Embodiments relate to a method for predicting power generation and remaining useful life to predict operational soundness of a power plant and a system for performing the same, the method including acquiring sensing data from each of a plurality of sensors included in a plurality of systems in the power plant, outputting each of a predicted power generation and a predicted remaining useful life from a measurement value in sensing data of an input sensor through a pre-trained prediction model, assessing the operational soundness in aspect of the power generation and the remaining useful life using a prediction result and a current result in each aspect, and determining the operational soundness of the system based on prediction uncertainty and an assessment result in aspect of the power generation and the remaining useful life.

A method for virtually testing a system control process for a process engineering system and a simulation device for virtually testing the system control process, wherein at least one preconfigured control module for controlling a component of the system is provided and the system control process is generated based on this control module and the control of the process engineering system is additionally simulated by the generated system control process, where at least one value of an input parameter of the system control process is predefined by a component-specific simulation model, and where the component-specific simulation model is contained in the preconfigured control module.

The ability to comprehend the context of a given programming artifact and extracting the underlying functionality is a complex task extending beyond just syntactic and semantic analysis of code. All existing automation capabilities, hence heavily depend on manual involvement of domain experts. Even recent approaches leveraging Machine Learning Capabilities are supervised techniques, whereby the dependency on domain experts still remains—in preparing suitable training sets. A method and system for automated classification of variables using unsupervised distribution agnostic clustering has been provided. The present disclosure focuses to tap the flexibility of the code and presents a domain agnostic approach using unsupervised machine learning which automatically extracts the context from source code, by classifying the underlying elements of the code. The method and system do not require any manual intervention and opens a wide range of opportunities in reverse engineering and variable level analysis space.

A control device is provided with: a first generation unit for generating a first instruction position on a plane of a control subject in each control period on the basis of a target trajectory; a first control unit for generating a first operation amount by model prediction control using a first dynamic characteristic model and the first instruction position; a second generation unit for generating a second instruction position on an orthogonal axis of the control subject in each control period; and a second control unit for generating a second operation amount by model prediction control using a second dynamic characteristic model and the second instruction position. On the basis of shape data indicating a surface shape of an object and the first instruction position, the second generation unit generates the second instruction position in such a manner that a distance between the control subject and a surface of the object is constant. As a result, the control subject follows the shape of the surface of the object with high accuracy.

Systems and methods generate and use a digital twin in a hydrocarbon system. The systems and methods can perform the following operations: (1) performing a plurality of simulations in a hyperdimensional space to generate outputs; (2) using the outputs of the plurality of simulations to generate one or more reduced order models (ROMs) using a regression technique or a machine learning technique; (3) generating a digital twin of the hydrocarbon system by instantiating the one or more ROMs at an operational point of the hydrocarbon system, and configuring the digital twin to use real-time data obtained from the hydrocarbon system; and (4) estimating values of one or more variables of the hydrocarbon system in real-time using the digital twin.

A method includes using a computational network to learn and predict time-series data. The computational network includes one or more layers, each having an encoder and a decoder. The encoder of each layer multiplicatively combines (i) current feed-forward information from a lower layer or a computational network input and (ii) past feedback information from a higher layer or that layer. The encoder of each layer generates current feed-forward information for the higher layer or that layer. The decoder of each layer multiplicatively combines (i) current feedback information from the higher layer or that layer and (ii) at least one of the current feed-forward information from the lower layer or the computational network input or past feed-forward information from the lower layer or the computational network input. The decoder of each layer generates current feedback information for the lower layer or a computational network output.

An occupancy tracking device configured to establish a network connection with an access point and to capture wireless signal distortion information for the network connection. The device is further configured to generate statistical metadata for the wireless signal distortion information. The device is further configured to input the wireless signal distortion information and the statistical metadata for the wireless signal distortion information into a machine learning model. The machine learning model is configured to determine a predicted occupancy level based on the wireless signal distortion information and the statistical metadata for the wireless signal distortion information. The predicted occupancy level indicates a number of people that are present within with the space. The device is further configured to obtain the predicted occupancy level from the machine learning model and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

Multiple control parts include a first control part and a second control part other than the first control part, the first control part showing the slowest response speed of a control amount with respect to a target value. A control device includes: a prediction part for predicting a future control amount for a first control target by using a first model indicating dynamic characteristics of the first control target corresponding to the first control part for each control cycle; and a generation part for generating a future target value of a second control target corresponding to the second control part from the future control amount. The second control part determines an operation amount for the second control target based on the future target value.

A measurement sensor is integrated with a control object so that a position separated from a processing object by the control object along a target trajectory is a measurement point. A control device includes: a first generator that generates a first command position of the control object on a plane; a first control part that generates a first operation amount using a model predictive control; a second generator that generates a second command position of the control object on an orthogonal axis that is orthogonal to the plane; and a second control part that generates a second operation amount using the model predictive control. The second generator generates the second command position so that the distance between the control object and the surface of an object is constant, based on the measurement result of the measurement sensor and the first command position.

The present disclosure describes a sensor system for measuring a process value of a physical system, including: a plurality of sensors, wherein each sensor is configured to generate a sense signal as a function of the process value at a given time; a system state corrector configured to determine an actual system state of the physical system at a given state update cycle; a system state predictor configured to determine a predicted system state of the physical system at a given prediction cycle from a previous system state at a previous state update cycle; a sense signal predictor configured to determine predicted sense signals at the given prediction cycle from the predicted system state by applying a first operation to the predicted system state using a sense signal model of the physical system for predicting the sense signals.