| 姓名 |
程聖凱(CHENG, SHENG-KAI)
查詢紙本館藏 |
畢業系所 |
資訊工程學系 |
| 論文名稱 |
深度學習電阻率成像雲端系統設計與實作
|
| 相關論文 | |
| 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]  至系統瀏覽論文 ( 永不開放)
|
| 摘要(中) |
為克服傳統電阻率成像因為耗時較長與容易受到人為影響而不易自動化的問題，有人提出以深度神經網路的方式為基礎的成像電阻率成像套件，此套件可以訓練好的神經網路，大幅縮短電阻率成像的時間。雖然訓練好的神經網路成像快速，但訓練過程卻非常耗時，也可能消耗許久時間訓練後跑出來的結果不盡如人意。再度生成訓練資料與改動參數重新訓練流程又是個複雜的手續，也無法知道程式完成時間點，預測的結果呈現也要直接至具有圖形處理器(Graphics Processing Unit， GPU)設備上才得知。為了克服上述問題，我們將使用到圖形處理器計算的程式改寫成應用程式介面(Application Programming Interface)，架設於有圖形處理器設備的裝置上作為雲端伺服器，再設計出一個圖形使用者介面(Graphical User Interface， GUI)客戶端應用程式負責傳入所需的參數。如此一來，只要在有網路的情況下，我們隨時隨地打開客戶端程式，便可知道訓練模型的進度，也隨時隨地可以停止或修改訓練資料與參數重新訓練或預測，更可以隨時觀看以前的預測結果，加上圖形化使用者介面，可以使這項技術普及率大幅提高。 |
| 摘要(英) |
In order to overcome the problem that the traditional resistivity images is challenging to be automation due to the long time consuming and anthropogenic influence, there is a proposal that package of resistivity images based on the Deep Neural Network can result in the positive neural network, which significantly shortens the time of the resistivity images. The positive neural network could be images quickly. However, the whole training is lengthy, and the result might be disappointing; even though renewing the process by generating the new training data and changing parameters is not only complicated but also not knowing the finish time, and the predicted outcomes have to be revealed until on the device, which has the function of the Graphics Processing Unit (GPU). For the purpose of solving the problem as mentioned above, we send the parameters to the Client by rewriting the calculated program with GPU into the Application Programming Interface (API) and setting it up on the device with GPU and then designs an application by Graphical User Interface (GUI) to the Client. As long as there is a network environment, we can receive the data of the training model and previous results, and even to stop or modify the training data and resend the parameters to the application for forecasting from time to time. With the GUI, the technology could increase its penetration of the market sharply. |
| 關鍵字(中) |
★ 實作 |
關鍵字(英) |
|
| 論文目次 |
摘要 I
ABSTRACT II
誌謝 III
目錄 IV
圖目錄 VI
第一章 緒論 - 1 -
1-1 研究背景 - 1 -
1-2 動機 - 1 -
1-3 目標 - 2 -
1-4 本文架構 - 3 -
第二章 基本技術 - 4 -
2-1 PYTHON - 4 -
2-2 TENSORFLOW - 4 -
2-3 FLASK - 4 -
2-4 TK INTER - 4 -
2-5 DOCKER - 5 -
2-6 雲端 - 5 -
第三章 實作 - 6 -
3-1 使用者圖形介面 - 8 -
3-2 應用程式介面 - 10 -
第四章 應用與結果 - 12 -
4-1 連接伺服器 - 12 -
4-2 生成資料 - 13 -
4-3 上傳資料 - 16 -
4-4 訓練模型 - 18 -
4-5 上傳模型 - 20 -
4-6 進行測試 - 21 -
4-7 結果觀看 - 23 -
第五章 結論 - 26 -
參考文獻 - 27 - |
| 參考文獻 |
1. Schmidhuber, Jürgen. "Deep learning in neural networks: An overview." Neural networks 61 (2015): 85-117.
2. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436-444.
3. Sun, Yi, et al. "Deepid3: Face recognition with very deep neural networks." arXiv preprint arXiv:1502.00873 (2015).
4. Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." nature 529.7587 (2016): 484-489.
5. Amodei, Dario, et al. "Deep speech 2: End-to-end speech recognition in english and mandarin." International conference on machine learning. 2016: 173-182.
6. Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 3431-3440
7. Wang, Shanshan, et al. "Accelerating magnetic resonance imaging via deep learning." 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI). IEEE, 2016: 514-517
8. Araya-Polo, Mauricio, et al. "Deep-learning tomography." The Leading Edge 37.1 (2018): 58-66.
9. 葉政凱. “基於深度學習的電阻率成像技術之研究” (2019)
10. Oliphant, Travis E. "Python for scientific computing." Computing in Science & Engineering 9.3 (2007): 10-20.
11. Abadi, Martín, et al. "Tensorflow: A system for large-scale machine learning." 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16). 2016: 265-283
12. Aslam, Fankar Armash, et al. "Efficient Way Of Web Development Using Python And Flask." International Journal of Advanced Research in Computer Science 6.2 (2015).
13. Lundh, Fredrik. "An introduction to tkinter." URL: www. pythonware. com/library/tkinter/introduction/index. htm (1999).
14. Merkel, Dirk. "Docker: lightweight linux containers for consistent development and deployment." Linux journal 2014.239 (2014): 2. |
| 指導教授 |
洪炯宗
|
審核日期 |
2020-8-24 |
| 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
|
