Abstract: Systems and methods are provided for the robust, multivariable control of a power generating asset via H-infinity loop shaping using coprime factorization. Accordingly, a controller of the power generating asset computes a gain value for an H-infinity (H?) module in real-time at predetermined sampling intervals using an actuator dynamic model. The controller then determines an acceleration factor based, at least in part, on the gain value of the H? module. Based, at least in part on the acceleration vector, the controller generates a control vector. An operating state of at least one component of the power generating asset is changed based on the control vector.

Abstract: A method for controlling the heating operation of an air conditioner.

Abstract: Systems and methods for training a surrogate model for predicting system states for a building management system based on generated data from a system identification model are disclosed herein. The system identification model is used to generate predicted system parameters of a zone of the building based on historic data from operation of the building equipment. The surrogate model is trained based on the predicted system parameters from the system identification model. Predicted future parameters of the variable state of the building are generated using the surrogate model. The surrogate model is re-trained based on new operational data from the building equipment. An updated series of predicted future parameters is generated using the re-trained surrogate model.

Abstract: According to an aspect, there is provided method for commissioning a drive. First, a local network is established between a wireless computing device, one or more wireless sensor devices and a drive. Each wireless sensor device is configured to measure one or more physical quantities associated with an industrial system controlled by the drive. The wireless computing device configures wirelessly the drive and the one or more wireless sensor devices for performing measurements. In response to receiving one or more measured values from the one or more wireless sensor devices, the wireless computing device displays the information on the one or more measured values on a display for providing guidance for a user of the wireless computing device for adjusting drive parameters of the drive.

Abstract: [Problem] To more easily and effectively absorb a difference in motion characteristic between a simulator model and a real robot. [Solution] Provided is an information processing device including a communication unit that receives a movement result of an autonomous moving body based on a control command value, and a parameter approximation unit that approximates a motion characteristic parameter of a simulator model for a movement simulation of the autonomous moving body on the basis of a movement result of the autonomous moving body, in which the parameter approximation unit approximates the motion characteristic parameter on the basis of similarity between a plurality of simulation results acquired on the basis of the different motion characteristic parameters in the movement simulation based on the control command value, and the movement result of the autonomous moving body.

Abstract: According to one or more embodiments of the disclosure, a device in a network obtains parameters for entropy testing of industrial equipment that controls a physical process. Entropy is added to commands sent to the industrial equipment during the entropy testing. The device receives packets that were generated during the entropy testing of the industrial equipment and include sensor data regarding the physical process. The device determines whether the sensor data is inconsistent by analyzing the sensor data using a machine learning model that models the physical process. The device initiates a corrective measure, when the sensor data is determined to be inconsistent.

Abstract: According to the present invention, an input operation unit receives an input related to generation and editing of a ladder diagram program. A partial circuit search unit searches and extracts a partial circuit corresponding to a ladder circuit of an inputted command name when the input operation unit receives the command name of the ladder circuit to be inserted into the ladder diagram program. A display unit outputs an input candidate corresponding to the partial circuit extracted by the partial circuit search unit. A replacement processing unit receives selection of the input candidate outputted from the display unit, and replaces, with the selected input candidate, the ladder circuit, the command name of which has been inputted by the input operation unit.

Abstract: Disclosed are systems and methods of generating electrical power utilizing a mining bank of individual blockchain miners. The blockchain miners operate as a revenue generating heat source that provides thermal energy into a power plant's Rankine or Brayton cycle turbines.

Abstract: A system for a semiconductor fabrication facility includes a manufacturing tool including a load port, a maintenance crane, a rectangular zone overlapping with the load port of the manufacturing tool, a plurality of first sensors at corners of the rectangular zone, an OHT vehicle, a second sensor on the OHT vehicle, a third sensor on the load port, and a control unit. The first sensors are configured to detect a location of the maintenance crane and to generate a first location data. The second sensor is configured to generate a second location data. The control unit is configured to receive the first location data of the maintenance crane and the second location data of the OHT vehicle. The control unit further sends signals to the second sensor and the third sensor or to cut off the signal to the second sensor.

Abstract: Disclosed is an electronic apparatus capable of managing cooling or heating and a controlling method thereof capable of operating even in the Internet of Things environment through a 5G communication network, and the electronic apparatus of the present disclosure can intensively control cooling or heating in an area where users are dense. The electronic apparatus capable of managing cooling or heating of the present disclosure can acquire a space where the user is located as a 3D structure, learn a distribution of the user distributed in the acquired 3D structure, and then control cooling or heating intensively in the area where the user can be distributed, thereby enabling efficient cooling or heating based on an area requiring the cooling or heating.

Abstract: A computer-implemented method is provided of controlling a heating, ventilation, and air-conditioning (HVAC) system having a heat pump and an auxiliary heater to heat an inside zone, the method comprising: setting values for an economic balance point and a temperature set point; setting a capacity balance point to be equal to the economic balance point; measuring an initial inside temperature in the inside zone and an outside temperature outside of a structure containing the inside zone; determining that the initial inside temperature is lower than the temperature set point; determining that the outside temperature is greater than the capacity balance point; activating the heat pump to heat the inside zone; waiting a heating interval while the heat pump is active; determining that the heat pump is not effective in heating the inside zone; and setting the capacity balance point to be equal to the outside temperature.

