7 results about "Expansion factor" patented technology

The invention discloses an automatic history fitting method based on multiple data assimilation of an improved set smoother, and relates to the technical field of petroleum engineering. The method comprises the following steps: firstly, constructing a plurality of oil reservoir models, setting a prior model, a real model and an initial expansion factor, obtaining the yield of each oil reservoir model by utilizing oil reservoir model numerical simulation, and calculating a residual error; based on corrected data covariance matrix singular value decomposition, production observation data disturbance enhancement, adaptive learning rate matrix scaling and geological boundary constraint, improving a set smoother, updating the permeability of each oil reservoir model, performing numerical simulation again, updating an expansion factor, judging whether the expansion factor meets a preset condition or not, continuing iteration if the expansion factor meets the preset condition, and if the expansion factor does not meet the preset condition, continuing iteration until the expansion factor meets the preset condition; and if not, updating the expansion factor of the iteration, ending the iteration, and outputting the permeability field of each updated oil reservoir model, thereby solving the problems of parameter overshoot, covariance statistical deviation and low calculation efficiency in oil reservoir history fitting, and facilitating history fitting of complex oil reservoir production parameters.

The invention discloses a control method and system for a robot and pneumatic software operation hand integrated system. The control method comprises the steps that the heading angle and the depth value of the robot and pneumatic software operation hand integrated system are monitored in real time; based on the heading angle, the depth value, the target heading angle and the target depth value, the deviation and the deviation change rate are calculated; inputting the deviation and the deviation change rate into a variable universe fuzzy PID controller to obtain a control quantity, and controlling a heading angle and a depth value of an integral system of the robot and the pneumatic software operation hand, so that the heading angle and the depth value of the integral system are close to a target heading angle and a target depth value; the variable universe fuzzy PID controller is obtained by introducing a scalability factor for controlling an input variable universe and a scalability factor for controlling an output universe into the fuzzy PID controller and adding a dynamic constraint mechanism of the scalability factors; a telescopic factor for controlling the input variable discourse domain is obtained based on the deviation; the scaling factor of the control output discourse domain is obtained based on the deviation change rate.

The invention discloses a double-loop real-time obstacle avoidance method and system based on geometric expansion, and the method comprises the steps: obtaining a first expansion factor of a moving object according to a current speed model, and building a constraint condition; acquiring a second expansion factor of each obstacle according to the circumcircle radius of each obstacle; according to the current position of the moving object and the center position of each obstacle, calculating the corresponding expansion factor circle center distance under each obstacle; under the condition that a plurality of obstacles exist, the threat degree of the second expansion factor and the first expansion factor of each obstacle is calculated according to the corresponding expansion factor circle center distance under each obstacle, and the current obstacle to be subjected to obstacle avoidance is determined; determining a spatial state according to the relationship between the corresponding expansion factor circle center distance under the current obstacle to be avoided and the first and second expansion factors; when the spatial state is an intersecting or tangent state, obtaining a comprehensive expansion factor based on the overlapping degree of the two expansion factors, and establishing a constraint condition; obtaining the center of the comprehensive expansion factor according to the comprehensive expansion factor and the unit direction vector; and obtaining an obstacle avoidance point based on the comprehensive expansion factor and the center of the comprehensive expansion factor. According to the invention, the obstacle avoidance point can be effectively calculated, so that obstacle avoidance is realized.

This invention discloses a low-power, high-throughput 5G LDPC decoder implementation method, specifically comprising: Step 1, calculating the parameters required for decoding from the input parameters; Step 2, based on the spreading factor Z... c The values ​​are stored in different blocks according to different storage methods; Step 3: Based on the expansion factor Z c The value of determines whether to perform layer splitting transformation on the base matrix and update the required posterior probability information of the variable nodes; Step 4: Update the information of the check nodes and the posterior probability information of the variable nodes within the layer; Step 5: Implement layered iterative decoding; Step 6: After updating the information of all check nodes in each layer and reaching the set number of iterations, the decoding iteration is completed, and the output processing of the decoding result begins. This method significantly reduces the required resources; and by utilizing the orthogonality of adjacent layers of the split matrix, the intra-layer pipelining operation improves the decoder throughput after reducing the decoding parallelism. A low-consumption, high-throughput 5G LDPC decoder implementation device is also disclosed.

The invention discloses an adaptive reinforcement learning driven accelerator multi-objective optimization method, and belongs to the technical field of optimization design of accelerators. The method comprises the following steps: firstly, analyzing an action mechanism of a convolution loop expansion factor, quantization precision and cache division key design variables, establishing a system-level state modeling method, and designing a DDPG framework suitable for continuous action space optimization as a learner of a configuration strategy; and meanwhile, a Python script runs at a CPU end to realize a lightweight searcher, and real-time search and distribution are carried out on block parameters of Loop-3 and Loop-4 of convolution operation, so that the calculation burden of a reinforcement learning agent is reduced. Finally, an adaptive configuration method driven by reinforcement learning and a matched search process are provided, and joint optimization of the expansion scale, quantization precision and partitioning parameters is achieved.

The invention discloses a QC-LDPC code construction method and system based on Logistic chaotic mapping, a medium and equipment, and belongs to the technical field of communication, and the method comprises the steps: obtaining the number of rows, the number of columns and an expansion factor of a basis matrix according to a code length and a code rate; generating a chaos sequence according to the Logistic mapping parameters, performing displacement value quantization in combination with an expansion factor to obtain an initial basis matrix, performing four-ring detection and elimination in combination with the expansion factor to obtain an optimized basis matrix, constructing a check matrix in combination with the expansion factor, and performing performance check: if not, performing four-ring detection and elimination according to the initial basis matrix and the expansion factor; a QC-LDPC code is constructed according to the basis matrix after iteration; and if so, constructing the QC-LDPC code according to the optimized basis matrix. According to the invention, the problems of poor security, performance-limited short loop and inflexible code rate adjustment caused by the structural regularity of the existing QC-LDPC code construction method are solved.

1. A computer-implemented method for obtaining a real-time map h(x) of the distribution in a D-dimensional space of a physical quantity f(x) describing a real phenomenon (1) characterizing a complex and uncertain system, the physical quantity f(x) being inaccessible to direct measurements, the method comprising: The spatial distribution of the physical quantity f(x) using a set of parameters (s) [Number 104] describing the complex and uncertain system using a mathematical model to simulate JPEG2025537042000106.jpg1423; A particular set of values ​​s n a library of approximations of the physical quantity f(x) by performing a plurality of simulations through the assignment of [Number 105] Steps to build JPEG2025537042000107.jpg1471; formula [Number 106] calculating the real-time map h(x) representing a real-time approximation of the physical quantity f(x) in JPEG2025537042000108.jpg2667, wherein a n , n=1,...,N is the magnitude of the difference |ν l is an expansion factor determined by solving a system of L equations with N unknowns to minimize |, where l=1,...,L, and the difference is [Number 107] JPEG2025537042000109.jpg1397, where x l is a D-dimensional point, [Number 108] JPEG2025537042000110.jpg1241 is a measurement process [Number 109] The impact on JPEG2025537042000111.jpg1214 l is the value measured at [Number 110] JPEG2025537042000112.jpg1114 is the measurement process for the first measurement involved [Number 111] This is the mathematical model of JPEG2025537042000113.jpg1113, [Number 112] JPEG2025537042000114.jpg1340 is the mathematical model applied to the function g [Number 113] JPEG2025537042000115.jpg1312 is point x l , where l=1,...,L is a value that the computer-implemented method is described.