請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83185
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃振康 | zh_TW |
dc.contributor.advisor | en | |
dc.contributor.author | 尤子澔 | zh_TW |
dc.contributor.author | Tzu-Hao Yu | en |
dc.date.accessioned | 2023-01-10T17:12:09Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-01-07 | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2002-01-01 | - |
dc.identifier.citation | 欣傳媒。2016。Cities Alive – 城市綠化立面研究。網址: https://www.xinmedia.com/article/105510。上網日期: 2021-10-14。 智慧農業。2020。智慧生物感測共通平台。網址: https://www.intelligentagri.com.tw/xmdoc/cont?xsmsid=0K254372926710605896&sid=0K254551268421593308。上網日期: 2022-09-13。 智慧農業。2021。智農是什麼。網址: https://www.intelligentagri.com.tw/xmdoc/cont?xsmsid=0J164373919378174143。上網日期: 2021-10-14。 藍, 春曉。2010。植生牆:水泥叢林的綠色奇蹟。網址: https://www.taiwan-panorama.com/Articles/Details?Guid=9bb2aa36-6276-420c-9e76-d9bfd2e49758。上網日期: 2021-10-14。 Amin, N., M. A. Azim, and K. Sopian. 2008. Development of cost effective charge controller with data acquisition options for PV powered sensor nodes. In "2008 33rd IEEE Photovoltaic Specialists Conference", 1-4. Bahl, P., and V. N. Padmanabhan. 2000. RADAR: An in-building RF-based user location and tracking system, 775-784. Bose, A., and C. H. Foh. 2007. A practical path loss model for indoor WiFi positioning enhancement. In "2007 6th International Conference on Information, Communications & Signal Processing", 1-5. Baoying, Q., H. Haifan, Z. Xinshuan, M. Caixia, and L. Daode. 2004. Simple Methods for Measuring the Leaf Area of Strawberry [J]. Journal of Fruit Science. 6. Canny, J. 1986. A computational approach to edge detection. IEEE Transactions on pattern analysis and machine intelligence. (6): 679-698. Corral, P., E. Peña, R. Garcia, V. Almenar, and A. C. d. C. Lima. 2008. Distance estimation system based on ZigBee, 405-411. Desai, A., I. Mukhopadhyay, and A. Ray. 2021. Techno-Economic-Environment Analysis of Solar PV Smart Microgrid for Sustainable Rural Electrification in Agriculture community. In "2021 IEEE 48th Photovoltaic Specialists Conference (PVSC)", 2281-2285. Desimone, R., B. M. Brito, and J. Baston. 2015. Model of indoor signal propagation using log-normal shadowing. In "2015 Long Island Systems, Applications and Technology", 1-4. Feng, T., and W. Chun. 2010. Calculating the Leaf-Area Based on Non-loss Correction Algorithm. In "2010 International Conference of Information Science and Management Engineering", 75-78. Gangopadhyay, S., and M. K. Mondal. 2016. A wireless framework for environmental monitoring and instant response alert. In "2016 international conference on microelectronics, computing and communications (MicroCom)", 1-6. Hongbin, T., and L. Shan. 2006. Comparison on Disc Method with Copy Method and Length-width Method for Measuring Leaf Area of Rice [J]. Plant Physiology Communications. 