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題名 防丟器的剖面追蹤研究
Profile Monitoring on the RSSI of Babyfinder
作者 徐伊萱
貢獻者 楊素芬<br>蔡紋琦
徐伊萱
關鍵詞 剖面追蹤
實驗設計
管制圖
即時追蹤
Profile Monitoring
Design of Experiments
Control Chart
Real-Time Detection
日期 2009
上傳時間 5-Sep-2013 15:09:58 (UTC+8)
摘要 　　本論文針對防丟器的剖面進行追蹤分析。防丟器包含發射器及接收器，發射器會發射訊號，接收器會記錄RSSI (Receive Signal Strength Index)與發射點數，其中RSSI表示訊號的強度。在工程理論上，RSSI與距離具有函數關係；然而環境中的干擾及事件發生都會影響此函數關係，特別是事件發生會嚴重地改變此函數關係，因此論文主要目的在於區別事件是否發生。
　　所謂的剖面指的是變數之間的函數關係，而論文中的剖面追蹤是利用管制圖的概念，用管制圖來監控剖面的參數估計值。如果管制圖上的點子出界，則表示事件發生而導致失控。
　　本論文以腳踏車是否被偷為例，嘗試一些實驗後找出顯著影響的因子設計實驗，包含17種腳踏車未被偷之情境與18種腳踏車被偷情境；欲利用未被偷的實驗建立試驗管制圖，而以被偷之情境來追蹤，用以驗證管制圖之有效性。
　　論文中主要透過分析防丟器產生的RSSI與距離的剖面、距離與發射點數的剖面來探討事件是否發生。另外剖面追蹤其實是種事後追蹤的方法，為了能即時追蹤，本論文亦採用預測區間的方式，來追蹤事件是否發生。
　　本論文建議監控距離與發射點數的剖面，因該方法的表現最好，另外建議增加防丟器上能紀錄距離的功能，此方法會更加合適。
　　本論文提出的即時追蹤方式並沒有特別好，因此一個比較好的即時追蹤方法是未來值得研究的方向。
The device of Babyfinder is designed to detect if an event occurs. The Babyfinder includes transceiver and receiver. The signal strength, Received Signal Strength Indicator (RSSI), generates once there are distances between transceiver and receiver. In wireless communication theory, the relationship between RSSI and distance should be expressed by the model that RSSI = a + b ln (distance) Nevertheless, some circumstance noises and user noises (or common causes), and/or events (special causes) may affect the variation of RSSI. Since the occurrence of events may change the functional relationship of RSSI and distance, to distinguish if the functional relationship is changed by the occurred events is the subject of this study. This study designs some events and noises experiments based on the real noise factors and special events. Two monitoring schemes are proposed to distinguish the occurred events and noise circumstance. One is the profile monitoring scheme, the other is the real time monitoring scheme. The two proposed approaches of profile monitoring scheme are considered to monitor the profile of RSSI and distance and that of distance and the number of transmitting points, respectively. The profile monitoring approach for distance and the number of transmitting points shows better performance. However, the profile monitoring is an after-event tracing approach. It cannot detect the occurred events in time. A better approach of real-time monitoring approach is worth to be proposed in the future study.
參考文獻 Chang, S. I. and Yadama, S. (2010). Statistical Process Control for Monitoring Nonlinear Profiles Using Wavelet Filtering and B-Spline Approximation. International Journal of Production Research, 48(4), 1049-1068.
Ding, Y., Zeng, L. and Zhou, S. (2006). Phase I Analysis for Monitoring Nonlinear Profile Signals in Manufacturing Processes. Journal of Quality Technology, 38 (3), 199-216.
Jeong, M. K, Lu, J. C. and Wang, N. (2006). Wavelet-based SPC Procedure for Complicated Functional Data. International Journal of Production Research, 44 (4), 729-744.
Jin, J. and Shi, J. (2001). Automatic Feature Extraction of Waveform Signals of In-process Diagnostic Performance Improvement. Journal of Intelligent Manufacturing, 12 (3), 257-268.
Kang, L. and Albin, S. L. (2000). On-Line Monitoring When the Process Yields a Linear Profile. Journal of Quality Technology, 32, 418-426.
Kim, K., Mahmoud, M. A. and Woodall, W. H. (2003). On the Monitoring of Linear Profiles. Journal of Quality Technology, 35(3), 317-328.
