Abstract: A model prediction-based control method for grid forming of multi-port autonomous reconfigurable solar plants includes: estimating and predicting system parameters by establishing a multi-port autonomous reconfigurable solar plant and a dynamic model of a synchronous generator model; converting an objective function into an unconstrained optimization problem, using Newton's method to achieve minimum computational burden within each calculation time step to find a solution in real time for obtaining an optimal angular frequency; based on results of the optimal angular frequency, updating an output voltage and a dq-axis current of the multi-port autonomous reconfigurable solar plant, and changing an arm modulation index of the plant, thereby realizing the plant's inertia and primary frequency modulation support. The model prediction-based control method provided by the present invention achieves rapid prediction during operation and improves frequency response, rapidity and system stability.

Abstract: The disclosure relates to running simulations in order to test software used to control a vehicle in an autonomous driving mode. For instance, logged data may be identified for a simulation. The logged data may have been collected by a first vehicle and may identifying an agent that is a road user. The logged data may be analyzed to identify one or more signals of intent of the agent including a logged path of the agent. One or more characteristics may be identified based on the one or more signals. The simulation may be run using the logged data by replacing the agent with an interactive agent having the one or more characteristics. The interactive agent may be capable of responding to actions performed by a simulated vehicle in the simulation using software for controlling a vehicle in an autonomous driving mode.

Abstract: Methods, systems, and storage media for determining the similarities of post content for mapping into a hyperspace. In an exemplary method, the disclosure comprises receiving a query at the processor. The method includes determining post data associated with the query. The post data comprises a plurality of posts provided to a social media platform by various users of the platform. The method includes determining, by the processor, a relationship between at least two posts of the plurality of posts. The method includes training, by the processor, a machine language model. The machine language model is based on the query and the relationship between the at least two posts. The method also generates a hyperspace based on the relationship between the at least two posts and the query.

Abstract: Aspects of the disclosure relate to generating assets for a simulation in order to test autonomous vehicle control software. For instance, data identifying a vehicle object, a polygon for the vehicle object, and a polygon for an open-door object associated with the vehicle object may be received. A rectangle may be fit to the polygon for the vehicle object. A polygon for the vehicle object without the open-door object may be determined based on the rectangle. A vehicle asset representing a vehicle may be adjusted based on the polygon for the vehicle object without the open-door object. A position and rotation of the rectangle may be used to position an open-door asset representing an open door for the simulation. The open-door asset is adjusted based on the adjustment to the vehicle asset. The simulation may be run with the positioned and adjusted open door asset in order to test the autonomous vehicle control software.

Abstract: The disclosure relates to running simulations in order to test software used to control a vehicle in an autonomous driving mode. For instance, logged data may be identified for a simulation. The logged data may have been collected by a first vehicle and may identifying an agent that is a road user. The logged data may be analyzed to identify one or more signals of intent of the agent including a logged path of the agent. One or more characteristics may be identified based on the one or more signals. The simulation may be run using the logged data by replacing the agent with an interactive agent having the one or more characteristics. The interactive agent may be capable of responding to actions performed by a simulated vehicle in the simulation using software for controlling a vehicle in an autonomous driving mode.

Abstract: A method includes receiving a set of real-world log data defining a real-world driving environment and generated by an autonomous vehicle (AV). The set of real-world log data includes at least one set of real-world parameters defining at least one real-world object observed by the AV operating within the real-world driving environment. The method further includes generating, based on the set of real-world log data, a set of simulated log data defining a simulated driving environment and causing a simulation to be performed using the set of simulated log data. The set of simulated log data comprises at least one set of simulated parameters defining at least one simulated object within the simulated driving environment, and generating the set of simulated log data includes perturbing at least one real-world parameter of the at least one set of real-world parameters to obtain the at least one set of simulated parameters.

Abstract: The disclosure relates to testing software for operating an autonomous vehicle. In one instance, a simulation may be run using log data collected by a vehicle operating in an autonomous driving mode. The simulation may be run using the software to control a simulated vehicle and by modifying a characteristic of an agent identified in the log data. During the running of the simulation, that a first type of interaction between the first simulated vehicle and the modified agent will occur may be determined. In response to determining that the particular type of interaction will occur, the modified agent may be replaced by a interactive agent that simulates a road user corresponding to the modified agent that is capable of responding to actions performed by simulated vehicles. That the particular type of interaction between the simulated vehicle and the interactive agent has occurred in the simulation may be determined.

