5results about How to "Improve traversal" patented technology

This invention discloses an energy-saving method and system based on data sensing, belonging to the field of energy-saving optimization technology. The method includes: using a sensor array to collect raw multi-source sensor data, performing data preprocessing and feature extraction to generate room occupancy status and a state vector; based on the room occupancy status, inputting the state vector into a macroscopic decision-making agent constructed based on a reinforcement learning algorithm to generate an optimal dynamic weight coefficient vector; based on the optimal dynamic weight coefficient vector, using an improved Hippo optimization algorithm to solve a multi-objective optimization function to generate an optimal equipment control parameter vector; executing the optimal equipment control parameter vector, returning to the sensor data acquisition step, calculating the immediate reward value after the decision, and updating the macroscopic decision-making agent using the corresponding state transition data. This invention solves the problems of poor flexibility, low optimization accuracy, and difficulty in balancing multiple objectives in existing energy-saving control strategies.

The application discloses a kind of multi-objective sled dog protection optimization method and system based on myxomycete dynamic weight, it is related to power distribution network relay protection technical field, including: obtaining fault current to establish action response characteristic model and sensitivity index, construct anti-time limit overcurrent protection double-objective optimization function;Global optimization is carried out using improved multi-objective sled dog algorithm, population is initialized by Piecewise chaotic mapping, dynamic weight is adaptively distributed by introducing myxomycete algorithm in local search stage, and individual position is updated in combination with local difference disturbance strategy;The solution set is objectively and comprehensively evaluated using entropy weight TOPSIS strategy and the optimal compromise setting value is output.The optimization method effectively resolves the conflict that traditional protection is difficult to consider both speed and sensitivity, and effectively guarantees the safe and reliable operation of the distribution network with distributed power supply.

The application discloses a ship cross section structure optimization method based on an improved bee colony algorithm. First, the discrete structure model is converted into a continuous optimization model based on a component library index, thereby laying a foundation for application of the bee colony algorithm. Secondly, in order to solve the latitude explosion caused by high-dimensional optimization, a uniformized Logistic mapping is designed for component section size initialization, so as to improve the randomness and ergodicity of the decision space. Since the bee colony algorithm has the problem of being good at exploration but poor at exploitation, the individual goodness and algorithm degree are considered, and the dynamic leading bee search and follower following strategy are respectively reconstructed, so as to balance the search ability of the algorithm and enhance the optimization quality. Finally, based on the section modulus sensitivity analysis, the continuous value obtained through iteration is normalized, so as to meet the actual engineering requirements. The method can adaptively adjust the global search and local search tendency of each individual, improves the efficiency and result precision of the ship structure optimization, and has practical value.

The present disclosure provides a pseudo-random number generator and a pseudo-random number generation method. The pseudo-random number generator comprises: a signal mapping circuit configured to map a plurality of input signals using a chaotic model to obtain a plurality of chaotic signals having a four-wing chaotic state; a signal sampling circuit electrically connected to the signal mapping circuit and configured to sample the plurality of chaotic signals to obtain a plurality of state variables corresponding to each chaotic signal in the plurality of chaotic signals; and a signal operation circuit electrically connected to the signal sampling circuit and configured to perform an integer operation on the plurality of state variables corresponding to each chaotic signal to obtain a plurality of groups of random numbers corresponding to the plurality of chaotic signals respectively, each group of random numbers in the plurality of groups of random numbers comprising a plurality of random numbers.

This invention discloses a multi-degree-of-freedom control system based on surface micro-electrical signals, comprising: an electromyography (EMG) signal acquisition module for acquiring surface EMG signals; a signal preprocessing module for preprocessing the acquired EMG signals; a feature extraction module for calculating the optimal parameters of multi-scale permutation entropy using a particle swarm optimization algorithm for the preprocessed signals; and based on the optimal parameters, performing multi-scale permutation entropy feature extraction on the preprocessed EMG signals to obtain feature data of the EMG signals; a signal classification module for classifying the EMG signals according to the feature dataset; and a feature matching module for matching the classified signals with preset actions. This invention solves the problem of limited application scenarios and efficiency of existing surface EMG signal-controlled robotic arms, which is caused by the low degree of freedom of the robotic arm.