A control system comprises a memory storing a sequence of sensor data received from one or more sensors. The control system has a processor which processes the sensor data to compute a sequence of derived sensor data values. An autoencoder receives the sequence of derived sensor data values and computes a forward prediction of the sequence of derived sensor data values, the autoencoder having been trained imposing a relationship on positions of the derived sensor data values encoded in a latent space of the autoencoder. A processor initiates control of an apparatus using the forward prediction.

An integrated energy optimizer having an edge side and a cloud side. The edge side may incorporate an energy optimizer, a building management system connected to the energy optimizer, a controller connected to the building management system, and equipment connected to the controller. The cloud side may have a cloud connected to the energy optimizer and to the building management system, and a user interface connected to the cloud. Data from the field sensor may go to the optimizer and the building management system. The data may be processed at the optimizer and the building management system for proper settings at the building management system.

A method of controlling and managing a process control system having a plurality of control loops includes implementing a plurality of control routines to control operation of the plurality of control loops, respectively, wherein the control routines may include at least one non-adaptive control routine. The method then collects operating condition data in connection with the operation of each control loop, and identifies a respective process model for each control loop from the respective operating condition data collected for each control loop. The identification of the respective process models may be automatic as a result of a detected process change or may be on-demand as a result of an injected parameter change. The process models are then analyzed to measure or determine the operation of the process control loops.

Embodiments include a computer-implemented method (and system) for performing automated batch data alignment for modeling, monitoring, and control of an industrial batch process. The method (and system) loads, scales, and screens plant historian batch data for an industrial batch process. The method (and system) selects a reference batch as basis of the batch alignment, defines and adds or modifies one or more batch phases, and selects one or more batch variables based on one or more profiles and corresponding curvatures of the batch data. The method (and system) estimates one or more weightings, adjust one or more tuning parameters and uses a sliding time window combined with DTW, DTI and GSS algorithms, performs the batch alignment in offline mode or online mode.

An information processing device includes a controller that: inputs information that is based on a raw material of a product that is produced at a plant to a physical model and acquires a first output result; inputs component information that is based on optical spectrum data that are obtained by spectroscopic sensing for the raw material and an output of the physical model to a machine learning model and acquires a second output result; and outputs information concerning a state of the plant based on the first output result and the second output result.

An information processing device includes a controller and a display. The controller acquires a first prediction result that indicates information concerning a state of a plant that is predicted by a physical model, and acquires a second prediction result that indicates information concerning a state of the plant that is predicted by a machine learning model that is generated based on data concerning the plant. The display displays the first prediction result and the second prediction result.

A cardboard sheet manufacturing system includes an information editing unit storing production state information, operation state information, and warping state information as acquisition information in a storage unit, the information editing unit deleting, in a case where the stored acquisition information includes prescribed information to be deleted, the information to be deleted from the storage unit and outputting the acquisition information stored in the storage unit as editing information. The system also includes an editing information storage unit storing the editing information output from the information editing unit, a prediction model calculation unit calculating a prediction model of the warping state based on the editing information stored in the editing information storage unit; and a control table update unit updating a target value of a control value of a control element in the cardboard sheet manufacturing apparatus, based on the prediction model.

Provided in embodiments of the present invention are a micro-grid reconstruction method and device, a micro-grid protection and control center and a storage medium. The method includes: monitoring and acquiring current operating data of a micro-grid in real-time; storing the acquired current operating data and corresponding time stamp information in a database; analyzing an operating state of the micro-grid based on the operating data and the corresponding time stamp information that are stored in the database; and determining a current control scheme for the micro-grid according to a current analysis result, and reconstructing the micro-grid according to the current control scheme. The technical solution mentioned above realizes flexible protection and control of the micro-grid and improves the operating automation and intelligence of a system.

A method may include obtaining one or more inputs in which each of the inputs describes at least one of: a state of an autonomous vehicle (AV) or a state of an object; and identifying a prediction context of the AV based on the inputs. The method may also include determining a relevancy of each object of a plurality of objects to the AV in relation to the prediction context; and outputting a set of relevant objects based on the relevancy determination for each of the plurality of objects. Another method may include obtaining a set of objects designated as relevant to operation of an AV; selecting a trajectory prediction approach for a given object based on context of the AV and characteristics of the given object; predicting a trajectory of the given object using the selected trajectory prediction approach; and outputting the given object and the predicted trajectory.

A vehicle includes a controller programed to: collect a set of data related to a driver of the vehicle; predict a driving setting for the driver using the set of data and an initial student-T process (STP) machine learning (ML) model; generate an updated STP ML model based on the prediction of the driving setting as to the set of vehicle data; transmit incremental learning related to the updated STP ML model to a server; and receive, from the server, a personalized driving setting for the driver output from a cloud STP ML model trained by the incremental learning.