A method solves a stochastic quadratic program (StQP) for a convex set with a set of general linear equalities and inequalities by an alternating direction method of multipliers (ADMM). The method determines an optimal solution, or certifies that no solution exists. The method optimizes a step size β for the ADMM. The method is accelerated using a conjugate gradient (CG) method. The StMPC problem is decomposed into two blocks. The first block corresponds to an equality constrained QP, and the second block corresponds to a projection onto the StMPC inequalities and anticipativity constraints. The StMPC problem can be decomposed into a set of time step problems, and then iterated between the time step problems to solve the decoupled problems until convergence.

A signal conversion circuit and a signal readout circuit are provided. The signal conversion circuit includes: an operational amplifier, configured to amplify an electric signal output by a sensing array; an input switched capacitor, wherein an end of the input switched capacitor is configured to receive the electric signal output by the sensing array, and another end of the input switched capacitor is coupled with an input end of the operational amplifier; and a feedback switched capacitor, wherein an end of the feedback switched capacitor is coupled with the input end of the operational amplifier, and another end of the feedback switched capacitor is coupled with an output end of the operational amplifier.

Systems, methods, apparatuses, and computer-readable media for computational model optimization. A plurality of sampled values for a hyperparameter of a computational model may be received, the plurality of sampled values comprising a subset of a plurality of possible values for the hyperparameter, each sampled value associated with a performance metric for the computational model with the sampled value assigned to the hyperparameter. A first candidate value from the plurality of possible values may be determined, the first candidate value having a distance to a first sampled value of the plurality of sampled values that exceeds a threshold distance, wherein the distance is in a space comprising the plurality of possible values. The first candidate value may be assigned to the hyperparameter of the computational model. A first performance metric may be determined for the computational model with the first candidate value assigned to the hyperparameter.

The embodiments of the invention provide an intelligent prediction method and apparatus for reservoir sensitivity, belonging to the technical field of reservoir sensitivity prediction. The method includes: acquiring a reservoir sensitivity influence factor item related to a reservoir sensitivity result to be predicted and numerical values of corresponding reservoir sensitivity influence factors; determining a corresponding type of database according to the reservoir sensitivity influence factor item; determining whether numerical values of reservoir sensitivity influence factors corresponding to core parameters in the numerical values of the reservoir sensitivity influence factors include a first upper boundary value or a first lower boundary value; and using, according to whether the first upper boundary value or the first lower boundary value is included, different intelligent sensitivity prediction models to calculate the reservoir sensitivity result to be predicted.

A controller includes a memory that stores instructions and a processor that executes the instructions. The instructions cause the controller to execute a process that includes receiving sensor data from a first sensor and a second sensor. The sensor data includes a time-series observation representing a first activity and a second activity. The controller generates models for each activity involving progressions through states indicated by the sensor data from each sensor. The controller receives from each sensor additional sensor data including a time-series observation representing the first activity and the second activity. The controller determines likelihoods that the models generated a portion of the additional sensor data and calculates a pair-wise distance between each sensor-specific determined likelihood to obtain calculated distances. The calculated distances for each sensor are grouped, and a relevance of each sensor to each activity is determined by executing a regression model using the grouped calculated distances.

A machine control system includes circuitry configured to acquire a second waveform that is generated by exponentiating a first waveform by a real number, and to operate a machine based on the second waveform. The first waveform represents a command of an operation of the machine. The real number has a value other than 0 and 1.

An exemplary method includes solving on a computing system an optimal power flow formulation for a plurality of generators in a power system. The solving includes computing using multi-threaded parallelism a plurality of constraints for the formulation, computing using multi-threaded parallelism a plurality of Jacobian functions of the constraints, and computing using multi-threaded parallelism a Hessian of Lagrangian functions. The method further includes outputting results of the solving, wherein the results comprise values of generation levels for the plurality of generators. Apparatus and program products are also disclosed.

An exemplary method includes solving on a computing system an optimal power flow formulation for a plurality of generators in a power system. The solving includes computing using multi-threaded parallelism a plurality of constraints for the formulation, computing using multi-threaded parallelism a plurality of Jacobian functions of the constraints, and computing using multi-threaded parallelism a Hessian of Lagrangian functions. The method further includes outputting results of the solving, wherein the results comprise values of generation levels for the plurality of generators. Apparatus and program products are also disclosed.

The embodiments of the invention provide an intelligent prediction method and apparatus for reservoir sensitivity, belonging to the technical field of reservoir sensitivity prediction. The method includes: acquiring a reservoir sensitivity influence factor item related to a reservoir sensitivity result to be predicted and numerical values of corresponding reservoir sensitivity influence factors; determining a corresponding type of database according to the reservoir sensitivity influence factor item; determining whether numerical values of reservoir sensitivity influence factors corresponding to core parameters in the numerical values of the reservoir sensitivity influence factors include a first upper boundary value or a first lower boundary value; and using, according to whether the first upper boundary value or the first lower boundary value is included, different intelligent sensitivity prediction models to calculate the reservoir sensitivity result to be predicted.

Apparatuses, methods of operating apparatuses, and corresponding computer programs are disclosed. In the apparatuses input circuitry receives input data comprising at least one data element and shift circuitry generates, for each data element of the input data, a bit-map giving a one-hot encoding representation of the data element, wherein a position of a set bit in the bit-map is dependent on the data element. Summation circuitry generates a position summation value for each position in the bit-map, wherein each position summation value is a sum across all bit-maps generated by the shift circuitry from the input data. Maximum identification circuitry determines at least one largest position summation value generated by the summation circuitry and output circuitry to generate an indication of at least one data element corresponding to the at least one largest position summation value. The statistical mode of the data elements in the input data is thereby efficiently determined.