114results about How to "Expand the search space" patented technology

The invention discloses an improved fuzzy neural network bus intelligent scheduling method based on a chaos theory, and belongs to the field of intelligent transportation. According to the improved particle swarm bus intelligent scheduling method based on the chaos theory, advantages and complementarity of various algorithms are fully utilized, a series of improvement measures are also introduced, such as conjugate gradient optimization, and inertia factor and constraint factor of the particle swarm algorithm etc., the mechanism and the search performance are researched from the theoretical and practical perspectives, problems of poor global search capability and premature convergence of the conventional optimization algorithm are fundamentally solved, the diversity of population can be obviously increased, the global search capability is obviously improved, the problem of fuzzy information can be effectively dealt with, the convergence speed is fast, and a new high-efficiency method is provided for bus intelligent scheduling.

The invention relates to a temperature compensation method and system for a silicon micro-accelerometer based on an improved PSO (Particle Swarm Optimization) optimized neural network, and the temperature compensation method and the temperature compensation system are designed for improving temperature compensation precision. The method comprises the following steps: acquiring a training sample ofPSO optimization and a BP (Back Propagation) neutral network; constructing the BP neutral network on the basis of the training sample; using an optimal extreme point optimized by an adaptive weight PSO as an initial weight value and a threshold value of a BP neutral network model; introducing mutation operation into a PSO algorithm, updating the particles, then reinitializing the particles at a certain probability and expanding a population search space which is continuously reduced in iteration by the mutation operation; establishing the BP neutral network by calling the parameters, realizing real-time temperature compensation of the silicon micro-accelerometer and outputting a compensation result. The temperature compensation method and the temperature compensation system disclosed by the invention have the advantages that the problems of solving of an optimal compensation result and temperature globality are solved, and finally improved compensation precision and global improvementof the silicon micro-accelerometer are realized.

The invention discloses a method for selecting a multi-objective immune optimization multicast router path, which mainly solves the problem of the optimization of multicast routers. The method has the following steps of: (1) determining an optimized target to generate a network model, and setting running parameters to generate an initial population; (2) eliminating an individual path loop; (3) calculating the individual target value to generate a current non-dominant population; (4) judging finishing conditions, if the finishing conditions are met, the current non-dominant population is output, or else, carrying out step (5); (5) calculating the individual crowding distance of the current non-dominant distance to generate an active population; (6) carrying out cloning and local searching operation on the active population; (7) carrying out recombination, variation and local search operation on the cloned population; (8) combining the current non-dominant population with the populations obtained in the steps (6) and (7) to eliminate the individual path loop; (9) calculating the individual value to refresh the current non-dominant population, and carrying out the step (4). The invention has the advantage of providing a flexible optimization scheme and is suitable for selecting the multicast router path.

A time-attitude based method for decomposition and synthesis of imaging satellite observation task is disclosed. The method is characterized by comprising the following steps: (1) determining the time-orientation vector of a target according to the weft height of the target and the position of a satellite; (2) establishing an external characteristic rectangle, with a long edge parallel to a star line and including all target points, as a target region, determining a pitch angle, and calculating the characteristic vector of a region target; (3) according to the width of the satellite, dividing bands; and (4) according to the region target borders, cutting the bands to a right length. The method can intuitively and efficiently conduct target decomposition and synthesis, and more easily generate element task information input to a task planning and scheduling model. The method puts forward a concept of time-attitude, which is not only easy to understand and calculate, but also provides a unified description method for target decomposition and synthesis, and has certain operability, generality and scalability.

