Defect features recognition in 3D industrial CT images

Abstract

Due to the limitations of production conditions, there is a certain probability that workpiece product has internal defects, which will have a certain impact on the performance of workpiece. Therefore, the internal defects detection of workpiece is essential. This study proposed a defect recognition method based on industrial computed tomography (CT) image to identify the internal defects of workpiece. The block fractal algorithm was used to locate the defect parts of the image, then the improved k-means clustering algorithm was used to segment the defect parts, and feature vector was extracted by Hu invariant moments. Finally, the firefly algorithm and radial basis function (RBF) neural network were combined to identify the defect. It was found from the experiments that the algorithm in this study had the accuracy of 97.89%, which proved the reliability of the algorithm and provided some suggestions for the defects recognition. Povzetek: Za prepoznavanje okvar na 3D slikah industrijskih izdelkov je uporabljena metoda vinske mušice.

Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures

“…Furthermore, imputed values can be assessed using cold-deck imputation [26], hot-deck imputation [31], expectationmaximization imputation [32,33], etc. Prediction models are also used to predict MVs after a model is built using the available information in the dataset [34]. Imputation methods are thought to be useful for manipulating MVs in situations such as when (I) the feature that holds MVs has a statistical impact on the output feature, (II) the MVs are of type MAR or MCAR, and (III) an instance does not hold MVs due to various features [35].…”

Prediction and Estimation of Book Borrowing in the Library: Machine learning

“…BPNN [14] and RBFNN [15] have been widely used in prediction and estimation. Compared with BPNN, RBFNN has more advantages in operation speed and structure and has been successfully applied in fields such as human face recognition [16] and defect detection [17]. Therefore, this study used RBFNN to establish the prediction and estimation model of book borrowing.…”

CBRG: A Novel Algorithm for Handling Missing Data Using Bayesian Ridge Regression and Feature Selection Based on Gain Ratio

“…Imputed value can also be estimated using KNN (K Nearest Neighbors) [3], cold deck imputation [19], expectation-maximization imputation [20]- [22], hot-deck imputation [23], etc. In techniques contain prediction models, a model is built based on the available information within the dataset, then this model is used to predict missing values [24]. Imputation methods are used to manipulate missing values if; i) the missing values are MCAR or MAR type, ii) the feature that contains missing values has statistical influence with the target feature, iii) deletion of instances decreases the size of the dataset, which in turn affects on building the predictive model, iv) an instance does not include missing values across many features [14].…”