Abstract: A method and system for set point control of a liquid level in a conical tank is disclosed. The method includes receiving set point signal, R(s), summing the set point signal, R(s), with a negative feedback signal, d{tilde over (f)}(s), and generating set point error signal, E(s), which is used as feedback at the input to drive the liquid level in the conical tank to a desired set point, thus controlling the liquid level of conical tank so that external disturbance, df(s), is minimized.

Abstract: Certain aspects of the present disclosure relate to a system including a first active control device, comprising: a flow control element; one or more sensors; a network interface configured to connect to a mesh network; a memory comprising computer-executable instructions; and a processor configured to: execute the computer-executable instructions; receive local sensor data from the one or more sensors; receive remote sensor data from a remote sensing device; control a position of the flow control element based on one or more of the local sensor data or the remote sensor data; store the local sensor data and remote sensor data in the memory; and transmit the local sensor data and the remote sensor data to a second active control device via the mesh network.

Abstract: A system for tool monitoring includes one or more tools of an energy industry system, the one or more tools configured to be disposed in one or more boreholes in one or more resource bearing formations, and one or more sensors connected to each tool of the one or more tools, each sensor of the one or more sensors configured to measure at least one parameter related to performance of each tool of the one or more tools. The system also includes a processing device configured to collect measurements of the at least one parameter from each sensor, generate an individual performance history record for each tool, and store the individual performance history record for each tool in a database configured to store individual performance history records for a plurality of tools in one or more energy industry systems.

Abstract: Systems and methods are disclosed for control voltage profiles, line flows and transmission losses of a power grid by forming an autonomous multi-objective control model with one or more neural networks as a Deep Reinforcement Learning (DRL) agent; training the DRL agent to provide data-driven, real-time and autonomous grid control strategies; and coordinating and optimizing power controllers to regulate voltage profiles, line flows and transmission losses in the power grid with a Markov decision process (MDP) operating with reinforcement learning to control problems in dynamic and stochastic environments.

Abstract: A heating, ventilation, and air conditioning (HVAC) control device configured to receive a user input for controlling an HVAC system, to determine whether the user input indicates an energy saving occupancy setting, and to identify a first plurality of time entries that are associated with a confidence level for a predicted occupancy status that is less than a predetermined threshold value in the predicted occupancy schedule. The device is further configured to modify the predicted occupancy schedule by setting the first plurality of time entries to an away status when the user input indicates an aggressive energy saving occupancy setting. The device is further configured to modify the predicted occupancy schedule by setting the second plurality of time entries to a present status when the user input indicates a conservative energy saving occupancy setting. The device is further configured to output the modified predicted occupancy schedule.

Abstract: Controlling heating and cooling in a conditioned space utilizes a fluid circulating in a thermally conductive structure in fluid connection with a hydronic-to-air heat exchanger and a ground heat exchanger. Air is moved past the hydronic-to-air heat exchanger, the air having fresh air supply and stale air exhaust. Sensors located throughout the conditioned space send data to a controller. User input to the controller sets the desired set point temperature and humidity. Based upon the set point temperature and humidity and sensor data, the controller sends signals to various devices to manipulate the flow of the fluid and the air in order to achieve the desired set point temperature and humidity in the conditioned space. The temperature of the fluid is kept less than the dew point at the hydronic-to-air heat exchanger and the temperature of the fluid is kept greater than the dew point at the thermally conductive structure.

Abstract: A method and a system for detecting anomalies in a water distribution system comprises a network of nodes, and is equipped with sensors of at least water velocity at a subset of the nodes. The water distribution system is modeled by a hydraulic model. The method comprises parametrizing the hydraulic model with initial values of a set of control variables, using the sensors to obtain values of state variables of the network at the nodes, using the hydraulic model to calculate predicted values of state variables, recursively calculating the values of control variables which, applied to the hydraulic model, permit to obtain the predicted values of state variables the closest to the observed values, and classifying nodes of the network based on the values of control variables.

Abstract: A control system includes a programmable logic control section controlling operation of a machine and a numerical control section controlling relative motion between a tool of the machine and a work piece. A method, performed in the control system, includes: evaluating an input signal, received by the programmable logic control section, in relation to a first condition, wherein the input signal includes information about a state of the tool or of a subtractive process performed via interaction of the tool and the work piece; and in response to the input signal satisfying the first condition, providing the information to the numerical control section. The state may for example be tool breakage, tool wear or wrong cutting data. An operator of the machine may for example specify via programs in the numerical control section how the machine is to respond to such states.

Abstract: In order to control a technical system by means of control model a data container is received, in which data container a control model having a training structure and model type information are encoded over all the model types. One of multiple model-type specific execution modules is selected for the technical system as a function of the model type information. Furthermore, operating data channels of the technical system are assigned input channels of the control model as a function of the model type information. Operating data of the technical system are acquired via a respective operating data channel and are transferred to the control model via an input channel assigned to this operating data channel. The control model is executed by means of the selected execution module, wherein control data are derived from the transferred operating data according to the training structure and are output to control the technical system.