Present disclosure discloses a method and a system for optimizing a model predictive control during an industrial process control operation. The method receives, using one or more sensors, one or more input parameters from each of a plurality of processing stages involved in the industrial process control operation. The method determines a pulp brightness value of each processing stage based on the one or more input parameters. Thereafter, the method implements a model trained on historical data, based on the determining, for controlling chemical dosage values of one or more chemical components at each of the plurality of processing stages such that an amount of the chemical dosage to be injected is determined based on at least one of the one or more input parameters of a current processing stage and the pulp brightness value of preceding processing stage, thereby attaining a target pulp brightness value.

A wind turbine control unit includes an upsampling module that receives a first control signal that includes a current control sample value and a predicted control trajectory. The upsampling module also calculates a second control signal in dependence on the current control sample value and the predicted control trajectory. The second control signal has a higher frequency than the first control signal. The upsampling module further outputs the second control signal for controlling an actuator.

The present application describes a machine learning method for detecting tamper. The method includes a step of training a model using one or more values obtained from one or more different sensors on an integrated module. The one or more values act as training data with respect to one or more of light, acceleration, magnetic field, rotation, temperature, pressure, humidity, and audio. The method also includes a step of predicting, via the trained model, tampering of the of the integrated module. The present application also describes a system for detecting tamper.

There is a need for more effective and efficient data modeling and/or data visualization solutions. This need can be addressed by, for example, solutions for performing data modeling and/or data visualization in an effective and efficient manner. In one example, solutions for generating a data model with dynamic relational awareness are disclosed. In another example, solutions for processing data retrieval queries using data models with dynamic relational awareness are disclosed. In yet another example, solutions for generating data visualizations using data models with dynamic relational awareness are disclosed. In a further example, solutions for integrating external data objects into data models with dynamic relational awareness are disclosed.

Systems, methods, techniques and communication networks relate to facilitating intelligent and/or efficient evaluation of resource upkeep and/or of resource transfer. Various workflows provide: scores provided by artificial-intelligence tools, facilitation of navigation of big-data population data, and supporting streamlined flows through transfer processes.

Disclosed is a process for creating an event prediction model that employs a data-driven approach for selecting the model’s input data variables, which, in one embodiment, involves selecting initial data variables, obtaining a respective set of historical data values for each respective initial data variable, determining a respective difference metric that indicates the extent to which each initial data variable tends to be predictive of an event occurrence, filtering the initial data variables, applying one or more transformations to at least two initial data variables, obtaining a respective set of historical data values for each respective transformed data variable, determining a respective difference metric that indicates the extent to which each transformed data variable tends to be predictive of an event occurrence, filtering the transformed data variables, and using the filtered, transformed data variables as a basis for selecting the input variables of the event prediction model.

According to an embodiment, an energy management system includes an acquirer, a predictor, and a demand and supply controller. The acquirer acquires information provided by an unspecified user and including at least one of current meteorological situations and predicted future meteorological situations inside of a management area and outside of the management area and social environment situation patterns inside of the management area and outside of the management area acquired via a network. The predictor predicts one or both of an amount of demand for the energy and an amount of power generation in the future inside of the management area by analyzing or evaluating the demand and the supply of the energy on the basis of the information acquired by the acquirer. The demand and supply controller controls an energy demand and supply balance inside of the management area on the basis of prediction results of the predictor.

A method for transmitting an application is disclosed. The method includes, for example, receiving, from a client, an input for initiating the application; generating an application bundle associated with the application, the application bundle including an address of a server, the address capable of enabling a program on the client to request, from the server, information needed for running the application; and transmitting the application bundle to the client.

According to one embodiment, an air-conditioning control device includes model storage and a processor. The model storage is configured to store a discomfort probability model which estimates a value of discomfort of an occupant by a pair of time elapsed after an energy-saving operation of an air conditioner is turned on and before the energy-saving operation is turned off by the occupant, and an air-conditioning state at the time when the energy-saving operation is turned off. The processor is configured to acquire a current air-conditioning state during the energy-saving operation of the air conditioner, and turn off the energy-saving operation, based on occupant's discomfort estimated from the discomfort probability model.

The embodiments described herein include one embodiment that provides a control method that includes connecting a first controller to a control system; receiving control system configuration data from a database, in which the configuration data comprises holistic state data of a second controller in the control system; and configuring operation of the first controller based at least in part on the configuration data received.