Abstract: A system for producing articles from glass tube includes a converter having a base with a plurality of processing stations and a turret moveable relative to the base. The turret indexes a plurality of holders for holding the glass tubes successively through the processing stations. The system further includes a thermal imaging system that includes a thermal imager coupled to the turret for movement with the turret. The thermal imaging system may also include a mirror coupled to the thermal imager and positioned to reflect infrared light from one of the plurality of holders to the thermal imager. The thermal imaging system may measure one or more characteristics of the glass tube during the conversion process. Processes for controlling the converter using the thermal imaging system to measure one or more process variables are also disclosed.

Abstract: The present invention relates to a computer implemented method (600) for optimization of a bioprocessing system (100) formed by interconnected or interconnectable bioprocessing units and configured to provide a desired system functionality, the method comprising obtaining (610) requirement data of a bioprocess, the requirement data being at least indicative of the desired system functionality, and indicative of a desired substance resulting from the operation of the bioprocess system, obtaining (620) unit data indicative of characteristics of a plurality of bioprocessing units, generating (630) at least one optimized or suitable bioprocessing system configuration capable to provide the desired system functionality, the bioprocessing system configuration comprising a selection of bioprocessing units from the plurality of bioprocessing units selected using the requirement data, the unit data and an objective function dependent on the requirement data and the unit data.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to surface data pipelines in an industrial automation environment. In some examples, a design component generates a control program configured for implementation by a programmable logic controller to control an industrial process. The design component adds program tags to the control program and implements the control program through the programmable logic controller. The design component establishes data pipelines that correspond to the program tags in the control program between data sources associated with the program tags and a machine learning system that consumes process data generated by the data sources. A pipeline management component generates a pipeline suggestion that indicates individual ones of the data pipelines and their corresponding program tags.

Abstract: The present disclosure provides a feedback controller and method for controlling an operation of a device at different control steps. The feedback controller comprises at least one processor, and the memory having instructions stored thereon that, when executed by the at least one processor, causes the feedback controller, for a control step, to collect a measurement indicative of a state of the device at the control step, and execute, recursively until a termination condition is met, a probabilistic solver parameterized on a control input to an actuator operating the device to produce a control input for the control step. The feedback controller is further configured to control the actuator operating the device based on the produced control input.

Abstract: Various devices, such as storage devices or storage systems are configured to recycle heat generated during normal operation. This recycled heat can be captured and converted into electricity that can be stored for later discharge and use. The recycling can be done through a variety of energy recycling modules that can be placed on various components within a storage device. These energy recycling modules can be a series of semiconductors that utilize various natural effects to convert heat applied on one side of the module into electricity that can be delivered to various power storing components or used in other areas of the storage device or system. These energy recycling modules can include versions that act as a generator of electricity by capturing heat, or as a component that actively cools via received power. These modules can be placed on heat-generating components within a storage device, such as a processor.

Abstract: The computing device may be configured to automatically determine curated configuration settings for a scene that is configured to control one or more zones. Each of the zones may be assigned a respective zone purpose. Each zone may be assigned at least one load control device that is configured to control a corresponding electrical load. Each of the one or more zones may be assigned a control type, which may be based on a respective product type and/or load type of the load control devices/electrical loads within each zone. The computing device may be configured to display one or more graphical user interfaces that a user of the computing device may interact with to select a curated configuration option for configuring the scene. The computing device may be configured to automatically determine curated configuration setting for the scene based on the selected curated configuration option and/or the zone purpose.

Abstract: A method for controlling a touchless lavatory is disclosed herein. The method includes forming one or more groups of sensors from a plurality of sensors, determining that a trigger event is detected from the first sensor, and identifying a first group of sensors of the one or more groups of sensors to which the first sensor belongs. The method further includes requesting an ambient light measurement from each of the plurality of sensor. The method further includes computing an average ambient light value based on the ambient light measurement, comparing the ambient light measurement of the first sensor to the average ambient light value of all the sensors, determining that the ambient light measurement of the first sensor is less than the average ambient light value of all the sensors, and determining that the first sensor trigger event is valid.

Abstract: A training method of a semiconductor process prediction model, a semiconductor process prediction device, and a semiconductor process prediction method are provided. The training method of the semiconductor process prediction model includes the following steps. The semiconductor process was performed on several samples. A plurality of process data of the samples are obtained. A plurality of electrical measurement data of the samples are obtained. Some of the samples having physical defects are filtered out according to the process data. The semiconductor process prediction model is trained according to the process data and the electrical measurement data of the filtered samples.

Abstract: A method for monitoring a first machine and a second machine is described. The machines are rotating machines. Sensor devices are coupled to the machines. The method includes synchronizing a first sensor device and a second sensor device; sensing, by the first sensor device, an operating parameter of the first machine; and sensing, by the second sensor device, an operating parameter of the second machine. The sensing of the second operating parameter is synchronized to the sensing of the first operating parameter. The method further includes broadcasting, by the first sensor device, a data packet comprising the sensed operating parameter of the first machine; receiving, by the second sensor device, the data packet sent by the first sensor device; and calculating, by the second sensor device, a performance indicator from the received operating parameter of the first machine and the sensed operating parameter of the second machine.

