feasible methods of solutions, for systems described by differential equations with long run average cost functions, the authors previously developed stochastic optimization algorithms. The main idea is that instead of finding the Optimal control of the systems, the authors turn the problem into an optimization problem by focusing their attention to the Threshold control type of policies. The underlying problem is then converted to an optimization problem, namely, finding the Optimal Threshold values. In this paper, after briefly discussing the convergence of the algorithm, the authors concentrate on the rate of convergence issues. >
Adriana Palacio - One of the best experts on this subject based on the ideXlab platform.
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Protecting against key-exposure: strongly key-insulated encryption with Optimal Threshold
Applicable Algebra in Engineering Communication and Computing, 2006Co-Authors: Mihir Bellare, Adriana PalacioAbstract:Key-insulated encryption schemes use a combination of key splitting and key evolution to protect against key exposure. Existing schemes, however scale poorly, having cost proportional to the number t of time periods that may be compromised by the adversary, and thus are practical only for small values of t . Yet in practice t might be large. This paper presents a strongly key-insulated encryption scheme with Optimal Threshold . In our scheme, t need not be known in advance and can be as large as one less than the total number of periods, yet the cost of the scheme is not impacted. This brings key-insulated encryption closer to practice. Our scheme is based on the Boneh-Franklin identity-based encryption (IBE) scheme [9], and exploits algebraic properties of the latter. Another contribution of this paper is to show that (not strongly) key-insulated encryption with Optimal Threshold and allowing random-access key updates (which our scheme and all others known allow) is equivalent to a restricted form of IBE. This means that the connection between key-insulated encryption and IBE is not accidental.
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protecting against key exposure strongly key insulated encryption with Optimal Threshold
IACR Cryptology ePrint Archive, 2002Co-Authors: Mihir Bellare, Adriana PalacioAbstract:A new framework for protection against key exposure was recently suggested by Dodis et. al. [16]. We take its realization further towards practice by presenting simple new schemes that provide benefits over previous ones in terms of scalability, performance and security. Our first contribution is a simple, practical, scalable scheme called SKIE-OT that achieves the best possible security in their framework. SKIE-OT is based on the Boneh-Franklin identity-based encryption (IBE) scheme [10] and exploits algebraic properties of the latter. We also show that the role of identity-based encryption is not coincidental by proving that IBE is equivalent to (not strongly) key-insulated encryption with Optimal Threshold and allowing random-access key updates.
Seungjin Park - One of the best experts on this subject based on the ideXlab platform.
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automatic hepatic tumor segmentation using statistical Optimal Threshold
International Conference on Computational Science, 2005Co-Authors: Seungjin Park, Kyungsik Seo, Jongan ParkAbstract:This paper proposes an automatic hepatic tumor segmentation method of a computed tomography (CT) image using statistical Optimal Threshold. The liver structure is first segmented using histogram transformation, multi-modal Threshold, maximum a posteriori decision, and binary morphological filtering. Hepatic vessels are removed from the liver because hepatic vessels are not related to tumor segmentation. Statistical Optimal Threshold is calculated by a transformed mixture probability density and minimum total probability error. Then a hepatic tumor is segmented using the Optimal Threshold value. In order to test the proposed method, 262 slices from 10 patients were selected. Experimental results show that the proposed method is very useful for diagnosis of the normal and abnormal liver.
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International Conference on Computational Science (1) - Automatic hepatic tumor segmentation using statistical Optimal Threshold
Lecture Notes in Computer Science, 2005Co-Authors: Seungjin Park, Kyungsik Seo, Jongan ParkAbstract:This paper proposes an automatic hepatic tumor segmentation method of a computed tomography (CT) image using statistical Optimal Threshold. The liver structure is first segmented using histogram transformation, multi-modal Threshold, maximum a posteriori decision, and binary morphological filtering. Hepatic vessels are removed from the liver because hepatic vessels are not related to tumor segmentation. Statistical Optimal Threshold is calculated by a transformed mixture probability density and minimum total probability error. Then a hepatic tumor is segmented using the Optimal Threshold value. In order to test the proposed method, 262 slices from 10 patients were selected. Experimental results show that the proposed method is very useful for diagnosis of the normal and abnormal liver.
