12 results about "Pseudo data" patented technology

The objective is to improve the accuracy of the generated abnormal signal data. [Solution] The signal management device 1 includes a generator that generates pseudo-data statistically similar to true data, with each of the sequences of intensity for each frequency component corresponding to the second data being treated as true data, and a discriminator that identifies whether the pseudo-data generated by the generator belongs to the class corresponding to true data or the class corresponding to pseudo-data. The device also includes a third learning unit configured to learn multiple generative models, each having an objective function set by the probability distributions of the first data and the second data determined from the ratios estimated by the first learning unit, and the second parameter estimated by the second learning unit.

When an application execution error occurs, an entry for the execution error is included in an error log. In instances where user data associated with the error should be preserved, a placeholder corresponding to the error is included in the error log. The placeholder contains pseudo data representing the user data, but is formatted in such a way to prevent the vendor from accessing the content of the data without additional authorization. When reviewing the log file, the vendor has visibility to the pseudo data contained in the placeholders, but is not able to discern the content of the user data from the pseudo data. In response to a request from the vendor to access instances of user data, the customer can selectively provide access to particular instances of user data that are deemed to not contain sensitive information.

The invention belongs to the technical field of equipment monitoring, and particularly relates to a bearing state recognition method and system based on pseudo data, a terminal and a storage medium, and the method comprises the steps: obtaining historical bearing vibration data, carrying out the data processing, and constructing an initial data set; inputting the initial data set into the constructed TimeVQ-VAE model, generating pseudo data of the bearing state, and constructing an enhanced training set based on the pseudo data; training a BiIndRNN (Bidirectional Independent Recurrent Neural Network) model by utilizing the enhanced training set; and adopting a particle swarm optimization (PSO) algorithm to obtain an optimal hyper-parameter combination of the BiIndRNN model. According to the method, the TimeVQ-VAE model is introduced, so that high-quality pseudo data (the similarity index u is greater than or equal to 0.9) highly similar to real data can be generated, model training difficulty caused by scarcity of fault samples and imbalance of the number of various types of samples in an industrial field is effectively relieved, and dependence on a large amount of real labeled data is reduced.

The objective is to improve the accuracy of the generated anomalous traffic data. [Solution] The communication management device 1 includes a generator 131 that generates pseudo-data statistically similar to true data, using each of the series of communication volume observed for each time period corresponding to the second data as true data, and a discriminator 132 that identifies whether the pseudo-data generated by the generator 131 belongs to the class corresponding to true data or the class corresponding to pseudo-data. The device also includes a third learning unit 13 configured to learn multiple generative models, each having an objective function set with the probability distribution of the second data and the probability distribution of the first data determined from the ratio estimated by the first learning unit 11, and the second parameter estimated by the second learning unit 12.

The invention discloses a Mongolian-Chinese neural machine translation method fusing a pre-training model and pseudo data enhancement, the model adopts a shared encoder and bidirectional decoder architecture, the shared encoder is composed of a fusion encoding layer and a structure sensing layer, and the bidirectional decoders respectively correspond to Mongolian-Chinese and Chinese-Mongolian translation directions; the fusion coding layer is used for extracting semantic features by adopting MongolBERT and XLM-R-Mongolian respectively, and performing linear weighted fusion through a learnable weight so as to generate cross-language semantic representation; a lightweight syntactic information integration mechanism is introduced into the structure perception layer, syntactic distance is embedded into position coding, and the structure perception capability is enhanced in combination with a relative position attention mechanism. In the training stage, a staged multi-task learning strategy is adopted, and Mongolian-Chinese bilingual data generated by a practical pseudo-parallel corpus enhancement method is used for model optimization. Complementation is formed in the aspects of model structure, data expansion, multi-task optimization and the like, and an extensible, efficient and robust solution is provided for neural machine translation under the low-resource condition.

The information processing device 1X is an AI based device to support a decision making and includes an acquisition means 20X, a calculation means 22X, a selection means 23X, and a generation means 24X. The acquisition means 20X is configured to acquire a plurality of sets each including a sample and a label. The calculation means 22X is configured to calculate, for each of the plurality of sets, accuracy of the label based on a difference between the label and a predicted result of the label predicted from the sample paired with the label. The selection means 23X is configured to select a set to be used for generating pseudo data from the plurality of sets, based on the accuracy. The generation means 24X is configured to generate the pseudo data, based on the selected set.

