博碩士論文 107325004 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:144 、訪客IP:3.142.171.180
姓名 陳威翰(Wei-Han Chen)  查詢紙本館藏   畢業系所 土木系營建管理碩士班
論文名稱 運用自然語言處理技術輔助工程專案合約利害關係人平台之研究
(Developing Cloud Computing based Project Management Platform among Contractual Parties using Natural Language Processing Technology)
相關論文
★ 運用深度神經網路建立H型鋼構件自動辨識系統之研究★ 運用關聯法則探討協力廠商對營造廠報價行為之研究
★ 探討影響台灣工程顧問公司落實法律遵循反貪腐關鍵因素之研究★ 以分包商角度探討對營造廠報價行為策略之研究
★ 運用SOMCM分群演算法開發設計雲端智慧平台之營運維護產業介面-以桃園市某園區為例★ 專案管理在履約爭議處理機制之比較與研析
★ H型鋼構件智慧塗裝路徑優化研究★ 以資料包絡分析法評估大型統包營造廠之經營績效-以上市櫃公司為例
★ 運用組織特徵映射圖動作軌跡相似度測量法探索預鑄工項生產效率與資源規劃之研究★ 預鑄專案成本估算策略之研究
★ 新建工程建造執照查核缺失要項之探討--以台北市為例★ 灰關聯分析探討古蹟與歷史建築再利用之研究
★ 營造工地管理人資量化與預測★ 公共建設專案現金管理與控制之研究
★ 營建業ERP整合PDA模型之研究★ 水庫營運效益評估之研究-以石門水庫為例
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 科技的改變並促進了營建產業的方便性,許多工具為營建業提供更好的結果,例 如發展建築資訊模型(BIM)、APP 等。然而,各營造廠或專案的工程平台之間的 分享串接仍未實現。本研究目的即為解決上述需求,建置『工程專案管理平台』 利用雲端計算及物聯網互動概念發展互動平台,整合營建工程項目模組,建置『工 程專案合約利害關係人互動平台』,透過資料探勘技術將合約利害關係人連結, 以進行深度管理。經由專家訪談結果彙整不同領域之專家意見,並彙整先前文字 表技術,建置關鍵字偵測演算法,分為三個階段依序為文字偵測、文字辨識與命 名實體辨識,並運用在平台關鍵字搜尋功能,減少使用者在大量資料庫中搜尋所 耗費之時間,提升平台自動化。平台建置分為前端、中繼及後端,前端及中繼層 的多台伺服器搭配伺服器負載平衡設備,可以在連線忙碌時合理分配工作負載, 有效利用伺服器容量,加快伺服器反應速度。本平台分為文件管理、進度管理及 品保管理,讓使用者多方串連達到資源共享的目的,亦能確保隨時能掌握工程之 進度與品質,使所有工程參與者皆能掌控專案進度並控管其品質,如發生進度逾 期或資金流量差異,也能提前做出預備方案,確保專案如期如質的進行。
摘要(英) Technological developments has made the construction industry efficient. The aim of this research is to solve communication interaction problems to build a project management platform using natural language processing technology integrating cloud computing and the Internet of Things (IoT) interactive concepts. Through collecting large amount of daily data, interviewing experts, adopting data mining tools, and constructing webpages with adequate algorithms, the interactive platform for construction contract stakeholders is developed for more in-depth management. The results show that a fully automatic tool facilitating contract management, cost management, scheduling, and documentation is achieved. It can effectively provide the construction practitioners to build and maintain big data in the industrial chain and to allow decision makers to obtain more efficient and affordable decisions through the platform.
關鍵字(中) ★ 雲端物聯網
★ 文字辨識
★ 互動平台
★ 深度學習
關鍵字(英) ★ Internet of Things (IoT)
★ cloud computing
★ interactive concept
★ project management
★ contractual management
★ natural language processing
論文目次 目錄
中文摘要i
英文摘要ii
目錄 iv
圖目錄 vi
表目錄 xvii
第一章 緒論1
1.1 研究背景與動機1
1.2 研究目的2
1.3 研究流程3
第二章 文獻回顧5
2.1 工程專案全生命週期5
2.2 雲端物聯網智慧平台5
2.3 場景文字偵測7
2.3.1 CTPN8
2.3.2 SegLink8
2.3.3 EAST10
2.3.4 PSENet12
2.4 場景文字辨識16
2.5 Named Entity Recognition (NER)18
第三章 研究方法22
3.1 專家訪談22
3.2 關鍵字偵測演算法26
第四章 平台建置38
4.1 系統架構38
4.2 平台案例說明39
第五章 結論與建議46
5.1 結論46
5.2 後續研究建議47
參考文獻 48
附錄 A 專家訪談問卷54
附錄 B 平台操作流程58
參考文獻 [1] P.-P. M.Institute., “PMBoK®-A Guide to the Project Management Body of Knowledge,” A Guid. to Proj. Manag. Body Knowl. (pmb. Guid., 2018.
