Please use this identifier to cite or link to this item:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90122Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 陳柏華 | zh_TW |
| dc.contributor.advisor | Albert Y. Chen | en |
| dc.contributor.author | 廖浚評 | zh_TW |
| dc.contributor.author | Chun-Ping Liao | en |
| dc.date.accessioned | 2023-09-22T17:30:20Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-09-22 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-11 | - |
| dc.identifier.citation | [1] I. Manisalidis, E. Stavropoulou, A. Stavropoulos, and E. Bezirtzoglou, “Environmental and Health Impacts of Air Pollution: A Review,” Front Public Health, vol. 8, 2020, doi: 10.3389/fpubh.2020.00014.
[2] M. Giugliano, G. Lonati, P. Butelli, L. Romele, R. Tardivo, and M. Grosso, “Fine particulate (PM2.5–PM1) at urban sites with different traffic exposure,” Atmos Environ, vol. 39, no. 13, pp. 2421–2431, 2005, doi: https://doi.org/10.1016/j.atmosenv.2004.06.050. [3] UNFCCC, “Electric Future,” Electric Future, May 23, 2022. https://unfccc.int/blog/electric-future (accessed Mar. 30, 2023). [4] ZETA, “https://www.zeta2030.org/,” 2023. https://www.zeta2030.org/ (accessed Mar. 30, 2023). [5] Norsk elbilforening, “Norwegian EV policy,” Norwegian EV policy, Jan. 03, 2023. https://elbil.no/english/norwegian-ev-policy/ (accessed Mar. 30, 2023). [6] GOV.UK, “Government takes historic step towards net-zero with end of sale of new petrol and diesel cars by 2030,” Nov. 18, 2020. https://www.gov.uk/government/news/government-takes-historic-step-towards-net-zero-with-end-of-sale-of-new-petrol-and-diesel-cars-by-2030 (accessed Mar. 30, 2023). [7] N. Li et al., “Potential impacts of electric vehicles on air quality in Taiwan,” Science of The Total Environment, vol. 566–567, pp. 919–928, 2016, doi: https://doi.org/10.1016/j.scitotenv.2016.05.105. [8] S.-H. Woo, H. Jang, S.-B. Lee, and S. Lee, “Comparison of total PM emissions emitted from electric and internal combustion engine vehicles: An experimental analysis,” Science of The Total Environment, vol. 842, p. 156961, 2022, doi: https://doi.org/10.1016/j.scitotenv.2022.156961. [9] “MOTC.” Jun. 22, 2023. [Online]. Available: https://stat.motc.gov.tw/mocdb/stmain.jsp?sys=100&funid=b3301 [10] W. J. Requia, M. Mohamed, C. D. Higgins, A. Arain, and M. Ferguson, “How clean are electric vehicles? Evidence-based review of the effects of electric mobility on air pollutants, greenhouse gas emissions and human health,” Atmos Environ, vol. 185, pp. 64–77, 2018, doi: https://doi.org/10.1016/j.atmosenv.2018.04.040. [11] R. Galvin, “Are electric vehicles getting too big and heavy? Modelling future vehicle journeying demand on a decarbonized US electricity grid,” Energy Policy, vol. 161, p. 112746, 2022, doi: https://doi.org/10.1016/j.enpol.2021.112746. [12] T. N. Skipper, A. S. Lawal, Y. Hu, and A. G. Russell, “Air quality impacts of electric vehicle adoption in California,” Atmos Environ, vol. 294, p. 119492, 2023, doi: https://doi.org/10.1016/j.atmosenv.2022.119492. [13] V. R. J. H. Timmers and P. A. J. Achten, “Non-exhaust PM emissions from electric vehicles,” Atmos Environ, vol. 134, pp. 10–17, 2016, doi: https://doi.org/10.1016/j.atmosenv.2016.03.017. [14] H. Huo, H. Cai, Q. Zhang, F. Liu, and K. He, “Life-cycle assessment of greenhouse gas and air emissions of electric vehicles: A comparison between China and the U.S.,” Atmos Environ, vol. 108, pp. 107–116, 2015, doi: https://doi.org/10.1016/j.atmosenv.2015.02.073. [15] L. Yang, B. Yu, B. Yang, H. Chen, G. Malima, and Y.