Abstract: The present disclosure relates to relates to an industrial premixed gas combustor using internal exhaust gas recirculation and an operating method thereof.

Abstract: An adaptive wearable article can include an electroadhesive clutch secured to a textile and extending around at least a portion of an opening. The clutch can include a first conductive member and a first polymeric substrate applied to the first conductive member and having a stiffness greater than a stiffness of the first conductive member, and the clutch can include a second electrode assembly comprising a second conductive member configured to partially overlay the first conductive member. The second electrode assembly can include a second conductive member and a second polymeric substrate applied to the second conductive member, the second polymeric substrate having a stiffness greater than a stiffness of the second conductive member, wherein the first and second conductive members are proximate each other with the first and second polymeric substrates distal with respect to each other.

Abstract: An adaptive article of apparel can include a textile with an opening that is configured to admit a body part of a wearer, and an electroadhesive clutch secured to the textile and extending around at least a portion of the opening. The electroadhesive clutch can be configured to permit or inhibit changing a size of the opening.

Abstract: An article of apparel can include an aperture. An electroadhesive clutch device can be coupled to, or integrated with, the article of apparel, and the clutch device can be configured to selectively open and close the aperture. An electric signal generator can be coupled to the clutch device and configured to provide one or more signals to the clutch device to thereby open or close the aperture.

Abstract: An article of apparel can include or use an electroadhesive clutch device. The device can include an elongate encasing for a first electrode assembly and a second electrode assembly each having a respective first and second electrode. The first and second electrodes can be at least partially overlapping and configured to slide or move relative to each other. The device can include an electrical signal generator configured to provide first and second signals to the first and second electrode assemblies, respectively, wherein the first electrode assembly is configured to slide laterally relative to the second electrode assembly when the first and second signals are not applied, and is configured to remain static relative to the second electrode assembly when the first and second signals are applied. The apparel can include a textile material to which the encasing is attached at least at a first end of the encasing.

Abstract: An electroadhesive clutch can be coupled to a textile. The clutch can include a first electrode assembly comprising a first conductive member, and a second electrode assembly comprising a second conductive member overlaying in part the first conductive member. In an example, the clutch includes or uses an elastic encasing within which the first and second electrode assemblies are positioned. The elastic encasing can form a first bond with the first conductive member at a first location of the elastic encasing and a second bond with the second conductive member proximate a second location of the elastic encasing different than the first location.

Abstract: Mechanisms are provided for intrusion detection based on regular expression matching. The mechanisms partition a regular expression (RegEx) rule set into a plurality of different partitions and distributes the plurality of different partitions to a plurality of different edge computing devices associated with a protected network of computing resources. The mechanisms route data packets of an incoming data stream to the plurality of edge computing devices, each of which processes the data packets to determine whether the data packets match RegEx rules in a partition distributed to the edge computing device. A determination is made as to whether the incoming data stream represents an intrusion based on a combination of results of the processing of the data packets by the plurality of edge computing devices.

Abstract: Systems and techniques that facilitate dynamic load scheduling are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory that can execute the computer executable components stored in memory. The computer executable components can comprise a carbon footprint component that generates estimated carbon footprints of a plurality of tasks for a plurality of datacenters based on carbon intensity forecasts and estimated workload requirements of the task, wherein the carbon intensity forecasts comprise ancillary carbon output, marginal carbon output, and average carbon output; and a scheduling component that schedules the plurality of tasks for one or more datacenters selected from the plurality of datacenters based on the estimated carbon footprints.

Abstract: Computer-implemented methods for managing instances of serverless functions in a cloud computing system are provided. Aspects include obtaining a service level objective for a serverless function, obtaining a command queue length for a graphical processing unit disposed on each of a plurality of compute nodes in the cloud computing system, and obtaining a request queue length of the serverless function. Aspects also include calculating a number of instances of the serverless function to deploy in the cloud computing system, wherein the number of instances is determined based on the service level objective and the request queue length of the serverless function, identifying compute nodes from the plurality of compute nodes to deploy each of the number of instances of the serverless function, and creating an instance of the serverless function on each of the identified compute nodes.

Abstract: An engineered textile includes a plurality of yarn strands with a subset extending in a substantially parallel and spaced arrangement and a bonding material extending across the plurality of yarn strands. The bonding material encapsulates a portion of each yarn strand to bond adjacent ones of the plurality of yarn strands together.

