Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation

Deep learning (DL) techniques are highly effective for defect detection from images. Training DL classification models, however, requires vast amounts of labeled data which is often expensive to collect. In many cases, not only the available training data is limited but may also imbalanced. In this paper, we propose a novel domain adaptation (DA) approach to address the problem of labeled training data scarcity for a target learning task by transferring knowledge gained from an existing source dataset used for a similar learning task. Our approach works for scenarios where the source dataset and the dataset available for the target learning task have same or different feature spaces. We combine our DA approach with an autoencoder-based data augmentation approach to address the problem of imbalanced target datasets. We evaluate our combined approach using image data for wafer defect prediction. The experiments show its superior performance against other algorithms when the number of labeled samples in the target dataset is significantly small and the target dataset is imbalanced

Development and Application of Big Data Analytics and Artificial Intelligence for Structural Health Monitoring and Metamaterial Design

Recent advances in sensor technologies and data acquisition platforms have led to the era of Big Data. The rapid growth of artificial intelligence (AI), computing power and machine learning (ML) algorithms allow Big Data to be processed within affordable time constraints. This opens abundant opportunities to develop novel and efficient approaches to enhance the sustainability and resilience of Smart Cities. This work, by starting with a review of the state-of-the-art data fusion and ML techniques, focuses on the development of advanced solutions to structural health monitoring (SHM) and metamaterial design and discovery strategies. A deep convolutional neural network (CNN) based approach that is more robust against noisy data is proposed to perform structural response estimation and system identification. To efficiently detect surface defects using mobile devices with limited training data, an approach that incorporates network pruning into transfer learning is introduced for crack and corrosion detection. For metamaterial design, a reinforcement learning (RL) and a neural network based approach are proposed to reduce the computation efforts for the design of periodic and non-periodic metamaterials, respectively. Lastly, a physics-constrained deep auto-encoder (DAE) based approach is proposed to design the geometry of wave scatterers that satisfy user-defined downstream acoustic 2D wave fields. The robustness of the proposed approaches as well as their limitations are demonstrated and discussed through experimental data or/and numerical simulations. A roadmap for future works that may benefit the SHM and material design research communities is presented at the end of this dissertation

Research on Distinguishing between Earthquake Signal and Non-Earthquake Signal for On-Site Earthquake Early Warning System

鑒於台灣地狹人稠，為增加強震波來臨前的預警時間以及解決區域型地震預警系統警報盲區的問題，現地型強震即時預警系統的建置勢在必行。現地預警系統利用地震初達波前幾秒的特性來預測此地震事件可能達到之最大地表加速度(Peak Ground Acceleration, PGA)，根據預測震度大小視情況發布警報。然而，由於現地型系統只使用單一地震測站所記錄的振動歷時資料，便容易受到其他非地震事件所造成的影響，例如車輛、行人、施工等等未知振動源，以及電子訊號等非振動訊號的干擾，而發生預警系統誤報的情形。故本研究擬使用支持向量分類法(Support Vector Classification, SVC)與奇異譜分析(Singular Spectrum Analysis, SSA)等人工智慧(Artificial Intelligence)與訊號處理方法嘗試解決這個問題，期能將容易被高估PGA的非地震事件在預測PGA前先行去除，以減少系統誤報的狀況，並確保實際震度大於四級的地震事件能夠分類正確。 支持向量分類法是一種機器學習(Machine Learning)的技術，其常被應用於資料識別等相關領域，而奇異譜分析為一種時間域訊號處理的分析方法，被廣泛應用在古典時間歷時分析、動力反應系統、多變量統計、多變性幾何、以及各類相關訊號處理的領域。本研究以這兩種理論為基礎，提出了兩種辨別流程法來區分地震事件與非地震事件，並使用來自於中央氣象局與國家地震中心現地測站的地震資料庫，以及收集自現地測站的非地震資料庫，將兩資料庫的加速度歷時資料進行兩階段的測試，以驗證本研究所提方法之可行性。The regional earthquake early warning system (EEWS) is not suitable for Taiwan due to the fact that most of the destructive seismic hazard comes from in-land earthquakes, which makes the lead-time before destructive earthquake wave arrives provided by the regional warning system can be null. On the other hand, on-site warning system can provide more lead-time at the region close to an epicenter since only the seismic information on the target site is required. Instead of leveraging the information of several stations, the on-site system extracts some P-wave features from the first few seconds of vertical ground acceleration of a single station and performs the prediction of the coming earthquake intensity at the same station according to these features. However, the system may be triggered due to some vibration signals that are not caused by an earthquake event or interference from electronic signals, which may result in false alarm at the station. Therefore, this research performs a study on the classification between true earthquake and non-earthquake events by means of Support Vector Classification (SVC) and Singular Spectrum Analysis (SSA). Support Vector Classification is a machine learning technique that has been widely used in automatic data identification in the past decades. Singular Spectrum Analysis is a signal processing algorithm which is popular for classical time series analysis, multivariate statistics, and dynamical systems. This research proposes two methods to distinguish the vibration signals caused by non-earthquake events from the one caused by earthquake events based on the above two algorithms. The feasibility of the proposed method will be verified by using data collected from Taiwan Strong Motion Instrumentation Program (TSMIP) and earthquake early warning stations of National Center for Research on Earthquake Engineering (NCREE).致謝 i 摘要 iii Abstract v 目錄 vii 圖目錄 ix 表目錄 xiii 第一章 緒論 1 1.1 前言 1 1.2 地震預警系統簡介 1 1.3 研究動機與目的 2 1.4 研究內容與架構 3 第二章 文獻回顧 5 2.1 前言 5 2.2 地震初達波特徵 6 2.3 支持向量分類法 (Support Vector Classification) 8 2.4 奇異譜分析 (Singular Spectrum Analysis) 9 2.5 系統分析方法 11 第三章 地震與非地震歷時資料庫 15 3.1 前言 15 3.2 中央氣象局地震歷時資料庫 15 3.3 現地測站地震與非地震歷時資料庫 15 3.4 訓練樣本資料庫建置 16 3.5 測試樣本資料庫建置 17 第四章 分析結果 28 4.1 前言 28 4.2 分析流程說明 28 4.2.1 辨別流程一 (Solution I) 28 4.2.2 辨別流程二 (Solution II) 28 4.3 支持向量分類法模型 (SVC Model) 29 4.4 奇異譜分析與快速傅立業轉換 31 4.4.1 判斷基準一 (Criterion I) 31 4.4.2 判斷基準二 (Criterion II) 32 4.5 分析結果 33 4.5.1 辨別流程一結果 (Solution I Result) 33 4.5.2 辨別流程二結果 (Solution II Result) 35 4.5.3 綜合比較 38 4.6 系統方法驗證結果 38 4.6.1 辨別流程一結果 (Solution I Result) 38 4.6.2 辨別流程二結果 (Solution II Result) 40 4.6.3 綜合比較 41 4.7 小結 42 第五章 結論與未來展望 93 5.1 結論 93 5.2 未來展望 94 參考文獻 9