Vehicle navigation control systems in autonomous driving rely on accurate predictions of objects within the vicinity of the vehicle to appropriately control the vehicle safely through its surrounding environment. Accordingly this disclosure provides methods and devices which implement mechanisms for obtaining contextual variables of the vehicle's environment for use in determining the accuracy of predictions of objects within the vehicle's environment.

An abnormality-detecting device for detecting abnormalities of a tool of a machine tool comprises: an acquiring unit for acquiring multiple measured values relating to the tool as measurement data (vibration information, cutting force information, sound information, main shaft load, motor current, power value); a normal model unit for learning the measurement data acquired during normal machining by one class machine learning and creating a normal model; an abnormality-diagnosing unit for acquiring measurement data during machining after creation of the normal model while diagnosing whether said measurement data is normal or abnormal on the basis of the normal model; and a re-diagnosing unit for re-diagnosing measurement data, which has been diagnosed to be abnormal by the abnormality-diagnosing unit, by a method different from the abnormality-diagnosing unit.

The present disclosure provides action imitation method as well as a robot and a computer readable storage medium using the same. The method includes: collecting at least a two-dimensional image of a to-be-imitated object; obtaining two-dimensional coordinates of each key point of the to-be-imitated object in the two-dimensional image and a pairing relationship between the key points of the to-be-imitated object; converting the two-dimensional coordinates of the key points of the to-be-imitated object in the two-dimensional image into space three-dimensional coordinates corresponding to the key points of the to-be-imitated object through a pre-trained first neural network model, and generating an action control instruction of a robot based on the space three-dimensional coordinates corresponding to the key points of the to-be-imitated object and the pairing relationship between the key points, where the action control instruction is for controlling the robot to imitate an action of the to-be-imitated object.

Methods, devices, and assemblies for controlling pressure in a supply grid are provided. The supply grid is suitable for supplying fluid to loads. The supply grid has first sensors for measuring the flow and/or the pressure of the fluid at first locations in the supply grid and a pump for pumping the fluid or a valve for controlling the flow of the fluid. The method includes: measuring the flow and/or pressure of the fluid at the first locations in the supply grid by the first sensors; predicting the pressure at the second location in the supply grid using a self-learning system based on the measured flows or pressures, wherein the self-learning system is trained to predict the pressure at a specified location in the supply grid; and actuating the pump or the valve at least also based on the pressure predicted by the trained system at the second location.

A system and method is provided for determining grasping positions for two-handed grasps of industrial objects. The system may include a processor configured to determine a three dimensional (3D) voxel grid for a 3D model of a target object. In addition, the processor may be configured to determine at least one pair of spaced apart grasping positions on the target object at which the target object is capable of being grasped with two hands at the same time based on processing the 3D voxel grid for the target object with a neural network trained to determine grasping positions for two-handed grasps of target objects using training data. Such training data may include 3D voxel grids of a plurality of 3D models of training objects and grasping data including corresponding pairs of spaced-apart grasping positions for two-handed grasps of the training objects. Also, the processor may be configured to provide output data that specifies the determined grasping positions on the target object for two-handed grasps.

An artificial intelligence (AI) device capable of being controlled according to a user's gaze includes a communication unit, a camera configured to capture an image of a user, and a processor configured to acquire user state information from the image of the user, acquire a gaze position of the user based on the acquired user state information, calculate a distance between the acquired gaze position and the camera, receive, from one or more external AI devices, one or more distances between gaze positions of the user respectively acquired by the external AI devices and cameras respectively provided in the external AI devices through the communication unit, and compare the calculated distance with the received one or more distances to select a controlled device.

The present invention is a servo control device that controls a servomotor based on a command position. The servo control device includes a correction unit that corrects the command position using a first neural network that performs processing based on parameters representing a network structure, and a servo amplifier that controls the servomotor based on a corrected command position outputted from the correction unit. The correction unit corrects the command position based on the corrected command position and the actual position of the servomotor.

In various examples there is a computer-implemented method performed by a digital twin at a computing device in a communications network. The method comprises: receiving at least one stream of event data observed from the environment. Computing at least one schema from the stream of event data, the schema being a concise representation of the stream of event data. Participating in a distributed inference process by sending information about the schema or the received event stream to at least one other digital twin in the communications network and receiving information about schemas or received event streams from the other digital twin. Computing comparisons of the sent and received information. Aggregating the digital twin and the other digital twin, or defining a relationship between the digital twin and the other digital twin on the basis of the comparison.

An abnormality-detecting device for detecting abnormalities of a tool of a machine tool comprises: an acquiring unit for acquiring multiple measured values relating to the tool as measurement data (vibration information, cutting force information, sound information, main shaft load, motor current, power value); a normal model unit for learning the measurement data acquired during normal machining by one class machine learning and creating a normal model; an abnormality-diagnosing unit for acquiring measurement data during machining after creation of the normal model while diagnosing whether said measurement data is normal or abnormal on the basis of the normal model; and a re-diagnosing unit for re-diagnosing measurement data, which has been diagnosed to be abnormal by the abnormality-diagnosing unit, by a method different from the abnormality-diagnosing unit.

Disclosed is a characterization method based on deep reinforcement learning for discrete manufacturing industry data. The method includes: collecting discrete manufacturing industry data, and creating a spatio-temporal database; dividing the discrete manufacturing industry data into a discrete feature and a continuous feature, creating a data coupling coding network, converting a coding vector in the coding network into a characterization vector, and creating a data characterization model; quantitatively characterizing discrimination of a data category by means of cluster evaluation indexes; and using weights of cluster evaluation indexes of different dimensions as dynamic rewards, creating a deep reinforcement learning model, and updating a neural network parameter of deep reinforcement learning through characterization of an interactive relation between a model and a discrete manufacturing decision-making analysis system.