130results about How to "Reduce distribution variance" patented technology

The invention discloses a fault diagnosis method based on adaptive manifold embedding dynamic distribution alignment. According to the method, the feature distortion of data in an original Euclidean space can be effectively avoided through the automatic calculation of the optimal subspace dimension and the calculation of the streaming kernel of a geodesic line and converted manifold feature representations; a similarity measure A-distance is introduced to define a self-adaptive factor; relative weights of condition distribution and edge distribution of sample data are dynamically adjusted, andtherefore, the distribution difference of source domain and target domain samples can be effectively reduced, the accuracy and effectiveness of rolling bearing fault diagnosis under variable workingconditions can be greatly improved. The method is high in interpretability, is lower in requirements for computer hardware resources, is higher in execution speed, and is excellent in diagnosis precision, algorithm convergence and parameter robustness. The method is especially suitable for multi-scene and multi-fault bearing fault diagnosis under variable working conditions, and can be widely applied to fault diagnosis tasks of complex systems such as machinery, electric power, chemical engineering and aviation under variable working conditions.

The invention discloses a migration method and system based on a deep residual error correction network. The method comprises the following steps: setting values of parameters in a pre-constructed target network model based on a source domain data set and a target domain data set in the network; based on the target network model with the set parameter values, carrying out image category classification on all the data in the target domain data set, and obtaining the category corresponding to each piece of data; labeling the corresponding data in the target domain data set based on the categorycorresponding to each piece of data to obtain a target domain data set with a label; wherein the target network model is constructed based on a residual error correction block and a loss function; wherein the source domain data set comprises a plurality of pictures and labels corresponding to the pictures; wherein the target domain data set comprises a plurality of pictures. According to the residual error correction block and the loss function provided by the concept of the invention, the generalization ability of the original network can be improved through deepening the network, so that thecross-domain image classification accuracy is improved.

The invention discloses a rolling bearing fault diagnosis method under variable load based on unsupervised characteristic alignment, and belongs to the domain of the rolling bearing fault diagnosis. For the problems that source domain data and target domain data belong to different distributions and a target domain sample does not contain a label science a certain load data is absent in the actualwork of the rolling bearing, the method comprises the following steps: acquiring time frequency characteristics of a vibration signal by combining variation modal decomposition with singular value decomposition, and constructing a multi-domain characteristic set by combining the vibration signal time domain and frequency domain characteristics; importing a sub-space alignment algorithm capable ofrealizing unsupervised domain adaption in the transfer learning, and performing improvement, and combining a kernel mapping method with a SA algorithm. The training data and the testing data are mapped to the same high-dimensional space, the state corresponding to other load data is identified by utilizing the known load data of the rolling bearing under the condition that the target domain lackslabel, and the method has high fault diagnosis accuracy rate.

The invention discloses a fault diagnosis method for a deep adversarial migration network based on a Wasserstein distance. The distance of the feature distribution of the two fields in the feature space is measured through the wassertein distance, the feature distribution is adapted, the difference between two fields is reduced, field-independent features are learned to train an effective classifier, the classifier is responsible for mapping the field-independent features to a category space to complete a classification task, and the problem of unsupervised transfer learning of vibration datawithout labels in a target domain is solved.

The invention discloses a brain-computer interface transfer learning method based on manifold embedding distribution alignment. The brain-computer interface transfer learning method comprises the following steps of acquiring EEG data of a source subject and EEG data of a target subject respectively; carrying out preprocessing and feature extraction on the EEG data; constructing a transfer learningmodel based on manifold embedding distribution alignment, and training the transfer learning model by using data to obtain a training model; and utilizing the classifier obtained by training to classify the label-free EEG data of the target subject. On the basis of Riemannian tangent plane mapping and manifold feature transformation, feature distribution alignment is integrated into classifier training, and an effective classifier is obtained through training. According to the method, the performance of the brain-computer interface system used by the target user can be effectively improved, and the training burden of the user is reduced.

