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DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 林忠緯(Chung-Wei Lin) | |
dc.contributor.author | Chia-Ching Fu | en |
dc.contributor.author | 傅家靖 | zh_TW |
dc.date.accessioned | 2021-06-15T13:24:38Z | - |
dc.date.available | 2022-08-12 | |
dc.date.copyright | 2020-08-21 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-10 | |
dc.identifier.citation | [1] R. I. Davis, A. Burns, R. J. Bril, and J. J. Lukkien, “Controller area net-work (can) schedulability analysis: Refuted, revisited and revised,”Real-TimeSystems, vol. 35, pp. 239–272, 2007. [2] S. Durga, S. Mohan, J. Dinesh, and A. Aneena, “Cuckoo based resource al-location for mobile cloud environments,” inComputational Intelligence, CyberSecurity and Computational Models, pp. 543–550. Springer, 2015. [3] X. Fu, B. Liang, Y. Huang, X. Ding, and J. Paisley, “Lightweight pyramidnetworks for image deraining,” inIEEE Transactions on Neural Networks andLearning Systems, pp. 1794–1807. IEEE, 2018. [4] B. Kim and S.-K. Park, “Ecu software updating scenario using ota technol-ogy through mobile communication network,” in2018 IEEE 3rd InternationalConference on Communication and Information Systems (ICCIS), pp. 67–72.IEEE, 2018. [5] C.-W. Lin, B. Kim, and S. Shiraishi, “Hardware virtualization and task allo-cation for plug-and-play automotive systems,”IEEE Design Test, pp. 1–1,2019. [6] Y. Liu, G. Xie, Y. Tang, and R. Li, “Improving real-time performanceunder reliability requirement assurance in automotive electronic systems,”IEEE Access, vol. 7, pp. 140 875 – 140 888, 2019. [Online]. Available:https://ieeexplore.ieee.org/abstract/document/8851139 [7] J. Redmon and A. Farhadi, “Yolov3: An incremental improvement. computervision and pattern recognition,” inIEEE Computer Vision Pattern Recong-nition, pp. 1–8. IEEE, 2018. [8] D. Ren, W. Zuo, Q. Hu, P. Zhu, and D. Meng, 'Progressive image deraining networks: A better and simpler baseline,' in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3932-3941. IEEE, 2019. [9] T. Wang, X. Yang, K. Xu, S. Chen, Q. Zhang, and R. Lau, “Spatial atten-tive single-image deraining with a high quality real rain dataset,” in2019IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),pp. 12 262–12 271. IEEE, 2019. [10] K. Watanabe, E. Kang, and C.-W. Lin, “Runtime monitoring for safety ofintelligent vehicles,” inACM/IEEE Design Automation Conference (DAC),pp. 1–6. IEEE, 2018. [11] S.-Y. Wu, “Deep-learning-based intrusion detection for cooperative adaptivecruise control,” 2020. [12] W. Yang, R.-T. Tan, J. Feng, J. Liu, Z. Guo, and S. Yan, “Deep joint rain detec-tion and removal from a single image,” in2017 IEEE Conference on ComputerVision and Pattern Recognition (CVPR), pp. 1685–1694. IEEE, 2017. [13] M.-H. Zarei, M.-A. Shirsavar, and N. Yazdani, “A qos-aware task allocationmodel for mobile cloud computing,” in2016 Second International Conferenceon Web Research (ICWR), pp. 43–47. IEEE, 2016. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/51065 | - |
dc.description.abstract | 隨著智慧型汽車的蓬勃發展,構成車輛的車內軟體也隨之增加。但我們發現環境狀況會對智慧型汽車車內軟體的效能有所影響,例如影像辨識與攻擊偵測軟體會因為環境變化而帶來不同的效能。另外一個問題是,有些車內軟體由於功能相容性的因素,是不能或必須被同時指派的,我們稱此問題為軟體相容性限制。因為上述兩個問題,我們應用了服務品質的數值來確保每個在特定環境下之任務可被服務品質達到要求的軟體所解決。並且,為了解決軟體相容性限制的問題,我們建立了軟體限制表來確保每次的軟體選擇皆不違反軟體相容性限制。為了在系統軟體中選擇適當的軟體來服務每個任務,我們引用了加權貪婪集合覆蓋演算法來選取所需要之軟體並以速率單調排程指派優先權得到最初解。最後,我們運用降火演算法在最初解上得到能產生總等待時間更小的軟體選擇與優先權排序的更優解。 | zh_TW |
