由於採用的地球原子不同，目前，台灣地區有兩種坐標系統存在，TWD67(Taiwan Datum 1967) 和TWD97(Taiwan Datum 1997)。在應用上，必須進行不同地球原子間之坐標轉換。坐標轉換方面，有許多方法可供選擇，如六參數轉換、支持向量機(Support Vector Machine, SVM)轉換等。 最小二乘支持向量機(Least Square Support Vector Machine, LSSVM)，為SVM的一種演算法，是一種非線性模型。LSSVM在運用上所需之參數少，能夠解決小樣本、非線性、高維度和局部極小點等問題。目前，LSSVM，已經被成功運用在影像分類和統計迴歸等領域上。 本研究將利用LSSVM採用不同之核函數：線性核函數(LIN)、多項式核函數(POLY)及徑向基核函數(RBF)進行TWD97和TWD67之坐標轉換。研究中並使用基因演算法來調整LSSVM的RBF核函數之系統參數(後略稱RBF+GA)，找出較佳之系統參數組合以進行坐標轉換。模擬與實測之地籍資料，將被用以測試LSSVM及六參數坐標轉換方法的轉換精度。 研究結果顯示，RBF+GA在各實驗區之轉換精度優於參數優化前RBF之轉換精度，且RBF+GA之轉換精度也較六參數轉換之轉換精度高。 進行參數優化後，RBF+GA相對於RBF的精度提升率如下：(1)模擬實驗區：參考點與檢核點數量比分別為1：1、2：1、3：1、1：2及1：3時，精度提升率分別為15.2%、21.9%、33.2%、12.0%、11.7%；(2)真實實驗區：花蓮縣、台中市及台北市實驗區之精度提升率分別為20.1%、32.4% 、22.5%。 / There are two coordinate systems with different geodetic datum in Taiwan region, i.e., TWD67 (Taiwan Datum 1967) and TWD97 (Taiwan Datum 1997). In order to maintain the consistency of cadastral coordinates, it is necessary to transform from one coordinate system to another. There are many coordinate transformation methods, such as, 2-dimension 6-parameter transformation, and support vector machine (SVM). Least Square Support Vector Machine (LSSVM), is one type of SVM algorithms, and it is also a non-linear model。LSSVM needs a few parameters to solve non-linear, high-dimension problems, and it has been successfully applied to the fields of image classification, and statistical regression. The goal of this paper is to apply LSSVM with different kernel functions (POLY、LIN、RBF) to cadastral coordinate transformation between TWD67 and TWD97. Genetic Algorithm will be used to find out an appropriate set of system parameters for LSSVM with RBF kernel to transform the cadastral coordinates. The simulated and real data sets will be used to test the performances, and coordinate transformation accuracies of LSSVM with different kernel functions and 6-parameter transformation. According to the test results, it is found that after optimizing the RBF parameters (RBF+GA), the transformation accuracies using RBF+GA are better than RBF, and even better than those of 6-parameter transformation. Comparing with the transformation accuracies using RBF, the transformation accuracy improving rate of RBF+GA are : (1) The simulated data sets: when the amount ratio of reference points and check points comes to 1:1, 2:1, 3:1, 1:2 and 1:3, the transformation accuracy improving rate are 15.2%, 21.9%, 33.2%, 12.0% and 11.7%, respectively; (2) The real data sets： the transformation accuracy improving rate of RBF+GA for the Hualien, Taichung and Taipei data sets are 20.1%, 32.4% and 22.5%, respectively.

隨著WiFi訊號在都市的涵蓋率逐漸普及，基於WiFi訊號強度值的定位方法逐漸發展。WiFi匹配定位(Matching Positioning)是透過參考點坐標與WiFi訊號強度(Received Signal Strength Indicator, RSSI)的蒐集，以最小二乘法(Least Squares, LS)計算RSSI模型參數；然後，利用模型參數與使用者位置的WiFi訊號強度，推估出使用者的位置。然而WiFi訊號強度容易受到環境因素影響，例如降雨、建物遮蔽、人群擾動等因素，皆會使訊號強度降低，若以受影響的訊號強度進行定位，將使定位成果與真實位置產生偏移。 為了降低訊號強度的錯誤造成定位結果的誤差，本研究嘗試透過具有穩健性的最小一乘法( Least Absolute Deviation, LAD)結合WiFi匹配定位，去克服WiFi訊號易受環境影響的特性，期以獲得較精確的WiFi定位成果。研究首先透過模擬資料的建立，測試不同粗差狀況最小一乘法WiFi匹配定位之表現，最後再以真實WiFi訊號進行匹配定位的演算，並比較最小一乘法WiFi匹配定位與最小二乘法WiFi匹配定位的成果差異，探討二種方法的特性。 根據本研究成果顯示，於模擬資料中，最小一乘法WiFi匹配定位相較於最小二乘法WiFi匹配定位，在面對參考點接收的AP訊號與檢核點接收的AP訊號強度含有粗差的情形皆能有較好的穩健性，且在參考點接收的AP訊號含有粗差的情況有良好的偵錯能力。而於真實環境之下，最小一乘法WiFi匹配定位之精度也較最小二乘法WiFi匹配定位具有穩健性；在室外資料的部份，最小一乘法WiFi匹配定位之精度為8.46公尺，最小二乘法WiFi匹配定位之精度為8.57公尺。在室內資料的部份，最小一乘法WiFi匹配定位之精度為2.20公尺，最小二乘法WiFi匹配定位之精度為2.41公尺。 / Because of the extensive coverage of WiFi signal, the positioning methods by the WiFi signal are proposed. WiFi Matching Positioning is a method of WiFi positioning. By collecting the WiFi signal strength and coordiates of reference points to calculate the signal strength transformation parameters, then, user’s location can be calculated with the LS (Least Squares). However, the WiFi signal strength is easily degraded by the environment. Using the degraded WiFi signal to positioning will produce wrong coordinates. Hence this research tries to use the robustness of LAD (Least Absolute Deviation) combining with WiFi Matching Positioning to overcome the sensibility of WiFi signal strength, expecting to make the result of WiFi positioning more reliable. At first, in order to test the ability of LAD, this research uses simulating data to add different kind of outliers in the database, and checks the performance of LAD WiFi Matching Positioning. Finally, this research uses real data to compare the difference between the results of LAD and LS WiFi Matching Positioning. In the simulating data, the test result shows that LAD WiFi Matching Positioning can not only have better robust ability to deal with the reference and check points AP signal strength error than LS WiFi Matching Positioning but also can detect the outlier in the reference points AP signal strength. In the real data, LAD WiFi Matching Positioning can also have better result. In the outdoor situation, the RMSE (Root Mean Square Error) of LAD WiFi Matching Positioning and LS (Least Squares) WiFi Matching Positioning are 8.46 meters and 8.57 meters respectively. In the indoor situation, the RMSE (Root Mean Square Error) of LAD WiFi Matching Positioning and LS (Least Squares) WiFi Matching Positioning are 2.20 meters and 2.41 meters respectively.