跳到主要內容

臺灣博碩士論文加值系統

(54.227.97.219) 您好!臺灣時間:2021/12/01 03:17
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:紀貞喜
研究生(外文):KEE-CHEN HEE
論文名稱:運用深度神經網路建立H型鋼構件自動辨識系統之研究
論文名稱(外文):Establishing recognition system for H-steel components using DNN
指導教授:陳介豪陳介豪引用關係
指導教授(外文):Jieh-Haur Chen
學位類別:碩士
校院名稱:國立中央大學
系所名稱:土木系營建管理碩士班
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:65
中文關鍵詞:模式識別H型鋼構件深度神經網路自動偵測
外文關鍵詞:pattern recognitionH-steel componentDeep Neural Networks (DNN)automatic detection
相關次數:
  • 被引用被引用:0
  • 點閱點閱:25
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
建立H型鋼構件的全自動化塗裝產線的第一步便是建立辨識系統。因此,本研究目的在於開發運用深度神經網絡的H型鋼構件自動辨識系統。透過文獻回顧,深度神經網路在辨識方面有著良好的表現。其為所提出的演算法提供核心計算能力,並根據其他8個步驟包括數據的輸入,偵測平面之計算與剔除,對剩餘平面進行分類,計算平行平面可能的間距,確定最佳辨識的構件資訊,對實際和辨識的構件進行比較,輸出至Matplotlib軟件使用所輸出的空間座標資訊建置三維模型,以及完成詳細的三維表面模型建置。透過從塗裝工廠隨機獲得的115件模擬案例和99件實際案例進行驗證, H型鋼構件整體的辨識準確率均高於99.12%,其中大部分的辨識率達到100%,而這些部分包括H型鋼構件的寬度,腹板厚度和翼緣板厚度。本研究的結果表明所提出的系統是可靠的,其平均準確率為99.73%,並且可被實際應用。
The first step to establish a fully automatic coating production line for H-steel components is to set up a recognition system. Therefore, the research purpose is to develop a recognition system for H-steel components using Deep Neural Networks (DNN). Literature review suggests that DNN performs well in recognition. It gives the proposed algorithm the core computation, followed by the other 8 steps of data input, calculation for detected area, classification for residual area, possible distances among parallels, determination for optimal recognition, comparison between actual and recognized components, output to Matplotlib software, 3D plot using output coordinates, and completion of detailed 3D plot. The evaluation is carried out using 115 simulated and 99 actual cases randomly obtained from the factories. The accuracy rates for all parts of H-steel components are higher than 99.12% where 100% recognition rate is reached for most parts such as H-steel width, base plate thickness, and wing plate thickness. The findings support that the proposed system is reliable with an average accuracy rate at 99.73% and applicable in reality.
摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vii
第一章、緒論 1
1.1研究背景與動機 1
1.2 研究問題 2
1.3 研究目的 2
1.4 研究範圍 2
1.5 研究流程 3
第二章、文獻回顧 6
2.1 表面辨識 6
2.2 模式識別於土木領域的應用 7
2.3 深度神經網路 7
2.4 自動化處理相關的表面資訊獲取方式 8
2.4.1 CAD模型取得表面資訊 8
2.4.2 圖像或影像辨識取得表面資訊 9
2.4.3 掃描方式取得表面資訊 9
2.5 立體光刻模型檔案 10
2.6 過往H型鋼構件之辨識成果 11
2.7 鋼構件塗裝 11
第三章、H型鋼構件自動辨識系統 13
3.1 研究假設 13
3.2 H型鋼構件STL模型表面辨識演算法 14
3.3 尺寸參數取得 16
3.4 塗裝表面輪廓模型建置 21
3.5 模型驗證 22
第四章、驗證結果分析與討論 27
4.1 特殊情況探討 28
4.2 綜合分析與討論 33
第五章、結論與建議 36
5.1 結論 36
5.2 後續研究建議 37
參考文獻 38
附錄一、尺寸參數獲取結果 41
附錄二、尺寸參數辨識率 48
[1] Huang, X., Bi, S., Dong, M., Chen, H., Fang, S., and Xi, N. (2016). "Automatic feature extraction and optimal path planning for robotic drawing." Proc., 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 19-24.
[2] 林榖欽、林建昌(2004),「視覺輔助H 型鋼橫向尺寸量測系統之研發」, 中國機械工程學會第二十一屆全國學術研討會, 國立中山大學,高雄,台灣,第 1-6頁。
[3] Gasparetto, A., Vidoni, R., Saccavini, E., and Pillan, D. (2010). "Optimal path planning for painting robots."ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis, Vol. 3, pp. 601-608.
[4] G, M., Elizabeth, S., and Mathew Koshy, S. (2018), "Circular mesh-based shape and margin descriptor for object detection", Pattern Recognition, Vol. 84, pp. 97-111.
[5] Clément, M., Kurtz, C., and Wendling, L. (2018), "Learning spatial relations and shapes for structural object description and scene recognition", Pattern Recognition, Vol. 84, pp. 197-210.
[6] Nandhakumar, N., and Aggarwal, J. K. (1985), "The artificial intelligence approach to pattern recognition—a perspective and an overview", Pattern Recognition, Vol. 18 No.6, pp. 383-389.
[7] Vento, M. (2015), "A long trip in the charming world of graphs for pattern recognition", Pattern Recognition, Vol. 48 No.2, pp. 291-301.
[8] Pathirage, C. S. N., Li, J., Li, L., Hao, H., Liu, W., and Ni, P. (2018), "Structural damage identification based on autoencoder neural networks and deep learning", Engineering Structures, Vol. 172, pp. 13-28.