dc.description.abstract | As the rapid growth in the intelligent vehicles field, software programs required to compose a vehicle increase. However, we notice that environmental conditions can affect performances of software programs such as object detection and intrusion detection. Moreover, some software programs cannot or should be scheduled together due to software program compatibility constraints caused by software program functionalities compatibility. Therefore, to solve the above mentioned problems, the Quality of Service (QoS) value is used to guarantee each runnable under certain environment condition is being served by software programs with the QoS value that is larger or equal to that defined on the runnable, and the software program constraint table is constructed to guarantee software program selections do not violate software program compatibility constraints. To obtain the software program selection among all software programs in the vehicular system, we apply the Weighted Greedy Set Cover algorithm to sift through software programs. Afterward, rate-monotonic scheduling is applied to assign priorities to all selected software programs, from which the initial solution is obtained. At last, we apply the Simulated Annealing algorithm on the initial solution for a better software program selection and priority assignment that lead to a lower total response time. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T13:24:38Z (GMT). No. of bitstreams: 1 U0001-1008202016105100.pdf: 877109 bytes, checksum: 2c380698c0fe7413bde028dcc63b2c07 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | Acknowledgements ii Abstract (Chinese) iii Abstract v List of Tables x List of Figures xi Chapter 1. Introduction 1 1.1 Related Work 2 1.1.1 Automotive Over-The-Air (OTA) update 2 1.1.2 Task Allocation 3 1.1.3 Timing Analysis 4 1.1.4 Quality of Service 4 1.2 Thesis Organization 5 Chapter 2. Motivating Examples 6 2.1 Raindrop Removal 6 2.2 Intrusion Detection 8 Chapter 3. System Model and Problem Formulation 10 3.1 System Model 10 3.1.1 Assumption 11 3.1.2 System Flow 11 3.1.3 Given 12 3.1.4 Decision Variables 13 3.1.5 Dependent Variables 13 3.1.6 Goal 13 3.2 Problem Formulation 14 Chapter 4. Algorithm 15 4.1 Computing the Total Response Time 15 4.2 Preprocessing Software Program Array 18 4.3 Weighted Greedy Set Cover 19 4.4 Coverage Check and Compatibility Check 22 4.5 Simulated Annealing 24 Chapter 5. Extension 28 5.1 Motivation 28 5.2 Software Programs Compatibility Constraints 29 5.3 Weighted Greedy Set Covering Modification 30 Chapter 6. Experimental Results 31 6.1 Easier and Smaller Test Case 32 6.1.1 With Software Programs Compatibility Constraints 32 6.1.2 Without Software Programs Compatibility Constraints 32 6.2 Easier and Larger Test Case 33 6.2.1 With Software Programs Compatibility Constraints 33 6.2.2 Without Software Programs Compatibility Constraints 33 6.3 Harder and Smaller Test Case 34 6.3.1 With Software Programs Compatibility Constraints 34 6.3.2 Without Software Programs Compatibility Constraints 35 6.4 Harder and Larger Test Case 36 6.4.1 With Sofware Programs Compatibility Constraints 36 6.4.2 Without Software Programs Compatibility Constraints 36 6.5 Discussion 37 Chapter 7. Conclusions 39 Bibliography 40 | |
dc.language.iso | en | |
dc.title | 適應式隨插即用車輛系統之軟體選擇 | zh_TW |
dc.title | Software Selection for Adaptive Plug-and-Play Automotive System | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 江蕙如(Iris Hui-Ru Jiang),蔡欣穆(Hsin-Mu Tsai),魏宏宇(Hung-Yu Wei) | |
dc.subject.keyword | 智慧型汽車,服務品質,加權貪婪集合覆蓋演算法,降火演算法,總回應時間, | zh_TW |
dc.subject.keyword | Intelligent Vehicle,Quality of Service,Weighted Greedy Set Cover Algorithm,Simulated Annealing Algorithm,Total Response Time, | en |
dc.relation.page | 41 | |
dc.identifier.doi | 10.6342/NTU202002826 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-11 | |
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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