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 control system (1) for controlling a plant (2) comprises a feedback loop including an integrator (7); a signal generator (32); and a scaling unit (10). The feedback loop comprises an input suitable for connection to an output (18) of the plant. The integrator integrates a signal received from the input to generate a state signal x. The signal generator generates a periodic base perturbation signal (34) with an initial amplitude. The scaling unit generates a scaling factor (30) having a first value if the variance of the state signal var(x) is zero, or a second value if the variance of the state signal is non-zero, wherein the second value is proportional to (formulae 1) The scaling unit is arranged to multiply (16) the initial amplitude of the periodic base perturbation signal by the scaling factor to produce a state dependent perturbation signal (35, 36), which is applied to an input of the plant.

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.

A method for determining oscillations occurring in a measuring signal. The method includes the steps of receiving a measuring signal, determining the extreme values of the received measuring signal, and ascertaining closed loops of the measuring signal, by a) identifying a closed loop in the measuring signal (a closed loop being formed by two half loops having identical oscillation width and opposite direction, b) storing the identified closed loop, c) removing the identified closed loop from the measuring signal, and d) repeating steps a) through c) until all closed loops have been ascertained.

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.

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 system and method for processing predictions in the presence of adversarial perturbations in a sensing system. The processor receives inputs from sensors and runs a neural network having a network function that generates, as outputs, predictions of the neural network. The method generates from a plurality of outputs a measurement quantity (m) that may be, at or near a given input, either (i) a first measurement quantity M.sub.1 corresponding to a gradient of the given output, (ii) a second measurement quantity M.sub.2 corresponding to a gradient of a predetermined objective function derived from a training process for the neural network, or (iii) a third measurement quantity M.sub.3 derived from a combination of M.sub.1, and M.sub.2. The method determines whether the measurement quantity (m) is equal to or greater than a threshold. If greater than the threshold, one or more remedial actions are performed to correct for a perturbation.

An adjustment method includes (a) causing a robot to execute an adjustment operation using candidate values of operation parameters and acquiring a measurement result of a sensor, (b) updating the candidate values of the operation parameters by executing optimization processing for the operation parameters using the measurement result, and (c) determining adjustment values of the operation parameters by repeating (a) and (b) until the optimization processing converges.

In order to carry out largely automated parameterisation of the closed-loop control parameters for closed-loop control of a hydraulic system comprising a servo drive, a method and a device for determining the closed-loop parameters of a closed-loop control unit of the hydraulic system are specified, wherein an actual system pressure of a hydraulic consumer of the hydraulic system is closed-loop controlled by means of a predefined set point rotational speed of a servo drive, wherein an actual rotational speed of the servo drive follows the predefined set point rotational speed, wherein an excitation signal is applied to the setpoint rotational speed, and the actual system pressure which is set here is measured, the dynamics of the hydraulic system are acquired from the actual rotational speed and/or the setpoint rotational speed and the actual system pressure, and the closed-loop control parameters are calculated from the acquired dynamics.

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.