44 results about "Time step" patented technology

A method includes providing a control algorithm that may include a constraint constraining production of the equipment below maximum values for a plurality of time steps, and dynamically adjusting the constraint by updating the maximum values as a function of predicted values of a dynamic variable for the plurality of time step. The dynamic variable affects an actual maximum production of the equipment. The method includes determining control decisions for the plurality of time steps by executing the control algorithm and controlling the equipment in accordance with the control decisions.

A determination device includes a memory configured to store instructions and a model for receiving an input of data relating to a determination target for each time step and for changing the state thereof for each time step. The determination device further includes a processor configured to execute the instructions to: identify a reference time step as a time step serving as the reference to the changing state of the determination target, and perform determination of the determination target based on the state of the model at a derivative time step from the reference time step.

This specification describes a simulation system that performs simulations of physical environments using a graph neural network. At each of one or more time steps in a sequence of time steps in a given time interval, the system can process a representation of a current state of the physical environment at the current time step using the graph neural network to generate a prediction of a next state of the physical environment at the next time step. Generally, the environment has discontinuous dynamics at one or more time points during the time interval.

The invention provides a time step determination method and device for solving a physical quantity of a transient physical process. The method comprises the following steps: acquiring first time step iteration information corresponding to a historical moment in the transient physical process; the first time step iteration information is used for representing the condition of iteratively updating the time step in the process of solving the convergence solution corresponding to the target physical quantity and the historical moment; the target physical quantity is a to-be-solved physical quantity in the transient physical process; determining a first predicted time step corresponding to the current moment according to the first time step iteration information corresponding to the historical moment and the first predicted time step corresponding to the previous moment; and iteratively updating the first prediction time step corresponding to the current moment to obtain a target time step enabling the target physical quantity to converge with a solution corresponding to the current moment. According to the method, the time step length enabling the physical quantity to converge in the transient physical process can be quickly determined.

The application discloses a soft measurement modeling method based on a residual time attention time convolution network and belongs to the field of soft measurement modeling. First, the time attention module is combined with the time convolution network, and the mutual influence between previous time steps and the current time step is integrated into the current time. Then, the residual connection is added on the basis of the time attention. Finally, the quality variable is predicted. The application can better extract the nonlinearity between data to improve the prediction performance and more accurately extract the relevant features between data.

The application provides an entity linking method and system based on dynamic time steps, and relates to the technical field of natural language processing. The method comprises the following steps: obtaining a plurality of entity mentions of a related topic in a document to be analyzed; obtaining candidate entities of each entity mention from a knowledge base; constructing a mapping entity association graph by using a dynamic time step method, and extracting global topic consistency features from the mapping entity association graph; obtaining target entities of each entity mention from the candidate entities according to the global topic consistency features, and associating each entity mention with the corresponding target entity. In this way, by using the dynamic time step method to construct the mapping entity association graph, in each time step, the candidate entities related to the topic are selected as the mapping entities of the mapping entity association graph, so that the correct global topic consistency features can be extracted from the mapping entity association graph, thereby improving the accuracy of entity linking.

An industrial printer control method and system relates to the field of program control technology. The method includes the following steps: obtaining the current print task of each industrial printer that shares the same ink tank; creating a dynamic time window for the current industrial printer and initializing the length of the dynamic time window to a first preset value; when other industrial printers are performing long-term ink extraction tasks, filling in preset data at each preset time step; when the dynamic time window is filled, outputting a compensation instruction to the current industrial printer; after receiving the compensation instruction, when the current industrial printer receives a print task, first performing an ink compensation operation on the ink supply pipeline of the current printer; the length of the dynamic time window is adjusted in real time according to the following steps: each time the current industrial printer receives a short-term ink extraction task, the length of the dynamic time window is reduced. This application can improve the printing effect of industrial printers.

