1802 results about "State-space representation" patented technology

In one embodiment of the invention, a tool tracking system is disclosed including a computer usable medium having computer readable program code to receive images of video frames from at least one camera and to perform image matching of a robotic instrument to determine video pose information of the robotic instrument within the images. The tool tracking system further includes computer readable program code to provide a state-space model of a sequence of states of corrected kinematics information for accurate pose information of the robotic instrument. The state-space model receives raw kinematics information of mechanical pose information and adaptively fuses the mechanical pose information and the video pose information together to generate the sequence of states of the corrected kinematics information for the robotic instrument. Additionally disclosed are methods for image guided surgery.

Line locator systems that fuse traditional sensors used in a combined pipe and cable locator (electromagnetic coils, magnetometers, and ground penetrating radar antennas) with low cost inertial sensors (accelerometers, gyroscopes) in a model-based approach are presented. Such systems can utilize inexpensive MEMS sensors for inertial navigation. A pseudo-inertial frame is defined that uses the centerline of the tracked utility, or an aboveground fixed object as the navigational reference. An inertial sensor correction mechanism that limits the tracking errors over time when the model is implemented in state-space form using, for example, the Extended Kalman Filter (EKF) is disclosed.

A methodology for process modeling and control and the software system implementation of this methodology, which includes a rigorous, nonlinear process simulation model, the generation of appropriate linear models derived from the rigorous model, and an adaptive, linear model predictive controller (MPC) that utilizes the derived linear models. A state space, multivariable, model predictive controller (MPC) is the preferred choice for the MPC since the nonlinear simulation model is analytically translated into a set of linear state equations and thus simplifies the translation of the linearized simulation equations to the modeling format required by the controller. Various other MPC modeling forms such as transfer functions, impulse response coefficients, and step response coefficients may also be used. The methodology is very general in that any model predictive controller using one of the above modeling forms can be used as the controller. The methodology also includes various modules that improve reliability and performance. For example, there is a data pretreatment module used to pre-process the plant measurements for gross error detection. A data reconciliation and parameter estimation module is then used to correct for instrumentation errors and to adjust model parameters based on current operating conditions. The full-order state space model can be reduced by the order reduction module to obtain fewer states for the controller model. Automated MPC tuning is also provided to improve control performance.

An anomaly detector for a Controller Area Network (CAN) bus performs state space classification on a per-message basis of messages on the CAN bus to label messages as normal or anomalous, and performs temporal pattern analysis as a function of time to label unexpected temporal patterns as anomalous. The anomaly detector issues an alert if an alert criterion is met that is based on the outputs of the state space classification and the temporal pattern analysis. The temporal pattern analysis may compare statistics of messages having analyzed arbitration IDs with statistics for messages having those analyzed arbitration IDs in a training dataset of CAN bus messages, and a temporal pattern is anomalous if there is a statistically significant deviation from the training dataset. The anomaly detector may be implemented on a vehicle Electronic Control Unit (ECU) communicating via a vehicle CAN bus. The anomaly detector does not rely on an database of messages and their periodicity from manufacturers (dbc files) and in that sense is truly a zero knowledge detector.

Line locator systems that fuse traditional sensors used in a combined pipe and cable locator (electromagnetic coils, magnetometers, and ground penetrating radar antennas) with low cost inertial sensors (accelerometers, gyroscopes) in a model-based approach are presented. Such systems can utilize inexpensive MEMS sensors for inertial navigation. A pseudo-inertial frame is defined that uses the centerline of the tracked utility, or an aboveground fixed object as the navigational reference. An inertial sensor correction mechanism that limits the tracking errors over time when the model is implemented in state-space form using, for example, the Extended Kalman Filter (EKF) is disclosed.

The described invention allows for rapid geolocation of one or more RF emitters using a single moving collection platform. Inaccuracies of conventional frequency of arrival (FOA) geolocation methods are overcome by solving simultaneously for emitter location and a potential emitter drift associated with an observed emitter frequency. Certain embodiments may utilize particle filtering algorithms to recursively update multimodal state densities that are typical of solutions involving both unknown emitter location and nonstationary emitter carrier drift. Moreover, certain properties of particle filters may be exploited to provide a geolocation solution given a complex multimodal state space composed of emitter location and a non-stationary emitter frequency required for FOA.

