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398results about How to "Improve learning ability" patented technology

Network constructing method for human face identification, identification method and system

The invention discloses a deeper layer network constructing method used for gender identification or age estimation based on human face. The method includes a step (101) dividing all training pictures into a plurality of groups; (102) extracting high layer features of a group of pictures based on a convolution neural network and thereby obtaining a first matrix composed of the high layer feature vectors, and extracting low layer and global features of the same group of the training images based on an artificial neural network and thereby obtaining a second matrix composed of the low layer feature vectors, obtaining a group of gender identification or age estimation results based on the extract first matrix, the second matrix and the defined judgment formula, wherein the values of a first weight matrix W1, a second weight matrix w2, an offset matrix b and an adjusting weight beta in the defined judgment formula are updated by utilizing an error back propagation algorithm and the final values of the parameters are obtained and the network construction is completed. Judgment of age and gender of a human face is performed based on the judgment formula determined according to the values of the parameters when the network construction is completed.
Owner:HENGFENG INFORMATION TECH CO LTD

Face image super-resolution reconstruction method based on an attribute description generative adversarial network

The invention discloses a face image super-resolution reconstruction method based on an attribute description generative adversarial network, and belongs to the field of digital image/video signal processing. The method is characterized in that a training stage comprises three parts of training sample preparation, network structure design and network training; the network structure design adopts agenerative adversarial network framework and is composed of a generative network and a discriminant network. The generation network comprises a face attribute coding and decoding module and a super-resolution reconstruction module; the discrimination network comprises an attribute classification module, an adversarial module and a perception module; wherein the network training process is carriedout in a mode that a generative adversarial network of a generative adversarial network framework and a discriminant network are alternately subjected to adversarial training; and a reconstruction stage: taking the LR face image and the attribute description information as input, and realizing image coding, attribute adding, image decoding and image reconstruction through a trained generation network. According to the invention, the enhancement of the face information of the low-resolution face image can be completed, and the accuracy of low-resolution face recognition can be improved.
Owner:BEIJING UNIV OF TECH

Virtual learning environment micro-expression recognition and interaction method based on double-flow convolutional neural network

The invention relates to a virtual learning environment micro-expression recognition and interaction method based on a double-flow convolutional neural network, and the method comprises the followingsteps: S1, carrying out the preprocessing of micro-expression data: carrying out the Euler video amplification of a micro-expression video, extracting an image sequence, carrying out the face positioning of the image sequence, and carrying out the cutting of the image sequence, and obtaining the RGB data of a micro-expression; extracting optical flow information from the data amplified by the Euler video to obtain an optical flow image of the micro-expression; s2, dividing the preprocessed data into a training set and a test set, and constructing a double-flow convolutional neural network by using a transfer learning method so as to learn space and time domain information of micro expressions; s3, carrying out maximum value fusion on the output of the double-flow convolutional neural network to enhance the recognition accuracy and obtain a final micro-expression recognition model; and S4, creating a virtual learning environment interaction system by using the micro-expression recognition model, and obtaining a user face image sequence through Kinect to carry out a micro-expression recognition task.
Owner:CHONGQING UNIV OF POSTS & TELECOMM

Rockburst grade predicting method based on information vector machine

The invention discloses a rockburst grade predicting method based on an information vector machine to mainly solve the problem that in the current underground engineering construction process, the rockburst geological disaster prediction effect is not good. A rockburst evaluation index and a grading standard are selected, and domestic and overseas great deep rock engineering rockburst instances are widely collected to establish an abundant training sample library. A cross validation strategy is utilized for training an IVM model with the superior statistics mode recognition performance, accordingly, a nonlinear mapping relation between the rockburst evaluation index and the rockburst grade is established, model initial parameter setting and the training sample library are adjusted according to the training result, and the IVM model predicting the rockburst grade is finally established. By means of the method to predict the rockburst grade, the complex mechanical analysis or calculation is not needed, only the input feature vector of a sample to be predicted needs to be input in the prediction model, and then the prediction value of the rockburst grade can be obtained. The method is economical, efficient and high in prediction precision and has good engineering application prospects.
Owner:GUANGXI UNIV

Power consumption prediction method for system

ActiveCN105260803AImprove regularitySimple and regular power consumptionForecastingElectric power systemData acquisition
For accurately measuring the power consumption, the invention provides a power consumption prediction method for a system. The method comprises S1 data acquisition: acquiring the power utilization information of residents, industrial users and business users through an intelligent ammeter, acquiring the influence factor information through an external system, and generating a history table for the users at the same time; S2 analysis of power utilization rules for the users: performing fitting analysis of the acquired power utilization information of the users by means of combination with the history table, and obtaining the power utilization rules for the users through analysis; S3 analysis of influence factors: setting a load fluctuation range according to the user type, and performing analysis of influence factors for the time point surpassing the fluctuation range to obtain the influence value of each influence factor for the power consumption of the users; and S4 power consumption prediction for a system: predicting the future short-term power consumption for the system by means of combination of analysis of power utilization rules and analysis of influence factors. The prediction method shows a brand new development direction for short-term power consumption prediction in future, and can be widely applied to the prediction field with great significance.
Owner:STATE GRID CORP OF CHINA +1

