150 results about "Error state" patented technology

The invention provides a CAN bus message period adjusting method and system based on dynamic load balancing, and the system measures and calculates a network load rate and an error state through a network load real-time detection module, carries out the priority grade division and parameter configuration for different messages, carries out the load partition definition according to the load rate, and carries out the adjustment of the CAN bus message period. And dynamically adjusting a message sending period according to a measurement result, adding a feedback mechanism to realize closed-loop control, and finally realizing load balancing of the system.

A finite time dynamic mechanical arm control method based on a speed reconstruction strategy comprises the steps that firstly, a mechanical arm system physical model containing joint torque is constructed, joint tracking errors are defined, and a mechanical arm system state space model is obtained; 2, constructing a finite-time high-bandwidth gain speed estimator, defining a state estimation error, and obtaining an estimation error dynamic equation; 3, defining an auxiliary state to obtain an auxiliary state dynamic equation; 4, constructing a finite time non-recursive adaptive controller; and 5, obtaining a final input torque of the controlled system, and outputting the final input torque as a joint torque. According to the method, through the processes of error modeling, state estimation, auxiliary conversion, control law design and closed-loop feedback, the dynamic tracking problem of the mechanical arm is converted into the finite time convergence problem of the error state, meanwhile, dynamic gain and robust compensation are introduced, the adaptability of a system to parameter uncertainty and external disturbance is improved, and the system reliability is improved. And a high-precision and high-robustness control target is realized.

The invention provides an automatic driving positioning integrity fault detection and identification adaptive evaluation method, relates to the technical field of vehicle control, and realizes real-time estimation and constraint of observation errors and state uncertainty by fusing GNSS and IMU and introducing error state Kalman filtering. According to the invention, residual-driven detection-identification-adaptation is introduced, and real-time identification, elimination and weight correction can be carried out on abnormal observation in a multi-dimensional observation space; through a dynamic threshold adjustment and uncertainty quantization mechanism, the filtering process can automatically adjust an observation noise model according to environmental changes, and it is ensured that the system keeps stable convergence and high-precision performance in multiple scenes. According to the method, optimal subset selection and whitening residual error construction are adopted in an ESKF updating link, so that the influence of the abnormal observation dimension on the state estimation covariance is effectively reduced. Through the integrity evaluation method combining the protection level and the alarm limit value, whether the positioning error meets the safety requirement or not can be judged.

The invention provides a numerical control machine tool intelligent control method and device combined with error analysis and a storage medium, and relates to the technical field of numerical control machine tool intelligent control. Setting U error hiding mode starting points and V error hiding mode ending points; performing propagation simulation according to error state transition, and simulating an error hidden cumulative map; configuring U error hiding mode starting points by using equipment basic data, performing propagation path simulation in an error hiding cumulative map, and obtaining M error simulation propagation paths; identifying a key error source sequence with the influence on the machining precision from large to small; setting an error compensation strategy and sending the error compensation strategy to the numerical control machine tool terminal for compensation control. The technical problems that in the prior art, deep analysis on an error propagation path is lacked, and the control precision of a numerical control machine tool is low are solved, and the technical effect that the control precision is improved by combining error analysis and a corresponding compensation strategy is achieved.

The invention provides a test system for prototype verification of a PCIE Switch switch chip. The system comprises an FPGA assembly, RC simulation equipment, EP simulation equipment and monitoring analysis equipment. In the application, the FPGA component integrates the tested component PCIE Switch and the test auxiliary component, supports hardware-level reconfiguration, can simulate topology and abnormal scenes of any RC device or EP device on the same platform, and does not need to replace physical hardware. Compared with an existing test system which depends on serial port log output faults, the method has the advantages that the error state is captured through FPGA hard logic, nanosecond fault tracing is achieved, and the test efficiency can be effectively improved. Besides, RC simulation equipment defines a test program through software, dynamically generates flow (such as normal / abnormal messages), and can replace physical RC equipment; the EP simulation device can respond to the test flow in real time, and can support multi-protocol version compatibility verification. Automatic testing of prototype verification of the PCIE Switch chip can be achieved through the monitoring and analyzing device, manual intervention is not needed, and input of testing manpower can be reduced.