A system and method of intelligent navigation with collision avoidance for a vehicle is provided. The system includes a global positioning system and a vehicle navigation means in communication with the global positioning system. The system also includes a centrally located processor in communication with the navigation means, and an information database associated with the controller, for identifying a location of a first vehicle and a second vehicle. The system further includes an alert means for transmitting an alert message to the vehicle operator regarding a collision with a second vehicle. The method includes the steps of determining a geographic location of a first vehicle and a second vehicle within an environment using the global positioning system on the first vehicle and the global positioning system on the second vehicle, and modeling a collision avoidance domain of the environment of the first vehicle as a discrete state space Markov Decision Process. The methodology scales down the model of the collision avoidance domain, and determines an optimal value function and control policy that solves the scaled down collision avoidance domain. The methodology extracts a basis function from the optimal value function, scales up the extracted basis function to represent the unscaled domain, and determines an approximate solution to the control policy by solving the rescaled domain using the scaled up basis function. The methodology further uses the solution to determine if the second vehicle may collide with the first vehicle and transmits a message to the user notification device.

The invention discloses a cloud computing system reliability modeling method considering a common cause fault, and belongs to the technical field of network reliability. The method comprises the steps of determining a state combination of a similar single server of a cloud computing system and performing simplification; calculating an existence probability of the simplified state combination of the similar single server by adopting a fault tree method; determining state combinations of similar servers of the cloud computing system, performing simplification, and calculating an existence probability of each state combination; enumerating state combinations of different servers of the cloud computing system, and calculating an existence probability of each state combination; and according to the state space of the cloud computing system, calculating the system reliability according to a given demand. According to the method, a common cause fault of all virtual machines running in the servers, caused by server faults, is considered, the state space modeling is adopted, and the state space is simplified, so that the problem of state space explosion during system scale increment is solved and the modeling efficiency is improved.

The invention relates to a heterogeneous multi-agent collaborative decision-making method based on a depth deterministic policy gradient, belonging to the collaborative decision-making field of a heterogeneous intelligent unmanned system, comprising the following steps of: firstly, defining heterogeneous multi-agent characteristic attributes and reward and punishment rules, defining multi-agent state space and action space, and constructing multi-agent motion environment for collaboratively making decision; then, establishing an actor module for decision-making action and a critic module for evaluating feedback based on the depth-deterministic strategy gradient algorithm, and training the parameters of the learning model; using the trained model to obtain the multi-agent state sequence; and evaluating the situation of the multi-agent motion state sequence according to the reward and punishment rules set in the environment. The invention may construct reasonable sports environment according to actual needs, achieve the purpose of intelligent sensing and strategy optimization through the synergy between multiple agents in the system, and has a positive effect on the development of the unmanned system field in China.

The invention discloses a method for estimating the charge state of a power cell, which comprises the following steps that: 1. a continuous state space model capable of expressing the relationship of each element in an expression circuit is obtained by an equivalent circuit model of a power cell; the relationship between the open-circuit voltage and the cell charge state is obtained through the standing experiment of the power cell, and the cell charge state is introduced into the continuous state space model of the power cell as a state variable; the power battery model under the noise environment is obtained by combining the noise information; and finally the continuous state space model is subjected to linearization and discretization so as to obtain a linear discrete state space model; 2. a relationship curve of the open-circuit voltage and the cell charge state is obtained through the standing experiment of the power cell, and a parameter k and a parameter d in the power cell model are obtained approximately; 3. parameters of the power cell model are obtained through the identification of current and voltage data collected by a data collecting system; and 4. the power cell charge state is estimated by utilizing a moving horizon estimation method based on the determined power cell model.

A well monitoring system particularly useful in detecting kicks in the well includes a well, a well system, and a computing apparatus. The well defines a wellbore and the well system includes at least one sensor measuring at least one well condition. The computing apparatus hosts a well monitoring software component that performs a method to detect a kick in a well. The method includes: storing a set of real-time data from a measurement of a well condition by the sensor, the measurements being correlative to an unplanned fluid influx into the well; modeling the operation of the well with a physics-based, state space model of the well system to obtain an estimate of the well condition; and applying the real-time data set and the estimate to a probabilistic estimator to yield a probability of an occurrence of a kick and a confidence measure for the probability.

A method for estimating joint angles of a multi-segment limb from inertial sensor data accurately estimates and tracks the orientations of multiple segments of the limb as a function of time using data from a single inertial measurement unit worn at the distal end of the limb. Estimated joint angles are computed from measured inertial data as a function of time in a single step using a nonlinear state space estimator. The estimator preferably includes a tracking filter such as an unscented Kalman filter or particle filter. The nonlinear state space estimator incorporates state space evolution equations based on a kinematic model of the multi-segment limb.

