A control method and a control system for a compressor in a refrigeration system, implements control logic based on the monitoring of parameters of the refrigeration system, where these parameters may include, for example, a previous load (C.sub.ant), a current load (C.sub.atu), a reference load (C.sub.ref), a measured cycle time (t.sub.cycle) and a target time (t.sub.target).

Adjustable fluidic oscillators are disclosed. A disclosed example oscillator includes a base having a cavity with a cross-sectional profile defining an oscillatory chamber between an inlet and an outlet of the oscillator, and a plunger to be received by the cavity and movable along a depth of the cavity to vary an aspect ratio of the oscillator.

Adjustable fluidic oscillators are disclosed. A disclosed example oscillator includes a base having a cavity with a cross-sectional profile defining an oscillatory chamber between an inlet and an outlet of the oscillator, and a plunger to be received by the cavity and movable along a depth of the cavity to vary an aspect ratio of the oscillator.

A field device, method, and non-transitory computer readable medium provide for cost of lost opportunity to operators in real-time using an advance process control. The field device includes a memory and a processor operably connected to the memory. The processor receives current values and average values for controlled variables and manipulated variables; determines costs of lost opportunity for each of controlled variable variance issues, limit issues, model quality issues, inferential quality issues, and variable model issues based on the current values and the average values of the controlled variables; and stores the costs of lost opportunity for the field device.

A field device, method, and non-transitory computer readable medium provide for cost of lost opportunity to operators in real-time using an advance process control. The field device includes a memory and a processor operably connected to the memory. The processor receives current values and average values for controlled variables and manipulated variables; determines costs of lost opportunity for each of controlled variable variance issues, limit issues, model quality issues, inferential quality issues, and variable model issues based on the current values and the average values of the controlled variables; and stores the costs of lost opportunity for the field device.

An artificial intelligence apparatus determines whether a washing option in a washing course according to the input of a user is changed when input of the user is received, and collect use history information when the washing option is changed. In addition, the artificial intelligence apparatus inputs the collected use history information to an option resetting model to acquire a second washing option for resetting the washing course, and resets the washing option included in the washing course according to the second washing option.

An artificial intelligence apparatus determines whether a washing option in a washing course according to the input of a user is changed when input of the user is received, and collect use history information when the washing option is changed. In addition, the artificial intelligence apparatus inputs the collected use history information to an option resetting model to acquire a second washing option for resetting the washing course, and resets the washing option included in the washing course according to the second washing option.

An electronic rack includes an array of server blades arranged in a stack. Each server blade contains one or more servers and each server includes one or more processors to provide data processing services. The electronic rack includes a coolant distribution unit (CDU) and a rack management unit (RMU). The CDU supplies cooling liquid to the processors and receives the cooling liquid carrying heat from the processors. The CDU includes a liquid pump to pump the cooling liquid. The RMU is configured to manage the operations of the components within the electronic rack such as CDU, etc. The RMU includes control logic to determine an optimal pump speed to minimize the total power consumption of the pump, acceleration servers and the host servers, based on the one or more parameters and the association between temperature and power consumption of the acceleration servers and the host servers. The RMU then controls the liquid pump based on the optimal pump speed.

An electronic rack includes an array of server blades arranged in a stack. Each server blade contains one or more servers and each server includes one or more processors to provide data processing services. The electronic rack includes a coolant distribution unit (CDU) and a rack management unit (RMU). The CDU supplies cooling liquid to the processors and receives the cooling liquid carrying heat from the processors. The CDU includes a liquid pump to pump the cooling liquid. The RMU is configured to manage the operations of the components within the electronic rack such as CDU, etc. The RMU includes control logic to determine an optimal pump speed to minimize the total power consumption of the pump, acceleration servers and the host servers, based on the one or more parameters and the association between temperature and power consumption of the acceleration servers and the host servers. The RMU then controls the liquid pump based on the optimal pump speed.

A method of optimizing a control loop of a converter, such as a control system of the converter, includes acquiring actual values of a drive system powered by the converter, inferring, based on at least one machine learning model and the actual values, one or more adjustments of control parameters of the control loop for improving the control accuracy, and outputting the one or more adjustments for adapting control parameter values.