Abstract: A system to create a stacked classifier model combination or classifier ensemble has been designed for identification of undisclosed flaws in software components on a large-scale. This classifier ensemble is capable of at least a 54.55% improvement in precision. The system uses a K-folding cross validation algorithm to partition a sample dataset and then train and test a set of N classifiers with the dataset folds. At each test iteration, trained models of the set of classifiers generate probabilities that a sample has a flaw, resulting in a set of N probabilities or predictions for each sample in the test data. With a sample size of S, the system passes the S sets of N predictions to a logistic regressor along with “ground truth” for the sample dataset to train a logistic regression model. The trained classifiers and the logistic regression model are stored as the classifier ensemble.

Abstract: A system to create a stacked classifier model combination or classifier ensemble has been designed for identification of undisclosed flaws in software components on a large-scale. This classifier ensemble is capable of at least a 54.55% improvement in precision. The system uses a K-folding cross validation algorithm to partition a sample dataset and then train and test a set of N classifiers with the dataset folds. At each test iteration, trained models of the set of classifiers generate probabilities that a sample has a flaw, resulting in a set of N probabilities or predictions for each sample in the test data. With a sample size of S, the system passes the S sets of N predictions to a logistic regressor along with “ground truth” for the sample dataset to train a logistic regression model. The trained classifiers and the logistic regression model are stored as the classifier ensemble.

Abstract: A system to create a stacked classifier model combination or classifier ensemble has been designed for identification of undisclosed flaws in software components on a large-scale. This classifier ensemble is capable of at least a 54.55% improvement in precision. The system uses a K-folding cross validation algorithm to partition a sample dataset and then train and test a set of N classifiers with the dataset folds. At each test iteration, trained models of the set of classifiers generate probabilities that a sample has a flaw, resulting in a set of N probabilities or predictions for each sample in the test data. With a sample size of S, the system passes the S sets of N predictions to a logistic regressor along with “ground truth” for the sample dataset to train a logistic regression model. The trained classifiers and the logistic regression model are stored as the classifier ensemble.

Abstract: A system to create a stacked classifier model combination or classifier ensemble has been designed for identification of undisclosed flaws in software components on a large-scale. This classifier ensemble is capable of at least a 54.55% improvement in precision. The system uses a K-folding cross validation algorithm to partition a sample dataset and then train and test a set of N classifiers with the dataset folds. At each test iteration, trained models of the set of classifiers generate probabilities that a sample has a flaw, resulting in a set of N probabilities or predictions for each sample in the test data. With a sample size of S, the system passes the S sets of N predictions to a logistic regressor along with “ground truth” for the sample dataset to train a logistic regression model. The trained classifiers and the logistic regression model are stored as the classifier ensemble.

Abstract: This invention discloses a UAV inspection method for power line based on human visual system. Image preprocessing module preprocesses the power line image of the input system. Power line detection module uses human visual attention mechanism to complete segmentation of the power line in the image. Binocular image registration module uses SURF algorithm to provide exact match of the feature points. The obstacle detection and early warning module uses binocular visual principle to calculate the three-dimensional coordinates of the matching point and the power line. The result output and feedback module calculates the vertical distance from the matching point to the power line according to the information about the space coordinates to complete feedback of the information about the obstacle with a threat to the power line. The method can accurately analyze the obstacle of power line in a quantitative manner, and the analysis result is stable and objective.

Abstract: This invention discloses a UAV inspection method for power line based on human visual system. Image preprocessing module preprocesses the power line image of the input system. Power line detection module uses human visual attention mechanism to complete segmentation of the power line in the image. Binocular image registration module uses SURF algorithm to provide exact match of the feature points. The obstacle detection and early warning module uses binocular visual principle to calculate the three-dimensional coordinates of the matching point and the power line. The result output and feedback module calculates the vertical distance from the matching point to the power line according to the information about the space coordinates to complete feedback of the information about the obstacle with a threat to the power line. The method can accurately analyze the obstacle of power line in a quantitative manner, and the analysis result is stable and objective.