本論文針對已有的光纖感測硬體架構研製一套創新的長周界入侵感測系統，此光纖感測硬體架構將光纖分成多區段的感測位置以分辨不同位置的入侵事件，系統的研製包含入侵事件判斷演算法及即時監控介面。
入侵事件的判斷法是以監度式學習的方法建立分類模組，從記錄的特徵資料訓練最佳的分類模組，以此分類模組判斷入侵與否。入侵事件的判斷計算是訊號擷取器操作在2.5kHz的取樣頻率下，連續擷取512筆訊號數值，經頻率域換算後，以本論文提出的兩個特徵參數計算法計算特徵值，再藉由訓練好的分類模組判斷是否為入侵事件。
本論文以兩個實驗測試入侵判斷演算法的判別效果，其一是改變作用於光纖的受力程度，測試系統能否辨別不同程度的外力，區分入侵與非入侵事件的訊號，並從此實驗找出最適合的門檻頻率，應用於特徵參數的計算上。另一個實驗則測試系統在實際環境下，能否從不同事件中區分入侵事件與環境擾動的差異，並觀察環境擾動對系統的影響，此實驗測試多種分類法，以找出適用於監控的分類方法，經實驗數據比較後，以二次識別分析有最佳的分類效果。從上述兩實驗可證明此入侵判斷的演算法有良好的辨識能力。
藉由本論文的入侵事件判斷演算法，監控系統能夠自動偵測防區內的異常事件，發生異常事件時，監控系統會即時記錄發生的時間及所擷取的訊號數值，並顯示於監控介面上。

In this thesis, we implement an innovative perimeter intrusion detection system, including a security interface and an algorithm for discriminating between intrusion and nuisance events, for the existing fiber optic sensing hardware architecture. While DAQ (Data Acquisition) continuously samples fiber sensor output under a sampling frequency of 2.5 kHz, the system first compute the high and low frequency features out of every 512 sample values and then use the features to discriminate between events.
We design two experiments to evaluate the effectiveness of the system. To test whether the system can discriminate between intrusion and nuisance events, the first experiment changes the force acting on the fiber. If the system works rightly, it will make right decision according to force measured. The second experiment is performed just like the first experiment in many ways, but moves the scene from the laboratory to the real world in order to take the interference of environment into account. In these two experiments, our system is capable to make right decisions in discriminating between events.
This system automatically detects abnormal events appeared in the Area. When an abnormal event occurs, the system records the time and sampled values of the event immediately. In the meantime, the security interface displays alarms.

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