42results about How to "Reduce the amount of training data" patented technology

The invention belongs to the technical field of computer vision and artificial intelligence, and particularly relates to a large-scale face recognition method based on a depth convolution neural network model. The method comprises steps of putting forward a residual error learning depth network model facing large-scale face recognition, wherein the residual error learning depth network model is formed by a convolution layer, a residual error layer and a full connection layer, and the residual error layer is formed by adding one path of multiple convolution layer cascade data and one path of original data to calculate the sum; and carrying out normalization operation in batch after each convolution layer in the model. According to the invention, by use of the characteristics of strong learning ability and good residual error learning convergence of the depth convolution neural network, layers of the model are increased in the aspect of the layer number of the network model; and in the aspect of residual error layer structure, the invention provides a highly efficient residual error layer structure. In the field facing the large-scale face recognition, the accuracy of the provided method is greatly improved compared with a base line model, and the accuracy of face retrieval in a million-class face recognition database can reach 74.25%.

The invention provides a power battery system residual available life prediction method based on a relevance vector machine and particle filter; the method comprises the following steps: using the relevance vector machine to extract collected power battery capacity declining characteristic vector values; building a power battery system aging model; using a particle filter theory to predict the power battery system residual available life through the aging model. The method can effectively reduce the training data bulk, can improve the algorithmic prediction precision, can ensure the RUL estimator stability, and can be expected to obtain accurate and reliable prediction results in real applications.

The invention discloses a state space probabilistic multi-time sequence prediction method based on a graph neural network, and the method comprises the steps: (1) obtaining a multi-time sequence, carrying out the preprocessing of the time sequence to construct a training set, and constructing a graph structure; (2) constructing a generation model for generating prior distribution and a time sequence of the hidden state according to a graph neural network and a multilayer perceptron, and constructing an inference network for generating approximate posteriori distribution of the hidden state according to the graph neural network and a recurrent neural network; (3) constructing a loss function according to the prior distribution and the approximate posteriori distribution of the implicit state, and optimizing a generation model and deducing parameters of the network by taking maximization of the loss function as a target; and (4) during application, obtaining the hidden state estimation of the to-be-predicted sequence at the latest moment by utilizing the inference network, then calculating to obtain the prior distribution of the hidden state by utilizing the generation model according to the hidden state estimation at the latest moment, and then calculating to obtain a time sequence observation estimation value according to the prior distribution of the hidden state.

The embodiment of the invention provides a computer-executed machine learning model training method, device and equipment, and the method comprises the steps: obtaining incremental data in a current time period, and enabling the incremental data to serve as a training sample set; based on the training sample set, conducting incremental training on the machine learning model trained in the last time period, and obtaining an initial machine learning model. And inputting each test sample in the test sample set into the initial machine learning model to obtain a test result. And based on the testresult, determining the accuracy of the initial machine learning model. And if the accuracy is greater than the first threshold, taking the initial machine learning model as a machine learning model trained in the current time period. And if the accuracy is not greater than the first threshold, adding test samples with wrong test results in the test sample set into the training sample set to obtain an updated training sample set, and training the initial machine learning model based on the updated training sample set to obtain a machine learning model trained in the current time period.

The invention discloses a sample data processing method for a cervix uteri image. The method comprises the following steps: classifying and establishing; preprocessing the data; performing segmentation; data enhancement: classifying the target image data, determining differences among the target image data, and performing enhancement processing for the differences; equalization processing: aimingat the total quantity difference among various target image data, supplementing a few types of samples by adopting data fitting to realize total quantity balance among the various target image data; constructing a data set: for each type of target image data after equalization processing, randomly dividing the target image data into a training data set, a verification data set and a test data setin proportion; and model construction: on the basis of the training data set and/or the verification data set and/or the test data set, mapping the training data set and/or the verification data set and/or the test data set to a contrast data set to obtain a classification corresponding to the sample data. According to the method, the problem of data imbalance in cervical image data classificationis solved, the precision and efficiency of image classification are improved, and the effect and quality of auxiliary diagnosis are improved.

The invention relates to the field of FPGA infrastructure optimization. In order to give comprehensive consideration on key parameters influencing circuit delay, allow coordinated exploration for variability of infrastructure-level parameters and transistor-level parameters in FGPA infrastructure design phase, embody and maintain inter-parameter physical significance, reduce training data quantity for a neural network and arrive at quick, precise, low-input delay estimation, the invention provides a precise low-speed low-input FPGA delay estimation method, comprising the steps of 1) determining fitting parameter Alpha and effective mobility Mu; 2) determining load capacitance of each transistor in each sub-circuit; 3) establishing an FPGA-macro delay model for each sub-circuit in the FGPA; 4) analyzing and normalizing; 5) solving weights Omega and Phi and number m of hidden neurons so that minimum training error Et and verification error Ev are achieved. The method of the invention is applied mainly in design and manufacture.

