In one embodiment, a network assurance service receives data regarding a monitored network. The service analyzes the received data using a machine learning-based model, to perform a network assurance function for the monitored network. The service determines that performance of the model is negatively affected by a sample rate of the received data. The service adjusts the sample rate of the data, based on the determination that the performance of the model is negatively affected by the sample rate of the received data.

A portable electronic device has a control unit including a processor whose clock frequency is variable, a temperature sensor, a housing that encases the control unit and temperature sensor, and a touch sensor that is able to detect touching to the surface of the housing. The control unit obtains a measured value of temperature from the temperature sensor, calculates an estimated value of surface temperature of the housing using the measured value and a first heat transfer model when the touch sensor has not detected touching, calculates the estimated value using the measured value and a second heat transfer model when the touch sensor has detected touching, decreases an upper limit for the clock frequency when the estimated value is higher than or equal to a threshold, and increases the upper limit for the clock frequency when the estimated value is lower than the threshold.

Various examples are provided that are related to boundary control in membrane distillation (MD) processes. In one example, a system includes a membrane distillation (MD) process comprising a feed side and a permeate side separated by a membrane boundary layer; and processing circuitry configured to control a water production rate of the MD process based at least in part upon a distributed heat transfer across the membrane boundary layer. In another example, a method includes determining a plurality of estimated temperature states of a membrane boundary layer separating a feed side and a permeate side of a membrane distillation (MD) process; and adjusting inlet flow rate or inlet temperature of at least one of the feed side or the permeate side to maintain a difference temperature along the membrane boundary layer about a defined reference temperature based at least in part upon the plurality of estimated temperature states.

A phase and an amplitude improving method includes performing a model establishing step, a phase compensating step, an amplitude compensating step and a compensation information generating step. The model establishing step includes establishing an inverter model. A voltage command is inputted to the inverter model to generate an actual voltage information. The voltage command includes a phase command information and an amplitude command information. The phase compensating step includes computing the phase command information and the actual voltage information to generate a compensating phase information. The amplitude compensating step includes computing the amplitude command information and the actual voltage information to generate a compensating amplitude information. The compensation information generating step includes generating a compensating voltage command. The compensating voltage command is inputted to the inverter model to generate a compensating actual voltage information.

A control device includes a timing detection unit, a setpoint path generation unit, and a control computation unit. The timing detection unit detects a timing at which an event indicating a change in a target setpoint or an event indicating application of a disturbance occurs, as a generation start timing at which generation of a path of a generation setpoint is started. The setpoint path generation unit determines the path of the generation setpoint in response to determination of the generation start timing at which generation of the path of the generation setpoint is started and outputs, in every control cycle, the generation setpoint that follows the determined path. In every control cycle, the control computation unit calculates a manipulated variable by performing control computation using a process variable and the generation setpoint as input values.

The disclosure provides a closed-loop controller for a controlled system comprising a comparison element generating an error e, a compensator generating a control u.sub.N value based on the error e, and a control allocator determining a manipulated parameter u.sub.M value based on the control u.sub.N. The control allocator typically utilizes a control effectiveness function and determines u.sub.M value by selecting one or more specific system x.sub.0 signals from the system state x.sub.i or system input y.sub.j values or system parameters values p.sub.k reported, defining a plurality of distributed x.sub.D around each specific system x.sub.0 signal, and minimizing an error function E(z.sub.i), where the error function E(z.sub.i) is based on errors which arise from use of the plurality of distributed x.sub.D in the control effectiveness function rather than one or more specific system x.sub.0 signals.

Building energy consumption is controlled by operating energy consumptive devices according to a control plan determined by: using a software building model to simulate building behavior over a simulation period in accordance with predicted circumstances and in accordance with multiple control plans; for at least one of the control plans, re-simulating building behavior a plurality of times each with a different perturbation imposed, with each perturbation corresponding to a different degree of participation in a grid market, to thereby determine an amount of participation in the grid market available to the building; resimulating building behavior a plurality of times imposing said determined participation amount as a constraint; and selecting an optimal control plan based on the last round of resimulations. The selected optimal control plan may also be derived through combining buildings or energy response attributes associated with different buildings to form synthetic resources for optimal deployment to the grid.

A method includes obtaining historical data associated with an industrial process, which is associated with multiple independent variables. The method also includes automatically excluding at least one portion of the historical data and automatically extracting data segments from at least one non-excluded portion of the historical data.

The method further includes iteratively performing model identification using the data segments to identify one or more models and using the model(s) to design, monitor, or tune at least one industrial process controller for the industrial process. Iteratively performing the model identification includes recursively analyzing the data segments to (i) select the data segment(s) associated with each variable that have a highest energy and provide a high signal to noise ratio and (ii) eliminate poorly performing segments associated with each variable. Iteratively performing the model identification also includes generating a model for each variable using the selected data segment(s) for that variable.

The present invention is directed towards methods and systems for characterizing sensors and developing classifiers for sensor responses on a utility grid. Experiments are conducted by selectively varying utility grid parameters and observing the responses of utility grid to the variation. Methods and systems of this invention then associate the particular responses of the utility grid sensors with specific variations in the grid parameters, based on knowledge of the areas of space and periods of time where the variation in grid parameters may affect the sensor response. This associated data is then used to updating a model of grid response.

The present invention relates to a servo motor controller for controlling a servo motor having a current limiting unit, and a tuningless nonlinear control method thereof. According to the present invention, the servo motor controller control method for controlling a servo motor including a current limiting unit comprises the steps of: detecting that the limited current is output according to a current command of the current limiting unit; adjusting a disturbance estimation gain of the servo motor controller in response to the output of the limited current; and generating a control input provided from the servo motor controller to the current limiting unit on the basis of the adjusted disturbance estimation gain.