博碩士論文 954303017 完整後設資料紀錄

DC 欄位 語言
DC.contributor資訊管理學系在職專班zh_TW
DC.creator陳玉珍zh_TW
DC.creatorYun-Chen Chenen_US
dc.date.accessioned2008-7-1T07:39:07Z
dc.date.available2008-7-1T07:39:07Z
dc.date.issued2008
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=954303017
dc.contributor.department資訊管理學系在職專班zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract在有限空間且電子設備排列密度極高並以冷氣及風扇強迫對流裝置散熱的叢集電腦機房,如何排列強迫對流裝置以能有效的散熱,讓電子設備處於正常的運轉狀態,對機房的管理而言,是一個很重要的課題。 在侷促空間中,加上冷氣及風扇氣流的因素,機房的流場顯得非常紊亂,要以流體力學軟體來模擬流場狀態,以找到機房強迫對流裝置的合適佈局是非常困難的事,因此,本研究應用基因演算法,並以類神經網路學習模型建立適應函數,以找到機房強迫對流裝置佈局之最佳近似解。zh_TW
dc.description.abstractTo keep the electronic installation at normal operation, effective heat dissipation of the extremely crowded cluster computer room is needed. Usually, fans and air conditioning equipments are used to assist this heat dissipation work. In the cramped space of crowded cluster computer room, airflow is exceptionally disorderly. How to arrange the fans and the air conditioning equipments to make heat dissipation effective is an important problem. Using Computational Fluid Dynamics (CFD) software to simulate the airflow to find solutions for effective heat dissipation is extremely difficult. The purpose of this research is to apply neural networks model to establish a fitness function first, then, have this fitness function used by genetic algorithms in the finding of best approximate solutions for solving the problem of effective heat dissipation in cluster computer rooms.en_US
DC.subject基因演算法zh_TW
DC.subject類神經網路zh_TW
DC.subject強迫對流zh_TW
DC.subjectforced convectionen_US
DC.subjectartificial neural networken_US
DC.subjectgenetic algorithm.en_US
DC.title應用基因演算法於叢集電腦機房強迫對流裝置佈局最佳近似解之研究zh_TW
dc.language.isozh-TWzh-TW
DC.titleApplying Genetic Algorithms to Cluster Computer Rooms for Best Approximate Solutions of the Forced Convection Equipment Layouten_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明