The invention discloses a method for repairing a fault of a wireless sensor network. The method comprises the following steps: S1, based on a fuzzy mathematics and linear program method, establishing a membership matrix which represents the membership degree of a redundant node on a fault node a replacement matrix which represents the redundant node on the fault node and restriction conditions; S2, according to the membership matrix and the replacement matrix, constructing a target function of the redundant node to replace the fault node; and S3, based on a genetic algorithm, selecting an optimal redundant node to replace the fault node. By utilizing the method provided by the invention, the defect that at present, the energy problem is not abundantly considered in the aspect of study on the fault repairing problem is solved, wherein the problem can be more appropriately described by using the used fussy theory, and various influence factors are considered in the calculation of the membership function, thus network conditions are comprehensively reflected; and by using a genetic simplex method, the defect that the genetic algorithm is early matured is solved, a searching space is developed, and the convergence in the later period of searching is good.

The optimal insulin to be delivered by an insulin infusion pump is determined by using a reinforcement learning algorithm aiming to the personalized glucose regulation. The algorithm optimizes the daily basal insulin rate and insulin: carbohydrate ratio for each patient, on the basis of his/her measured glucose profile. The proposed algorithm is able to learn in real-time patient-specific characteristics captured in the daily glucose profile and provide individualised insulin treatment. An automatic and personalised tuning method contributes in the optimisation of the algorithm's performance.

According to the multi-skill personnel scheduling method in the research and development project combination, which can solve the technical problems that a multi-skill personnel scheduling decision inthe research and development project combination is high in complexity and high in difficulty. The method mainly comprises the steps of generating an initial scheme of multi-skill personnel scheduling in a project combination according to a traditional serial progress production mechanism; Establishing a project combination multi-skill personnel multi-target scheduling model by considering the learning effect of personnel; When a learning effect and a plurality of scheduling targets are considered, applying a multi-target improved ant colony algorithm to iteratively solve the multi-skill multi-target scheduling model to obtain a Pareto solution set; And for the Pareto solution set, solving an optimal solution of the model by adopting an approximate ideal solution sorting method, and taking the optimal solution as a personnel configuration scheme for the project combination multi-skill personnel multi-target scheduling problem. On the basis of considering the cost and the constructionperiod of a traditional project personnel scheduling model, the personnel learning effect is further considered, the personnel skill training decision-making target is increased, rapid solving of themodel is achieved, and the decision-making requirements of enterprise managers are met.

The invention discloses a fruit-fly-optimization-algorithm-based multi-station assembling sequence planning method. The method comprises: a priority constraint relationship between parts is expressed by using a priority sequence graph; a priority relationship matrix, an integrated interference matrix, a station capability list, and an assembling information table are constructed; and an interference relationship between parts as well as a relationship between stations is described. A coding system of a fruit fly algorithm is provided; and three searching stages including an odour searching stage, a vision searching stage, and a cooperative searching stage of the fruit fly are designed by considering the local and global searching capabilities of the fruit fly algorithm. A fitness function expression close to practical engineering is put forward by considering influences of an assembling operation cost, a replacement tool cost, a clamping changing cost and a transport cost comprehensively. According to a priority sequence matrix, initial sequence evolution is guided; and a product assembling sequence and a station assembling sequence are optimized by using the fruit fly optimization algorithm.

Disclosed are methods and systems for expanding the search space in a Peer-to-Peer (“P2P”) network. In one embodiment, the search space is expanded by increasing the time-to-live value of the search request message. The P2P network may include a plurality of nodes, and the method for increasing the search space in the network includes the steps of receiving a search request message; changing a time-to-live value carried by the message; and forwarding the message to at least one peer node.

The invention discloses a method and apparatus for obtaining object detectors, and belongs to the technical field of detection. The method comprises: according to an SVM, training an image training sample, obtaining a first root filter, and according to a first root filter, obtaining first part filters corresponding to each object part; performing model transformation on the first root filter and each first part filter, and obtaining a root filer after the transformation and part filters after the transformation; according to a latent SVM, training the root filer after the transformation and the part filters after the transformation, obtaining a second root filter and second part filters, and taking the first root filter, the second root filter, the first part filters and the second part filters as the obtained object detectors. According to the invention, the model transformation is carried out on the first root filter and the first part filters, so that the types of filters can be increased, the search space of the object detectors is expanded, objects with changeable postures and visual angles can be effectively matched, and accordingly the object detection precision is improved.