Mahmoud, M. A. and Woodall, W. H. (2004). Phase I Analysis of Linear Profiles with Calibration Applications. Technometrics, 46(4), 380-391.
Mahmoud, M. A. (2008). Phase I Analysis of Multiple Linear Regression Profiles. Communications in Statistics—Simulation and Computation, 37(10), 2106-2130.
Mahmoud, M. A., Parker, P. A., Woodall, W. H. and Hawkins, D. M. (2007). A Change Point Method for Linear Profile Data. Quality and Reliability Engineering International, 23(2), 247-268.
Mahmoud, M. A., Morgan, J. P. and Woodall, W. H. (2009). The Monitoring of Simple Linear Regression Profiles with Two Observations per Sample. Journal of Applied Statistics, (Manuscript ID: CJAS-2009-0012.R1).
Moguerza, J. M., Munoz, A. and Psarakis, S. (2007). Monitoring Nonlinear Profiles using Support Vector Machines. Lecture Notes in Computer Science, 4756, 574-583.
Montgomery, D. C., Peck, E. A. and Vining, G. G. (2001). Introduction to Linear Regression Analysis. (3rd Ed.). New York: Wiley.
Noorossana, R. and Amiri, A. (2007). Enhancement of Linear Profiles Monitoring in Phase II. AmirKabir Journal of Science and Technology, 18, 19-27 in Farsi
Reis, M. S. and Saraiva, P. M., (2006). Multiscale Statistical Process Control of Paper Surface Profiles. Quality Technology and Quantitative Management, 3 (3), 263-282.
Shiau, J. J. H., Huang, H. L., Lin, S. H. and Tsai, M. Y. (2009). Monitoring Nonlinear Profiles with Random Effects by Nonparametric Regression. Communications in Statistics - Theory and Methods, 38(10), 1664-1679.
Shiau, J. J. H., Lin, S. H. and Chen, Y. C. (2006). Monitoring Linear Profiles Based on a Random-effect Model. Technical Report. Institute of Statistics, National Chiao Tung University, Hsinchu, Taiwan.
Walker, E. and Wright, S. (2002). Comparing Curves Using Additive Models. Journal of Quality Technology, 34 (1), 118-129.
Woodall, W. H, Spitzner, D. J., Montgomery, D. C. and Gupta, S. (2004). Using Control Charts to Monitor Process and Product Quality Profiles. Journal of Quality Technology, 36(3), 309-320.
Woodall, W. H. (2007). Current Research on Profile Monitoring. Revista Producao, 17(3), 420-425.
Zhou, S. Y., Sun, B. C. and Shi, J. J. (2007). An SPC Monitoring System for Cycle-based Waveform Signals using Haar Transform. IEEE Transactions on Automation Science and Engineering, 3(1), 60-72.
Zou, C., Tsung, F. and Wang, Z. (2007). Monitoring General Linear Profiles using Multivariate Exponentially Weighted Moving Average Schemes. Technometrics, 49(4), 395-408.
Zou, C., Zhang, Y. and Wang, Z. (2006). Control Chart Based on Change-point Model for Monitoring Linear Profiles. IIE Transactions. 38(12), 1093-1103.
Zou, C., Zhou, C., Wang, Z. and Tsung, F. (2007). A Self-Starting Control Chart for Linear Profiles Journal of Quality Technology, 39(4), 364-375.
