33results about How to "Improve search rate" patented technology

Generating and using a high-speed, scalable, and easily updateable data structure are described. The proposed data structure provides minimal perfect hashing functionality while intrinsically supporting low-cost set-membership queries. In other words, in some embodiments, it provides at most one match candidate in a set of known arbitrary-length bit strings that is used to match the query.

A cell searching apparatus and method in an asynchronous mobile communication system allocates an adjacent SCG to each base station according to adjacent degrees. When a mobile terminal is powered on, the mobile terminal receives PSCs and SSCs from the base stations. Multiple searching of SSCs is then performed based on each slot synchronization followed by a frame synchronization and SCG detection through the first searched SSC. If the detected SCG is not an SCG of a base station to which the mobile terminal belongs, the cell searching apparatus detects an SCG with a large size among the adjacent SCGs and detects an SC by using the greatest SCG. Frame synchronization and SCG detection are therefore quickly performed in a cell searching, resulting in a quick search searching.

Disclosed is a repeated data processing method. In a corresponding relationship between representative values of fingerprints of data blocks and fingerprint values of the data blocks, the corresponding fingerprint values of the data blocks belonging to the same data stream are stored together, and are continuously stored in the corresponding relationship according to the sequence in the data stream; and in all-fingerprint comparison of the data blocks, the continuously stored fingerprint values are loaded into a memory and compared, so that the searching efficiency of repeating data is effectively improved.

The invention discloses an automobile comparative analysis system based on big data, relates to the technical field of automobile analysis, and solves the technical problem that the search accuracy is not high and the advantages of big data cannot be fully utilized in the process of vehicle analysis and search; the invention sets a business terminal module, which includes public terminal units and private terminal units. The business terminal module separates the public from the private, which not only ensures the accuracy of the comparison between public terminal units and vehicles, but also avoids the cumbersome process of comparing private terminal units with vehicles, which helps to improve The working efficiency of the present invention; the present invention is provided with comparative analysis module, and this setting is used for analyzing primary selection vehicle, and comparative analysis module analyzes vehicle feature information sequentially, and each step in the analysis process all generates corresponding analysis result, has guaranteed The business terminal module can obtain accurate comparison results; the present invention sets a passer-by bounty module, which fully utilizes the advantages of big data and helps to improve the search rate of vehicles.

The invention discloses a radar multi-target tracking optimization method based on the chaotic neural network. The method is mainly characterized in that state one-step prediction of a tth target at the k time, measurement prediction of the tth target at the k time, the measurement prediction information of a j'th measurement for the tth target at the k time, a one-step prediction error covariance matrix of the tth target at the k time, an information covariance matrix of the tth target at the k time, Kalman gain of the tth target at the k time, an nk*T'-dimension measurement-target association matrix at the k time, an (nk+1)*T'-dimension effective likelihood function matrix of association of nk meausurements and T' targets at the k time, an (nk+1)*T'-dimension normalization matrix of association of the nk meausurements and the T' targets at the k time, an (nk+1)*T'-dimension accurate probability matrix of association of the nk meausurements and the T' targets at the k time, a state equation of the tth target at the k time and an error covariance matrix of the tth target at the k time are sequentially calculated; the t is respectively made to be 1 to T', the error covariance matrix of the T'th targets at the k time is acquired, and real-time tracking for the T'th targets is carried out by a radar according to the error covariance matrix of the T'th targets at the k time.

The invention discloses a robust controller of a permanent magnet synchronous motor based on a fuzzy-neural network generalized inverse and a construction method thereof. The construction method of the invention comprises the following steps of: combining an internal model controller and a fuzzy-neural network generalized inverse to form a compound controlled object; serially connecting two linear transfer functions and one integrator with the fuzzy-neural network with determined parameters and weight coefficients to form the fuzzy-neural network generalized inverse, serially connecting the fuzzy-neural network generalized inverse and the compound controlled object to form a generalized pseudo-linear system, linearizing a PMSM (permanent magnet synchronous motor), and decoupling and equalizing the linearized PMSM into a second-order speed pseudo-linear subsystem and a first-order current pseudo-linear subsystem; and respectively introducing an internal-model control method in the two pseudo-linear subsystems to construct the internal model controller. The robust controller of the invention has the advantages of overcoming the dependence and local convergence of the optimal gradient method on initial values and solving the problems of randomness and probability caused by using the simple genetic algorithm, obtaining the high performance control, anti-disturbance performance andadaptability of the motor and simplifying the control difficulty, along with simple structure and high system robustness.

A hardware-software partitioning method based on a greedy simulated annealing algorithm comprises the following steps: (1) specifying the hardware-software partitioning problem as 0-1 knapsack problem, initially partitioning a task set by a greedy algorithm with low time complexity, and setting the partitioning result as the initial value of the simulated annealing algorithm; and (2) performing the simulated annealing algorithm, which is mainly composed of two layer loops, namely an inner loop and an outer loop, wherein the inner loop can generate a new partition according to a disturbance model, and determine and accept the new partition according to an acceptance criteria; and the outer loop is used for determining whether or not to exit the loop process according to a temperature threshold and the number of new partitions which are not accepted continuously. The method provided by the invention can reduce algorithm operation time, improve search quality and reduce calculation complexity.

The invention discloses a method for improving the searched rate of a website and belongs to the field of the Internet and search engines. According to the method for improving the searched rate of the website, factors and modes which influence a search engine are collected, iteration is carried out with the linear iterative algorithm to obtain a weight, a quantifiable grade of the ranking of the website then can be obtained, and reasonable advice on website optimization is put forward according to the value of the product of each influence factor and the weight. The method for improving the searched rate of the website has the advantages that cost is reduced and promotion efficiency of the website is improved.

Generating and using a high-speed, scalable, and easily updateable data structure are described. The proposed data structure provides minimal perfect hashing functionality while intrinsically supporting low-cost set-membership queries. In other words, in some embodiments, it provides at most one match candidate in a set of known arbitrary-length bit strings that is used to match the query.