65results about How to "Reduce training data" patented technology

The invention discloses a deep convolutional neural network-based human face occlusion detection method. The method comprises the steps of performing block segmentation on an input image to obtain a target pre-selected region; constructing a first deep convolutional neural network, training the first deep convolutional neural network comprising a first deep convolutional network and a first multilayer perceptron connected with the first deep convolutional neural network to obtain required parameters, extracting features of the target pre-selected region, and performing classification; predicting the position of a human head through a second multilayer perceptron according to the extracted features; filtering the credibility of a classification type which is the human head and the predicted position of the human head through non-maximum suppression to remove an overlapped duplicate detection box; and obtaining a human head block in combination with original image segmentation, constructing a multi-task learning policy-based second deep convolutional neural network, and judging whether the left eye, the right eye, the nose and the mouth of the human head block are occluded or not. According to the method, the occluded human face can be accurately detected and the specific occluded part of the human face can be judged; and the method is mainly used for crime pre-warning of videos of a camera in front of an automatic teller machine.

The embodiment of the invention provides a training method and system for a language model, wherein the method specifically comprises the following steps: acquiring seed corpuses of all fields; screening big data corpuses according to a vector space model of seed corpuses of all fields to obtain seed screening corpuses in the corresponding fields; respectively training by utilizing the seed screening corpuses of all fields to obtain the screening model of the corresponding field; fusing the screening models of all the fields to obtain corresponding screening fusion model. According to the training method and system for language model disclosed by the embodiment of the invention, the parameter reasonableness of the language model can be improved on the premise of reducing a computation burden and saving time.

The invention discloses a negative selection intrusion detection method based on the variable self-body radius, and the method is mainly used for solving the problem of poor detection effect because the self-body space formed by setting a fixed autologous radius can not be better covered in the traditional method. The negative selection intrusion detection method is implemented through the following steps: 1) pretreating a KDD (Knowledge Discovery In Database) 99data set; 2) selecting parts of normal data from the data set to serve as an self-body so as to form a self-body set; 3) randomly generating a foreign body, and setting the variable self-body radius for all self-bodies by utilizing a distance characteristic between the self-body and the generated foreign body; (4) training a detector set D; and (5) detecting the test data by use of the detector set D so as to judge whether the test data is normal or abnormal. The negative selection intrusion detection method based on the variable self-body radius has the advantages of high positive detection rate and low misinformation rate, the effect of the negative selection intrusion detection method can be effectively improved under the condition that the self-body data amount is small, the negative selection intrusion detection method is used for identifying the abnormal network data, and ensuring the network safety.

The invention relates to an identity authentication method and device for wearable intelligent equipment. The method and the device have the beneficial effects that on the basis of the principle of intra-body communication and in combination with the bioidentification technology, when users use the wearable intelligent equipment, S21 parameters after test signals flow through user bodies are acquired in a test frequency band to determine the optimal frequency band, the S21 parameters are acquired and processed in the optimal frequency band to obtain final authentication templates used for representing user identifies; authentication of the user identifies is achieved by comparing acquired user identity authentication data with the authentication templates; the privacy information of the users is effectively protected by utilizing nonreproducible uniqueness of the biological characteristics of human bodies and the advantages that the biological characteristics of human bodies can not be lost or forgotten and are difficult to counterfeit, thus well ensuring the privacy and safety of the users, avoiding potential risks caused when the users use the wearable intelligent equipment and being greatly beneficial to popularization and application of the wearable intelligent equipment.

The invention discloses a small hydropower station group generating capacity prediction method based on big data driving, and the method comprises the steps: dividing small hydropower station groups according to regions for the current situation that the small hydropower station groups are wide in point range and are managed disorderly; obtaining historical meteorological data and generating capacity data of each region, preprocessing bad data by adopting a data mining technology, and then performing normalization processing; and training the historical meteorological data and the power generation data by using two layers of long-term and short-term memory neural networks and one layer of full-connection neural network, and performing optimization calculation by taking the minimum root-mean-square error of the power generation true value of the test data and the power generation predicted value of the training model as an optimization target to finally obtain the power generation prediction model of the small hydropower station group. According to the method, the generating capacity of the small hydropower station group can be accurately predicted, comprehensive and ordered management of the small hydropower station is realized, and safe and stable operation of a power grid is ensured.

The invention discloses a small hydropower station generating capacity prediction system based on a cloud edge fusion architecture and an implementation method thereof, and the method comprises the steps: collecting the real-time meteorological data and small hydropower station generating data of a measured region from an external system, directly uploading the real-time meteorological data and small hydropower station generating data to a cloud computing server through a VPN channel to achieve the human-computer interface of the system through the Internet; enabling the cloud computing serverto transmit the meteorological data and the small hydropower station power generation data to an edge server for storage. A small hydropower station generating capacity prediction model is constructed, small hydropower station generating capacity prediction is carried out by utilizing the model, then a prediction result is returned to a cloud computing server, system core analysis functions of data analysis, mining, prediction and the like are completed, and finally the prediction and analysis results are transmitted to terminal equipment through the Internet to be displayed. The method is atypical case of organic combination application of cloud computing and edge computing technologies, and effectively solves the problems of storage, transmission and display of power grid sensitive data while improving the reliability of the system.

The invention discloses a multi-feature fusion sign language recognition method based on adaptive hidden markov, which comprises the following steps: 1. Firstly, extracting multiple features from a sign language video database and performing front-end fusion, namely constructing a feature pool set; then, an adaptive hidden Markov model of sign language video under different features in the featurepool set is constructed, and a feature selection strategy to obtain the appropriate back-end score fusion features. After selecting the back-end scoring fusion features, the scoring vectors under each back-end scoring fusion feature are calculated, and different weights are assigned to them, and then the back-end scoring fusion is carried out, so as to obtain the optimal fusion effect. The invention can realize the accurate recognition of sign language categories in sign language video and improve the robustness of the recognition.

Various embodiments set forth systems and techniques for training a personalized prediction model. The techniques include generating, based on interaction data associated with one or more users and a first weight associated with the interaction data, a first set of training data; generating, based on the personalized prediction model, a predicted enjoyment signal associated with playback of a digital content item; generating, based on the first set of training data and the predicted enjoyment signal, a second set of training data; and updating one or more parameters of a personalized ranking model based on the second set of training data.