Abstract: A control system for an operable system such as a flow control system or temperature control system. The system operates in a control loop to regularly update a model with respect at least one optimizable input variable based on the detected variables. The model provides prediction of use of the input variables in all possible operation points or paths of the system variables which achieve an output setpoint. In some example embodiments, the control loop is performed during initial setup and subsequent operation of the one or more operable elements in the operable system. The control system is self-learning in that at least some of the initial and subsequent parameters of the system are determined automatically during runtime.

Abstract: Systems and methods for safety-enabled control. Input values provided to a control system can be validated. Command gating can be performed for control values provided by the control system. Validation of input values and command gating for control values can be performed in accordance with respective validation windows. Validation windows can be dynamically adjusted based on data received via a sensor or interface.

Abstract: A reactive power-voltage control method for integrated transmission and distribution networks is provided. The reactive power-voltage control method includes: establishing a reactive power-voltage control model for a power system consisting of a transmission network and a plurality of distribution networks; performing a second order cone relaxation on a non-convex constraint of the plurality of distribution network constraints to obtain the convex-relaxed reactive power-voltage control model; solving the convex-relaxed reactive power-voltage control model to acquire control variables of the transmission network and control variables of each distribution network; and controlling the transmission network based on the control variables of the transmission network and controlling each distribution network based on the control variables of the distribution network, so as to realize coordinated control of the power system.

Abstract: A method and system are provided for improving predictions of electrical power usage. In the method and system, load and/or environmental data is classified into data sets that correspond to different modes of operation of an electrical load. Different predictive models are also provided for each set of classified data. The predictive models may provide more efficient and/or more accurate predictions of power usage since each model is limited to a particular mode of operation.

Abstract: A system and method for providing a mobile interface for marking and organizing images is described herein. In some instances, the system may present, on a display device, an image region that displays a capturable image that is capturable by an image sensor of the wireless mobile electronic device and a preview region including a series of one or more annotatable image previews. The system may receive an input instructing the wireless mobile electronic device to capture a second image using the image sensor of the wireless mobile electronic device, generate a second image preview representing the second image, and update the graphical user interface to include the second image preview at the preview region. The system may receive an input including an annotation using an input device of the wireless mobile electronic device and update the graphical user interface to represent the annotation at the preview region.

Abstract: A model predictive maintenance (MPM) system for building equipment. The MPM system includes an equipment controller configured to operate the building equipment to affect a variable state or condition in a building. The MPM system includes an operational cost predictor configured to predict a cost of operating the building equipment over a duration of an optimization period. The MPM system includes a budget manager configured to generate one or more budget constraints. The MPM system includes an objective function optimizer configured to optimize an objective function subject to the one or more budget constraints to determine a maintenance and replacement schedule for the building equipment. The objective function includes maintenance and replacement costs of the building equipment and the predicted cost of operating the building equipment.

Abstract: A diagnostic device is a device for estimating the tension of a belt of an industrial machine for transmitting power, and is provided with a control unit configured to control a diagnostic operation in which the belt is driven, a data acquisition unit configured to acquire at least feedback data at the time of the diagnostic operation, a preprocessing unit configured to analyze frequency-gain characteristics of the feedback data and create, as input data, a range including a resonant frequency and an anti-resonant frequency in the characteristics, and a machine learning device configured to perform processing related to machine learning, based on the data created by the preprocessing unit. The diagnostic device supports inference or abnormality detection of the value of the belt tension.

Abstract: The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.

Abstract: The invention relates to a method for characterizing the effects of coupling of a radiofrequency transceiver apparatus comprising at least one transmit path and at least one receive path, and to an apparatus implementing the method. The method comprises the calculation of coefficients of a correcting filter, with the steps of: transmitting a known signal over a transmit path, receiving a signal over a receive path, calculating the coefficients of the correcting filter on the basis of the known signal and of the signal received over said receive path. The method further comprises a step, carried out during the transmission of a useful signal over the transmit path, of filtering the signal transmitted over the transmit path by means of said correcting filter in order to determine the transmitted signal received by coupling effect over the receive path.