The invention discloses a humanoid robot joint motor control method with model prediction control, and relates to the technical field of robot motion control. The method is used for solving the problems of control instruction infeasibility and model disturbance caused by voltage saturation during high-speed motion. An error state space model of a joint motor system is established, and non-linear terms such as gravity are used as nominal feed-forward quantities for stripping; then, the rotating speed of the motor and the bus voltage of the driver are collected in real time, the back electromotive force is calculated, and the voltage margin is mapped into a dynamic torque physical limit value at the current moment; and meanwhile, estimating an unmodeled lumped disturbance value of the model by using an extended state observer. And finally, taking the dynamic limit as a real-time inequality constraint, introducing a disturbance value correction prediction equation, constructing and solving a quadratic programming problem, extracting an optimal compensation torque, superposing the optimal compensation torque with an inverse dynamic feedforward torque, and converting the superposed torque into a current signal to drive a motor, thereby realizing high-dynamic and high-precision robust control under the physical boundary constraint.

The invention discloses an engineering measurement error dynamic correction method based on multi-sensor data fusion, and relates to the technical field of engineering measurement. Three error sources of sensor physical characteristics, environmental interference and manual operation are defined as estimable state variables, and differentiation processing is performed according to different characteristics of errors: a dynamic model is established to embed the sensor and environmental errors into a state space, so that real-time compensation based on the model is realized; according to the scheme, a gross error recognition mechanism based on data-driven statistical characteristics is established, real-time diagnosis and processing are carried out on personal errors, joint optimal estimation is carried out on unified state vectors by adopting a recursive estimation algorithm, and high-precision corrected physical quantities and estimated values of all error states are synchronously output. The measurement precision and reliability are obviously improved; by means of robust processing of gross errors, stability of the system in a non-ideal environment and online self-diagnosis and health monitoring of the measurement system are enhanced.

The invention discloses a T-S fuzzy model-based two-stage anti-attack coupling memristive neural network finite time bipartite synchronization control method. The method comprises the steps of establishing a memristive neural network model and a symbol topological structure; converting the memristive neural network model into a fuzzy coupling memristive neural network model, defining a synchronous error state variable, and establishing an error system of the memristive neural network; converting the error system into a form based on differential inclusion; a two-stage finite time synchronous controller is designed, in the first stage, when the state of an error system meets the condition, a pulse controller based on a hybrid trigger mechanism is designed to resist spoofing attacks; and in the second stage, a controller combining finite time control and fixed time control mechanisms is designed to resist DoS attacks. According to the invention, a two-stage controller is designed, and the controller integrates pulse safety control, feedback control and a finite time and control mechanism, and can simultaneously resist spoofing attacks with energy constraints and DoS attacks with random outbreak features.

A control device and method for a braking system. The method includes reading out or checking, based on at least one signal, whether a predefined error state exists at a brake-actuation hydraulic unit, which is mounted on an ESC hydraulic unit, and performing at least one of the following steps if it is determined that the at least one predefined error state exists at the brake-actuation hydraulic unit: activating at least one pump of the ESC hydraulic unit in order to draw in a firmly predefined or specified brake fluid volume from a brake fluid reservoir of the braking system, and / or controlling at least one electric motor of the vehicle which can be operated in a regenerative mode, to effect a firmly predefined or specified motor braking torque not equal to zero at at least one wheel of the vehicle and / or at least one axle of the vehicle.

The invention discloses a mechanical arm control method and system based on real-time attitude estimation, and the method comprises the steps: obtaining a real-time point cloud image of a mechanical arm, and carrying out the attitude estimation based on the real-time point cloud image to obtain an initial attitude of the mechanical arm; the target posture of the mechanical arm is determined, the target posture and the initial posture are analyzed, and the posture error difference is determined; the attitude error difference is analyzed, the error state of the mechanical arm in each direction is determined, and a motion optimization coefficient matrix of the mechanical arm is determined based on the error state; the operation parameters of the mechanical arm are optimized on the basis of the motion optimization coefficient matrix, and motion of the mechanical arm is controlled on the basis of the optimized operation parameters; and after the mechanical arm moves, the steps are repeated, and closed-loop circulation control over the mechanical arm is completed. Visual monitoring is converted into real-time dynamic monitoring from static monitoring, continuous closed-loop deviation correction can be achieved in the movement process of the mechanical arm, accordingly, dynamic deviation correction and accurate operation are achieved, and the grabbing and assembling success rate and efficiency of the mechanical arm are greatly improved.

The invention belongs to the technical field of computer network communication, and provides an RDMA-oriented reliable connection fault rapid recovery method and system, the method is executed by a first communication end, communication is established between the first communication end and a second communication end through a queue pair QP, and the queue pair QP is used for bearing service data transmission. The first communication end reserves resources after the QP enters an error state, and sends a PROBE control frame to the opposite end through the control plane queue pair; the second communication end decides to reuse the original QP or allocate a new QP (reuse an original protection domain, a memory registration area and a completion queue) according to the local QP state, and returns a QP number and a start packet serial number in an RESP frame; and the first communication end updates the local QP context and drives the state machine to switch to a ready sending state. The whole process does not need to destroy the QP and does not depend on CM handshake, the recovery time delay is less than 0.5 millisecond, and the method is transparent to the upper layer application and is suitable for the RoCE v2 network environment.