This invention discloses a method for generating tabular data based on local context retrieval and fine-tuning, comprising: obtaining raw tabular data samples for machine learning training from a public benchmark database, performing preprocessing and data partitioning to obtain feature columns and label columns, and constructing a training set; fine-tuning the local calibration parameters of a TabPFN model based on the training set to obtain a parameter-fine-tuned TabPFN model; obtaining pseudo-data samples based on the training set by manually setting the target number of samples and target category labels; and iteratively updating the pseudo-data samples and the parameter-fine-tuned TabPFN model using an iterative sampling method based on dynamic local context to finally obtain a tabular dataset. This invention, through local calibration parameter fine-tuning and dynamic local context iterative sampling, can generate high-quality, diverse tabular datasets, effectively improving the realism and usability of the generated data.

The application discloses a drive system cross-sensor collaborative diagnosis method based on dynamic competition force balance, which comprises the following steps: obtaining and preprocessing monitoring data of sensors in a drive system; inputting the preprocessed monitoring data as a sample set, constructing a cross-sensor collaborative distillation network, and the cross-sensor collaborative distillation network comprising a teacher model and a student model; the teacher model and the student model respectively performing up-to-standard discrimination, and constructing a heterogeneous sensor dynamic competition force balance mechanism after reaching the standard; iteratively training the student model through optimization of distillation loss and dynamic competition force balance mechanism regularization loss; inputting monitoring data to be diagnosed into an optimal model obtained based on iterative training to perform diagnosis and output corresponding diagnosis results; relying on the cross-sensor collaborative distillation network, establishing the association between multi-sensor fusion fault features and single-sensor local features, and enabling missing data to effectively participate in model training without generating additional pseudo data in the whole process.

This enables data analysis using external language models while reducing the risk of data leakage. [Solution] The sensitive data analysis support device uses its own first language model to extract data from validation data that shows cases related to the clinical question, generates pseudo-data from the extracted data that is statistically similar to the extracted data and has reduced personal identifiability, generates a pseudo-analysis specification that is formatted for the pseudo-data based on the clinical question and validation data, inputs the pseudo-analysis specification and pseudo-data into an external second language model to obtain a draft analysis code for the pseudo-data based on the pseudo-analysis specification, and formats the draft source code for analysis of the extracted data based on the analysis specification.

This invention relates to a method for generating CSI pseudo-data, comprising the following steps: inputting noisy data into a CSI data generation model to obtain CSI pseudo-data. The CSI data generation model includes: an encoder for extracting feature maps from the input image; a diffusion model for generating pseudo-feature maps based on the feature maps; and a decoder for restoring the pseudo-feature maps back to CSI pseudo-data. This invention can generate reliable multi-class CSI pseudo-data, and by embedding the encoder and decoder into the diffusion model, it achieves a lightweight CSI data generation model, saving significant computational power.

A system for implementing data security using dynamic data fragmentation and storage is disclosed. The system accesses digital content and splits the digital content into a set of data shards according to a splitting rule. The splitting rule indicates that each data shard combined with a respective pseudo data shard should appear as the digital content. The system generates a pseudo data shard such that the first data shard together with the pseudo data shard appears as the digital content. The pseudo data shard comprises pseudo information that is a counterpart to the original information indicated in other data shards. The system communicates the first data shard and the pseudo data shard to a database.

The objective is to improve the accuracy of the data on the number of abnormal packets generated. [Solution] The communication management device 1 includes a generator 131 that generates pseudo-data statistically similar to true data, using each of the sequences of packet counts observed for each time period corresponding to the second data as true data, and a discriminator 132 that identifies whether the pseudo-data generated by the generator 131 belongs to the class corresponding to true data or the class corresponding to pseudo-data. The third learning unit 13 is configured to learn multiple generative models, each having an objective function set with the probability distribution of the second data and the probability distribution of the first data determined from the ratio estimated by the first learning unit 11, and the second parameter estimated by the second learning unit 12.