A method for the autonomous construction and/or design of at least one component part of a component includes the step of determining a state (s.sub.i) of the component part by a state module, wherein a state (s.sub.i) is defined by parameters (p.sub.i) such as data and/or measured values of at least one property (e.sub.i) of the component part. The state (s.sub.i) is transmitted to a reinforcement learning agent, which uses a reinforcement learning algorithm. A calculation function (ƒ.sub.i) and/or an action (a.sub.i) is selected on the basis of a policy for a state (s.sub.i) for the modification of at least one parameter (p.sub.i) by the reinforcement learning agent. A modeled value for the property (e.sub.i) is calculated using the modified parameter (p.sub.i). A new state (s.sub.i+1) is calculated by an environment module on the basis of the modeled value for the property (e.sub.i).

Systems and methods for tuning a closed-loop controller for a hot melt liquid dispensing system are disclosed. In an example method, based on a set temperature setpoint, the hot melt liquid dispensing system is maintained at a steady state with respect to a temperature process variable and a heater duty cycle control variable. The heater duty cycle control variable is brought to a sustained oscillation. An amplitude and an ultimate period are determined. An ultimate gain is determining based on the step value and the amplitude. A proportional, integral, or derivative constant is determined based the ultimate period and/or ultimate gain. The closed-loop controller is implemented using the proportional, integral, or derivative constant.

The present disclosure is applicable to the technical field of metering and distributing of an adhesive dispensing machine, and provides a machine learning-based flexible intelligent adhesive dispensing method. Specific steps are as follows: first, establishing a reference process database; second, performing coarse scanning on a product tray by a scanning module, identifying a product model number, directly calling adhesive dispensing data fitted in a previous period by the central control system according to the product model number, and controlling the adhesive dispensing module to roughly adjust an adhesive dispensing posture; three, performing precise scanning on a body of each shaped charge in the product tray through the scanning module, performing real-time classification and fitting on the data of the product tray through the central control system, and correcting the called adhesive dispensing data through the fitted data; and fourth, executing an adhesive dispensing action by an adhesive dispensing machine table according to newest adhesive dispensing data. According to the method, the mixed-line and mixed-type continuous automatic adhesive dispensing operation of shaped charges is realized, which greatly reduces or eliminates the debugging cost, shortens the production cycle of new products, and has relatively strong industrial promotion and application value.

A method may include receiving data representative of one or more commands generated by a model-less controller to control operations of devices within a system and output parameters associated with the devices of the system. The method may also include determining whether the data is indicative of a change in operational characteristics of the system and generating a model representative of the operational characteristics of the system as a function of the data based on a Bayesian optimization algorithm in response to the data being indicative of the change. The method may also involve transmitting an excitation input to the devices in response to the data not being indicative of the change, receiving updated output parameters associated with the devices of the system after the excitation input is transmitted, and generating the model based on the updated output parameters and the excitation input.

A control system includes a vision system including an imaging device and a VAR monitoring system configured to determine a power adjustment phase of the VAR process based on the images from the vision system and a process parameter. The VAR monitoring system includes a vision analysis module configured to analyze the images from the vision system to detect a melt marker based on a remelt image process model, and a prediction module configured to predict an operational characteristic of the VAR process that is associated with the power adjustment relative to a melt marker location and a remelt prediction model. The VAR monitoring system is configured to initiate the power adjustment phase in response to the melt marker satisfying a predetermined melt marker condition, the operational characteristic of the VAR process satisfying a predetermined operational condition, or a combination thereof.

A heating and cooling system that includes a condenser coil configured to receive a refrigerant. A first compressor and a second compressor that pump the refrigerant through the condenser coil. A first condenser fan and a second condenser fan that push air over the condenser coil. A controller that receives a signal indicative of an ambient air temperature, a signal indicative of an operational status of the first compressor, and a signal indicative of an operational status of the second compressor. The controller controls operation of the first condenser fan and the second condenser fan in response to the signal indicative of the ambient air temperature, the signal indicative of the operational status of the first compressor and the signal indicative of the operational status of the second compressor.

This learning device provides a learned model to an adjuster containing a learned model learned to output a predetermined compensation amount to a controller, in a control system including the controller outputting a command value obtained by compensating a target value based on a compensation amount and a control object controlled to process an object to be processed. The learning device includes: an evaluation part obtaining operation data including the target value, command value and control variable and evaluates the quality of the control variable; a learning part generating candidate compensation amounts based on the operation data, and learning, as teacher data, the generated candidate compensation amount and the specific parameter of the object, and generating a learned model; and a setting part providing the learned model to the adjuster if the evaluated quality is within an allowable range.

Provided is an optimization system (1) including: a plurality of individual systems (20); and a host system (12) configured to communicate to and from the individual systems (20). Each of the individual systems (20) includes: a device (electric device (30)) which is connected to an energy source (electric power system (22)), and is configured to receive energy from the energy source, or transmit energy to the energy source; and an optimization calculation module (50) configured to execute optimization calculation so that an objective function is minimized under a state in which parameters of the energy through the device are set to the objective function and a constraint condition, respectively. The host system (12) includes a host calculation module (70) configured to derive an incentive based on a plurality of optimization calculation results each derived by a corresponding one of the individual systems (20). The optimization calculation module (50) is configured to again execute the optimization calculation based on the incentive derived by the host calculation module (70).

An underwater data center includes a data center positioned in a water environment, powered by one or more sustainable energy sources. One or more data center nodes is coupled to the data center or included in the data center. A controller is coupled to the one or more data center nodes. A housing member houses the data center node under water. A passive cooling system coupled to the data center. The passive cooling operates by at least one of convention or conduction without moving fluid in the housing. The underwater data center is coupled to a sustainable energy source that provides energy to the underwater data center. The controller is configured to redistribute excess power from the sustainable energy source to an alternate source responsive to determine that the power from the sustainable energy source is greater than an amount needed to power the system.

A vehicle, and a system a method of navigating a vehicle. The system includes a trajectory planning module and a trajectory tracking module. The trajectory planning module operates at a processor of the vehicle to generate a trajectory for the vehicle. The trajectory tracking module operates at the processor to track the trajectory to navigate the vehicle. The trajectory planning module and the trajectory tracking module run asynchronously from each other.