Abstract: Provided is a model selection apparatus including a candidate model storage unit configured to store a plurality of candidate models each of which is generated by reinforcement learning using, as at least a part of a reward, an output of an evaluation model configured to output an indicator obtained by evaluating a state of equipment and is capable of output an action, a state data acquisition unit configured to acquire a plurality of pieces of state data representing the state of the equipment in a case where each of manipulated variables based on outputs of the plurality of candidate models is applied to a controlled object in the equipment, an indicator acquisition unit configured to acquire a plurality of indicators, a model selection unit configured to select an object model for controlling the controlled object, and an object model output unit configured to output the object model.

Abstract: Various systems and methods are presented regarding monitoring and controlling operation of a process. A visual representation of the process can be created based on a supermodel comprising models (representing one or more devices) and nodes (representing respective device variables and constraints). Further, the process can be represented by levels, wherein devices at each level can be self-aware and have onboard artificial intelligence, such that a device at any level can auto-configure itself in accordance with a requirement placed upon it. Field-level devices (IFLDs) can be smart devices which auto-configure based upon a requirement from a higher-level device. Accordingly, system awareness can be incorporated across all levels of the process enabling overall and device-specific optimization of the process.

Abstract: A machine learning apparatus capable of reducing an error between a machined workpiece and a target shape when the workpiece is machined based on a workpiece model modeling the target shape of the workpiece. A machine learning apparatus includes a state observation section configured to observe machining state data of a machine tool configured to machine the workpiece, and measurement data of an error between a shape of the workpiece machined by the machine tool based on the workpiece model and the target shape, as a state variable representing a current state of environment in which the workpiece is machined, and a learning section configured to learn the correction amount in association with the error by using the state variable.

Abstract: There is provided a distributed control device including a first power supply circuit that outputs a first power supply voltage, a power failure detection circuit that outputs a power failure notification signal, a first drive power supply circuit that outputs a first drive voltage, a second drive power supply circuit that outputs a second drive voltage, a control circuit operated by using the first drive voltage, and a first drive device operated by using the second drive voltage. The power failure detection circuit outputs the power failure notification signal including power failure information indicating that the commercial power supply is abnormal. The first switching circuit stops supply of the first power supply voltage to the second drive power supply circuit in accordance with a first switching control signal based on the power failure notification signal including the power failure information.

Abstract: This control device 10 comprises a model construction unit 11 that constructs a model for simulating a control object 20, a problem subdivision unit 12 that subdivides the model constructed by the model construction unit 11, a control measure calculation unit 13 that predicts the future status of the control object 20 using the model subdivided by the problem subdivision unit 12 and that calculates a control measure for the control object 20 on the basis of the predicted future status, and an operation command generation unit 14 that generates operation commands to the control object 20 on the basis of the control measure calculated by the control measure calculation unit 13.

Abstract: Systems and methods for a centrally designed vehicle HVAC system that cools vehicle components as well as the cabin and/or delivery containers. The HVAC system is designed with the ability to divert HVAC capacity from one vehicle system to another, allowing for more optimized HVAC usage. In some examples, the HVAC system disclosed herein allows for a more optimized HVAC system size, including decreased HVAC system weight and power consumption. In various implementations, the HVAC system uses back office prediction and remote control to make the determination of HVAC system bias based on current weather conditions as well as forecasted weather conditions. The HVAC system provides a comfortable vehicle cabin while ensuring the thermal condition of vehicle systems and components are managed to avoid interruptions to service.

Abstract: [Solving means] A remote-control apparatus, including a measurement value receiving unit for receiving a measurement value related to equipment from a local-control apparatus configured to control the equipment; a calculating unit for calculating a control value corresponding to the measurement value received by the measurement value receiving unit and a delay amount, by using a model configured to calculate a control value that should be used for control of the equipment when there is caused control delay including communication delay with the local-control apparatus from a delay amount corresponding to the control delay and a measurement value; and a control value transmitting unit for transmitting the control value calculated by the calculating unit to the local-control apparatus, is provided.

Abstract: Setpoint control processing is provided which includes generating, for a current state of a process, an artificial intelligence recommended control setpoint for the process, and obtaining, by one or more processors, historical process state instances, and associated control setpoints, related to the current state of the process. The artificial intelligence recommended control setpoint for the process is compared, by the one or more processors, to the associated control setpoints of the historical process state instances related to current state of the process. Based on a result of the comparing, a control system setpoint for the process is updated for optimal control of the process for a current process optimization objective, and the process is operated using the updated control system setpoint to optimally control the process for the current process optimization objective.

Abstract: A numerical control device comprising: a tool data acquisition unit configured to acquire tool data of a rotary multi-edge tool; a machining information acquisition unit configured to acquire machining information including a feed direction, a feed speed, and a number of rotations for the rotary multi-edge tool to move with respect to a workpiece; an oscillation condition acquisition unit configured to acquire an oscillation condition for the rotary multi-edge tool; and a movement command calculation unit configured to calculate a movement command for the rotary multi-edge tool to perform a relative movement with respect to the workpiece, the relative movement being coincident with a composite motion resulting from superposition of a basic movement in the feed direction at the feed speed and an oscillation determined based on the tool data, the number of rotations, and the oscillation condition.

Abstract: Various embodiments described herein relate to advanced process control for assets and/or processes using instance-based learning. In this regard, an event indicator related to a change event associated with operation of an asset is received. In response to the event indicator, one or more insights for one or more real-time settings for the asset are determined based at least in part on a comparison between a current operating condition digital signature for the asset and historical operating condition digital signature for the asset. Additionally, the one or more real-time settings for the asset are adjusted based on the one or more insights to provide one or more adjusted settings for the asset.

