Abstract: A machine learning (ML) based system with neural networks for reducing bacterial sequence contamination in constructing a phage genome utilizing next-generation sequencing (NGS) data is present. The system includes a phage NGS dataset and includes a NGS reads screener to filter out low-quality reads. An NGS integrator is employed to pre-assemble the filtered reads into contigs. The contigs are then classified using a contig classifier including an autoencoder based on a gapped pattern graph convolutional network (GP-GCN) to identify their origin phage genome so as to minimize a bacterial sequence contamination in the NGS dataset. A graph generator creates a copy-number-aware bipartite conjugate graph from the classified contigs. A phage sequence assembler analyses the graph for assembling the contigs into a potential phage sequence.

Abstract: Disclosed are an accelerated test data analysis method and apparatus based on a grey forecast model, a device.

Abstract: The present disclosure discloses a method and apparatus for estimating product lifetime based on a multi-stress accelerated test, and a device. This method can be applied in the field of data processing technologies, specifically including: determining, in response to a lifetime estimation request for a target product, a target characteristic lifetime of the target product based on a target stress condition for the target product and correspondence relationships between candidate stress conditions and candidate characteristic lifetimes; and determining, based on an optimal lifetime model for the type to which the target product belongs and the target characteristic lifetime, a target average life of the target product.

Abstract: A performance degradation-based product reliability weak link evaluation method includes: acquiring a performance degradation parameter of each component of a to-be-evaluated product corresponding to each test time point; evaluating the performance degradation parameter of each component corresponding to each test time point through a fault time evaluation model, to obtain a pre-fault operating duration of each component; evaluating the pre-fault operating duration of each component through a confidence-based unreliability evaluation method, to obtain an unreliability evaluation result of each component; generating at least one component service life distribution model corresponding to each component according to the unreliability evaluation result, and selecting a corresponding target component service life distribution model; and evaluating mean time between failures corresponding to each component according to the target component service life distribution model, and selecting a reliability weak link of th

Abstract: The present disclosure relates to a method and an apparatus for processing accelerated test data based on multiple performance degradation, and a device.

Abstract: The present disclosure relates to a method and an apparatus for reliability evaluation based on multiple performance degradation, a computer device, a storage medium, and a computer program product. The method includes: for each of the multiple performances of a target product, obtaining a plurality of candidate functions that the performance possibly follows, determining a target function that satisfies a preset selection condition from the plurality of candidate functions, and determining a reliability function corresponding to the performance based on the target function; determining coupling relation information between the multiple performances of the target product, and obtaining redundancy information of the multiple performances of the target product; and performing a reliability evaluation on the target product based on the coupling relation information, the redundancy information, and the reliability function of each performance.

Abstract: The present disclosure relates to a method and an apparatus for processing accelerated test data based on multiple performance degradation, and a device.

Abstract: A method for processing accelerated degradation test data based on Wiener process includes obtaining accelerated degradation test data of a to-be-tested product, selecting a target time conversion model from a predefined set of time conversion models based on the accelerated degradation test data, constructing a nonlinear Wiener degradation process based on the target time conversion model, determining a product reliability function of the to-be-tested product, and testing the reliabilities of the to-be-tested product based on the product reliability function to obtain product reliability test results of the to-be-tested products.

Abstract: Disclosed are a fault prediction method and apparatus for a power conversion device, and a power conversion system. The method includes: acquiring multiple output voltages of a detecting coil in a preset time period, wherein an electromagnetic induction is generated between the detecting coil and a switching-on circuit of a switching transistor in the power conversion device; extracting each output frequency corresponding to each output voltage of the detecting coil; predicting time when the power conversion device fails according to a change trend of each output frequency. The apparatus includes a detecting coil and a data processing device; the detecting coil is connected to the data processing device and is a closed coil; the data processing device is configured to: acquire an output voltage of the detecting coil, extract corresponding output frequency, and predict time when the power conversion device fails according to a change trend of each output frequency.