3: 496-498. Ji, M., J. Chen, Z. Liu, Y. Tong, F. Duan, T. S. Durrani, and J. Jiang. 2017. Multi-level quantization and blind equalization based direct transmission method of digital baseband signal. Physical Communication. 25: 348-354. Kaemarungsi, K., and P. Krishnamurthy. 2004. Modeling of indoor positioning systems based on location fingerprinting. In "Ieee Infocom 2004", 1012-1022. Kalman, R. E. 1960. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering. 82(1): 35-45. Klee, U., T. Gehrig, and J. McDonough. 2006. Kalman filters for time delay of arrival-based source localization. EURASIP Journal on Advances in Signal Processing. 2006: 1-15. Kotanen, A., M. Hannikainen, H. Leppakoski, and T. Hamalainen. 2003. Positioning with IEEE 802.11 b wireless LAN. In "14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, 2003. PIMRC 2003.", 2218-2222. Kodali, R. K., and A. Sahu. 2016. An IoT based weather information prototype using WeMos. In "2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)", 612-616. Lü, C., H. Ren, Y. Zhang, and Y. Shen. 2010. Leaf Area Measurement Based on Image Processing. In "2010 International Conference on Measuring Technology and Mechatronics Automation", 580-582. Lihua, M., L. Zhiyi, and Z. Hongzhi. 2001. A geometric rectification method for imaging system by display projecting. Acta Photonica Sinica. 30(5): 624. Mackey, A., P. Spachos, and K. N. Plataniotis. 2018. Enhanced indoor navigation system with beacons and kalman filters. In "2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)", 947-950. Nakano, M., N. Komuro, and K. Kawamoto. 2019. Indoor Positioning Method based on BLE Location Fingerprint with Statistics Approach. In "2019 IEEE 8th Global Conference on Consumer Electronics (GCCE)", 1160-1163. Nordin, N., and F. Dressler. 2012. Effects and implications of beacon collisions in co-located IEEE 802.15. 4 networks. In "2012 IEEE Vehicular Technology Conference (VTC Fall)", 1-5. Patwari, N., J. N. Ash, S. Kyperountas, A. O. Hero, R. L. Moses, and N. S. Correal. 2005. Locating the nodes: cooperative localization in wireless sensor networks. IEEE Signal processing magazine. 22(4): 54-69. Phutcharoen, K., M. Chamchoy, and P. Supanakoon. 2020. Accuracy Study of Indoor Positioning with Bluetooth Low Energy Beacons. In "2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON)", 24-27. Sadowski, S., and P. Spachos. 2018. Rssi-based indoor localization with the internet of things. IEEE Access. 6: 30149-30161. Shipkovenski, G., T. Kalushkov, E. Petkov, and V. Angelov. 2020. A Beacon-Based Indoor Positioning System for Location Tracking of Patients in a Hospital. In "2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)", 1-6. Singh, A., Y. Shreshthi, N. Waghchoure, and A. Wakchaure. 