Provided is an electronic device for controlling surface heart and a method of controlling the electronic device. The electronic device includes a speaker, a temperature sensor, a memory, and a processor electrically coupled to the speaker, the temperature sensor, and the memory. The processor obtains first temperature information based on impedance information measured in a coil included in the speaker; obtains second temperature information measured by the temperature sensor, the second temperature information based on a heat source disposed adjacent to the speaker; predicts a surface temperature of a surface area of the electronic device, opposite to an internal area in which the speaker is disposed, based on the first temperature information and the second temperature information using a nonlinear approximation function; and controls an audio signal input to the speaker based on the predicted surface temperature.

There is proposed a model update device and method, and a process control system, which are capable of significantly reducing the labor, time, and monetary cost required for model update and are also easily applicable to a plant in which the existing model predictive control is introduced, without causing a loss of operating profit.

By converting a data format of operation data of a target process into a delay coordinate format and solving a regression problem including a regularization term for the operation data converted into the delay coordinate format, an update model reflecting secular change information of the target process is generated, and the model is replaced with the generated update model to be updated.

Embodiments are directed towards automatically identifying, configuring, monitoring, controlling, managing, and maintaining a machine, via collection computers in communication with the machine components. The components include ID Tags that store identification data, such as a component type and a unique identifier. Interrogation of the ID Tags enables the automatic identification and configuration of the machine. Data provided by the sensors, during usage of the machine, enables the remote monitoring and managing of the usage, as well as maintaining of the machine. Machine maintenance includes automatically predicting and scheduling the replacement of various components. Embodiments provide suggestions for suppliers of replacement components, as well as suggestions for alternative components that may be better optimized for the configuration and usage of the machine. Heuristics and crowd-generated data, via machine user social networks, inform predictive analyses employed to automatically identify, configure, manage, operate, and maintain the machine.

The present disclosure presents techniques for improving operational efficiency of climate control systems. A climate control system may include climate control equipment, a sensor that measures temperature in a building, and a control system that controls operation of the equipment using a first temperature schedule, which associates each time step with a temperature setpoint, when the building is occupied. When not occupied, the control system determines an expected return time based on historical occupancy data associated with the building, determines the temperature setpoint associated with the expected return time, determines candidate schedules each expected to result in the inside air temperature meeting the temperature setpoint, determines efficiency metrics each associated with one of the candidates based on historical performance data resulting from previous operation of the climate control system, and controls operation of the equipment based on a second temperature schedule selected from the candidates based on associated efficiency metrics.

A method and apparatus for controlling power production. In one embodiment, the method comprises determining a predicted weather event; determining a predicted power production impact for a distributed generator (DG) array based on the predicted weather event; and controlling power production from one or more components of the DG array to compensate for the predicted power production impact.

A control device controls a water supply device that supplies irrigation water to an open farm field in which a plant grows. The control device includes a storage unit, a calculation unit, and an output unit. The storage unit stores an environment value of each of a plurality of divided areas obtained by dividing the farm field and weather forecast of the farm field. The calculation unit calculates, based on an environment value and the weather forecast, an irrigation schedule in which supply time and amount of the irrigation water individually supplied to each of the plurality of divided areas during the irrigation period are determined. The output unit outputs to the water supply device, a control signal to control supply and no supply of the irrigation water to each of the plurality of divided areas based on the irrigation schedule.

A method for predicting a failure of a power control unit of a vehicle is provided. The method includes obtaining data from a plurality of sensors of the power control unit of a vehicle subject to simulated multi-load conditions, implementing a machine learning algorithm on the data to obtain machine learning data, obtaining new data from the plurality of sensors of power control unit of the vehicle subject to real multi-load conditions, implementing the machine learning algorithm on the new data to obtain test data, predicting a failure of the power control unit based on a comparison between the test data and the machine learning data.

A Thermal Performance Forecast approach is described that can be used to forecast heating and cooling fuel consumption based on changes to user preferences and building-specific parameters that include indoor temperature, building insulation, HVAC system efficiency, and internal gains. A simplified version of the Thermal Performance Forecast approach, called the Approximated Thermal Performance Forecast, provides a single equation that accepts two fundamental input parameters and four ratios that express the relationship between the existing and post-change variables for the building properties to estimate future fuel consumption. The Approximated Thermal Performance Forecast approach marginally sacrifices accuracy for a simplified forecast. In addition, the thermal conductivity, effective window area, and thermal mass of a building can be determined using different combinations of utility consumption, outdoor temperature data, indoor temperature data, internal heating gains data, and HVAC system efficiency as inputs.

A device with automated device temperature control is described. In one example, the device includes a processor and a weather forecast engine coupled to the processor. The weather forecast engine obtains weather data of a geographical location in which the device is located. The weather data includes values of environmental parameters. The weather data is then shared with a prediction engine. The device further includes a control engine coupled to the processor. The control engine receives a first threshold operating temperature determined for the device based on the weather data, a device model, and a mapping table from the prediction engine. The control engine then initiates a temperature control device, connected to the device, to cool the device if a current device temperature of the device is greater than the first threshold operating temperature.

According to some embodiments, an available algorithm data store may contain information about a pool of available algorithms. An algorithm selection platform coupled to the available algorithm data store may access the information about the pool of available algorithms and compare the information about each of the pool of available algorithms with at least one requirement associated with the current algorithm executing in the real environment. The algorithm selection platform may then automatically determine algorithm execution context information and, based on said comparison and the algorithm execution context information, select at least one of the pool of available algorithms as a potential replacement algorithm. An indication of the selected at least one potential replacement algorithm may then be transmitted (e.g., to be evaluated in a shadow environment by an algorithm evaluation platform).