Abstract: A load control system may include control-target devices, control-source devices, and a system controller. The control-target devices may enter an association mode to associate with control-source devices. The control-target device may receive an identifier from the control-source device and store the identifier in memory when the control-target device to associate the control-target device and the control-source device. The system controller may retrieve the association information from the control-target device after integration into the load control system. The system controller may receive association information that is defined using other association procedures, such as an association procedure performed on a network device and uploaded to the system controller. The system controller may maintain the association information defined using the different association procedures after the system controller is integrated into the load control system.

Abstract: A system may include sensor device comprising a sensor configured to measure sensor data indicating an operational parameter of industrial automation equipment associated with an industrial automation process. The system may also include communication circuitry configured to transmit the sensor data. Additionally, the system includes a processor configured to receive the sensor data. Further, the system includes a non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause the processor to perform operations including identifying an operational state of the industrial automation equipment based on the sensor data. The operations may also include determining a discrepancy between the sensor data and the operational state. Further, the operations may include modifying an operation of the processor from a first operational mode to a second operational mode of a plurality of operational based on the comparison.

Abstract: Examples relate to adjusting load power consumption based on a power option agreement. A computing system may receive power option data that is based on a power option agreement and specify minimum power thresholds associated with time intervals. The computing system may determine a performance strategy for a load (e.g., set of computing systems) based on a combination of the power option data and one or more monitored conditions. The performance strategy may specify a power consumption target for the load for each time interval such that each power consumption target is equal to or greater than the minimum power threshold associated with each time interval. The computing system may provide instructions the set of computing systems to perform one or more computational operations based on the performance strategy.

Abstract: Systems and methods for safety-enabled control by: establishing a wireless communication channel with a plurality of remote control units via the wireless interface device; in response to establishing the wireless communication channels, operating a system-under-control in a supervised mode based on input received from at least one of the plurality of remote control units; in response to a mode switch command received from a first remote control unit of the plurality of remote control units, providing the other remote control units with a request for a mode switch confirmation; and, in response to confirming receipt of a safety-rated input from an autonomous control system and receipt of a mode switch confirmation from each of the other remote control units, operating the system-under-control in an autonomous mode based on input received from the autonomous control system.

Abstract: A control system includes a control target and plural devices, and the control target and the devices are connected to a network. The control system includes a program storage provided in each device storing a control program that calculates control data using data exchanged with the control target, an execution module and a disposition module. The execution module is included in each device and causes a processor to execute programs including a disposed control program. The disposition module disposes control programs in one or more of the devices. The disposition module determines a device in which the control program is disposed from among the plural devices based on a communication delay time between the devices and information about a resource usable for execution of the control program of each device for each control program.

Abstract: A method for operating a device to produce a product includes capturing at least one quality data record that includes measured values of one or more quality parameters that each correspond to one property of the product. The method captures at least one associated machine data record including actual values of several adjustable machine parameters of the device, chronologically assigns the quality data record to the machine data record, and generates a first data record comprising chronologically-correlated measured values and actual values. The preceding steps are repeated at least once to generate at least one second data record. A correlation is determined between the quality parameter(s) and the machine parameter(s). The method provides a corresponding target value for at least one of the adjustable machine parameters on the basis of the control model, proceeding from a target value of the quality parameter(s).

Abstract: A method and apparatus for optimizing operation simulation of a data center are disclosed, and the method includes: constructing a data center simulation model; acquiring a state data set and an action data set of the data center; inputting the state data set and the action data set into a first state prediction model to obtain a next state data set; judging whether the next state data set meets a state safe judgment condition; if so, inputting the state data set and the action data set into a second state prediction model to obtain a next state data set; optimizing network parameters of a reinforcement learning algorithm; and determining an action data set corresponding to a real-time state data set of the data center by using the trained reinforcement learning algorithm.

Abstract: A controller is a controller forming, with another controller, a redundant controller. The controller includes a communication function unit that performs communication with an external device capable of executing control calculation needed for process control for a plant, and a redundancy management unit that switches states of the controller between an active state where a result of the control calculation obtained from the external device via the communication function unit is reflected in the process control and a standby state where the result of the control calculation is not reflected in the process control. The redundancy management unit switches the states of the controller to bring one of the controller and the another controller into the active state.

Abstract: A predictive control method and system are provided for controlling a device or system. The control method and system involves receiving a setpoint signal as input; and performing closed loop control of the device or system by outputting a control signal according to the setpoint signal and a response model of the device or system using a predictive control algorithm. The predictive control algorithm is configured to implement control according to a polynomial representation for regulation, sensitivity and tracking and further implement non-linearity or time delay compensation using the response model. The closed loop control is tunable using an adjustable single parameter for accelerating or decelerating the closed loop control relative to an open loop control scenario.

Abstract: Element actions of actuators incorporated in an automated manufacturing machine are stored in a manner associated with program elements to perform the element actions. An action chart is used to describe an operation of the automated manufacturing machine, with one element action having an end linked to a start of another element action using a logical operation. The action chart describing the operation of the automated manufacturing machine is read. The element actions on the action chart are converted into the program elements. The program elements are combined together in accordance with the action chart. This allows automatic generation of a control program for controlling the operation of the automated manufacturing machine.

