98 results about "Dynamic system model" patented technology

A method for synthesizing a sequence includes defining a switching linear dynamic system (SLDS) with a plurality of dynamic systems. In a Viterbi-based method, a state transition record for a training sequence is determined. The corresponding sequence of switching states is determined by backtracking through the state transition record. Parameters of dynamic models are learned in response to the determined sequence of switching states, and a new data sequence is synthesized, based on the dynamic models whose parameters have been learned. In a variational-based method, the switching state at a particular instance is determined by a switching model. The dynamic models are decoupled from the switching model, and parameters of the decoupled dynamic model are determined responsive to a switching state probability estimate. Similar methods are used to interpolate from an input sequence.

A 3-D multi-physics design environment (“3-D design environment”) for designing and simulating multi-physics devices such as MEMS devices is discussed. The 3-D design environment is programmatically integrated with a system modeling environment that is suitable for system-level design and simulation of analog-signal ICs, mixed-signal ICs and multi-physics systems. A parameterized MEMS device model is created in a 3-D graphical view in the 3-D design environment using parameterized model components that are each associated with an underlying behavioral model. After the MEMS device model is completed, it may be exported to a system modeling environment without subjecting the model to preliminary finite element meshing.

A method generates a synthetic textured data signal by first acquiring a time-invariant input textured data signal. The input textured data signal is sampled to construct an observation matrix. The observation matrix is eigen-coding and factoring to identify a linear dynamic system modeling the input textured data signal. Then, the linear dynamic system can be run forward from an initial state using a quadratic regulator and a random noise signal to generate the synthetic textured data signal.

Methods for adaptive real time control of a system for thermal processing substrates, such as semiconductor wafers and display panels. Generally, the method includes creating a dynamic model of the thermal processing system, incorporating wafer bow in the dynamic model, coupling a diffusion-amplification model into the dynamic thermal model, creating a multivariable controller, parameterizing the nominal setpoints, creating a process sensitivity matrix, creating intelligent setpoints using an efficient optimization method and process data, and establishing recipes that select appropriate models and setpoints during run-time.

The invention provides an abnormal behavior detection method based on a linear dynamic system and a deep network. An LDS dynamic system model and a TSN deep learning network are combined, the space-time feature information of actions is extracted through the TSN, then action features are connected in series through an LDS to form complete behavior features, and finally the behavior type is judgedthrough a support vector machine (SVM). According to the method, the 3D convolutional network is established on the basis of the residual network, and the 3D convolutional kernel is established in a 2D+1D form, so that the network parameter quantity is reduced, and the problem that the original 3D network cannot preload the weight is solved. A residual 3D network is introduced into the TSN structure, so that the feature extraction capability of the network is improved. The number of network layers is increased, and the fitting capability of the network is improved. According to the invention,the high-precision identification of long-sequence abnormal actions can be realized, and finally the accurate monitoring of abnormal behaviors is realized.

The invention discloses a patient movement demand-based assistance lower limb rehabilitation robot self-adaptation control method. By collecting the joint angle and joint angle speed signal of the lower limb of a patient in real time, the expected track self-adaptation tracking control is realized by a robustness variable-structure control method; then, by using a man-machine dynamics system model, the rehabilitation degree and the active movement ability of the patient are studied in real time by using a RBF (Radial Basis Function) neural network; the forward feed assistance of a lower limb rehabilitation robot is further estimated; next, the real-time assistance of the robot is subjected to self-adaptation attenuation according to the track tracking errors; the continuous self-adaptation patient rehabilitation demand-based assistance control is realized; finally, the tracks subjected to the patient rehabilitation demand-based assistance self-adaptation control correction are input into a lower limb rehabilitation robot joint movement controller; the on-line movement is performed; and the continuous and seamless patient rehabilitation demand-based assistance lower limb rehabilitation robot self-adaptation control is realized.

A target in a sequence of measurements is tracked by modeling the target with a switching linear dynamic system (SLDS) having a plurality of dynamic models. Each dynamic model is associated with a switching state such that a model is selected when its associated switching state is true. A set of continuous state estimates is determined for a given measurement, and for each possible switching state. A state transition record is then determined by determining and recording, for a given measurement and for each possible switching state, an optimal previous switching state, based on the measurement sequence, where the optimal previous switching state optimizes a transition probability based on the set of continuous state estimates. A measurement model of the target is fitted to the measurement sequence. The measurement model is the description of the influence of the state on the measurement. It couples what is observed to the estimated target. Finally, a trajectory of the target is estimated from the measurement model fitting, the state transition record and parameters of the SLDS, where the estimated trajectory is a sequence of continuous state estimates of the target which correspond to the measurement sequence. The set of continuous state estimates is preferably obtained through Viterbi prediction. The optimal previous switching state can be an optimal prior switching state, or can be an optimal posterior switching state.

