Browsing by Author "Yen, Neil Y."

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A GA-based approach for finding appropriate granularity levels of patterns from time series
(2016)
Chen, Chun-Hao
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Tseng, Vincent S.
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Yu, Hsieh-Hui
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Hong, Tzung-Pei
;
Yen, Neil Y.
In our previous approach, we proposed an algorithm for finding segments and patterns simultaneously from a given time series. In that approach, because patterns were derived through clustering techniques, the number of clusters was hard to be setting. In other words, the granularity of derived patterns was not taken into consideration. Hence, an approach for deriving appropriate granularity levels of patterns is proposed in this paper. The cut points of a time series are first encoded into a chromosome. Each two adjacent cut points represents a segment. The segments in a chromosome are then divided into groups using the cluster affinity search technique with a similarity matrix and an affinity threshold. With the affinity threshold, patterns with the desired granularity level can be derived. Experiments on a real dataset are also conducted to demonstrate the effectiveness of the proposed approach.
Coalitional Power Control and Time Allocation for Multiuser Cognitive Networks
(2013-09-01)
Chan, Yu-Wei
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Chien, Feng-Tsun
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Park, James J.
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Hung, Jason C.
;
Yen, Neil Y.
This paper considers the problem of resource allocation for a cooperative cognitive radio network with multiple primary users (PUs) and multiple secondary users (SUs), in which secondary users (SUs) relay traffics for primary users, in exchange for dedicated transmission time for their own transmission needs. However, since the PUs and SUs are rational, they aim at maximizing their own utility. Therefore, the problem of conflicting interests between them exists. We formulate the problem as a coalitional game to analyze the cooperation behavior between PUs and SUs. We propose to form the grand coalition by grouping all PUs and SUs in a set, and to use the core and the Shapley value solution concepts, respectively to analyze the stability and fairness of cooperative resource allocation. The optimal fraction of time allocation and power allocation for PUs and SUs are derived by an iterative updating algorithm, respectively. Simulation results show the benefits of our proposed approach as compared with other approaches.