Abstract: A method for protecting a motor from overloading includes: acquiring a present effective value of a phase current of a motor; performing time integration on the present effective value of the phase current, to obtain a first integral value; obtaining a first threshold; performing overload detection according to the first threshold and the first integral value; determining a target limiting current for operation of the motor if an overload occurs; and controlling the operation of the motor based on the target limiting current.

Abstract: The present disclosure relates to antibodies that specifically binding to TL1A and uses thereof, in particular to antibodies that specifically bind to TL1A or antigen-binding fragments thereof, nucleic acids encoding the antibodies or antigen-binding fragments thereof, vectors containing the nucleic acids, and host cells containing the nucleic acids or the vectors. Pharmaceutical compositions comprising the antibodies as well as methods of treatment using the antibodies are also provided.

Abstract: A vehicle includes a motor and a motor controller. A damping control method for the vehicle includes: obtaining a rotation speed of the motor; filtering the rotation speed through a first low-pass filter to obtain a first filtered rotation speed, filtering the rotation speed through a second low-pass filter to obtain a second filtered rotation speed, and obtaining, by a vehicle controller, a rotation speed fluctuation value based on the first filtered rotation speed and the second filtered rotation speed; obtaining, by the vehicle controller, an active damping adjustment coefficient and an active damping clipped torque based on the rotation speed; obtaining, by the vehicle controller, an active damping torque based on the rotation speed fluctuation value, the active damping adjustment coefficient, and the active damping clipped torque; and controlling, by the motor controller, the motor based on the active damping torque.

Abstract: A method for protecting a motor from overloading includes: acquiring a present effective value of a phase current of a motor; performing time integration on the present effective value of the phase current, to obtain a first integral value; obtaining a first threshold; performing overload detection according to the first threshold and the first integral value; determining a target limiting current for operation of the motor if an overload occurs; and controlling the operation of the motor based on the target limiting current.

Abstract: An image processing method and apparatus, an electronic device and a storage medium are provided. The method includes: at least one image to be processed and at least one attribute filtering condition of an event to be monitored are obtained; event detection is performed on the at least one image to be processed to obtain an intermediate detection result of the event to be monitored; event attribute extraction is performed on the at least one image to be processed to obtain at least one attribute of the event to be monitored; and a target monitoring result of the event to be monitored is obtained according to the intermediate detection result, the at least one attribute and the at least one attribute filtering condition of the event to be monitored.

Abstract: The health of a tool is predicted based on temporally ordered input data representing parameters indicative of tool health. A sliding time window is used to partition input data into temporally displaced data sets. Non-linear regression models determine, based on the data sets, a set of predictive values relating to tool health at a future time. A tool-health metric is then determined based on one or more of the predictive values.

Abstract: Complex process control and maintenance are performed utilizing a nonlinear regression analysis to determine optimal tool-specific adjustments based on operational metrics, process adjustments and maintenance activities.

Abstract: Complex process control and maintenance are performed utilizing a nonlinear regression analysis to determine optimal maintenance activities and process adjustments based on an urgency metric.

Abstract: The health of a tool is predicted based on temporally ordered input data representing parameters indicative of tool health. A sliding time window is used to partition input data into temporally displaced data sets. Non-linear regression models determine, based on the data sets, a set of predictive values relating to tool health at a future time. A tool-health metric is then determined based on one or more of the predictive values.

Abstract: Complex process control and maintenance are performed utilizing a nonlinear regression analysis to determine optimal maintenance activities and process adjustments based on an urgency metric.

Abstract: Non-linear regression models of a complex process and methods of modeling a complex process feature a filter based on a function of an input variable, the output of which is a predictor of the output of the complex process.

Abstract: Complex process prediction and optimization are performed utilizing an orthogonal transform to represent time-varying continuous signals as discrete values, which are then subjected to a nonlinear regression analysis to relate between the discrete values and process metrics.