The invention relates to a positioning method and apparatus, a computer readable storage medium and a computer device. The method comprises the steps of obtaining a to-be-positioned target signal set;converting the target signal set into a target fingerprint vector through a trained vectorization model; obtaining the similarity between the target fingerprint vector and each fingerprint vector ina fingerprint library; selecting at least one fingerprint vector from the fingerprint library according to the obtained similarity; and determining position information corresponding to the target signal set according to the position information corresponding to the selected at least one fingerprint vector. According to the scheme provided by the invention, the positioning accuracy can be improved.

The invention provides a cow face detection and recognition method based on deep learning. The method comprises the steps of bovine face data acquisition and preprocessing, bovine face data conversion, bovine face feature extraction model construction and bovine face feature vector database construction, bovine face detection model construction, bovine face part detection and feature extraction byusing the constructed bovine face feature extraction model and bovine face detection model, and bovine face recognition by using an image retrieval technology. According to the method, the data volume required by model training can be effectively reduced, the cattle face part is accurately drawn, the influence of environmental factors of the non-cattle face part on the recognition accuracy is reduced, multiple cattle face feature values are comprehensively used for cattle face recognition, and the cattle face recognition accuracy is improved.

The present invention relates to the field of intelligent traffic, and relates to a camera masking intelligent detection method, in particular to a camera masking detection method using video images and based on machine learning method. The specific technological process of the method comprises the following steps: image down sampling, Gabor filtering processing, PCA dimensionality reduction processing, data training and masking judgment. The uniform LBP features of an image are used as features of learning classification, so that a support vector machine is trained. The trained parameters are used for judging whether the new image is masked or not. A collected image sample is subject to Gabor filtering processing, so that stability and accuracy of the uniform LBP features of the image sample are improved. The uniform LBP features are subject to PCA dimensionality reduction processing to reduce trained data size of the support vector machine. The method is reliable in principle, rapid in judgement, high in judging accuracy, low in implementation cost, and friendly in applying environment.

The invention relates to an FPGA architecture exploration method. The method aims at combining a precise model with a GA algorithm, and achieves trade-off optimization between delay and area by adjusting size of the transistor. To achieve the goal, the FPGA architecture exploration method comprises the following steps of (1) determining key parameters that affects the delay of the FPGA; (2) establishing corresponding ELmore delay model for each circuit; (3) combining the Elmore delay model of the FPGA with a nerve net, establishing a KBNN delay model, conducting training to the KBNN delay model, determining a weight omega and phi that minimize a training error Et and a verification error Ev, and the number of hidden neurons m; (4) establishing an improved minimum width transistor area model, and estimating the area of the FPGA island; (5) combining the delay model, the area model and the GA algorithm, achieving fast adjustment of the size of the transistor. The method is mainly applied to an FPGA design occasion.

The invention discloses a melody MIDI accompaniment generation method based on a deep neural network, and belongs to the technical field of artificial intelligence and music science and technology. The melody MIDI accompaniment generation method comprises the steps of collecting an MIDI data set with a body tailoring label, processing the MIDI data set to obtain a data set, encoding through MuMIDIto obtain entries, inputting the entries into a GC-Transformer model for training until a total loss function converges, completing training of the GC-Transformer model, finally encoding MIDI fragments only containing melody, and inputting the MIDI fragment into a trained GC-Transformer model, and finally outputting the generated accompaniment MIDI fragment. The melody MIDI accompaniment generation method has the advantages of short generation time, high generation quality, less hardware resource consumption, less training data volume and the like.

The invention relates to the field of vehicle intelligent perception, and discloses a vehicle behavior identification method. The method comprises the steps of obtaining the current transverse distance of a front vehicle relative to a main vehicle and the first duration of transverse distance change of the front vehicle; wherein the front vehicle is a vehicle which runs in front of the host vehicle and is adjacent to the host vehicle, and the front vehicle and the host vehicle run on different lanes; determining a first lane changing two-degree-of-freedom chi-square value of the front vehicle based on the current transverse distance and the first duration, and determining a first lane changing probability of the front vehicle relative to a lane where the host vehicle is located based on the first lane changing two-degree-of-freedom chi-square value; and based on the first lane changing probability and a first set lane changing probability threshold value, identifying the lane changing behavior of the front vehicle. According to the method, the cut-in and cut-out behaviors of the vehicle are identified by using the chi-square distribution of two degrees of freedom, and the vehicle behavior identification efficiency is improved on the basis of ensuring the vehicle behavior identification accuracy.

