Abstract: Cooling devices, such as fans, installed in a computing device can be automatically identified by reading an encoded signal from the cooling device's tachometer signal line. A cooling device can temporarily output an encoded signal in response to a trigger event, such as a power on event (e.g., powering-on of the cooling device). A controller, such as a baseboard management controller (BMC), can receive the encoded signal and decode the signal to determine identification information about the cooling device, such as vendor information and/or model information. The identification information about the cooling device can be stored, logged, output, and/or used to customize the operation of the cooling device.

Abstract: A home appliance is disclosed. The disclosed home appliance comprises: a communication device for performing communication using a plurality of communication methods; and a processor for, when wireless identification information of a home appliance is received from an external server via a first communication method, controlling the communication device to operate in an access point (AP) mode in which communication is performed in a second communication method by using the received wireless identification information, wherein the processor is connected to a terminal device via the second communication method, and if wireless configuration information for an access point accessed by the terminal device is received from the terminal device, the processor controls the communication device to access the access point via a third communication method by using the received wireless configuration information.

Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.

Abstract: To determine system settings for an industrial system, digital twin data of a digital twin of the industrial system is retrieved. System simulations of the industrial system are performed based on the digital twin data to explore candidate system settings for the industrial system prior to application of one of the candidate system settings to the industrial system. At least one optimization objective or at least one constraint used in the system simulations is changed while the system simulations are being performed on an ongoing basis. The results of the system simulations are used to identify one of the candidate system settings for application to the industrial system.

Abstract: A computer-implemented learning method for optimizing a control policy controlling a system is provided. The method includes receiving states of the system being operated for a specific task, initializing the control policy as a function approximator including neural networks, collecting state transition and reward data using a current control policy, estimating an advantage function and a state visitation frequency based on the current control policy, updating the current control policy using the second-order approximation of the objective function, a second-order approximation of the KL-divergence constraint on the permissible change in the policy using a quasi-newton trust region policy optimization, and determining an optimal control policy, for controlling the system, based on the average reward accumulated using the updated current control policy.

Abstract: Some embodiments provide irrigation controllers comprising: a housing; a control unit including a first microcontroller configured to execute irrigation programs and a first set of code; and a removable plug-in device that removably mates with a portion of the irrigation controller and communicationally couples to the first microcontroller, wherein the plug-in device comprises a memory storing a second set of code to replace at least a portion of the first set of code, wherein the plug-in device is configured to re-flash at least a portion of the first set of code allowing a copy of the second set of code to overwrite at least the portion of the first set of code; wherein the first set of code comprises a bootloader that writes the copy of the second set of code over the first set of code with the exception of the bootloader that is not written over.

Abstract: A process is provided for the management of environmental data for tobacco production. The process includes inserting at least one sensor into a collection of tobacco leaves and connecting one or more of the sensors to a communication node for collecting data. A DNCP server is adapted to receive at least one communication from a communication node to identify a data gateway among a plurality of available gateways. Environmental data is transmitted from the sensor and communication node to the gateway for transmission to a server.

Abstract: A method of managing oilfield activity with a control system is provided having a plurality of virtual sensors and integrating the virtual sensors into a virtual sensor network. The method includes determining interdependencies among the virtual sensors, obtaining operational information from the virtual sensors, and providing virtual sensor output to the control system based on the determined interdependencies and the operational information.

Abstract: A method for optimizing production of sheet-metal parts, the production comprising cutting out and singularizing the sheet-metal parts and bending the sheet-metal parts, wherein the method includes: (A) training a neural network, which is executed on a Monte Carlo tree search framework, by means of supervised learning and self-play with reinforcement learning; (B) recording constraints for the sheet-metal parts, the constraints comprising geometric data of the sheet-metal parts; (C) creating an optimized production schedule by way of the neural network; and (D) outputting the production schedule.

Abstract: A control mechanism scheduler for a water resource infrastructure receives operating data and disturbance data, the operating data describing infrastructure components of the water resource infrastructure, the disturbance data comprising a disturbance signal describing a disturbance expected to disturb the water resource infrastructure. The control mechanism scheduler generates classes for disturbance signals, generates simulations of the water resource infrastructure, and generates schedules of setpoints for control mechanisms actuable to control the infrastructure components of the water resource infrastructure in accordance with approaching a predetermined objective.

