Abstract: The present invention discloses a method for scheduling of service processes in hybrid cloud considering virtual machine deployment and an interval-based charging mode. The method includes: acquiring a task set TS, a hybrid cloud CPS, a physical machine set PMS, a periodic charging mode TCM, a maximum number of iterations Maxiter, a group population Gpop, a maximum generation number E of sparks, a maximum explosion amplitude R, an initial temperature T, and a cooling rate cr; in an initial phase, first generating an initial firework population; performing random encoding on each firework into a real number list, represented by a priority list of tasks; decoding each firework into a complete scheduling solution and calculating an objective value of the solution; and in an iteration phase, continuously updating the firework, and outputting the best solution once a stop condition is satisfied.

Abstract: A moving object detection method and a moving object detection system are provided. The method includes: predetermining a background image corresponding to a scene monitored by a video monitoring device; performing a subtraction processing on a grayscale image to be detected and the background image to acquire a difference image; and binarizing the difference image and determining a moving object in the grayscale image to be detected, where the determining the background image includes: dividing a first grayscale image frame and a second grayscale image frame in a grayscale image frame sequence captured by the video monitoring device into image blocks to acquire a first image block set and a second image block set respectively, and determining the background image using a difference between the first image block set and the second image block set.

Abstract: A moving object detection method and a moving object detection system are provided. The method includes: predetermining a background image corresponding to a scene monitored by a video monitoring device; performing a subtraction processing on a grayscale image to be detected and the background image to acquire a difference image; and binarizing the difference image and determining a moving object in the grayscale image to be detected, where the determining the background image includes: dividing a first grayscale image frame and a second grayscale image frame in a grayscale image frame sequence captured by the video monitoring device into image blocks to acquire a first image block set and a second image block set respectively, and determining the background image using a difference between the first image block set and the second image block set.

Abstract: A feature extraction and classification method based on support vector data description is provided, which includes: calculating, for each sample, Euclidean distances from the sample to spherical centers of multiple hypersphere models corresponding to different data categories, where the multiple hypersphere models are acquired in advance by training using a support vector data description algorithm; substituting, for each sample, the Euclidean distances and radiuses of the hypersphere models respectively corresponding to the Euclidean distances into a new feature relation equation, to acquire a new feature sample corresponding to the sample, where the new feature samples constitute a new feature sample set; and performing classification on the new feature sample set using a preset classification algorithm, to acquire a classification result. A feature extraction and classification system based on support vector data description having the above advantages is further provided.

Abstract: A gene expression data classification method and a gene expression data classification system are provided. With the gene expression data classification method, a gene feature data set is acquired, and then the gene feature data set is clustered using a clustering algorithm to obtain clustering sets, the number of which is a first preset parameter, the clustering sets are processed to obtain a second sample matrix, a second training set and a feature index set, to reduce dimensionality of gene expression data. In this way, redundancy among the gene expression data is reduced, thereby greatly reducing calculation resources and calculation time consumed in a subsequent process of performing feature selection on the second training set.