A method for estimating joint angles of a multi-segment limb from inertial sensor data accurately estimates and tracks the orientations of multiple segments of the limb as a function of time using data from a single inertial measurement unit worn at the distal end of the limb. Estimated joint angles are computed from measured inertial data as a function of time in a single step using a nonlinear state space estimator. The estimator preferably includes a tracking filter such as an unscented Kalman filter or particle filter. The nonlinear state space estimator incorporates state space evolution equations based on a kinematic model of the multi-segment limb.

The present invention provides a method, system, and apparatus to monitor cardiovascular signals such as arterial blood pressure (ABP), pulse oximetry (POX), and intracranial pressure (ICP). The system can be used to calculate and monitor useful clinical information such as heart rate, respiratory rate, pulse pressure variation (PPV), harmonic phases, pulse morphology, and for artifact removal. The method uses a statistical state-space model of cardiovascular signals and a generalized Kalman filter (EKF) to simultaneously estimate and track the cardiovascular parameters of interest such as the cardiac fundamental frequency and higher harmonics, respiratory fundamental frequency and higher harmonics, cardiac component harmonic amplitudes and phases, respiratory component harmonic amplitudes and phases, and PPV.

A computer is programmed to automatically select a state or a set of states of a digital circuit that are visited during simulation, for use as one or more initial states by a formal verification tool. Such automatic selection of one or more simulation states reduces the set of all simulation states to a small subset, thereby to address the state space explosion problem. Depending on the embodiment, the programmed computer uses one or more criteria provided by a library and / or by the user, in making its selection of states. Such criteria may be based on a property (assertion / checker) of the digital circuit and / or a signal generated during simulation. Furthermore, after such criteria (also called “primary criteria”) are applied, the selected states may be pruned by application of additional criteria (also called “secondary criteria”) prior to formal analysis.

The invention relates to a heterogeneous cloud wireless access network resource allocation method based on deep reinforcement learning, and belongs to the technical field of mobile communication. Themethod comprises the following steps: 1) taking queue stability as a constraint, combining congestion control, user association, subcarrier allocation and power allocation, and establishing a random optimization model for maximizing the total throughput of the network; 2) considering the complexity of the scheduling problem, the state space and the action space of the system are high-dimensional,and the DRL algorithm uses a neural network as a nonlinear approximation function to efficiently solve the problem of dimensionality disasters; and 3) aiming at the complexity and the dynamic variability of the wireless network environment, introducing a transfer learning algorithm, and utilizing the small sample learning characteristics of transfer learning to enable the DRL algorithm to obtain an optimal resource allocation strategy under the condition of a small number of samples. According to the method, the total throughput of the whole network can be maximized, and meanwhile, the requirement of service queue stability is met. And the method has a very high application value in a mobile communication system.

A probabilistic digital signal processor using data from multiple instruments is described. Initial probability distribution functions are input to a dynamic state-space model, which operates on state and / or model probability distribution functions to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data from multiple instruments with the prior to generate a posterior probability distribution function passed (1) to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled in an iterative algorithm or (2) iteratively to the dynamic state-space model. For example, the probabilistic processor operates on fused data using a physical model, where the data originates from a mechanical system or a medical meter or instrument, such as an electrocardiogram or pulse oximeter to generate new parameter information and / or enhanced parameter information.

The invention provides an automobile automatic driving test case generation method based on scenes and tasks, and the method comprises the following steps: 1), selecting typical traffic behaviors in acomplex and multi-element road traffic scene, and constructing a test task library; 2) defining terms of a traffic scene and related elements thereof, describing concepts, attributes and hierarchicalrelationships of scene elements, and constructing an ontology model of the road traffic scene; 3) generating a traffic scene based on the traffic scene model and the traffic behavior task library, the traffic scene being generally composed of a plurality of traffic participants, a specific time-space road environment and a traffic scene, and generally including a plurality of driving tasks; and 4) further describing a traffic scene in detail by defining a parameter range and correlation of variables in the state space, and selecting representative discrete values for combination by applying boundary value analysis, equivalence class division and other methods in software testing to generate a plurality of test cases.

