A method is described for acquiring data for relay-based controller tuning of a controller controlling a system. The method includes determining, based on controller output constraint, a relay amplitude representing an amplitude of a relay signal to be added to steady state controller output to obtain a sum signal to be fed to the system as system input during the acquiring data for relay-based controller tuning; and starting acquiring controller output data and system output data when detecting a steady state of system output while controller output is fed to the system as system input.

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 system, including a first power conditioner module (PCM) configured to provide a first power output, wherein the first PCM includes a first plurality of input/output (TO) points configured to electrically couple to a plurality of terminals. The system also includes a second power conditioner module (PCM) configured to provide a second power output, wherein the second PCM includes a second plurality of input/output points configured to electrically couple to the plurality of terminals. The second PCM also includes a control system configured to perform a verification operation on a first set of data received from the first PCM, wherein the first PCM received the first set of data from the first plurality of TO points, and send a second set of data to the first PCM, wherein the second set of data is received via the second plurality of TO points, and wherein the first PCM is configured to perform an additional verification operation on the second set of data.

A test system for testing a specimen include (a) a set of actuators for applying a desired time history of load to a specimen, (b) a drive unit connected to each actuator, (c) power generating elements (current/pneumatic/hydraulic) and (d) a controller connected to the drive units, the controller generates a drive signal for the drive unit based on feedback received from output of the specimen and an error derived from the feedback and an input command. The controller generates the drive signal by compensating varying system parameters which are introduced due to nonlinear response of the test system and the specimen, wherein the controller does not require (i) additional measured variable other than a feedback received from the specimen and (ii) a numerical model of the test system and the specimen.

A dynamic decoupling control method for a multi-degree-of-freedom precision comprises defining a dynamic decoupling controller and parameterizing elements in the form of a finite impulse response (FIR) filter, applying a nominal decoupling control method to measure an actual position signal of an actual system and an output of a nominal decoupling controller, calculating a virtual control quantity, and optimizing an indicator function to obtain an estimated value of a coefficient to be optimized of the dynamic decoupling controller. Decoupling at medium and high frequency bands can be effectively realized with improved accuracy of decoupling, and an algorithm flow is simplified. The method is prone to engineering implementation.

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 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 method for inducing a covert misclassification performed on a non-transitory computer readable medium, the method includes identifying a target position. The method further includes creating a spectral perturbation tensor. The spectral perturbation tensor is configured to shift a projection of an initial spectrum towards the target position. Additionally, the method includes combining the spectral perturbation tensor to the initial spectrum. Further, the method includes classifying the combination of the spectral perturbation tensor and the initial spectrum with an established classifier, thereby designing the spectral perturbation tensor such that the combination is misclassified.

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.

The invention discloses a context awareness control device, system and method. The context awareness control device comprises a connection module, a sensor module, an emission control module, an information processing module and an adjustment control module. The sensor module collects environmental information and transmits the environmental information to the information processing module. The information processing module collects use information of a terminal device through the connection module. The adjustment control module makes a comparison between the function situation information and the pre-stored better environment value according to the detected currently executing function, outputs a first environment control signal according to the comparison results to adjust the working state of the terminal device and emits a second environment control signal through the emission control module to adjust the working state of the electrical equipment.

A plant or refinery may include equipment such as condensers, regenerators, distillation columns, rotating equipment, compressors, pumps, turbines, or the like. Different operating methods may impact deterioration in equipment condition, thereby prolonging equipment life, extending production operating time, or providing other benefits. Mechanical or digital sensors may be used for monitoring equipment to determine whether problems are developing. For example, sensors may be used in conjunction with one or more system components to perform invariant mapping, monitor system operating characteristics, and/or predict pressure, volume, surges, reactor loop fouling, gas quality, or the like. An operating condition (e.g., of one or more pieces of equipment in the plant or refinery) may be adjusted to prolong equipment life or avoid equipment failure.