Abstract: A method includes receiving a set of feature models, each feature model of the set of feature models corresponding to a respective feature associated with processing of a component, receiving a set of target properties for processing the component, where the set of target properties includes, for each feature, a respective target, determining, based on the set of feature models, one or more sets of predicted processing parameters in view of the set of target properties, generating one or more candidate process recipes each corresponding to a respective one of the one or more sets of predicted processing parameters, where the one or more candidate process recipes each correspond to a set of predicted properties including, for each feature, a respective predicted property value resulting from component processing, and selecting, from the one or more candidate process recipes, a process recipe for processing the component.

Abstract: A calibration system configured to calibrate a handheld controller is disclosed. The calibration system includes a tracking camera, a displayer and a processing unit. The tracking camera is configured to capture streaming images involving the handheld controller. The displayer is configured to display a calibration test instruction. The processing unit is configured to receive first movement data generated by the handheld controller while the handheld controller moving; receive the streaming images captured by the tracking camera generated while the handheld controller moving; calculate second movement data according to the streaming images; calculate calibration parameters by comparing the first movement data and the second movement data; and, transmit the calibration parameters to the handheld controller. The calibration parameters are utilized by the handheld controller in generating a third movement data.

Abstract: Monitoring, systems and methods for data collection in an industrial environment are disclosed. A system may include a data collector communicatively coupled to a plurality of input channels and to a network infrastructure, wherein the data collector collects data based on a selected data collection routine, a data storage structured to store a plurality of collector routes and collected data, a data acquisition circuit structured to interpret a plurality of detection values from the collected data, and a data analysis circuit structured to analyze the collected data, and sense a change in operation and determine an aggregate rate of data being collected from the plurality of input channels. If the aggregate rate exceeds a throughput parameter the data analysis circuit alters the data collection to reduce the amount of data collected or, based on the sensed change, modify a collector route.

Abstract: An embodiment may involve persistent storage containing a machine learning trainer application configured to apply one or more learning algorithms. One or more processors may be configured to: obtain alert data from one or more computing systems; generate training vectors from the alert data, wherein elements within each of the training vectors include: results of a set of statistics applied to the alert data for a particular computing system of the one or more computing systems, and an indication of whether the particular computing system is expected to fail given its alert data; train, using the machine learning trainer application and the training vectors, a machine learning model, wherein the machine learning model is configured to predict failure of a further computing system based on operational alert data obtained from the further computing system; and deploy the machine learning model for production use.

Abstract: The present disclosure provides an Industrial Internet of Things system conducive to system scalability and control method thereof. The system comprises a user platform, a service platform, a management platform, a sensor network platform, and an object platform. The service platform or the sensor network platform adopts independent layout. The service platform and the sensor network platform both include sub platforms and each sub platform is provided with a database, a processor, and/or an information channel. The object platform includes a production line, and the production line is configured with a plurality of sensors. In each sub platform of the sensor network platform, a same communication protocol is used with the sensors connected to a same sub platform of the sensor network platform. Any sub platform of the sensor network platform is connected with a unique sub platform of the service platform through the management platform.

Abstract: A rapid material development process for a powder bed fusion additive manufacturing (PBF AM) process generally utilizes a computational fluid dynamics (CFD) simulation to facilitate selection of a simulated parameter set, which can then be used in a design of experiments (DOE) to generate an orthogonal parameter space to predict an ideal parameter set. The orthogonal parameter space defined by the DOE can then be used to generate a multitude of reduced volume build samples using PBF AM with varying laser or electron beam parameters and/or feedstock chemistries. The reduced volume build samples are mechanically characterized using high throughput techniques and analyzed to provide an optimal parameter set for a 3D article or a validation sample, which provides an increased understanding of the parameters and their independent and confounding effects on defects and microstructure.

Abstract: A measurement apparatus comprising at least one device interface adapted to connect an auxiliary measurement device and/or a device under test, DUT, to said measurement apparatus; a user interface adapted to input by a user settings for performing a measurement by said measurement apparatus and an artificial intelligence, AI, module adapted to provide current settings of said measurement apparatus, wherein said artificial intelligence, AI, module is machine learned on the basis of connected devices and/or settings during historic measurements performed by said measurement apparatus.

