610 results about "State-transition matrix" patented technology

Error-free state tables are automatically generated from a specification of a group of desired performable functions, such as are provided in a programming language in a formal notation such as Backus-Naur form or a derivative thereof by discriminating tokens corresponding to respective performable functions, identifications, arguments, syntax, grammar rules, special symbols and the like. The tokens may be recursive (e.g. infinite), in which case they are transformed into a finite automata which may be deterministic or non-deterministic. Non-deterministic finite automata are transformed into deterministic finite automata and then into state transitions which are used to build a state table which can then be stored or, preferably, loaded into a finite state machine of a hardware parser accelerator to define its personality.

A method that enables a tester to efficiently perform confirmation behavior of a Web page that utilizes DHTML or difference in behavior of the Web page among various browsers, or assignment of information relating to accessibility of the Web page or verification of the information. A method is provided that analyzes change in a Web page by determining states that can dynamically occur in response to an external event in a Web page that utilizes DHTML by analyzing at least one of DOM, style information for when rendered on a browser, input data from a user, and the value of a global variable in JAVA®SCRIPT, storing data that can identify each of the states, identifying state transition between the stored states, and reproducing the stored states.

A transition detection circuit detects a transition of a state of a pixel clock, and outputs a detection signal according to a timing of the detected state transition. A control signal generation circuit generates a control signal according to the detection signal and phase data indicating a transition timing of the pixel clock. A pixel clock control circuit causes the state of the pixel clock to transit according to the control signal.

A method for managing main memory including DRAM and NVRAM in a computer depending on the operation state of the computer is provided. The method includes: (a) upon start of the computer, loading a program and the like into the DRAM, and loading predetermined read-only data and the like into the NVRAM; (b) in a state transition from a normal operation to a suspend state, moving data in the DRAM to the NVRAM; (c) in a state transition from the suspend state to the normal operation, reading data from the NVRAM for program execution; (d) in the case where a data write to the NVRAM occurs, stopping the data write, and moving data in a data area of the NVRAM subjected to the data write, to the DRAM; and (e) performing the data write to the DRAM to which the data has been moved.

A computer-implemented method for dialog state tracking employs first and second latent variable models which have been learned by reconstructing a decompositional model generated from annotated training dialogues. The decompositional model includes, for each of a plurality of dialog state transitions corresponding to a respective turn of one of the training dialogues, state descriptors for initial and final states of the transition and a respective representation of the dialogue for that turn. The first latent variable model includes embeddings of the plurality of state transitions, and the second latent variable model includes embeddings of features of the state descriptors and embeddings of features of the dialogue representations. Data for a new dialog state transition is received, including a state descriptor for the initial time and a respective dialogue representation. A state descriptor for the final state of the new dialog state transition is predicted using the learned latent variable models.

A state controller of a data transfer processing circuit switches an operation of an A-device or a B-device between a host operation and a peripheral operation by state transition. A power supply switch circuit connects a power supply circuit with a VBUS line based on transition of the state controller. A power supply switching circuit of a power supply control circuit connects the VBUS line or the power supply circuit with the data transfer processing circuit based on a switching signal. The switching signal is generated based on an output signal of a switch circuit or a control signal from the state controller.

A computing device for state transitions of recursive state machines and a computer-implemented method for the definition, design and deployment of domain recursive state machines for computing devices of that type; such devices are intended for the simulation of large systems involving human and automated components, particularly the type generally called “Enterprise Applications”; such devices are also applicable to a much wider range of fields, such as cognitive modelling or robotics. The commonality between the computing device and the computer-implemented method is the Subject Predicate Object Protocol (SPOP) which is used to capture instructions by the computer-implemented method for the definition, design and deployment of recursive state machines and also is the protocol used by the computing device for state transitions of recursive state machines to communicate inbound and outbound events based on the captured instructions.

