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.

Abstract: A method for teaching a planar transport device, in which an operating behavior of at least one handling element of the planar transport device, which is configured as an electrodynamically movable mover and is configured for handling products, is taught by an operator interaction of an operator, which is designed differently from a manual writing of a programming command. The operator interaction takes place directly, preferably free of an additional operator input device, at the handling element in order to specify the operating behavior of the handling element.

Abstract: Approaches for controlling operations of a sucker rod pump system of a production well that produces reservoir fluids from a reservoir, which employs a gateway device disposed at a wellsite corresponding to the well and a cloud-computing system operably coupled to the gateway device. The gateway device may communicate time-series operational data characterizing operation of the sucker rod pump system to the cloud-computing system. The cloud-computing system can receive and store the time-series operational data communicated from the gateway device and repeatedly perform operations that i) process the time-series operational data to train a machine learning model that predicts run time of the sucker rod pump system given feature data extracted the time-series operational data and ii) use the resultant machine learning model to determine new operating parameter data for use in controlling the sucker rod pump system, wherein the new operating parameter data maximizes production from the well.

Abstract: A device and method for analyzing a technical system or process that includes a first function and a second function. A first model models a functional relationship of input and output variables of the first function and an influence of a first disturbance variable. The second model models a functional relationship of input and output variables of the second function and an influence of a second disturbance variable. An observation is provided that includes input variables and output variables of the first function and of the second function. A prediction regarding an effect of changing the first function under the influence of the first disturbance variable and/or changing the second function under the influence of the second disturbance variable is determined, and of these functions, that function whose change has a higher likelihood of achieving a desired effect is selected.

Abstract: The present technology relates to artificial intelligence assisted device troubleshooting. In an implementation, an interface service of a human machine interface application trains a machine learning model on the content of an embeddings database. The interface service then receives an input comprising a context of an automation system design. The interface service generates a prompt that includes an instruction for the ML model to identify an anomaly type associated with the context of the automation system design and to generate a solution that addresses the anomaly type. The interface service transmits the prompt to the ML model and receives a response from the ML model that includes the anomaly type and the requested solution. After receiving a response, the interface service may modify the automation system design based on the content of the response and surface a graphical user interface that includes the modified design.

Abstract: Exemplary embodiments are disclosed of controllers with machine learning and systems including the same. In exemplary embodiments, a controller is configured to include or be operable for executing a machine learning process during which the controller learns normal level(s) of parameter(s) (e.g., system component(s) and/or sensor(s) parameters, etc.) to be monitored across a full operational range of a system component, which, in turn, enables the controller to compare the monitored parameter(s) to the learned normal level(s) for detection of issue(s) or problem(s) associated with the monitored parameter(s), e.g., regardless of the RPM at which the engine, machine, motor, or other system component is operating, etc.

Abstract: A numerical control apparatus 3 comprises: a pulse generation unit 34 that moves a plurality of machine elements of a machine tool 2 along a plurality of control axes on the basis of a movement pulse for the machine tool 2; an interference checking unit 36 that performs interference checking calculation for a plurality of interference-checking subject sets; and an interference checking assistance device 5 that assists the interference checking calculation. The interference checking assistance device 5 comprises: a first storage unit 51 that stores machine element-control axis linkage information; a second storage unit 52 that stores individual axis subordinate relationship information; and a checking subject set extraction unit 54a that, on the basis of the movement pulse, the machine element-control axis linkage information, and the individual axis subordinate relationship information, extracts one or more checking subject sets from among all combinations of the machine elements.

Abstract: An information processing system includes: a vehicle that can feed power to outside; a plurality of off-grid terminals that each manage power of a corresponding one of a plurality of off-grid areas; and a server. The server transmits information of the plurality of off-grid areas to the vehicle, and when information of a selected area selected by a user of the vehicle from the plurality of off-grid areas is received from the vehicle, the server performs a matching process for matching the vehicle and the selected area with each other, and transmits, to the vehicle, information instructing to feed power to the selected area.