Abstract: Apparatus, systems, methods, and related computer program products for managing demand-response programs and events. The systems disclosed include an energy management system in operation with an intelligent, network-connected thermostat located at a structure. The thermostat controls an HVAC system to cool the structure using a demand response event implementation profile over the demand response event period. The thermostat can also receive a requested change to the setpoint temperatures defined by the demand response event implementation profile and access a determination of an impact on energy shifting that would result if the requested change is incorporated into the demand response event implementation profile. This determination can be communicated to the energy consumer.

Abstract: A system including a deep learning processor obtains response data of at least two data types from a set of process stations performing operations as part of a manufacturing process. The system analyzes factory operation and control data to generate expected behavioral pattern data. Further, the system uses the response data to generate actual behavior pattern data for the process stations. Based on an analysis of the actual behavior pattern data in relation to the expected behavioral pattern data, the system determines whether anomalous activity has occurred as a result of the manufacturing process. If it is determined that anomalous activity has occurred, the system provides an indication of this anomalous activity.

Abstract: A system to supply emergency power to an elevator includes one or more lithium batteries, an inverter coupled to the lithium batteries, a battery management system (BMS) coupled to the inverter, an Energy Management Software (EMS) coupled to the BMS, and a relay coupled to utility power, wherein the relay operates to provide utility power to the system and notifies the EMS that utility power is on, wherein the EMS initiates charging of the BMS system, wherein the BMS sends charging command to the inverter, which operates until the state of charge is 100%, where the inverter goes into a standby mode.

Abstract: Systems and methods for testing a power plant emulation are disclosed. Software for an energy management system is deployed to a computer system comprising computing devices. A model of a power plant site is deployed to a test framework. Test commands and/or use cases are injected, via the test framework to public energy management system end points. The test commands and/or use cases are used to emulate and test the model of at least the portion of the power plant site and to generate simulated operating parameter values. The generated simulated operating parameter values of the model are compared with expected values. A determination is made as to whether the model passed testing using the comparison. After determining the model passed testing, software, corresponding to at least a portion of the model, is made available for deployment to operate a physical version of the power plant site.

Abstract: Apparatus and methods for automatically testing mobile devices are disclosed according to various embodiments. In one example, a disclosed apparatus includes: a robot having a retention device into which the mobile device to be tested is positioned; a test computer having a processor and a non-transitory computer readable storage medium storing test software for testing the mobile device; and a user monitor electrically connected to the test computer and configured for providing a result of the testing of the mobile device. The mobile device is wirelessly connected to the test computer and has a test application installed thereon corresponding to the test software. The robot is configured for performing interaction and manipulation of the mobile device in cooperation with the test application and the test software during the testing.

Abstract: Systems and methods for managing power on an electric power grid including a server for communicating IP-based messages over a network with distributed power consuming devices and/or power supplying devices, the IP-based messages including information including a change in state of the power consuming device(s), a directive for a change in state of the power consuming device(s), a priority message, an alert, a status, an update, a location with respect to the electric power grid, a function, device attributes, and combinations thereof.

Abstract: A system including a deep learning processor obtains response data of at least two data types from a set of process stations performing operations as part of a manufacturing process. The system analyzes factory operation and control data to generate expected behavioral pattern data. Further, the system uses the response data to generate actual behavior pattern data for the process stations. Based on an analysis of the actual behavior pattern data in relation to the expected behavioral pattern data, the system determines whether anomalous activity has occurred as a result of the manufacturing process. If it is determined that anomalous activity has occurred, the system provides an indication of this anomalous activity.

Abstract: A system including a deep learning processor receives one or more control signals from one or more of a factory's process, equipment and control (P/E/C) systems during a manufacturing process. The processor generates expected response data and expected behavioral pattern data for the control signals. The processor receives production response data from the one or more of the factory's P/E/C systems and generates production behavioral pattern data for the production response data. The process compares at least one of: the production response data to the expected response data, and the production behavioral pattern data to the expected behavioral pattern data to detect anomalous activity. As a result of detecting anomalous activity, the processor performs one or more operations to provide notice or cause one or more of the factory's P/E/C systems to address the anomalous activity.

Abstract: A method for adjusting a blend composition includes: receiving target blend composition data associated with a target blend composition having a target blend property; controlling blend component flow streams to cause blend components to be added to a blend tank to form a blend composition; determining a property of the blend composition; comparing the property of the blend composition with the target blend property; generating control flow instructions based on the property of the blend composition and a machine learning analysis of historical blend data; and adjusting a blend component flow stream of the blend component flow streams to form a modified blend composition in the blend tank.

Abstract: A method for optimizing a medical procedure room set up comprises obtaining a medical procedure room setup input associated with a medical practitioner identifier, a medical procedure identifier, and a medical procedure room identifier for the medical procedure room set up. A virtual image guidance for the medical procedure room set up based on the received input is provided. The method comprises acquiring data associated with one or more of item, equipment, and personnel in the medical procedure room setup and analyzing prestored data associated with item, equipment, and personnel in one or more reference medical procedure room setups corresponding to the received medical procedure room setup input. The method comprises comparing the acquired data with the analyzed prestored data associated with the respective item, equipment, and personnel in the one or more reference medical procedure room setups and providing recommendation for optimizing the medical procedure room set up.

