Abstract: A building management system includes building equipment operable generate training data relating to behavior of a building system and a controller configured to perform a system identification process that includes generating a prediction error function based on the training data and a system model, generating initial guesses of one or more parameters of the system model, running an optimization problem of the prediction error function for a first group of iterations, discarding, after the first group of iterations, a portion of the initial guesses based on one or more criteria and ranking a remaining portion of the initial guesses, running the optimization problem of the prediction error function for a top-ranked initial guess of the remaining portion to local optimality to identify a first set of values of the one or more parameters, and identifying the one or more parameters as having the first set of values.

Abstract: Techniques are described for implementing automated control systems for target battery systems based at least in part on battery state information gathered from active excitation of the batteries, such as to maximize battery life while performing other battery power use activities. The excitation of a target battery system may occur while it is in use, by repeatedly introducing small defined variations as input to the battery system while the battery system is otherwise used to supply or receive electricity. Corresponding small variations in output of the battery system from the excitation activities are then measured by hardware sensors, aggregated and analyzed to generate a current model of the internal state of the one or more batteries, and then used to assist in controlling further operations of the battery system, including in some cases to update a previously existing model of the battery system.

Abstract: A smart power cord with integrated sensors and wireless interfaces to manage devices in a facility such as a hotel. Each cord may monitor power delivered to an attached device, and may measure variables such as temperature, humidity, and pressure. It may also act as a gateway to other devices in the vicinity. A central control station may manage the devices in the facility via the smart power cords. Data collected from smart power cords may be analyzed to provide alerts and management reports. Illustrative alerts may warn of attempted theft when a device is unplugged, of environmental conditions that suggest mold risks or HVAC problems, or of a staff member requesting assistance via an emergency alert forwarded through a smart power cord. Locations of smart power cords may be automatically configured using either cooperative or non-cooperative localization based on measurement of signal characteristics such as RSSI.

Abstract: Systems and methods of configuring process control systems for operating process plants including multi-variable devices are disclosed herein. Multi-variable devices generate data associated with primary parameters and subsequent parameters. To facilitate configuration and use of subsequent parameters within process control systems, new device objects may be generated to represent the subsequent parameters. The new device objects may be generated as child device objects of parent device objects representing the multi-variable devices. Each child device object may also have a unique tag to identify the new device object within the process control system. The subsequent parameters of the multi-variable devices may thus be used within the process control system by reference to the tags of the corresponding child device objects.

Abstract: A method and system for optimizing the operation of a geo-exchange system, by generating predictive models pertaining to energy demand and energy capacity for a particular building or district, based on data from sensors associated with components of a district geo-exchange system, historical and real-time operational data associated with district modules, including weather forecast data and current weather conditions.

Abstract: A system on chip (SoC) includes an internal read-only memory (ROM) configured to store a first boot loader; a first internal static random access memory (SRAM) configured to receive a second boot loader output from a booting device, store the second boot loader, and perform a booting sequence according to control of the first boot loader; a second internal SRAM configured to receive a third boot loader output from the booting device, store the third boot loader, and perform a wake-up sequence according to control of the first boot loader; and a dynamic random access memory (DRAM) controller configured to load an operating system (OS) from the booting device into a DRAM according to control of the second boot loader.

Abstract: A method for controlling loads of a wind turbine includes receiving sensor signals from one or more sensors being indicative of a movement of a nacelle of the wind turbine from a reference point. More particularly, the movement corresponds, at least, to a tilt and/or a displacement of the wind turbine tower and/or nacelle. The method also includes generating a deflection profile of the tower along its overall length from a bottom end to a top end thereof based on the sensor signals. Further, the method includes determining at least one of a thrust or a tower load of the wind turbine from the deflection profile. In addition, the method includes implementing a control action for the wind turbine based on the thrust and/or the tower load.

Abstract: A cart that moves through a three-dimensional object printing system includes a platform and a cleaning device configured to remove material from a surface as the platform passes the surface. The cart further includes a rechargeable power supply configured to be connected to the cleaning device. A controller onboard the cart is operatively connected to the rechargeable power supply and the cleaning device. The controller is configured to connect the rechargeable power supply to the cleaning device and operate the cleaning device to clean the surface.

Abstract: A controller configured to communicatively couple to at least two sets of equipment. At least one set of equipment includes heating, ventilation, and air conditioning (HVAC) equipment, refrigeration equipment, or building automation equipment. For each set of equipment, the controller determines an operation schedule for the respective set of equipment. The controller also communicates the operation schedule to the respective set of equipment, for each set of equipment. The controller may receive a request to perform mass scheduling. The request indicates which sets of equipment and which settings of the operation schedule are to be configured using the mass scheduling. The controller may also communicate the same values for the settings that the request indicates to configure using the mass scheduling to each set of equipment that the request indicates to configure using the mass scheduling.

Abstract: A system and method are described for virtualizing Internet of Things (IoT) devices and hubs.

Abstract: An equipment management system including a controller and a server. The controller communicatively couples to one or more remote systems. At least one of the remote systems includes a heating, ventilation, and air conditioning (HVAC) system, a refrigeration system, or a building automation system. The controller receives telemetry data associated with the operation of the one or more remote systems and with individual components of the one or more remote systems. The controller communicates at least a portion of the received telemetry data to the server. The server determines one or more operational commands for the one or more remote systems. The one or more operational commands are determined based on the telemetry data communicated from the controller to the server. The server communicates the one or more operational commands to the controller. The controller communicates the one or more operational commands to the one or more remote systems.