描述 碩士
國立政治大學
統計研究所
97354005
98
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0097354005
資料類型 thesis
dc.contributor.advisor 楊素芬<br>蔡紋琦zh_TW
dc.contributor.author (Authors) 徐伊萱zh_TW
dc.creator (作者) 徐伊萱zh_TW
dc.date (日期) 2009en_US
dc.date.accessioned 5-Sep-2013 15:09:58 (UTC+8)-
dc.date.available 5-Sep-2013 15:09:58 (UTC+8)-
dc.date.issued (上傳時間) 5-Sep-2013 15:09:58 (UTC+8)-
dc.identifier (Other Identifiers) G0097354005en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/60428-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計研究所zh_TW
dc.description (描述) 97354005zh_TW
dc.description (描述) 98zh_TW
dc.description.abstract (摘要) 　　本論文針對防丟器的剖面進行追蹤分析。防丟器包含發射器及接收器，發射器會發射訊號，接收器會記錄RSSI (Receive Signal Strength Index)與發射點數，其中RSSI表示訊號的強度。在工程理論上，RSSI與距離具有函數關係；然而環境中的干擾及事件發生都會影響此函數關係，特別是事件發生會嚴重地改變此函數關係，因此論文主要目的在於區別事件是否發生。
　　所謂的剖面指的是變數之間的函數關係，而論文中的剖面追蹤是利用管制圖的概念，用管制圖來監控剖面的參數估計值。如果管制圖上的點子出界，則表示事件發生而導致失控。
　　本論文以腳踏車是否被偷為例，嘗試一些實驗後找出顯著影響的因子設計實驗，包含17種腳踏車未被偷之情境與18種腳踏車被偷情境；欲利用未被偷的實驗建立試驗管制圖，而以被偷之情境來追蹤，用以驗證管制圖之有效性。
　　論文中主要透過分析防丟器產生的RSSI與距離的剖面、距離與發射點數的剖面來探討事件是否發生。另外剖面追蹤其實是種事後追蹤的方法，為了能即時追蹤，本論文亦採用預測區間的方式，來追蹤事件是否發生。
　　本論文建議監控距離與發射點數的剖面，因該方法的表現最好，另外建議增加防丟器上能紀錄距離的功能，此方法會更加合適。
　　本論文提出的即時追蹤方式並沒有特別好，因此一個比較好的即時追蹤方法是未來值得研究的方向。		zh_TW
dc.description.abstract (摘要) The device of Babyfinder is designed to detect if an event occurs. The Babyfinder includes transceiver and receiver. The signal strength, Received Signal Strength Indicator (RSSI), generates once there are distances between transceiver and receiver. In wireless communication theory, the relationship between RSSI and distance should be expressed by the model that RSSI = a + b ln (distance) Nevertheless, some circumstance noises and user noises (or common causes), and/or events (special causes) may affect the variation of RSSI. Since the occurrence of events may change the functional relationship of RSSI and distance, to distinguish if the functional relationship is changed by the occurred events is the subject of this study. This study designs some events and noises experiments based on the real noise factors and special events. Two monitoring schemes are proposed to distinguish the occurred events and noise circumstance. One is the profile monitoring scheme, the other is the real time monitoring scheme. The two proposed approaches of profile monitoring scheme are considered to monitor the profile of RSSI and distance and that of distance and the number of transmitting points, respectively. The profile monitoring approach for distance and the number of transmitting points shows better performance. However, the profile monitoring is an after-event tracing approach. It cannot detect the occurred events in time. A better approach of real-time monitoring approach is worth to be proposed in the future study.en_US
dc.description.tableofcontents 1 INTRODUCTION 1
2 DATA COLLECTION AND THE DESIGN OF EXPERIMENTS 5
2.1 The Description of Experiments 5
2.2 The Data Processing 10
3 DATA ANALYSIS FOR THE BABYFINDER EXPERIMENTS 14
3.1 The Profile Monitoring Scheme 15
3.1.1 Control charts for monitoring the profile of Y given lnX 15
3.1.2 Control charts for monitoring the profile of X given t 28
3.2 Real Time Monitoring Scheme 33
3.3 Performance Comparisons of the Profile Monitoring and Real Time Monitoring Schemes 48
4 SUMMARY AND CONCLUSIONS 51
REFERENCES 52		zh_TW
dc.format.extent 1082200 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0097354005en_US
dc.subject (關鍵詞) 剖面追蹤zh_TW
dc.subject (關鍵詞) 實驗設計zh_TW
dc.subject (關鍵詞) 管制圖zh_TW
dc.subject (關鍵詞) 即時追蹤zh_TW
dc.subject (關鍵詞) Profile Monitoringen_US
dc.subject (關鍵詞) Design of Experimentsen_US
dc.subject (關鍵詞) Control Charten_US
dc.subject (關鍵詞) Real-Time Detectionen_US
dc.title (題名) 防丟器的剖面追蹤研究zh_TW
dc.title (題名) Profile Monitoring on the RSSI of Babyfinderen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) Chang, S. I. and Yadama, S. (2010). Statistical Process Control for Monitoring Nonlinear Profiles Using Wavelet Filtering and B-Spline Approximation. International Journal of Production Research, 48(4), 1049-1068.
