949results about How to "Fast learning" patented technology

A method and system for call processing to assist call center agents at a call center, where a call in the form of an unstructured voice signal is received from a caller at the call center, the received call is transcribed into readable text data, and keywords are identified in the readable text data to determine a context for the voice signal based on the identified keywords, based on the context identifying and extracting matching entities from a data store, and presenting the extracted entities and possible new queries to the call center agent based on the set of most relevant entities.

The present invention provides methods, devices, systems, and instruments related to medical implants and surgical instruments produced to precisely fit individual subjects. In particular, the present invention utilizes a combination of medical imaging, quantitative image analysis, CAD, CAM, and additive manufacturing processes to personalized biocompatible devices.

A method and apparatus for providing learning capability to processing device, such as a computer game, educational toy, telephone, or television remote control, is provided to achieve one or more objective. One of a plurality of actions (e.g., game actions, educational prompts, listed phone numbers, or listed television channels) to be performed on the processing device is selected. A user input indicative of a user action (e.g., a player action, educational input, called phone number, or watched television channel) is received. An outcome of the selected action and/or user action is determined. An action probability distribution having probability values corresponding to the plurality of actions is then updated based on the determined outcome. The next action will then be selected based on this updated action probability distribution. The foregoing steps can be modified based on a performance index to achieve the objective of the processing device so that it learns.

The invention relates to a recognition method of digital music emotion, belonging to the field of computer pattern recognition; the recognition method solves the problem that the existing recognition method of digital music emotion can not recognize sampling-based digital music format, the sorting technology based on a multi-class support vector machine is adopted, acoustic characteristic parameters and music theory characteristic parameters are combined, so as to carry out emotion recognition of digital music; the recognition method comprises the following steps: (1) pretreatment; (2) characteristic extraction; (3) training the multi-class support vector machine; (4) recognition. The music emotion is classified into happiness, impassion, sadness and relaxation, the emotion recognition is carried out based on a sampling-based digital music format file, the common acoustic characteristics in the speech recognition field are not only extracted, and a series of music theory characteristics are extracted according to the theory characteristics of music; meanwhile, the sorting method based on the support vector machine is adopted, the leaning speed is rapid, the sorting precision ratio is high and the recognition efficiency is improved.

In a method and a device for entering control commands into a controller of a manipulator, such as a robot, a first force or movement or sequence of forces or movements is detected, and the detected force, movement or sequence is electronically compared with stored forces, movements or sequences, respectively. Each of the stored forces or movements or sequences has a control command associated therewith. Upon the comparison indicating a coincidence between the detected force or movement or sequence with one of the stored forces or movements or sequences, the control command associated with that one of the stored forces, stored movements or stored sequences is automatically supplied as an input to the controller for operating the manipulator.

The invention discloses an unmanned aerial vehicle (UAV)-unmanned ground vehicle (UGV) combined formation cooperative control method, comprising the following steps: step 1, establishing nonlinear dynamical models of unmanned vehicles in UAV-UGV combined formation; step 2, processing the nonlinear dynamical models of a UAV and a UGV via equivalent transformation, and taking acceleration as a common control target quantity, obtaining a unified control model taking acceleration as a control input in the combined formation; step 3, establishing a ground-air combined formation structure based on a virtual pilot to obtain a stable control signal for the UAV-UGV combined formation and obtain an error model of the combined formation, wherein the control signal is acceleration obtained in step 2 as a common control target quantity; and step 4, designing a UAV-UGV combined formation controller by adopting a RBF (Radial Basis Function) network algorithm according to the control model, the error model and the acceleration serving as a control signal and a control target quantity at the same time, so that the combined formation is stable and reliable.

The invention discloses an adaptive control system based on a radial basis function (RBF) neural network sliding mode control for a micro-electromechanical system (MEMS) gyroscope, and the system comprises a gyroscope and a control circuit, wherein the control circuit comprises a sliding mode controller and an RBF neural network; the difference of displacement of the three-axis gyroscope in the directions of three coordinate axes x, y and z and displacement of a reference model is taken as the input of the sliding mode controller. In the adaptive control system, an adaptive sliding mode control method is applied in controlling the gyroscope, so as to improve the stability and reliability of the system; and the RBF neural network is adopted to carry out adaptive learning on upper boundary of uncertain interference, thus reducing the influence of measurement error and external interference, effectively lowering the occurrence of buffeting, and achieving a better control effect.

A method of presenting alternate routes to a user of a personal navigation device includes providing navigation instructions for guiding the user on an original route selected by the user, receiving and analyzing dynamic traffic data, determining the presence of a first alternate route according to the dynamic traffic data, the first alternate route potentially requiring a shorter travel time than the original route, and prompting the user with a choice of following the first alternate route, wherein the first alternate route is described using the name of at least one street associated with the first alternate route.