The invention belongs to the technical field of model training, and discloses a machine learning model training method and system for a large-scale machine learning system. The machine learning model training system for the large-scale machine learning system comprises a data acquisition module, a data preprocessing module, a parameter range determination module, a central control module, a model training module, a model test module, a model evaluation module, a model optimization module, a data storage module and an update display module. The training sample set is processed through the data preprocessing module, the feature subsets of the training sample set are obtained, and the model training data volume is reduced; the machine learning model is trained based on incremental learning, so that the accuracy of model training can be improved; the optimal parameter value is determined in the value range of each parameter through the model evaluation module and the model optimization module, and the model parameters are adjusted, so that the training efficiency of the model in machine learning is improved.

The invention discloses an electric quantity prediction method under edge equipment based on sparse anomaly perception. The method comprises the following steps: S1, collecting electric quantity dataof K target buildings by edge equipment; S2, performing sparse anomaly marking on the abnormal data at the edge equipment end by adopting a sparse anomaly sensing method; S3, calculating a sparse abnormal discard probability and obtaining a total training set; S4, with a machine learning regression algorithm and a five-fold cross validation training model, randomly discarding abnormal data by utilizing a sparse abnormal discarding probability during each-fold cross validation and not participating in training; and S5, carrying out electric quantity prediction on to-be-predicted data by utilizing the machine learning model, and multiplying model prediction output by the sparse abnormal discarding probability to obtain final prediction. According to the method, a problem that a machine learning regression model based on mean square error loss is sensitive to abnormal data is solved; and meanwhile, the training data volume is reduced, the model training speed is increased, randomness is introduced, and the prediction precision and generalization ability of the model are improved.

The invention provides an ultra-short-term power load prediction method, which is characterized by comprising the following steps of: firstly, randomly generating a sufficient number of low-dimensional delay variable combinations according to multi-dimensional observation variables; then, mapping the data to a load at a specific future time point through a neural network prediction algorithm, obtaining a plurality of networks according to the mapping, and calculating a plurality of prediction values by each network; and finally, calculating a final prediction value through an aggregation estimation method. According to the method, the random distributed embedded framework and the BP neural network are combined, and the distribution information is created by utilizing the interaction between the high-dimensional variables to replace a time sequence lacking due to less training data, so that the amount of training data required by prediction is effectively reduced, the prediction precision is remarkably improved, and the stability is enhanced.

The embodiment of the invention provides a data processing method. The data processing method comprises the steps: obtaining at least one sample of a target object; performing feature extraction on atleast one sample by using a first neural network to obtain at least one piece of first feature data, wherein the first feature data is used for indicating features of at least one sample; performingfeature extraction on at least one piece of first feature data by using a second neural network to obtain at least one piece of second feature data, wherein the second feature data is used for indicating change features of the corresponding first feature data; and making a decision according to at least one second feature data to obtain decision data. According to the data processing method provided by the embodiment of the invention, the first neural network and the second neural network are used for carrying out feature extraction on the sample twice, so the data capable of representing theevent represented by the sample and the decision data are obtained, the training data volume and the training time used by the deep learning model are reduced, and the training efficiency of the deeplearning model is improved.

The invention provides a near-field source positioning method based on an auto-encoder and a parallel network. The method comprises the steps of: generating near-field source data received by an array under the condition of a single information source, constructing an auto-encoding network and a parallel full-connection network, inputting the near-field source data into a combined network of the auto-encoding network and the parallel full-connection network, and when angle information is obtained, calculating out a single spectrum peak search mode. According to the method, the signal received by the array is subjected to subspace segmentation, and direct output of the DOA spatial spectrum under the condition of unknown signal source number is realized through the parallel full-connection network, so that the efficiency of the algorithm is greatly improved. The capability of multi-source positioning can be obtained only by training single-source data, so that the data volume of training is greatly reduced, and meanwhile, the training difficulty of the neural network is reduced to a certain extent. And an offline training and online testing process is adopted, so that the algorithm complexity in an actual use process is greatly reduced.