The invention discloses a multi-source domain adaptive model and method based on partial feature alignment, and the method comprises the steps: enabling a feature selection module for partial featureextraction to generate a selection vector of a feature level according to the similarity of each feature dimension of a source domain and a target domain on the basis of a conventional convolutional neural network or residual neural network feature extractor, wherein after the selection vector acts on the initial feature map, part of features highly related to the target domain in the source domain can be screened out. On the basis, the invention further provides three partial feature alignment loss functions for intra-class, inter-domain and inter-class features, so that the purified featuremap has better distinguishability for a classifier, and partial features related to a source domain and a target domain are highlighted. The method is used for the multi-source field self-adaptive classification data set, and compared with an existing multi-source field self-adaptive model, the classification accuracy is higher, and the feature selection effect is better.

The invention discloses a cross-domain migration electronic nose drift suppression method based on migration samples. The method comprises steps of projecting the source domain data and the target data to a subspace, performing edge maximum mean difference minimization processing, condition maximum mean difference minimization processing and separability maximization processing on sets of different domain data, and performing maximization processing on discrimination information to obtain a conversion basis P, a corresponding projection source domain data set and a projection target domain data set; calculating an unknown output weight of the adaptive extreme learning machine according to the projection source domain data set and the projection target domain data set to obtain a final adaptive extreme learning machine; and performing a drift suppression test on the target domain data of the unknown label. The method has the beneficial effects that the discrimination information of thesource domain and the target domain is stored while drift is inhibited. The edge distribution difference and the condition distribution difference are minimized, and the robustness and the classification accuracy of the model are improved. Knowledge migration is realized in a feature layer and a decision layer, and migration samples are fully utilized.

The invention discloses a domain self-adaptive equipment operation inspection system and method. An image acquisition processing module is used for acquiring a to-be-detected inspection image of a power transmission line and preprocessing the image; the to-be-detected inspection image is input into a preset weather condition classification model to identify weather conditions to which the to-be-detected inspection image belongs, and a weather condition classification result is obtained; a corresponding preset domain self-adaptive equipment operation inspection model is selected according to the weather condition classification result; and the to-be-detected inspection image is input into a preset domain self-adaptive equipment operation inspection model to obtain a detection result of theto-be-detected inspection image, wherein the detection result comprises one or a combination of equipment category, working condition and position information of a detection object in the to-be-detected inspection image. According to the invention, the to-be-detected sample of the high-voltage transmission line domain self-adaptive equipment operation detection system is not restricted by sample annotation and regional or weather conditions, and the equipment operation detection result of the target domain in the domain self-adaptive scene has the same detection performance as that of the source domain.

The invention relates to a traceablility SNP marker in a pig GIGYF2 gene and a detection method thereof. The SNP marker which is positioned in a genomic DNA fragment in the pig GIGYF2 gene is obtained through DNA pool sequencing. The genomic DNA fragment in the pig GIGYF2 gene, which is represented by SEQ ID NO.1 and comprises bps with the number of 452, comprises parts of twenty-three introns, and there is a base mutation from 187A to 187C at the 187th bit. The distribution case of the SNP marker of the invention in allelic genes in a test colony comprising ten pig kinds or lines is analyzed, and results show that frequencies of the marker in the allelic genes of different kinds or lines are close, so the polymorphism is abundant, there is a small difference among the frequencies of the allelic genes of different kinds or lines, and heterozygosities are greater than 0.3, so a case that the SNP marker of the invention can be used for pig product DNA traceablility is preliminarily determined.

The invention relates to a cross-modal brain tumor image segmentation method with a finite source domain label, which is used for carrying out cross-modal image segmentation by effectively utilizing unmarked data of a source domain and a target domain under the condition that source domain labels are scarce aiming at a multi-modal brain tumor data set. The method comprises the following specific steps: constructing a source domain segmentation network, carrying out multi-scale consistency regular constraint, constructing a cross-modal adaptive network, carrying out double-circulation feature alignment through two sub-networks, then integrating the source domain segmentation network into the cross-modal adaptive network, and carrying out multi-scale consistency regular constraint on the cross-modal adaptive network. And forming a joint segmentation framework of a semi-supervised source domain and an unsupervised target domain, performing cross-modal adaptive segmentation model training optimization by using an end-to-end training mode, and testing a new target domain brain tumor image to obtain a segmentation result. The method can effectively reduce the domain difference, alleviates the problem of source domain label scarcity, and can be applied to the field of brain tumor cross-modal image segmentation.

