54 results about "Virtual particle" patented technology

A system and method for decentralized cooperative control of a team of agents for geographic and other search tasks. The approach is behavior-based and uses probability particle approach to the search problem. Agents are attracted to probability distributions in the form of virtual particles of probability that represent hypotheses about the existence of objects of interest in a geographic area or a data-space. Reliance on dependable, high-bandwidth communication is reduced by modeling the movements of other team members and the objects of interest between periodic update messages.

The present invention proposes a multi-agent cooperative target search method based on the particle swarm algorithm, which introduces the particle swarm algorithm for virtual navigation, and uses entity agents with certain communication and perception capabilities to replace the virtual particle realization source in the particle swarm algorithm Location search. For the first time in the particle swarm algorithm, the moving distance and search time of the particles are considered, and the weight cost function is established. According to the target position generated by each generation of the particle swarm, the path with the least cost is planned for the agent through the local search strategy. Accordingly, the multi-agent system can greatly reduce energy consumption, enhance battery life, and improve search efficiency without affecting the target search accuracy. The invention is a multi-agent target search method with generality, and the particle swarm algorithm based on it can be any particle swarm variant.

The invention discloses a system for predicating ball mill grind size distribution by a fast Monte Carlo method and a method thereof, and the system comprises a data acquiring module, a particle size mass fraction calculating module, a representative particle diameter calculating module, a virtual particle number calculating module, a breaking rate calculating module, a breaking probability calculating module, a grinding particle number calculating module and a size distribution calculating module; the method comprises the following steps of: setting the initial time (t=0) of the ball mill, and acquiring relative data; calculating the mass fraction of each particle size of each initial material; calculating the representative particle diameter of each particle size; calculating the virtual particle number of each particle size; calculating the breaking rate of each particle size; calculating the breaking probability of each particle size; acquiring the number of grinding particles in each particle size after grinding by the ball mill; calculating grinded size distribution. In the system and the method, many breaking events are allowed to occur in each time interval, thereby precisely observing a microscopic grinding process and obtaining the size distribution change of the material in the ball mill before and after grinding.

The invention provides a method for analyzing and evaluating micro-nano particle dispersion and distribution. The method includes the following steps of firstly, collecting a micro-nano particle microscopic image, and graying the image; secondly, conducting binaryzation on the image through an OTSU method, and removing impure points and ineffective communication domains in the image; thirdly, separating the connected communication domains through the morphological algorithm, and filling holes in the communication domains; fourthly, adding virtual particles, segmenting the image through the watershed algorithm, and obtaining segmented characteristic communication domains; fifthly, calculating related data of the characteristic communication domains; sixthly, evaluating micro-nano particle dispersion and distribution. By means of the method, the more accurate result can be obtained; a large number of micro-nano particle distribution microscopic images can be rapidly processed in a batched mode; the micro-nano particle distribution condition can be analyzed and evaluated comprehensively from the aspects of uniformity, density and clustering degree.