748 results about "State-space representation" patented technology

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.

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 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.

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.

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 provides a reinforcement learning based air combat maneuver decision making method of a UAV. A motion model of an airplane platform is created; principle factors that influence the air combat situation are analyzed; on the basis of the motion model and analysis on the air combat situation factors, a dynamic fuzzy Q learning model of air combat maneuver decision making is designed, and essential factors and an algorithm flow of reinforcement learning are determined; a state space of air combat maneuver decision making is fuzzified and serves as state input of reinforcement learning; typical air combat motions are selected as basic motions of reinforcement learning, and the triggering intensities of fuzzy rules are summed in a weighted manner, and a continuous motion space is covered; and on the basis of an established air combat dominant function, a return value of reinforcement learning is set in a rewards and punishment values weighing-superposing method. Thus, the autonomous maneuver decision making capability of the UAV during air combat can be improved effectively, the robustness is higher, an autonomous searching optimization performance is higher, and decisionsmade by the UAV are improved continuously in continuous simulation and learning.

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.

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 a matrix converter switch open circuit fault diagnosis method based on prediction control. The method comprises the following steps of under the condition that a matrix converter works normally, establishing relation models between an output voltage and an input voltage, and between an input current and a load current; determining numbers of all the switch combination states of the matrix converter; establishing a state space model and acquiring predicted values of the load current, the input current and the input voltage in a next sampling period; determining an evaluation function; using a finite set model prediction control strategy to select a switch state capable of making an evaluation function value be minimum in all the switch combination states in each sampling period and using the switch state as the switch state of the next sampling period; establishing the state space model and acquiring a change rule of a fault phase load current after a switch open circuit fault is generated; carrying out on-line monitoring on the load current, according to a finite set model, predicting and controlling the selected switch state so as to carry out fault diagnosis, and identifying a fault switch position. By using the method, an open circuit fault can be timely diagnosed.

The invention discloses a multiwave beam-based depth-sounding joint inversion method for a sound velocity profile and a submarine topography. The method comprises the following steps of: (1) transmitting multiwave beams to the seafloor of a sounded sea area through a transmitting transducer array of a multiwave-beam depth sounding system, and receiving echo signals through a receiving transducer array of the multiwave-beam depth sounding system; (2) obtaining the arrival angle and the arrival time of echoes according to the received echo signals by using the multiwave-beam depth sounding system; (3) establishing a state space model formed by using a state equation and a sounding equation; (4) obtaining inversion values of the sound velocity gradient and the seafloor depth of the sounded sea area by using a sequential filter method according to the established state space model and the arrival angle and the arrival time of received echoes, and obtaining an estimate of the sound velocity profile of the sounded sea area by using the inversion value of the sound velocity gradient; and further, calculating the water temperature profile of the sounded sea area by using the estimate of the sound velocity profile. According to the multiwave beam-based depth-sounding joint inversion method for the sound velocity profile and the submarine topography, disclosed by the invention, the estimates of the sound velocity profile and the depth of the seafloor can be obtained quickly and accurately.

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 exhaustively simulating a design. The present invention provides a digital circuit design verification method wherein, prior to a property checking process for each property of a non-reduced RTL model, a reduced RTL model is determined, which reduced RTL model 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, and thus speeding up verification tasks.

The method for the identifying of multiple input, multiple output (MIMO) Hammerstein models that includes modeling of the linear dynamic part of a Hammerstein model with a state-space model, and modeling the nonlinear part of the Hammerstein model with a radial basis function neural network (RBFNN).

The invention discloses a real time residual life prediction method of a gear, and belongs to the technical field of mechanical reliability. The method comprises following implementation steps of 1, using a sensor to monitor degeneration of the power gear in real time; 2, carrying out feature extraction to the fatigue state of the gear; carrying out degeneration evaluation to the wear degeneration performance of the gear; 3, building a parameter variable gear degeneration state space model; 4, detecting a sudden change state point in the degeneration process of the gear; 5, correcting a prediction model according to the detected sudden change point information; carrying out state evaluation again; and 6, predicting the residual life of the gear according to the gear state evaluation and a gear fault threshold value. The method has the advantages that the accuracy of predicting the degeneration state and the real time residual life of the gear can be effectively improved, thus providing the basis for preventative maintenance of the gear.

Systems and methods for modeling and forecasting cyclical demand systems in the presence of dynamic control or dynamic incentives. A method for modeling a cyclical demand system comprises obtaining historical data on one or more demand measurements over a plurality of demand cycles, obtaining historical data on incentive signals over the plurality of demand cycles, constructing a model using the obtained historical data on the one or more demand measurements and the incentive signals, wherein constructing the model comprises specifying a state-space model, specifying variance parameters in the model, and estimating unknown variance parameters.

The invention discloses a distributed multi-mobile-robot formation control method based on ESO, comprising steps of 1) performing modeling on a multi-mobile-robot to obtain a distributed discrete state space deviation model of a multi-mobile-robot, 2) designing a performance index according to the distributed discrete state space deviation model of the multi-mobile-robot and calculating a prediction control quantity of the subsystem, 3) designing an extension state observer of the mobile robot to obtain a disturbance estimation value of the mobile robot, and 4) utilizing the obtained disturbance estimation value to design a combination controller based on the extension state observer. The invention provides a distributed multi-mobile-robot formation control method which is simple and easy to implement, and has a good anti-interference capability.

