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206results about How to "Improve generalization ability" patented technology

Magnetic resonance reconstruction method based on deep learning and data consistency

The invention discloses a magnetic resonance reconstruction method based on deep learning and data consistency, and relates to the field of magnetic resonance reconstruction methods. The method comprises the steps: 1, collecting K-space data and integrating the K-space data into a network, formed by the sequential stacking of a convolutional neural network and a data consistency layer, for completing the network construction; 2, converting the undersampled data in the K-space data into a roll pleat image as the input of the built network, converting the full-sampled data of the K-space data into a complete image as the tag data of the built network, and obtaining a mapping relation between the network input and output through a back propagation training network; 3, inputting the corresponding images of a test set into the trained network, and performing the forward propagation to obtain an output image to complete the magnetic resonance reconstruction. The method solves the problems ofpoor reconstruction performance and stability caused by a condition that a conventional magnetic resonance reconstruction method based on deep learning does not fully utilize the collected data and can only deal with a single channel, achieves implementation supervision, improves the learning ability, and improves reconstruction performances.
Owner:朱高杰

Cross-library micro-expression recognition method and device based on optical flow attention neural network

ActiveCN110516571AImprove recognition rateImprove generalization abilityNeural architecturesAcquiring/recognising facial featuresOptical flowNerve network
The invention discloses a cross-database micro-expression recognition method and a device based on an optical flow attention neural network. The method comprises the following steps: (1) acquiring twodifferent micro-expression databases as a training set and a test set; (2) converting the micro-expression video into a face image sequence; (3) extracting a starting frame, a peak frame and a termination frame from each face image sequence, calculating the starting frame and the peak frame to obtain a first single-channel optical flow graph, and calculating the peak frame and the termination frame to obtain a second single-channel optical flow graph; (4) forming a fusion feature graph by the first single-channel optical flow graph, the second single-channel optical flow graph and the peak frame of each face image sequence; (5) establishing an optical flow attention neural network, and taking the fusion feature graphs corresponding to the training set and the test set as input for training; and (6) processing the micro-expression video to be identified to obtain a fusion feature map, and inputting the fusion feature map into the optical flow attention neural network to obtain a micro-expression category. The method is high in generalization capability and high in recognition accuracy.
Owner:SOUTHEAST UNIV

Intelligent sleep phase detection and sleep quality evaluation system and method

The invention belongs to the technical field of sleep state monitoring, and discloses an intelligent sleep phase detection and sleep quality evaluation system and method. The intelligent sleep phase detection and sleep quality evaluation system comprises an eeg signal acquisition module, a background server and an on-line service platform, wherein the eeg signal acquisition module is used for acquiring sleep eeg data, and is used for completing acquisition of the sleep eeg data through eeg signal detecting components; the background server is used for receiving acquired original sleep eeg datato complete the treatment of sleep eeg signals, so that sleep eeg automatic phase separating is realized, and sleep quality is evaluated; and the on-line service platform is used for tracking the sleep trend of a user and providing services including sleep psychological consultation, expert suggestion and the like. The intelligent sleep phase detection and sleep quality evaluation method comprises the following steps of obtaining an eeg signal, performing pretreatment, performing feature extraction, performing mode recognition and classification and outputting the sleep phase. The evaluationresult can be pushed to a mobile terminal so that professional sleep psychological consultation and expert advice are provided for the user, are completely independent of the working experience of anoperator, and can be used as beneficial supplement and explanation for clinical diagnosis.
Owner:川北医学院

Polarization SAR image classification method based on complex contour wave convolution neural network

The invention discloses a polarization SAR image classification method based on a complex contour wave convolution neural network, and a problem of low classification accuracy in the prior art is mainly solved. The method comprises the steps of (1) inputting and normalizing a polarization coherent matrix T of a polarization SAR image to be classified, (2) according to the normalized matrix, constructing characteristic matrixes of a training data set and a test data set, (3) constructing a complex convolution neural network, and thus obtaining a complex contour wave convolution neural network, (4) training the complex contour wave convolution neural network by using the training data set, and obtaining a trained model, and (5) inputting the characteristic matrix of a test data set into the trained model to carry out classification, and obtaining a classification result. According to the method, the convolution neural network is extended to a complex domain to carry out operation and extract image characteristics of multiple scales, multiple directions and multiple resolution characteristics, the classification precision of the polarization SAR image is effectively improved, and the method can be used for target detection and identification.
Owner:XIDIAN UNIV

Partial discharge pattern identification method for crosslinked polyethylene cable

