A method for managing air quality may include, at one or more processors, receiving sensor data comprising a plurality of air quality parameters for an environment, wherein the sensor data is generated by one or more environment quality monitoring devices located in the environment, predicting an adverse air quality event based on the sensor data, and automatically controlling one or more devices to mitigate the adverse air quality event. An environment quality monitoring device may include a housing, a plurality of sensors in the housing and configured to generate sensor data comprising a plurality of environment quality parameters, a network communication device configured to communicate the sensor data over a network, and an alert configured to indicate an environment quality score of the ambient environment, where the environment quality score is based on at least a portion of the sensor data.

The invention discloses a MIMO different-factor full-form model-free control method. In view of the limitations of the existing MIMO full-form model-free control method with the same-factor structure, namely, at time k, different control inputs in the control input vector can only use the same values of penalty factor and step-size factors, the invention proposes a MIMO full-form model-free control method with the different-factor structure, namely, at time k, different control inputs in the control input vector can use different values of penalty factors and/or step-size factors, which can solve control problems of strongly nonlinear MIMO systems with different characteristics between control channels widely existing in complex plants. Compared with the existing control method, the inventive method has higher control accuracy, stronger stability and wider applicability.

A prediction control device controls an actuator for automatic driving of a vehicle including: a command value generation unit generating an operation amount for the actuator and an operation amount candidate as a predicted value; an output prediction unit outputting a control amount candidate as a predicted value corresponding to the actuator output by using an operation model; an evaluation function calculation unit expressing constraint conditions for the automatic driving; a situation degree detection obtaining a measure of giving priority to ride comfort or giving priority to danger avoidance of an own vehicle while traveling; and a responsiveness adjusting unit obtaining a next operation amount candidate from the situation degree from the situation degree detection unit. The operation command value generation unit generates an operation amount for the actuator, and the responsiveness adjusting unit adjusts the output of the evaluation function according to the situation degree.

Disclosed herein is an artificial intelligence device. An an artificial intelligence device according to an embodiment of the present invention includes a communication unit that obtains an external environmental factor and an internal environmental factor collected by a sensor and a processor that provides the internal environmental factors to an environmental factor prediction model to predict a subsequent internal environmental factor and allows a ventilation system and an air cleaning system to operate cooperatively with each other based on the predicted internal environmental factor.

State of the art techniques used for Flue Gas Desulpharization (FGD) process monitoring fail to comprehend the relationship between various process parameters, which is crucial in determining the performance of a FGD process being monitored. The disclosure herein generally relates to industrial process monitoring, and, more particularly, to a method and system for performance optimization of Flue Gas Desulphurization (FGD) Unit. The system identifies Key Performance Indicators (KPIs) associated with the process being monitored, and identifies parameters associated with each KPI. This information is used to generate several predictive models, from which a predictive model having the highest value of composite model score amongst the predictive models is selected as the predictive model for processing the input data, which is then used to perform optimization of the FGD process.

A model predictive maintenance system for building equipment that performs operations including obtaining an objective function defining a cost of operating and performing maintenance on the equipment as a function of operating and maintenance decisions for time steps within a time period of a life cycle horizon and including performing a first computation of the objective function under a first scenario where maintenance is performed on the equipment during the period, a result of the first computation indicating a first cost. The operations include performing a second computation under a second scenario in which maintenance is not performed during the period, a result indicating a second cost. The operations include initiating an automated action to perform maintenance on the equipment in accordance with decisions defined by the first scenario if the first cost is less than or equal to the second cost.

An environmental control system of a building including a first building device operable to affect environmental conditions of a zone of the building by providing a first input to the zone. The system includes a second building device operable to independently affect a subset of the environmental conditions by providing a second input to the zone and further includes a controller including a processing circuit. The processing circuit is configured to perform an optimization to generate control decisions for the building devices. The optimization is performed subject to constraints for the environmental conditions and uses a predictive model that predicts an effect of the control decisions on the environmental conditions. The processing circuit is configured to operate the building devices in accordance with the control decisions.

There is a need for solutions for efficiently and reliably perform sensor-based predictive data analysis. This need can be addressed by, for example, solutions for performing cross-sensor predictive data analysis. In one example, a method for performing cross-sensor predictive data analysis includes identifying sensor input data objects comprising one or more image data objects; determining sensor feature data objects based on the sensor input data objects; generating predictions for a target predictive entity associated with the sensor input data objects by processing the sensor feature data objects using a cross-sensor predictive inference model; and performing prediction-based actions based on the cross-sensor predictions.

Provided is a state data of the technical system are captured and fed into a controller, which is configurable by control parameters, in order to control the technical system on the basis of the state data. Furthermore, complexity data quantifying a present computation complexity for the controller are captured and transmitted to a control planner. The control planner takes the complexity data as a basis for ascertaining an updated control parameter that renders the control currently more performant, according to a predefined performance measure, than as a result of the previous control parameter. The controller is then reconfigured by the updated control parameter.

A heating, ventilation, and air conditioning (HVAC) control device configured to receive a user input for controlling an HVAC system, to determine whether the user input indicates an energy saving occupancy setting, and to identify a first plurality of time entries that are associated with a confidence level for a predicted occupancy status that is less than a predetermined threshold value in the predicted occupancy schedule. The device is further configured to modify the predicted occupancy schedule by setting the first plurality of time entries to an away status when the user input indicates an aggressive energy saving occupancy setting. The device is further configured to modify the predicted occupancy schedule by setting the second plurality of time entries to a present status when the user input indicates a conservative energy saving occupancy setting. The device is further configured to output the modified predicted occupancy schedule.