A control device has a learning model that outputs an estimated value of a second physical quantity that is unobservable under a condition of manufacturing a SiC crystal, from a first physical quantity that is observable under the condition of manufacturing the SiC crystal. The control device generates a basic learning model by mechanical learning using, as teacher data, a simulation result of a simulation model based on structural data of a SiC crystal manufacturing apparatus. The control device acquires measured values of the first physical quantity and the second physical quantity measured under a condition that the SiC crystal is unable to be manufactured while the second physical quantity is observable, and generates the learning model that corrects an output of the basic learning model based on the measured values.

Systems and methods are directed to controlling components of a utility grid. The system can receive data samples including signals detected at one or more portions of a utility grid. The system can construct a matrix having a first dimension and a second dimension. The system can train a machine learning model based on the matrix to predict values for signals of the utility grid not provided in the matrix. The system can receive bounds for one or more input variables, constraints on one or more output variables, and a performance objective for the utility grid. The system can determine, based on the machine learning model and via an optimization technique, an adjustment to a component of the utility grid that satisfies the performance objective. The system can provide the adjustment to the component of the utility grid to satisfy the performance objective.

In some examples, an unmanned aerial vehicle (UAV) employs one or more image sensors to capture images of a scan target and may use distance information from the images for determining respective locations in three-dimensional (3D) space of a plurality of points of a 3D model representative of a surface of the scan target. The UAV may compare a first image with a second image to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. Further, based at least on the difference, the UAV may determine, while the UAV is in flight, an update to the 3D model including at least one of an updated location of at least one point in the 3D model, or a location of a new point in the 3D model.

Tools and techniques are described to automate commissioning of physical spaces. Controllers have access to databases of the devices that are controlled by them, including wiring diagrams and protocols, such that the controller can automatically check that each wire responds correctly to stimulus from the controller. Controllers also have access to databases of the physical space such that they can check that sensors in the space record the correct information for device activity, and sensors can cross-check each other for consistency. Once a physical space is commissioned, incentives can be sought based on commissioning results.

A setpoint adjustment apparatus for a displacement meter (10) includes a determiner (343) to determine whether a measurement value acquired by an acquirer (341) in measurement of a reference workpiece using an applying setpoint, to be used in measurement of the reference workpiece, is within the range of a desired measurement value (352), and a changer (345) to change the applying setpoint. When the measurement value is within the range of the desired measurement value (352), the applying setpoint used in acquisition of the measurement value is employed as an applying setpoint for inspection of a measurement target (1). When the measurement value is out of this range, the applying setpoint used in acquisition of the measurement value is changed to a different applying setpoint, and whether the measurement value from the reference workpiece using this applying setpoint is within the range of the desired measurement value (352) is determined.

The present invention relates to an intelligent gardening system, for monitoring and controlling gardening apparatuses in a gardening area, including: multiple sensors that collect environmental information of the gardening area; one or more gardening apparatuses that perform gardening work according to a control instruction; and a control center that generates the control instruction based on the environmental information; wherein the sensors, the gardening apparatuses and the control center communicate with each other to form an Internet of Things.

The disclosed apparatus, systems, and methods relate to a location query mechanism that can efficiently determine whether a target entity is located within a region of interest (ROI). At a high level, the location query mechanism can be configured to represent a ROI using one or more polygons. The location query mechanism can, in turn, divide (e.g., tessellate) the one or more polygons into sub-polygons. Subsequently, the location query mechanism can use the sub-polygons to build an index system that can efficiently determine whether a particular location is within any of the sub-polygons. Therefore, when a computing device queries whether a particular location is within the region of interest, the location query mechanism can use the index system to determine whether the particular location is within any of the sub-polygons.

Mitigating the reality gap through optimization of one or more simulated hardware parameters for simulated hardware components of a simulated robot. Implementations generate and store real navigation data instances that are each based on a corresponding episode of locomotion of a real robot. A real navigation data instance can include a sequence of velocity control instances generated to control a real robot during a real episode of locomotion of the real robot, and one or more ground truth values, where each of the ground truth values is a measured value of a corresponding property of the real robot (e.g., pose). The velocity control instances can be applied to a simulated robot, and one or more losses can be generated based on comparing the ground truth value(s) to corresponding simulated value(s) generated from applying the velocity control instances to the simulated robot. The simulated hardware parameters and environmental parameters can be optimized based on the loss(es).

A building management system includes a building efficiency improvement system and method configured to monitor and control subsystems and equipment for improved efficiency of operation. A user device is configured to display a user interface for monitoring and controlling one or more building equipment efficiency parameters and settings. The building efficiency management system further includes a controller configured to collect and analyze data from equipment, generate displays of the operational status and efficiency levels, generate sets of alternative equipment control algorithms based on efficiency objectives, and present users with a set of alternative equipment control algorithms displayed via graphic user interface elements on the user device. The user device further provides a means to select and implement an alternate equipment control algorithm. The controller is further configured to receive inputs from the user device commanding changes to equipment controls and process transactions associated with changes to equipment configuration.

Systems and methods are directed controlling components of a utility grid. The system can receive signals. The system can determine one or more statistical metrics based on the signals. The system can generate an input matrix. The system can input the input matrix into a machine learning model. The system can predict, based on the input matrix and via the machine learning model, the value for the signal of the utility grid at a time period for which the value is not provided in the input matrix. The system can provide a command to control a component of the utility grid responsive to the value for the signal of the utility grid predicted by the machine learning model.