A system comprises active magnetic emitters positioned within an area, passive magnetic emitters configured to be moved within the area, a magnetic field detector configured to measure a strength and direction of a magnetic field within the area, and a processor in communication with the magnetic field detector. The passive magnetic emitters are configured to be integrated in, coupled to, or secured to at least one tracked object or tracked subject within the area. The processor is configured to evaluate at least one change in the measured strength and direction of the magnetic field end send a signal to a visual effect actuator or visual effects display to initiate a visual effect based on the at least one change. A method and computer program product relating to the system is also provided.

A system and a method for regulating charging and discharging of an energy storage device as part of an electrical power distribution network is described. The invention is a smart control algorithm for a bi-directional switch in which an energy storage device, such as a battery set, is charged when electricity prices are low and discharged when electricity prices are high. The invention uses two different types of pricing data: forecasted price data and real-time price data. The forecasted price data is used to set a threshold. When the real-time price data of electricity exceeds this threshold, the energy storage device is set to discharge and send power to the grid. Otherwise the energy storage device is set to charge. The threshold is set periodically, typically in 30 minute to several hour intervals to capture the latest data.

In a quality control method applied in manufacturing, product information of a product is obtained. Manufacturing parameters corresponding to the product information are queried. The manufacturing parameters are input into a product quality prediction model which is trained to obtain the value of at least one quality inspection of each product. If such quality inspection value is not equal to a standard value or is not within a standard value range, an incorrect manufacturing parameter is identified from all the manufacturing parameters applicable to each product, the incorrect manufacturing parameter being output when identified.

A control system for a culinary instrument includes an order management module configured to generate parameters for a user interface. The control system includes an order prioritization module configured to assign priorities to orders based on a set of prioritization rules, which establish a sequence of the set of orders. The control system includes an order wait time module configured to estimate wait times for the orders. The order wait time module estimates wait times for the orders based on a current status of the culinary instrument and a model of predicted interruptions of the culinary instrument. The order management module is configured to transform the user interface by the parameters in response to the estimated wait times. The control system includes an instrument control module configured to control the culinary instrument to prepare a food item specified by an order specified as next by the sequence.

Deep learning models and other complex models provide accurate representations of complex industrial processes. However, these models often fail to satisfy properties needed for their use in closed loop systems such as Advanced Process Control. In particular, models need to satisfy gain-constraints. Methods and systems embodying the present invention create complex closed-loop compatible models. In one embodiment, a method creates a controller for an industrial process. The method includes accessing a model of an industrial process and receiving indication of at least one constraint. The method further includes constructing and solving an objective function based on at least one constraint and the model of the industrial process. The solution of the objective function defines a modified model of the industrial process that satisfies the received constraint and can be used to create a closed-loop controller to control the industrial process.

Scenario discriminative hybrid motion control for robots and methods of use are disclosed herein. A method may include determining a number of objects in a space, determining when a goal is within the space, and selectively switching between a plurality of control schemes based on the number of objects in the space and whether the goal is within the space. The plurality of control schemes including a model predictive control scheme, a simplified model predictive control scheme, and a proportional-integral-derivative scheme. Selectively switching between the plurality of control schemes reduces power consumption of an automated system compared to when the automated system utilizes only the model predictive control scheme.

A control mechanism scheduler for a water resource infrastructure receives operating data and disturbance data, the operating data describing infrastructure components of the water resource infrastructure, the disturbance data comprising a disturbance signal describing a disturbance expected to disturb the water resource infrastructure. The control mechanism scheduler generates classes for disturbance signals, generates simulations of the water resource infrastructure, and generates schedules of setpoints for control mechanisms actuable to control the infrastructure components of the water resource infrastructure in accordance with approaching a predetermined objective.

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.

Various examples of systems and methods are provided for Lyapunov control for uncertain systems. In one example, a system includes a process plant and a robust Lyapunov controller configured to control an input of the process plant. The robust Lyapunov controller includes an inner closed loop Lyapunov controller and an outer closed loop error stabilizer. In another example, a method includes monitoring a system output of a process plant; generating an estimated system control input based upon a defined output reference; generating a system control input using the estimated system control input and a compensation term; and adjusting the process plant based upon the system control input to force the system output to track the defined output reference. An inner closed loop Lyapunov controller can generate the estimated system control input and an outer closed loop error stabilizer can generate the system control input.

The subject of the invention is a method for calibrating a direct neural interface. The calibration is performed by considering a so-called input calibration tensor, formed on the basis of measured electrophysiological signals and so-called output calibration tensor, formed on the basis of measured output signals. The method comprises the application of a least squares multivariate regression implemented by considering a covariance tensor and a cross-covariance tensor which are established on the basis of input and output calibration tensors corresponding to a current calibration period. The method takes into account covariance and cross-covariance tensors established during an earlier calibration period prior to the current calibration period, these tensors being weighted by a forget factor.