A method includes translating at least one application source code file associated with a legacy controller in a distributed control system to instructions executable by a controller simulation computing device, wherein the legacy controller is associated with a legacy operating system and the controller simulation computing device is associated with a second operating system different from the legacy operating system. The method also includes simulating operation of the legacy controller using the instructions and an emulation of the legacy operating system in the controller simulation computing device. The method further includes determining configuration data for the legacy controller during the simulated operation of the legacy controller. In addition, the method includes saving the configuration data to a configuration data file.

A powered balancing mobility device that can provide the user the ability to safely navigate expected environments of daily living including the ability to maneuver in confined spaces and to climb curbs, stairs, and other obstacles, and to travel safely and comfortably in vehicles. The mobility device can provide elevated, balanced travel.

A method and system includes predicting energy consumption of a vehicle using a statistical model. The method includes obtaining a plurality of input vectors for plurality of points in time, wherein each input vector includes a plurality of variables with a weight vector. Thereafter, the energy level for each input vector is captured for each point in time. Subsequent to capturing the energy level, the method includes predicting a change in energy level of the vehicle using the statistical model.

A control system estimates a numerical value range within which a command value can fall from a distribution of second data relating to the command value in a learning data set used to construct a prediction model, and in such a manner that a first acceptable range prescribed by a preset first threshold value with respect to a command value for a subject device is extended, decides a second threshold value with respect to the command value for the subject device on the basis of the estimated numerical value range. Further, in an operational phase, on the basis of an output value from the prediction model, the control system decides a command value for the subject device within a second acceptable range prescribed by the decided second threshold value, and controls an operation of the subject device on the basis of the decided command value.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing material processing. In one aspect, a method includes collecting, from a set of sensors, a set of current manufacturing conditions. Based on the set of current manufacturing conditions collected from the sensors, a set of current qualities of a material currently being processed by manufacturing equipment is determined. A baseline production measure for processing the material according to the set of current qualities is obtained. A candidate set of manufacturing conditions that provide an improved production measure relative to the baseline production measure is determined. A set of candidate qualities for the material produced under the candidate set of manufacturing conditions is determined. A visualization that presents both of the set of candidate qualities of the material and the set of current qualities of the material currently being processed is generated.

Described herein are systems, methods, and non-transitory computer readable media for triggering a sensor operation of a second sensor (e.g., a camera) based on a predicted time of alignment with a first sensor (e.g., a LiDAR), where operation of the second sensor is simulated to determine the predicted time of alignment. In this manner, the sensor data captured by the two sensors is ensured to be substantially synchronized with respect to the physical environment being sensed. This sensor data synchronization based on predicted alignment of the sensors solves the technical problem of lack of sensor coordination and sensor data synchronization that would otherwise result from the latency associated with communication between sensors and a centralized controller and/or between sensors themselves.

Responding to grid events is provided. The system determines, based on an event, to modify an electrical load of a site. The system selects a parameter for the site to adjust to modify the electrical load. The system identifies a script constructed from previously processed interactions between a human-machine interface of the building management system to adjust the parameter for the site. The system establishes a communication session with a remote access agent executed by a computing device of the site to invoke the building management system of the site. The system generates a sequence of commands defined by the script to adjust the one or more parameters for the site. The system transmits the sequence of commands to cause the remote access agent to execute the sequence of commands on the human-machine interface of the building management system to modify the electrical load of the site.

An apparatus, method and computer program is described comprising detecting a first object in a first image of a sequence of images using a neural network (22), wherein the means for detecting the first object provides an object area indicative of a first location of the first object; and tracking the first object (24), wherein the means for tracking the first object further comprises generating a predicted future location of the first object and generating an updated location of the first object using the neural network. The means for generating the predicted future location of the first object may, for example, receive said object area indicative of a first location of the first object and may receive said updated location information of the first object.

According to examples, system control may include accessing a plurality of procedures to control an operation of a system to prevent an occurrence of a fault in the system and/or a reduction of an output of the system. A highest ranked procedure may be learned from the plurality of procedures to control the operation of the system to prevent the occurrence of the fault in the system and/or the reduction of the output of the system. Real-time data associated with the system may be accessed. Further, the operation of the system may be controlled by applying the highest ranked procedure to prevent the occurrence of the fault in the system and/or the reduction of the output of the system.

A machine controller, geometry data and measured physical data of a machine is provided. The geometry data and the physical data are input to a machine learning module and to a simulation module of the machine controller. By the input data, the simulation module generates first values of a first physical property of a component of the machine on a discretized grid. Furthermore, an evaluator is provided for evaluating a physical compatibility of the first values with second values of a second physical property of the component, and for generating a residual quantifying the compatibility. The evaluator evaluates the compatibility of the first values with output data of the machine learning module and generates a resulting residual. Moreover, the machine learning module is trained to minimize the resulting residual, thus configuring the machine controller for controlling the machine by the output data of the trained machine learning module.