The invention discloses a wind turbine generator fault diagnosis method, which comprises the following steps: according to the vibration signal characteristics of a wind turbine generator gearbox, carrying out variational mode decomposition on signals under different working conditions to obtain a series of intrinsic mode function components, and respectively solving multi-scale permutation entropies of the intrinsic mode function components; combining the multi-scale permutation entropy and the original signal time domain feature into a feature vector, and inputting the feature vector into atransfer learning algorithm; the covariance of a source domain and a target domain being minimized through a linear transformation matrix, the distribution difference of signal data of the source domain and the target domain being reduced through second-order statistics alignment, and then inputting the feature vectors of the aligned signal data of the source domain and the target domain into a support vector machine for fault classification. According to the method, the problem of poor classification effect caused by different distribution of the vibration signal data under different workingconditions can be solved, and the method has higher accuracy in wind turbine generator fault diagnosis under variable working conditions.

The invention discloses an image classification method based on subspace projection and dictionary learning. Firstly, a discrimination dictionary is initialized through a training set sample with a label, and then a class label of a test sample is predicted by using the discrimination dictionary. A test set sample with high reliability is selected and a low dimensional subspace is learned from thetest set sample with false tags and the discriminant dictionary is updated in this low dimensional space. The updated discriminant dictionary is used to reclassify the test set samples, the pseudo-label obtained by this iteration is compared with the pseudo-label obtained by the previous iteration, the samples with the same attribute obtained by the two iterations are called stable samples. If the number of stable samples exceeds eighty percent of the number of samples in the test set after one iteration, the pseudo-label obtained by this iteration is output as the classification result afterthe iteration is over. Compared with the prior art adaptive image classification method, the algorithm of the invention can obtain higher classification accuracy.

The invention discloses a domain adaptive pedestrian re-identification method based on mutual divergence learning. The method comprises the following steps: preparing a pedestrian data set; pre-training the source domain data set, and extracting feature vectors of pictures from the target domain data set; performing density-based clustering on the images of the target domain data set, and taking the number of the cluster as a pseudo label; adding the outliers into a training sample by using an adversarial strategy; mixing the clustered samples and the outliers, sending the mixture into a network, correcting noise of a pseudo tag by adopting mutual divergence learning, inputting a pedestrian image to be queried into a trained pedestrian re-identification model to obtain a pedestrian feature vector to be identified, performing similarity comparison on the pedestrian feature vector to be identified and attribute features in a candidate library, and obtaining a pedestrian re-identification result. According to the invention, the distribution difference between the source domain and the target domain is reduced, the knowledge of the source domain is effectively utilized, and finally, the framework can learn the characteristics with robustness and discrimination.

The invention relates to a domain self-adaption technology in the field of machine learning, and provides a Grassmann manifold domain self-adaption method based on symmetric matrix space subspace learning. In order to reduce the difference between the probability distribution of the source domain data and the target domain data, the method comprises the following steps of firstly establishing mapping from a Grassmann manifold to a symmetric matrix space, then mapping the Grassmann manifold matrix data of the source domain and the target domain to the symmetric matrix space, and constructing asubspace in the symmetric matrix space; subjecting the projection of the original data in the subspace to a mean value similarity criterion; establishing an objective function of the subspace learning, optimizing and solving the objective function to obtain the objective subspace, matching the projection probability distribution of the original data in the objective subspace on the objective subspace, that is, achieving the domain self-adaption of the original data through twice transformation from the Grubman manifold to the symmetric matrix space and then from the symmetric matrix space to the subspace of the symmetric matrix space.

The invention belongs to the technical field of equipment fault diagnosis, and discloses a variable working condition bearing fault diagnosis method based on a domain adversarial hybrid graph convolutional network. According to the specific technical scheme, the method comprises the specific steps that firstly, a convolutional neural network is used for conducting feature extraction on input data, obtained features serve as nodes, secondly, an adjacent matrix is obtained through a graph generation layer, an association graph is constructed according to the input matrix, and thirdly, the association graph is obtained. Inputting the constructed graph into a multi-receptive-field mixed graph convolutional network to extract structural information, capturing local structural information and global structural information of the graph from the perspective of the graph convolutional network and graph sampling and aggregation by using the multi-receptive-field mixed graph convolutional network, and finally, extracting the structural information of the graph from the perspective of the multi-receptive-field mixed graph convolutional network. And fault classification is carried out on the obtained features through a transfer learning method, so that the fault diagnosis capability of the model on a target domain is improved, and the expression capability, the generalization performance and the robustness of the model are remarkably improved.