The invention provides a preset time fuzzy control method for a reentrant manufacturing system under intermittent state feedback, and the method comprises the steps: constructing a nonlinear error dynamic model, and employing a fuzzy modeling technology to represent the system as a combination of a plurality of linear subsystems; introducing a time scaling function, and transforming the error state of the global fuzzy model by using the time scaling function to obtain an error scaling state; an event triggering mechanism is designed to realize intermittent state feedback so as to reduce the communication cost; based on the Lyapunov stability theory, a sufficient condition that the system is stable within preset time is deduced, and a controller gain is obtained by solving a linear matrix inequality of the sufficient condition, so that the system still has robustness under external interference. Agile response and low-communication-cost operation of the reentrant manufacturing system within the preset deadline are realized.

The present disclosure includes devices methods and systems related to detecting stall error state in a work machine. A method for detecting stall error state in a work machine may include determining one or more base operation states. The method may include receiving speed indication data from one or more speed detection systems. The method may include comparing the one or more base operation states to one or more of the speed indication data. The method may include determining a stall error state based at least in part on the comparing of the base operation states and speed indication data. The method may include adjusting one or more combine operation parameters based at least in part on the stall error state.

The invention relates to the technical field of numerical control machine tools, and discloses a thermal error dynamic modeling and compensation method for a numerical control machine tool. The method comprises the following steps: continuously collecting heat state original data through a temperature sensing device arranged at a key part of a numerical control machine tool; real-time preprocessing is carried out on the thermal state original data to eliminate noise and abnormal values, and a standardized thermal data sequence is generated; key thermal characteristic parameters are extracted from the standardized thermal data sequence, and the key thermal characteristic parameters are input into a self-adaptive thermal error prediction module to output a current thermal error prediction value; comparing the current thermal error predicted value with a preset thermal error threshold value to generate a thermal error state indication signal; starting a compensation strategy selection mechanism when the thermal error state indication signal indicates abnormity; the compensation strategy selection mechanism retrieves a compensation scheme library according to the thermal error state indication signal to determine an adaptive compensation instruction; and executing the adaptive compensation instruction to drive the numerical control machine tool to adjust processing parameters for thermal error correction.

The invention discloses a 2-D motorcade dynamic performance sliding mode control method for error adjustment based on a virtual position, and relates to the field of motorcade cooperative control. According to the method, a virtual position following strategy is designed under a 2-D motorcade cooperative control technical framework for the first time. A virtual position corresponding to each following vehicle is set, so that the following vehicles can predict the path change of a motorcade in advance through the respective corresponding virtual positions, and the vehicle course angle error in the following process is remarkably reduced; a novel finite time performance function is designed, a convergence region can be sensitively transformed according to an initial value of a spacing tracking error, and control parameters are automatically adjusted in real time according to an error state. Through the function, the vehicle distance error can be quickly and stably converged to a predefined area within finite time under the condition that a traditional pilot-front vehicle following strategy is adopted. The motorcade can automatically adapt to control requirements under different driving conditions, and stable operation of the motorcade under various complex road conditions is ensured.

Disclosed herein is a computer-implemented quantum error correction method for decoding errors in a quantum computer system. The quantum computer system comprises a decoder apparatus and a register of quantum devices. The method comprises receiving, at the decoder apparatus, syndrome data in an initial state, the syndrome data being representative of an error state of the quantum devices in the register of quantum devices, and graph data representative of a graph comprising a plurality of error nodes and a plurality of check nodes. The error nodes represent error mechanisms that can occur on the register of quantum devices. Each check node is associated with one or more measurements which can be performed on the register of quantum devices. The initial state of the syndrome data indicates a check value of marked or unmarked for each check node based on an outcome of its associated one or more measurements. The method further comprises determining, based on a statistical model of error rates associated with the error mechanisms, an error probability associated with each of the one or more error nodes; and generating modified graph data by iteratively performing linear operations on the graph data and syndrome data based on a current state of the syndrome data to determine an independent error node associated with each marked check node. The method also comprises determining, by the decoder apparatus, a correction for the error state based on the modified graph data and, optionally, based on the error probabilities associated with the one or more error nodes.