The invention relates to a discrete sliding mode repetitive control method for a periodic servo system, which comprises the following steps: identifying a difference equation model of the current servo system, and converting the difference equation model into a state space model; selecting a linear switching function; forming a discrete reaching law, and constructing an ideal switching dynamic state according to the formed discrete reaching law; constructing a discrete sliding mode repetitive controller model according to the ideal switching dynamic state; and using current control variables as the control command for a controlled servo system to enable the servo system to change with reference signals. The invention can weaken the system chattering, and simultaneously inhibit the influence of periodic disturbance on the system, thereby improving the control quality and realizing the accurate control on periodic tracking tasks.

The invention relates to a computer software security test method, and in particular relates to a dynamic and static combined software security test method. The test method comprises the following steps: firstly carrying out disassembly and intermediate language transformation on an executable program so as to generate a function call graph (CG) and a control flow graph (CFG) of a file; finding out a vulnerable point of a system by means of static analysis of the function CG, and constructing a test case generation execution path by virtue of a dynamic analysis method; searching a called function based on the function CG, finding out a specific path for triggering the vulnerable point on a first-grade basic block according to the CFG if the function is located on the generated execution path, and then ending the loophole mining process corresponding to the sensitive point; and if the path can not be found, reconfiguring the test case generation execution path and then searching the called function in a cyclic manner. The dynamic and static combined software security test method has the advantages of better solving the problem of path state space blast caused by single Fuzz dynamic test, and greatly improving the path coverage hit rate and the software test analysis efficiency.

The invention provides an SOC (state of charge) estimation method for controlling equivalent charging and discharging of a lithium battery. The SOC estimation method includes: establishing a first-order RC (resistor and capacitor) equivalent circuit model of a single lithium battery, and determining a state equation and an observation equation of a lithium battery system; determining a Kalman filtering discrete state space model, a state parameter variable and an observation parameter variable; updating an predicted value of the state parameter variable and a covariance matrix of measuring errors, and acquiring an innovation sequence according to an observed voltage value at the end of the lithium battery; introducing a self-adaptive fading factor to track and correct a predicted covariance matrix of the lithium battery system; calculating a Kalman filtering gain matrix of the discrete state space model, and updating the optimal estimated value and an error covariance matrix value at the present moment; acquiring a statistical property of process noise; acquiring an SOC estimation value at the present moment, and putting the parameters of the present moment in recursive iteration calculation of strong-tracking self-adaptive Kalman filtering for the next moment. By the aid of the SOC estimation method, real-time and precise estimation of SOC of the lithium battery is realized.

The invention discloses a wind power plant active power control method based on model prediction control. Wind turbine generators are classified according to the wind speeds of the wind turbine generators, a local linearization state-space model of each type of generators is established by adopting an equivalent modeling method, dynamic response characteristics of different types of generators are taken into account based on a designed model prediction controller, power grid dispatching values are distributed to each type of generators according to certain priority, then active reference values obtained by each type of generators are revised by the model prediction controller, and revision values are distributed to all the generators in proportion. For the goal of wind power plant active power control, the operational difference among the generators is taken into account, and the power grid dispatching value can be quickly tracked based on the model prediction control.

The invention discloses a method for carrying out filtering estimation on charged states of a lithium ion battery. The method comprises steps of acquiring parameter data of the surface of the lithium ion battery according to a preset time threshold, and establishing an equivalent circuit model of the lithium ion battery; acquiring a mathematical model of the lithium ion battery according to equivalent circuit model, acquiring parameters of the equivalent circuit model by use of a deviation compensation recursive least square algorithm and acquiring open-circuit voltage of the lithium ion battery; acquiring a state space model according to a model and the mathematical model of the lithium ion battery in a noise environment; estimating the charged states of the lithium ion battery by use of a Kalman filter recursive algorithm and acquiring a first charged state estimation value and a precision value of the Kalman filter; acquiring a second charged state estimation value by use of the H-infinite filter recursive algorithm; and calculating weights of the first charged state estimation value and the second charged state estimation value, and calculating a charged state estimation value of the lithium ion battery according to the weights.

The invention discloses a storage battery surplus energy monitoring method and a monitoring device. The monitoring device comprises the following steps of: constructing a storage battery state space model; utilizing off-line sample data to carry out off-line identification on the state space model of a storage battery, and utilizing a model parameter off-line identification result and on-line sample data to carry out on-line identification on the state space model of the storage battery; constructing a state observer according to a model parameter on-line identification result and a storage battery state space model, and carrying out observation on a storage battery open-circuit voltage; and obtaining the surplus energy of the storage battery according to the corresponding relation of the storage battery open-circuit voltage and the surplus energy of the storage battery. The monitoring method and the monitoring device provided by the invention can carry out monitoring on the surplus energy of the storage battery through a method of the on-line identification model parameter, effectively reduce storage battery time-varying nonlinear characteristic, the influence of measuring errors and noises and the like on a surplus energy monitoring result, improve monitoring accuracy, reduce measuring errors, and strengthen the instantaneity of the monitoring.

A “property checker” uses light-weight symbolic execution to prove that software programs satisfy safety properties by simultaneously performing program testing and program abstraction. A simple example of safety properties includes conditions that must be satisfied for proper program execution, such as whether an application properly interfaces with API methods or functions. Program tests are an “under-approximation” of program behavior, and abstractions are an “over-approximation” of the program. This simultaneous testing either finds a test-case that reaches an error state, or finds an abstraction showing that no path in the state space of the program can reach any error state. If a test-case reaches an error state, the property checker has discovered a violation of the safety property. Conversely, if no path in the state space can reach any error state, the property checker has proved that the program satisfies the desired safety property.