114results about How to "Increase time complexity" patented technology

The invention relates to the field of agricultural outlier detection, in particular to an outlier detection method based on agricultural big data. The method comprises the following steps: a data collection step of collecting agricultural production data, agricultural soil data and agrometeorological resource data, and integrating the data into a training data set; The step of constructing iTree tree is to select m sample points from the training dataset and continuously randomly select splitting attributes and splitting points until the termination condition is reached; the step of constructing iTree tree is to select m sample points from the training dataset. Constructing an isolated forest algorithm model, initializing the number t of iTree trees in the isolated forest and the set m ofsubsamples taken when constructing the iTree trees, entering the step of constructing the iTree trees in a loop, and constructing mutually independent iTree trees, wherein the set of all iTree trees constitutes the isolated forest algorithm model; An outlier judging step of calculating an outlier score s (x), and judging whether the test data x is an outlier by the outlier score s (x). The invention applies the isolated forest algorithm model to the outlier detection of the agricultural big data, and can effectively improve the detection effect of the outlier of the agricultural big data.

There is provided a method and an apparatus for updating a background model used for background subtraction of an image. The method comprises: receiving an image (220), and classifying a region (226) in the image as foreground by performing background subtraction using a background model (240). The background model comprises a collection of background samples (248, 248b, 248c, 248d) for each pixel (228) in the image. The collection of background samples is arranged in a list of background images (242a, 242b, 242c, 242d). The method further comprises: replacing image contents in the region (226) of the image which is classified as foreground by image contents of a corresponding region (246) in a background image in the list, and adding the image to the list of background images by rearranging a collection of pointers (244a, 244b, 244c, 244d) which each points to one of the background images in the list, such that one of the pointers instead points to the image.

The invention discloses a subway tunnel disease detection method based on deep learning, and mainly solves the problems that the subway tunnel detection amount is large, and the existing detection algorithm is low in efficiency and large in missing detection amount. The method specifically comprises the following steps: making a to-be-detected subway tunnel image database, including a subway tunnel normal region picture database and a subway tunnel disease region database; based on an improved Faster R-CNN target detection algorithm, carrying out target positioning and accurate identification and classification on the subway tunnel diseases; detecting a disease state in the to-be-detected area by adopting a deep learning-based method; and finally, inputting a to-be-tested image into the detection model to obtain a tunnel disease detection result. The solution thought of the traditional algorithm is image-preprocessing-artificial feature extraction-classification. According to the method, the problems existing in current subway tunnel disease detection are solved by utilizing the thought of deep learning, namely image-feature extraction network-classification and regression.

The invention discloses an expression recognition method based on reverse synergetic salient region features. The whole recognition process is mainly divided into five parts, namely preprocessing, detection of an expression salient region, feature extraction on the salient region, weight assignment and recognition and classification. The method comprises the specific steps that (1) a face region is divided in a training test sample; (2) a reverse synergetic salient detection algorithm is utilized to extract the expression salient region from the divided part; (3) an LBP operator and an HOG operator are utilized to perform feature extraction on the salient region; (4) a support vector machine is utilized to perform preliminary classification on all salient local features, and weight assignment is performed; and (5) a multi-classification decision-making mechanism is used for recognition and classification. The method is combined with the relevancy between expressions, the relevancy is utilized to extract a local region containing rich expression information, and therefore the calculated amount is greatly reduced; and meanwhile, the multi-classification decision-making mechanism is used for classification, so that the recognition rate is increased accordingly.

The invention provides a D2D power distribution fast optimization algorithm in presence of multichannel cellular subscribers. The communication speed optimization problem of cellular network based D2D subscribers is a complex non-convex and non-linearity optimization problem. Aiming at the multichannel cellular subscribers and multichannel D2D subscribers, the transmission power pi of the cellular subscribers and the transmission power qi of the D2D subscribers can be optimized and solved rapidly under the conditions of independent power limitation of the cellular subscribers and independent power limitation of the D2D subscribers by applying the algorithm. The transmission power obtained by adoption of the optimization algorithm can ensure requirements of the cellular subscribers on the communication speeds on all the cellular frequency bands and can maximize the sum of the communication speeds of all the D2D subscribers on all the cellular frequency bands. The algorithm has the advantages of a fast rate of convergence, low calculated amount, easy implementation, high result precision and the like.