32results about How to "Avoid "negative transfer"" patented technology

The present invention provides a rolling bearing fault diagnosis method in various working conditions based on feature transfer learning, and relates to the field of fault diagnosis. The objective ofthe invention is to solve the problem that a rolling bearing, especially to various working conditions, is low in accuracy of diagnosis. The method comprise the steps of: employing a VMD (VariationalMode Decomposition) to perform decomposition of vibration signals of a rolling bearing in each state to obtain a series of intrinsic mode functions, performing singular value decomposition of a matrixformed by the intrinsic mode functions to solve a singular value or a singular value entropy, combining time domain features and frequency domain features of the vibration signals to construct a multi-feature set; introducing a semisupervised transfer component analysis method to perform multinuclear construction of a kernel function thereof, sample features of different working conditions are commonly mapped to a shared reproducing kernel Hilbert space so as to improve the data intra-class compactness and the inter-class differentiation; and employing the maximum mean discrepancy embedding to select more efficient data as a source domain, inputting source domain feature samples into a SVM (Support Vector Machine) for training, and testing target domain feature samples after mapping. Therolling bearing fault diagnosis method in various working conditions has higher accuracy in the rolling bearing multi-state classification in various working conditions.

The invention discloses an adversarial-learning-based multi-source-domain adaptive migration method and system. The method comprises: step one, pre training is carried out by using all-source-domain data and a representation network and a classifier of a target model are initialized; step two, multi-path adversarial adversarial processing is carried out on multi-source-domain data and target-domain data and a representation network and a multi-path discriminator of the target model are updated; step three, adversarial scores between the source domains and the target domain are calculated; stepfour, target domain classification is carried out based on the classifiers and the adversarial scores of all source domains; step five, a target domain pseudo sample with a high confidence coefficient is selected for fine tuning of the representation network and the classifier of the target model; and step six, the steps from the step two to the step five are carried out again until model convergence is realized or a maximum iteration number of times is reached, and then training is stopped. According to the invention, reliance on the hypothesis of consistency of the single-source-domain tagset and the target domain is eliminated; and a negative migration phenomenon existing in the multi-source domain adaptation process is avoided effectively.

The invention discloses a far infrared pedestrian detection method for changed scenes. A sample extension target data set is screened out of auxiliary data on the basis of the Boosting-style inductive transfer learning algorithm DTL Boost. At first, a sample importance measurement model based on the k-nearest neighbor is utilized for evaluating the similarity between the auxiliary data and the target data, and corresponding initial weights are distributed for different samples in the auxiliary data. In the training process, the prediction inconsistency degree of member classifiers is explicitly defined, iterative updating is carried out on the current weights of the auxiliary data and the target data sample through the prediction error rate of the current member classifiers, a sample extension training set with the positive transfer ability is screened out of the auxiliary data, and the different member classifiers are encouraged to learn different parts or aspects of the target data. In this way, an integrated classifier with the stronger generalization ability is obtained through training, and the robustness of pedestrian detection in the new scene is enhanced.

The invention discloses a text topic classification model based on multi-source-domain integrated migration learning. The model is composed of a target domain data module, a tagging module, an integrated learning module for multi-source-domain tag determination and a correct data module. According to a classification method for the text topic classification model based on multi-source-domain integrated migration learning, first, data without class tags is classified through the tagging module; and next, data with tags is determined, the data correctly classified through three classifiers is selected and added into the target domain data module, classification is performed through the three classifiers to obtain data with dummy tags and different types of text topics, one type of text topics is selected to serve as target domain data, other types of text topics are used as source domain data and added into the target domain data, and a Softmax classifier is used to test the correct rate. In this way, the negative migration phenomenon brought by single-source-domain migration is effectively avoided, data composition comes from all aspects of a target domain, and data balance can be better met.

The invention belongs to the technical field of side-scan sonar image recognition, and particularly relates to a side-scan sonar image target classification method based on style migration. Accordingto the method, a saliency detection method and a style migration network are used, a conventional optical image is converted into a side-scan-imitating sonar image, the distance between a source fieldand a target field is shortened, the number of basic features capable of being directly migrated is increased, and migration learning efficiency can be effectively improved; meanwhile, a migration learning method is used for migrating a fully-trained deep learning network, and the characteristic that the basic characteristics of the image have universality is utilized, so the number of optimization parameters can be effectively reduced, and the problem that a deep learning technology cannot be applied due to insufficient training samples is avoided. According to the method, a style migrationand transfer learning method is used for migrating a convolutional neural network trained by using the artificially generated side-scan-imitating sonar image, so transfer learning efficiency is improved, and the phenomenon of negative migration is prevented.

The invention relates to a method for quickly mixing a high-order attention domain adversarial network based on transfer learning. The method comprises the steps of designing a quick mixing high-orderattention and domain adversarial adaptive network for a to-be-processed image data set; preprocessing the source domain and the target domain; importing the preprocessed source domain and the preprocessed target domain into the designed network in batches in sequence, obtaining weighted feature maps through a fast mixing high-order attention network, then inputting the weighted fine feature mapsinto a domain adversarial adaptive network for training, and finally performing probability operation through a full connection layer; respectively calculating average image classification accuracy ofthe source domain and the target domain; enabling a gradient inversion layer in back propagation to take a reverse gradient direction to form adversarial training, then performing iterative training,and applying a fast mixed high-order attention and domain adversarial adaptive network trained on a source domain directly to a target domain to carry out image classification. According to the invention, the recognition rate and migration capability of the unsupervised domain adaptive network in migration learning are improved.

