Abstract: The present disclosure discloses a rolling ring shrink fitting tool for rotary equipment, including a lifting frame; a movement slot is formed in a bottom surface of a top of the lifting frame; an output end of a first motor is connected with a main gear; guide rollers are embedded in a lifting seat through bearings; shrink fitting plates are two semicircular structures; lifting lugs are welded on outer sides of the shrink fitting plates; tooth rings are arranged outside the shrink fitting plates; stop rods are fixed on inner walls of the tooth rings; supporting screw rods are embedded at sunken positions on inner walls of the shrink fitting plates through bearings.

Abstract: The present disclosure discloses a rolling ring shrink fitting tool for rotary equipment, including a lifting frame; a movement slot is formed in a bottom surface of a top of the lifting frame; an output end of a first motor is connected with a main gear; guide rollers are embedded in a lifting seat through bearings; shrink fitting plates are two semicircular structures; lifting lugs are welded on outer sides of the shrink fitting plates; tooth rings are arranged outside the shrink fitting plates; stop rods are fixed on inner walls of the tooth rings; supporting screw rods are embedded at sunken positions on inner walls of the shrink fitting plates through bearings.

Abstract: The present disclosure discloses an engineering machinery equipment, and a method, system, and storage medium for safety control thereof, and relates to the field of artificial intelligence, automatic control, and engineering machinery technologies. A method can include: acquiring spatial sensing data of a work area; performing obstacle detection based on the spatial sensing data to determine a position of an obstacle within the work area; determining a safe working range of a mechanical structural component of the engineering machinery equipment based on the position of the obstacle within the work area; and controlling a working range of the mechanical structural component of the engineering machinery equipment based on the safe working range.

Abstract: A scalable solution to robot behavioral navigation following natural language instructions is presented. An example of the solution includes: receiving, by a pre-trained sequential prediction model, a navigation graph of the task environment, instructions in natural language and an initial location of the robot in the navigation graph, wherein the navigation graph comprises nodes indicating locations in the task environment, coordinates of the nodes, and edges indicating connectivity between the locations; and predicting sequentially, by the pre-trained sequential prediction model, a sequence of single-step behaviors executable by the robot to navigate the robot from the initial location to a destination.

Abstract: The present disclosure discloses an engineering machinery equipment, and a method, system, and storage medium for operation trajectory planning thereof, and relates to the field of artificial intelligence, automatic control, and engineering machinery technologies.

Abstract: Described herein are systems and methods for inverse reinforcement learning to leverage the benefits of model-based optimization method and model-free learning method. Embodiments of a framework combining human behavior model with model predictive control are presented. The framework takes advantage of feature identification capability of a neural network to determine the reward function of model predictive control. Furthermore, embodiments of the present approach are implemented to solve the practical autonomous driving longitudinal control problem with simultaneous preference on safe execution and passenger comfort.

Abstract: The present disclosure provides a method and apparatus of playing a video, an electronic device, and a storage medium, which relates to a field of video processing. The method may include: receiving a video, and distinguishing a content of interest and a content of no interest in the video according to tag information added to the video, wherein the tag information contains tag information added to the video subsequent to recognizing the content of interest and the content of no interest in the video by a machine model pre-trained; and playing the content of interest and the content of no interest at different playing speeds, wherein a playing speed for the content of interest is greater than that for the content of no interest.

Abstract: Embodiments of the present disclosure disclose a method, apparatus, device, and storage medium for controlling a guide robot, and relate to the field of artificial intelligence, robots, and multi-sensor fusion technologies. A specific embodiment of the method includes: acquiring a state of the guide robot, a state of a user, and a position of an obstacle; generating a state update equation for a combined system of the guide robot and the user based on the state of the guide robot and the state of the user; generating a collision-free global path based on the position of the obstacle; generating a control command based on the state update equation for the combined system and the collision-free global path; and driving the guide robot to move based on the control command.

Abstract: A radio frequency power amplifier, a chip, and a communication terminal. The radio frequency power amplifier comprises a power amplifier circuit (5), an output matching circuit (2), a power detection circuit (3), and a bias comparison circuit (4). The output power on a main signal path is measured by the power detection circuit (3), and an equivalent voltage proportional to the output power is obtained and input to the bias comparison circuit (4); the equivalent voltage value is adjusted by means of the bias comparison circuit (4) and compared with a control voltage (1) to provide a bias voltage and/or collector voltage for the power amplifier circuit (5), thereby forming a closed-loop circuit, such that the radio frequency power amplifier can work in a stable state when gains and output power are in different power levels.

Abstract: Described herein are embodiments of a neural network-based task planner (TaskNet) for autonomous vehicle. Given a high-level task, the TaskNet planner decomposes it into a sequence of sub-tasks, each of which is further decomposed into task primitives with specifications. TaskNet comprises a first model for predicating the global sequence of working area to cover large terrain, and a second model for determining local operation order and specifications for each operation. The neural models may include convolutional layers for extracting features from grid map-based environment representation, and fully connected layers to combine extracted features with past sequences and predict the next sub-task or task primitive. Embodiments of the TaskNet are trained using an excavation trace generator and evaluate its performance using a 3D physically-based terrain and excavator simulator.