-M. Wei, “Life cycle environmental assessment of electric and internal combustion engine vehicles in China,” J Clean Prod, vol. 285, p. 124899, 2021, doi: https://doi.org/10.1016/j.jclepro.2020.124899. [16] A. Yu, Y. Wei, W. Chen, N. Peng, and L. Peng, “Life cycle environmental impacts and carbon emissions: A case study of electric and gasoline vehicles in China,” Transp Res D Transp Environ, vol. 65, pp. 409–420, 2018, doi: https://doi.org/10.1016/j.trd.2018.09.009. [17] P. Lyu, P. (Slade) Wang, Y. Liu, and Y. Wang, “Review of the studies on emission evaluation approaches for operating vehicles,” Journal of Traffic and Transportation Engineering (English Edition), vol. 8, no. 4, pp. 493–509, 2021, doi: https://doi.org/10.1016/j.jtte.2021.07.004. [18] R. Joumard, M. André, R. Vidon, and P. Tassel, “Characterizing real unit emissions for light duty goods vehicles,” Atmos Environ, vol. 37, no. 37, pp. 5217–5225, 2003, doi: https://doi.org/10.1016/j.atmosenv.2003.02.001. [19] S. Lacour, R. Joumard, and M. André, “Exploring ways to improve instantaneous emission models for passenger cars,” International Journal of Vehicle Design, vol. 27, no. 1–4, pp. 76–85, 2001, doi: 10.1504/ijvd.2001.001953. [20] N. R. Council and others, Modeling mobile-source emissions. National Academies Press, 2000. [21] L. Yu, Y. Xu, G. Song, Y. Hao, S. Guo, and Q. Shi, “Development and application of macroscopic emission model for China,” Transp Res Rec, vol. 2123, no. 1, pp. 66–75, 2009. [22] C. Ying, Y. Lei, W. Hong-tu, H. A. O. Yan-zhao, and S. Guo-hua, “Comparative study of MOBILE and COPERT emission models based on PEMS,” Journal of Transportation Systems Engineering and Information Technology, vol. 11, no. 3, p. 176, 2011. [23] G. K. D. Saharidis and G. E. Konstantzos, “Critical overview of emission calculation models in order to evaluate their potential use in estimation of Greenhouse Gas emissions from in port truck operations,” J Clean Prod, vol. 185, pp. 1024–1031, 2018, doi: 10.1016/j.jclepro.2018.02.036. [24] H. Perugu, “Emission modelling of light-duty vehicles in India using the revamped VSP-based MOVES model: The case study of Hyderabad,” Transp Res D Transp Environ, vol. 68, pp. 150–163, 2019. [25] S. Vallamsundar and J. Lin, “MOVES Versus MOBILE: Comparison of greenhouse gas and criterion pollutant emissions,” Transp Res Rec, vol. 2233, no. 1, pp. 27–35, 2011. [26] H. Abou-Senna, E. Radwan, K. Westerlund, and C. D. Cooper, “Using a traffic simulation model (VISSIM) with an emissions model (MOVES) to predict emissions from vehicles on a limited-access highway,” J Air Waste Manage Assoc, vol. 63, no. 7, pp. 819–831, 2013. [27] J. Bluett and G. Fisher, “Validation of a vehicle emission model using on-road emission measurements,” in Proceedings of the 17th International Clean Air and Environmental Conference, Hobart, Australia, 2005. [28] D. Elst, R. T. M. Smokers, and J. W. De Koning, “Evaluation of the Capabilities of On-Board Emission Measurement System for the Purpose of Generating Real-Life Emission Factors,” TNO Report, vol. 4, 2004. [29] L. Zhang, R. Long, H. Chen, and J. Geng, “A review of China’s