6 results about "Minimum entropy" patented technology

The present application provides a method for bearing fault diagnosis across working conditions by using a minimum entropy optimized prototype contrast network, and relates to the technical field of intelligent fault diagnosis. In the pre-training stage, an auxiliary domain discriminator is constructed to assist DA with the discriminant information of the classifier, the classification difficulty is evaluated by sample entropy, and the performance degradation in the DA process is inhibited. In the training stage, the learning vector quantization method is adopted to find the prototype. Through intra-domain prototype contrast learning, the sample features are closely gathered around the same prototype in the feature space, while being separated from the different prototypes. Then, the intra-class consistency of the features is enhanced, and the inter-class distinguishability is improved, so as to realize the precise alignment in the feature space. In addition, the cross-domain instance-prototype learning aligns the semantic structure in the shared embedding space, alleviates the negative transfer problem through the fine-grained alignment strategy, and improves the generalization ability of the model. Through the pseudo-label generation and the weighted loss function, the generalization performance of the model in the cross-working-condition small sample scene is improved.

Technological advancements are disclosed that utilize inertial sensor data for multiple classes to select a combination of filters to extract information though features to train a machine learning core decision tree. A determination is made whether the data for a class includes a frequency peak or dominating frequency that contains significant information about the class. In response to the data for the class including a frequency peak, a peak-based frequency range is determined. An entropy value is calculated for multiple frequency ranges in the data for the class. An entropy-based frequency range is selected from the multiple frequency ranges having a minimum entropy value. A frequency of interest is selected from the peak-based frequency range and the entropy-based frequency range for the class. A combination of filters is selected for each frequency of interest for each class and a decision tree is trained based on selected filter combination.

The invention relates to a self-adaptive quantum random number generation method based on real-time entropy estimation, and belongs to the technical field of quantum random number generation. According to an existing integrated quantum random number generator, due to the non-stationary characteristic of a physical entropy source, the entropy rate dynamically drifts, and after-processing parameters are fixed, randomness safety and generation efficiency are difficult to guarantee at the same time. According to the method, the state transition probability of the original random bit stream is counted in real time, the confidence interval correction is introduced to calculate the minimum entropy, the input bit width is dynamically adjusted accordingly, the Toeplitz matrix is reconstructed for random extraction, and self-adaptive adjustment of the compression ratio is achieved. According to the method, the compression ratio is increased to ensure the security of the output random number when the entropy source quality is reduced, and the compression ratio is reduced to improve the generation efficiency when the entropy source quality is improved, so that the dynamic balance between the randomness security and the generation efficiency is realized on the premise of keeping integration and low cost.

The invention belongs to the technical field of information security, and particularly relates to a random number security rapid evaluation method and device based on a neural network. According to the method, a random bit stream is mapped into a high-dimensional time state sequence by utilizing the high-speed nonlinear mapping capability of a photon reserve pool, and key time structure characteristics are highlighted by virtue of a time sequence mode attention mechanism, so that sensitive detection on random changes of a physical entropy source is realized. Compared with an existing method depending on a statistical test or a large-scale deep learning model, the method fully utilizes the high-speed calculation capability of the photon reserve pool neural network and the extraction capability of a time sequence mode attention mechanism on a key time structure, does not need complex model training, is light and efficient in calculation process, and is easy to implement. The method can be easily integrated with optical random number generator hardware, and is suitable for real-time minimum entropy evaluation of a high-speed physical entropy source.

This invention provides an RBAC+ABAC-supported access control method for AI knowledge bases, belonging to the field of AI knowledge base technology. This invention employs a gradient boosting decision tree and a Bloom filter to construct a simplified decision model. An optimization algorithm based on thermodynamic entropy finds the policy configuration with the minimum entropy value through simulated annealing, eliminating rule conflicts and simplifying the logical structure. During vector retrieval, the permission encoding vector and semantic vector are concatenated to form a joint vector to construct a permission-aware index. During answer generation, a permission context fingerprint is attached, and secondary propagation of permissions is verified through a permission re-verification gateway. Operation logs are recorded using a hash chain structure to ensure the immutability of audit traceability. This invention solves the technical problem that AI knowledge bases cannot simultaneously guarantee the efficiency of permission decisions and the maintainability of policy rules when performing fine-grained access control.

The present application relates to a kind of mechanical fault diagnosis systems based on minimum entropy deconvolution and stochastic resonance, comprising: the vibration data of mechanical rotating component is collected as original signal;After frequency scaling, the original signal is input into bistable stochastic resonance system, and correlation kurtosis is used as index function, system parameters are adaptively adjusted, so that the desired signal is output, the numerical solution of nonlinear system output is obtained using 4-order Runge-Kutta algorithm, the impact component in original signal is amplified using stochastic resonance phenomenon, and the signal after noise reduction is obtained;The signal after noise reduction is filtered again using minimum entropy deconvolution, and minimum entropy deconvolution filter signal is obtained;The signal after minimum entropy deconvolution filtering is subjected to hilbert envelope spectrum analysis, and the fault of mechanical rotating component is diagnosed.The present application can autonomously extract the frequency of fault signal by designing corresponding index under the condition that the fault frequency of unknown signal is unknown, and accurate fault diagnosis and positioning are realized.