臺灣博碩士論文加值系統

在無所不在計算的環境中，情境推理對於情境感知是一個非常重要的議題，藉此才能得知更多或者所需的資訊並且自動提供適當的服務。在過去的研究當中都是針對某一時刻下的情境資訊來進行推理，例如：依據會議室中的人數以及執行的軟體，就可以推理出會議室正在進行的活動為何。然而有很多資訊是需要藉由不同時間下的情境才有辦法推理得知。有鑑於此，本篇論文中針對智慧家居的環境定義一個以RDF與OWL為基礎的時間情境推理模型(TempCRM)，由於在日常家居生活中有許多潛在危安狀況是因為一連串不同時間下的情境所導致出來，其危險層級也大都是隨著時間增加而提升。在TempCRM模型中，我們使用RDF與OWL用來描述家庭中各種事件的情境資訊，並且定義一組一階述語邏輯規則在這些資訊上進行時間相關的時間情境推理，最後計算出家居的危險層級。透過一個假想狀況劇本的驗證，說明我們所定義的TempCRM模型可以有效地計算出家居危險層級，提供更好的時間推理機制與提升家居安全的有效方法。

In the ubiquitous computing environment, context reasoning is an important issue of context-awareness. It is used to deduce desired or higher-level context and then to provide suitable services automatically. The previous context-reasoning approaches are mainly non-temporal. The reasoning is according to the real-time contexts without time information. However, temporal contexts are very important information for context-awareness. Therefore, a temporal context reasoning model based on resource description framework (RDF) and Web ontology language (OWL) is proposed in this paper. TempCRM is also applied to a smart home for inferring its dangerous level. In a home environment, a potential dangerous situation is caused by a series of temporal events. A temporal event is represented as a RDF-based temporal context. A smart home ontology is defined for the terms and relationships used in the temporal context. Then, a set of reasoning rules can be defined for inferring and computing the dangerous level. In the simulation study, a script with dangerous situations is designed to evaluate the dangerous level generated by TempCRM. The result illustrates that TempCRM is effective to alarm the inhabitant and thus prevent the occurrence of an incident from the temporal contexts.

目錄
中文摘要 I
Abstract II
誌謝 III
圖目錄 VI
表目錄 VIII
1. 簡介 1
1.1. 概論 1
1.2. 研究背景及動機 2
1.3. 論文架構 5
2. 相關研究 6
2.1. 情境感知相關研究 6
3. TempCRM時間情境推理模型 15
3.1. 情境述語 16
3.2. 智慧家居本體論 20
3.3. RDF時間情境資訊 22
3.4. 裝置狀態的機率密度函數 26
3.5. 時間推理規則 28
4. 模擬分析與結果 34
5. 結論與未來工作 40

圖目錄
圖1：EasyMeeting 架構圖 7
圖2：Cobra架構圖 8
圖3：policy ontology定義描述rules的字彙用來規範不同的agent對於個人資訊的存取權限 9
圖4：Semantic Space中所定義的上層及延伸領域的context ontology 10
圖5：Semantic Space情境架構圖 11
圖6：基於使用者目前的活動、位置、以及計算實體 12
圖7：ADBM的架構概要圖 13
圖8：ECA時間推理規則 14
圖9：TempCRM模型的架構圖 15
圖10：智慧家居上層ontology與智慧家居領域ontology的定義 22
圖11：一個使用RDF來描述資源的範例 23
圖12：一個使用RDF來描述情境資訊的範例 24
圖13：TempCRM模型中描述某一事件的RDF範例 25
圖14：裝置Di之使用時間的機率常態分佈圖 27
圖15：裝置Di之使用時間TE轉換為危險層級的分佈圖 28
圖16：瓦斯未關狀況下的推理規則 29
圖17：以RDF來呈現推理規則的流程 31
圖18：大門未關危安狀況下的推理規則 31
圖19：危險層級推理的模擬結果 36
圖20：改善遮蔽效應後的模擬結果 38


表目錄
表1：一個家居事件假想的模擬劇本 34
表2：模擬使用的參數設定值 35

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