144results about How to "Slow convergence" patented technology

The invention discloses a self-adaptive composite service optimization method based on multi-agent reinforced learning. The method combines conceptions of the reinforced learning and agents, and defines the state set of reinforced learning to be the precondition and postcondition of the service, and the action set to be the Web service; parameters for Q learning including the learning rate, discount factors and Q value in reinforced learning are initialized; each agent is used for performing one composite optimizing task, and can perceive the current state, and select the optimal action under the current state as per the action selection strategy; the Q value is calculated and updated as per the Q learning algorithm; before the Q value is converged, the next round learning is performed after one learning round is finished, and finally the optimal strategy is obtained. According to the method, the corresponding self-adaptive action strategy is worked out on line as per the environment change at the time, so that higher flexibility, self-adaptability and practical value are realized.

A method and apparatus for reducing motion blur in a motion blurred image are provided. The method includes blurring a guess image based on the motion blurred image as a function of blur parameters of the motion blurred image. The blurred guess image is compared with the motion blurred image and an error image is generated. The error image is blurred and pixels in the blurred error image are weighted based on the steepness of edges proximal to corresponding pixels in the motion blurred image. The blurred and weighted error image and the guess image are combined thereby to update the guess image and correct for motion blur.

The invention discloses a short-term prediction method for occupancy of an effective parking space of a parking lot. The short-term prediction method comprises the steps as follows: 1) determining a time sequence of the occupancy of the effective parking space of the parking lot; 2) carrying out wavelet decomposition to the time sequence of the occupancy of the effective parking space through a wavelet function, thus obtaining a low-frequency coefficient vector and a high-frequency coefficient vector; implementing the wavelet reconstruction to the low-frequency coefficient vector and high-frequency coefficient vector, so as to obtain the time sequence of N+1 reconstructions; 3) establishing a wavelet neural network model to the time sequence of the N+1 reconstructions for predicting, thus obtaining N+1 prediction results; and 4) accumulating the N+1 prediction results, so as to obtain the prediction results corresponding to the time sequence of the occupancy of the effective parking space. According to the short-term prediction method disclosed by the invention, a wavelet analysis-wavelet neural network combinational prediction model is raised to perform short-term prediction to the occupancy of the effective parking space of the parking lot according to the short-term variation characteristic of the occupancy of the effective parking space of the parking lot, therefore, the prediction accuracy and the stability are improved.

The invention relates to a mobile robot path planning method. The method comprises the following steps: finding the shortest path of an environment; taking grids safe from a dynamic barrier on the shortest path as local target points when a robot detects in progress the dynamic barrier and will bump into the dynamic barrier in the environment; determining the motion range of the dynamic barrier; the robot advancing along the grids with high pheromone concentration; and obtaining an optimal path that is away from the dynamic barrier and passes by a specified point. The problem of low convergence speed of an ant colony algorithm due to restrictions of the ant colony algorithm itself is solved. A group of optimal solutions is obtained after parameters of a conventional ant colony algorithm are optimized by a particle swarm algorithm. A genetic algorithm is adopted to prevent local optimum, other parameters of the above parameters keep unchanged, and the pheromone concentration is amplified by times. In this way, pheromone concentration differences on different paths are more obvious, and the convergence speed of the algorithm is increased.

The invention discloses a driving motor system performance evaluation method for an electric vehicle. The driving motor system performance evaluation method analyzes from different dimensions such as motor control performance, motor body design and enterprise qualification and ability of the driving motor system according to performance characteristics of a driving motor used for the electric vehicle, adopts an analytic hierarchy process to determine a driving motor performance evaluation index system and index weights thereof, establishes a BP neural network model for driving motor system performance evaluation, organically integrates a bat algorithm with a particle swarm algorithm to form a bat-particle particle swarm hybrid algorithm, and optimizes parameters of the neural network structural model by adopting the bat-particle particle swarm hybrid algorithm. Simulation examples show that, through training and testing data samples, the driving motor system performance evaluation method which optimizes the neural network based on the analytic hierarchy process and the bat-particle particle swarm hybrid algorithm has the advantages of fast evaluation speed and high accuracy rate, achieves satisfying evaluation results, and has certain promotion value in evaluation, selection and application of a driving motor system for the electric vehicle.