[2] 謝光玉 T A - Ta QuangNgoc, “公司在採用雲端運算之下的競爭優勢和資源配置
TT - Competitive Advantage and Resource Configurations of companies in Cloud computing business,” 國立交通大學, 新竹市, 2010.
[3] 張家瑋 T A - Jia-WeiZhang, “雲端平台大數據資料庫研究-以報關訊息資料為例 TT - Big Data Computing Performance Analysis on Cloud Platform: A Case Study of Custom Application Messages,” 龍華科技大學, 桃園縣, 2014.
[4] 許賓鄉 T A - PIN-HSIANGHSU, “雲端服務模式探討:以銀髮族居家安全健康互 動雲端服務為例 TT - Model of Cloud Services – Senior Home Care Cloud Service Case Study,” 國立臺灣科技大學, 台北市, 2012.
[5] K.-H.彭康桓 TA - Peng, “雲端商業智慧服務策略之研究 TT - An Innovative Approach in the Business Intelligence Service via Cloud Computing,” 國立交通大學, 新竹市, 2009.
[6] 黃健原 T A - Chien-YuanHuang, “物聯網在智慧住宅節能與安全之應用 TT - Application of Internet of Things on Intelligent Residential Energy Conservation and Safety,” 國立臺北科技大學, 台北市, 2017.
[7] G.Aloi et al., “A mobile multi-technology gateway to enable IoT interoperability,” in Proceedings - 2016 IEEE 1st International Conference on Internet-of-Things Design and Implementation, IoTDI 2016, 2016, pp. 259–264.
[8] X.Ren, S.Du, andY.Zheng, “Parallel RCNN: A deep learning method for people detection using RGB-D images,” in 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017, pp. 1– 6.
[9] S.Ren, K.He, R.Girshick, andJ.Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, Jun.2015.
[10] Z.Tian, W.Huang, H.Tong, P.He, andY.Qiao, Detecting Text in Natural Image with Connectionist Text Proposal Network, vol. 9912. 2016.
[11] B.Shi, X.Bai, andS.Belongie, “Detecting Oriented Text in Natural Images by Linking Segments,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3482–3490.
[12] X.Zhou et al., “EAST: An Efficient and Accurate Scene Text Detector,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2642–2651.
[13] M.Jaderberg, K.Simonyan, A.Vedaldi, andA.Zisserman, “Reading Text in the Wild with Convolutional Neural Networks,” Int. J. Comput. Vis., vol. 116, Dec.2014.
[14] Z.Zhang, C.Zhang, W.Shen, C.Yao, W.Liu, andX.Bai, “Multi-oriented Text Detection with Fully Convolutional Networks,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 4159–4167.
[15] J.Long, E.Shelhamer, andT.Darrell, “Fully convolutional networks for semantic segmentation,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3431–3440.
[16] K.-H.Kim, Y.Cheon, S.Hong, B.Roh, andM.Park, “PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection,” Aug.2016.
[17] O.Ronneberger, P.Fischer, andT.Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation, vol. 9351. 2015.
[18] W.Wang et al., Shape Robust Text Detection with Progressive Scale Expansion Network. 2019.
[19] T.Lin, P.Dollár, R.Girshick, K.He, B.Hariharan, andS.Belongie, “Feature Pyramid Networks for Object Detection,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 936–944.
[20] B.Shi, X.Bai, andC.Yao, “An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 11, pp. 2298–2304, 2017.
[21] D.Nadeau andS.Sekine, “A survey of named entity recognition and classification,” Lingvisticae Investig., vol. 30, no. 1, pp. 3–26, 2007.
[22] K.Humphreys et al., “University of Sheffield: Description of the LaSIE-II system as used for MUC-7,” Proc. Seventh Messag. Underst. Conf., Jun.2001.
[23] G. R.Krupka andK.Hausman, “IsoQuest Inc.: Description of the NetOwl Extractor System as Used for {MUC}-7,” in Seventh Message Understanding Conference ({MUC}-7): Proceedings of a Conference Held in Fairfax, Virginia, April 29 - May 1, 1998, 1998.