Systems and methods for computing with multiple nodes. In some embodiments, a method includes writing, during each of a sequence of time steps, by each node of a plurality of nodes, to a shared memory, the shared memory being shared by the nodes, wherein: each of the nodes includes a hardware-maintained coherence domain and is connected to the other nodes, and each of the nodes includes a respective portion of the shared memory.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a prediction characterizing an environment. In one aspect, a method includes obtaining a respective observation characterizing a state of an environment for each time step in a sequence of multiple time steps, comprising, for each time step after a first time step in the sequence of time steps: processing a network input that comprises observations obtained for one or mor preceding time steps to generate a plurality of acquisition decisions; obtaining an observation for the time step, wherein the observation includes data corresponding to modalities that are selected for acquisition at the time step, does not include data corresponding to modalities that are not selected for acquisition at the time step; and processing a model input that includes the observation for each time step in the sequence of time steps to generate the prediction.

The application discloses a logical time management service method and system for joint tests, wherein participants set time control strategies and time limited strategies, and initiate logical time advancing requests and request processing callback requests to middleware according to preset time steps, and register callback functions; a time adjuster calculates a timestamp lower limit to provide data guarantee for time advancing; the middleware receives the logical time advancing requests, checks whether the participants have the right of the time control strategies, updates the current timestamp lower limit, receives the processing callback requests, checks FIFO queues and processes queue messages, checks the time limited strategies of the participants, checks TSO queue states and processes queue messages, checks the relationship between the time requested by the participants and the timestamp lower limit, and calls the callback functions allowed by the time advancing to the participants; the application provides an efficient and accurate logical time management mechanism, and solves the problem that the middleware of the existing joint test platform does not support logical time management.

The embodiment of the invention provides a seismic phase signal pickup method based on time window length transformation, and the method comprises the steps: S1, calculating the theoretical travel time of a seismic phase, and calculating the theoretical arrival time of the seismic phase through a ray tracing method; s2, selecting a time window length, setting a time window at the theoretical arrival time, and determining initial actual arrival time through a time domain long and short window method; and S3, changing the length of the time window, reducing the scale of the time window, setting the time window at the initial actual arrival time, and determining the final actual arrival time through a time domain long and short window method. According to the embodiment of the invention, the pickup precision is high, the seismic phase signals can be extracted more accurately by combining the theoretical travel time and the long and short window method, especially in complex or low-signal-to-noise-ratio seismic data, misrecognition or missed recognition can be reduced, the automation degree is high, automatic extraction of the seismic phase signals can be realized, manual intervention is reduced, and the efficiency is improved. A large amount of time and labor cost are saved, and the working efficiency is greatly improved.

Disclosed in the embodiments of the present disclosure are a non-terrestrial communication method and apparatus. The method comprises: acquiring, at the current time step, the data transmission rate between each terminal among at least one terminal and a network device; inputting state information at the current time step into a first model, so as to obtain a first action executed by the network device in the state of the current time step, wherein the first action is used for indicating the displacement of the network device at the current time step; and on the basis of the first action, determining the position of the network device at a subsequent time step, wherein the first model is used for determining the position of the network device at each time step within a first period, and the total data transmission rate of the at least one terminal is the largest within the first period. Thus, a network device can move rationally even when a terminal is moving, thereby effectively ensuring the throughput of a system, making the system achieve communication with a short delay and a high data rate, and improving the communication efficiency of the system.

To provide a technique capable of integrating history data in the order of occurrence in actual time even when integrating history data in which a plurality of time returns exist.SOLUTION: According to an embodiment, an information processing device includes an interface configured to acquire first history data and second history data in which process data of a device and a time stamp are stored in respective rows, and a processor configured to arrange the first history data and the second history data in order of index numbers assigned to the respective rows, determine whether a time of an n-th row of the first history data is later than a time of an m-th row of the second history data, insert the m-th row between the n-th row and an (n-1) - th row when it is determined that the history data of the n-th row is later than the time of the m-th row, and assign new index numbers to respective rows of third history data generated by inserting the second history data into the first history data in order of arrangement.SELECTED DRAWING: Figure 1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting agent response times. One of the methods includes continually updating, at each time step of a plurality of time steps, an accumulated measure of surprise for the agent due to the movements of another entity in the traffic environment. A distribution of previously predicted trajectories is obtained at a previous time step for the other entity in the environment. A measure of surprise is computed from the perspective of the agent. An accumulated measure of surprise is updated for the time step using the computed measure of surprise for the agent. If the accumulated measure of surprise crosses a threshold at a particular point in time, a predicted response time for the agent is generated based on the particular point in time that the accumulated measure of surprise crosses the threshold.