road traffic carbon emissions,” J Clean Prod, vol. 207, pp. 569–581, 2019. [30] M. Boughédaoui, R. Joumard, and R. Kerbachi, “On-board emission measurement of high-loaded light-duty vehicles in Algeria,” J Air Waste Manage Assoc, vol. 58, no. 1, pp. 45–54, 2008. [31] B. Giechaskiel et al., “Vehicle emission factors of solid nanoparticles in the laboratory and on the road using Portable Emission Measurement Systems (PEMS),” Front Environ Sci, vol. 3, no. DEC, p. 168711, Dec. 2015, doi: 10.3389/FENVS.2015.00082/BIBTEX. [32] R. Smit, L. Ntziachristos, and P. Boulter, “Validation of road vehicle and traffic emission models–A review and meta-analysis,” Atmos Environ, vol. 44, no. 25, pp. 2943–2953, 2010. [33] R. P. Roess, Traffic engineering. United states of Anerica, 2004. [34] L. A. Klein, M. K. Mills, and D. R. P. Gibson, Traffic detector handbook. US Department of Transportation, Federal Highway Administration, Research …, 2006. [35] S. Martinet, Y. Liu, L. Jean-Soro, M. Goriaux, and M. André, “In-situ estimation of non-regulated pollutant emission factors in an urban area of Nantes, France, with fleet composition characterization,” Transp Res D Transp Environ, vol. 76, pp. 193–210, Nov. 2019, doi: 10.1016/J.TRD.2019.09.023. [36] J. Zhao, H. Xu, H. Liu, J. Wu, Y. Zheng, and D. Wu, “Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors,” Transp Res Part C Emerg Technol, vol. 100, pp. 68–87, Mar. 2019, doi: 10.1016/J.TRC.2019.01.007. [37] R. Jiang et al., “Traffic experiment reveals the nature of car-following,” PLoS One, vol. 9, no. 4, p. e94351, 2014. [38] S. Kaufmann, B. S. Kerner, H. Rehborn, M. Koller, and S. L. Klenov, “Aerial observations of moving synchronized flow patterns in over-saturated city traffic,” Transp Res Part C Emerg Technol, vol. 86, pp. 393–406, 2018. [39] R. Krajewski, J. Bock, L. Kloeker, and L. Eckstein, “The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), IEEE, 2018, pp. 2118–2125. [40] R. Ke, Z. Li, J. Tang, Z. Pan, and Y. Wang, “Real-Time Traffic Flow Parameter Estimation From UAV Video Based on Ensemble Classifier and Optical Flow,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 1, pp. 54–64, 2019, doi: 10.1109/TITS.2018.2797697. [41] Y.-X. Huang et al., “Experimental study and modeling of car-following behavior under high speed situation,” Transp Res Part C Emerg Technol, vol. 97, pp. 194–215, 2018. [42] N. Caceres, J. P. Wideberg, and F. G. Benitez, “Review of traffic data estimations extracted from cellular networks,” IET Intelligent Transport Systems, vol. 2, no. 3, pp. 179–192, 2008, doi: 10.1049/IET-ITS:20080003. [43] N. Buch, S. A. Velastin, and J. Orwell, “A review of computer vision techniques for the analysis of urban traffic,” IEEE Transactions on intelligent transportation systems, vol. 12, no. 3, pp. 920–939, 2011. [44] W. Zhang, G. Jordan, and V. Livshits, “Generating a vehicle trajectory database from time-lapse aerial photography,” Transp Res Rec, vol. 2594, no. 1, pp. 148–158, 2016. [45] N. Seenouvong, U. Watchareeruetai, C. Nuthong, K. Khongsomboon, and N. Ohnishi, “A computer vision based vehicle detection and counting system,” in 2016 8th International conference on knowledge and smart technology (KST), IEEE, 2016, pp. 224–227. [46] Y. Liu, B. Tian, S. Chen, F. Zhu, and K. Wang, “A survey of vision-based vehicle detection and tracking techniques in ITS,” in Proceedings of 2013 IEEE international conference on vehicular electronics and safety, IEEE, 2013, pp. 72–77. [47] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2014. [48] K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2961–2969. [49] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” in Advances in Neural Information Processing Systems, C. Cortes, N. Lawrence, D. Lee, M. Sugiyama, and R. Garnett, Eds., Curran Associates, Inc., 2015. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2015/file/14bfa6bb14875e45bba028a21ed38046-Paper.pdf [50] W. Liu et al., “Ssd: Single shot multibox detector,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, Springer, 2016, pp. 21–37. [51] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788. [52] G. R. Nagendra, L. Chen, G. A. Covic, and J. T. Boys, “Detection of EVs on IPT highways,” IEEE J Emerg Sel Top Power Electron, vol. 2, no. 3, pp. 584–597, 2014. [53] I. Mocanu, C. Aichinger, M. Czuka, A. Fuchs, S. Gasparoni, and P. Saleh, “Dynamic and Acoustic Behaviour of Electric Versus Combustion Vehicles,” Transportation Research Procedia, vol. 14, pp. 2517–2526, 2016, doi: https://doi.org/10.1016/j.trpro.2016.05.332. [54] M. Takagi, K. Fujimoto, Y. Kawahara, and T. Asami, “Detecting hybrid and electric vehicles using a smartphone,” in Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014, pp. 267–275. [55] M. Wüstenberg, H. Blunck, K. Grønbæk, and M. B. Kjærgaard, “Distinguishing electric vehicles from fossil-fueled vehicles with mobile sensing,” in 2014 IEEE 15th International Conference on Mobile Data Management, IEEE, 2014, pp. 211–220. [56] D. Švorc, T. Tichý, and M. Růžička, “Detection of the electric vehicle using thermal characteristics,” in 2020 Smart City Symposium Prague (SCSP), 2020, pp. 1–5. doi: 10.1109/SCSP49987.2020.9133981. [57] Y.-H. Ho(何語萱), T.-C. Hsiao(蕭大智), and A. Y. Chen(陳柏華), “ELECTRIC MOTORCYCLE EMISSION ANALYSIS THROUGH COMPUTER VISION,” 中國土木水利工程學刊, vol. 34, no. 7, pp. 631–637, 2022, doi: 10.6652/JoCICHE.202211_34(7).0007. [58] J. A. G. Nijhuis et al., “Car license plate recognition with neural networks and fuzzy logic,” in Proceedings of ICNN’95-international conference on neural networks, IEEE, 1995, pp. 2232–2236. [59] A. Szentkuti, H. Skala Kavanagh, and S. Grazio, “Infrared thermography and image analysis for biomedical use,” Period Biol, vol. 113, no. 4, pp. 385–392, 2011. [60] A. Szentkuti, H. Skala Kavanagh, and S. Grazio, “Infrared thermography and image analysis for biomedical use,” Period Biol, vol. 113, no. 4, pp. 385–392, 2011. [61] C. Meola, “Infrared thermography in the architectural field,” The Scientific World Journal, vol. 2013, 2013. [62] N. P. Avdelidis and A. Moropoulou, “Applications of infrared thermography for the investigation of historic structures,” J Cult Herit, vol. 5, no. 1, pp. 119–127, 2004. [63] A. Gupta, A. Anpalagan, L. Guan, and A. S. Khwaja, “Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues,” Array, vol. 10, p. 100057, 2021. [64] M. Bandi, “A Review On Thermal Infrared Semantic Distribution for Nightfall Drive,” in 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), IEEE, 2022, pp. 645–653. [65] P. Dollar, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: An evaluation of the state of the art,” IEEE Trans Pattern Anal Mach Intell, vol. 34, no. 4, pp. 743–761, 2011. [66] C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.” 