Abstract: The disclosure relates to running simulations in order to test software used to control a vehicle in an autonomous driving mode. For instance, logged data may be identified for a simulation. The logged data may have been collected by a first vehicle and may identifying an agent that is a road user. The logged data may be analyzed to identify one or more signals of intent of the agent including a logged path of the agent. One or more characteristics may be identified based on the one or more signals. The simulation may be run using the logged data by replacing the agent with an interactive agent having the one or more characteristics. The interactive agent may be capable of responding to actions performed by a simulated vehicle in the simulation using software for controlling a vehicle in an autonomous driving mode.

Abstract: The disclosure relates to testing software for operating an autonomous vehicle. In one instance, a simulation may be run using log data collected by a vehicle operating in an autonomous driving mode. The simulation may be run using the software to control a simulated vehicle and by modifying a characteristic of an agent identified in the log data. During the running of the simulation, that a first type of interaction between the first simulated vehicle and the modified agent will occur may be determined. In response to determining that the particular type of interaction will occur, the modified agent may be replaced by a interactive agent that simulates a road user corresponding to the modified agent that is capable of responding to actions performed by simulated vehicles. That the particular type of interaction between the simulated vehicle and the interactive agent has occurred in the simulation may be determined.

Abstract: Aspects of the disclosure relate to generating assets for a simulation in order to test autonomous vehicle control software. For instance, data identifying a vehicle object, a polygon for the vehicle object, and a polygon for an open-door object associated with the vehicle object may be received. A rectangle may be fit to the polygon for the vehicle object. A polygon for the vehicle object without the open-door object may be determined based on the rectangle. A vehicle asset representing a vehicle may be adjusted based on the polygon for the vehicle object without the open-door object. A position and rotation of the rectangle may be used to position an open-door asset representing an open door for the simulation. The open-door asset is adjusted based on the adjustment to the vehicle asset. The simulation may be run with the positioned and adjusted open door asset in order to test the autonomous vehicle control software.

Abstract: The disclosure relates to testing software for operating an autonomous vehicle. In one instance, a simulation may be run using log data collected by a vehicle operating in an autonomous driving mode. The simulation may be run using the software to control a simulated vehicle and by modifying a characteristic of an agent identified in the log data. During the running of the simulation, that a first type of interaction between the first simulated vehicle and the modified agent will occur may be determined. In response to determining that the particular type of interaction will occur, the modified agent may be replaced by a interactive agent that simulates a road user corresponding to the modified agent that is capable of responding to actions performed by simulated vehicles. That the particular type of interaction between the simulated vehicle and the interactive agent has occurred in the simulation may be determined.

Abstract: The present invention discloses a high-linearity GaN-based millimeter wave device and preparation method thereof.

Abstract: Aspects of the disclosure relate to improving realism in simulations for testing software for operating a vehicle in an autonomous driving mode. In one instance, an initial observation of a road user object may be identified in a log data segment captured by a perception system of a vehicle. The perception system having one or more sensors. The initial observation includes a point in time and an initial location of the road user object. A distance traveled by the road user object from a start of the log data segment to the point in time may be determined. A starting location for the road user object may be determined using the distance traveled. A trajectory for the road user object may be determined between the starting location and the initial location of the road user object. The trajectory may be appended to the log data segment.

Abstract: The disclosure relates to running simulations in order to test software used to control a vehicle in an autonomous driving mode. For instance, logged data may be identified for a simulation. The logged data may have been collected by a first vehicle and may identifying an agent that is a road user. The logged data may be analyzed to identify one or more signals of intent of the agent including a logged path of the agent. One or more characteristics may be identified based on the one or more signals. The simulation may be run using the logged data by replacing the agent with an interactive agent having the one or more characteristics. The interactive agent may be capable of responding to actions performed by a simulated vehicle in the simulation using software for controlling a vehicle in an autonomous driving mode.