The invention discloses an industrial printer control method and system, and relates to the technical field of program control, and the method comprises the following steps: obtaining the current printing task of each industrial printer sharing the same ink bin; a dynamic time window is created for the current industrial printer, and the length of the dynamic time window is initialized to be a first preset value; when other industrial printers are executing the long-time ink pumping task, preset data are filled in every preset time step length; after the dynamic time window is filled, a compensation instruction is output to the current industrial printer; after the compensation instruction is received, when the current industrial printer receives a printing task, first ink compensation operation is carried out on an ink conveying pipeline of the current printer; the length of the dynamic time window is adjusted in real time according to the following steps that when the current industrial printer receives the short-time ink pumping task every time, the length of the dynamic time window is reduced. The printing effect of the industrial printer can be improved.

The application provides a kind of embedded device system time synchronization method and system based on multiple bus interfaces, comprising: step 1: listening to data packet in various bus interfaces corresponding to each slave device;Step 2: analyzing data packet, extracting command code, obtaining interface identification of corresponding bus interface;Step 3: binding command code and interface identification and storing in list according to listening order;Step 4: traversing list, corresponding response is carried out according to command code type;Step 5: when command code type is time request, the system time of master device obtained is sent to slave device according to interface identification corresponding interface;Step 6: listening to data packet sent by master device;Step 7: analyzing data packet, extracting time for time;Step 8: time for time response is carried out according to time for time;Step 9: after slave device completes time for time, sending time for time completion data packet to master device.The application has high universality and expansibility, and reduces development cost.

The present invention relates to a discrete event parallel processing method, terminal device, and storage medium. The method comprises: when a new event is generated during thread processing, the newly generated event is stored in the event queue corresponding to the thread in order of minimum processing time, and the minimum processing time of the event is stored in the event queue as the timestamp of the event; when all threads in the thread pool have completed execution, the event with the smallest sorted timestamp is extracted from each event queue of each thread, and the minimum value of the timestamps of all extracted events is calculated; the next frame timestamp is calculated based on the minimum timestamp and the time step; the minimum timestamp in the event queue corresponding to each thread is compared with the next frame timestamp to determine whether the thread is processing at the time corresponding to the next frame timestamp. The present invention adopts a parallel discrete event simulation method, which can fully utilize the computing performance of multiple CPUs and improve the operation efficiency of the simulation.

A computer-implemented method for selecting an action from a set of possible actions performed in a system when the system is in a current state of a set of possible states in a time step, the method comprising: performing a selection of an action, the action, starting from the current state, directs the system in successive time steps with the highest probability along a path corresponding to a series of actions and states in successive times and having a gain at least equal to or higher than a predetermined value; the system is caused to perform the action.

Methods and apparatus are proposed for computing a power reduction policy to control radio emissions of a base station over a time window, with the objective to keep the time-averaged radio emissions over the time window below an authorized value set by regulations, with minimum impact on the performance of the overall system. The time window is subdivided into a plurality of time periods starting at a plurality of time steps, and the policy control comprises, at least at a first time step in the time window: generating a predicted average radio emissions over subsequent time periods in the time window, based on radio emissions historical data; finding couples of values of a power reduction factor and an average radio emissions that minimize a cost function for each time step in the time window, based on the predicted average radio emissions.