System and method for addressing immense, long-standing problem of bandwidth management, for example, in enterprise networks, VPNs, real-time and stored video services, mobile applications, wireless networks, and cloud computing applications. Described features include an automatic closed-loop control system infrastructure encompassing multiple time-scales and performing control actions optimized to the extent possible with respect to administrator-provided performance metrics. One aspect utilizes available or innovatively accessible means of session and QoS control (settings in configuration files, gateway APIs, QoS parameters, application bit-rate settings, etc.) within the context of practical multiple-vendor products in evolving multiple-service networks. Another aspect utilizes available or innovatively accessible means of session and QoS observations (values in reporting log files, gateway APIs, network monitoring, etc.) within the context of practical multiple-vendor products in evolving multiple-service networks. Traffic-measurement controlled adaptive reservations for distributed myopic single-service gatekeepers effectively shapes the permitted state-space boundary over a range of arbitrary curvatures.

The invention discloses a transmission line oscillation positioning system based on micro inertial measurement combination. The transmission line oscillation positioning system comprises a monitoring center and a pole and tower monitoring host machine which are connected with each other, wherein the pole and tower monitoring host machine is wirelessly connected with at least two wireless inertialsensor nodes; and each wireless inertial sensor node comprises a triaxial acceleration sensor and a triaxial gyroscope. By the method for positioning by adopting the system, the wireless inertial sensor nodes acquire acceleration values and state space angles of a lead wire monitoring point in three directions; the monitoring center processes and analyzes the data of each monitoring point by adopting algorithms, such as Fourier transformation, a least square method, digital filtering, Kalman filtering, matrix coordinate transformation, frequency domain integral operation so as to fit to acquire an oscillation trace of a whole line, and corrects the oscillation line according to a digital model of a relation between an oscillation characteristic value and a micro meteorological condition; therefore, the precision of oscillation monitoring is improved and the most direct and intuitive monitoring of transmission line oscillation can be realized.

The present invention relates to a method of circuit verification in digital design and in particular, relates to a method of register transfer level property checking to enable the same. Today's electrical circuit designs frequently contain up to several million transistors, and circuit designs need to be checked to ensure that circuits operate correctly. Formal methods for verification are becoming increasingly attractive since they confirm design behavior without exhausting simulating a design. A digital circuit design verification method, prior to a property checking process for each property of a non-reduced RTL model, determines a reduced RTL model which retains specific signal properties of a non-reduced RTL model which are to be checked. A linear signal width reduction causes an exponential reduction of the induced state space. Reducing state space sizes in general goes hand in hand with reduced verification runtimes, thus speeding up verification tasks.

The present invention discloses an SOC (state of charge) estimation method. According to the method, a battery OCV-SOC relationship module, a parameter acquisition module, an offline identification parameter value, a parameter discrete state space model, a battery parameter online identification module, a battery dynamic parameter update module and a battery SOC estimation module. The method includes the following specific steps that: 1, a discharge-standing experiment is performed, an OCV-SOC relationship expression is obtained through fitting, parameter values in an equivalent circuit model are identified; 2, a battery second-order RC system discrete state space model is established, and the battery model parameters are identified online and dynamically updated; and 3, the SOC of a battery is estimated online. With the method of the invention adopted, the defects of inaccuracy and accumulative error of the initial value of the SOC of a battery of in an ampere-hour integration method can be eliminated. The method is applicable to the dynamic change of the characteristics of the battery, can improve the accuracy of SOC online estimation and can be widely applied to the electric vehicles and storage battery management system field. The method has the advantages of high battery model precision, fast convergence, high stability and high reliability.

The invention discloses a method and device for estimating a power battery charge state on line. The method comprises the steps that a capacity correction factor of a power battery is calculated according to the accumulated released energy of the power battery, the internal resistance of the power battery is calculated, a state space model of the power battery is established, and the state space model of the power battery is substituted into an unscented Kalman filter to obtain the estimated value of the battery charge state. According to the method and device for estimating the power battery charge state on line, the capacity correction factor of the power battery is calculated according to the accumulated released energy of the power battery, the accumulated released energy of the power battery is the sum of energy consumed by load acting and energy consumed by the internal resistance from the first time up to now, the accumulated released energy of the power battery can reflect the using history of the battery like the cycle index and is more beneficial to accurate measuring in numerical value, and therefore the method of correcting the related parameters of the battery through the accumulated released energy of the power battery is better in operability and is more accurate in estimated value.

