群眾資訊近幾年被廣泛的使用在各種應用中，尤其是在災難警告與訊息回報上。利用群眾資訊可幫助災難緊急應變中心在實體感測器遭受損害時，即時獲得當地真實情況資訊，以有效做出應變決策。但是，群眾資訊的資訊來源在無法驗證可靠度的情況下，會因為資料中夾雜許多未知的錯誤訊息，影響災難緊急應變中心的判讀，導致決策的不確定性。目前災難緊急應變中心主要採用的資料處理方法，為最大概似期望值演算法(Expectation Maximization Algorithm)簡稱EM演算法，此方法透過概似估計不斷重複迭代感測資訊集合，直到函數收斂得到資訊來源可靠度。然而EM演算法的缺點包含了過程繁瑣、時間複雜度高以及需要大量的資料量才能得到具一定可靠度的結果，由此可知，EM演算法在回報資訊不足的情況下會降低災難緊急應變中心做決策的可靠度。因此，本論文的研究問題為：在實體感測器資料量不足的情況下，如何融合最少的群眾資訊，達到具一定可靠度的決策結果。為此，我們提出一個結合二進制檢定與NP檢定的資料融合方法來解決此問題。為驗證資料融合方法之效果，我們與Dong Wang[5]等人提出的EM演算法做效能比較，採相同情境與設定下進行比較分析。實驗結果顯示，本論文所提出的資料融合方法在已知群眾資訊可靠度的情況下，利用較少的群眾資訊仍然可以達到與使用EM演算法相同的可靠度。

In recent years, crowdsourcing is widely used in the application of disaster decision support system. In this paper, we present data fusion methods which enable a crowdsourcing enhanced system to use human sensor data and physical sensor data synergistically to improve its sensor coverage and the quality of its decisions. The data fusion methods are built based on the Neyman-Pearson Test (NP Test). They are building blocks of a central unit in a crowdsourcing support system for disaster surveillance.
Recently, a statistic method “Expectation Maximization Algorithm” using likelihood estimation to calculate data reliability iteratively is adopted to solve unobserved latent problem. However, EM Algorithm not only has high time complexity but also requires large data volume. Accordingly, emergency operation center cannot provide the best decision when data volume is small.
In this paper, we identify the problem as Trustworthiness problem, i.e., conformity to truth, which studies how to make highly reliable decision when data volume is limited. To solve the Trustworthiness problem, we propose a data fusion method, which outputs highly reliable decision from crowdsourcing data. Compared with EM algorithm, our method achieves better performance than EM Algorithm according to the performance data.

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