Abstract: A wafer defect test apparatus in which a defect prediction performance is improved and a simulation time is shortened is provided.

Abstract: Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to visualize machine learning model status in an industrial automation environment. In some examples, a machine learning component receives process inputs associated with industrial devices in the industrial automated environment. The machine learning component processes the inputs to generate machine learning outputs and transfers the machine learning outputs to influence one or more functions of the industrial devices. The machine learning component reports operational data characterizing the machine learning outputs. A Human Machine Interface (HMI) component displays a visualization of the machine learning component and receives the operational data from the machine learning component.

Abstract: Methods and systems for thermal management of hardware resources are disclosed. To improve the likelihood of the computer implemented services being provided, the systems may include information transmission topologies that are more likely to properly transmit operating point information (e.g., actual fan rates, rates at which fans are to operate, etc.) between management entities such as thermal managers and entities managed by the thermal managers such as fans (and/or other types of gas flow control components). By doing so, the system may be more likely to provide thermal management services as intended by using the transmitted information that is more likely to be accurate.

Abstract: A method is for status monitoring at least one of two or more current-carrying electrical components, suppliable with electrical energy from an electrical energy source via a common current feed line and from the common current feed line in each case via a separate current supply line, individually openable or closable. In an embodiment, the method includes detecting measured values of an electrical variable in the common current feed line before and after opening or closing one or a plurality of the current supply lines; calculating a difference between a measured value detected before opening or closing and a measured value detected after opening or closing; and determining a status change of at least one of the two or more components based upon a time series of the difference.

Abstract: A robotic system comprising an end effector-mounted sensor is disclosed. In various embodiments, a robotic arm is manipulated to move a sensor to a position such that an object of interest is within a read range of the sensor. A sensor data read by the sensor is received via a communication interface. The sensor data is used to determine an attribute of the object; and the determined attribute of the object is used to determine a plan to grasp and move the object.

Abstract: An offline programming device includes an input unit that receives input of a plurality of teaching points, a creation unit that determines intermediate point located between adjacent teaching points and creates an operation program for the robot, a simulation unit that simulates a movement trajectory of the robot when the operation program is executed, and a display unit that displays a GUI screen representing the movement trajectory. The GUI screen includes a first display area showing a time series sequence of the plurality of teaching points and a second display area. When an error is detected in the movement trajectory, a section between the teaching points including the point in time when the error occurs is displayed in the first display area according to a first error display method.

Abstract: A method includes initiating a connection with a semiconductor manufacturing system. The method further includes generating a set of tool data items associated with the semiconductor manufacturing system. The method further includes providing a graphical user interface (GUI) presenting the set of tool data items associated with the semiconductor manufacturing system and receiving and via the GUI, user input selecting one or more of the tool data items. The method further includes adding configuration data associated with one or more of the tool data items to a data collection plan. The method further includes validating the configuration data by locating, in the manufacturing system, a configuration data file associated with the configuration data. The method further includes executing one or more data collection operations based on the data collection plan.

Abstract: The present application provides an optical system and a method of operating an overlay measuring apparatus. The overlay measuring apparatus is adapted to determine relative positions of two or more successive patterned layers of a device. The overlay measuring apparatus includes a stage and an imaging assembly. The device is placed on the stage. The imaging assembly includes a plurality of optical heads and a plurality of overlay marks assembled on the optical heads. The relative positions of the two or more successive patterned layers of the device are determined using light reflected from the device and passing through the overlay mark mounted on the respective optical head.

Abstract: A method and device for controlling an air conditioner, and a computer readable storage medium. The method for controlling an air conditioner includes: upon detecting a power-on or power-off command corresponding to an internal unit of a central air conditioner, acquiring a current predicted load of the central air conditioner; determining whether the predicted load meets an external unit change condition; and if so, adjusting an operating state of an external unit of the central air conditioner on the basis of the predicted load.

Abstract: Various embodiments of the present technology generally relate to solutions for improving industrial automation programming and data science capabilities with machine learning. More specifically, embodiments include systems and methods for implementing machine learning engines within industrial programming and data science environments to improve performance, increase productivity, and add functionality. In an embodiment, a system comprises a machine learning-based analysis engine configured to perform an analysis of operational data from an industrial automation environment. The analysis engine is further configured to perform an analysis of control logic and identify, based on the analysis of the operational data and the analysis of the control logic, a variable that is in the control logic but is not used in the operational data. The system further comprises a notification component configured to surface a notification that the variable is in the control logic but is not used in the operational data.

Abstract: An extension device for one or more automation devices in an industrial system is provided. Industrial data processing units capable of performing data processing based on one or more artificial neural networks are provided. To enable and/or accelerate one or more computations in an industrial system, thereby simplifying integration of artificial intelligence into the industrial system, and to simplify data exchange between an extension device capable of processing data using artificial intelligence and an automation device, one or more results of the one or more computations are obtained. The results indicate one or more states of the industrial system. The one or more results are provided via a process state model shared with the automation device to monitor and/or control the industrial system.