The invention discloses an open set fault diagnosis method for a bearing of a high-speed motor train unit. The open set fault diagnosis method comprises the steps of collecting a vibration signal of the bearing of the high-speed motor train unit in operation through an acceleration sensor; aiming at an open set diagnosis scene of a constant working condition, inputting training data with a label to train a one-dimensional convolutional neural network; inputting labeled source domain data and unlabeled target domain data to train the bilateral weighted adversarial network according to an open set diagnosis scene with working condition changes; establishing an extreme value theoretical model by utilizing the characteristics of the training data or the source domain data, inputting the characteristics of the test sample or the target domain sample into the established extreme value theoretical model, outputting the probability that the test sample or the target domain sample belongs to an unknown fault type, and if the probability is greater than a threshold value, determining that the test sample or the target domain sample belongs to the unknown fault type, and if not, determining that the test sample or the target domain sample belongs to the known fault type. The type of the test sample or the target domain sample is determined according to the label predicted value so as to realize the fault diagnosis of the bearing of the high-speed motor train unit.

The invention provides a large model preference alignment method improved by using an online synchronization strategy, a new preference alignment process is provided based on a synchronization strategy optimization and online learning method, transformation is carried out from a training process, links such as synchronous sampling and online parameter updating are added, and the optimization of the large model preference alignment is realized. Reward model updating and data distribution alignment in the model training process are achieved, and finally the financial question and answer ability is effectively improved. On the basis of keeping the high efficiency of the original method, the distribution difference between the strategies can be effectively reduced, the optimization direction of the optimal strategy is ensured, meanwhile, the supervision information of preference data is updated approximately in real time, the reliability of the reward model in the high reward system is enhanced, the human preferences are better aligned, and the method is suitable for popularization and application. Therefore, the reliability and generalization in the financial service field are ensured.

The invention provides a medical image classification unsupervised domain self-adaption and multi-granularity alignment system and a medical image classification unsupervised domain self-adaption and multi-granularity alignment method. According to the method, a coarse-to-fine progressive feature optimization strategy is provided, that is, a fine-grained feature encoder is initialized by using the training weight of a coarse-grained feature encoder, and alignment optimization of fine-grained features is guided while original semantic information is reserved. Specifically, a staged and layer-by-layer unfreezing fine adjustment method is adopted, and the adaptive capacity of fine-grained features is enhanced on the premise that the invariance of a coarse-grained domain is not damaged. According to the strategy, the generalization and convergence efficiency of the model on target domain data is remarkably improved. And experimental verification is carried out on a plurality of cross-domain medical image classification data sets. Results show that no matter on a small-scale data set or a large-scale data set, the method provided by the invention can obtain performance obviously superior to that of an existing method, and generalization ability and robustness are stronger.

The invention discloses a layout circuit board of a multi-device parallel power module. In the layout circuit board of the multi-device parallel power module, a first lotus-shaped BNC interface (comprising a load positive electrode output terminal and a load negative electrode output terminal) is located in the center of a PCB, and a plurality of parallel silicon carbide devices in the silicon carbide device parallel module are arranged in a circumferential mode with the first lotus-shaped BNC interface as the circle center; and a plurality of parallel decoupling capacitors in a decoupling capacitor parallel module are arranged circumferentially by taking the first lotus-shaped BNC interface as a circle center. The arrangement positions of the parallel devices are optimized, so that parasitic parameters of the circuit are matched, the distribution difference of stray inductance of the circuit is reduced, the current coupling effect is eliminated, the transient current imbalance of themulti-device parallel power module can be changed, and the good current sharing characteristic of the parallel silicon carbide devices is realized.

