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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林宗男(Tsung-Nan Lin) | |
| dc.contributor.author | Yen-Chih Chou | en |
| dc.contributor.author | 周彥志 | zh_TW |
| dc.date.accessioned | 2021-06-16T23:30:33Z | - |
| dc.date.available | 2012-08-01 | |
| dc.date.copyright | 2012-08-01 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-07-28 | |
| dc.identifier.citation | [1] S. Gezici, “A survey on wireless position estimation,” Wireless Personal Communications, vol. 44, pp. 263–282, 2008, 10.1007/s11277-007-9375-z. [Online]. Available: http://dx.doi.org/10.1007/s11277-007-9375-z 1
[2] D. Munoz, F. B. Lara, C. Vargas, and R. Enriquez-Caldera, Position Location Techniques and Applications. Academic Press, 2009. 1 [3] M. McGuire, K. Plataniotis, and A. Venetsanopoulos, “Data fusion of power and time measurements for mobile terminal location,” Mobile Computing, IEEE Transactions on, vol. 4, no. 2, pp. 142 – 153, march-april 2005. 1 [4] S. Golden and S. Bateman, “Sensor measurements for wi-fi location with emphasis on time-of-arrival ranging,” Mobile Computing, IEEE Transactions on, vol. 6, no. 10, pp. 1185 –1198, oct. 2007. 1 [5] P. Bahl and V. Padmanabhan, “Radar: an in-building rf-based user location and tracking system,” in INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, vol. 2, 2000, pp. 775 –784 vol.2. 1, 49 [6] A. Agiwal, P. Khandpur, and H. Saran, “Locator: location estimation system for wireless lans,” in Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots, ser. WMASH ’04. New York, NY, USA: ACM, 2004, pp. 102–109. [Online]. Available: http://doi.acm.org/10.1145/1024733.1024747 1 [7] M. Youssef and A. Agrawala, “The horus wlan location determination system,” in Proceedings of the 3rd international conferenceon Mobile systems, applications, and services, ser. MobiSys ’05. New York, NY, USA: ACM, 2005, pp. 205–218. [Online]. Available: http://doi.acm.org/10.1145/1067170.1067193 1 [8] S.-H. Fang, T.-N. Lin, and P.-C. Lin, “Location fingerprinting in a decorrelated space,” Knowledge and Data Engineering, IEEE Transactions on, vol. 20, no. 5, pp. 685 –691, may 2008. 1 [9] H. Lim, L.-C. Kung, J. C. Hou, and H. Luo, “Zero-configuration, robust indoor localization: Theory and experimentation,” in INFOCOM 2006. 25th IEEE International Conference on Computer Communications. Proceedings, april 2006, pp. 1 –12. 1 [10] Y. Chen, Q. Yang, J. Yin, and X. Chai, “Power-efficient access-point selection for indoor location estimation,” Knowledge and Data Engineering, IEEE Transactions on, vol. 18, no. 7, pp. 877 – 888, 2006. 2, 31 [11] R. Xu and I. Wunsch, D., “Survey of clustering algorithms,” Neural Networks, IEEE Transactions on, vol. 16, no. 3, pp. 645 –678, may 2005. 2, 24, 46 [12] P. H. R. Duda and D. Stork, in Pattern Classification. 2, 14, 24 [13] A. Ben-Hur, D. Horn, H. Siegelmann, and V. Vapnik, “A support vector clustering method,” in Pattern Recognition, 2000. Proceedings. 15th International Conference on, vol. 2, 2000, pp. 724 –727 vol.2. 2 [14] A. Smailagic and D. Kogan, “Location sensing and privacy in a contextaware computing environment,” Wireless Communications, IEEE, vol. 9, no. 5, pp. 10 – 17, oct. 2002. 4 [15] J. Hightower and G. Borriello, “Location systems for ubiquitous computing,” Computer, vol. 34, no. 8, pp. 57 –66, aug 2001. 4 [16] T. Rappaport, J. Reed, and B. Woerner, “Position location using wireless communications on highways of the future,” Communications Magazine, IEEE, vol. 34, no. 10, pp. 33 –41, oct 1996. 4 [17] R. S. Simon and A.-Z. Alejandro, in Antennas and propagation for wireless communication systems. 6 [18] J. S. Seybold, Frontmatter. John Wiley and Sons, Inc., 2005, pp. i–xv. [Online]. Available: http://dx.doi.org/10.1002/0471743690.fmatter 6 [19] V. V. Saeed, in Advanced Digital signal processing and noise reduction. 6 [20] S.-H. Fang and T.-N. Lin, “Robust wireless lan location fingerprinting by svd-based noise reduction,” in Communications, Control and Signal Processing, 2008. ISCCSP 2008. 