The invention discloses a robot simulation learning method based on a dynamic system model. Simulation of teaching movement of the robot is achieved through learning, specifically, the demonstration motion is modeled into a nonlinear dynamic system model through a gaussian mixture model, in addition, the stability of the motion model is guaranteed through the method with the additional stability constraint conditions, the parameter learning problem of the motion model is converted into a constraint optimization problem, so that a complete description of the motion model is obtained, and finally, the motion model obtained through learning is used as a control strategy to guide the robot to imitate the teaching motion. The method is used for teaching motion of target point fixation, the method has good stability, all the generated motion trails are converged to a target point, and has good expression capacity for simple and complex teaching movement, the generalization ability of the motion model is good, a motion track which is smooth and can be converged to a target can be generated outside the teaching movement range.

The invention relates to a touch information classified computing and modelling method based on machine learning. The method comprises the following steps: acquiring a touch sequence of a training set sample, modelling by adopting a linear dynamic system model, extracting dynamic characteristics of a sub touch sequence, calculating distance of the dynamic characteristics of the sub touch sequence by adopting Martin distance, clustering a Martin matrix by adopting a K-medoids algorithm, constructing a code book, carrying out characterization on each touch sequence by adopting the code book to obtain a system packet model, putting the system packet model of the training set sample and a training set sample label into an extreme learning machine for training a classifier, and putting the system packet model of a to-be-classified sample into the classifier to obtain a label for type of an object. The touch information classified computing and modelling method has the advantages that the actual demand of a robot on stable and complaisant grasping of a non-cooperative target is met, data foundation is provided for completion of a precise operation task, and other sensing results can be fused and computed, so that the description and recognition capability on different targets is enhanced by virtue of multi-source deep perception, and a technical foundation is laid for implementation of intelligent control.

Portions of an input measurement sequence are classified into a plurality of regimes by associating each of a plurality of dynamic models with one a switching state such that a model is selected when its associated switching state is true. In a Viterbi-based method, a state transition record is determined, based on the input sequence. A switching state sequence is determined by backtracking through the state transition record. Finally, portions of the input sequence are classified into different regimes, responsive to the switching state sequence. In a variational-based method, the switching state at a particular instance is also determined by a switching model. The dynamic model is then decoupled from the switching model. Parameters of the decoupled dynamic model are determined responsive to a switching state probability estimate. A state of the decoupled dynamic model corresponding to a measurement at the particular instance is estimated, responsive to the input sequence. Parameters of the decoupled switching model are then determined responsive to the dynamic state estimate. A probability is estimated for each possible switching state of the decoupled switching model. A switching state sequence is determined based on the estimated switching state probabilities. Finally, portions of the input sequence are classified into different regimes, responsive to the determined switching state sequence.

A system and method for monitoring and fault detection in dynamic systems. A “cross-covariance” matrix is used to construct and implement a principle component analysis (PCA) model and / or partial least squares (PLS) model. This system is further utilized for monitoring and detecting faults in a dynamic system. Time series information is synchronized, with respect to a set of training data. Based on historical data, consistency of correlations between variables can be checked with respect to a given time stamp. Temporal PCA (T-PCA) can be used for clearly monitoring changes in tags (time series) synchronization, even when the temporal behaviors of the dynamic system are not clear.

This invention provides one product cloud shape property analysis method based on data dynamic memory module, which comprises the following steps: reading the output data from digital device into memory and establishing linear memory structure for data; then adopting 3D R*-tree for rapid space polymer sorting on data and to establish data dynamic memory; then in the browsing process, using current data point as aim point to get the local reference data set by use of Bezier curve accuracy near to point set to estimate curve rate ; finally mapping the data curve rate as RGB color mode.

A method for automatically decomposing a dynamic system model into separate submodels for ultimate execution on diverse target platforms is provided. Embodiments of the invention provide a way for a system designer to indicate which portions of a previously-specified system model are to become submodels. Optionally, the designer may also indicate the associated target platform for the execution of each submodel. A system submodel for each portion designated by the designer is generated, keeping all critical system-level information consistent throughout the entire system model. Each of these system submodels may then be employed to generate a software version of each system submodel. Each software submodel may then be transferred to its specified target platform and executed in cooperation with the other software submodels to effect an overall system simulation executing across the associated target platforms. Alternately, one or more system submodels may be replaced by the actual components of the dynamic system represented by those system submodels.