A transient simulation method for an oscillating circuit includes: selecting a node of the oscillating circuit as a reference point and determining a time point for entering periodic oscillation; setting a length of a time step pattern, performing transient simulation on the oscillating circuit and monitoring a physical quantity at the reference point; after the physical quantity reaches a preset reference value, obtaining a time step pattern, wherein the number of time step patterns is equal to the value of the length, and the reference value is within a variation range of the signal amplitude; continuing to perform transient simulation on the oscillating circuit, and when the physical quantity at the reference point reaches the reference value again, applying the obtained time step pattern to adjust the time step and time point of the simulation. The method of the present invention monitors the change of a physical quantity at a node in the oscillating circuit, obtains a time step pattern for adjusting the time point and time step of subsequent transient simulations, and can greatly reduce periodic variations in transient waveforms when the system is in periodic oscillation.

The invention relates to a nuclear power digital twin multi-model engine time synchronization method and system. The method comprises the following steps: S1, establishing a global time reference, so that all model engines perform state updating and event triggering based on the same time reference; and S2, under the global time reference, setting a fixed time step length for all model engines so as to ensure synchronization and model engine calculation and interaction cycle alignment. Wherein the interaction cycle refers to a complete work cycle which must be completed by the model engine within a time step length. And S3, performing timestamp marking on events and communication data generated by all the model engines, and performing queue management on the events and the communication data according to timestamps. And S4, optimizing loading durations of all model engines based on a parallel loading mode and a resource pre-allocation mechanism. The method is used for dynamic interaction of multiple model engines in a nuclear power digital twin platform, global time logic consistency can be ensured, and model collaboration efficiency and real-time performance are improved.

A method (70) for implementing a surface code measurement circuit (50) on a lattice (46) of qubits (14) of a Margarner four-terminal lattice (66, 68) includes: assigning (70A), in a sequence of time steps, a set of single qubit projection measurement loops on each of three auxiliary qubits (14A) of the lattice; allocating (70B), in a sequence of time steps, a set of dual qubit projection measurement loops on each of the four data qubits (14D) of the lattice together with one of the three auxiliary qubits; allocating (70C), in a sequence of time steps, a set of dual qubit projection measurement loops on two or more pairs of auxiliary qubits selected from the three auxiliary qubits of the lattice; and advancing (70D) to traverse each of the time steps of the sequence, performing the allocated single-qubit projection measurement and dual-qubit projection measurement therein. In the method, a measurement circuit corresponds to a stabilizer of a surface code, and a measurement that generates a stabilizer operator is measured.

Techniques for utilizing adjusted timestamps and updated hardware internal timers to drive sensor data (e.g., camera and / or video frames) are discussed herein. Timestamps can be adjusted to be adjusted timestamps based on simulation times and adjustment times. The adjustment times can include delays associated with frames being driven out of buffers. The adjustment times can further include delays associated with frames propagating through simulation devices. Internal timers can be updated to be updated timers based on numbers of timesteps between signals of primary clocks.

The invention provides a path planning method and device, a program product and a storage medium. In the method, a deep learning model comprises an encoder and a decoder; the encoder is used for generating a node-embedded graph embedding containing each node according to the graph structure; the decoder is used for predicting a prediction node yt of the tth time step at the tth time step so as to obtain a distribution path of the prediction node containing each time step; the method comprises the following steps: after the decoder predicts the prediction node yt, updating the predicted commitment time of the prediction node yt; and updating node embedding of the prediction node yt and updating graph embedding based on the updated predicted commitment time of the prediction node yt, so that the decoder executes prediction of the prediction node yt + 1 at the (t + 1) th time step according to the updated graph embedding and the updated node embedding of the prediction node yt.

The invention discloses a logic time management service method and system for a joint test, and the method comprises the steps: enabling a participant to set a time control strategy and a time limitation strategy, initiating a logic time propulsion request and a request for processing callback to middleware according to a preset time step length, and registering a callback function; the time regulator calculates a timestamp lower limit to provide data guarantee for time marching; the middleware receives the logic time advancing request, checks whether the participant has the authority of the time control strategy, and updates the lower limit of the current timestamp; receiving and processing a callback request, checking an FIFO queue and processing a queue message, checking a participant time limited strategy, checking a TSO queue state and processing the queue message, checking a relation between participant request time and a timestamp lower limit, and calling a callback function allowing time advance to the participant; the invention provides an efficient and accurate logic time management mechanism, and solves the problem that the existing joint test platform middleware does not support logic time management.