The invention discloses a method for analyzing wing structure aero-elasticity stability based on aerodynamic force uncertain order reduction. The method is based on unsteady aerodynamic force model order reduction method based on CFD technology; uncertainty of numerical calculation and aerodynamic parameters during aerodynamic force identification process is taken into comprehensive consideration and is uniformly quantified as an uncertain bounded interval noise sequence in an identification model; uncertain order reduction of the aerodynamic force model is realized by means of interval set membership identification algorithm; unsteady aerodynamic uncertain order reduction model based on CFD technology is established; mathematical model of uncertain aero-elasticity system of state space form is established coupled with structural motion equation; an effective method for predicting system robustness stability boundary from the prospective of section state matrix characteristic values. The aero-elasticity system uncertainty modeling thinking and stability boundary prediction technology provided by the invention gives consideration to calculation efficiency, analysis precise and system robustness.

Disclosed is a rolling bearing remaining life prediction method based on feature fusion and particle filtering. According to an index calculation process, firstly, original features are extracted from bearing vibration signals, the extracted original features are clustered by the adoption of a relevance clustering method, then, one typical feature is selected from each cluster to form optimal feature sets, and finally the feature sets are fused by the adoption of a weight fusion method into a final recession index. According to a life prediction process, firstly, smoothing and resampling are carried out on the recession index, the time interval is adjusted to be an expected value, state-space model initial parameters are calculated by the adoption of least square fitting, then, model parameters are updated in real time according to new observation data, and finally the remaining life of a bearing can be predicted. According to the rolling bearing remaining life prediction method based on feature fusion and particle filtering, the difference between the life prediction result and a true value is small, and the application effect is good.

The invention relates to a method for predicting dynamic risk and vulnerability at fine scale and belongs to the scientific field of global information. The method is mainly characterized in that an optimized Bayesian network is searched from multi-source heterogeneous spatiotemporal data on the basis of a grid format with certain resolution at fine scale; domain knowledge is combined to improve the network; therefore, the uncertain estimation of disaster risk and the vulnerability, namely probability estimation, is carried out. In the method, a nuclear density method is put forward to train a sample according to a sample derivative grid; an optimized discretization method is put forward to discretize continuous variables so as to provide discrete state space input for the network; a simulated annealing optimization algorithm is adopted to search an optimized network structure; and a method of accurate reasoning combined with approximate reasoning to predict the probabilities of risk and the vulnerability is adopted. The method provided by the invention can position the positions of the disaster risk and the vulnerability in real time at the fine spatial scale, estimate the spatial distribution of the risk probability and has important theoretical significance and practical value for improving the effects on the reduction and relief of disaster and building an intelligent public emergency pre-warning system by the state.

The invention provides a dynamic spectrum access method on the basis that the policy planning restricts Q learning, which comprises the following steps: cognitive users can divide the frequency spectrum state space, and select out the reasonable and legal state space; the state space can be ranked and modularized; each ranked module can finish the Q form initialization operation before finishing the Q learning; each module can individually execute the Q learning algorithm; the algorithm can be selected according to the learning rule and actions; the actions finally adopted by the cognitive users can be obtained by making the strategic decisions by comprehensively considering all the learning modules; whether the selected access frequency spectrum is in conflict with the authorized users is determined; if so, the collision probability is worked out; otherwise, the next step is executed; whether an environmental policy planning knowledge base is changed is determined; if so, the environmental policy planning knowledge base is updated, and the learning Q value is adjusted; the above part steps are repeatedly executed till the learning convergence. The method can improve the whole system performance, and overcome the learning blindness of the intelligent body, enhance the learning efficiency, and speed up the convergence speed.

The invention discloses an adaptive code rate video transmission method and an adaptive code rate video transmission system based on reinforced learning. The method comprises the steps of inputting astate space corresponding to a video block needing to be downloaded into a code rate prediction neural network, and outputting a code rate strategy by the code rate prediction neural network; downloading the video block needing to be downloaded according to the code rate strategy output by the code rate prediction neural network; after each video block is completely downloaded, computing a video playing quality index corresponding to each video block and returning the video playing quality index back to the code rate prediction neural network; and performing training by the code rate prediction neural network according to the returned video playing quality index and the state space corresponding to the completely downloaded video block. According to the method and the system provided by the invention, the algorithm well adapts to multiple different network conditions while the quality of service is improved, the labor time cost of rule setting and parameter optimization is greatly reduced, the efficiency problem caused by artificial feature selection and rule setting is avoided, and the video quality experience is also greatly improved while the configuration and debug time is saved.