A method of handling a failure in a pipeline between a source of alerts and a Security Incident & Event Management (SIEM) platform involves: deserializing data from at least one error state object in an error log; transferring the deserialized data to an offline environment; reconstituting at least some tasks of the pipeline, including the failed task, using contents of the error log and contents from a task log; re-running the at least some tasks of the pipeline; identifying a cause of the failed task; based upon the cause of the failed task, transferring at least one of a modified task or replacement task for use in the pipeline; instantiating a new version of the pipeline incorporating the at least one of the modified task or the replacement task; and re-executing the new version of the pipeline to process alerts and provide the processed alerts to the SIEM platform.

The invention provides a fault recovery method and system, driving equipment and a storage medium, and belongs to the technical field of driving equipment.The method comprises the steps that in the running process of the driving equipment, an equipment identifier of a target peripheral component with a fault in multiple peripheral components of the driving equipment is determined; obtaining working state information of the target peripheral component according to the equipment identifier, wherein the working state information is used for reflecting whether the power supply state of the target peripheral component is normal when the fault occurs; determining a fault recovery strategy of the target peripheral component according to the working state information; and controlling the target peripheral component to perform fault recovery according to the fault recovery strategy. In this way, fault recovery is only carried out on the peripheral component with the fault, operation of other peripheral components is not affected, normal operation of the system can be guaranteed while the error state can be cleared away in time, unnecessary system restarting is avoided, data loss is prevented, the influence of the fault on operation of the driving equipment is reduced, and the driving efficiency is improved. And the availability and the stability of the system are improved.

The invention discloses a data secondary correction-based current transformer error state online detection method and system. The method comprises the following steps of: acquiring historical data and real-time test data of a current transformer to form current data; performing primary correction on the test data to obtain primary correction data of the current data; performing secondary correction on the primary correction data to obtain secondary correction data of the current data; calculating a Q statistic by using the secondary correction data, comparing the Q statistic with a statistic control limit Qc, and judging whether the error state of the current transformer is abnormal or not; the method has the advantage that the online detection of the error state of the current transformer can be realized under the condition that the fluctuation of the unbalance degree is relatively large.

The invention provides a vehicle multi-source sensing fusion positioning method, system and device, and belongs to the technical field of automatic driving positioning. The method comprises the following steps: under an error state Kalman filtering framework based on a manifold space, decomposing a real state quantity of a vehicle into a nominal state quantity and an error state quantity; using IMU data to predict a state through median integration; and asynchronously fusing multi-source observation such as an encoder, a GNSS (Global Navigation Satellite System) and a lane line, updating an error state through a linearized observation model and a Kalman gain, and finally merging and outputting a calibrated vehicle pose. According to the method, deep fusion and incremental updating of multi-source data are realized through a unified ESKF framework, error accumulation of pure inertial navigation and positioning jump during mode switching are effectively inhibited, and the real-time performance, output continuity and overall robustness of the system are remarkably improved while the positioning precision is ensured.

The invention relates to a sensor device (12) for determining a position state of a rotatably and / or longitudinally displaceably mounted component (5) for a vehicle part (2) of a motor vehicle (1), comprising at least one processing circuit (15) which is adapted to process the position state of the component (5), the at least one processing circuit (15) is adapted to generate at least one electrical sensor signal (17), which is dependent on the position state and has a sensor signal voltage, and to output the sensor signal via the at least one signal output (18), checking whether an error state related to the sensor device (12) exists, in which the generation of the at least one sensor signal (17) is interrupted, and in the event of the error state, generating at least one error signal (24) having an error signal voltage that differs from the at least one sensor signal voltage, the error signal is output via at least one signal output (18).

The invention discloses a knowledge distillation-based model training method and device, electronic equipment and a medium. The method comprises the steps that question cue words are input into a student model, and the student model gradually generates student answer marks according to the question cue words; for each student answer mark, calculating the difference between the student answer mark and a teacher answer mark generated by the teacher model; if the difference is equal to or greater than a preset difference threshold, determining the current state of the student model as a target divergence error state; according to an error path generated by the student model based on the target bifurcation error state and a rescue path generated by the teacher model based on the target bifurcation error state, calculating comparison sorting loss; and training the student model according to the comparison sorting loss to obtain a trained student model. According to the method, the key error node can be accurately identified, the student model is effectively taught to recover from the error state, and the reasoning effect of the student model is improved.

