隨著工業4.0的興起，智慧型裝置、網路與感測元件的普及也帶動了工業大數據分析的發展，從感測器獲得的資料種類愈趨複雜與多元化，對於輸出目標也從常見的單一輸出需求轉化為多輸出需求，此外，隨著數據的產生越來越大量且快速，數據也時常出現遺失值與雜訊產生等問題。因此對遺失數據的插補與多輸出預測之方法，仍是目前大數據分析中較具有研究價值的問題。
本論文所研究的資料為多重輸入與多重鑽頭加工品質輸出預測的工業大數據資料。本研究將實驗分為兩個獨立的部分，第一部分實驗為將初始不完整的資料集中，含有缺失值的數據刪除後，再使用eXtreme梯度增強模型篩選出對於預測值相對重要的特徵，以此來建立含有不同缺失比率的數據集，接著利用不同的插補方法包括卷積神經網路插補法與混合傳統-量子卷積神經網路插補法來將這些數據集做完整的插補動作，進而分析不同插補方法在數據比率不同的情況下，比較數據集的還原程度，以及還原數據所花費的時間。
第二部分實驗首先利用主成分分析法將原數據集進行特徵的降維，接著再分別使用卷積神經網路預測法與混合傳統-量子卷積神經網路預測法進行預測多鑽頭機台加工品質的動作。實驗結果顯示，所提出的混合傳統-量子卷積神經網路插補法與卷積神經網路插補法得出的準確度並無顯著差異，然而混合傳統-量子卷積神經網路預測法與卷積神經網路預測法的預測結果，經過雙樣本t檢定後顯示，混合傳統-量子卷積神經網路在多目標預測方面，有著優於卷積神經網路的準確性。

The advent of Industry 4.0 has brought about a surge in the adoption of intelligent devices, networks, and sensing components, consequently driving the advancement of industrial big data analysis. As the nature of data acquired from sensors grows increasingly intricate and varied, the objectives for output have transitioned from traditional single output requirements to encompass multi-output requirements. Furthermore, the rapid generation of data has introduced challenges such as the occurrence of missing values and the generation of noise within the data. Consequently, the methods for addressing missing data through interpolation and achieving multi-output prediction remain areas of significant research value within the field of big data analytics.
This thesis focuses on the analysis of industrial big data for the purpose of multiple output prediction of drill manufacturing quality. The study is divided into two distinct parts. In the first part, datasets with missing values are extracted from the initial incomplete data set. Subsequently, the eXtreme gradient boosting model is employed to identify the relatively significant features for predicting the target values. Multiple data sets with varying missing ratios are created, and different imputation methods, including convolutional neural network imputation and mixed classical-quantum convolutional neural network imputation, are employed to perform comprehensive data imputation on these sets. The analysis entails comparing the efficacy of different imputation methods under varying data ratios by assessing the degree of data restoration and the time required for restoration.
In the second part of the experiment, the principal component analysis method is utilized to reduce the dimensionality of the original dataset. Subsequently, the convolutional neural network prediction method and the mixed classical-quantum convolutional neural network prediction method are employed to predict the quality of multi-drill machining actions. The experimental results indicate that there is no significant difference in accuracy between the proposed mixed classical-quantum convolutional neural network imputation method and the convolutional neural network imputation method. However, when comparing the prediction results of the mixed classical-quantum convolutional neural network prediction method with those of the convolutional neural network prediction method, the two-sample t-test demonstrates that the mixed classical-quantum convolutional neural network outperforms the convolutional neural network in terms of multi-objective prediction accuracy.

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