Abstract: The invention relates to a method for outputting a manufacturing file for producing an optical element (100) from a curable material (50) by using an additive manufacturing technology, comprising the steps of:—acquiring the desired geometry of the optical element,—obtaining a discretization of the desired geometry in volume units described by data relative to position parameters of the volume units and to the dimension of the volume units,—associating at least a volume unit with a kinetic parameter that relates to the curing pace imposed to the curable material of the volume unit,—producing, using at least one processor, a manufacturing file comprising said data and said kinetic parameter for manufacturing at least the optical element, and—outputting said manufacturing file.

Abstract: A method of manufacturing a dental restoration including a construction step (S2) of preparing construction data for a dental tool machine for machining the dental restoration from a workpiece. The construction data defines at least the geometry of the dental restoration within the workpiece. The method includes a starting step (S3) of starting machining of the dental restoration from the workpiece in accordance with the construction data. The method also includes a preparing step (S4) of starting preparing, before termination of the construction step (S2), the dental tool machine at least through equipping it with all the consumables essential for starting the starting step (S3), and completing preparing the dental tool machine before start of the starting step (S3).

Abstract: A computer-implemented resource management method for modular plants may include: receiving data identifying a required module type to be assembled into the modular plant as part of a module pipeline including one or more modules; and executing an optimization algorithm to select, from a plurality of modules having the required module type, a module for inclusion in the module pipeline on the basis of one or more predetermined optimization criteria.

Abstract: A computer-implemented method for determining automatically a machine safety and/or a product quality of a flexible cyber-physical production system with a configuration adaptable during a production process including production steps executed by machines forming equipment of a physical factory of the cyber-physical production system to produce a product according to a product recipe, wherein the machine safety and/or product quality are calculated during runtime of the flexible cyber-physical production system by processing a meta-model of the flexible cyber-physical production system stored in a computer readable storage medium, is provided.

Abstract: According to one embodiment, a method, computer system, and computer program product for mitigating computer fan noise. The embodiment may include retrieving a previously generated baseline profile for a user participating in a real-time e-conference call via a computing device. The embodiment may include identifying real-time hardware and software metrics of the computing device. The embodiment may include calculating a current fan indicative noise (FIN) score for the call based on the baseline profile and the identified metrics. The embodiment may include determining whether the current FIN score is above a threshold value. In response to determining that the FIN score is above a threshold value, the embodiment may include determining whether the computing device is at risk of damage. In response to determining that the computing device is not at risk of damage, the embodiment may include limiting operation of one or more fans of the computing device.

Abstract: A planning method for a power distribution automation system based on a reliability constraint is provided. The method includes: defining installation states of components and principles of a fault isolation, a load transfer and a fault recovery after a branch fault occurs; establishing a reliability evaluation optimization model for a power distribution network based on a mixed integer linear programming, the reliability evaluation optimization model comprising a target function and constraint conditions; solving the reliability evaluation optimization model to obtain an optimum planning scheme; and planning the power distribution automation system based on the optimum planning scheme.

Abstract: The present disclosure relates to methods for determining a maximum power setpoint for a wind turbine comprising: determining an ambient temperature, determining a temperature of one or more wind turbine components and determining a current power output of the wind turbine. The methods further comprise determining the maximum power setpoint based at least partially on a thermodynamic model of the wind turbine components, the ambient temperature, the temperature of the components of the wind turbine and on the present power output of the wind turbine. The present disclosure further relates to methods for determining a setpoint reduction and to wind turbine control systems and wind turbines configured for such methods.

Abstract: A continuous flow manufacturing system includes a tool, configured to hold a part. The system also includes a machine-readable identifier, associated with the tool and encoded with a tool-identification. The system further includes a plurality of workstations. Each one of the plurality of workstations includes a production machine, configured to perform a manufacturing operation on a part, and a reader, configured to read the machine-readable identifier. The system also includes a computing device, configured to retrieve process-data, associated with the tool-identification and a corresponding one of the plurality of workstations, and configured to update the process-data, subsequent to performing the manufacturing operation associated with the corresponding one of the plurality of workstations.

Abstract: Methods and systems are disclosed for controlling device movement based on a movement command. Issues on accurately controlling movement of a device such as a programmable robot and rover are addressed by first determining a command type of the movement command, followed by determining specific types of controllers for a high-level controller and a low-level controller based on the determined command type. When the command type is a linear movement, a Proportional-Integral-Derivative (PID) controller is used at the high-level controller and a Proportional/Proportional-Integral (PPI) controller is used at the low-level controller to accurately control a target distance. When the command type is an angular movement, the PPI controller is used at the high-level controller and the PID controller is used at the low-level controller to accurate control the end-heading of the device.

Abstract: A Multi-Purpose Dynamic Simulation and run-time Control platform includes a virtual process environment coupled to a physical process environment, where components/nodes of the virtual and physical process environments cooperate to dynamically perform run-time process control of an industrial process plant and/or simulations thereof. Virtual components may include virtual run-time nodes and/or simulated nodes. The MPDSC includes an I/O Switch which delivers I/O data between virtual and/or physical nodes, e.g., by using publish/subscribe mechanisms, thereby virtualizing physical I/O process data delivery. Nodes serviced by the I/O Switch may include respective component behavior modules that are unaware as to whether or not they are being utilized on a virtual or physical node. Simulations may be performed in real-time and even in conjunction with run-time operations of the plant, and/or simulations may be manipulated as desired (speed, values, administration, etc.).