2018. Indoor navigation system using bluetooth low energy beacons. In "2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)", 1-5. Singh, D. K., H. Jerath, and P. Raja. 2020. Low Cost IoT Enabled Weather Station. In "2020 International Conference on Computation, Automation and Knowledge Management (ICCAKM)", 31-37. Sornalatha, K., and V. Kavitha. 2017. IoT based smart museum using Bluetooth Low Energy. In "2017 third international conference on advances in electrical, electronics, information, communication and bio-informatics (AEEICB)", 520-523. Spachos, P., and K. N. Plataniotis. 2020. BLE beacons for indoor positioning at an interactive IoT-based smart museum. IEEE Systems Journal. 14(3): 3483-3493. Tasaki, K., T. Takahashi, S. Ibi, and S. Sampei. 2020. 3D Convolutional Neural Network-Aided Indoor Positioning Based on Fingerprints of BLE RSSI, 1483-1489. Vo, Q. D., and P. De. 2015. A survey of fingerprint-based outdoor localization. IEEE Communications Surveys & Tutorials. 18(1): 491-506. Wang, M., and J. Brassil. 2015. Managing large scale, ultra-dense beacon deployments in smart campuses. In "2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)", 606-611. Zhong, F. H. W. 2006. Algorithm of Image Segment Based on Complicated Background. Journal of Academy of Armored Force Engineering. 2. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/83185 | - |
dc.description.abstract | 植生牆 (Living Wall)是將植物栽種於牆面,或讓植物依附於牆面或間接結構之上,是各種綠化工程中最經濟快速、效益也最高的選擇。但是高空綠化的環境嚴苛,維護來說是一大工程。為了減少人力維護的成本與風險,本研究旨在展現植生牆自動維護系統的概念,希望未來能讓植生牆的維護走向自動化。自動維護系統包含能源監控、植物影像與環境數據的取得、植物綠葉面積與輪廓的影像處理以及維護裝置的移動與定位。在能源部分,維護裝置藉由太陽能作為電力供給來源,使用微控制器製作具有物聯網 (Internet of Things, IoT) 功能之太陽能電流電壓監控裝置,太陽能板之電流與電壓數據將即時上傳至雲端、同時記錄在記憶卡中,實現太陽能發電的監控與評估。另外,製作小型氣象站於戶外收集溫度、濕度、光照等數據,即時上傳至雲端,完成小區域的環境數據收集。在嵌入式系統有限的運算資源下,利用簡易的影像處理方法完成植物綠葉面積的計算與輪廓的取得,能應用於植物生長狀況的評估,與作為判斷修剪需求的依據。在維護裝置的移動上,製作能自動循跡移動的定位車,模擬裝置維護時的移動,並且能為定位實驗節省約 30% 的時間。使用信標 (Beacon) 進行定位,完成多邊定位法與指紋特徵定位法的實驗,分別得到0.796 m與0.186 m的平均誤差,最後使用神經網絡 (Neural Networks) 對指紋特徵進行訓練,得到0.054 m的平均誤差與80.4%的定位準確率。本研究在植生牆自動維護系統的各領域中皆有實踐與討論,希望能提供一些實驗數據與經驗,並激發更多想法與討論。 | zh_TW |
dc.description.abstract | Living Walls are aimed to plant plants on the wall or let plants attach to the wall or other indirect structure. It is the most economical, fastest and efficient choice for various green projects. However, the maintenance work of high-altitude greening is harsh. To reduce the cost and risk of maintenance, this study focuses to demonstrate the automatic maintenance system of living wall for the possible automation in the future. The automatic maintenance system included energy monitoring, acquisition of plant images and environmental data, image processing of green leaf area and edge of plant, and movement and positioning of maintenance devices. In terms of energy, the maintenance device used solar energy as a source of power supply. Utilizing a microcontroller to establish a solar current and voltage monitoring device with Internet of Things (IoT) functions, the current and voltage data of the solar panel were uploaded to the cloud in real-time and recorded on the memory card to realize the monitoring and evaluation of solar energy. In addition, a small weather station was built to collect data such as temperature, humidity, and outdoor illuminance. The data was uploaded to the cloud in real-time to complete the collection of environmental data in small regions. Based on the limited computing resources from the embedded system, the simple image processing method was used to complete the calculation of green leaf area and the acquisition of the edge of the plant, which could be applied to the evaluation of the plant growth status and the judgement for pruning needs. For the movement of the maintenance device, a positioning vehicle that could automatically track and move was established to simulate the movement of the device during maintenance, saving about 30% of the time for the positioning experiments. The experiments of the multilateral positioning and fingerprint positioning method was completed, with average errors of 0.796 m and 0.186 m respectively. Finally, the neural network was used to train the fingerprint features, getting an average error of 0.054 m and a positioning accuracy of 80.4%. This research practiced and discussed various aspects of living wall automatic maintenance systems, providing experimental data and experience to stimulate more ideas and discussions in the future. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-01-10T17:12:09Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-01-10T17:12:09Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 目錄 目錄 i 圖目錄 v 表目錄 viii 第一章 前言 1 1.1背景 1 1.2研究目的 3 第二章 文獻探討 5 2.1智慧農業 5 2.1.1 物聯網 6 2.1.2 智慧感測共通平台 6 2.1.3 能源使用 7 2.2影像處理技術 8 2.2.1 植物綠葉面積 8 2.2.2 葉片輪廓 9 2.3定位 10 2.3.1 定位技術 10 2.3.2 信標 16 2.3.3 定位的方法 17 2.3.3.1 多邊定位法 17 2.3.3.2 指紋特徵定位法 19 2.3.4 信標定位遇到的問題 20 2.3.5 信標定位的實際應用 21 第三章 研究方法 23 3.1能源監控與物聯網 24 3.1.1 硬體選用與架構 24 3.1.2 流程架構 28 3.1.3 物聯網 30 3.1.3.1 環境參數選擇 30 3.1.3.2 小型氣象站 31 3.2影像處理 33 3.2.1 面積計算 34 3.2.1.1灰階處理 34 3.2.1.2 平滑處理 35 3.2.1.3 銳化處理 36 3.2.1.4 二值化處理與面積計算 36 3.2.2 邊緣檢測 37 3.3信標定位 39 3.3.1 信標 39 3.3.2 信標定位車 40 3.3.2.1 硬體架構 41 3.3.2.2 流程架構 42 3.3.3 信標訊號干擾實驗 43 3.3.3.1 信標數量對訊號的影響 44 3.3.3.2 信標設置距離對訊號的影響 44 3.3.3.3 信標發送頻率對訊號遺失現象的影響 45 3.3.3.4 多路徑效應對訊號的影響 45 3.3.4 信標定位方法 47 3.3.4.1 多邊定位法 49 3.3.4.2 指紋特徵定位法 52 3.3.4.3 指紋特徵權重定位法 54 3.3.4.4 基於深度學習之指紋特徵定位法 56 第四章 結果與討論 58 4.1電流電壓監控裝置 58 4.1.1電流電壓監控裝置 58 4.1.2 電流與電壓校正 59 4.1.3 電流電壓監控裝置小結 64 4.2小型氣象站 67 4.3影像處理 69 4.3.1 影像處理之使用者介面 69 4.3.2 影像處理之結果與討論 71 4.3.2.1 灰階處理 72 4.3.2.2 平滑與銳化處理之比較 73 4.3.2.3 植物綠葉面積計算 74 4.3.2.4 邊緣檢測比較 76 4.3.3 影像處理小結 76 4.4信標定位車 77 4.4.1 信標定位車成品 77 4.4.2 移動結果 78 4.5信標定位 79 4.5.1 信標數量對訊號影響之結果 79 4.5.2 信標設置距離對訊號影響之結果 80 4.5.3 信標發送頻率對訊號遺失現象影響之結果 80 4.5.4 多路徑效應對訊號影響之結果 81 4.5.5 多邊定位法 83 4.5.6 指紋特徵定位法 84 4.5.6.1 指紋特徵定位法 84 4.5.6.2 指紋特徵加權定位法 86 4.5.6.3基於深度學習之指紋特徵定位法 87 4.5.7 信標定位小結 87 第五章 結論與未來研究 89 5.1結論 89 5.2未來研究 90 參考文獻 92 圖目錄 圖 1. 