The invention discloses a small sample image classification method and system based on multi-scale cross-modal prompt enhancement, and relates to the technical field of small sample image classification, and the method comprises the steps: obtaining a query image and a support image small sample data set with a labeled category; inputting the acquired data into a trained small sample image classification model, extracting visual features of a query image and enhanced visual features of each support image through a feature extractor based on hierarchical multi-modal prompt enhancement in the model, and performing global average pooling on all the extracted enhanced visual features to obtain an enhanced visual feature of each support image; obtaining an enhanced visual feature prototype of each category, calculating similarity scores of the categories according to the visual features of the query image and the enhanced visual feature prototype of each category, determining the category with the highest score as a classification result of the query image, and outputting the classification result; wherein the small sample image classification model is obtained by training a global-local self-guiding supervision mechanism. According to the invention, the precision and robustness of small sample image classification can be significantly improved.

The invention relates to an elastic wave full waveform inversion method based on an optimal transmission function, and belongs to the field of seismic velocity modeling. The method comprises the following steps: firstly, establishing an observation system, selecting an excitation source with a certain frequency and a speed model, and receiving an actual seismic record and a simulated seismic record through the excitation source; secondly, introducing an optimal transmission function as a target function of inversion, and establishing a target functional formula; a timing wave field and a reverse time wave field are obtained through forward modeling and reverse time continuation, derivation is carried out on a target functional formula to obtain a gradient calculation formula, the gradient calculation formula is substituted into the timing wave field and the reverse time wave field, and the gradient is obtained. And optimizing the obtained gradient by using an Adam gradient optimization algorithm, and substituting the optimized gradient into iterative inversion to update the model. And repeating the steps until an inversion result meeting the precision requirement is obtained. Model experiments show that compared with a traditional L2 objective function, the method has the advantages that the event continuity of the inversion model is enhanced, the deep structure is clearer, and the overall precision is greatly improved.

The invention discloses an enterprise electricity consumption prediction method. The method comprises the following steps: 1) data acquisition; 2) introducing the maximum mean value difference, and carrying out similarity analysis; 3) modeling a long short-term memory network of transfer learning; and 4) electric quantity prediction: predicting the electric quantity of the target domain enterprise at the future moment by using the improved model in the step 3). The problem of insufficient data volume during electric quantity prediction is effectively solved, the model training time is saved, and the electric quantity prediction precision is improved.

The invention relates to the technical field of target detection, and particularly provides a semi-supervised learning-based target detection method, system and device and a storage medium, and the method comprises the steps: obtaining a data set which comprises a marked image and an unmarked image; generating a pseudo label for the unmarked image based on the marked image by using a dense detector; dividing the pseudo labels into reliable labels and soft labels according to the scores of the pseudo labels by using a pseudo label distributor; the pseudo label is updated to the data set, the updated data set is utilized to train a target detection model, and the target detection model identifies a target object in the sample image after training; the soft label is set to only participate in the preset partial loss calculation in the training process so as to reduce the training error caused by the soft label. According to the invention, an efficient semi-supervised learning target detection framework is constructed, and the accuracy of a target detection model is improved.

The invention discloses a fault diagnosis method based on the Wasserstein distance-based deep adversarial migration network. The Wasserstein distance is used to measure the distance of the feature distribution of two fields in the feature space, and to adapt the feature distribution to reduce the difference between the two fields. , learn domain-independent features to train an effective classifier, responsible for mapping domain-independent features to category space, complete classification tasks, and solve unsupervised transfer learning problems without labeled vibration data in the target domain.

The invention discloses a multi-sensor data fusion method based on a deep migration network. The multi-sensor data fusion method comprises the following steps of performing data preprocessing on acquired source domain data and target domain data of a plurality of sensors; constructing training data and test data; training a feature coding network and a classifier by using a deep neural network; calculating the maximum mean error loss and the corresponding classification loss of each coding network, calculating the inconsistent loss of all classifiers to the target domain sample output, optimizing the data classification, the maximum mean difference and the total loss generated by the classifier output, and obtaining an overall loss function; and inputting the target domain test data into each coding network to extract features, and outputting a final data fusion result. According to the fusion method, redundant and complementary information of different position sensors is effectively utilized, and a stable fusion result and high diagnosis precision are obtained. Meanwhile, the distribution difference between the test data and the training data can be effectively reduced.

