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研究生:王崴弘
研究生(外文):Wei-Hung Wang
論文名稱:基於支持向量機及模糊推理之地震預警系統研製
論文名稱(外文):Implementation of Earthquake Early Warning System Based on Support Vector Machine and Fuzzy Inference
指導教授:許獻聰許獻聰引用關係
指導教授(外文):Shiann-Tsong Sheu
學位類別:碩士
校院名稱:國立中央大學
系所名稱:通訊工程學系
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:2015
畢業學年度:103
語文別:中文
論文頁數:53
中文關鍵詞:地震預警系統加速度感測器支持向量機模糊推理
外文關鍵詞:EEW SystemG-SensorSupport Vector MachineFuzzy Inference
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台灣位於歐亞大陸板塊 (Eurasian Plate) 與菲律賓海板塊 (Philippine Sea Plate) 的交接處,屬於環太平洋火山帶 (Ring of Fire) ,容易因板塊移動而造成地震,是世界上地震最為頻繁的區域之一,每每造成人民生命財產的損失。一旦地震發生時,大多數民眾只能在當下做最本能的反應,不容易快速得知地震資訊,甚至提早得知地震即將發生,所以,地震預警系統 (Earthquake Early Warning System, EEWS) 的開發與研究,就變成一個迫切需要討論的議題。
目前,環太平洋火山帶上各個國家,都投入相當多的人力以及經費在地震預警系統的研製,其中以日本、美國、台灣三個國家的研究成果最為突出。但綜觀上述研究,可以歸納出一些共同的問題。首先,特殊的儀器設備,容易造成建置成本的提高。再者,觀測站的數量,無法隨心所欲的增加。最後,系統與人民的隔閡大,人民不易得知如何快速得取得地震預警。這三個面向的問題,造成我們目前依然依賴媒體透過電視、廣播或社群軟體提供的地震資訊,地震預警系統並沒有真正的進入人民的日常生活當中。
因此,本論文以全新的視角審視地震預警系統,並以通訊領域的觀點出發,提出利用加速度感測器 (G-Sensor) ,結合支持向量機 (Support Vector Machine) 、模糊推理 (Fuzzy Inference) 等理論的演算法,並實作地震預警函式庫。由於加速度感測器目前的應用廣泛,例如:智慧型手機、平板電腦、硬碟等等我們每天都會接觸的設備皆有內建,所以能將配有加速度感測器的裝置都視為一個小型的地震觀測站,只要擁有上網的能力並運行包含地震預警函式庫的APP或電腦程式,就能以最低的成本形成最大的地震觀測網,一舉解決上述的三個問題,發揮地震預警系統最大的能力。
Taiwan is located in a seismically active zone and faces the problem of a large number of earthquakes. Many disastrous earthquakes usually cause the loss of lives and properties over the years. Therefore, how to design a series of preventative measures and reduce the risk of damages caused by earthquakes is an important issue. So far we still do not have a better way to get earthquake warning immediately. As a result, the Earthquake Early Warning System (EEWS) would be a useful tool and has become the urgent need of human beings.
Nowadays countries neighboring the Ring of Fire put a lot of efforts and resources on investigating the EEWS. Among them, Japan, The United States of America, and Taiwan have the most abundant achievements. Constructing the EEWS has some common problems. Firstly, the need of the special equipment for seismic wave sensing costs highly. Secondly, the number of earthquake detection stations cannot increase arbitrarily due to the lack of funds. Thirdly, people are not familiar with the EEWS. Until now, people get the earthquake news from television, radio stations or social APPs. The EEWS does not fully come into our lives.
In this thesis, we propose a new algorithm and architecture of the EEWS named as the EQ-system. The hardware part of EQ-system is a G-sensor for detection. The software part of EQ-system is a library of earthquake early warning called LibEQ. LibEQ combines several theories such as Support Vector Machine, Fuzzy Inference and so on. We can build a biggest earthquake detection network with low cost. People can get earthquake alarm by APP. Consequently, for earthquake early warning, EQ-system can produce the best possible results.
中文摘要 i
ABSTRACT ii
CONTENTS iii
LIST OF FIGURES v
LIST OF TABLES vi
1. INTRODUCTION 1
2. RELATED WORKS 3
2-1 The Types of Earthquake Early Warning System 3
2-2 The Earthquake Early Warning System in Japan 4
2-3 The Earthquake Early Warning System in United States of America 4
2-4 The Earthquake Early Warning System in Taiwan 5
3. IMPLEMENTATION OF EQ-SYSTEM AND LIBEQ 7
3-1 The Architecture of EQ-System 7
3-2 The Hardware Part of EQ-System 9
3-2-1 Sensor 9
3-2-2 Server 10
3-3 The Software Part of EQ-System 11
3-3-1 The Introduction of Modules 11
3-3-2 System Parameters 12
3-3-3 EQ_dectection 13
3-3-4 EQ_SVM and EQ_SVMgen 16
3-3-5 EQ_fuzzy 18
3-3-6 EQ_credibility 20
3-3-7 EQ_magnitude 21
3-3-8 EQ_interface 22
3-3-9 EQ_density 23
3-3-10 EQ_judgment 23
3-3-11 The Architecture of Modules 24
4. EXPERIMENTAL RESULTS AND ANALYSIS 26
4-1 Sensor APP and Server APP 26
4-1-1 The Configurations of APP 26
4-1-2 The Interaction of Sensor and Server 27
4-2 Result and Analysis 29
4-3 Scenario Analysis 32
5. FUTURE WORKS 36
5-1 Sample Rate Optimization 36
5-2 Compare with Other Smartphone Based EEWS 37
6. CONCLUSIONS 38
7. REFERENCES 39
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