4results about How to "Short calculation time" patented technology

This application provides a method and apparatus for cutting quantum circuits. The method includes dividing the quantum circuit to obtain two bits of data; generating cutting position data based on the quantum circuit and the two bits; cutting the quantum circuit based on the cutting position data to obtain circuit cutting information, which includes two initial quantum sub-circuits; updating the wire cutting position and gate cutting position to obtain new cutting position data if either initial quantum sub-circuit does not meet the bit number constraint; determining the circuit cutting cost for generating new circuit cutting information based on the new cutting position data if the new initial quantum sub-circuit meets the bit number constraint; and determining the target circuit cutting information from multiple new circuit cutting information or from existing circuit cutting information based on the circuit cutting cost.

This invention belongs to the technical field of robotic arm motion planning and discloses a robotic arm motion planning method and system based on an error-driven adaptive gradient model. The method includes: S1 establishing the joint velocity at two adjacent moments with respect to the adaptive coefficient λ based on a Lyapunov function. n The gradient relationship is obtained; S2. The joint velocity at time n+1 is calculated using the gradient relationship, and the error between the calculated joint velocity at time n+1 and the adaptive coefficient is updated using the central finite difference method; S3. n = n+1; return to step S2 until the joint velocities at all times are obtained; S4. The joint position is calculated using the relationship between the joint velocity and position; the velocity of the end effector is calculated using the robotic arm motion planning model and the joint velocities at each time, thereby realizing the motion planning of the robotic arm. This invention solves the problem of low computational efficiency in robotic arm motion planning.

This application belongs to the field of radar reconnaissance technology and provides a single-source radiation determination method based on temporal information entropy. It first calculates the Time Difference of Arrival (TDOA) of all adjacent pulses, then sets a clustering threshold and a first-class cluster center, and uses a clustering algorithm to obtain multiple clusters based on the TDOA. Next, it reduces the number of clusters and removes invalid elements by merging and removing harmonics. The distribution probability of each TDOA is calculated based on the number of indices in the merged clusters after harmonic removal. The temporal information entropy is calculated based on the distribution probability of each TDOA. A set information entropy discrimination threshold is set; if the temporal information entropy does not exceed the threshold, the pre-sorted signal is considered to be composed of a single radiation source; otherwise, it is composed of multiple radiation sources. This method can accurately determine the composition of radiation source signals in the radar pre-sorting clustering results, avoiding further PRI main sorting processing of single-source signals and reducing redundant sorting calculations.

An early fault diagnosis method for cross-stage multi-condition based on permutation spectrum difference measurement, including: collecting vibration signals of mechanical equipment under different states in the manufacturing test stage and the actual use stage, dividing the vibration signals into multiple subsequences through a combination of characteristic parameters, and reconstructing the subsequences into the original phase space; obtaining all possible permutation patterns according to the embedding dimension of the vibration signals, and converting the original phase space into a symbolic phase space; counting the number of occurrences of various permutation patterns in the symbolic phase space to obtain permutation spectrum characteristics; obtaining the difference degree between the vibration signals collected under different states; establishing a parameter selection model to select the best parameter combination; calculating the permutation spectrum characteristics of each time series using the best parameter combination, inputting the permutation spectrum characteristics of the data in the manufacturing test stage into a multi-class SVM classifier to obtain a training model, and inputting the permutation spectrum of the data in the actual use stage into the training model to identify the fault type and operating condition corresponding to the time series.