The invention discloses a circuit solving method for batch pipeline calculation tasks based on identifiers, and relates to the technical field of numerical calculation hardware acceleration, and the method comprises the following steps: an upper computer transmits initial parameters and configuration information of a trajectory equation to a data transmission control module in batches through a PCIe interface, a parameter cache module receives and caches the parameters, and the data transmission control module transmits the parameters to the data transmission control module; meanwhile, batch configuration information is read to determine the number of trajectory equations; an identifier distribution module sets an identifier cut-off value according to the number of equations, distributes incremental hardware identifiers for each group of trajectory equations by adopting a polling arbitration mode, binds parameters and then writes the parameters into an RAM (Random Access Memory); the state management module analyzes track configuration algorithm information and selects an RK4-Adams4 or RK6-Adams6 algorithm combination, the data path control module starts operation and distributes parameters to the function solving module for iterative calculation, and in the process, a lost identifier is detected and an error state is marked; and the algorithm iteration module caches results after completing calculation, and finally, the batch results are transmitted back to the upper computer through PCIe according to an identification sequence.

Examples provide an electronic device including at least one electronic processor configured to request first accessibility data associated with a first user interface (“UI”) displayed by a first device and including at least one of (a) information identifying one or more UI elements in the first UI or (b) information identifying one or more UI events in the first UI; train, based on at least the first accessibility data and an error state associated with the first device, an artificial intelligence (“AI”) model; request second accessibility data associated with a second UI displayed by a second device and including (a) information identifying one or more UI elements in the second UI or (b) information identifying one or more UI events in the second UI; and detect, based on at least the second accessibility data and the AI model, the error state on the second device.

An integrated circuit (IC) for testing an I3C device is provided. The IC may include a start / stop detection logic block to receive a serial data line (SDA) signal and a serial clock line (SCL) signal, and output a start detection signal and a stop detection signal, a SCL counter block to receive the SCL signal and the start and stop detection signals, and output a SCL count value, a SCL positive edge detection signal, and a SCL negative edge detection signal, a SDA inversion logic block to receive the SCL count value, a SCL programmed count value, and the SCL positive and negative edge detection signals, and output a SDA invert enable signal based on a comparison of the SCL count value and the SCL programmed count value, and a multiplexer to receive the SDA invert enable signal, and output the SDA signal or an inverted SDA signal.

A quantum computing system and a method of performing a quantum error correction code are disclosed. Syndrome data is received representative of an error state of a plurality of quantum devices. Occurrence of a runtime trigger event is determined. A hypergraph modification rule associated with the runtime trigger event is retrieved from a decoding hypergraph modification map data structure comprising a plurality of predefined hypergraph modification rules. A decoding hypergraph is modified in accordance with the hypergraph modification rule to generate a modified decoding hypergraph. A correction for the error state is determined by decoding the syndrome data with the modified decoding hypergraph.

Methods and apparatus relating to a memory failure detection mechanism within a die architecture. In some examples, a die package includes decoder logic that receives a plurality of data words, and a first error correction code for each of the data words. The decoder logic generates, for each of the data words, a second error correction code based on a corresponding one of the data words. Further, the decoder logic generates, for each of the data words, an error state based on the first error correction code and the second error correction code corresponding to each of the data words. The die package also includes error generation logic that receives the error state for the data word from the decoder logic and generates error data based on a combination of the error states for the data word. The error generation logic may store the error data in a memory device.

The application discloses a kind of multi-motor synchronous control methods based on neural network prediction compensation, comprising the following steps: collecting each motor speed, position, current and bus voltage signal, sampling time alignment is carried out to the collected signal, constructs motor state vector set and generates synchronous reference quantity;According to the synchronous reference quantity, the synchronization error of each motor is calculated, and the coupling error characteristics are established in combination with the topological connection relationship of multi-motor;The fractional order memory processing is carried out to the coupling error characteristics, and the working condition characteristics are fused to form a unified input vector;The radial basis function neural network outputs disturbance prediction result and prediction compensation result;Based on coupling error, memory error and prediction compensation result, generate synchronous control quantity, and map to each motor control increment;According to error state and control increment, update network weight and fractional order parameter, generate each motor drive control instruction and issue to corresponding driver.

A power tool comprises a housing, a motor mounted in the housing, and a too holderl operatively coupled to the motor. The power tool also includes a gyro sensor module mounted on the housing, which comprises at least one gyro sensor configured to detect angular rotation of the housing about a first axis and angular inclination of the first axis from a reference first axis. A controller is configured to receive sensor signals from the gyro sensor module, determine a first error condition of the power tool based on a threshold angular velocity being exceeded, and determine a second error condition of the power tool based on a threshold angular inclination being exceeded.

There is a demand for a gaming machine that allows players to play games smoothly. [Solution] A settlement sound can be output when one game value is settled, and a predetermined error sound can be output when a predetermined error state occurs, and the lowest sound, which is the lowest sound among the predetermined error sounds output in the predetermined error state, is higher than the highest sound, which is the highest sound among the settlement sounds output based on the settlement of one game value.