[24] W. J.Black, F.Rinaldi, andD.Mowatt, “FACILE: Description of the NE System Used for MUC-7,” in Seventh Message Understanding Conference (MUC-7): Proceedings of a Conference Held in Fairfax, Virginia, April 29 - May 1, 1998, 1998.
[25] C.Aone, L.Halverson, T.Hampton, andM.Ramos-Santacruz, “SRA: Description of the IE2 System Used for MUC-7,” in Seventh Message Understanding Conference (MUC- 7): Proceedings of a Conference Held in Fairfax, Virginia, April 29 - May 1, 1998, 1998.
[26] D.Appelt et al., SRI International FASTUS system: MUC-6 test results and analysis. 1995.
[27] A.Mikheev, M.Moens, andC.Grover, “Named Entity Recognition Without Gazetteers,” in Proceedings of the Ninth Conference on European Chapter of the Association for Computational Linguistics, 1999, pp. 1–8.
[28] O.Etzioni et al., “Unsupervised named-entity extraction from the Web: An experimental study,” Artif. Intell., vol. 165, no. 1, pp. 91–134, 2005.
[29] S. R.Eddy, “Hidden Markov models,” Curr. Opin. Struct. Biol., vol. 6, no. 3, pp. 361– 365, 1996.
[30] J. R.Quinlan, “Induction of decision trees,” Mach. Learn., vol. 1, no. 1, pp. 81–106, 1986.
[31] J. N.Kapur, Maximum Entropy Models in Science and Engineering. Wiley, 1990.
[32] L.Li, S.Ma, andY.Zhang, “Optimization algorithm based on genetic support vector machine model,” in Proceedings - 2014 7th International Symposium on Computational Intelligence and Design, ISCID 2014, 2015, vol. 1, pp. 307–310.
[33] Y.Nuo, X.Yan, Z.Yu, S.Huang, andJ.Guo, “A Khmer NER method based on conditional random fields fusing with Khmer entity characteristics constraints,” in Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017, 2017, pp. 7464–7471.
[34] R.Collobert, J.Weston, L.Bottou, M.Karlen, K.Kavukcuoglu, andP.Kuksa, “Natural Language Processing (Almost) from Scratch,” J. Mach. Learn. Res., vol. 12, pp. 2493– 2537, 2011.
[35] Z.Huang, W.Xu, andK.Yu, “Bidirectional LSTM-CRF Models for Sequence Tagging,” Aug.2015.
[36] J.Li, A.Sun, J.Han, andC.Li, “A Survey on Deep Learning for Named Entity Recognition,” CoRR, vol. abs/1812.0, 2018.
[37] E. F.Tjong Kim Sang andF.DeMeulder, “Introduction to the CoNLL-2003 Shared Task: Language-independent Named Entity Recognition,” in Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003 - Volume 4, 2003, pp. 142–147.
[38] S.Pradhan, A.Moschitti, N.Xue, O.Uryupina, andY.Zhang, “CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes,” in Joint Conference on EMNLP and CoNLL - Shared Task, 2012, pp. 1–40.
[39] V.VanAsch, “Macro-and micro-averaged evaluation measures [[basic draft]],” Belgium: CLiPS, pp. 1–27, 2013.
[40] P.-H.Li, R.-P.Dong, Y.-S.Wang, J.-C.Chou, andW.-Y.Ma, “Leveraging Linguistic Structures for Named Entity Recognition with Bidirectional Recursive Neural Networks,” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017, pp. 2664–2669.
[41] M.Honic, I.Kovacic, G.Sibenik, andH.Rechberger, “Data- and stakeholder management framework for the implementation of BIM-based Material Passports,” J. Build. Eng., vol.23, pp. 341–350, 2019.
[42] 林昭嘉 T A - Chao-chiaLin, “結合雲端資料庫與智慧型手持行動裝置於施工管理 跨平台之應用 TT - The Application of Combining Cloud Database and Intelligent Mobile Devices on Construction Management,” 國立臺北科技大學, 台北市, 2017.
[43] S.-T.張守德 TA - Chang, “物聯網與深度學習影響下 伺服器的演進與未來發展
TT - The Evolution and Future Development in Server Effected by Internet of Things and Deep Learning,” 輔仁大學, 新北市, 2017.
指導教授 陳介豪(Jieh-Haur Chen) 審核日期 2020-1-16
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明