2117results about How to "Improve learning effect" patented technology

The invention provides a Chinese text classification method based on a super-deep convolution neural network structure model. The method comprises the steps of collecting a training corpus of a word vector from the internet, combining a Chinese word segmentation algorithm to conduct word segmentation on the training corpus, and obtaining a word vector model; collecting news of multiple Chinese news websites from the internet, and marking the category of the news as a corpus set for text classification, wherein the corpus set is divided into a training set corpus and a test set corpus; conducting word segmentation on the training set corpus and the test set corpus respectively, and then obtaining the word vectors corresponding to the training set corpus and the test set corpus respectively by utilizing the word vector model; establishing the super-deep convolution neural network structure model; inputting the word vector corresponding to the training set corpus into the super-deep convolution neural network structure model, and conducting training and obtaining a text classification model; inputting the Chinese text which needs to be sorted into the word vector model, obtaining the word vector of the Chinese text which needs to be classified, and then inputting the word vector into the text classification model to complete the Chinese text classification.

The invention provides a video perception-fused multi-task synergetic recognition method and system, and belongs to the technical field of multisource heterogeneous video data processing and recognition. According to the method and system, a biological vision perception mechanism is combined to research feature-synergetic shared semantic descriptions of multisource heterogeneous video data and universal feature descriptions of the multisource heterogeneous video data are obtained; an environment-suitable calculation theory is utilized to establish task-synergetic feature association learning and task prediction mechanism and realize an environment-suitable perception task association prediction mechanism; and long-time dependency is combined to put forward a context-synergetic vision multi-task deep synergetic recognition mode, realize a multi-task deep synergetic recognition model with long-time memory and solve the problem that the video multi-task recognition is bad in generalization, low in robustness and high in calculation complexity. According to the method and system, an intelligent, generalized and mobile video common feature description method and the multi-task deep synergetic recognition model are put forward, so that the development in the field of intelligent information push and personalized control services of smart city multisource heterogeneous video data canbe prompted.

The invention discloses a 1D-CNN based distributed optical fiber sensing signal feature learning and classification method, which solves of a conventional distributed optical fiber sensing system thatdistinguishable features of an event extracted manually have poor adaptability to complex changing environments and the system is time-consuming and laborious. The method includes the following steps: conducting time division on obtained distributed optical fiber sensing sound and vibration signals of space points and establishing a typical event signal data set; constructing a one-dimensional convolutional neural network (1D-CNN) model, and performing iterative updating training on a network through the typical event signal data training set to obtain a typical event signal feature set; after training different types of classifiers by the typical event signal feature set, selecting the best classifier. During testing, test data is input into an optimal 1D-CNN and distinguishable featuresof an event are obtained, and then a classification result is obtained by inputting the best classifier.

Provided is a student answer information real-time collection and efficient and intelligent correcting system and use method in a teaching process, belonging to a teaching system. A teacher lesson preparation module is used for teachers to compile exercises, input standard answers and scores, and upload data to a server; students upload answers to the server at student clients; an efficient and intelligent correcting module is used for automatically correcting objective questions, preprocessing subjective questions, automatically correcting the questions capable of being intelligently corrected, sending the questions incapable of being intelligently corrected to teachers for correction, and transmitting correction results to the server; the server feeds back the correction results to the student clients. The system and method can realize ''networking, digital and customized'' education, allow students to ''learn anywhere and anytime'', deeply fuse with current education, reach the effect of ''classroom application, regular application, and universal application'', and be easily and extensively copied in areas with poor education resources to realize educational equity and teaching in accordance with the aptitude of students.

The invention discloses a deep feature and transfer learning-based rolling bearing fault diagnosis method under variable working conditions, relates to the technical field of fault diagnosis, and aimsto solve the problem of low state identification accuracy of different fault positions and different performance degradation degrees of a rolling bearing under the variable working conditions. The method comprises the following steps: firstly, carrying out feature extraction on the vibration signal frequency domain amplitude of the rolling bearing by adopting SDAE to obtain vibration signal deepfeatures, and forming a source domain feature sample set and a target domain feature sample set; then, adopting the JGSA to carry out domain adaptation processing on the source domain feature sample and the target domain feature sample, the purpose of reducing distribution offset and subspace transformation difference of feature samples between domains is achieved, and domain offset between different types of feature samples is reduced. And finally, completing rolling bearing multi-state classification under variable working conditions through a K nearest neighbor algorithm. Compared with other methods, the method disclosed by the invention shows better feature extraction capability under the variable working condition of the rolling bearing, the sample feature visualization effect of therolling bearing is optimal, and the fault diagnosis accuracy of the rolling bearing under the variable working condition is high.

The invention discloses an image de-noising method based on a convolution pair neural network, comprising the following steps: building a convolution pair neural network model, wherein the convolution pair neural network model includes multiple convolution pairs and corresponding activation layers; selecting a training set, and setting the training parameters of the convolution pair neural network model; according to the convolution pair neural network model and the training parameters thereof, training the convolution pair neural network model with the goal of loss function minimizing to form an image de-noising neural network model; and inputting a to-be-processed image to the image de-noising neural network model, and outputting a de-noised image. Through the image de-noising method based on a convolution pair neural network disclosed by the invention, the learning ability of the neural network is enhanced greatly, accurate mapping from noisy images to clean images is established, and real-time de-noising is realized.

The invention provides an on-line learning platform system based on the Web. The on-line learning platform system is carried by a Web server and a data base server to enable users to conduct on-line learning activities through the network. The on-line learning platform system comprises a course administrator subsystem, a student subsystem, a teacher subsystem, an academic administrator subsystem, a shopping mall subsystem and a headquarter administrator subsystem. The subsystems are independent with each other and share information. By means of the on-line learning platform system based on the Web, the learning enjoyment of students and interactivity of the students and teachers are increased, and the learning effect is improved.