The invention discloses a method and system for predicting wind speed. The method includes: obtaining the original sequence of wind speed data; using a particle swarm algorithm to determine the optimal preset scale parameter and optimal bandwidth parameter of the variational mode decomposition method, and converting the original sequence Decompose into several modal function subsequences; use the differential evolution algorithm to determine the kernel parameters of the least squares support vector machine model of each modal function subsequence, the variation factor of the mutation operation decreases with the increase of the evolutionary algebra, and the generated mutant individuals It is related to the optimal individual of the previous generation, and the crossover probability factor of the crossover operation increases with the increase of the evolutionary algebra; according to the autocorrelation of each modal function subsequence and each kernel parameter, determine the least squares of each modal function subsequence Support vector machine wind speed prediction sub-model, and predict the decomposition wind speed of each sub-sequence through each wind speed prediction sub-model; determine the final wind speed prediction value according to each decomposition wind speed. The method and system provided by the invention can accurately predict wind speed.

The invention discloses an automatic balance control method based on APSO-BP for double counterweight plates. Based on a BP neural network, balance data of each time are fully utilized and the balancing parameters are fitted and optimized to realize automatic balancing control. The input parameters are the initial position A1 and B1 of the two balancing blocks, rotating speed N, unbalance force (shown in the description), the optimization objective is system vibration response (shown in the description), and the optimal output is the target positions A2, B2 of the two balancing blocks. Basedon the initial position, rotational speed and system vibration response of the counterweight, the BP neural network is used to fit the system vibration value, and the adaptive particle swarm optimization is used to obtain the optimal target position of the two counterweight blocks. The invention can effectively optimize the particle swarm optimization based on the neural system fitting input-output relationship by using the improved adaptive weight adjustment method, which is conducive to promoting global optimization, accelerating the convergence rate and increasing the accuracy of the automatic balance system control and the effectiveness of the data.

The invention discloses an LED chip mounter pick-and-paste path optimization method based on a hybrid genetic algorithm, and relates to a chip mounter pick-and-paste path optimization method. The objective of the invention is to solve the problems of long pick-up path and low LED patch production work efficiency of an existing method. The method comprises the following steps of: 1, preparing before production, and importing machine parameters and PCB data file information; 2, according to the machine parameters and the PCB data file information determined in the step 1, determining a feeder slot allocation position enabling the movement distance of the chip mounting head to be the shortest; 3, planning a parallel chip mounting head chip picking path, taking the determined feeder slot position distribution midpoint as a starting point and an ending point for optimizing the chip mounting path, searching a feasible solution of the chip mounting path by adopting a hybrid genetic algorithm,and reserving the shortest solution of the chip mounting path; and 4, outputting the solution with the shortest mounting path searched in the step 3 as an optimal solution. The method is applied to the fields of electric appliance technology and electrical engineering.

The invention provides a translation rule extract method based on a tree-to-tree translation model, comprising the following steps: 1), analyzing source language strings and target language strings in training corpus, and outputting the source language compact shared syntax forest of the training corpus and the target language compact shared syntax forest of the training corpus; and 2), extracting translation rules according to the source language compact shared syntax forest of the training corpus and the target language compact shared syntax forest of the training corpus. Since the testing corpus is translated according to the translation rules, the translation quality is effectively improved.

The embodiment of the invention provides a relay charging path planning method and system for a small unmanned aerial vehicle. The method comprises the following steps: according to a heuristic valueof pheromone concentration between adjacent target points, a heuristic value of a path distance between the adjacent target points and a heuristic value of a time window of the adjacent target pointsin an ant colony algorithm, establishing an initial path by utilizing an adjacent point search algorithm; inserting a charging station into the initial path according to the maximum endurance mileageof an unmanned aerial vehicle to form an initial charging path; and determining a final charging path by performing removal operator optimization and insertion operator optimization on the initial charging path. According to the technical scheme, an optimal charging station insertion heuristic algorithm is designed in consideration of selection of the charging station and determination of the charging level; and in view of the complexity of the problem, a local search algorithm is introduced, the search space in the ant colony algorithm iteration process is expanded, and the search probabilityof the optimal solution is increased.