The technology disclosed offers a declarative and visualization framework that implements a state machine for multi-step progression of interaction with an entity. The declarative framework is usable over and over for a broad range of applications because it provides a simple rule-based authoring tool that can be used for specifying different elements and components of a complex state machine, including state definitions, state transition triggers, state transition conditions and state transition actions. Once defined, the state machine is automatically generated and visually implemented based on the declarative input provided by a non-technical user.

A semiconductor memory device including an on-chip ECC circuit and having a data retention mode which includes, in the order of state transition, an encoding state EEST by an error correction circuit in which the error correction circuit carries out calculation of parity bits of data of the memory cells, a burst self-refresh state BSST in which the memory cells are self-refreshed in a burst with a period shorter than in ordinary self-refresh, a power-off state PFST in which an internal power supply circuit is partially turned off, a power-on state PNST in which the internal power supply circuit, partially turned off, is turned on, and a decoding state EDST by the error correction circuit in which the error correction circuit corrects errors of the memory cells. In case a command for exiting from the data retention mode in the encoding state, transition may be made to an idle state IST so that re-entry may be made from the decoding state EDST to the BSST.

The invention belongs to the technical field of navigation, and concretely relates to an inertial navigation / mileometer autonomous integrated navigation method. The method comprises the following steps: 1, initially aligning an inertial navigation system, and initializing parameters of a Kalman filter; 2, carrying out inertial navigation calculation to obtain a discrete state transfer matrix and a measurement matrix; 3, calculating based on the summation measurement of displacement errors to obtain a filtering observation; 4, carrying out mileometer information fault detection; 5, carrying out Kalman filtering; and 6, correcting. The method solves technical problems of too large mileometer quantification noise and reduced combination navigation precision of present inertial navigation / mileometer integrated navigation methods; and the method which adopts a displacement integration coupling based inertial navigation / mileometer integrated navigation strategy abandons a quantization noise introduced in the traditional methods utilizing a speed, which is obtained by performing calculation on a displacement, as an observation, and substantially improves the reliability of an integrated navigation system.

A method, a computer program product, a computer system and a method for supporting an application. The method includes: starting one or more applications and creating a corresponding application task button representing each application of the one or more applications; grouping two or more of the applications into a group, creating an application group task button for the group and displaying the application group task button on a taskbar displayed on a computer screen; displaying on the taskbar the application task button of each application of the one or more applications that is not grouped; upon selection of the application group task button, displaying a pop-up menu, the pop-up menu including the application task button of each application of the group and a show-in-taskbar task button; and upon selection of the show-in-taskbar task button, displaying in the taskbar the application task buttons of the applications of the group.

Provided in the invention is an improved-gray-Markov-model-based power equipment fault prediction method. The method comprises: original data collection is executed on power equipment; apriority step ratio checking on is executed according to fault interval data in the original data; under the circumstances that the apriority step ratio checking is done successfully, a GM (1,1) gray model is established directly; under the circumstances that the apriority step ratio checking is not done successfully, original data transformation is executed and then a GM (1,1) gray model is established; according to the markov chain property, an initial prediction value is determined based on the GM (1,1) gray model, state division is carried out, and then a state transition matrix is established, thereby obtaining a Markov prediction model; and an improved Markov prediction model is obtained based on the obtained Markov prediction model, and a calculation result of the gray model is corrected by using the improved Markov prediction model, thereby obtaining a final prediction value.

The invention provides a GNSS / INS / vehicle integrated navigation method for agricultural machinery operation in the technical field of navigation. The method comprises the following steps: S1, inertial navigation calculation is carried out after correction of zero offset of an inertial measurement unit (IMU), and a state transition matrix and a system covariance matrix are calculated through Kalman filtering; S2, whether GNSS information is updated is judged; if the GNSS information is updated, the GNSS information is extrapolated, filtering estimation is carried out by taking the position and speed differences between INS and GNSS as observations, the dynamic condition of the carrier is judged, correction platform error and IMU zero offset error are fed back, and the method returns to S1 for loop execution; or, the current information is the position, speed and attitude information of the carrier; and S3, whether vehicle information is updated is judged; if the vehicle information is updated, the vehicle information is extrapolated, filtering estimation is carried out by taking the speed difference between INS and the vehicle as an observation, and correction platform error and IMU zero offset error are fed back; or, the current information is the position, speed and attitude information of the carrier. The method has high navigation accuracy, and can achieve high navigation accuracy with a low-cost IMU.

