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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林風(Phone Lin) | |
| dc.contributor.author | En-Hau Yeh | en |
| dc.contributor.author | 葉恩豪 | zh_TW |
| dc.date.accessioned | 2021-06-07T23:55:49Z | - |
| dc.date.copyright | 2013-09-02 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-08-20 | |
| dc.identifier.citation | [1] L. Zhang, B. Tiwana, R.P. Dick, Z. Qian, Z.M. Mao, Z. Wang, and L. Yang. Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), pages 105 – 114, October 2010.
[2] M. Kim, J. Kong, and S. W. Chung. Enhancing online power estimation accuracy for smartphones. Consumer Electronics, IEEE Transactions on, 58(2):333 – 339, May 2012. [3] M. Simard, S.S. Saatchi, and G. De Grandi. The use of decision tree and multiscale texture for classification of jers-1 sar data over tropical forest. IEEE Transactions on Geoscience and Remote Sensing, 38(5):2310 – 2321, September 2000. [4] E. Zio. A study of the bootstrap method for estimating the accuracy of artificial neural networks in predicting nuclear transient processes. IEEE Transactions on Nuclear Science, 53(3):1460 – 1478, June 2006. [5] H. Qiao, J. Peng, Z.-B. Xu, and B. Zhang. A reference model approach to stability analysis of neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 33(6):925 – 936, December 2003. [6] R. Roscher, B.Waske, andW. Forstner. Incremental import vector machines for classifying hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 50(9):3463 – 3473, September 2012. [7] Dustin Boswell. Introduction to support vector machines, 2002. [8] M. Karasuyama and Ichiro Takeuchi. Multiple incremental decremental learning of support vector machines. IEEE Transactions on Neural Networks, 21(7):1048–1059, July 2010. [9] C.-C. Chang and C.-J. LIN. Libsvm: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(27), April 2011. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/17081 | - |
| dc.description.abstract | 今天，智慧型手機具備了多種網路營運商提供的無線通訊服務(如UMTS和Wi-Fi)，亦即異質無線網路。異質無線網路的環境下行動手機會消耗更多的電量以在不同的通訊協定之間切換。在這篇論文中，我們提出一種運用使用者儲存在社群網路中的日常行為行程的節電機制以達到減少行動電話耗能的目的。我們使用機器學習的技術以預測使用者使用無線網路服務的使用情形。這篇論文也提供了效能評估的方法。 | zh_TW |
| dc.description.abstract | Today, smartphones are equipped with multiple communications protocols to access the
wireless communications services (eg., UMTS plus Wi-Fi) provided by network opera- tors, that is, the heterogeneous wireless networks. The heterogeneous wireless networks environment result in that the mobile phone consumes more power to switch between different wireless technologies. In this paper, we propose an energy conservation mechanism to save the power consumption of mobile phones based the user’s daily schedule stored in his social calendar. We utilize the machine learning technique to predict the user’s usage of wireless communications services. Performance evaluation for our mechanism is also provided in this paper. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-07T23:55:49Z (GMT). No. of bitstreams: 1 ntu-102-R00922095-1.pdf: 2843552 bytes, checksum: dcb1f67e5e4e0703a0708176c6972ec8 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 1 Introduction 1
2 The UEC Mechanism 5 3 Part I: Modeling, Collecting, and Predicting of User Behavior 7 4 Part II: Automatic Component Controller 9 4.1 The Power Model of Hardware Component . . . . . . . . . . . . . . . . 12 4.2 Wi-Fi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.3 3G . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.4 Ring Tone or Vibration for Incoming Call . . . . . . . . . . . . . . . . . 15 5 Machine Learning 17 5.1 SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 6 Saving Power Estimation 23 7 Simulation 25 8 Conclusion 33 Bibliography 35 | |
| dc.language.iso | en | |
| dc.subject | 智慧型手機節能實作 | zh_TW |
| dc.subject | 智慧型手機之節能 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 使用行為模擬 | zh_TW |
| dc.subject | Simulation Platform | en |
| dc.subject | Smartphone Power Saving | en |
| dc.subject | Implementation | en |
| dc.subject | Machine Learning | en |
| dc.title | 依據使用者行為之手機節能機制 | zh_TW |
| dc.title | User-Based Energy Conservation Mechanism for Mobile
Handset | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林一平(Yi-Bing Lin),施吉昇(CS Shih),修丕承(Pi-Cheng Hsiu) | |
| dc.subject.keyword | 智慧型手機之節能,機器學習,使用行為模擬,智慧型手機節能實作, | zh_TW |
| dc.subject.keyword | Smartphone Power Saving,Machine Learning,Simulation Platform,Implementation, | en |
| dc.relation.page | 36 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2013-08-20 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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|---|---|---|---|
| ntu-102-1.pdf 未授權公開取用 | 2.78 MB | Adobe PDF |
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