A non-transitory computer-readable storage medium stores instructions, which when executed by a processing device of a diagnostic server, cause the processing device to perform certain operations. The operations include receiving, from a processing chamber, (i) measurement values of a combined signal that is based on an injection of an alternating signal wave onto a first output signal of a controller of the processing chamber, and (ii) measurement values of a second output signal of the controller that incorporates feedback from the processing chamber. The operations further include generating, based on the measurement values of the combined signal and the measurement values of the second output signal of the controller, a baseline bode fingerprint pertaining to a state associated with the processing chamber. The operations further include storing, in computer storage, the baseline bode fingerprint to be used in performing diagnostics of the processing chamber.

An intellectual quality management method is disclosed. A heatmap risk interface is created according to the required data and the parameter configuration which are calculated using a time dependent risk priority number (RPN) equation. An intellectual audit scheduling algorithm is defined via the heatmap risk interface to automatically generate at least one audit plan. An audit program corresponding to the audit plan is performed and a plurality of problem points are selected. Intellectual root cause category recommendation is performed to the questions points. intellectual corrective actions and preventive action recommendations are performed to the problem points according to the intellectual root cause category recommendation to obtain at least one optimum corrective action and at least one preventive action. Corrective actions are performed to each audit unit according to the corrective action to solve the problem points and prevention actions are performed to each audit unit according to the preventive action.

A non-transitory computer-readable storage medium stores instructions, which when executed by a processing device of a diagnostic server, cause the processing device to perform certain operations. The operations include receiving, from a processing chamber, (i) measurement values of a combined signal that is based on an injection of an alternating signal wave onto a first output signal of a controller of the processing chamber, and (ii) measurement values of a second output signal of the controller that incorporates feedback from the processing chamber. The operations further include generating, based on the measurement values of the combined signal and the measurement values of the second output signal of the controller, a baseline bode fingerprint pertaining to a state associated with the processing chamber. The operations further include storing, in computer storage, the baseline bode fingerprint to be used in performing diagnostics of the processing chamber.

A non-transitory, computer-readable recording medium stores a program of reinforcement learning by a state-value function. The program causes a computer to execute a process including calculating a temporal difference (TD) error based on an estimated state-value function, the TD error being calculated by giving a perturbation to each component of a feedback coefficient matrix that provides a policy; calculating based on the TD error and the perturbation, an estimated gradient function matrix acquired by estimating a gradient function matrix of the state-value function with respect to the feedback coefficient matrix for a state of a controlled object, when state variation of the controlled object in the reinforcement learning is described by a linear difference equation and an immediate cost or an immediate reward of the controlled object is described in a quadratic form of the state and an input; and updating the feedback coefficient matrix using the estimated gradient function matrix.

A non-transitory computer-readable storage medium stores instructions, which when executed by a processing device of a diagnostic server, cause the processing device to perform certain operations. The operations include receiving, from a processing chamber, (i) measurement values of a combined signal that is based on an injection of an alternating signal wave onto a first output signal of a controller of the processing chamber, and (ii) measurement values of a second output signal of the controller that incorporates feedback from the processing chamber. The operations further include generating, based on the measurement values of the combined signal and the measurement values of the second output signal of the controller, a baseline bode fingerprint pertaining to a state associated with the processing chamber. The operations further include storing, in computer storage, the baseline bode fingerprint to be used in performing diagnostics of the processing chamber.

Embodiments disclosed herein include a method for auto-tuning a system. In an embodiment, the method comprises determining if the system is in a steady state. Thereafter, the method includes exciting the system. In an embodiment, the method comprises storing process feedback measurements from the excited system to provide a set of stored data. In an embodiment, the set of stored data is a subset of all available data generated by the excited system. In an embodiment, the method further comprises determining when the excited system returns to the steady state, and tuning the system using the set of stored data.

A crop prediction system performs various machine learning operations to predict crop production and to identify a set of farming operations that, if performed, optimize crop production. The crop prediction system uses crop prediction models trained using various machine learning operations based on geographic and agronomic information. Responsive to receiving a request from a grower, the crop prediction system can access information representation of a portion of land corresponding to the request, such as the location of the land and corresponding weather conditions and soil composition. The crop prediction system applies one or more crop prediction models to the access information to predict a crop production and identify an optimized set of farming operations for the grower to perform.

An intellectual quality management method is disclosed. A heatmap risk interface is created according to the required data and the parameter configuration which are calculated using a time dependent risk priority number (RPN) equation. An intellectual audit scheduling algorithm is defined via the heatmap risk interface to automatically generate at least one audit plan. An audit program corresponding to the audit plan is performed and a plurality of problem points are selected. Intellectual root cause category recommendation is performed to the questions points. intellectual corrective actions and preventive action recommendations are performed to the problem points according to the intellectual root cause category recommendation to obtain at least one optimum corrective action and at least one preventive action. Corrective actions are performed to each audit unit according to the corrective action to solve the problem points and prevention actions are performed to each audit unit according to the preventive action.

The present disclosure provides a method and system for optimizing a control process. The method and system comprise using a sensor to generate a feedback signal that represents a measured performance index for an extremum seeking control (ESC) method and sending the feedback signal to an ESC conditioning circuit that applies a logarithmic transformation to the feedback signal to obtain a modified feedback signal. An ESC controller applies the modified feedback signal to the ESC method to generate an output value that is used to control an actuator to maximize the performance of a machine or process.

A receiver device and an eye pattern-based control parameter adjustment method are provided. The receiver device includes a receiving circuit and a control circuit. The control circuit performs an iterative operation to determine an optimized control parameter, and updates current control parameters of the receiving circuit to the optimized control parameter after completing the iterative operation. The receiving circuit processes an input signal according to the current control parameters to generate recovered data. The iterative operation includes: updating the current control parameters of the receiving circuit to candidate control parameters; checking a size relationship between an optimized eye mask and a current eye pattern; and increasing the optimized eye mask according to the current eye pattern when the optimized eye mask does not conflict with the current eye pattern, and updating the optimized control parameters to the candidate control parameters corresponding to the new eye mask.