Abstract: Systems and methods are presented to provide a first machine learning model to a collaboration platform. The systems and methods receive a second machine learning model from the collaboration platform that indicates the second machine learning model is based on the first machine learning model. The systems and methods test the second machine learning model using criteria corresponding to the first machine learning model to determine whether the second machine learning model is valid. In turn, the systems and methods publish the second machine learning model to a repository in response to determining that the second machine learning model is valid.

Abstract: An article of apparel can include an aperture. An electroadhesive clutch device can be coupled to, or integrated with, the article of apparel, and the clutch device can be configured to selectively open and close the aperture. An electric signal generator can be coupled to the clutch device and configured to provide one or more signals to the clutch device to thereby open or close the aperture.

Abstract: An adaptive article of apparel can include a textile with an opening that is configured to admit a body part of a wearer, and an electroadhesive clutch secured to the textile and extending around at least a portion of the opening. The electroadhesive clutch can be configured to permit or inhibit changing a size of the opening.

Abstract: An electroadhesive clutch can be coupled to a textile. The clutch can include a first electrode assembly comprising a first conductive member, and a second electrode assembly comprising a second conductive member overlaying in part the first conductive member. In an example, the clutch includes or uses an elastic encasing within which the first and second electrode assemblies are positioned. The elastic encasing can form a first bond with the first conductive member at a first location of the elastic encasing and a second bond with the second conductive member proximate a second location of the elastic encasing different than the first location.

Abstract: A support garment can include a textile layer forming a supportive region and configured to adjustably permit or inhibit displacement of a body part of the wearer that is positioned adjacent or proximate the supportive region. The garment can include an electroadhesive clutch device to help control displacement. In an example, the garment includes an underwear garment or a portion thereof.

Abstract: An electronic circuit includes word lines; bit lines intersecting the word lines at a plurality of grid points; and resistive processing units located at the grid points. Baseline stochastic pulse input units are coupled to the word lines; differential stochastic pulse input units are coupled to the word lines; and bitline stochastic pulse input units are coupled to the bit lines. Control circuitry coupled to the pulse input units is configured to cause each of the baseline stochastic pulse input units to generate a baseline pulse train using base input data, each of the differential stochastic pulse input units to generate a differential pulse train using differential input data defining differences from the base input data, and each of the bitline stochastic pulse input units to generate a bitline pulse train using bit line input data. Neural network weights can thus be stored in the resistive processing units.

Abstract: Computer-implemented methods for adaptively training a machine learning model for estimating energy consumption in a cloud computing system are provided. Aspects include detecting an update event in the cloud computing system and collecting energy consumption data for the cloud computing system for a time period after the occurrence of the update event. Aspects also include retraining a power consumption model based at least in part on the data collected during the time period and storing the power consumption model in the cloud computing system.

Abstract: Described are techniques for modeling power in the multi-tenant private cloud environment. An absolute power model is trained to estimate the absolute power in the multi-tenant private cloud environment. The absolute power model is composed of both independent and dependent inferences. Furthermore, a dynamic power model is trained to estimate the dynamic power in the multi-tenant private cloud environment based on the deconstructed independent inferences. The dynamic power model is composed of only the deconstructed independent inferences. The absolute power model and the dynamic power model are then combined into a combined model to model the power in the multi-tenant private cloud environment after validating the dynamic power model. The combined model may then be utilized to estimate the power used in the multi-tenant private cloud environment if the error metrics of the combined model indicate that a measured error of the combined model is less than a threshold value.

Abstract: Techniques for performing improved machine learning using decision trees are disclosed. In one example, a system includes a plurality of decision tree structures, and configuration logic operatively coupled to the plurality of decision tree structures. The configuration logic selectively configures the plurality of decision tree structures to form at least one of: one or more combined decision tree structures, wherein a combined decision tree structure comprises multiple interconnected ones of the plurality of decision tree structures; and one or more individual decision tree structures, wherein an individual decision tree structure comprises a single one of the plurality of decision tree structures.

Abstract: Provided is a method, system, and computer program product for using machine learning to allocate resources to workloads at run time in an optimized manner to minimize resource consumption. A processor may generate training data from a retrospective analysis of historical resource management data associated with a computing system. The processor may train a machine learning model to optimize resource management of the computing system at run time using the training data. The processor may obtain optimization recommendations for a current state of the computing system from the machine learning model. The processor may implement the optimization recommendations to manage the current state of the computing system.