The present invention discloses a method and system for controlling an underwater robot locus based on deep reinforcement learning. The system comprises a learning phase and an application phase. In the learning phase, a simulator simulates the running process of an underwater robot and collects the data of the simulated running underwater robot, the data comprises the state of each moment and the target state of the next moment corresponding to each moment, and the learning is performed aiming at a decision neural network, an auxiliary decision neural network, an evaluation neural network and an auxiliary evaluation neural network through the data. In the application phase, the state o the underwater robot at the current moment and the target state of the underwater robot at the next moment are obtained and input to the decision neural network obtained through the final learning in the learning phase, and the decision neural network is configured to calculate the propulsive force required by the underwater robot at the current moment. The method and system for controlling underwater robot locus based on the deep reinforcement learning can realize accurate control of the underwater motion track.

The invention discloses an intelligent contract security vulnerability mining method and system based on artificial intelligence. The invention provides an intelligent contract security vulnerabilitymining method based on artificial intelligence. The method comprises the following steps of disassembling a binary system of the intelligent contract according to an instruction set of an intelligentcontract, converting the system into smart contract assembly, dividing the byte code into different modules according to an intelligent contract byte code jump instruction; and then according to thesource code block and the target code block of the jump instruction, generating a control flow graph of the intelligent contract byte code for deep learning, thereby greatly reducing the manual intervention cost, and furthest avoiding negligence on security vulnerability detection caused by manual intervention errors in other defenses. The technology disclosed by the invention can be applied to anindustrial platform in a large range; the technology disclosed by the invention has an intelligent learning function, and when a novel vulnerability sample is found, the characteristics of the vulnerability sample can be quickly learned, so that the large-range diffusion of vulnerabilities is avoided.

The invention discloses a rolling bearing fault diagnosis method based on improved variational model decomposition and an extreme learning machine. The method comprises: vibration signals of a rollingbearing under different types of faults are collected, the vibration signals are filtered by means of maximum correlation kurtosis deconvolution, parameter optimization is carried out on the maximumcorrelation kurtosis deconvolution method by using a particle swarm algorithm, and an enveloped energy entropy after signal deconvolution is used as a fitness function; the mode number of variationalmodel decomposition is improved by an energy threshold and improved variational model decomposition of the filtered vibration signals is realized to obtain mode matrixes of the corresponding vibrationsignals; singular value decomposition is carried out on the mode matrixes to obtain a singular value vector and a rolling bearing fault feature set is constructed; and the fault feature set is trained by using an extreme learning machine and a rolling bearing fault diagnosis model is established. Therefore, stable feature extraction of the complex vibration signal of the rolling bearing is realized, so that the diagnostic accuracy is improved.

Provided herein is a role-playing game with physical game devices, cards, and dice as well as rules for their use. Gamers assume the roles of imaginary characters in a fictitious storyline and use game devices, cards and dice to facilitate conflict resolution in the domains of physical combat, magical forces, and technological abilities. Game devices are interactive slide rules that force strategic resource allocation and exhibit dynamic attrition. Cards representing skill with certain devices may be inserted into ports of those name devices to modify their performance. Cards representing maaic words combine together according to the desian on the card perimeter and rules of grammar, allowing characters to compose many magic effects from a relatively smaller set of magic words. Gamma components are customizable and may be obtained from retail outlets. Internet download sites, trading with other enthusiasts, and winning components at tournaments.

The invention discloses an intelligent detection and quantitative recognition method for the defect of concrete. According to the method, a concrete test piece is subjected to impact echo signal sample acquisition, signal noise reduction treatment and characteristic value extraction so as to construct a recognition model for analysis components including feature extraction, defect inspection, defect diagnosis and defect quantification and positioning; and the model is used for detecting and recognizing to-be-detected concrete. The intelligent detection and quantitative recognition method provided by the invention is directed at disadvantages of conventional detection technology for concrete defects and based on theoretical analysis, value simulation and model testing, employs advanced signal processing and artificial intelligence technology and fully digs out characteristic information of a testing signal, thereby establishing the model for intelligent rapid detection and classified recognition based on wavelet analysis and an extreme learning machine; and the model has good classified recognition performance, realizes intelligent rapid quantitative recognition and evaluation of the variety, properties and scope of the defect of concrete and further improves the innovation and application level of non-destructive testing technology for the defect of concrete.

