Abstract: An exemplary embodiment of the present disclosure discloses a method of setting a model threshold value for detecting an anomaly of a facility monitoring system, the method including: acquiring sensor data output from each sensor; extracting a feature value for the sensor data of each sensor; acquiring output data by inputting input data including the extracted feature value to a trained neural network model; and comparing the input data and the output data and setting a threshold value for detecting an anomaly based on a calculated comparison result value.

Abstract: An industrial monitoring system comprises a monitoring device attached to an industrial device by a bond. Sensor data collected by the monitoring device during a commissioning period is received and used to train a machine learning model. Subsequent to the commissioning period, additional sensor data is collected by the monitoring device. An abnormal state of the bond between the monitoring device and industrial device is determined based on the additional sensor data and a characteristic inferred by the trained machine learning model. A notification of the abnormal state is generated.

Abstract: Examples of the usage of a command of a command line interface includes the command with a set of parameters and corresponding parameter values. The examples are generated from telemetry data, which does not contain parameter values, and from web-based sources that may contain multiple parameter values. A machine learning model is used to predict the data type of a parameter value when the parameter is used with a particular command. The predicted data type is then used to select an appropriate parameter value for the example from multiple known parameter values or to generate a parameter value when no known parameter value exists.

Abstract: In some aspects, a computing system can determine a set of attributes based on analyzing input data using attribute templates written in a production-ready programming language. The computing system can generate attribute definitions for the set of attributes using the attribute templates and deploy the attribute definitions for the set of attributes to a production environment of a software program. The software program is written in a programming language compatible with the production-ready programming language. The computing system can monitor the performance of the set of attributes in the production environment of the software program and cause the attribute definitions of the plurality of attributes to be modified based on the monitoring.

Abstract: Systems, apparatuses, and methods related to an extended memory neuromorphic component for performing operations in memory are described. An example apparatus can include a plurality of computing devices. Each of the computing devices can include a processing unit and a memory array. The example apparatus can further include a communication subsystem coupled to the at least one of the plurality of computing devices and to a neuromorphic component. At least one of the plurality of computing devices can receive a request from a host to perform an operation, receive an indication of data to be access in a memory device to perform the operation, and send an indication to the neuromorphic component to monitor the data to be accessed. The neuromorphic component can intercept data and determine that a portion of the data should be flagged.

Abstract: Technologies for secure I/O include a compute device, which further includes a processor, a memory, a trusted execution environment (TEE), one or more input/output (I/O) devices, and an I/O subsystem. The I/O subsystem includes a device memory access table (DMAT) programmed by the TEE to establish bindings between the TEE and one or more I/O devices that the TEE trusts and a memory ownership table (MOT) programmed by the TEE when a memory page is allocated to the TEE.

Abstract: An inspection-plan based inspection method includes receiving data characterizing an inspection plan associated with inspection of one or more nodes in an inspection site by an inspection device. A first step of the inspection plan includes a first set of operating parameters of the inspection device associated with the inspection of a first node of the one or more nodes and a first set of constraints associated with one or more inspection criteria at the first node by the inspection device. The method also includes generating a first control signal configured to instruct the inspection device to inspect the first node of the one or more nodes. The first control signal is based on one or more of the first set of operating parameters and a user input. The method further includes receiving data characterizing the inspection measurement of the first node by the inspection device.

Abstract: In one implementation, a method of sharing a physical device between multiple virtual machines is provided. The method includes receiving, from a first virtual machine, a request to access a physical device of a computing device. The method also includes assigning, by a processing device, the physical device to the first virtual machine in view of power state information associated with the physical device of the computing device, wherein the power state information is received from one or more other virtual machines of the computing device.

Abstract: An example system for deployment of a compacting head in a waste compartment includes an eutectic alloy fusible link positioned within a waste compartment that breaks at or above a predetermined temperature, a spring tensioned deploy lever that is deployed based on a break of the eutectic alloy fusible link, a pivotal joint assembly that collapses based on deployment of the deploy lever, and a compacting head of a trash compactor that is deployed to compact contents of the waste compartment based on collapse of the pivotal joint assembly.

Abstract: In a device engineering method, data of a plant network hierarchy (PNH) and registered devices (RD) in a device management server are synchronized to a device simulation server (SS). In a simulation mode, at least one function of device management system is executed via communicating with simulated devices (SD), while the device management system being not communicatively coupled to any physical control station configured to control physical field devices in a real plant. Virtual parameters are introduced into the SD. For device configuration, simulation is made of configurable device parameters; non-configurable device parameters; and device status, with SD generated from DD files in the PNH for the RD in the FDCS. Parallel communications including sending communication requests from a CRH component to the SD in the PNH from the CRH component in the FDCS to the SD in the PNH are executed simultaneously.