Abstract: A model-based approach to determining an optimal configuration for a data center may use an environmental chamber to characterize the performance of various data center configurations at different combinations of temperature and altitude. Telemetry data may be recorded from different configurations as they execute a stress workload at each temperature/altitude combination, and the telemetry data may be used to train a corresponding library of models. When a new data center is being configured, the temperature/altitude of the new data center may be used to select a pre-trained model from a similar temperature/altitude. Performance of the current configuration can be compared to the performance of the model, and if the model performs better, a new configuration based on the model may be used as an optimal configuration for the data center.

Abstract: A measurement operation parameter adjustment apparatus that enables efficient measurement of the placement position of an object to be measured even in the case where there are variations in the placement positions, the sizes, and the product types of objects to be measured includes a machine learning device. The machine learning device observes measurement operation parameter data representing the measurement operation parameter of the measurement operation and measurement time data representing time taken to perform the measurement operation as a state variable representing a current environmental state and performs learning or decision-making using a learning model obtained by modeling adjustment of the measurement operation parameter based on the state variable.

Abstract: An objective of the present invention is to provide a future state estimation method and future state estimation device with which, given that the space in which the estimation is carried out is finite, it is possible to rapidly estimate the states of a controlled object and the peripheral environment thereof in the form of probability density distribution for infinite future.

Abstract: A power control unit (100) and method of use thereof for varying the supply of electricity to an electrical apparatus using a wireless communications link between a controller (20) and the power control unit (100). The power control unit (100) is adapted to alternatively communicate with the controller (20) using a non-peer-to-peer communications standard or a peer-to-peer communications standard such as Wi-Fi Direct.

Abstract: A procedure and an installation for the control of home appliances that uses only the data that is necessary, avoiding saturation of the memories of the elements involved and/or the communications bandwidth. The procedure is simple and reliable so that it does not require the intervention of an expert user or a service person. The procedure includes the standardization of the data identifiers of each home appliance by identifying the identifier that contains a value for each data point and checking that the scale of each data point is equivalent to a predetermined scale. The installation includes a standardization system for such purpose.

Abstract: A substrate processing apparatus includes a process station for processing a substrate; a cassette station integrated with the process station; a substrate carriage equipped for transferring the substrate between said process station and the cassette station through a passage located at an interface between the process station and said cassette station; and a substrate scanner equipped at said interface between the process station and the cassette station for capturing surface image data during transportation of the substrate that passes through the passage.

Abstract: A machine learning device includes a sparse modeling processing unit and a selection unit. The sparse modeling processing unit acquires individual importance degrees for each of explanatory variable candidates, the individual importance degrees being acquired by using respective sparse modeling methods different from each other, each of the sparse modeling methods taking input data including a specified objective variable in a learning model used for industrial activity and the explanatory variable candidates that are candidates for an explanatory variable for explaining the specified objective variable. The selection unit calculates a comprehensive importance degree for each of the explanatory variable candidates based on the individual importance degrees of each of the explanatory variable candidates, and selects an explanatory variable of the learning model from among the explanatory variable candidates based on the comprehensive importance degree.

Abstract: The method may comprise receiving historical data (e.g., mineralogy data, irrigation data, raffinate data, heat data, lift height data, geographic data on ore placement and/or blower data); training a predictive model using the historical data to create a trained predictive model; adding future assumption data to the trained predictive model; running the forecast engine for a plurality of parameters to obtain forecast data for a mining production target; comparing the forecast data for the mining production target to the actual data for the mining production target; determining deviations between the forecast data and the actual data, based on the comparing; and changing each of the plurality of parameters from the forecast data to the actual data to determine a contribution to the deviations for each of the plurality of parameters.

Abstract: A controller for controlling a system is provided.

Abstract: A system and a method for regulating charging and discharging of an energy storage device as part of an electrical power distribution network is described. The invention is a smart control algorithm for a bi-directional switch in which an energy storage device, such as a battery set, is charged when electricity prices are low and discharged when electricity prices are high. The invention uses two different types of pricing data: forecasted price data and real-time price data. The forecasted price data is used to set a threshold. When the real-time price data of electricity exceeds this threshold, the energy storage device is set to discharge and send power to the grid. Otherwise the energy storage device is set to charge. The threshold is set periodically, typically in 30 minute to several hour intervals to capture the latest data.