2022. [67] T.-Y. Lin et al., “Microsoft COCO: Common Objects in Context,” CoRR, vol. abs/1405.0312, 2014, [Online]. Available: http://arxiv.org/abs/1405.0312 [68] FLIR Systems Inc., “The Teledyne FLIR Free ADAS Thermal Dataset v2,” https://www.flir.com/oem/adas/adas-dataset-agree/, Jan. 19, 2022. https://www.flir.com/oem/adas/adas-dataset-agree/ (accessed Mar. 30, 2023). [69] Y. Du et al., “StrongSORT: Make DeepSORT Great Again.” 2023. [70] N. Wojke, A. Bewley, and D. Paulus, “Simple Online and Realtime Tracking with a Deep Association Metric,” in 2017 IEEE International Conference on Image Processing (ICIP), 2017, pp. 3645–3649. doi: 10.1109/ICIP.2017.8296962. [71] G. Welch, G. Bishop, and others, “An introduction to the Kalman filter,” 1995. [72] H. W. Kuhn, “The Hungarian Method for the Assignment Problem,” Naval Research Logistics Quarterly, vol. 2, no. 1–2, pp. 83–97, Mar. 1955, doi: 10.1002/nav.3800020109. [73] W. Yuchi et al., “Evaluation of random forest regression and multiple linear regression for predicting indoor fine particulate matter concentrations in a highly polluted city,” Environmental pollution, vol. 245, pp. 746–753, 2019. [74] C. D. Whiteman, S. W. Hoch, J. D. Horel, and A. Charland, “Relationship between particulate air pollution and meteorological variables in Utah’s Salt Lake Valley,” Atmos Environ, vol. 94, pp. 742–753, 2014. [75] “Non-contact temperature measurement for industry and R&D.” https://www.optris.global/?gclid=Cj0KCQjwy9-kBhCHARIsAHpBjHjtCiKVqcMM7vN0tN5Z-oypH9s8CHcz44dtzE9BQpdwyGSw3kVTLioaAh_cEALw_wcB (accessed Jun. 26, 2023). [76] M. Weilenmann, J.-Y. Favez, and R. Alvarez, “Cold-start emissions of modern passenger cars at different low ambient temperatures and their evolution over vehicle legislation categories,” Atmos Environ, vol. 43, no. 15, pp. 2419–2429, 2009. [77] D. Han et al., “A review of studies using hydrocarbon adsorption material for reducing hydrocarbon emissions from cold start of gasoline engine,” Renewable and Sustainable Energy Reviews, vol. 135, p. 110079, 2021. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90122 | - |
| dc.description.abstract | 城市地區空氣污染水平的上升已成為許多研究的焦點，因其對人口健康的不良影響。道路上的車輛排放被確認為污染的主要來源之一。許多國家提出將車輛完全電動化作為減少道路污染的措施，然而，電動車（EVs）相較於傳統車輛對污染的實際影響仍然不確定。因此，開發一種準確區分道路上的EVs的模型可以使我們更好地了解EVs對道路污染的影響。
由於EVs和傳統車輛在外觀上沒有顯著差異，基於可見光的目標檢測方法非常不可靠。然而，熱成像技術可以準確區分這兩種車輛之間的差異。 本研究提出了一種從深度學習模型和我們收集的熱數據中進行轉移學習的方法。特別地，我們使用車輛檢測模型計算不同類型車輛的比例，並應用車輛汙染排放分析來分析EVs, EMs 對環境的汙染。 這項研究可應用於評估個人對排放物的暴露及相關健康影響。這項工作還提供了一種可靠的方法，使用熱成像技術區分道路上的EVs和傳統車輛，並可擴展到識別其他類型的EVs，如電動摩托車（EMs）、電動巴士和電動卡車。提取的數據預計還可在環境分析、交通控制、智慧城市和其他相關研究領域中提供支援。 | zh_TW |
| dc.description.abstract | The increasing level of air pollution in urban areas has become a focus of many studies due to its detrimental impact on the health of the population. Vehicular emissions on roads have been identified as one of the primary sources of pollution. Numerous countries have proposed the complete electrification of vehicles as a measure to reduce pollution on roads; however, the actual impact of Electric Vehicles (EVs) versus conventional vehicles on pollution remains uncertain. Therefore, developing an accurate model to distinguish EVs on roads can enable us to better understand the impact of EVs on road pollution.