3rd International Symposium on, march 2008, pp. 295 –298. 7 [21] W.-J. Chang and J.-H. Tarng, “Effects of bandwidth on observable multipath clustering in outdoor/indoor environments for broadband and ultrawideband wireless systems,” Vehicular Technology, IEEE Transactions on, vol. 56, no. 4, pp. 1913 –1923, july 2007. 7 [22] Y. Qi, H. Kobayashi, and H. Suda, “Analysis of wireless geolocation in a non-line-of-sight environment,” Wireless Communications, IEEE Transactions on, vol. 5, no. 3, pp. 672 – 681, march 2006. 7 [23] L. Cong and W. Zhuang, “Nonline-of-sight error mitigation in mobile location,” Wireless Communications, IEEE Transactions on, vol. 4, no. 2, pp. 560 – 573, march 2005. 7 [24] R. S. Simon and A.-Z. Alejandro, in Antennas and propagation for wireless communication systems. 9 [25] P. Castro and R. Munz, “Managing context data for smart spaces,” Personal Communications, IEEE, vol. 7, no. 5, pp. 44 –46, oct 2000. 10 [26] T. Roos, P. Myllymaki, H. Tirri, P. Misikangas, and J. Sievanen, “A probabilistic approach to wlan user location estimation,” International Journal of Wireless Information Networks, vol. 9, pp. 155–164, 2002, 10.1023/A:1016003126882. [Online]. Available: http://dx.doi.org/10.1023/A:1016003126882 10 [27] M. Youssef, A. Agrawala, and A. Udaya Shankar, “Wlan location determination via clustering and probability distributions,” in Pervasive Computing and Communications, 2003. (PerCom 2003). Proceedings of the First IEEE International Conference on, 2003, pp. 143 – 150. 10, 31 [28] G. Baudat and F. Anouar, “Kernel-based methods and function approximation,” in Neural Networks, 2001. Proceedings. IJCNN ’01. International Joint Conference on, vol. 2, 2001, pp. 1244 –1249 vol.2. 20, 34 [29] L. Bottou, C. Cortes, J. Denker, H. Drucker, I. Guyon, L. Jackel, Y. LeCun, U. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: a case study in handwritten digit recognition,” in Pattern Recognition, 1994. Vol. 2 - Conference B: Computer Vision Image Processing., Proceedings of the 12th IAPR International. Conference on, vol. 2, oct 1994, pp. 77 –82 vol.2. 21 [30] C.-W. Hsu and C.-J. Lin, “A comparison of methods for multiclass support vector machines,” Neural Networks, IEEE Transactions on, vol. 13, no. 2, pp. 415 –425, mar 2002. 21, 39 [31] ——, “A comparison of methods for multiclass support vector machines,”Neural Networks, IEEE Transactions on, vol. 13, no. 2, pp. 415 –425, mar 2002. 22, 23 [32] R. Rifkin and A. Klautau, “In defense of one-vs-all classification,” J.Mach. Learn. Res., vol. 5, pp.101–141, December 2004. [Online]. Available: http://portal.acm.org/citation.cfm?id=1005332.1005336 23 [33] Netstumbler. http://www.netstumbler.com. 44 [34] H.-S. Seok, K.-B. Hwang, and B.-T. Zhang, “Feature relevance network-based transfer learning for indoor location estimation,” Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 41, no. 5, pp. 711 –719, sept. 2011. 45 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/65218 | - |
| dc.description.abstract | 群聚演算法應用在室內定位上可以增進定位準確度和減少運算量。儘管如此，傳統的群聚演算法無法直接的處理。而這個問題是目前在結合監督式與非監督式學習時會遇到的主要議題。而這份研究主要提出了一個基於支持向量機下的新穎群集配置演算法簡稱SVM-C，而此演算法成功地解決了結合群集配置算法及室內定位時會發生的問題。SVM-C 主要以兩個級別間的距離為判斷依據，而非像傳統群集配置演算法是使用兩個平均值間的歐式距離作為判斷依據。這篇論文主要時
坐在現實的無線網路環境中，而實驗結果也呈現出在傳統的定位演算法及維度排序下，相較於K-means 與SVC，定位的精準度分別增加了19.61%和15.31％。而在不同的定位演算法及不同的維度排序下，SVM-C 皆占了優勢。 | zh_TW |
| dc.description.abstract | Clustering approaches have been used in location fingerprinting systems to improve positioning accuracy and reduce computational overhead. However, traditional methods can not use the data collected directly, and this problem is the main issue in combining the supervised and unsupervised learning. This study proposes a novel clustering algorithm based on SVM called SVM-C and it solves the problem about the clustering algorithm applying in the classification. The SVM-C approach focuses on the distance between the classes. It utilizes the margin between two canonical hyperplanes to cluster them instead of using the Euclidean distance between two average points.