A cross-project defect prediction method based on feature distribution alignment and neighborhood instance selection specifically comprises the following steps: selecting source projects from a software defect data set, combining all the source projects to form a source project set, and selecting a target project; calculating a covariance matrix of the source item set and a covariance matrix of the target item; eliminating the correlation between the features of the source item set, filling the feature correlation of the target item into the source item set, and selecting instances with high similarity with instances in the target item from the source item set data after feature alignment to form a training instance set TS; and training a Logistic model by using the training instance set TS, and performing defect prediction classification on each instance in the target project by using the Logistic model. According to the cross-project software defect prediction method, the selection of the training data required by the model is achieved by adopting the feature distribution alignment method and the neighborhood instance selection method, the difference between projects and instances in the cross-project software defect prediction method is effectively solved, and the defect prediction performance is improved.

The invention provides a battery charge state prediction method comprising the following steps: obtaining to-be-predicted data of a target battery, the to-be-predicted data comprising a terminal voltage, a terminal discharge current and a terminal voltage change rate; inputting the to-be-predicted data into a pre-trained battery state-of-charge prediction model based on transfer learning, and obtaining a state-of-charge prediction result corresponding to the target battery output by the battery state-of-charge prediction model; wherein the battery state-of-charge prediction model comprises a transfer learning model and a Transformer model which are obtained through training of training data and are based on time distribution search and matching. Therefore, the state of charge of the battery can be accurately and quickly predicted.

The invention relates to the technical field of unmanned aerial vehicle fault diagnosis, in particular to an unmanned aerial vehicle sensor fault diagnosis method based on weighted migration domain adaptive learning. Comprising the following steps: constructing a multi-scale neural network model which comprises three multi-scale neural network modules including a feature extractor, a domain discriminator and a classifier; the feature extractor extracts high-dimensional feature information of the data; the domain discriminator carries out confrontation training with the feature extractor in a model training process, so that the domain discriminator cannot distinguish whether data is from a source domain or a target domain; the classifier identifies and classifies the sample features output by the feature extractor; obtaining a source domain sample data set and a target domain sample data set; the constructed multi-scale neural network model is trained; and performing fault identification and diagnosis on the target domain data through the trained multi-scale neural network model. The method has the advantages that a better learning model is established for unmanned aerial vehicle fault diagnosis under the condition of lacking a target domain data set label, and fault diagnosis of a target domain data set is realized.

The invention discloses a rolling bearing life prediction method based on a dynamical model and a cross-domain graph network, and the method comprises the steps: building a simulation model based on a five-degree-of-freedom dynamical model of a rolling bearing, and combining a multi-target particle swarm optimization algorithm with a bearing actual measurement signal to dynamically update the parameters of the dynamical simulation model. And the defect evolution process of the simulation model in different degradation stages is explored, and the full-life-cycle simulation data of the rolling bearing are generated, so that the defect that the full-life-cycle data of the bearing are difficult to collect in actual engineering is overcome. Designing a multi-layer cross-domain graph neural network model, performing nonlinear transformation on graph node features to extract space information of neighbor nodes, and extracting time dependence of the neighbor nodes by combining a hidden layer state at a previous moment; and taking the complete labeled simulation data as source domain data, taking the incomplete label-free actual measurement data as target domain data, reducing the distribution difference between the source domain data and the target domain data through a multi-layer domain adaptation mechanism, and finally realizing cross-domain prediction of the service life of the bearing.

The invention provides an aero-engine fault diagnosis method based on a transferable neural network. By using a basic thought of transfer learning, the method overcomes an over-idealized hypothesis problem that training data and a test data set obey the same distribution in a traditional aero-engine gas path fault diagnosis method based on data-driven. Two fault diagnosis algorithms UD-RVFL and UJD-RVFL are provided by combining a domain self-adaption thought and an RVFL algorithm, so as to realize that on the premise that the main advantage that an original RVFL network topology structure is simple is reserved, the distribution difference between data of a source domain and data of a target domain can be reduced by learning feature representation of migratable data, and data attributes and feature structures of the source domain are reserved as much as possible. According to the invention, a transfer learning strategy is adopted, edge distribution and conditional distribution differences existing between data sets can be reduced, and therefore, the fault diagnosis precision is improved.