Abstract: Systems and methods of operating power tools. The method includes receiving a command to start a recording mode at a first electronic processor of a first power tool, and receiving at the first electronic processor, a measured parameter from a sensor of the first power tool while a first motor of the first power tool is operating. The method also includes generating a recorded motor parameter by recording the measured parameter, on a first memory of the first power tool, when the first power tool operates in the recording mode, and transmitting, with a first transceiver of the first power tool, the recorded motor parameter. The method further includes receiving the recorded motor parameter at an external device, transmitting the recorded motor parameter to a second power tool via the external device, and receiving the recorded motor parameter via a second transceiver of the second power tool.

Abstract: A method for developing machine-learning (ML) based tool including initializing an input dataset, which is pre-processed by a first model to harmonize the dataset. Historical data similar to the input data set is fetched from a historical database. Based thereupon a controller recommends a method and a control-setting associated with the identified model for the visual inspection process to a user. Thereafter, the dataset is annotated by a second model to define a labelled data set. A plurality of features are extracted with respect to the data set through a feature extractor. A machine-learning classifier operates upon the extracted features and classifies the dataset with respect to one or more labels. A meta controller communicates with one or more of the first model, the second model, the feature extractor and the selected classifier for assessing a performance of at least one of first model and the feature extractor.

Abstract: An apparatus for generating a temperature prediction model is disclosed. The apparatus for generating a temperature prediction model includes the temperature prediction model configured to provide a simulation environment, and a processor configured to set a hyperparameter of the temperature prediction model, train the temperature prediction model, in which the hyperparameter is set, so that the temperature prediction model, in which the hyperparameter is set, outputs a predicted temperature, update the hyperparameter on the basis of a difference between the predicted temperature, which is outputted from the trained temperature prediction model, and an actual temperature, and repeat the setting of the hyperparameter, the training of the temperature prediction model, and the updating of the hyperparameter on the basis of the difference between the predicted temperature and the actual temperature by a predetermined number of times or more to set a final hyperparameter of the temperature prediction model.

Abstract: As described herein, a system, method, and computer program are provided for a microservice lifecycle operator. In use, at least one specification for a microservice is identified. Further, a lifecycle of the microservice is managed, using a lifecycle operator and the at least one specification.

Abstract: Provided is a home appliance of determining an operation command corresponding to an occupant through learning based on setting information of the home appliance according to the occupant to provide an operation satisfying all occupants. An air conditioner according to an embodiment of the disclosure includes: an outdoor unit; and an indoor unit including a heat exchanger, wherein the indoor unit includes: a communicator configured to communicate with an access point (AP); and a controller configured to receive information about a terminal connected to the access point through the communicator, and change at least one of operation temperature or an operation mode when a new terminal is connected to the access point or a terminal connected to the access point is disconnected from the access point.

Abstract: A clothing management using a moving hanger is provided. A method of controlling a clothing styler according to an embodiment of the present disclosure determines a weight of clothing using a current RPM (cRPM) determined based on a PWM value provided to a motor and a prediction model based on the pre-trained artificial neural and may perform a suitable control operation according to the determined weight. The clothing styler of the present disclosure may be associated with an artificial intelligence module, an unmanned aerial vehicle (UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device, and a device related to a 5G service.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing self qualification of cloud based applications. One of the methods includes analyzing, by a self-qualified process (SQP), a set of risks for a cloud based software platform, wherein each risk comprises a set of features that are being evaluated according to a risk definition; performing, by the SQP, one or more tests related to one or more risks of the set of risks; and based on an outcome of each of the tests, generating a measure of a qualification state for the cloud based software platform, wherein the measure of the qualification state is used to determine self-qualification of the software platform.

Abstract: A method and system provides for asynchronous two-way binding between a user interface and a data model which are implemented on different frameworks. The system includes data, data model, and bridge controllers. The data controller provides a front-end binding framework that interacts with a user interface of a user device to manage a bindable property or method for a view on the user device. The data model controller provides a back-end binding framework that manages a data model, the front-end binding framework and the back-end binding framework being different types of frameworks. The bridge controller implements asynchronous two-way binding for the bindable property or method between the front-end binding framework and the back-end binding framework to update the bindable property or method in the data model when data changes at the user interface and to update the view on the user device when data changes at the data model.

Abstract: A machine learning apparatus includes a first information acquiring unit that acquires first information including at least one of a shape of a workpiece, a material of the workpiece, a cutting path of a cutting process, a type of a tool, and an amount of wear of the tool; a second information acquiring unit that acquires second information correlated with an evaluation of a burr occurring on the workpiece due to the cutting process; and a learning unit that executes learning processing using a plurality of pieces of the first information and a plurality of pieces of the second information, and generates a learning model that outputs a cutting condition, according to another piece of first information that is different from the plurality of pieces of first information.

Abstract: This application provides a method for training a neural network model and an apparatus. The method includes: obtaining annotation data that is of a service and that is generated by a terminal device in a specified period; training a second neural network model by using the annotation data that is of the service and that is generated in the specified period, to obtain a trained second neural network model; and updating a first neural network model based on the trained second neural network model. In the method, training is performed based on the annotation data generated by the terminal device, so that in an updated first neural network model compared with a universal model, an inference result has a higher confidence level, and a personalized requirement of a user can be better met.