[9] Zeghal, M., and Abdel‐Ghaffar Ahmed, M. (1992), "Analysis of behavior of earth dam using strong‐motion earthquake records", Journal of Geotechnical Engineering, Vol. 118 No.2, pp. 266-277.
[10] AbdelRazig Yassir, A., and Chang, L.-M. (2000), "Intelligent model for constructed facilities surface assessment", Journal of Construction Engineering and Management, Vol. 126 No.6, pp. 422-432.
[11] Zhang, M., Cheng, W., and Wang, Y. (2018), "Multiple-fault classification for hot-mix asphalt production by machine learning", Journal of Construction Engineering and Management, Vol. 144 No.5, pp. 04018024.
[12] Brilakis, I., German, S., and Zhu, Z. (2011), "Visual pattern recognition models for remote sensing of civil infrastructure", Journal of Computing in Civil Engineering, Vol. 25 No.5, pp. 388-393.
[13] Wang, M. (2020), "Applying Internet information technology combined with deep learning to tourism collaborative recommendation system", PLOS ONE, Vol. 15 No.12, pp. e0240656.
[14] Qin, S., Zhang, Q., Zhang, J., Liang, L., Zhao, Q., Wu, H., and Cao, L. (2020), "Application of deep neural network for generating resonance self-shielded cross-section", Annals of Nuclear Energy, Vol. 149, pp. 107785.
[15] Radaideh, M. I., Pigg, C., Kozlowski, T., Deng, Y., and Qu, A. (2020), "Neural-based time series forecasting of loss of coolant accidents in nuclear power plants", Expert Systems with Applications, Vol. 160, pp. 113699.
[16] Su, M., Hayati, D. W., Tseng, S., Chen, J., and Wei, H. (2021), "Smart care using a DNN-based approach for activities of daily living (ADL) recognition", Applied Sciences, Vol. 11 No.1.
[17] Luo, X. J., Oyedele, L. O., Ajayi, A. O., Akinade, O. O., Owolabi, H. A., and Ahmed, A. (2020), "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings", Renewable and Sustainable Energy Reviews, Vol. 131, pp. 109980.
[18] Chen, H., and Xi, N. (2008), "Automated tool trajectory planning of industrial robots for painting composite surfaces", The International Journal of Advanced Manufacturing Technology, Vol. 35 No.7-8, pp. 680-696.
[19] Sahir Arıkan, M. A., and Balkan, T. (2000), "Process modeling, simulation, and paint thickness measurement for robotic spray painting", Journal of Robotic Systems, Vol. 17 No.9, pp. 479-494.
[20] Ciszak, O., Juszkiewicz, J., and Suszyński, M. (2020), "Programming of Industrial Robots Using the Recognition of Geometric Signs in Flexible Welding Process", Symmetry, Vol. 12 No.9, pp. 1429.
[21] Hajari, N., Lugo, G., Sharma, H., and Cheng, I. (2020), "Marker-less 3d object recognition and 6d pose estimation for homogeneous textureless objects: an RGB-D approach", Sensors (Basel, Switzerland), Vol. 20.
[22] Yuan, Q., Chen, I. M., Lembono, T., Landén, S., and Malmgren, V. (2018), "Automatic robot taping system with compliant force control", The International Journal of Advanced Manufacturing Technology, Vol. 94.
[23] Gasparetto, A., Vidoni, R., Pillan, D., and Saccavini, E. "Automatic Path and Trajectory Planning for Robotic Spray Painting." Proc., ROBOTIK 2012; 7th German Conference on Robotics, pp. 211-216.
[24] Chen, R., Wang, G., Zhao, J., Xu, J., and Chen, K. (2018), "Fringe pattern based plane-to-plane visual servoing for robotic spray path planning", IEEE/ASME Transactions on Mechatronics, Vol. 23 No.3, pp. 1083-1091.
[25] Nastase-Dan (2012), "Standard tessellation language in rapid prototyping technology", The Scientific Bulletin of VALAHIA University – MATERIALS and MECHANICS pp. 81-85.
[26] Camardella, L., Vilella, O., and Breuning, H. (2017), "Accuracy of printed dental models made with 2 prototype technologies and different designs of model bases", American Journal of Orthodontics and Dentofacial Orthopedics, Vol. 151.
[27] Yang, Y., Chen, Z., Song, X., Zhu, B., Hsiai, T., Wu, P.-I., Xiong, R., Chen, Y., Zhou, Q., and Shung, K. K. (2016), "Three dimensional printing of high dielectric capacitor using projection based stereolithography method", Nano Energy, Vol. 22, pp. 414-421.
[28] Wang, Y., Chen, R., and Liu, Y. (2020), "A double mask projection exposure method for stereolithography", Sensors and Actuators A: Physical, Vol. 314, pp. 112228.
[29] Lyon, S. B., Bingham, R., and Mills, D. J. (2017), "Advances in corrosion protection by organic coatings: What we know and what we would like to know", Progress in Organic Coatings, Vol. 102, pp. 2-7.
[30] Bonner, P. E., and Stanners, J. F. (2013), "Protection of steel by metal spraying: A review", British Corrosion Journal, Vol. 1 No.9, pp. 339-343.
[31] Jamali, S., and Mills, D. (2012). Inhomogeneity of organic coatings and its effect of protection.
[32] P, P. V., and Al-Maadeed, M. (2019), "Self-repairing composites for corrosion protection: a review on recent strategies and evaluation methods", Materials (Basel), Vol. 12 No.17.
[33] Jamali, S. S., and Mills, D. J. (2014), "Steel surface preparation prior to painting and its impact on protective performance of organic coating", Progress in Organic Coatings, Vol. 77 No.12, pp. 2091-2099.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top