59 results about "Dynamic topic model" patented technology

The invention provides a spatial robot prediction control method based on a quantum particle swarm optimization algorithm. Firstly, a lagrangian dynamic model of a spatial robot system is establishedon the basis of an extended mechanical arm method, and a discrete state space model is established by combining the dynamic model with a kinematic model; secondly, a corresponding discrete model prediction controller is designed on the basis of a system extended state space model and a Laguerre polynomial; finally, rolling optimization is conducted on performance indexes under the constraint condition through the quantum particle swarm optimization algorithm, and prediction errors are subjected to feedback correction, so that effective tracking of tail end desired trajectory is achieved. According to the control method, effective tracking of the tail end desired trajectory can be achieved under the given constraint condition, and task space trajectory planning does not need to be carried out in advance; the situation that a global optimum solution cannot be found through a conventional quadratic programming algorithm under multi-constraint conditions can be avoided; energy consumptioncan be optimized while the requirement of tracking precision is met.

The invention discloses a breast cancer prognosis survival rate prediction method based on a dynamic Cox model, and belongs to the technical field of a tumor mortality risk regression model algorithm.The method includes: using a log probability regression model to estimate the patient's positive lymph node ratio LNR level; combining the ratio with other features to fit a Cox regression model of Bayesian method based dynamical estimation model parameters; and predicting a survival rate-time characteristic curve of the patient. The simulation results show that the Cox regression model of the Bayesian method based dynamical estimation model parameters of two logarithmic pseudo-marginal LPML indexes of the combined model is superior to the conventional Cox proportional risk model using LNR index features; and the Cox regression model of the dynamical estimation parameters can further improve the prediction precision of a survival analysis model, the computational complexity is within an acceptable range, and the prediction effect is relatively accurate.

The invention discloses a dynamic model-based unmanned car model prediction controller design method. According to the method, a novel vehicle dynamic model is established; an unmanned car model prediction controller on the basis of the novel vehicle dynamic model; (1) the novel vehicle dynamic model obtained through linearization at the current working point is utilized to predict an output stateof a system in a prediction time domain; (2) the obtained predicted output of the system and given reference output are utilized to construct an optimization problem; and (3) the optimization problemis solved, and a first component of U (k) is taken as an optimal control quantity of the current moment to act on the system. According to the method, vehicle speed is added to serve as the control quantity, and error compensation is introduced to make up accumulative errors caused by model precision, linearization and discretization in the prediction process, so that unmanned cars can have a better trajectory tracing effect.

The invention provides an intelligent contract forming method. The method is characterized by comprising the following steps of converting a non-formalized intelligent contract description document into a description document in a formalized specification; according to the description document in the formalized specification of an intelligent contract, creating dynamic models of the intelligent contract; achieving conversion among different dynamic models of the intelligent contract; verifying the correctness of the dynamic models of the intelligent contract; after the correctness verificationis successful, automatically generating intelligent contract codes according to the dynamic models of the intelligent contract; conducting consistency detection on the description document in the formalized specification of the intelligent contract and the generated intelligent contract codes.

A system and method that facilitate modeling unobserved speech dynamics based upon a hidden dynamic speech model in the form of segmental switching state space model that employs model parameters including those describing the unobserved speech dynamics and those describing the relationship between the unobserved speech dynamic vector and the observed acoustic feature vector is provided. The model parameters are modified based, at least in part, upon, a variational learning technique. In accordance with an aspect of the present invention, novel and powerful variational expectation maximization (EM) algorithm(s) for the segmental switching state space models used in speech applications, which are capable of capturing key internal (or hidden) dynamics of natural speech production, are provided. For example, modification of model parameters can be based upon an approximate mixture of Gaussian (MOG) posterior and/or based upon an approximate hidden Markov model (HMM) posterior using a variational technique.

The invention provides a static meteorological satellite channel modeling method, which comprises the following steps of: according to the spatial distribution situation of a satellite-to-ground link, successively analyzing the influence of free-space loss, ionospheric scintillation, atmospheric absorption, multipath and shadow on a channel link; according to the weather situation, establishing a static meteorological satellite Rice channel model and a Suzuki channel model; and according to a Markov chain, linking the Rice channel model and the Suzuki channel model in one dynamic model to reflect the influence on the transmission characteristic of the static meteorological satellite channel by the weather state change. The method disclosed by the invention has an important value on the signal modulation, the coding mode and the power control technology of static meteorological satellite communication.

The invention discloses a regional land use control policy simulation and effect analysis method, and relates to the field of land use and cover change simulation. By integrating the system dynamics model, the cellular automata model and GIS, the method realizes the real-time data exchange between the three at the grid level, realizes the effective interaction between graphs and numbers, and improves the efficiency of land use change. The accuracy of the simulation can expand the scope of application of the model, and provide better auxiliary tools for the regulation and control of land policies, which can be effectively used in the simulation and effect analysis of land use regulation policies in various regions, dimensions, and scales.