Abstract: A learning control device according to one embodiment includes a memory and a hardware processor. The memory stores a correction value to be used in a learning trial. The processor generates a feedback signal on the basis of a tracking error for a target state of a control target. The feedback signal is used for causing an operation result state of the control target to track the target state. The feedback signal is generated such that a control target waveform of transition of an output signal representing the operation result state becomes a waveform obtained by calculating a coefficient on the whole of a base control target waveform. The processor outputs, to a feedback communication path, a calculation result obtained by calculating the coefficient on the correction value. The processor updates the correction value stored in the memory in accordance with a signal communicated through the feedback communication path.

Abstract: A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

Abstract: A computer-implemented control method of constrained controlling of a computer-controlled system. The system is controlled according to a control input, which is safe if a constraint quantity resulting from the controlling of the computer-controlled system exceeds a constraint threshold. A current control input is determined based on previous control inputs and corresponding previous noisy measurements. The computer-controlled system is controlled according to the current control input, thereby obtaining a current noisy measurement of the resulting constraint quantity. The current control input is determined based on a mutual information between a first random variable representing the constraint quantity resulting from the current control input and a second random variable indicating whether a further control input is safe.

Abstract: A hydro-photovoltaic complementary operation chart application method for a clean energy base includes: divide a hydro-photovoltaic complementary operation chart into two sub-operation charts by runoff probability and critical probability; predict runoff and predicted photovoltaic output during the operation cycle, select the sub-operation chart by the runoff probability, determine hydropower output of a reservoir in the current month according to water level of the reservoir at the beginning of the current month and the operation area, and obtain the water level of the reservoir at the end of the current month through the runoff calculation; obtain long-term hydropower output process and reservoir level process in the clean energy base until the hydropower output and water level of the reservoir in all months of the operation cycle are calculated, and calculate hydropower generation probability to complete operation.

Abstract: A system, apparatus, method, and non-transitory computer readable medium for performing improved and/or optimal sensor placement may include a computing device caused to, receive a plurality of fault conditions and an initial sensor suite associated with at least one object to be simulated, the initial sensor suite including a plurality of candidate physical sensors for the at least one object, perform failure mode analysis of the initial sensor suite, the performing the failure mode analysis including generating at least one dependency-matrix (D-matrix) based on the plurality of fault conditions and the plurality of candidate physical sensors, and generate a recommended sensor suite associated with the at least one object based on results of the failure mode analysis, the recommended sensor suite including at least one recommended sensor, the at least one recommended sensor being a subset of the plurality of candidate physical sensors.

Abstract: An example method includes, at a weather forecast time, determining a lag between a target renewable energy site and a first nearby site for which respective power measurements are correlated, selecting a first forecast look-ahead time, determining if the first forecast look-ahead time is less than or equal to the lag, determining a series of lagged power measurements at a time of forecast which constitute a series of correlation-based forecasts for power generation at the target site based on the lag, generating a set of ramp predictors incorporating correlation-based forecasts from the first site and the first forecast look-ahead time, receiving power forecast errors, applying sets of decision trees to the predictors and the power forecast errors to obtain predicted forecast errors, and generating second power forecasts for the set of look-ahead times of the target site based on the first power forecasts and the predicted forecast errors.

Abstract: An etching processing apparatus includes a storage that stores a learned model of each group generated by each learning processing in a case where each processing condition acquired during execution of a specific step of an etching processing, is classified into a plurality of groups according to a difference in effects when executing the specific step, and the learning processing is performed for each group; an updating unit that updates the learned model of a specific group when an effect of executing the specific step on a test wafer using setting data included in a processing condition associated with the specific group is not equivalent to an effect associated with the specific group; and a searching unit that searches for, using the updated learned model, setting data capable of obtaining the effect associated with the specific group when the specific step is executed on the test wafer.

Abstract: A system, method, and medium for corrective action to achieve baseline condition including a communication component and a processor. The communication component receives input data associated with one or more operating conditions of an equipment utilized for baseline activity and captures baseline data relating to a system baseline associated with a corrective action recommendation. The processor predicts an optimal operating condition based on at least one of a statistical model, a mathematical model, or a machine learning model of the equipment and determine the corrective action recommendation based on the optimal operating condition. The corrective action recommendation is associated with the system baseline. The processor also detects one or more anomalies from the baseline data deviating from the system baseline beyond a predetermined range, and re-evaluates the optimal operating condition based on the anomaly or anomalies.

Abstract: A method for diagnosing a fault in an electric power distribution network includes measuring three-phase current signals from local and remote terminals of a protected zone on the network, and computing differential current signals based on the measured three-phase current signals. The differential current signals are preprocessed using a filter to smooth the differential current signals. A maximal overlap discrete wavelet transform is applied on the smoothed differential current signals to obtain a plurality of detail coefficients. Observation signals comprising the plurality of detail coefficients are provided to a deep reinforcement learning (DRL) agent comprising a temporal convolution attention-based neural network (TCAN). The TCAN DRL agent is trained using a proximal policy optimization (PPO) method to propose a trip action corresponding to a trip or no trip command for a fault condition. Responsive to receiving a trip command, a trip signal is transmitted to a circuit breaker.

Abstract: A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing platform offers a variety of industrial applications to end customers—including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM)—and implements associated architectural features that address a number of issues relating to data sharing, security, scalability, and other concerns.