The invention discloses a partial discharge pattern identification method for a crosslinked polyethylene cable, and belongs to the technical field of online monitoring and fault diagnosis of electrical equipment. The partial discharge pattern identification method comprises the following steps: adopting online or offline monitoring data of a cable, performing partial discharge signal feature extraction based on the natural mode singular value decomposition technology, and selecting the sample data to realizing learning of a cable partial discharge detection limit learning machine model, so as to realize partial discharge detection of the XLPE (Crosslinked Polyethylene) power cable. The partial discharge pattern identification method disclosed by the invention aims to running state of seven cables, based on the natural mode singular value decomposition technology, performs feature extraction on the corresponding raw signals corresponding to different running states of the cables, and determines the target vector form of the sample data, so as to complete partial discharge sample data set construction of the XLPE power cable, and learn and test the constructed partial discharge detection ELM model of the cable. The partial discharge pattern identification method disclosed by the invention can accurately and quickly identify the insulation defect and partial discharge mode of the power cable, so as to ensure safe and healthy operation of cable equipment, and provide the basis for the power cable repair scheme.
Owner:NORTHEAST DIANLI UNIVERSITY +2

Method and device for classifying sheet media

The invention relates to the processing technology of image information, in particular to a method and a device for classifying sheet media quickly according to the image information. The method comprises the following steps of: acquiring the image information of the sheet media to be identified; performing normalization processing on the acquired image information according to a preset standard image dimension so as to obtain the image information to be identified; performing feature extraction on the image information to be identified to form a feature vector; and sending the feature vector into a classifier which is trained by standard sheet media to obtain a classification result, wherein the normalization processing comprises the following steps of: identifying whether the dimension of the acquired image information is smaller than that of the preset standard image, if so, directly returning an unknown type of the sheet media; and otherwise, calculating the geometric center of the acquired image information; and intercepting the image information with the same dimension as that of the preset standard image by taking the geometric center as the center to form the image information to be identified. By the method, the discrimination degree of the feature vectors in different classifications can be improved effectively and the generalization capability of the classifier can be enhanced.
Owner:GRG BAKING EQUIP CO LTD

Dense stacking target detection method based on automatic labeling and transfer learning

The invention discloses a dense stacking target detection method based on automatic labeling and transfer learning. The dense stacking target detection method comprises the following steps: establishing a labeled training image set by high-resolution image segmentation; inputting the labeled training image set into a pre-trained target detection model YOLOv3, optimizing a priori frame size and a loss function of the YOLOv3 model, and performing fine adjustment on the model by using the training image set; and finally, inputting a to-be-detected image into the fine-tuned YOLOv3 model, outputting the classification of the target sub-regions and the positions of the sub-regions, splicing output result images to recover the output result images into an original image, and counting a total counting result. The dense stacking target detection method provided by the invention has strong anti-interference performance and robustness, and has low requirements on an image photographer and a photographing illumination condition; through an unsupervised learning method, quasi-automatic annotation of the image is realized, and the workload of manual annotation is greatly reduced, and the model training efficiency is improved; and the dense stacking target detection method can be used for image recognition of a large number of densely stacked targets which are mutually shielded, and is suitable for automatic counting scenes of various densely stacked targets.
Owner:NANJING COLLEGE OF INFORMATION TECH

Video target tracking method based on twin network fusion multi-template features

The invention relates to a video target tracking method based on twin network fusion multi-template features, and provides a semi-supervised template online updating strategy, when a to-be-tracked target in a video sequence has complex conditions such as occlusion, deformation and illumination change, the target change and the occluded condition are evaluated by calculating an APCE value and template similarity, when the appearance of the target is greatly changed, the features extracted from the previous frame of picture are fused with the original template features to obtain a new template with higher expression capability, so that the method is favorable for adapting to various complex conditions; in order to improve the generalization ability of the model and adapt to multiple types oftargets, a regularization technology is adopted in the training process to prevent model overfitting; in order to further improve the speed of the algorithm, only an original template is adopted fortracking in a non-complex situation, so that the calculated amount is greatly reduced, and the method provided by the invention achieves a higher running speed than other methods under the condition of obtaining better tracking performance.
Owner:HENAN UNIV OF SCI & TECH

Wishart and SVM (support vector machine)-based polarimetric SAR (synthetic aperture radar) image classification method

The invention discloses a Wishart and SVM (support vector machine)-based polarimetric SAR (synthetic aperture radar) image classification method. The technical problems of low classification accuracy and low classification efficiency of a conventional polarimetric SAR classification method in case of fewer training samples are mainly solved. The method is implemented by the following steps: inputting an image; performing filtration; performing Cloude decomposition; calculating a similar matrix of a characteristic set F; calculating a similar matrix of a coherence matrix characteristic set T; calculating a final similar matrix; performing classification by virtue of an SVM; calculating accuracy. When the method is used for classification, the problem of more misclassified points caused by noise in the prior art is solved, crosstalk between polarization channels can be avoided, polarimetric information and counting correlation can be maintained, the contour and edge of a polarimetric SAR image are clearer, the quality of the classified image is improved, higher polarimetric SAR image classification accuracy and higher noise adaptability are achieved, and the method can be used for the target identification and tracking of the polarimetric SAR image.
Owner:XIDIAN UNIV

