Abstract: The present disclosure provides a self-learning collaborative control method for active steering and yaw moment for a motor vehicle, including a first step of constructing fundamental formulas which are stored in a vehicle ECU, and a second step of calculating an active steering angle ?C and a yaw moment Mc on line by the vehicle ECU according to following sub-steps during a driving process of the motor vehicle, and controlling a driving state of the motor vehicle according to ?C and Mc. The second step includes a first sub-step of collecting raw real-time parameter values, a second sub-step of performing calculation by the identifier and the control target reference model, a third sub-step of calculating ?C and Mc. The present disclosure can realize the self-learning collaborative control of active steering and yaw moment without requiring a system control model and correct a driver's steering operation.

Abstract: Computer-implemented methods, apparatus, and systems for data storage and data query are described. During data storage, the number of neighboring graph nodes in each starting graph node in directed graph graph data to be stored is determined, and a data storage mode is determined according to the number of neighboring graph nodes. When the data storage mode is not an ultra-large node data storage, node data, neighbor information, outgoing edge index feature information, and outgoing edge data of the starting graph node are stored in the same data fragment. When the data storage mode is an ultra-large node data storage, node data, neighbor information, outgoing edge index feature range information, and outgoing edge data are stored in a starting graph node data fragment, and the outgoing edge data and outgoing edge data storage address information of the starting graph node are stored in at least two outgoing edge data fragments.

Abstract: A query request is received for querying graph data, including a filtering condition for an attribute value of a first attribute, and used to query a destination edge in a neighboring edge of a first node that satisfies the filtering condition. Graph data includes point data of the first node and edge data of a neighboring edge stored in a single-point data block corresponding to the first node and comprising index data of the edge data used to index a first attribute of the edge data, record the attribute value of the first attribute, and record a storage location of an edge corresponding to the attribute value of the first attribute in the single-point data block. Using the index data and filtering condition, a storage location of the destination edge in the single-point data block is determined and data of the destination edge obtained.

Abstract: A load modulation circuit for radio frequency identification including a first load modulation module and a second load modulation module connected to a coupling circuit. Under weak field intensity, load modulation is mostly realized by the first load modulation module, with the second load modulation module contributing far less than the first load modulation module as regards to the load modulation waveform and load modulation depth. As field intensity increases, the first load modulation module contributes less and less to the load modulation waveform and load modulation depth. While field intensity increases, the second load modulation module contributes more and more to the load modulation waveform and load modulation depth under the control of a variable voltage. Under strong filed intensity, load modulation is mostly realized by the second load modulation module.

Abstract: A load modulation circuit for radio frequency identification including a first load modulation module and a second load modulation module connected to a coupling circuit. Under weak field intensity, load modulation is mostly realized by the first load modulation module, with the second load modulation module contributing far less than the first load modulation module as regards to the load modulation waveform and load modulation depth. As field intensity increases, the first load modulation module contributes less and less to the load modulation waveform and load modulation depth. While field intensity increases, the second load modulation module contributes more and more to the load modulation waveform and load modulation depth under the control of a variable voltage. Under strong filed intensity, load modulation is mostly realized by the second load modulation module.

Abstract: A regulator for regulating the output from a high-voltage pump, Vpump, to provide a regulated load voltage, Vpp, to a load in MOSFET integrated circuits. The regulator includes a MOSFET switch which when enabled in a first state connects Vpp to the integrated circuit voltage level, Vcc, and which when disabled in a second state allows Vpp to be driven to levels greater than Vcc. The regulator includes a multipath control circuit for controlling the switch state and for controlling Vpp. A first current path, Iramp, controls the rise-time parameters of Vpp and a second current path, Idis, controls the fall-time parameters of Vpp. The rise-time parameters and the fall-time parameters are separately controlled.

Abstract: A regulator for regulating the output from a high-voltage pump, Vpump, to provide a regulated load voltage, Vpp, to a load in MOSFET integrated circuits. The regulator includes a MOSFET switch which when enabled in a first state connects Vpp to the integrated circuit voltage level, Vcc, and which when disabled in a second state allows Vpp to be driven to levels greater than Vcc. The regulator includes a multipath control circuit for controlling the switch state and for controlling Vpp. A first current path, Iramp, controls the rise-time parameters of Vpp and a second current path, Idis, controls the fall-time parameters of Vpp. The rise-time parameters and the fall-time parameters are separately controlled.