The invention discloses a method and a system for equalizing the distribution of physical downlink control channels. The method includes the following steps: a base station sequentially chooses idle C-RNTIs (cell radio network temporary identifier), and respectively calculates a search space corresponding to each C-RNTI, and if the resource utilization rate of a CCE (control channel element) occupied by one of the search spaces is less than a designated value, the C-RNTI is put into a to-add list; one or more C-RNTIs are chosen from the to-add list and sent to a terminal; after storage, the terminal sends an acknowledgement message to the base station; after receiving the acknowledgement message, the base station locally updates the search spaces corresponding to the terminal, and PDCCH (physical downlink control channel) data corresponding to the terminal are sent on all the search spaces corresponding to the terminal; and the terminal searches the corresponding PDCCH data on the corresponding search spaces. The technical scheme provided by the invention can equalize the distribution of PDCCHs on system resources and reduce the occurrence of PDCCH resource collision.

The invention provides a DV-HOP indoor positioning method based on immune particle swarm optimization. An immune particle swarm optimization algorithm is introduced into the DV-HOP indoor positioningmethod, and a particle swarm optimization method is improved by using an immune algorithm; the situation with the maximum iteration accuracy and best adaptation is found via crossing and mutation operations of a particle swarm; the diversity maintenance capacity of the particle swarm is improved; the search space of a solution is enlarged; the convergence speed of the algorithm is improved; and the accuracy and the overall search capacity are both significantly improved. Therefore, compared with the traditional DV-HOP indoor positioning method based on the standard particle swarm optimizationalgorithm, the method provided by the invention is significantly enhanced in positioning accuracy. Compared with the indoor positioning method of a ranging mode, the method provided by the invention does not need the support of external hardware, is low in cost, small in overhead and reduced in human input. Under the condition of ensuring the relatively high positioning accuracy, the method provided by the invention is reduced in cost overhead, and beneficial to popularization and application in a large scale.

The invention discloses a state maintenance authorization management method and system. The method comprises the following steps: receiving a pre-stored first random number and a first hash message authentication code sent by a terminal after receiving an authorization instruction, and a generated second random number; when the authorization instruction is a first authorization instruction, and when the first hash message authentication code is verified to be correct according to the authentication code of a preset object, the received first random number, a pre-stored third random number and a salt, judging that the terminal passes the authorization, and generating a fourth random number; calculating a second hash message authentication code according to the authentication code of the preset object, the salt, the second random number and the fourth random number; and sending the fourth random number, the second hash message authentication code and a terminal authorization pass result to the terminal. According to the state maintenance authorization management method and system disclosed by the invention, every time when terminal passes the authorization, the random number is regenerated, a new hash message authentication code is calculated and is sent to the terminal for the next time authorization judgment, the authorization state is recorded, and the application demands of complex occasions are satisfied.

The invention provides a planning method of ship hull plate curved surface forming collaborative machining based on dual mechanical arms. The problems that a unified analysis and task planning model lacks for a multi-task planning method at present, and a constraint condition is not considered in an actual task distribution process can be solved. The method specially comprises the following stepsof S1, carrying out modeling analysis; S2, through determining of a heating direction of heating paths, determining a beginning end and a tail end of each heating path, and calculating distance cost between the heating paths; S3, selecting and defining variables; S4, determining a constraint condition; S5, determining a ship hull plate curved surface forming dual-mechanical-arm optimizing target;and S6, improving an artificial bee colony algorithm. The improved artificial bee colony algorithm is adopted for carrying out distribution on ship hull plate curved surface forming dual-mechanical-arm machining tasks, the improved artificial bee colony algorithm is adopted for carrying out solving of task planning, and the task distribution problem of dual-mechanical-arm collaborative machining can be effectively solved.