Since EVs and conventional vehicles have no significant visual differences, visible light-based object detection is highly unreliable. However, thermal imaging can accurately distinguish the differences among these two types of cars. This study presents a transfer learning approach from a deep learning model and an open-source dataset with the thermal data we collected. Particularly, we count the portion of different type of cars with the car detection model and applied vehicle emission analysis to analyze the emission contribution by EVs and electric motorcycles (EM). This study could be applied for assessment of personal exposure to emissions and related health impacts. This work also provides a reliable method for distinguishing between EVs and conventional vehicles on roads using thermal imaging, which can be extended to the identification of other types of EVs such as EMs, electric buses, and electric trucks. The extracted data is expected to also facilitate in different domains such as environmental analysis, traffic control, smart cities, and other related research. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-09-22T17:30:20Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-09-22T17:30:20Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Table of Contents
Acknowledgments i 摘要: ii Abstract: iii Table of Contents v List of Figures vi List of Table vi 1 Introduction 1 1.1 Background 1 1.2 Objective 2 1.3 Research flowchart 3 2 Literature review 5 2.1 EV emission on road 5 2.2 Measurement of Vehicle Emission 6 2.3 Collect traffic data 8 2.4 Distinguish EV from other cars 9 2.5 Utilize of thermal image 11 2.6 Research gap and summery 12 3 Methodology 13 3.1 Traffic data collection 14 3.1.1 Transfer learning with YOLOv7 and FLIR dataset 14 3.1.2 EV and EM classifier construction 16 3.1.3 Vehicle counting by categories by StrongSORT 16 3.2 Data collection 17 3.3 Analysis of Pollution Emission Contributions by Vehicle Types 20 4 Result 22 4.1 Model construction 22 4.2 Trace model and vehicle counting model Performance Evaluation and Validation 30 4.3 Analysis of Pollution Emission Contributions by Vehicle Types 33 5 Conclusion 36 5.1 Contribution 36 5.1.1 EV and EM classifier 36 5.1.2 Analysis of Pollution Emission Contributions by Vehicle Types 37 5.2 Limitation 39 5.3 Future work 39 6 Reference 40 | - |
| dc.language.iso | en | - |
| dc.subject | 空氣污染 | zh_TW |
| dc.subject | 轉移學習 | zh_TW |
| dc.subject | 物體識別 | zh_TW |
| dc.subject | 電動車 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 熱成像 | zh_TW |
| dc.subject | 污染物 | zh_TW |
| dc.subject | pollutant | en |
| dc.subject | transfer learning | en |
| dc.subject | deep learning | en |
| dc.subject | object detection | en |
| dc.subject | thermal imaging | en |
| dc.subject | electric vehicle | en |
| dc.subject | air pollution | en |
| dc.title | 基於熱影像視覺車流資料採集之電動車污染排放分析 | zh_TW |
| dc.title | ELECTRIC VEHICLE EMISSION ANALYSIS THROUGH THERMAL IMAGE-BASED VEHICLE CLASSIFICATION | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 謝尚賢;周建成;許聿廷 | zh_TW |
| dc.contributor.oralexamcommittee | Shang-Hsien Hsieh;Chien-Cheng Chou;Yu-Ting Hsu | en |
| dc.subject.keyword | 轉移學習,深度學習,空氣污染,污染物,電動車,熱成像,物體識別, | zh_TW |
| dc.subject.keyword | transfer learning,deep learning,object detection,thermal imaging,electric vehicle,air pollution,pollutant, | en |
| dc.relation.page | 47 | - |
| dc.identifier.doi | 10.6342/NTU202303841 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-08-12 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| Appears in Collections: | 土木工程學系 | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| ntu-111-2.pdf | 1.83 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.