Browsing by Author "Chen, Chien-Liang"

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An Approximate Dynamic Programming Model for Link Scheduling in WMNs with Gateway Design Constraint
(2014)
Chen, Chien-Liang
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Liu, Wan-Yu
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Chang, Shu-Huai
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Lin, Chun-Cheng
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工業工程與管理學系
The design objective of wireless mesh networks is generally focused on transmitting data packets as many as possible in a given time. Wireless mesh network models may be categorized into many different types according to different programming objectives, wherein the link scheduling model achieves the purpose of effective transmission of packets by certain transmission constraints and the programming of the open status of packet transmission links in each stage. In this paper, the approximate dynamic programming approach for a new wireless mesh network scheduling model having node programming information saving locations and network link gateway designs is orchestrated. The experiment results show that, in addition to maintaining many wireless network characteristics, the scheduling algorithm is effectively executed and approximate dynamic programming effectively simulates dynamic programming and has performances superior to genetic algorithm.
An Extreme Surface Proximity Push for Embedded SiGe in pMOSFETs Featuring Self-Aligned Silicon Reflow
(2010-09-01)
Lin, Da-Wen
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Chen, Chien-Liang
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Chen, Ming-Jer
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Wu, Chung-Cheng
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電子工程學系及電子研究所
This letter proposes a novel process to modulate the distance, or proximity, between the tip of embedded silicon-germanium (e-SiGe) and the channel region in pMOSFETs. Traditionally, sophisticated etching treatment is adopted in a spacer structure; however, process-induced variation in the e-SiGe proximity may lead to serious variation in pMOSFET performance. In this letter, an extremely close proximity is achieved using self-aligned silicon reflow (SASR) in hydrogen ambient. As opposed to conventional approaches which have e-SiGe proximity determined by spacer width, the tip of e-SiGe with SASR can be positioned flush with the gate edge, as corroborated by both the TEM analyses and TCAD simulation. A significant improvement in pMOSFET performance is also measured.
Detecting Hierarchical Community Structures in Social Networks Using Integer Linear Programming
(2013-01-01)
Lin, Chun-Cheng
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Kang, Jia-Rong
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Chen, Jyun-Yu
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Chen, Chien-Liang
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工業工程與管理學系
Detection of hierarchical community structures is one of the most crucial tasks for analyzing complicated social networks. In a hierarchical community structure, the super node at a higher level represents a nested structure so that the relationship of subcommunities in a community can be observed. Most of the previous works focused on designing metaheuristics for detecting hierarchical community structures, which may be computationally efficient, but cannot always guarantee the community partition optimality. Hence, this paper proposes an integer linear programming model for detecting the hierarchical community structure in social networks, which takes into account the number of levels and the limit of community size of each level. Our experimental results show that our model can find a reasonable hierarchical community structure, where the interaction between communities at different levels can be comprehended more clearly.
Nurse Scheduling with Joint Normalized Shift and Day-Off Preference Satisfaction Using a Genetic Algorithm with Immigrant Scheme
(2015-01-01)
Lin, Chun-Cheng
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Kang, Jia-Rong
;
Chiang, Ding-Jung
;
Chen, Chien-Liang
;
工業工程與管理學系
To make a fair and satisfactory nurse shift schedule, this paper proposes a novel preference satisfaction function, in which numbers of the preferred work shifts and days-off of the nursing staff are balanced, and ranks for preferences and number of the preference ranks satisfied so far are also considered. Such a preference function is capable of equivalently and fairly planning the nurse preference schedule to improve the total satisfaction. Additionally, distributed sensors can be applied to collect the information on hospital beds to provide the schedule planner to determine the lowest required amount of manpower for each work shift, to avoid the working overload of the nursing staff. To solve the nursing schedule problem, we propose a genetic algorithm (GA) with an immigrant scheme, in which utilization of the immigrant scheme is helpful in efficiently reducing amount of infeasible solutions due to practical scheduling constraints, so that the GA can efficiently find better solutions for larger-scale problems. Performance of the proposed GA with and without solution recovery scheme is evaluated by conducting a comprehensive experimental analysis on three different-size instances.