Abstract: The disclosure relates to testing software for operating an autonomous vehicle. In one instance, a simulation may be run using log data collected by a vehicle operating in an autonomous driving mode. The simulation may be run using the software to control a simulated vehicle and by modifying a characteristic of an agent identified in the log data. During the running of the simulation, that a first type of interaction between the first simulated vehicle and the modified agent will occur may be determined. In response to determining that the particular type of interaction will occur, the modified agent may be replaced by a interactive agent that simulates a road user corresponding to the modified agent that is capable of responding to actions performed by simulated vehicles. That the particular type of interaction between the simulated vehicle and the interactive agent has occurred in the simulation may be determined.

Abstract: A breaststroke leg strength training device, including a lower panel, and a prostrate platform connected with the panel through an elastic band and located below one side of the panel; a slit consistent with a breaststroke leg movement trajectory is formed in the panel, and an elastic band attachment column is respectively arranged at positions on both sides of the slit and on the panel; two foot pedal middle shafts are arranged in the slit and extend to the lower side of the panel; and the elastic band is connected with the attachment columns through a fixed beam fixed to the platform and the two foot pedal middle shafts. The device has a smart structural design, a trainer can comprehensively train all the muscle groups involved in the breaststroke leg actions in a continuous and efficient manner, and the breaststroke leg actions of the trainer can be specified and corrected.

Abstract: A method, station (STA), and access point (AP) for transmitting in a WLAN. A station includes a controller and a transceiver, the transceiver configured to receive, from an AP, a channel sounding sequence including a null data packet announcement frame (NDPA) including information indicating STAs to transmit a feedback report, a null data packet (NDP) containing pilots to compute the feedback report and a trigger frame (TF) to trigger transmission of the feedback report. An AP includes a controller and a transceiver, the controller configured to generate a NDPA including information indicating STAs to transmit a feedback report, a NDP containing pilots to compute the feedback report, and a TF to trigger transmission of the feedback report from at least one of the STAs, and the transceiver is operably connected to the controller and configured to transmit a channel sounding sequence that includes the NDPA, the NDP, and the TF.

Abstract: A breaststroke leg strength training device, including a lower panel, and a prostrate platform connected with the panel through an elastic band and located below one side of the panel; a slit consistent with a breaststroke leg movement trajectory is formed in the panel, and an elastic band attachment column is respectively arranged at positions on both sides of the slit and on the panel; two foot pedal middle shafts are arranged in the slit and extend to the lower side of the panel; and the elastic band is connected with the attachment columns through a fixed beam fixed to the platform and the two foot pedal middle shafts. The device has a smart structural design, a trainer can comprehensively train all the muscle groups involved in the breaststroke leg actions in a continuous and efficient manner, and the breaststroke leg actions of the trainer can be specified and corrected.

Abstract: A method, station (STA), and access point (AP) for transmitting in a WLAN. A station includes a controller and a transceiver, the transceiver configured to receive, from an AP, a channel sounding sequence including a null data packet announcement frame (NDPA) including information indicating STAs to transmit a feedback report, a null data packet (NDP) containing pilots to compute the feedback report and a trigger frame (TF) to trigger transmission of the feedback report. An AP includes a controller and a transceiver, the controller configured to generate a NDPA including information indicating STAs to transmit a feedback report, a NDP containing pilots to compute the feedback report, and a TF to trigger transmission of the feedback report from at least one of the STAs, and the transceiver is operably connected to the controller and configured to transmit a channel sounding sequence that includes the NDPA, the NDP, and the TF.

Abstract: The preparation method includes the following steps: dissolving aripiprazole in an acidic solution having an acidifier so as to obtain a medicament having acidic solution; then, performing a wet granulation on or preparing a suspension with the obtained medicament having acidic solution, an alkalizer, and an excipient so as to obtain the aripiprazole medicament formulation; the excipient comprising an antioxidant. The aripiprazole medicament formulation obtained through the preparation method has a significantly reduced amount of related substances, great solubility, great stability, high bioavailability, reduced individual differences, and enhanced wettability and content uniformity of insoluble medicaments.