A target tracking method uses sensor(s) producing target signals subject to positional and / or angular bias, which are updated with sensor bias estimates to produce updated target-representative signals. Time propagation produces time-updated target states and sensor positional and angular biases. The Jacobian of the state dynamics of a target model produces the state transition matrix for extended Kalman filtering. Target state vector and bias covariances of the sensor are time propagated. The Kalman measurement residual is computed to produce state corrections, which are added to the time updated filter states to thereby produce (i) target state updates and (ii) sensor positional and angular bias updates. The covariance of a state vector comprising target states and sensor positional and angular biases is propagated, producing measurement updated state covariance including (i) target position and velocity measurement covariance updates and (ii) the sensor positional and angular bias measurement covariance updates.

The invention relates to a system and method for integrating domain information into state transitions of a Finite State Transducer (“FST”) for natural language processing. A system may integrate semantic parsing and information retrieval from an information domain to generate an FST parser that represents the information domain. The FST parser may include a plurality of FST paths, at least one of which may be used to generate a meaning representation from a natural language input. As such, the system may perform domain-based semantic parsing of a natural language input, generating more robust meaning representations using domain information. The system may be applied to a wide range of natural language applications that use natural language input from a user such as, for example, natural language interfaces to computing systems, communication with robots in natural language, personalized digital assistants, question-answer query systems, and / or other natural language processing applications.

The invention discloses a vehicle collision prediction method based on vehicle distance probability distribution under a highway model. The method includes the steps of a vehicle periodically (under 10Hz) broadcasts current motion statuses Beacons (speed, acceleration and GPS); the density of vehicles in the surrounding environment is dynamically calculated to build a vehicle distance distribution probability model; a minimum safety distance required to avoid collision when two adjacent vehicles emergently brake is dynamically calculated according the motion status of one vehicle and the motion status of the adjacent vehicle ahead; the collision probability (the probability for the vehicle distance being smaller than the minimum safety distance) of the two adjacent vehicles is calculated according to vehicle distance probability distribution; a multi-vehicle collision Markov chain and a state transition matrix are established, and expectation for the number of vehicle collisions on the whole section at certain time is estimated. The method is high in innovation level and extensibility; the defects of poor GPS data precision and instability in the current vehicle-location-based collision prediction algorithm are well made up; the method plays an excellent role especially in GPS satellite signal blind areas and has promising application prospect.

Capacitive proximity sensing is carried out by detecting a relative change in the capacitance of a “scoop” capacitor formed by a conductor and a surrounding ground plane. Charge is transferred between the “scoop” capacitor and a relatively large “bucket” capacitor, and a voltage of the bucket capacitor is applied to an input threshold switch. A state transition (e.g., from low to high, or high to low) of the input threshold switch is detected and a value (TouchVal) indicative of a number of cycles of charge transfer required to reach the state transition is determined. The presence or absence of an object or body portion in close proximity to or contact with a device can be determined by comparing TouchVal with a predetermined threshold value (TouchOff). In order to lessen the time required for detection, and / or improve the sensitivity thereof, the bucket capacitor may initially be charged to a repeatable non-zero reference level closer to the charge level that will cause a state transition. TouchOff can be adjusted to take into account environmentally induced (non-touch related) changes in the capacitance of the scoop capacitor. Power management may be provided in a user operated data input device utilizing the inventive proximity sensing.

A string matching system comprises a state transition table generator for generating a state transition table based on a matching condition described in a regular expression, and an automaton for including a state that makes a transition according to the state transition table generated by the state transition table generator; if, in the state transition table generated based on the matching condition, there exists no next transition destination state with respect to a current-state and input-characters tuple, the automaton makes a transition to the initial state without proceeding to read input characters.Furthermore, the string matching system comprises a state transition table generator for generating a state transition table based on matching conditions described in a regular expression, and an automaton that makes a transition according to the state transition table generated by the state transition table generator; if no next-transition destination state with respect to a current-state and input-character tuple exists in the state transition table generated based on the matching condition, then the state transition table generator determines an exclusion character based on which the automaton make a transition to a predetermined state without proceeding to read input characters, to generate a state transition table.