The present invention provides a navigation path planning method based on strategy reuse and reinforcement learning, and belongs to the technical field of navigation path planning, which solves the problem of insufficient reuse of the source strategy in the existing method. According to the method provided by the present invention, a function representing state importance is introduced to assist strategy selection, strategy reuse and strategy library reconstruction, so that the purpose of rapidly planning a navigation path in a road network map is achieved; compared with the existing traditional path planning method, a reinforcement learning algorithm based on strategy reuse is used in the algorithm ARES-TL, the complete strategy library is updated in real time, the algorithm time is savedby occupying some space storage strategy library, and the reinforcement learning algorithm can cope with the online micro-updated map; compared with the same type of strategy reuse method, by using the algorithm ARES-TL of the present invention, the negative migration caused by the reuse of the irrelevant source strategy is avoided with respect to PRQL and OPS-TL, and exploration efficiency is improved and navigation tasks are accurately completed; and the method provided by the present invention can be applied to the technical field of navigation path planning.

The invention discloses a cross-subject EEG fatigue state classification method based on generative adversarial domain self-adaptation. The method comprises the following steps: firstly, acquiring andpre-treating data, and removing artifacts; secondly, performing EEG feature extraction through PSD, and acquiring a two-dimensional sample matrix from a three-dimensional EEG time sequence; distinguishing a source domain data set and a target domain data set to obtain a training set and a test set which are not overlapped; training a classification model GDANN by using part of label-free target domain data and random data conforming to Gaussian distribution; and finally, evaluating the accuracy of the classification result by using a confusion matrix. The generative adversarial network and the domain invariant thought are further combined, the problem that EEG signal data sets are rare and difficult to obtain is solved, the problem that source domain data and target domain data are not matched is balanced, negative migration is avoided to a certain extent, a high-precision cross-subject fatigue detection classifier is trained, and the method is expected to have a wide application prospect in actual brain-computer interaction.

The invention discloses a multi-virtual power plant decentralized self-discipline optimization method. The method comprises the following steps: S1, initializing parameters; S2, classifying tasks andforming an initial knowledge matrix; S3, acquiring information; S4, determining an optimization individual action; S5, calculating an objective function value of each agent; S6, calculating a rewardfunction; S7, updating the knowledge matrix; S8, information feedback: each agent returns the current optimal solution to an information center; and S9, judging whether the maximum number of iterations is reached or not, and if yes, outputting an optimal knowledge matrix of the corresponding task, otherwise, returning to S3. By applying the multi-virtual power plant decentralized self-discipline optimization method, the technical problems that the existing distribution network regulation and control cannot meet the requirement that a plurality of virtual power plants participate in the power market in real time for profit-by-profit, and the grid connection behavior of distributed equipment is effectively controlled to support the safe and effective operation of the distribution network aresolved.

The invention provides a cross-domain medical care equipment recommendation method and system with a privacy protection function. The method comprises the following steps: acquiring ID data of a target domain about users and items of medical health care equipment; performing one-hot coding representation on the user ID data and the project ID data of the target domain; expanding the one-hot coding representation of the target domain by using the auxiliary domain user domain dependence feature and the auxiliary domain user domain non-dependence feature to obtain an expanded feature; and using the extended features and the trained factorization machine model to predict the score of the target domain user to the item, and recommending the medical health care equipment according to the score result. The method can ensure that other platforms cannot reversely derive the original score information according to the disclosed user potential factor vector, and can effectively solve the problem of original data privacy leakage of the user in the auxiliary domain data migration process.

The invention discloses a laser welding method and device and a storage medium thereof. The method comprises the steps that a bad factor sample set in the laser welding process is collected as a target data set; an existing sample set with the highest similarity with the target data set is determined as a source data set; pre-training is continuously conducted on a deep learning model by using thesource data set to obtain a first pre-training model; structure adjustment is performed on the first pre-training model by using the target data set to obtain a second pre-training model; and parameters of the first pre-training model are migrated to the second pre-training model to obtain a final model. The source data set which has the highest similarity with and is matched with the target dataset can be confirmed, and negative migration is avoided; and the defect that a small amount of sample data is prone to over-fitting is overcome in a transfer learning mode, and the model convergencespeed is increased.

The embodiment of the invention discloses a figure image recognition method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: acquiring a figure image; performing feature extraction on the figure image through a feature extraction network included in a preset image recognition model, obtaining a global feature map, wherein the preset image recognition model further comprises a joint recognition network, the joint recognition network comprises a deconvolution branch network group, and the deconvolution integral branch network group is used for generating a figure joint thermodynamic diagram group; inputting the global feature map into the joint recognition network to obtain a figure joint thermodynamic diagram group; and generating a figure image recognition result based on the figure joint thermodynamic diagram group, and sending the figure image recognition result to a display terminal for display. According to the embodiment, the accuracy of generating the character image recognition result can be improved.