Abstract: A 3D printer is disclosed herein. The 3D printer comprises a build material distributor, an agent delivery device and a controller. The controller obtains slice image data of a sliced virtual build volume including a 3D object to be generated, where the slice image data is indicative, for each slice, of the portions of corresponding build material layers to be solidified. The controller is to determine print data corresponding to each slice, the print data defining a density of printing agent to be applied to regions of each layer based on the slice image data; and to obtain additional data relating to a portion of a slice that corresponds to a sloped portion of the 3D object. The controller is further to modify the determined print data to modify the density of printing agent to be printed at a portion of a given build material layer based on the additional data.

Abstract: System and methods are disclosed to charge an electric vehicle (EV) and manage grid power consumption. The system includes a building switchgear coupled to a building meter; an independent system operator (ISO) accepted meter coupled to the building switchgear, the ISO meter including a telemetry unit to communicate with an ISO; and a battery energy storage system (BESS) coupled to the building switchgear, and an ISO or System Performance Meter, wherein the BESS selectively provides power in response to a customer power demand or an EV charging request to prevent a customer grid power consumption from spiking and peaking at grid imbalance highest cost on peak times.

Abstract: A method for controlling an output voltage of at least one transformer in a power grid comprises providing the one transformer(s) with a power electronics converter and an output voltage controller, determining a grid frequency, comparing the grid frequency with a reference value, producing an error signal, applying the error signal as feedback to the controller, and generating a control action to cause the controller to change the output voltage of the transformer comprising a power electronics converter. The power electronics converter is an AC-AC converter, and the method comprises connecting the AC-AC converter to either a primary side or a secondary side of the transformer. The AC-AC converter has a first AC side and a second AC side and the method comprises connecting two terminals on the first AC side to a winding of the transformer, and connecting the second AC side to an external circuit line.

Abstract: Embodiments relate to the methodology of direct manufacture of a customized labial/lingual orthodontic tube by using a ceramic slurry-based additive manufacturing (AM) technology. For example, a method of manufacturing customized ceramic labial/lingual orthodontic tubes by additive manufacturing may comprise measuring dentition data of a profile of teeth of a patient, based on the dentition data, creating a three-dimensional computer-assisted design (3D CAD) model of the patient's teeth, and saving the 3D CAD model, designing a virtual 3D CAD tube structure model for a single labial or lingual tube structure based upon said 3D CAD model, importing data related to the 3D CAD tube structure model into an additive manufacturing machine, and directly producing the tube with the additive manufacturing machine by layer manufacturing from an inorganic material including at least one of a ceramic, a polymer-derived ceramic, and a polymer-derived metal.

Abstract: A system including a deep learning processor receives one or more control signals from one or more of a factory's process, equipment and control (P/E/C) systems during a manufacturing process. The processor generates expected response data and expected behavioral pattern data for the control signals. The processor receives production response data from the one or more of the factory's P/E/C systems and generates production behavioral pattern data for the production response data. The process compares at least one of: the production response data to the expected response data, and the production behavioral pattern data to the expected behavioral pattern data to detect anomalous activity. As a result of detecting anomalous activity, the processor performs one or more operations to provide notice or cause one or more of the factory's P/E/C systems to address the anomalous activity.

Abstract: A method (600) for configuring one or more components of a process plant includes receiving (602) a change request (226) and receiving (604) a system dependency graph (228) corresponding to the process plant. The method (600) further includes selecting (606) a subset of components (230) among the plurality of components based on configuration of the process plant and identifying (608) a subset of nodes (232) among the plurality of nodes by traversing a path in the system dependent graph (228). The method (600) also includes computing (610) an impact parameter (234) value based on a traversed path and computing (612) a plurality of change parameter values (236) based on the traversed path. The method (600) further includes determining (614) an effort estimate (210) based on the impact parameter (234) value and the plurality of change parameter values (236) using a machine learning technique.

Abstract: A manufacturing system and method obtain a first digital model of teeth, create a modified digital model of the teeth by adding a locator feature to the first digital model of the teeth, and create a physical model of the teeth based on the modified digital model. A portion of the physical model is created based on the locator feature. A dental aligner device is formed on the physical model of the teeth with extraneous material of the dental aligner device extending away from the dental aligner device. The extraneous material is removed from the dental aligner device from the physical model of the teeth. The forming of the dental aligner device and/or the removing of the extraneous material is performed by positioning the physical model using the locator feature.

Abstract: Systems and methods of managing power distribution in a portion of an electrical power grid with at least one power storage, including: receiving at least one power consumption rule from at least one consumer of the power grid, analyzing power consumption data from at least one power consumption meter connected to the power grid, applying the at least one power consumption rule on the analyzed power consumption data, based on forecasted data, and managing power consumption for the at least one consumer, based on the result of the at least one power consumption rule, and also based on a power capacity status of the at least one power storage.

Abstract: The management device includes a management control section configured to set attachment position setting information including an attachment position of a feeder in a tendency such that the feeder is to be replaced in association with production switching to be attached to an attachment portion closer to a storage section based on production plan information including multiple pieces of mounting condition information.

Abstract: A system including a deep learning processor obtains response data of at least two data types from a set of process stations performing operations as part of a manufacturing process. The system analyzes factory operation and control data to generate expected behavioral pattern data. Further, the system uses the response data to generate actual behavior pattern data for the process stations. Based on an analysis of the actual behavior pattern data in relation to the expected behavioral pattern data, the system determines whether anomalous activity has occurred as a result of the manufacturing process. If it is determined that anomalous activity has occurred, the system provides an indication of this anomalous activity.