The invention discloses a strapdown inertial navigation system(SINS) / global navigation satellite system (GNSS) combined navigation filter system comprising a primary binding module, an earth parameter calculation module, an initial alignment module, a strapdown inertial navigation calculation module, an improved filter algorithm parameter calculation module, a normal-mode combined navigation filtering module, a fault-mode combined navigation filtering module and a feedback correction output module. The improved filter algorithm parameter calculation module calculates a sate transition matrix, a system drive noise covariance matrix, and a measurement noise covariance matrix that are needed by an improved kalman filtering algorithem based on data provided by an SINS and a GNSS and transmits the information to the normal-mode combined navigation filtering module and the fault-mode combined navigation filtering module to carry out filtering calculation according to the result and a status flag. According to the invention, the combined navigation filtering algorithm is modularized, thereby obviously improving precision of the combined navigation and accelerating the convergence speed of the combined navigation filtering algorithm; and the kalman filtering divergence can be effectively inhibited.

The invention discloses a vehicle operating condition multi-scale predicting method based on the Markov chain. The method establishes a Markov chain prediction model for the vehicle operating condition. The method comprises the steps of computing a state transferring matrix by maximum likelihood estimation according to the history information of vehicle operating condition; performing the vehicle operating condition predicting of different time scales according to the obtained state transferring matrix by utilizing the Markov chain and Monte Carlo analogy method; restoring the predicted outcomes of different time scales into data under a history operating condition sampling frequency through linear interpolation; dividing the predicted outcomes of different time scales into different confidence grades according to simulated sample quantity, and computing the linear weight coefficient under different confidence grades of the predicted outcome every time by adopting a linear weighting method; and merging all the predicted values of each scale of predicted outcome every time according to the weight coefficients and merging the different scales of predicted outcomes under the original data frequency to obtain the vehicle operating condition multi-scale predicting outcome. The vehicle operating condition multi-scale predicting method based on the Markov chain can meet the predicting precision requirements of the vehicle operating condition and the requirements of vehicle real-time control.

An intelligent control method for vertical recovery of carrier rockets based on deep reinforcement learning is disclosed. A method of autonomous intelligent control for carrier rockets is studied. Theinvention mainly studies how to realize attitude control and path planning for vertical recovery of carrier rockets by using intelligent control. For the aerospace industry, the autonomous intellectualization of spacecrafts is undoubtedly of great significance whether in the saving of labor cost or in the reduction of human errors. A carrier rocket vertical recovery simulation model is established, and a corresponding Markov decision-making process, including a state space, an action space, a state transition equation and a return function, is established. The mapping relationship between environment and agent behavior is fitted by using a neural network, and the neural network is trained so that a carrier rocket can be recovered autonomously and controllably by using the trained neural network. The project not only can provide technical support for the spacecraft orbit intelligent planning technology, but also can provide a simulation and verification platform for attack-defense confrontation between spacecrafts based on deep reinforcement learning.

The invention relates to optimizing software code execution during state transitions. The system handles changes in execution context using differential rule checking techniques. For instance, when a thread executing in a data processing system changes state, its new state may be subject to different rules than its previous state. To enforce these rules, the thread may be associated with software code that causes certain restrictions, such as memory restrictions, to be applied to the thread. In an interpretive environment, this can be implemented by detecting a state transition in an active thread, and responding to the state transition by associating the thread with a dispatch table that reflects its state change. The dispatch table may cause the thread to be associated with code that enforces those restrictions. In one example, different dispatch tables can be provided, each table reflecting a different state of a thread, and each causing a thread to be subject to different restrictions. In another example, the same dispatch table can be rewritten to accommodate the changed state of the thread.

