38results about How to "Strong parallelism" patented technology

The invention relates to a method for enhancing image resolution by convolution filtering and anti-aliasing analyzing. The method includes steps of preprocessing an original image O to obtain a preprocessed imaged L; sequentially carrying out down-sampling filtering processing and up-sampling filtering processing for the preprocessed image by filters to obtain a low-pass portion of the original image O; subtracting the low-pass portion from the original image O to obtain a high-pass portion of the original image O; carrying out self-similarity processing for the preprocessed image L, searching for positions on the preprocessed image L similar to the high-pass portion, and matching high-pass information into the preprocessed image L by mapping relation to obtain a clear image S with pixels the same as those of the preprocessed image L; and judging whether the image S obtained after the self-similarity processing has aliasing or not, and carrying out anti-aliasing edge processing for the image S obtained after the self-similarity processing and the preprocessed image L to obtain a smoother high-definition image R if the image S obtained after the self-similarity processing has the aliasing. Compared with the prior art, the method has the advantages of capability of increasing definition and resolution of the image, high practicality and the like.

The invention discloses an ordered binary decision diagram modeling method for distribution network fault recovery. The method includes a first step of carrying out partition on a power distribution network, and using an on-off state as a decision variable, a second step of forming an adjacent matrix of the power distribution network according to a forming method of the adjacent matrix of an undirected graph, a third step of forming a reachable matrix of the power distribution network, a fourth step of forming boolean functions of each restriction of fault recovery, a fifth step of combining to generate a final binary decision diagram, and obtaining a contractive solution space, a sixth step of carrying out load flow calculation on feasible solutions one by one in the solution space after contraction, and carrying out safety verification, and a seventh step of obtaining an optimal recovery scheme according to priority levels of optimized objective functions. The modeling method has the advantages of effectively reducing the solution complexity of fault recovery problems, overcoming the defect that a traditional artificial intelligence algorithm is prone to premature convergence in local optimal solution, and improves understanding reliability and the like.

The invention discloses a separable active noise control system. A controller is used for noise control in a stable sound field environment. The controller is used to divide an active noise controller into two independent modules, namely a training module and a control module. The control module has a function of simulating a sound field path, and is mainly used for the simulation of the sound field path from the reference source to the control source, and is used to reduce the sound pressure in the specific area. The training module and the control module can work in a combined manner, and are mainly used for the cooperation of the sound field paths during the active noise control. The system provided by the invention can work in the fixed control working mode and the self-adaption working mode, and is advantageous in that in the fixed control working mode, the control module can work independently; the function is single, the size is small, the cost is low, the power consumption is low, the parallelism is strong, and the processing time delay can be reduced; in the self-adaption working mode, the control module and the training module can work in a cooperative manner, and the connection is convenient and fast, the operation is simple, and the use efficiency is high, and in addition, one training module is used for a plurality of control modules, and therefore the system debugging installation can be facilitated.

The invention provides a PM2.5 concentration data analysis and prediction model building method. The method comprises the steps of: firstly, decomposing a change process of PM 2.5 into pollution generation, diffusion, thinning and settling; dividing a PM 2.5 monitoring area into a plurality of cells and building a cellular automata model for each process; training parameters in each model by using historical data to obtain a PM 2.5 data prediction model. The cellular automata models are adopted to simulate and predict the change of the PM 2.5 concentration, so that a PM 2.5 concentration change process can be effectively and rapidly predicted.

The invention discloses an NCC image matching algorithm enzyme numerical film system, which includes a surface film for storing an image to be matched, a template image and an initialized enzyme, and simultaneously outputs position coordinates for calculating the matching position of the template image in the image to be matched; ComputeT film, used to calculate the parameters required for the template image to perform NCC calculation; ComputeMatchingCood film, used to calculate the matching position of the template image in the image to be matched; several ComputeNccValue lk Membrane, which is used to calculate the NCC value of the template image at each position in the original image after moving in parallel. The membrane system of the present invention has strong parallelism, so that the template image does not need to be slidingly matched on the original image, but calculates the value of NCC with all sub-images at the same time, allowing multiple time-consuming NCC calculations to be performed in parallel under serial calculations Execution, the computational time complexity is constant and has nothing to do with the data size, thus greatly reducing the time complexity of the entire NCC image matching algorithm.

The embodiment of the invention provides a method and system for managing Internet of Videos devices. The method comprises the following steps: periodically transmitting a parameter collection commandto a plurality of Internet of Videos devices; parsing the received parameter collection command according to an Internet of Videos protocol; obtaining target parameters from the plurality of Internetof Videos devices according to the parsed parameter collection command; performing reverse parsing on the target parameters from the plurality of Internet of Videos devices according to the Internetof Videos protocol to obtain target parameters in a Jason format; storing the target parameters of the plurality of Internet of Videos devices in the Jason format in a database; receiving an operationcommand of the plurality of Internet of Videos devices; parsing the operation command according to the Internet of Videos protocol; operating the plurality of Internet of Videos devices according tothe parsed operation command to obtain operation results; performing reverse parsing on the operation results of the plurality of Internet of Videos devices according to the Internet of Videos protocol to obtain operation results in the Jason format; and returning the operation results of the plurality of Internet of Videos devices in the Jason format to a management platform.

A target detecting and tracking system using a background differencing method based on FPGA comprises a FPGA device, a SD card, an LED indicator light, a DDR2SDRAM, two seven-segment digital tubes, an LTM display screen and a minimum video detecting and tracking system, wherein the minimum video detecting and tracking system is embedded in the FPGA and is integrated in a SOPCbuilder environment with an AVLON bus as a standard. The target detecting and tracking system using the background differencing method based on FPGA is mainly used for detecting and tracking moving objects in static scenes. A target detecting and tracking method using the background differencing method based on FPGA comprises the steps of a. reading AVI format video files with the SD card, b. converting the AVI format video files into multiframe images, c. detecting and tracking the videos based on the images, d. obtaining the moving objects which are to be tracked, and e. displaying tracking results on the LTM display screen. Due to the fact that an FPGA platform is adopted by the system, the system is high in parallelism performance, and computing speed is improved. The target detecting and tracking method is good in tracking effect and adaptability, particle filter algorithm which is suitable for non-linear non-Gaussian dynamic models, and the target detecting and tracking system is similar with real scenes and is good in adaptability and high in tracking accuracy.