10results about How to "Improve inference accuracy" patented technology

The model generation method according to the present disclosure is an information processing method executed by a computer, and includes machine learning that implements an inference model using a plurality of training images. The inference model includes a compression module and an inference module configured to infer a solution to a task for a sub-region in an input image. The compression module is configured to generate compressed information by compressing information about an extended region that includes the sub-region and is wider than the sub-region. The inference module is configured to derive the solution to the task from information about the sub-region and the compressed information obtained by the compression module.

The invention relates to the technical field of silicon carbide defect detection, and discloses a silicon carbide product defect detection method and system based on an intelligent sensor, and the method comprises the steps: obtaining initial time sequence data; executing spatial feature mapping according to the data, extracting a preliminary boundary of a purity non-uniform region, and obtaining a purity deviation feature vector of the preliminary boundary; performing multi-time-domain evolution analysis based on the vector to obtain an optimized defect transition trajectory sequence; carrying out time domain differential analysis on the sequence, and extracting a dynamic evolution path index; the purity is judged, and an evolution influence quantization matrix is generated; further fusing the associated information to obtain a defect evolution influence factor; and generating a defect detection report based on the factor to obtain a final detection result. According to the method, defect dynamic evolution tracking under the high-temperature working condition can be achieved.

This application provides a monolithic 3D integrated chip, relating to the field of semiconductor technology, primarily addressing the problem that current resistive random access memory (RRAM)-based logic architectures remain relatively fixed and lack flexible reconfigurability. The monolithic 3D integrated chip includes a logic layer, a memory computing layer, and a reconfigurable layer stacked sequentially, with interconnections between these layers. The logic layer transmits computation signals and data to the memory computing layer and configuration data and signals to the reconfigurable layer. The memory computing layer, under the control of the computation signals, performs matrix multiplication or addition on the computation data and outputs the computation results. The reconfigurable layer, under the control of the configuration signals, performs reconfigurable processing on the computation results according to the configuration data. This application endows the chip with flexible programmability and reconfigurability.

The invention relates to the technical field of biomedical signal processing, and discloses a calcium ion imaging signal peak inference method and system based on deep learning, and the method comprises the following steps: segmenting an original noisy calcium ion signal time sequence through interval sampling to generate a recovery sequence, and training a one-dimensional U-Net neural network model; processing the historical noisy calcium ion signal time sequence by using a one-dimensional U-Net neural network model to obtain a historical de-noised signal, and training a one-dimensional convolutional neural network model by using the historical de-noised signal; and denoising the original noisy calcium ion signal time sequence to be processed, and inputting the denoised original noisy calcium ion signal time sequence into the one-dimensional convolutional neural network model to deduce a neuron absolute spike rate sequence. According to the method, noise can be suppressed through self-supervised denoising without external truth value data, the signal-to-noise ratio is improved by using a two-stage framework, a neuron discrete discharge event is accurately solved, and the problems that the prior art depends on a truth value database and the inference precision is low under the low signal-to-noise ratio are solved.

The invention provides a deep counterfeit content detection system and method based on a multi-modal large model, and the method comprises the steps: taking a video clip as input, employing a phased Video Swin Transform as a backbone network, and extracting multi-scale deep space-time visual features in video data; a Mel-frequency cepstrum coefficient fragment is used as input, a cascaded ERes2Net module is used for constructing an audio feature extraction network, a multi-stage fusion mechanism is introduced, extracted audio features are injected into a visual feature extraction branch stage by stage, and fused features are formed; according to the method, features are aggregated through parallel space-time pooling and a time attention mechanism, then internal association of audio-visual features is deeply mined in a local region, among all regions and in three dimensions of local-global through a core local-global interaction module, and finally, a deep forging detection result is output through a full connection layer. According to the scheme, the optimal effect is achieved on the largest deep counterfeiting detection data set in the industry.

The present disclosure provides a data processing method, comprising: performing enhancement processing on original data collected by an event information collection device according to at least one data enhancement transformation, to obtain at least one piece of enhanced data, wherein each piece of the enhanced data corresponds to at least one data enhancement transformation; obtaining at least one piece of initial prediction result corresponding to at least one piece of to-be-predicted data according to the at least one piece of to-be-predicted data, wherein the at least one piece of to-be-predicted data comprises the at least one piece of enhanced data, or one piece of the original data and the at least one piece of enhanced data; and obtaining a target prediction result according to the at least one piece of initial prediction result. The present disclosure also provides a data processing device, an electronic device and a computer readable medium.

This specification provides a method for fine-tuning a code model, based on a code library containing multiple source codes and corresponding patch codes. The method includes: obtaining a first representation vector corresponding to any first source code among the multiple source codes; the first representation vector is determined by performing a first encoding on the text of the first source code and a second encoding on the first abstract syntax tree corresponding to the first source code; based on the first representation vector, performing a similarity-based search in the code library to obtain a target context; the target context includes several other source codes whose similarity to the first source code meets a first threshold and their corresponding patch codes; generating prompt text based on the first source code and the target context, inputting it into the code model to obtain an inferred patch; and fine-tuning the parameters of the code model based on the inferred patch and the first patch code corresponding to the first source code.

This application relates to an intelligent simulation system and method. The intelligent simulation system includes at least: a simulation configuration specification generation system for generating a simulation configuration specification based on user-input simulation tasks; the simulation configuration specification describes simulation configuration information; a simulation task resource inference system for inferring the computational resources required to execute the simulation task based on the simulation configuration specification; a simulation orchestration system for determining a target solver from multiple solvers based on the simulation configuration specification, so that the target solver executes the simulation task based on the computational resources; and a simulation fault repair system for generating repair instructions and a diagnostic report when the target solver fails to simulate. Through multi-system collaborative coupling, the intelligent simulation system enables users to trigger the simulation workflow through natural language interaction, achieving end-to-end automation from mesh generation to solution analysis. This significantly reduces the user's familiarity with the underlying software operations and enables standardized delivery of simulation expert engineering capabilities.

This invention discloses an AI-driven big data fusion-based anti-misoperation interlocking system for wind farm booster stations, relating to the field of anti-misoperation interlocking technology for wind farm booster stations. It includes a data acquisition module that collects real-time electrical parameters, operating status, and environmental parameters of the equipment, and uploads them after preprocessing; a big data fusion storage module that categorizes and stores multiple types of data according to a hierarchical strategy, optimizing access and retrieval; an AI intelligent analysis and decision-making module that integrates multi-source data to generate precise interlocking strategies; an anti-misoperation interlocking execution module that executes interlocking in a three-level mode and provides status feedback; a communication interaction module that achieves internal and external data interaction through a dual-mode architecture; a status monitoring module that monitors the operating status of each module and identifies faults; and an emergency handling module that initiates a response mechanism and restores operation after self-checking following fault resolution. This invention relies on AI and big data fusion technology, dynamically adapting interlocking thresholds to equipment and environmental conditions; supporting unattended operation and intelligent maintenance, providing reliable assurance for the safe operation of wind farm booster stations.