6results about How to "Avoid computation" patented technology

The invention discloses a parameter deentanglement-based classification method and device and a readable storage medium, and belongs to the field of computer vision and deep learning. The method comprises the steps of obtaining an image data set; preprocessing the image data set to obtain a standardized image data set; dividing the standardized image data set into a training set and a test set; importing the training set into a pre-constructed skeleton network structure based on parameter deentanglement diversified representation for training to obtain an updated classification model; evaluating the updated classification model by using the test set to obtain a classification model with optimal performance; and carrying out real-time classification processing on the image data based on the classification model with the optimal performance to obtain a classification result. According to the method, the feature representation capability of the model is improved by utilizing the classification model based on parameter deentanglement diversified representation, so that the accuracy of the model on a classification task is remarkably improved, and higher classification accuracy is realized.

The invention discloses a transient interaction characteristic analysis method for an optical storage direct-flexible system in a high-temperature and high-humidity environment, and the method comprises the steps: firstly building a differential algebraic model of a dynamic behavior, and initializing a state and parameters; then, adopting an SAS method to construct an approximate solution, and dynamically adjusting model parameters according to real-time temperature and humidity; establishing a linear equation of a coupling relationship, recursively solving a high-order coefficient, and calculating residual error evaluation precision; solving the high-order nonlinear model by utilizing a parallelization calculation technology, constructing a local RLC topology to carry out transient simulation, recording a time-varying result of a system state, and analyzing a coupling effect of each component; and finally, adjusting the key variable by adopting a self-adaptive control algorithm. According to the invention, based on the SAS calculation method, for the dynamic response of the optical storage direct-flexible system in the high-temperature and high-humidity environment, efficient transient analysis is realized, and the operation reliability and the regulation and control capability of the system are remarkably improved. The method is applied to an on-line monitoring and control platform of a power system, and real-time optimization is realized.

The present invention discloses a time-controlled fine-grained traceability method for large-scale supply chain data, which belongs to the technical field of fine-grained traceability processing; including: building a large-scale supply system; initializing, generating public parameters and master keys; encrypting user data; tracing the encrypted user data; decrypting user data; obtaining data. The present invention adopts an aggregated access control tree method, including all attribute nodes of the user, adopts a basic access strategy, and implements fine-grained access control according to different user attributes, effectively solving the increased amount of calculation in the encryption and decryption process; controls the timeliness of user permissions by dynamically updating access strategies, and uses smart contracts and blockchain timestamp verification to ensure the fairness and non-tamperability of access control, solves the problem of access control for different identities, implements access authority control in the time dimension, and ensures the privacy and security of data.

The application discloses an electromagnetic data matching degree calculation method, a signal processing method and a signal processing system, and belongs to the field of electronic reconnaissance. The method comprises the following steps: S1, calculating the search length of the electromagnetic signals to be matched and setting an initial displacement; S2, calculating the matching search granularity; S3, calculating the matching degrees of different displacements between the electromagnetic signals to be matched; S4, calculating a new initial displacement and a new matching search granularity; S5, repeating steps S3 to S4 until the matching degree search granularity meets the accuracy requirement or reaches a minimum value, and calculating the minimum value matching degree as the optimal matching degree of the electromagnetic signals to be matched, and completing the electromagnetic data matching when the target is detected or identified according to the matching degree. The application improves the accuracy and speed of the matching degree of the non-periodic data of the electromagnetic signals, reduces the communication amount and the calculation amount when the practical equipment is applied, makes the data matching real-time higher, consumes less resources, and guarantees the matching accuracy.

The present application relates to the technical field of computer-aided drug research and development, and particularly relates to a method for realizing Motif sequence constraint in a DFA graph of mRNA codon optimization, which comprises the following steps: step A: locating the position of a Motif subgraph in the DFA graph; step B: extending the local DFA graph; step C: deleting the Motif subgraph; and step D: repeating step A until the Motif subgraph is completely deleted. The present application avoids explicitly arranging and combining candidate codons, and instead implicitly includes the constraint in the path of the DFA graph. The DFA graph is processed before optimization starts, thereby avoiding the large amount of calculation required for post-processing of codon arrangement and combination. Thus, the method solves the problem of Motif sequence constraint in mRNA codon optimization, and provides an effective solution for long-sequence mRNA codon design.

The application discloses an ISAR image adaptive quantization method based on histogram entropy, and belongs to the technical field of radars. An M*N dimension ISAR amplitude floating-point data matrix model in units of dB is firstly constructed; then a quantization boundary value coordinate axis of the ISAR image quantization is constructed; then the optimal upper and lower quantization boundaries of the ISAR amplitude data are obtained; finally, the optimal quantization image is obtained by quantization based on the obtained upper and lower quantization boundaries. The application uses the entropy of the ISAR image as an evaluation index of adaptive quantization, has universal applicability, and reduces the problem of non-uniform quantization of different targets, different resolutions and different signal-to-noise ratio environments during ISAR imaging. By using the histogram entropy of the ISAR image, the calculation amount of the entropy operation of the whole image is avoided, and only the histogram needs to be quantized, so that the operation scale of the two-dimensional image is reduced to the scale of the quantization order W, and the engineering application of real-time processing is more favorable. The histogram entropy can be used for different quantization bit numbers.