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4570 results about "Object function" patented technology

Voice identification method using long-short term memory model recurrent neural network

The invention discloses a voice identification method using a long-short term memory model recurrent neural network. The voice identification method comprises training and identification. The training process comprises steps of introducing voice data and text data to generate a commonly-trained acoustic and language mode, and using an RNN sensor to perform decoding to form a model parameter. The identification process comprises steps of converting voice input to a frequency spectrum graph through Fourier conversion, using the recursion neural network of the long-short term memory model to perform orientational searching decoding and finally generating an identification result. The voice identification method adopts the recursion neural network (RNNs) and adopts connection time classification (CTC) to train RNNs through an end-to-end training method. These LSTM units combining with the long-short term memory have good effects and combines with multi-level expression to prove effective in a deep network; only one neural network model (end-to-end model) exits from a voice characteristic (an input end) to a character string (an output end) and the neural network can be directly trained by a target function which is a some kind of a proxy of WER, which avoids to cost useless work to optimize an individual target function.
Owner:SHENZHEN WEITESHI TECH

Vehicle autonomous parking path programming method used for multiple parking scenes

ActiveCN105857306APlanning results are safe and feasibleEasy to trackControl devicesRange of motionParking guidance and information
The invention provides a vehicle autonomous parking path programming method used for multiple parking scenes. The method is used for automatically parking a vehicle in a parking space through an autonomous parking system when the autonomous parking system detects the available parking space. The method includes the steps that target parking space information is detected, and a parking scene is determined; the initial state and target state of the to-be-parked vehicle are determined; a vehicle kinematics differential equation is established; state variables and control variables of the vehicle are segmented, equidistance sampling is performed on each segment according to certain time step, and to-be-optimized variables are obtained; an equality constraint, boundary constraints and inequality constraints of the to-be-optimized variables are formed; motion range constraints of the to-be-parked vehicle are formed according to the motion range limit in the parking process of the vehicle; an optimization objective is determined, and an objective function is established; and by means of a nonlinear programming solver, an optimal solution of a parking path is obtained. The vehicle autonomous parking path programming method is suitable for the multiple parking scenes, the design is reasonable, abundant information can be provided so as to control autonomous parking of the vehicle, and the security coefficient is high.
Owner:HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI

Multiple video cameras synchronous quick calibration method in three-dimensional scanning system

A synchronous quick calibration method of a plurality of video cameras in a three-dimensional scanning system, which includes: (1) setting a regular truncated rectangular pyramid calibration object, setting eight calibration balls at the vertexes of the truncated rectangular pyramid, and respectively setting two reference calibration balls at the upper and lower planes; (2) using the video cameras to pick-up the calibration object, adopting the two-threshold segmentation method to respectively obtain the corresponding circles of the upper and lower planes, extracting centers of the circles, obtaining three groups of corresponding relationships between circle center points in the image and the centres of calibration ball in the space, solving the homography matrix to obtain the internal parameter matrix and external parameter matrix and obtaining the distortion coefficient, taking the solved video camera parameter as the initial values, and then using a non-linear optimization method to obtain the optimum solution of a single video camera parameter; (3) obtaining in sequence the external parameter matrix between a plurality of video cameras and a certain video camera in the space, using the polar curve geometric constraint relationship of the binocular stereo vision to establish an optimizing object function, and then adopting a non-linear optimization method to solve to get the optimum solution of the external parameter matrix between two video cameras.
Owner:NANTONG TONGYANG MECHANICAL & ELECTRICAL MFR +1

Logistics distribution control method with soft time windows

A logistics distribution control method with soft time windows includes the following steps: (A1) a network model is built, cost resistances are assigned to roads to which network data are concentrated, and taking road nodes into consideration, toll weights are assigned to road traffic light intersections and toll stations; (A2) an optimized vehicle routing model with soft time windows (VRPTW) is built, a target function is established with lowest transportation costs, and the transportation costs are respectively composed of fixed distribution vehicle cost, transportation cost, vehicle waiting cost and delay cost; (A3) a fuzzy clustering analysis algorithm is designed, and a method based on the integration of quantitative analysis and qualitative analysis is adopted for clustering; (A4) a heuristic optimized vehicle routing algorithm is designed, the optimized vehicle routing algorithm is adopted for distribution target nodes in each class, and thereby a distribution result can be obtained. The logistics distribution control method with soft time windows adopts the distances of actual delivery road network routes between distribution nodes as a calculation basis and also takes the actual traffic capacities of roads, large network node number and transportation time needed by distribution nodes into consideration.
Owner:ZHEJIANG UNIV OF TECH
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