The invention discloses a false data injection attack design and defense method for a direct current microgrid. For a network communication message in the secondary control layer of the direct currentmicrogrid, malicious false data are injected. At the same time, considering the initial state estimation bias and measurement noise existing in the original detection process, a covert data injectionattack method is designed to cause disturbance to current/voltage without affecting the existing attack detection indicators. The covert data injection attack acquired through analysis affects the average voltage value of coupling points in the direct current microgrid. A proportional-integral dynamic average consistency algorithm is used to estimate the average voltage value of the coupling points in the direct current microgrid. The covert data injection attack is detected based on the average voltage deviation of the coupling points. Finally, a proportional-integral compensation mechanismis used to compensate the average voltage deviation of the coupling points. The secondary control input is adjusted. Voltage balance in the direct current microgrid is re-established.

The invention discloses field programmable gate array (FPGA) implementation equipment and an FPGA implementation method for self-adaptive clutter suppression of external radiation source radar. The method comprises the following steps of inputting four paths of channelized data to be processed into a finite impulse response (FIR) filter module through a first in first out (FIFO) memory inside a self-adaptive clutter suppression module of an FPGA chip; inputting two paths of data, channelized by an auxiliary antenna, into a step length calculation module through the FIFO memory, and calculating the step length through the step length calculation module; simultaneously inputting the output of the step length calculation module and two paths of output of the FIR filter module into a weight updating module; inputting an updated weight into the FIR filter module; and performing synchronous parallel output on clutter suppression results through in-phase/quadrature (I/Q) channels. The self-adaptive clutter suppression is controlled by five global clocks with the same rate and different phases. The self-adaptive clutter suppression of the external radiation source radar can be better realized. The problem of difficulty in meeting of a requirement on real-time performance caused by large conventional clutter suppression equipment amount is solved. The equipment and the method are high in processing efficiency, high in calculation speed and low in equipment complexity, and are used for implementing the self-adaptive clutter suppression of the external radiation source radar.

The invention provides an AUV (Autonomous Underwater Vehicle) track deviation estimation method based on multi-point terrain matching and positioning. The AUV track deviation estimation method comprises the following steps: (1) estimating a searching region of the terrain matching and positioning; (2) carrying out terrain matching and positioning under unknown tidal difference and measurement error conditions; (3) estimating measurement errors and estimating a positioning confidence interval; (4) estimating positioning errors; (5) estimating multi-point terrain matching and positioning initial track deviation; (6) carrying out track correlation and eliminating an incorrect positioning point; (7) carrying out secondary fitting of a track. The AUV track deviation estimation method provided by the invention has the advantages that characteristics that reckoning navigation is slowly changed in a time domain and an airspace and a terrain matching and positioning result has no diffusivity in the time domain and the airspace are combined, and a plurality of dense terrain matching and positioning points are obtained on a track line; track line of the reckoning navigation is fitted with multi-point positioning information so that the positioning precision and the positioning reliability are greatly increased.

A response of a channel may be estimated by correlating a received signal and a training sequence, by forming a matrix Γ based on a desired shape for the peaks of the correlation, by extracting a vector y from the received signal, and by estimating the channel response from a least-squares solution based on the matrix Γ, the vector y, and a matrix formed from the elements of the known training sequence.

Various configurations of improved wake-modifying devices for a power boat are provided herein. All such configurations provide a water channel and (optional) wedge for capturing and redirecting significantly more of the water flowing past the transom of the boat than conventional trim tabs consisting primarily of a flat plate. This increases the upward force applied to the deployed wake-modifying device and raises the stern of the boat on the non-surf side to list the boat to the surf side, producing a larger wake on the surf side. Some configurations provide a convergent water channel to produce longer, smoother wakes on the surf side of the boat. Other configurations provide a divergent water channel, which produces a comparatively shorter, taller wake on the surf side of the boat.

The invention discloses a method for controlling the component and thermal treatment technological process of pre-hardening plastic die steel. The method includes the steps that firstly, an orthogonal test is designed according to structure characteristics and performance requirements of the pre-hardening plastic die steel, and thermal treatment technological parameters which have obvious influences on performance are found out through the test; then chemical components, the thermal treatment technological parameters and performance indexes are collected; an artificial neural network model is built, the model is trained, input of the model is the chemical components and the thermal treatment technological parameters, and output of the model is the performance indexes; the trained artificial neural network model is used for predicting steel performance and studying influence laws of input to output according to the chemical components and the thermal treatment technological parameters. According to the method, the complicated non-linear relationship between the chemical components, the thermal treatment technological parameters and mechanical properties is built, therefore, the component and thermal treatment technological process can be effectively controlled, the pre-hardening plastic die steel with excellent performance can be generated, and the service life of dies can be prolonged.