This thesis applies the proposed algorithms to realistic wireless local area networks. Experimental results demonstrate that the SVM-C outperforms the K-means and SVC reducing the mean localization error by 19.61\% and 15.31\% respectively under the traditional AP-selection schemes. The experiments based on different fingerprinting approaches and different AP-selection schemes also confirm the advantages of the proposed algorithms. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T23:30:33Z (GMT). No. of bitstreams: 1 ntu-101-R99942122-1.pdf: 1812196 bytes, checksum: 087f3e26082a80adf1e72598917112e9 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | List of Figures iii
List of Tables v 1 Introduction 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 BACKGROUND of INDOOR LOCATION SYSTEM 4 2.1 Wireless Location Estimation . . . . . . . . . . . . . . . . . . . . 4 2.2 RSS Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Location Fingerprinting System . . . . . . . . . . . . . . . . . . . 9 2.3.1 Offline Stage . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3.2 Online Stage . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3 MACHINE LEARNING MECHANISM 12 3.1 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.1 Maximum Likelihood . . . . . . . . . . . . . . . . . . . . . 13 3.1.2 Support Vector Machine . . . . . . . . . . . . . . . . . . . 15 3.1.2.1 Linearly separable case . . . . . . . . . . . . . . . 15 3.1.2.2 Linearly non-separable case . . . . . . . . . . . . 17 3.1.2.3 Nonlinear extension by kernel trick . . . . . . . . 20 3.1.2.4 Extension to multiclass classification problem . . 21 3.2 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.1 K-means Clustering . . . . . . . . . . . . . . . . . . . . . . 24 3.2.2 Support Vector Clustering . . . . . . . . . . . . . . . . . . 26 3.2.3 Cluster Boundaries . . . . . . . . . . . . . . . . . . . . . . 26 3.2.3.1 Cluster Assignment . . . . . . . . . . . . . . . . . 28 3.2.3.2 Parameter Selection of SVC . . . . . . . . . . . . 29 3.2.4 In the Online Stage . . . . . . . . . . . . . . . . . . . . . . 30 4 Proposed Clustering Algorithm 32 4.1 Support Vector Machine Clustering . . . . . . . . . . . . . . . . . 32 4.2 In the Online Stage . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3 Discussion with Traditional Clustering Algorithm . . . . . . . . . 40 5 Experimental Results 44 5.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5.2 Quality of Clustering Algorithm . . . . . . . . . . . . . . . . . . . 45 5.3 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . 47 5.4 The Computing to Reduce by Clustering Algorithm . . . . . . . . 47 5.5 Alternative Fingerprinting Approach . . . . . . . . . . . . . . . . 49 6 CONCLUSION 57 Bibliography 59 | |
| dc.language.iso | en | |
| dc.subject | 指紋辨識 | zh_TW |
| dc.subject | 無線網域 | zh_TW |
| dc.subject | 群集配置演算法 | zh_TW |
| dc.subject | 支持向量機 | zh_TW |
| dc.subject | 非監督式學習 | zh_TW |
| dc.subject | fingerprinting | en |
| dc.subject | WLAN | en |
| dc.subject | cluster algorithm | en |
| dc.subject | SVM | en |
| dc.subject | unsupervised learning | en |
| dc.title | 以新穎指紋辨識位置之群集配置基於支持向量機應用在室內定位上 | zh_TW |
| dc.title | Indoor Positioning by a Novel Location Fingerprinting Algorithm of SVM-based Cluster Assignment | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 廖婉君,蔡子傑,陳俊良 | |
| dc.subject.keyword | 指紋辨識,無線網域,群集配置演算法,支持向量機,非監督式學習, | zh_TW |
| dc.subject.keyword | fingerprinting,WLAN,cluster algorithm,SVM,unsupervised learning, | en |
| dc.relation.page | 62 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-07-30 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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