The invention discloses a joint combat system modeling method based on a hyper-network theory. The mapping relationship between a physical entity and a model in the combat system is combined. Work division is carried out according to roles of action entities in joint operation to create system nodes. Different types of information flows are designed. A combat ring is created so that the requirements of different levels and different granularities of modeling are met according to a joint combat system; a coarse-grained and fine-grained joint combat system model is established and comprises a reconnaissance intelligence system model, a command and control system model, a firepower strike system model and a support guarantee system model, and a joint combat system dynamic model is constructedby combining a combat task, a time sequence and an organization structure transformation rule. The mapping relationship between the physical entity and the model in the joint operation system is fully considered, the network characteristics and the information characteristics in the joint operation system are highlighted, and the static and dynamic system modeling problem of the joint operation system is solved.

A method of detecting whether a receiver coil is near a transmit coil in a wireless power transfer system (WPTS), the method involving: applying a pseudo-random signal to the transmit coil; while the pseudo-random signal is being applied to the transmit coil, recording one or more signals produced within the WPTS in response to the applied pseudo-random signal; by using the one or more recorded signals, generating a dynamic system model for some aspect of the WPTS; and using the generated dynamic system model in combination with stored training data to determine whether an object having characteristics distinguishing the object as a receiver coil is near the transmit coil.

The invention discloses a supervision-based industrial process fault detection method of a linear dynamic system model, and is applied to fault detection under the condition of obtainable quality variables in an industrial process. By use of the linear dynamic system model and the quality variables, the linear dynamic system model with supervision is established, and the dynamics of a process and the randomness of data are taken into consideration. Compared to other conventional methods, the method provided by the invention not only improves the fault detection effect of the industrial process, but also enhances the grasp of process operators for process states, and enables industrial production to be safer and product quality to be more stable; and the reliance of the conventional fault detection method on process knowledge is improved to a quite large degree, and automatic implementation of the industrial process is better facilitated.

The invention discloses a soft measurement modeling method based on a monitored linear dynamic system model. The method achieves the soft measurement modeling of the dynamic process of the industrial production in a noise environment, and the prediction of the quality variable which is hard to predict directly. Based on the monitored linear dynamic system model, the method builds an effective soft measurement model, and overcomes the process dynamic nature and the data collection randomness in the industrial production. Compared with other methods in the prior art, the model built by the method is more accurate, the prediction of the model is more accurate, so the product quality is more stable. Besides, the dependency of the soft measurement modeling on the process knowledge is reduced, and the automatic implementation of the industrial process is benefited.

A system and method for determining a patient's susceptibility to develop cardiorespiratory instability wherein physiological parameters are analyzed with respect to a dynamics systems model to produce and indicator associated with a probability that the patient will become unstable is provided.

The invention discloses a microgyroscope fuzzy self-adaptation control method based on a T-S model. The microgyroscope fuzzy self-adaptation control method based on the T-S model comprises the following steps: establishing a T-S fuzzy module based on a microgyroscope nonlinearity model, and acquiring an fuzzy dynamic system model through single point fuzzification, product reasoning and center equal-weighted defuzzification; designing a reference model based on controlling tracks, designing a local linear state feedback controller to each T-S fuzzy submodel based on a parallel distribution compensation method, and enabling the fuzzy dynamic system model tracks to track the reference model tracks; and designing a parameter estimator due to the facts that both manufacturing errors and environmental interference exist and the parameters of the T-S fuzzy model are unknown. An improved type self-adaptation control algorithm is designed based on a Lyapunov theory for enabling an overall situation of both track control errors and parameter estimating errors to be gradual and stable. The microgyroscope fuzzy self-adaptation control method based on the T-S model is applied to the microgyroscope nonlinearity model, tests and verifies feasibility and effectiveness of the microgyroscop nonlinearity model which is controlled on a microgyroscope track control module.

The invention discloses an industrial process fault diagnosis method based on a switching linear dynamic system model. The method comprises the steps of firstly carrying out independent repeated sampling on normal operation data and various known fault data of the industrial process, and then establishing the switching linear dynamic system model through a learning algorithm of the switching linear dynamic system model; and then acquiring a diagnosis result of the current monitoring data by using a Gaussian sum filtering method, that is, judging whether the current data is located at a normal operating condition, and if not, judging which fault the current data is located. Compared with other method at present, the industrial process fault diagnosis method not only improves a fault diagnosis effect of the industrial process, enhances mastering of a process operator for the process state, enables industrial production to be safer, and enables the product quality to be more stable. In addition, the industrial process fault diagnosis method improves the dependency of a fault diagnosis method for process knowledge to a great extent, thereby being more conducive to automation implementation of the industrial process.