The invention discloses a frequency domain blind separation method and a frequency domain blind separation system with low complexity. The method comprises the following steps: acquiring frequency domain separation signals collected by a microphone array; carrying out correlation analysis on the separation signals of the current frequency point and the pervious frequency point, thus completing thelocal sorting of the separation signal of each frequency point; carrying out correlation analysis on the separation signal of each frequency point after local sorting and the global central point, thus completing the global sorting of the separation signal of each frequency point; carrying out amplitude adjustment on the separation signals after global sorting; and carrying out Fourier transformon the adjusted separation signals, thus obtaining the separation signals of the time domain. With the method combining local sorting with global sorting provided by the invention, the accuracy and robustness of separation can be improved, meanwhile, the iteration times of the sorting process is reduced, and the computation complexity is relatively low.

An encryption method based on iteration FrFT and chaotic single-channel color images is characterized by comprising the steps that firstly the color images are placed in chaos and divided into three parts, then two parts are encrypted to obtain a middle result, and finally the amplitude of the middle result and the third part are encrypted to obtain a final cryptograph. The FrFT and the paired Logistic chaotic mapping are combined, so that the encrypted image has the nonlinearity and randomness, the safety and convergence rate of an encryption system are improved, the problem that an existing encryption method is low in convergence rate is solved, meanwhile, the complexity of the color image encryption system is reduced, the system is simple, and operation is convenient.

The invention discloses a satellite video dynamic target tracking method fusing a correlation filter and motion estimation, and relates to the field of spaceflight earth observation information processing and analysis. The satellite video dynamic target tracking method comprises the steps: obtaining a satellite video, and segmenting the satellite video into continuous single-frame images; selecting a to-be-tracked target from the single-frame image, constructing image features of the target, and inputting the image features into a related filter to obtain the position of the target; performingmotion estimation processing on the position of the target through a trajectory average algorithm and a Kalman filter, and when the Kalman filter is not stable, taking the processing result of the trajectory average algorithm as a motion estimation result; and when the Kalman filter is stable, taking a processing result of the Kalman filter as a motion estimation result. The satellite video dynamic target tracking method is suitable for satellite video ground target tracking, especially when the target is shielded, can still effectively track the target, and can complete multiple applicationssuch as target high-precision tracking, sensitive target real-time positioning and traffic flow monitoring.

The compositional event based modeling of integrated applications (CINEMA) tool provides a way to extend a modeling environment using legacy event based applications, such as Graphical User Interface (GUI) APplications (GAPs). CINEMA allows modelers to extend the toolbox of the modeling environment by creating modeling elements that represent GUI objects of GAPs. CINEMA generates source code that allows an integrated system to control and manipulate the GUI objects of GAPs represented by the modeling elements used to create a model of the integrated system.

The invention relates to a high-precision numerical simulation calculation method for carrying out static characteristic analysis on concrete gravity dam based on h-p type finite element method, and belongs to the technical field of simulation. The high-precision numerical simulation calculation method for carrying out static characteristic analysis on concrete gravity dam based on h-p type finiteelement method comprises the following steps of building a calculation model of the hydraulic concrete gravity dam; calculating a displacement field and a stress field of the hydraulic concrete gravity dam model by a h-p type finite element method; judging whether the obtained displacement field and stress field meet the precision requirement or not, if the obtained displacement field and stressfield do not meet the precision requirement, encrypting the grid again, improving the order of the interpolation polynomial, and repeating the steps until a satisfactory result is obtained. The invention provides a novel high-precision finite element calculation and analysis method for a concrete gravity dam in order to solve the problems that an existing concrete gravity dam model based on traditional finite element static analysis is too long in calculation time, too large in error and low in precision.

The invention belongs to the technical field of seismic and logging joint inversion, and discloses a CNN well-seismic joint inversion method and system, a storage medium, equipment and application. The method comprises the steps: searching an inversion mapping operator f1: y-> x from seismic data y to logging data x, i.e. X = f1 (y), with the seismic data y as the input and the logging data x as the output; reconstructing a logging curve in the forward direction; and reversely updating the weight and the bias. A four-layer network structure containing two hidden layers comprises an input layer, a first convolution layer, a second convolution layer and an output layer, and the two hidden layers are convolution layers. Some virtual logging curves are interpolated by using a Kriging interpolation technology, and virtual logging data and real logging data are used as training data for convolutional neural network learning. Under the condition that a real well is not additionally added, the number of learning samples can be increased through virtual well logging, an inversion mapping operator is searched for in a wider range, and over-fitting of local training data is prevented.