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研究生:柯紜凱
研究生(外文):Yun-Kai Ke
論文名稱:以灰色與模糊建模方法即時分析生態環境之變化
論文名稱(外文):Using Grey and Fuzzy Modeling for Real-Time Analysis of Eco-environmental Variations
指導教授:黃有評黃有評引用關係
指導教授(外文):Yo-Ping Huang
口試委員:張玉山練光祐李祖添
口試日期:2012-07-13
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電機工程系所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:100
中文關鍵詞:灰色模型無線感測網路生物多樣性型態樹模糊預警
外文關鍵詞:Grey ModelWireless Sensor NetworksBiodiversityPattern TreeFuzzy early warning
相關次數:
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各種環境因子與生態的變化息息相關,若能系統化分析其間之非生物相互關係,將有助於改善生態環境之永續發展。環境監測系統一般須仰賴硬體設備量測之精準度與大量人力輔助收集數據,再進行分析與評估。然而此種方法較無法即時評估環境狀況,且須侷限於某些特定地區進行監測。為解決該等問題,本論文提出以智慧型手機為平台,結合低功耗的Zigbee無線感測網路及大型牆面觸控系統建構生態監控與預警系統。本論文主要以水質因子、溫度、濕度、二氧化碳及光照度作為生物多樣性的分析變數,利用灰關聯分析找出調查水域中水質因子之重要程度,並搭配模糊方法與資料探勘技術建立型態樹,藉此了解環境因子與鳥類棲息地之非生物相互關係。此外,本論文利用灰色模型與模糊推論即時預測各環境因子之變化,當變化值超過門檻時,將發送警報通知,以提供研究人員或社區民眾採取必要的因應措施。本論文除提出完整的架構與理論探討外,實驗結果顯示,即時環境因子預測結果,溫度誤差1.27%、濕度誤差1.29%、二氧化碳誤差4.23%,整體灰預測誤差較其他兩種預測方法誤差低,而型態樹平均運算子權重加入灰關聯分析水質因子重要程度做調整後,實驗顯示準確率高達94.59%,其結果更具參考價值,亦證明本系統之可行性。

A variety of environmental factors are closely related to ecological changes. Analyzing the abiotic relationship between them systematically will help improve the ecological environment of sustainable development. Most environmental monitoring systems rely on hardware accuracy and manual data collecting before analyzing and evaluating. However, this method cannot assess environmental conditions in real-time, and is bounded to certain area. To solve these problems, this study proposes an ecological monitoring and early warning system that combines low-power Zigbee wireless sensor network and large-scale multi-touch wall display. In this study, the main factors of biological diversity analysis include water quality, temperature, humidity, carbon dioxide and light intensity. Meanwhile, gray relational analysis is used to identify the importance of water quality factors in the target waters. And finally, with the fuzzy method and data mining technique, we can build the pattern trees to find out the mutual relationship of abiotic environmental factors and bird habitat. In addition, grey model and fuzzy inference system are used to predict changes of each environmental factor. When these changes exceed the threshold values, the devised system will send an alert notification to researchers or the local resident in order to take necessary actions. In addition to the comprehensive framework and theoretical discussion, the experimental results show that the real-time environmental factors prediction with the temperature error of 1.27%, humidity error of 1.29%, carbon dioxide error of 4.23%. The overall error is lower than the other two forecasting methods. Experiments showed the grey relational analysis with pattern tree adjustment has accuracy rate up to 94.59% that makes the results more valuable and also verifies the feasibility of the proposed system.

摘要 i
ABSTRACT ii
誌謝 iv
目錄 v
表目錄 viii
圖目錄 x
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 研究方法 3
1.4 論文架構 4
第二章 相關技術及應用探討 5
2.1 灰色系統理論 5
2.1.1 灰關聯分析 5
2.1.2 灰色預測 7
2.2 模糊理論 13
2.2.1 模糊集合 13
2.2.2 歸屬函數 15
2.3 型態樹 17
2.3.1 杰卡德相似量度 17
2.3.2 模糊集合運算 18
2.3.3 型態樹建構流程 19
2.4 辛普森多樣性指數 22
2.5 熱舒適度 23
2.6 網路地圖 24
2.7 Android 作業系統 25
2.7.1 系統版本 25
2.7.2 系統架構 26
2.7.3 生命週期 28
2.8 微處理器 31
2.9 Zigbee無線傳輸模組 32
2.10 感測器介紹 35
2.10.1 SHT71 溫濕度感測器 35
2.10.2 MG811 二氧化碳感測器 36
第三章 系統架構與設計 37
3.1 系統架構 37
3.1.1 硬體架構 37
3.1.2 軟體架構 47
3.2 系統流程 49
3.2.1 伺服端 49
3.2.2 手機端 57
3.2.3 網頁端 60
3.2.4 環境資訊量測系統 61
第四章 實驗結果與分析 63
4.1 實驗環境 63
4.2 即時預測分析 64
4.3 非生物相互關係分析 71
4.4 資訊檢索介面 79
4.4.1 網頁資訊檢索 79
4.4.2 智慧型手機資訊檢索 84
4.4.3 伺服端操作介面 91
第五章 結論與未來展望 94
5.1 結論 94
5.2 未來展望 96
參考文獻 97


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