Abstract: According to one embodiment, a data collection system includes an event data collector, a state machine generator, a state machine list, and a state machine driver. The event data collector collects sense signals respectively as a plurality of event data. The state machine generator generates a state machine as a model corresponding to the workpiece. One of the sense signals is acquired when the workpiece is fed into the processing system. The state machine generator generates the state machine and generates an ID for the state machine when the event data collector collects one of the plurality of event data corresponding to the one of the sense signals. The state machine driver drives the state machine retained in the state machine list by sending, to the state machine retained in the state machine list, an event corresponding to another one of the sense signals.

Abstract: A control parameter is adjusted without the need to operate a driver of a control target. A control device (10) includes a model identification unit (12), a parameter determination unit (132), a data evaluation unit (131) and an operation data calculation unit (133). The model identification unit (12) identifies a physical model of an operation of the control target. The operation data calculation unit (133) calculates operation data by using the physical model and the control parameter. The data evaluation unit (131) calculates an evaluation value by using normative data of the operation of the control target and the operation data. The parameter determination unit (132) determines the control parameter by using the evaluation value.

Abstract: A method for controlling operation of a wind turbine includes collecting training data, training a machine learning model, obtaining recent data, and applying the machine learning model the recent data to output a reference power or reference power differential corresponding to the recent data. The machine learning model is then applied to the recent data to output at least one of estimated power or estimated power differential corresponding to values of the pitch setpoints and the tip speed ratio setpoints which differ from the recent data. A turbine setting is determined by comparing the estimated power or estimated power differential to the reference power or reference power differential, and then applying the turbine setting to the wind turbine if the estimated power or estimated power differential is greater than or equal to a threshold amount above the reference power or reference power differential.

Abstract: Environmental characteristics of habitable environments (e.g., hotel or motel rooms, spas, resorts, cruise boat cabins, offices, hospitals and/or homes, apartments or residences) are controlled to eliminate, reduce or ameliorate adverse or harmful aspects and introduce, increase or enhance beneficial aspects in order to improve a “wellness” or sense of “wellbeing” provided via the environments. Control of intensity and wavelength distribution of passive and active Illumination addresses various issues, symptoms or syndromes, for instance to maintain a circadian rhythm or cycle, adjust for “jet lag” or season affective disorder, etc. Air quality and attributes are controlled. Scent(s) may be dispersed. Noise is reduced and sounds (e.g., masking, music, natural) may be provided. Environmental and biometric feedback is provided. Experimentation and machine learning are used to improve health outcomes and wellness standards.

Abstract: A method of selecting an instrument set for an orthopedic implant procedure. The method utilizes a computer programmed with a data structure configured to store (i) a set of implant components and implant instruments; and (ii) a set of surgical instruments utilized in performing the procedure. The method selects a sub-set of implant components and implant instruments based upon a determination of component types and then selects a sub-set of surgical instruments based upon a determination of surgical techniques. The method arranges the sub-set of surgical instruments and implant instruments into a substantially specific order based upon a determination of a sequence of bone cuts to be performed in the procedure.

Abstract: Provided is a processing apparatus where the exchange of an operating program for an apparatus body is not required each time a jig unit is exchanged. The processing apparatus includes an apparatus body and a jig unit. A master control device is mounted on the jig unit. Operations of the jig unit and the apparatus body are controlled by an operating program in the master control device.

Abstract: A control method for dynamically controlling active and reactive power capability of a wind farm includes obtaining one or more real-time operating parameters of each of the wind turbines. The method also includes obtaining one or more system limits of each of the wind turbines. Further, the method includes measuring at least one real-time wind condition at each of the wind turbines. Moreover, the method includes continuously calculating an overall maximum active power capability and an overall maximum reactive power capability for each of the wind turbines as a function of the real-time operating parameters, the system limits, and/or the real-time wind condition. Further, the method includes generating a generator capability curve for each of the wind turbines using the overall maximum active and reactive power capabilities and communicating the generator capability curves to a farm-level controller of the wind farm that can use the curves to maximize the instantaneous power output of the wind farm.

Abstract: An industrial machinery includes: a drive mechanism driving a control target that moves work or a tool; a motor; a first sensor detecting a position of the control target; a second sensor detecting a position of the motor; a current controller controlling a supply current to the motor; a servo controller outputting a torque instruction to the current controller; and a numerical controller calculating a processing force of the control target to the work based on position information on the control target acquired from the first sensor, position information on the motor acquired from the second sensor, and the torque instruction, the numerical controller determining that the tool is in failure if an absolute value of a first component of the processing force becomes equal to or larger than a first threshold value while processing the work, the first component having a frequency lower than a predetermined frequency.

Abstract: Aspects of the present invention include a device including processing circuitry that detects a first user action according to a first signal received from a first sensor while operating in a first mode, the first user action being gripping and lifting up of the device, changes an operation mode from the first mode to a second mode based on the first user action, detects a second user action according to a second signal received from a second sensor while operating the second mode, the second user action being a touch operation, performs an operation based on the second user action, and changes the operation mode from the second mode to a third mode after a predetermined period of time elapses, the second mode consuming more power than the first mode, and the third mode consuming more power than the first mode.