The invention discloses a computer-assisted psychological consulting automatic question-answering robot system and relates to a robot system for psychological consulting. The system aims to solve the problems that existing adjuvant therapy equipment of psychological consultants is incapable of working independently, feeding consultation back and monitoring in real time, feeding consulting results back timely and building psychological consulting files of the whole process of the consulting therapy and is not strong in expansibility. According to the system, a psychological consulting question feature extraction module is used for extracting key features of psychological consulting questions input by users; a dialogue control logic module is used for reading and processing the extracted key features and giving a candidate answer set; a knowledge base of the robot system is used for storing answers of the psychological consulting questions; a dialogue historical analysis module is used for analyzing the dialogue history of the psychological consulting users; an answering management module is used for giving a best psychological consulting answer according to the dialogue history; and an answering generating module is used for outputting the best answer and storing the answer into a dialogue journal. The system disclosed by the invention is used for assisting the psychological consulting.

An athletic training apparatus includes a generally horizontal base adapted to be disposed on a ground surface and a first sensor adapted to measure a weight of an athlete. A force receiving area adapted to be disposed in proximity of and above an end of the base is included and operably connected to a second sensor such that when the athlete exerts a force on the force receiving area, the second sensor measures the force exerted by the athlete.

A voice response system attempts to respond to spoken user input and to provide computer-generated responses. If the system decides it cannot provide valid responses, the current state of user session is determined and forwarded to a human operator for further action. The system maintains a recorded history of the session in the form of a dialog history log. The dialog history and information as to the reliability of past speech recognition efforts is employed in making the current state determination. The system includes formatting rules for controlling the display of information presented to the human operator.

The invention relates to the technical field of gait recognition, and discloses a gait recognition method based on a dynamic vision sensor. The invention creatively provides a spatiotemporal mode analysis method based on the dynamic vision sensor. Through an impulsive neural network model based on a Tempotron algorithm, the goal of training and recognizing gait data recorded by the dynamic vision sensor can be achieved, so that the finally obtained gait recognition has high biological authenticity; therefore the gait recognition can be performed on a plurality of objects; the problem of high difficultly of the gait detection in the complicated background can be solved; and the high accuracy of the gait recognition can be ensured. Meanwhile, the invention also provides two coding modes; the fast convergence can be realized in the training process; and the good recognition correctness rate is obtained. Particularly, by combining the fixed period mobile window data segment sample division mode, the correctness rate of the gait recognition can reach 85 percent or higher; the practical values are very high; and the practical popularization and application are convenient.

The invention discloses a method for controlling a micro gyro based on a radial basis function (RBF) neural network sliding mode. Single-input single-output neural sliding mode control can be realized by using a switching function as the input of an RBF neural network, using a sliding mode controller as the output of the RBF network and using the learning function of the neural network; and a control effect can be achieved by integrating the advantages of sliding change structure control, an adaptive algorithm and the RBF neural network. The adaptive algorithm is used for adjusting the link weight of the RBF neural network in real time on line according to accessible conditions, so that a system finally achieves a sliding mode surface, completes tracking, and can adapt to sliding mode control strategies and timely correct and estimate all rigid errors, damping and the like; and the stability of a provided adaptive sliding mode controller exists according to the Lyapunov stability theorem, the system has good robustness, and digital simulation of the three-dimensional micro gyro proves that the method for controlling the micro gyro is valid.

A method for transferring PMMA-coated graphene can transfer graphene to a wide variety of different substrate surfaces. It transfers graphene to different surfaces by using of Poly(methyl methacrylate) (PMMA), polymer such as sponge, and deionized (DI) water. This method comprises easy steps of coating CVD graphene with a layer of PMMA; placing the PMMA-coated CVD graphene onto a polymer to form a PMMA-coated CVD graphene on the surface of a polymer; putting this polymer with PMMA-coated CVD graphene in DI water, and finally scooping up the PMMA-coated CVD graphene with one target substrate. In this way, it transfers the CVD graphene to a target substrate surface.

The invention discloses a fault diagnosis method based on local mean decomposition (LMD), singular value decomposition (SVD) and an extreme learning machine (ELM), and aims to increase the accuracy of bearing fault diagnosis under a variable condition. The method comprises the following steps: firstly, decomposing a nonlinear and unstable original vibration signal into a series of product functions (PFs) by adopting an efficient adaptive signal processing method LMD, wherein each PF component is a product of an envelope signal and a pure frequency modulated signal having physical meaning; secondly, processing the PF components by adopting the SVD to compress a feature vector scale and obtain a more stable feature vector value; lastly, classifying a bearing fault state by applying the ELM having higher application computation efficiency and higher classification accuracy based on an extracted feature vector. By adopting the fault diagnosis method based on LMD-SVD-ELM, a set of complete and valid variable-condition fault diagnosis scheme is provided for a bearing. The method has a very good practical engineering application value.