Planning method of intelligent vehicle autonomous running dynamic trajectory and system of the same

The invention discloses a planning method of an intelligent vehicle autonomous running dynamic trajectory and belongs to the technical field of intelligent transportation. The planning method of the intelligent vehicle autonomous running dynamic trajectory comprises (1) a step of generating a trajectory of an intelligent vehicle through searching for an optimum solution of a mathematical model by means of an optimum control method, (2) a step of extracting features of the trajectory generated in the first step, and (3) a step of judging whether the features meet presupposed constraint conditions or not; if the features meet the presupposed constraint conditions, regarding the trajectory as a final output trajectory; if the features do not meet the presupposed constraint conditions, adjusting weight W<1>, W<2>, and W<3> according to presupposed weight adjustment rules and then returning to the first step. The invention further discloses a planning system of the intelligent vehicle autonomous running dynamic trajectory. The planning system of the intelligent vehicle autonomous running dynamic trajectory comprises a trajectory generation module, a feature extraction module, an estimation module, and a weight adjustment module. Compared with the prior art, the planning method of the intelligent vehicle autonomous running dynamic trajectory and the system of the same have a stronger learning ability and a better adaptability to the unknown environment.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS

Micro-expression recognition method based on space-time appearance movement attention network

ActiveCN112307958ASuppression identifies features with small contributionsTake full advantage of complementarityCharacter and pattern recognitionNeural architecturesPattern recognitionNetwork on
The invention relates to a micro-expression recognition method based on a space-time appearance movement attention network, and the method comprises the following steps: carrying out the preprocessingof a micro-expression sample, and obtaining an original image sequence and an optical flow sequence with a fixed number of frames; constructing a space-time appearance motion network which comprisesa space-time appearance network STAN and a space-time motion network STMN, designing the STAN and the STMN by adopting a CNN-LSTM structure, learning spatial features of micro-expressions by using a CNN model, and learning time features of the micro-expressions by using an LSTM model; introducing hierarchical convolution attention mechanisms into CNN models of an STAN and an STMN, applying a multi-scale kernel space attention mechanism to a low-level network, applying a global double-pooling channel attention mechanism to a high-level network, and respectively obtaining an STAN network added with the attention mechanism and an STMN network added with the attention mechanism; inputting the original image sequence into the STAN network added with the attention mechanism to be trained, inputting the optical flow sequence into the STMN network added with the attention mechanism to be trained, integrating output results of the original image sequence and the optical flow sequence through the feature cascade SVM to achieve a micro-expression recognition task, and improving the accuracy of micro-expression recognition.
Owner:HEBEI UNIV OF TECH +2

Method and device for predicting life of satellite lithium ion battery

The invention discloses a method and device for predicting the life of a satellite lithium ion battery. The method comprises the following steps: analyzing the characteristics of cycle life test data of the satellite lithium ion battery so as to obtain a fault evolution characteristic quantity; carrying out de-relax effect on capacity degradation data corresponding to the fault evolution characteristic quantity; carrying out least squares support vector machine LSSVM unsupervised learning training according to the capacity degradation data after the de-relax effect so as to complete the construction of an LSSVM model for life prediction; predicting the battery capacities of the battery in different periods through the LSSVM model, and carrying out battery failure threshold value extrapolation according to the battery capacities so as to realize the real-time prediction of the remaining useful life RUL of the lithium battery. According to the method provided by the invention, the LSSVM model for life prediction is constructed after de-relax effect is carried out on the capacity degradation data, and then life prediction is carried out through the model, so that the prediction result is correct and the problem that the process of measuring the life of the lithium ion battery is not correct is solved.
Owner:BEIJING AEROSPACE MEASUREMENT & CONTROL TECH

Color-spun yarn color matching method based on least square support vector machine

InactiveCN107103181ASimplified color rangeReduce inventorySpecial data processing applicationsYarnFiber
The invention relates to a color-spun yarn color matching method based on a least square support vector machine. The method is characterized by comprising the following steps that reference color fibers are determined, and the corresponding color data value is acquired; a part of or all reference color fibers are selected, combined in different ways and mixed in different mixing ratios for spinning and sample preparation, and the reflectance values of all samples are measured; the reflectance value of a standard sample and the corresponding proportional relation serve as training samples, and an LS-SVM function model is trained; a corresponding sample recipe C is calculated; according to the prediction recipe C, sample making is conducted, a simulated sample is obtained, and the reflectance value of simulated sample is measured; through the comparison, the chromatic aberration between a target sample and the simulated sample is obtained, if the chromatic aberration meets a preset value, the proportion is determined as an optimum recipe, otherwise, recipe modification is conducted; the optimum recipe is input in an LS-SVM training set for training. The types of the reference color fibers adopted by the method are less, after the recipe obtained by calculation is verified and modified, the accuracy of the optimum recipe is high, color matching is simple and reliable, and the efficiency and the practicability are high.
Owner:DONGHUA UNIV