The invention discloses an intelligent current tracking control method used for an active filter and based on T-S fuzzy modeling and relates to current control methods used for active power filters. The method comprises the steps that a T-S model of the active filter is established on the basis of a nonlinear model of the active filter and is composed of three control rules, and a fuzzy dynamic system model of the active filter is obtained by means of single-point fuzzification, product inference and center equal weighed defuzzification; a reference model is designed according to an expected dynamic response; a partial linear state feedback controller is designed for each T-S fuzzy submodel based on a parallel distributed compensation method, so that the trajectory of the fuzzy dynamic system model of the active filter tracks the trajectory of the reference model; because of uncertainty of parameters and existence of external interference, parameters of the T-S fuzzy model of the active filter are unknown, and a parameter estimator is designed; moreover, an improved self-adaptive control algorithm is designed based on the Lyapunov theory, so that global asymptotic stability of current control errors and parameter estimated errors is achieved.

A software development practices expert system and method is described. The expert system utilizes control systems engineering designs, as well as dynamic systems models to inform and guide the selection, assembly, composition, publishing and presentation, enactment, assessment, learning and analysis, refactoring, improvement and simulation of software development practices into approaches or methods to software development. The expert system collects software endeavor result data to correlate efficacy of software development practice usage, and to recalibrate dynamic systems models and control systems engineering design parameters. Such designs and models configure and improve an inference-based rule engine to provide advice to users contained within a rule repository and knowledge base.

The invention discloses a multivariate data analysis method oriented to dynamic system model verification and belongs to the technical field of model verification. The multivariate data analysis method includes the steps of firstly, subjecting standardized experimental data to data dimension reduction based on PCA (principal component analysis) and subjecting multivariate data to PCA; secondly, performing error assessment for dynamic responses; thirdly, computing response scores based on an SME (subject matter expert); fourthly, computing EEARTH (enhanced error assessment of response time histories) scores based on PCA; fifthly, enabling a decision maker to decide to accept or refuse a predicting result of a simulation model for a corresponding physical experiment. The multivariate data analysis method oriented to dynamic system model verification has the advantages that not only can time curve characteristics of the dynamic responses be analyzed comprehensively, but also a potential principal component of the multivariate data can be found out, influence of multivariate data correlativity on a verification result is eliminated, the verification result contradicting with the multivariate dynamic response quantity is avoided, and the problem of multivariate correlation dynamic response quantity analysis of a dynamic system is handled effectively.

The invention discloses a self-adaptive learning preset performance control method of a nonlinear system. The self-adaptive learning preset performance control method comprises the steps of establishing an Euler-Lagrange dynamic system model at first, then performing preset performance control on the Euler-Lagrange dynamic system model, designing a nominal preset performance controller, and finally designing a learning-based self-adaptive controller based on self-adaptive dynamic planning. The self-adaptive learning preset performance control method of the nonlinear system designs a low-complexity robust self-adaptive controller only relying on input / output data of the system without the need of an exact dynamic model of the system, thus prior design of transient and steady-state performance of a controlled Euler-Lagrange system can be realized, and the shortcoming that the traditional control based on data learning relies heavily on an initial admission control strategy can be avoided.

A system and method reduces noise in a time series signal. A primary signal including stationary and non-stationary noise is modeled by a dynamic system having a continuum of states. A secondary signal including time series data is added to the primary signal to form a combined signal. The generic noise in the combined signal is estimated from samples of the combined signal using the dynamic system modeling the generic noise. Then, the estimated generic noise is removed from the combined signal to recover time series data.

The invention discloses a video keyframe extraction method based on a linear dynamic system. The method comprises the steps of (1), collecting a video data file to be processed; (2), initializing a video segment, calculating linear dynamic system model parameters of the video segment, and calculating the reconstruction error of the video segment according to the model parameters; (3), increasing the length of the video segment frame by frame, and repeating the step (2) till the reconstruction error is beyond a pre-set threshold value; (4), taking the finally determined middle frame of the video segment as the keyframe of the segment; and (5), initializing the next new video segment after the previous video segment, and repeating the steps (2)-(3) till the video data file is ended. By means of the video keyframe extraction method based on the linear dynamic system disclosed by the invention, the description capability of keyframes in semantic contents can be obviously improved; and thus, the video keyframe extraction method can be applied in the services, such as internet video content retrieval, sensitive video detection and filtering and intelligent video monitoring.