This application discloses a method and related apparatus for predicting channel time series at future times. The method includes: firstly, acquiring the channel time series at M times to obtain X(t) m )={x n (t m Let X(t) = |m=1,2,…,M} (n=1,2,…,N), where N is the number of channel time series within a given time moment, and M and N are both positive integers. Next, based on the stated X(t)... m )={x n (t m Construct a mapping Ψ for each m = 1, 2, ..., M (n = 1, 2, ..., N). p , among which, Ψ p (X(t m ))=x n (t m+p‑1 (p=2,3,…,L), where L-1 is the number of time steps to be predicted, and X(t) is used as the time step to be predicted. m (m=1,2,…,M-p+1) as input, with x n (t m+p‑1 Using (m=1,2,…,M-p+1) as the output, we perform a Gaussian process regression (GPR) fitting to obtain Ψ. p (p = 2, 3, ..., L). Finally, X(t) m (m = M - p + 2, M - p + 3, ..., M) respectively input the Ψ p (p = 2, 3, ..., L), we get x n (t m+p‑1 This application generates predicted values ​​for (n = 1, 2, ..., N; m = 1, 2, ..., M; p = 2, 3, ..., L), thus achieving multi-step prediction of channel time series. Compared with existing single-step prediction, this application does not accumulate or amplify errors during the prediction process, resulting in more accurate prediction results.

Embodiments of the present specification provide a diffusion model processing method and device, a picture processing method and device, wherein the diffusion model processing method comprises determining a time step set of a diffusion model and a time step interval corresponding to the time step set; determining a first time step from the time step set and determining a target time step corresponding to the first time step according to the time step interval; inputting a noisy picture corresponding to the first time step and the target time step into the diffusion model to obtain predicted noise corresponding to the noisy picture; and processing the diffusion model according to target noise corresponding to the noisy picture and the predicted noise. The method divides the time step set into time step intervals, and during subsequent diffusion model training, the diffusion model shares time step conditions within a time step interval, i.e., the first time step shares the target time step within the corresponding time step interval, thereby reducing the time step condition, greatly reducing the training burden, and improving the model training performance.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating predictions characterizing an environment. In one aspect, a method includes obtaining, for each time step in a sequence of a plurality of time steps, a respective observation characterizing a state of an environment, including, for each time step subsequent to a first time step in the sequence of time steps: processing a network input including observations obtained for one or more previous time steps, generating a plurality of acquisition decisions; obtaining an observation for the time step, where the observation includes data corresponding to modalities selected for acquisition at the time step and does not include data corresponding to modalities not selected for acquisition at the time step; and processing a model input to generate a prediction, the model input comprising an observation for each time step in the sequence of time steps.

A computer-implemented method for determining a mean and variance of measurement data comprises the following steps performed by a computer hardware component: acquiring measurement data for a current time step using a sensor; updating the plurality of intermediate data for the previous time step based on the measurement data for the current time and based on the forgetting factor to obtain a plurality of intermediate data for the current time step; and determining an average value and a variance for the current time step based on the plurality of intermediate data for the current time step.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for predicting agent response times. One of the methods includes continually updating, at each time step of a plurality of time steps, an accumulated measure of surprise for the agent due to the movements of another entity in the traffic environment. A distribution of previously predicted trajectories is obtained at a previous time step for the other entity in the environment. A measure of surprise is computed from the perspective of the agent. An accumulated measure of surprise is updated for the time step using the computed measure of surprise for the agent. If the accumulated measure of surprise crosses a threshold at a particular point in time, a predicted response time for the agent is generated based on the particular point in time that the accumulated measure of surprise crosses the threshold.