Private encryption protocol message classification method based on sparse representation and convolutional neural network

The invention relates to the technical field of network information, in particular to a private encryption protocol message classification method based on sparse representation and a convolutional neural network, which comprises the following steps: obtaining and preprocessing network traffic data to obtain a data set file and a label file; importing the data set file into a sparse auto-encoder for unsupervised feature learning to obtain input data with smaller dimension; and training the two-dimensional convolutional neural network by using the training set after sparse representation and thetraining set label, performing convolution and pooling, and minimizing errors to obtain a classifier. According to the classification method disclosed by the invention, the classification characteristics of the private encryption protocol message are automatically learned from the original network flow, and classification identification is realized; the method does not depend on the IP address and port number information of the header of the network traffic data packet, and the generalization ability of the classification model is high; sparse representation is used for learning local features of private encryption protocol messages, a two-dimensional convolutional neural network is used for learning global features of the private encryption protocol messages, and the recognition precision of the classifier is improved.
Owner:NO 30 INST OF CHINA ELECTRONIC TECH GRP CORP

Aviation three-level AC power generator rotary rectifier online fault diagnosis method

The invention discloses an aviation three-level AC power generator rotary rectifier online fault diagnosis method based on an optimization extreme learning machine, and belongs to the technical field of power generator state monitoring and fault diagnosis. The method comprises the following steps that 1) an overall simulation model of an aviation three-level AC power generator is established and the fault mode and fault testable points of a rotary rectifier are determined; 2) corresponding voltage or current information under various fault modes is acquired by utilizing the testable points, feature extraction is performed on fault information and normalization processing is performed, and a training sample set and a test sample set are determined; 3) the parameters of a mind evolutionary algorithm, the number of nodes in each layer of the extreme learning machine and an excitation function are set, the extreme learning machine is optimized, the optimal input weight and threshold are outputted, and an extreme learning machine model is established and the model is verified by using the test sample set; and 4) the model passing verification in the step 3) is used for online intelligent fault diagnosis, and a fault signal and feature extraction method is maintained to be consistent with that of step 2).
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS

Unmanned aerial vehicle aeromagnetic holoaxial gradient magnetic disturbance compensation method based on feedforward network

The invention provides an unmanned aerial vehicle aeromagnetic holoaxial gradient magnetic disturbance compensation method based on a feedforward network. The method includes the steps that calibrated flight data is subjected to preprocessing operation of wavelet transform and normalization, normalized feedforward network input vectors are transmitted into the established feedforward network to be trained, a regularized cost function is adopted, stable feedforward network parameters are iteratively obtained through an error back-propagating algorithm, and a finally convergent network serves as a compensation network; exploration flight data is subjected to identical preprocessing operation and transmitted to the obtained compensation network to be calculated, estimation of an airplane disturbing magnetic field is obtained, and magnetic disturbance compensation is obtained. The stable network parameters are obtained by training the feedforward network, the network serves as the compensation network for compensating for the exploration flight data, the problems of inverse matrix instability and over-fitting in a least squares algorithm are effectively avoided, the generalization performance of the feedforward network is expanded, and unmanned aerial vehicle aeromagnetic holoaxial gradient magnetic disturbance compensation is achieved.
Owner:INST OF ELECTRONICS CHINESE ACAD OF SCI

Electricity load type identification method

The invention discloses an electricity load type identification method, which is realized through an electricity load identification device consisting of an information collection module, an information processing module and a communication module. The electricity load type identification method simultaneously adopts electricity load starting current characteristics including starting process time, a starting current maximum value, and starting current maximum value time and a load current frequency spectrum characteristic of the electricity load as identification characteristics for the electricity load, and the characteristic information is rich. The electricity load type identification method adopts a combination classifier comprising a support vector machine classifier and a Bayes classifier to perform identification classification, performs comprehensive identification in consideration of characteristics of two classifiers, and thus has high identification accuracy. The provided methods for obtaining starting current characteristics and load current frequency spectrum characteristics are simple and reliable. The electricity load identification device can be used in some collective public places like a students 'dormitory, a large-scale pedlars' market, etc, where the electricity load management is needed, and can also be used in other places where need to perform electricity load type statistics and electricity appliance management.
Owner:HUNAN UNIV OF TECH
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