A controller for controlling a vapor compression system is provided. The controller is configured to control an operation of the VCS with different combinations of setpoints for different actuators of the VCS to estimate a cost of operation of the VCS for each of the different combinations of setpoints, and compute, using a Bayesian optimization of the combinations of setpoints and their corresponding estimated costs of operation, a probabilistic surrogate model, wherein the probabilistic surrogate model defines at least first two order moments of the cost of operation in the probabilistic mapping. The controller is further configured to select an optimal combination of setpoints having the largest likelihood of being a global minimum at the surrogate model according to an acquisition function of the first two order moments of the cost of operation.

A load control method of an indenter based on fuzzy predictive control and a system, acquiring the actual measured force value of a sensor in the loading stage; acquiring the expected force value of the nth cycle in the loading stage; calculating a first error and a change rate; establishing and optimizing a fuzzy predictive controller; determining the movement steps of a motor in the loading stage; acquiring the actual measured value of the sensor in the full load stage; acquiring the expected force value of the nth cycle in the full load stage and calculating a second error; controlling the movement of the motor; acquiring the actual measured force value of the sensor in the unloading stage; acquiring the expected force value of the nth cycle in the unloading stage; calculating a third error and a change rate; determining the movement steps of the motor in the unloading stage.

A defect profiling and tracking system for a process-manufacturing enterprise is provided. The system includes a memory and a processor. The processor is configured to access entity data for a plurality of entities of the process-manufacturing enterprise and process parameter data for one or more deviating entities. The processor is configured to analyze the entity data and the process parameter data for each of the deviating entities to determine a plurality of relationships between quality defects and the process parameters to generate a unique entity specific process signature (EPS) for each entity. The processor is configured to receive real-time process parameter data for one or more entities to generate a real-time process signature for the one or more entities and compare the real-time process signature of each entity with EPS corresponding to the entity to detect one or more EPS matches that are indicative of a quality defect.

A load control method and a load control system of an indenter based on fuzzy predictive control, are provided. The method includes acquiring an actual measured force value of a sensor and an expected force value of the n.sup.th cycle in the loading stage; calculating a first error and a change rate; establishing and optimizing a fuzzy predictive controller; determining movement steps of a motor in the loading stage; acquiring the actual measured value of the sensor and an expected force value of the n.sup.th cycle in the full load stage; controlling the movement of the motor; acquiring the actual measured force value of the sensor and an expected force value of the n.sup.th cycle in the unloading stage; calculating a third error and a change rate; and determining the movement steps of the motor in the unloading stage.

A controller for controlling a vapor compression system is provided. The controller is configured to control an operation of the VCS with different combinations of setpoints for different actuators of the VCS to estimate a cost of operation of the VCS for each of the different combinations of setpoints, and compute, using a Bayesian optimization of the combinations of setpoints and their corresponding estimated costs of operation, a probabilistic surrogate model, wherein the probabilistic surrogate model defines at least first two order moments of the cost of operation in the probabilistic mapping. The controller is further configured to select an optimal combination of setpoints having the largest likelihood of being a global minimum at the surrogate model according to an acquisition function of the first two order moments of the cost of operation.

Disclosed within is a closed loop controller having: (a) a signal processing and statistics subsystem sampling an input data stream from at least one sensor, calculating real-time continuous statistics in the input data stream based on a sliding window technique, and outputting one or more classifications based on the real-time statistics; and (b) an intelligent fuzzy logic controller receiving the one or more classifications from the signal processing and statistics subsystem, accessing a heuristic rule set based on expert knowledge, and outputting a noninvasive stimulation pattern based on the one or more classifications and the heuristic rule set.

A method of generating the knowledge base used for a programmable fuzzy controller comprising the steps of determining the relevant input and output variables to be controlled; creating artificial potential fields for each of said variables; sampling each of said potential fields in order to generate fuzzy membership functions; compiling said fuzzy membership functions into fuzzy sets; and mapping inputs fuzzy set to output fuzzy sets through a rule base. The relevant input and output variables are including: minimum, maximum, and equilibrium values; an importance weight; a non-linearity value; a control direction; and information as to whether said variable is an input or output variable. Further provided is a programmable fuzzy controller whose fuzzy knowledge base is obtained by the method described.

Systems and methods for controlling an operation of devices for an occupant. A processor to iteratively train a personalized thermal comfort model (PTCM) during an initialization period. Receive a sequence of unlabeled real-time data. A transmitter requests the occupant to label an instance of unlabeled data, when there is a disagreement between the labels of stored historical labeled data (LD) similar to received unlabeled data and a predicted label on the new unlabeled data that exceeds a threshold. The processor, in response to receiving the labeled data, trains the PTCM using different weights of the personalized LD than to the historical LD. Retrains PTCM using the historical database and the updated personalized database. A controller controls the set of devices based on the retrained PTCM.

Disclosed within is a closed loop controller having: (a) a signal processing and statistics subsystem sampling an input data stream from at least one sensor, calculating real-time continuous statistics in the input data stream based on a sliding window technique, and outputting one or more classifications based on the real-time statistics; and (b) an intelligent fuzzy logic controller receiving the one or more classifications from the signal processing and statistics subsystem, accessing a heuristic rule set based on expert knowledge, and outputting a noninvasive stimulation pattern based on the one or more classifications and the heuristic rule set.

Systems and methods for controlling an operation of devices for an occupant. A processor to iteratively train a personalized thermal comfort model (PTCM) during an initialization period. Receive a sequence of unlabeled real-time data. A transmitter requests the occupant to label an instance of unlabeled data, when there is a disagreement between the labels of stored historical labeled data (LD) similar to received unlabeled data and a predicted label on the new unlabeled data that exceeds a threshold. The processor, in response to receiving the labeled data, trains the PTCM using different weights of the personalized LD than to the historical LD. Retrains PTCM using the historical database and the updated personalized database. A controller controls the set of devices based on the retrained PTCM.