Embodiments of gas concentrating systems and methods are provided. These systems and methods comprise configuration of hardware and software components to monitor various sensors associated the systems and methods of concentrating gas as described herein. These hardware and software components are further configured to utilize information obtained from sensors throughout the system to perform certain data analysis tasks. Through analysis, the system may, for example, calculate a time to failure for one or more system components, generate alarms to warn a user of pending component failure, modify system settings to improve functionality in differing environmental conditions, modify system operation to conserve energy, and/or determine optimal setting configurations based on sensor feedback.

A method for managing a power load of a grid includes performing a statistic-based distribution estimation of a composite load model using static and dynamic models with Gibbs sampling; deriving a distribution estimation of load model coefficients; and controlling grid power based on a simulation, a prediction, a stability analysis or a reliability analysis with the load model coefficients.

An arrangement and a method for controlling a dewatering process including measuring values representing operating parameters of the gravitational sedimentation device and using the values as input values for a predictive multivariable model for predicting an operating state of the gravitational sedimentation device. The disclosure further relates to obtaining reference values for the operating parameters, determining at least one predicted output for an operating parameter of the gravitational sedimentation device, and comparing the predicted output to at the reference values to determine control values that will affect changes in operation of the gravitational sedimentation device.

A mobility device that can accommodate speed sensitive steering, adaptive speed control, a wide weight range of users, an abrupt change in weight, traction control, active stabilization that can affect the acceleration range of the mobility device and minimize back falls, and enhanced redundancy that can affect the reliability and safety of the mobility device.

A method for interacting with a building management system (BMS) using intelligent software agents. The method includes receiving a user request from a multi-input device configured to accept vocal and textual inputs, and contextualizing the user request for a space and/or place and a corresponding user. The method further includes constructing a user skill level from the user request, and activating a customized BMS optimization process, the customized BMS optimization process determined by the intelligent software agents from the user skill level, the user request, and the space and/or place.

A building system includes one or more storage devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to receive an unstructured user question from a user device of a user and query a graph database based on the unstructured user question to extract context associated with the unstructured user question from contextual information of a building stored by the graph database, wherein the graph database stores the contextual information of the building through nodes and edges between the nodes, wherein the nodes represent equipment, spaces, people, and events associated building and the edges represent relationships between the equipment, spaces, people, and events. The instructions further cause the one or more processors to retrieve data from one or more data sources based on the context and compose a presentation based on the retrieved data.

The present application relates to the control and management of an energy system in coordination with a fluctuating energy market and, in one form provides a method for managing supply of energy to a number of energy consuming devices connected to a power distribution system of an energy supply system. The method may include the steps of: (1) monitoring one or more parameters affecting energy usage, (2) acquiring a first set of values based on the parameters, (3) calculating a set of predicted future values based on the first set of values, (4) identifying a context related to the first set of values, and (5) presenting control actions to increase efficiency of energy supply in response to the context.

This technology relates to an information processing apparatus, an information processing method, and a program for permitting relatively easy comparison and examination of learning histories. An information processing apparatus includes a control section configured to perform control to display multiple prediction models as models trained by machine learning, and respective pieces of model information regarding the prediction models. This technology can be applied, for example, to information processing apparatuses that perform learning and prediction by machine learning.

An energy control system (ECS) for controlling an energy storage system (ESS's) that includes energy storage devices(s) or an energy storage combination (ESDC's) including ≥1 of the energy storage devices and ≥1 of the energy storage combinations. A power conversion system is coupled to an output of the ESDC, and a transformer is coupled to an output of the power conversion system. The ECS includes an ECS server and an ESS adapter configured for providing an interface between ECS server and the ESS. The ECS server is configured for reading status data from the ESS and submitting schedules including selected charging and discharging times to the ESS, monitoring or displaying a variance between an expected performance of the ESS based on the schedules and an actual ESS performance, and responsive to the variance being determined to be above a predetermined threshold, sending an update of the schedules to the ESS.

A method for predicting yield relating to a process of manufacturing semiconductor devices on a substrate, the method including: obtaining a trained first model which translates modeled parameters into a yield parameter, the modeled parameters including: a) a geometrical parameter associated with one or more selected from: a geometric characteristic, dimension or position of a device element manufactured by the process and b) a trained free parameter; obtaining process parameter data including data regarding a process parameter characterizing the process; converting the process parameter data into values of the geometrical parameter; and predicting the yield parameter using the trained first model and the values of the geometrical parameter.