Method for monitoring health of rotary machine suitable for working condition changing condition

The invention relates to a method for monitoring health of a rotary machine suitable for a working condition changing condition. The method comprises the steps that (1) a monitor model is constructed, wherein a relevance vector machine is used for fitting the function relation of health characteristic parameters and a working condition, the function relation is used as the parameters of a self-adaption threshold model, and the self-adaption threshold model is constructed; (2) a health state is monitored, wherein through test signals of the rotary machine to be detected, the constructed self-adaption threshold model is used for detecting whether test data exceed a threshold value or not, the machine is judged to be healthy if the test data do not exceed the threshold value, and otherwise, the machine is judged to break down. According to the method, the relevance vector machine is used for fitting the mean value of health characteristics and the function relation of standard deviation changing along with working condition parameters, and the method has the advantages that the relevance vector machine has strong learning capacity, the problems of the local minimum, the over-fitting and the under-fitting of a neural network can be solved, the relevance vector machine has better sparsity than a support vector machine, and obtained results are simpler and more practical. The method has the advantages of being high in monitor precision and capable of being used under rotating speed changing and load changing conditions.
Owner:NAT UNIV OF DEFENSE TECH

Vegetation classification method based on machine learning algorithm and multi-source remote sensing data fusion

The invention relates to the field of ecological environment monitoring, and discloses a vegetation classification method based on a machine learning algorithm and multi-source remote sensing data fusion, which is used for efficiently realizing identification and classification of vegetation types in a target area. The method comprises the following steps: acquiring a low-altitude remote sensing image of terrestrial plants in a sample area by using an unmanned aerial vehicle, and acquiring a digital orthoimage and a digital surface model of the sample area based on the low-altitude remote sensing image; extracting elevation information of the digital surface model; acquiring an SAR image of a sample region corresponding to the aerial photography time of the unmanned aerial vehicle by utilizing satellite remote sensing; carrying out wave band and image fusion on the digital orthoimage, the elevation information and the SAR image; performing inversion model training and inversion model precision evaluation on the fused image through sample area actual measurement data and a machine learning algorithm to obtain an inversion model meeting requirements; and finally, classifying terrestrial plants in the target area based on the inversion model. The method is suitable for terrestrial plant ecological environment monitoring.
Owner:CHINA HYDROELECTRIC ENGINEERING CONSULTING GROUP CHENGDU RESEARCH HYDROELECTRIC INVESTIGATION DESIGN AND INSTITUTE

Wavelet neural network-based distribution network single-phase short circuit line selection method

InactiveCN105759167AImprove the success rate of line selectionAccurate extractionFault location by conductor typesSimulationDistributed power
The invention discloses a wavelet neural network-based distribution network single-phase short circuit line selection method, which belongs to the technical field of distribution network protection. The method comprises the following steps: 1) db4 wavelets are selected to serve as a wavelet packet basis function to decompose transient zero-sequence current in each feeder line of the distribution network, and the sampling frequency is 10KHz; 2) the modulus maximum of the transient zero-sequence current is calculated; 3) the modulus maximum obtained in the second step and the polarity of the modulus maximum are used for training the neural network; and 4) the BP neural network after the training in the third step is applied to the distribution network with single-phase short circuit, and a fault line is determined according to a different output result. The method of fault line selection by using the wavelet neural network disclosed by the invention has good reliability and practicability, a single-phase grounding fault line can be effectively eliminated, stable operation of the distribution network system is facilitated, and an important role is played in planning and applications of a distributed power supply.
Owner:JIANGSU ELECTRIC POWER CO +3

Deep network model constructing method, and facial expression identification method and system

The invention discloses a deep network model constructing method, which comprises the steps of: step S1) establishing a deep network model used for facial expression identification, and initializing parameters of the deep network model, wherein the deep network model comprises a convolutional neural network used for extracting high-level features of pictures, a reconstructed network used for extracting low-level features of the pictures and a joint decision network used for identifying facial expressions; step S2) dividing all training pictures into N groups; step S3) sequentially inputting each group of the pictures into the deep network model, and training parameters in the deep network model based on a gradient descent method; step S4) regarding the parameters of the deep network modelobtained in the step S3) as initial values of model parameters, and re-dividing all the training pictures into N groups, then jumping to the step S3), and carrying out the process repeatedly until allthe trained model parameters no longer change when compared to the initial values of the model parameters. The invention further discloses a facial expression identification method and a facial expression identification system.
Owner:INST OF ACOUSTICS CHINESE ACAD OF SCI
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