A novel electronic system provides fast three-dimensional model generation, social content sharing of dynamic three-dimensional models, and monetization of the dynamic three-dimensional models created by casual consumers. In one embodiment, a casual consumer utilizes a dedicated real-time 3D model reconstruction studio with multiple camera angles, and then rapidly create dynamic 3D models with novel computational methods performed in scalable graphics processing units. In another embodiment, uncalibrated multiple sources of video recording of a targeted object are provided by a plurality of commonly-available consumer video recording devices (e.g. a smart phone, a camcorder, a digital camera, etc.) located at different angles, after which the uncalibrated multiple sources of video recording are transmitted to a novel cloud computing system for real-time temporal, spatial, and photometrical calibration and 3D model reconstruction. The dynamic 3D models can be uploaded, listed, and shared among content creators and viewers in an electronic sharing platform.

The invention relates to a dynamic model allocating method based on matrix transformation. The method comprises the steps of firstly, transforming and generating complex models into model files which can be loaded conveniently by programs according to dynamic model operation requirements in expense analysis and assessment processes, and designing transformation matrixes to be adapted to uncertain input and output parameters of different models; dynamically calling successfully registered models, analyzing input matrix content and loading corresponding data for calculation; and finally, graphically displaying and saving output results according to analysis of the output matrix content. By means of the dynamic model allocating method, dynamic model allocation based on matrix transformation is designed and achieved, all kinds of models in expense analysis and assessment processes can be integrated and called widely, the problem that staff members fail to use complex models is solved effectively, the complexity and workloads of expense analysis and assessment through models are reduced for staff members, and the optimum and scientific application of expense analysis and assessment models is achieved.

The invention discloses an emergency public opinion evolution analysis method based on sentiment analysis and theme features. The emergency public opinion evolution analysis method mainly comprises the steps: firstly carrying out the word segmentation of a data text through employing a word segmentation method based on character string matching according to the features of Chinese word segmentation; secondly, utilizing synonym linguistic data to extend weights and features of words after word segmentation, and calculating emotion values; then calculating and carrying out time slicing on the data based on the emotion difference value; and finally, taking the data after the time slicing as the input of a dynamic topic model (DTM) so as to obtain the change and the popularity of the topic word of the emergency in a time sequence, predicting the development trend of the emergency, and verifying the effect of sentiment analysis in a topic evolution process by comparing with a topic evolution result only considering the text similarity slicing.

The invention discloses a microgrid equivalent modeling method based on LSTM neural network. The method comprises specific steps of 1, collecting The disturbance data of the common coupling point of the microgrid during the disturbance period; 2, according to the equivalent modeling requirement of the microgrid, determining the number of input and output nodes of the LSTM neural network, and offline training the LSTM neural network by utilize the disturbance data collected in the step 1; 3, according to the neural network trained offline in the step 2, obtaining a nonlinear equivalent model which can represent the running state of the microgrid. The invention utilizes artificial neural network to have good ability to deal with complex non-linear problems, and at the same time can well capture the dynamic characteristics of the electric power system, and the structure and parameters of the dynamic model are determined by the structure and parameters of the LSTM neural network. Only themeasured values of the common coupling points of the micro-grid are needed, and the specific parameters and topological structure of the micro-grid system are not required to be mastered. Moreover, adefinite model is not required to be established in advance when the micro-grid system is equivalent. Once the model is trained and tested, the dynamic equivalent model based on LSTM neural network can meet the needs of system simulation and analysis.

The invention provides a social network user multi-label classification method based on a dynamic multi-view learning model, and the method comprises the following steps: building multi-view representation of a user for a specific social network data set; based on the user representation, constructing a deep fusion representation model among the multi-view data; using a dynamic routing model, updating parameters, and optimizing multi-view characteristics; and introducing a shared representation model, and constructing an objective function for the features in the step 3; and through model optimization, obtaining an optimal shared representation matrix, and finally, realizing multi-label classification of any user by utilizing the shared matrix. According to the method, multi-label efficient classification of network users is realized. The problems of model learning performance reduction, limited view fusion quantity, incapability of meeting multi-classification task requirements of the model and the like caused by data loss are solved, and the method can be widely applied to scenes such as user accurate analysis, abnormal user detection, user relationship mining and unknown user identification in the network.

The invention discloses a robot dynamics model identification method, and relates to the field of robot dynamics models. The dynamics model identification method comprises three cycles from inside tooutside; the inner-layer cycle is configured to converge a covariance matrix of errors in a dynamics model to a constant value, so that the statistical characteristics of the errors are more accurately calculated; the middle-layer cycle is configured to remove data points not conforming to the model in the data so as to improve the consistency of model parameter estimation; and the outer-layer cycle is configured to update nonlinear friction force model parameters so as to improve the accuracy of model parameter estimation. Compared with a traditional dynamics model identification method, thenovel method is used for analyzing a parameter identification problem from the point of statistics, an iterative weighted least square method is adopted to remove an outlier, a new friction force model is adopted to improve the identification precision, and dynamics model parameters can be identified accurately and robustly in an off-line mode, so that a firm foundation is laid for the design andapplication of a robot.