1 植生牆是現代設計常見的作法 1 圖 1. 2 植生牆在不同都市結構中具備不同的環境效益 2 圖 1. 3 兩廳院的工人正在修剪整理2.5層樓高的植生牆 3 圖 2. 1 智慧農業相關概念與技術 6 圖 2. 2 WIFI、BLE、ZIGBEE、LORAWAN四種方法的平均誤差直方圖 15 圖 2. 3 WIFI、BLE、ZIGBEE、LORAWAN四種方法的平均功耗直方圖 15 圖 3. 1 植生牆自動維護系統架構圖 24 圖 3. 2 具IOT功能之電流電壓監控裝置硬體架構圖 25 圖 3. 3 分壓電路示意圖 28 圖 3. 4 具IOT能力之電流電壓監控裝置流程架構圖 29 圖 3. 5 小型氣象站架構圖 32 圖 3. 6 影像處理流程圖 34 圖 3. 7 NRF51822 BEACON 39 圖 3. 8 信標定位車移動路徑示意圖 41 圖 3. 9 信標定位車硬體架構示意圖 43 圖 3. 10 探討信標數量與RSSI值波動關係的實驗示意圖 44 圖 3. 11 探討信標設置距離與RSSI值波動關係的實驗示意圖 45 圖 3. 12 信標定位實驗佈置示意圖 46 圖 3. 13 遮蔽物對多路徑效應影響之實驗示意圖 47 圖 3. 14 實驗場域示意圖 48 圖 3. 15 多邊定位法定位流程圖 49 圖 3. 16 多邊定位中A值校正方法示意圖 51 圖 3. 17 指紋特徵定位法定位流程 52 圖 3. 18 完成校正後信標1的指紋數據庫實例 53 圖 3. 19 指紋特徵權重定位法定位流程 54 圖 3. 20 基於深度學習之指紋特徵定位法定位流程 56 圖 3. 21 深度學習架構 57 圖 4. 1 電流電壓監控裝置 58 圖 4. 2 電流與電壓感測器接線示意圖 61 圖 4. 3 電流與電壓誤差之關係折線圖 62 圖 4. 4 太陽能板電流數據折線圖 65 圖 4. 5 太陽能板電壓數據折線圖 65 圖 4. 6 溫度數據折線圖 67 圖 4. 7 濕度數據折線圖 68 圖 4. 8 光照數據折線圖 68 圖 4. 9 土壤濕度數據折線圖 68 圖 4. 10 影像處理之使用者介面 71 圖 4. 11 影像處理結果(一) 71 圖 4. 12 影像處理結果(二) 72 圖 4. 13 RGB影像與灰階影像 72 圖 4. 14 經過平滑與銳化處理之影像 73 圖 4. 15 經過二值化處理之影像 74 圖 4. 16 經人工處理與二值化處理後之影像 75 圖 4. 17 影像處理與人工判別之影像相減後差異部分 75 圖 4. 18 使用CANNY邊緣檢測與修改邊緣檢測之影像 76 圖 4. 19 信標定位車 77 圖 4. 20 信標數量對RSSI值標準差影響之實驗結果 79 圖 4. 21 信標設置距離對RSSI值標準差影響之實驗結果 80 圖 4. 22 多路徑效應對訊號標準差之影響 82 圖 4. 23 距離與訊號強度關係散佈圖 84 圖 4. 24 指紋特徵定位準確率分布圖 85 圖 4. 25 訊號遺失率與標準差之關係折線圖 87 表目錄 表 2. 1 依原理區分之不同的定位方法與特點 11 表 2. 2 依觀測量區分之不同的定位方法 12 表 2. 3 不同室內定位技術的優缺點 13 表 3. 1 LINKIT 7697與ARDUINO NANO規格比較 25 表 3. 2 AJP-S660太陽能板規格表 26 表 3. 3 NRF51822 BEACON之基本規格 40 表 3. 4 信標編號與放置座標 48 表 4. 1 固定電壓下電流量測與校正之結果 59 表 4. 2 無電流下電壓量測與校正之結果 60 表 4. 3 電流與電壓校正結果 61 表 4. 4 電流與電壓二次校正之結果 63 表 4. 5 太陽能電流電壓數據紀錄(2022年5月2日) 66 表 4. 6 訊號發送間隔對信標訊號遺失率的影響 81 表 4. 7 各信標之訊號遺失率 82 表 4. 8 多邊定位法實驗結果 83 表 4. 9 指紋特徵定位結果 85 表 4. 10 指紋特徵加權定位法之定位結果 86 | - |
dc.language.iso | zh_TW | - |
dc.title | 影像分析與信標輔助定位應用於植生牆的維護 | zh_TW |
dc.title | Applications of Image Analysis and Beacon Aided Positioning in Maintenance of Living Wall | en |
dc.title.alternative | Applications of Image Analysis and Beacon Aided Positioning in Maintenance of Living Wall | - |
dc.type | Thesis | - |
dc.date.schoolyear | 110-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 葉仲基;曾百由 | zh_TW |
dc.contributor.oralexamcommittee | ;; | en |
dc.subject.keyword | 太陽能,IoT,小型氣象站,信標,指紋特徵定位, | zh_TW |
dc.subject.keyword | solar energy,IoT,small weather station,beacon,fingerprint positioning, | en |
dc.relation.page | 94 | - |
dc.identifier.doi | 10.6342/NTU202203664 | - |
dc.rights.note | 同意授權(全球公開) | - |
dc.date.accepted | 2022-09-23 | - |
dc.contributor.author-college | 生物資源暨農學院 | - |
dc.contributor.author-dept | 生物機電工程學系 | - |
顯示於系所單位: | 生物機電工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
U0001-2009202217354100.pdf | 4.66 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。