Ding, Y., Zeng, L. and Zhou, S. (2006). Phase I Analysis for Monitoring Nonlinear Profile Signals in Manufacturing Processes. Journal of Quality Technology, 38 (3), 199-216.
Jeong, M. K, Lu, J. C. and Wang, N. (2006). Wavelet-based SPC Procedure for Complicated Functional Data. International Journal of Production Research, 44 (4), 729-744.
Jin, J. and Shi, J. (2001). Automatic Feature Extraction of Waveform Signals of In-process Diagnostic Performance Improvement. Journal of Intelligent Manufacturing, 12 (3), 257-268.
Kang, L. and Albin, S. L. (2000). On-Line Monitoring When the Process Yields a Linear Profile. Journal of Quality Technology, 32, 418-426.
Kim, K., Mahmoud, M. A. and Woodall, W. H. (2003). On the Monitoring of Linear Profiles. Journal of Quality Technology, 35(3), 317-328.
Mahmoud, M. A. and Woodall, W. H. (2004). Phase I Analysis of Linear Profiles with Calibration Applications. Technometrics, 46(4), 380-391.
Mahmoud, M. A. (2008). Phase I Analysis of Multiple Linear Regression Profiles. Communications in Statistics—Simulation and Computation, 37(10), 2106-2130.
Mahmoud, M. A., Parker, P. A., Woodall, W. H. and Hawkins, D. M. (2007). A Change Point Method for Linear Profile Data. Quality and Reliability Engineering International, 23(2), 247-268.
Mahmoud, M. A., Morgan, J. P. and Woodall, W. H. (2009). The Monitoring of Simple Linear Regression Profiles with Two Observations per Sample. Journal of Applied Statistics, (Manuscript ID: CJAS-2009-0012.R1).
Moguerza, J. M., Munoz, A. and Psarakis, S. (2007). Monitoring Nonlinear Profiles using Support Vector Machines. Lecture Notes in Computer Science, 4756, 574-583.
Montgomery, D. C., Peck, E. A. and Vining, G. G. (2001). Introduction to Linear Regression Analysis. (3rd Ed.). New York: Wiley.
Noorossana, R. and Amiri, A. (2007). Enhancement of Linear Profiles Monitoring in Phase II. AmirKabir Journal of Science and Technology, 18, 19-27 in Farsi
Reis, M. S. and Saraiva, P. M., (2006). Multiscale Statistical Process Control of Paper Surface Profiles. Quality Technology and Quantitative Management, 3 (3), 263-282.
Shiau, J. J. H., Huang, H. L., Lin, S. H. and Tsai, M. Y. (2009). Monitoring Nonlinear Profiles with Random Effects by Nonparametric Regression. Communications in Statistics - Theory and Methods, 38(10), 1664-1679.
Shiau, J. J. H., Lin, S. H. and Chen, Y. C. (2006). Monitoring Linear Profiles Based on a Random-effect Model. Technical Report. Institute of Statistics, National Chiao Tung University, Hsinchu, Taiwan.
Walker, E. and Wright, S. (2002). Comparing Curves Using Additive Models. Journal of Quality Technology, 34 (1), 118-129.
Woodall, W. H, Spitzner, D. J., Montgomery, D. C. and Gupta, S. (2004). Using Control Charts to Monitor Process and Product Quality Profiles. Journal of Quality Technology, 36(3), 309-320.
Woodall, W. H. (2007). Current Research on Profile Monitoring. Revista Producao, 17(3), 420-425.
Zhou, S. Y., Sun, B. C. and Shi, J. J. (2007). An SPC Monitoring System for Cycle-based Waveform Signals using Haar Transform. IEEE Transactions on Automation Science and Engineering, 3(1), 60-72.
Zou, C., Tsung, F. and Wang, Z. (2007). Monitoring General Linear Profiles using Multivariate Exponentially Weighted Moving Average Schemes. Technometrics, 49(4), 395-408.
Zou, C., Zhang, Y. and Wang, Z. (2006). Control Chart Based on Change-point Model for Monitoring Linear Profiles. IIE Transactions. 38(12), 1093-1103.
Zou, C., Zhou, C., Wang, Z. and Tsung, F. (2007). A Self-Starting Control Chart for Linear Profiles Journal of Quality Technology, 39(4), 364-375.		zh_TW