• Journal of the Chungcheong Mathematical Society
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• v.27 no.1
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• pp.145-149
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• 2014

I consider the optimal consumption and portfolio selection problem with nonnegative wealth constraints using the dynamic programming approach. I use the constant relative risk aversion (CRRA) utility function and disutility to derive the closed-form solutions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.6B
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• pp.358-366
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• 2007

Recently, FCC(Federal Communications Commission) has considered for that unlicensed device leases licensed devices' channel to overcome shortage of communication channels. Therefore, IEEE 802.22 WRAN(Wireless Regional Area Networks) working group progresses CR (Cognitive Radio) technique that is able to sense and adopt void channels that are not being occupied by the licensed devices. Channel selection is of the utmost importance because it can affect the whole system performance in CR network. Thus, we propose a policy-based dynamic channel selection architecture for cognitive radio network to achieve an efficient communication. We propose three kinds of method for channel selection; the first one is weighted channel selection, the second one is sequential channel selection, and the last one is combined channel selection. We can obtain the optimum channel list and allocates channels dynamically using the proposed protocol.

• Journal of KIISE:Software and Applications
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• v.35 no.2
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• pp.91-104
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• 2008

Service-Oriented Computing (SOC) is the development method that published services are selected and composed at runtime to deliver the expected functionality to service clients. SOC should get maximum benefits not only supporting business agility but also reducing the development time. Services are selected and composed at runtime to improve the benefits. However, current programming language, SOC platforms, business process modeling language, and tools support either manual selection or static binding of published services. There is a limitation on reconfiguring and redeploying the business process to deliver the expected services to each client. Therefore, dynamic selection is needed for composing appropriate services to service clients in a quick and flexible manner. In this paper, we propose Dynamic Selection Handler (DSH) on ESB. we present a design method of Dynamic Selection Handler which consists of four components; Invocation Listener, Service Selector, Service Binder and Interface Transformer. We apply appropriate design patterns for each component to maximize reusability of components. Finally, we describe a case study that shows the feasibility of DSH on ESB.

• Journal of the Chungcheong Mathematical Society
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• v.25 no.2
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• pp.277-281
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• 2012

I study an optimal consumption and portfolio selection problem with regime-switching using a dynamic programming method. With constant relative risk aversion (CRRA) utility I obtain optimal solutions in closed-form.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5360-5376
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• 2017

In this paper, we study the cross-layer design for the AMC/HARQ-based wireless networks, and propose a new dynamic transmission-mode selection scheme to improve system spectrum efficiency. In the proposed scheme, dynamic thresholds for transmission-mode selection in each packet transmission and retransmission are jointly designed under the constraint of the overall packet error rate. Comparing with the existing schemes, the proposed scheme is inclined to apply higher modulation order at the first several (re)transmissions, which corresponds to higher-rate transmission modes thus higher average system spectrum efficiency. We also extend the cross-layer design to MIMO (Multi-input Multi-output) communication scenarios. Numerical results show that the proposed new dynamic transmission-mode selection scheme generally achieves higher average spectrum efficiency than the conventional and existing cross-layer design.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.121-127
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• 2004

This paper presents a new vertex selection scheme for shape approximation. In the proposed method, final vertex points are determined by "two-step procedure". In the first step, initial vertices are simply selected on the contour, which constitute a subset of the original contour, using conventional methods such as an iterated refinement method (IRM) or a progressive vertex selection (PVS) method In the second step, a vertex adjustment Process is incorporated to generate final vertices which are no more confined to the contour and optimal in the view of the given distortion measure. For the optimality of the final vertices, the dynamic programming (DP)-based solution for the adjustment of vertices is proposed. There are two main contributions of this work First, we show that DP can be successfully applied to vertex adjustment. Second, by using DP, the global optimality in the vertex selection can be achieved without iterative processes. Experimental results are presented to show the superiority of our method over the traditional methods.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2008.04a
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• pp.342-349
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• 2008

A selection method of primary degrees of freedom in dynamic condensation for nonproportional damping structures is proposed. Recently, many dynamic condensation schemes for complex eigenanalysis have been applied to reduce the number of degrees of freedom. Among them, iterative scheme is widely used because accurate eigenproperties can be obtained by updating the transformation matrix in every iteration. However, a number of iteration to enhance the accuracy of the eigensolutions may have a possibility to make the computation cost expensive. This burden can be alleviated by applying properly selected primary degrees of freedom. In this study, which method for selection of primary degrees of freedom is best fit for the iterative dynamic condensation scheme is presented through the results of a numerical experiment. The results of eigenanalysis of the proposed method is also compared to those of other selection schemes to discuss a computational effectiveness.

• Industrial Engineering and Management Systems
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• v.15 no.1
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• pp.63-76
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• 2016

Multiperiod portfolio selection problem attracts more and more attentions because it is in accordance with the practical investment decision-making problem. However, the existing literature on this field is almost undertaken by regarding security returns as random variables in the framework of probability theory. Different from these works, we assume that security returns are uncertain variables which may be given by the experts, and take absolute deviation as a risk measure in the framework of uncertainty theory. In this paper, a new multiperiod mean absolute deviation uncertain portfolio selection models is presented by taking transaction costs, borrowing constraints and threshold constraints into account, which an optimal investment policy can be generated to help investors not only achieve an optimal return, but also have a good risk control. Threshold constraints limit the amount of capital to be invested in each stock and prevent very small investments in any stock. Based on uncertain theories, the model is converted to a dynamic optimization problem. Because of the transaction costs, the model is a dynamic optimization problem with path dependence. To solve the new model in general cases, the forward dynamic programming method is presented. In addition, a numerical example is also presented to illustrate the modeling idea and the effectiveness of the designed algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2A
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• pp.139-148
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• 2011

This paper presents the minimum-cost network selection scheme which determines the transmission instance in the multi-band maritime communication system, so that the shipment-related real-time information can be transmitted within the maximum allowed period. The transmission instances and the corresponding network selection process are modeled by a Markov Decision Process (MDP), for the channel model in the 2-state Markov chain, which can be solved by stochastic dynamic programming. It derives the minimum-cost network selection rule, which can reduce the network cost significantly as compared with the straight-forward scheme with a periodic transmission.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.386-393
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• 2012

Portfolio selection methods based on stochastic receding horizon approach, which were recently reported in the field of financial engineering, can explicitly consider the dynamic characteristics of wealth evolution and various constraints in the process of performing optimal portfolio selection. In view of the theoretical value, versatility, and effectiveness that receding horizon approach has achieved in many engineering problems, dynamic portfolio selection methods based on stochastic receding horizon optimization technique have the possibility of becoming an important breakthrough. This paper observes through theoretical investigations that the SDP(semi-definite program)-based portfolio selection procedure can be simplified, and has obtained meaningful performance on returns from simulation studies applying the simplified version to Korean financial markets.