8 results about "Experimental data processing" patented technology

This invention claims protection for an improved wavelet thresholding denoising method. The method decomposes a noisy signal through several layers of wavelet decomposition, obtaining the corresponding high-frequency wavelet coefficients for each layer. The amplitude of the corresponding wavelet coefficients is analyzed; those for useful signals are lower, while those for noise are higher. An improved threshold function is applied to process the wavelet coefficients at each layer, retaining coefficients below the threshold and discarding those above. These wavelet coefficients are then reconstructed using inverse wavelet transform, outputting the denoised signal. This invention constructs a new threshold function to address the shortcomings of traditional wavelet denoising algorithms in noise reduction. Experimental data processing results show that the new threshold function effectively suppresses the influence of random errors in the inertial measurement unit. Compared to the original compromise threshold algorithm, the signal-to-noise ratio is improved, the root mean square error is reduced, and the scheme of this invention achieves better signal smoothness and excellent denoising effect compared to traditional denoising schemes.

This invention relates to the fields of structural engineering experimental data processing and machine learning technology, and discloses a hysteresis curve parameter recognition method based on machine vision phenomenology. It obtains initial estimates of hysteresis model parameters using traditional parameter recognition methods. These initial estimates serve as a benchmark for applying physical constraints to perform adaptive intelligent sampling in the parameter space, generating multiple parameter combinations. The force-displacement curves corresponding to each parameter combination are converted into standard grayscale images. A Siamese convolutional neural network model is constructed and trained to learn the mapping relationship between image similarity and curve fitting goodness. The similarity between the generated image and the original experimental image is evaluated using the trained model, and the parameter combination corresponding to the highest score is selected. Further multi-layer physical rationality verification is then performed to select the optimal hysteresis model parameters. This invention integrates the physical basis of traditional methods with the visual shape recognition capabilities of CNNs, significantly improving parameter recognition accuracy and robustness while ensuring that the recognition results conform to physical laws.

This paper relates to the field of automated data processing and provides an experimental data processing method, apparatus, and system. The method includes: sending a task preparation sequence instruction to a data computing system, causing the data computing system to create task-related computing processes according to the task preparation sequence instruction, create data objects within the computing processes, and assign initial values; sending a data acquisition instruction to a device control terminal, receiving data acquired by the device control terminal through operation of the experimental equipment; generating a data modification instruction based on the acquired data and sending it to the data computing system; sending a data calculation instruction to the data computing system, causing the data computing system to perform calculations on the data of the relevant task computing processes according to the data calculation instruction; and sending a data output instruction to the data computing system, causing the data of the relevant computing processes to be stored in a data object database according to the data output instruction. This paper can realize the entire business process and has the advantages of ease of use, ease of learning, and versatility.

This application discloses a method, apparatus, device, and storage medium for high-resolution phase information extraction. The method includes: deconstructing and separating overlapping positive and negative phase images of a sample using a deep convolutional neural network model based on an attention mechanism; establishing a differential phase image based on the deconstructed and separated positive and negative phase images; generating network training data using an X-ray phase difference microscopy physical model; and directly using the trained model for experimental data processing to extract the absolute and differential phase information of the sample. The above-mentioned solution provided by this application can quickly and accurately extract the absolute and differential phase information of a sample from overlapping positive and negative X-ray phase microscopy data. Furthermore, this invention uses numerical simulation to generate network training data, eliminating the need to collect training data on an experimental system, greatly reducing experimental difficulty and saving experimental time.

The application discloses an intelligent auxiliary system for medical scientific research training based on an artificial intelligence large model, and particularly relates to the field of artificial intelligence and scientific research data processing, and is used for solving the problems of multi-source dispersion, non-uniform structure, missing steps difficult to identify and dependence on manual verification of experimental records in the existing medical scientific research process; a digital experimental record file is constructed by image acquisition on an experimental table record carrier and in combination with target detection and text recognition, logical breakpoints are recognized based on numerical jump and semantic co-occurrence, and jump record recognition is realized by using phenotype feature distribution and optimal transport calculation; missing steps are further reasoned and completed by dynamic programming, and finally, a complete experimental process draft is generated in combination with a structured template, automatic analysis, abnormal identification, process completion and multi-modal data fusion processing of scientific research data are realized, and the intelligent level of experimental data processing and the standardization of the scientific research process are improved.

The application provides a medical experiment data processing method and system, which realizes intelligent integration and efficient processing of multi-modal medical experiment data by introducing an adaptive mixed model, has the characteristics of data preprocessing automation, multi-dimensional feature extraction, adaptive model selection and strong visual interaction. The system can automatically identify and adapt to different types of data processing algorithms, effectively improving the accuracy and efficiency of data processing; through multi-modal feature fusion and time alignment technology, cross-data source correlation analysis is realized; at the same time, user-defined view and real-time feedback optimization are supported, which significantly enhances the practicability and flexibility of the system, provides comprehensive, intuitive and efficient data analysis support for medical researchers, and effectively promotes the deep mining and scientific discovery of medical experiment data.

The embodiment of the application belongs to the field of big data, and relates to an AB experiment data processing method, comprising the following steps: acquiring AB experiment data through a spring service; adding attribute labels to candidate events in the AB experiment data according to an attribute list configured in a TiDB database to obtain to-be-processed events; storing the to-be-processed events in a clickhouse table of a clickhouse database; and executing a preset SQL task to perform data processing on the clickhouse table to obtain an AB experiment data processing result, wherein the attribute labels are used to extract data in the to-be-processed events. The application further provides an AB experiment data processing device, a computer device and a storage medium. In addition, the application also relates to blockchain technology, and the AB experiment data can be stored in a blockchain. The application improves the processing efficiency of AB experiment data.

This disclosure provides an experimental data processing method, apparatus, storage medium, and electronic device. The method includes acquiring experimental group service data of a target strategy; extracting a preset identifier from the experimental group service data; and generating first experimental data based on the preset identifier; wherein the preset identifier indicates whether the target solution determined by the target strategy is transmitted to the user terminal for execution. By identifying the preset identifier in the experimental group service data, it can be determined whether the target strategy reaches the user, i.e., whether the target solution determined by the target strategy is transmitted to the user terminal corresponding to the experimental group service data for execution. Generating the first experimental data based on the experimental group service data that reaches the user reduces interference from experimental group service data that cannot reach the user, thus improving the accuracy of the first experimental data.