Abstract: A method, an apparatus, and a storage medium for determining a destination on a map are provided. In the method, N segments of a text are acquired; a recursive order of the N segments is determined based on a grammatical relationship between the N segments of the text; a matching model is selected for each of the N segments from multiple models based on a meaning of the segment; each of the segments is input in the recursive order with an initial region of the map or an update region output by the matching model for the last segment prior to the segment in the recursive order, into the matching model of the segment; and the update region output by the matching model for the last segment in the recursive order is used as the destination in the map.

Abstract: A scalable solution to robot behavioral navigation following natural language instructions is presented. An example of the solution includes: receiving, by a pre-trained sequential prediction model, a navigation graph of the task environment, instructions in natural language and an initial location of the robot in the navigation graph, wherein the navigation graph comprises nodes indicating locations in the task environment, coordinates of the nodes, and edges indicating connectivity between the locations; and predicting sequentially, by the pre-trained sequential prediction model, a sequence of single-step behaviors executable by the robot to navigate the robot from the initial location to a destination.

Abstract: Embodiments of the present disclosure relate to a method and apparatus for updating information. The method may include: acquiring road network structure information of a target road network and vehicle information of a target number of vehicles in the target road network, the vehicle information including initial state information, perception information and positioning information, and the vehicle information being constrained by the road network structure information; selecting a target vehicle from the target number of vehicles; determining, based on a vehicle dynamics model, a reference speed at which the target vehicle passes a preset time step; and updating vehicle information of a vehicle in the target road network based on the reference speed of the target vehicle.

Abstract: Disclosed in the present invention are a radio frequency power amplifier based on power detection feedback, a chip, and a communication terminal. The radio frequency power amplifier comprises multiple stages of amplifier circuits and at least one power detection feedback circuit; the input end of the power detection feedback circuit is connected to the output end of a current stage of amplifier circuit, and the output end of the power detection feedback circuit is connected to the input ends of the current stage of amplifier circuit and at least one stage of amplifier circuit located prior to the current stage of amplifier circuit. The power detection feedback circuit generates, according to the detected output power of the current stage of amplifier circuit, a control voltage varying inversely with the output power, so that the power detection feedback circuit outputs current varying positively with the control voltage.

Abstract: The present disclosure discloses an engineering machinery equipment, and a method, system, and storage medium for operation trajectory planning thereof, and relates to the field of artificial intelligence, automatic control, and engineering machinery technologies.

Abstract: The present disclosure discloses an engineering machinery equipment, and a method, system, and storage medium for safety control thereof, and relates to the field of artificial intelligence, automatic control, and engineering machinery technologies. A method can include: acquiring spatial sensing data of a work area; performing obstacle detection based on the spatial sensing data to determine a position of an obstacle within the work area; determining a safe working range of a mechanical structural component of the engineering machinery equipment based on the position of the obstacle within the work area; and controlling a working range of the mechanical structural component of the engineering machinery equipment based on the safe working range.

Abstract: Embodiments of the present disclosure disclose a method, apparatus, device, and storage medium for controlling a guide robot, and relate to the field of artificial intelligence, robots, and multi-sensor fusion technologies. A specific embodiment of the method includes: acquiring a state of the guide robot, a state of a user, and a position of an obstacle; generating a state update equation for a combined system of the guide robot and the user based on the state of the guide robot and the state of the user; generating a collision-free global path based on the position of the obstacle; generating a control command based on the state update equation for the combined system and the collision-free global path; and driving the guide robot to move based on the control command.

Abstract: Disclosed in the present invention are a radio frequency power amplifier based on current detection feedback and a chip. The radio frequency power amplifier comprises multiple stages of amplifier circuits and at least one current detection feedback circuit; the input end of the current detection feedback circuit is connected to the input end of a current stage of amplifier circuit among the multiple stages of amplifier circuits by means of a corresponding resistor, and the output end of the current detection feedback circuit is connected to the input end of at least one stage of amplifier circuit prior to the current stage of amplifier circuit. The current detection feedback circuit generates, according to the detected quiescent operating current of the current stage of amplifier circuit, a control voltage varying inversely with the quiescent operating current, so that the current detection feedback circuit outputs current varying positively with the control voltage.

Abstract: Described herein are embodiments of a neural network-based task planner (TaskNet) for autonomous vehicle. Given a high-level task, the TaskNet planner decomposes it into a sequence of sub-tasks, each of which is further decomposed into task primitives with specifications. TaskNet comprises a first model for predicating the global sequence of working area to cover large terrain, and a second model for determining local operation order and specifications for each operation. The neural models may include convolutional layers for extracting features from grid map-based environment representation, and fully connected layers to combine extracted features with past sequences and predict the next sub-task or task primitive. Embodiments of the TaskNet are trained using an excavation trace generator and evaluate its performance using a 3D physically-based terrain and excavator simulator.

Abstract: Described herein are systems and methods for inverse reinforcement learning to leverage the benefits of model-based optimization method and model-free learning method. Embodiments of a framework combining human behavior model with model predictive control are presented. The framework takes advantage of feature identification capability of a neural network to determine the reward function of model predictive control. Furthermore, embodiments of the present approach are implemented to solve the practical autonomous driving longitudinal control problem with simultaneous preference on safe execution and passenger comfort.