The invention discloses a dynamical probability load flow calculation method with consideration of wind power integration. A draught-fan output model is established on the basis of the Markov chain and a state scenario tree, draught-fan output historical data are normalized, states are partitioned, and a state transfer matrix is obtained according to the change relations between the states; according to the draught-fan output state at the current moment and the state transfer matrix, the most possible states of the draught-fan output at the next moment and the probabilities of the most possible states are predicted, repeated analysis is conducted, and the state scenario tree in a future period is generated; multi-period and multi-state distribution of the draught-fan output is obtained through the state scenario tree; probability load flow calculation based on the semi-invariant method and improved Von-Mises series expansion is conducted on each time section. Calculation results can reflect the relations between probability load flows of a power grid in different time periods, probability distribution of the draught-fan output states can be reasonably predicted, the change rule of the draught-fan output states is accurately described, and the method is used for comprehensively assessing safety and stability of a power system achieving wind power integration.

The invention discloses a power system transient stability simulating method based on implicit numerical integration. Compared with an existing power system transient stability numerical simulation implicit trapezoidal integration method, the power system transient stability simulating method employs a power-angle integration formula with a smaller local truncation error, namely, enables a non-linear differential equation set for describing a power system transient process to be expressed as a linear portion and a non-linear portion. An accurate analysis expression of a state transition matrix is obtained by reasonably selecting a system matrix of the linear portion as a singular matrix, and a group of implicit integration formulas is obtained by leading linear integrable functions to be approximate to the non-linear portion of the differential equation set. The local truncation error of the power-angle implicit integration formulas of the generator refers to O (h5) which is larger than a local truncation error O (h3) of implicit trapezoidal integration, the calculated quantity of integration each time is equivalent to that of the implicit trapezoidal integration. By means of the high-precision implicit integration formulas, iteration times of each integration step under the same iteration precision condition are decreased, so that the simulated calculated quantity is remarkably decreased.

A power consumption calculation apparatus is disclosed, including: a work load measurement part, a management part, and a power consumption calculation part. The work load measurement part measures a work load of a job executed by an image forming apparatus and records a measured value, at predetermined time intervals. The management part manages state transition data indicating power consumption and a required time for each of states of the image forming apparatus which transits in response to an execution of each job. The power consumption calculation part calculates a power consumption amount corresponding to one job execution at each predetermined time interval when it is determined that the job is executed, based on the measured value and the state transition data.

The invention discloses a meteorological threat assessment method based on a discrete dynamic Bayesian network. The method comprises the following steps: collecting an observed weather type, intensity information and UAV (Unmanned Aerial Vehicle) position and attitude information; performing a quantization treatment according to a divided quantization level, and establishing an observation evidence list; using expert knowledge or experience to establish a conditional probability transfer matrix between states, and determining a state transfer matrix between time slices; establishing a discrete dynamic Bayesian network model between a meteorological threat level, a meteorological factor and the UAV; and using a Hidden Markov Model reasoning algorithm to calculate the final meteorological threat level. The meteorological threat assessment method based on the discrete dynamic Bayesian network provided by the invention realizes the organic combination of a continuous observation value and the discrete dynamic Bayesian network, and reasons out the probability distribution of a meteorological threat degree in combination with the HMM (Hidden Markov Model) reasoning algorithm, so that the effectiveness, the practicability and the accuracy of meteorological assessment can be greatly improved.

A state transition definition table describes state transition definitions. A state transition table generation class is generated from the state transition definition table. A state transition management module controls the state transition of screens utilizing a state transition table generated from a state transition table generation class, based on the input of events derived from screens. A state transition management module controls the display of screens based on the state transition thereof.

A computer implemented method, system, and computer usable program code for classifying a data stream using high-order models. The data stream is divided into a plurality of data segments. A classifier is selected for each of the plurality of data segments. Each of a plurality of classifiers is clustered into states. A state transition matrix is computed for the states. The states of the state transition matrix specify one of the high-order models for classifying the data stream.