Abstract: An acquisition unit (10) acquires normal sample data and non-normal sample data. A model generation unit (120) generates a normal model representing the normal sample data. A change unit (141) generates a non-normal feature vector of the non-normal sample data, and generates a non-normal changed vector obtained by changing an element of the non-normal feature vector. When the non-normal changed vector and the normal model are similar to each other, a verification unit (142) executes a process using sample data represented by the non-normal changed vector. The verification unit (142) verifies whether an anomalous event is detected by a detection device. Upon verification that an anomalous event is not detected, the verification unit (142) determines whether an anomalous event is present, independently of the detection device.

Abstract: A method for detecting a slow node includes: obtaining a generated record for a first storage node, the generated record including a storage node generation time, and a number of times and consuming time for transmitting data to second storage nodes other than the first storage node; obtaining a valid record from the generated record, the valid record being generated within a preset time period, and the preset time period being within a time period between the storage node generation time and a current time; determining an average consuming time for the first storage node transmitting the data to each of the second storage nodes, based on the number of times and the consuming time in the valid record; and detecting the slow node in the second storage nodes based on the average consuming time.

Abstract: Techniques for predicting an amount of at least one biomaterial produced or consumed by a biological system in a bioreactor are provided. Process conditions and metabolite concentrations are measured for the biological system as a function of time. Metabolic rates for the biological system, including specific consumption rates of metabolites and specific production rates of metabolites are determined. The process conditions and the metabolic rates are provided to a hybrid system model configured to predict production of the biomaterial. The hybrid system model includes a kinetic growth model configured to estimate cell growth as a function of time and a metabolic condition model based on metabolite specific consumption or secretion rates and select process conditions, wherein the metabolic condition model is configured to classify the biological system into a metabolic state. An amount of the biomaterial based on the hybrid system model is predicted.

Abstract: An optimization method includes: identifying, for each of a plurality of replicas each of which has a plurality of state variables, an amount of change in strength of interaction that corresponds to a change in a distance between the replica and another replica in a state space in a case where a value of a first state variable among the plurality of state variables of the replica is updated, the state space indicating a space which a combination of values of the plurality of state variables is able to take; and determining whether or not to update the value of the first state variable, based on a proposal probability that corresponds to the amount of change in the strength of interaction in the case where the value of the first state variable is updated and based on an acceptance probability that corresponds to a target probability distribution.

Abstract: An I/O server service interacts with multiple containerized controller services each implementing the same control routine to control the same portion of the same plant. The I/O server service may provide the same controller inputs to each of the containerized controller services (e.g., representing measurements obtained by field devices and transmitted by the field devices to the I/O server service). Each containerized controller service executes the same control routine to generate a set of controller outputs. The I/O server service receives each set of controller outputs and forwards an “active” set to the appropriate field devices. The I/O server service and other services, such as an orchestrator service, may continuously evaluate performance and resource utilization in the control system, and may dynamically activate and deactivate controller services as appropriate.

Abstract: The present disclosure provides a switch circuit and a control method thereof, a smart switch and a control system. The switch circuit includes a wireless communication module, a switch module coupled to a load, a switch-on power obtaining circuit, an energy storage circuit coupled with the wireless communication module and a switch-off power obtaining circuit. The load is coupled with a neutral wire. The switch-on power obtaining circuit is configured to output a first current to the energy storage circuit in response to the switch module being switched on. The switch-off power obtaining circuit is configured to output a second current to the energy storage circuit via the second power supply terminal in response to the switch module being switched off. The second current is less than the first current.

Abstract: A computer-implemented method for creating a control process for a technical system using a Bayesian optimization method, the control process being created and executable based on model parameters of a control model, the following steps being performed in order to optimize the control process: furnishing a quality function that corresponds to a trainable regression function, and that assesses a quality of a control process of the technical system based on model parameters; executing a Bayesian optimization method based on the quality function in order to iteratively ascertain an optimized model parameter set having model parameters, such that during execution of the Bayesian optimization method, a model parameter domain that indicates the permissible value ranges for the model parameters is expanded, by an amount equal to an expansion distance, with respect to those dimensions for which the model parameter ascertained in the current iteration lies at a range boundary.

Abstract: The embodiments described herein include one embodiment that a control method including executing an infeasible search algorithm during a first portion of a predetermined sample period to search for a feasible control trajectory of a plurality of variables of a controlled process, executing a feasible search algorithm during a second portion of the predetermined sample period to determine the feasible control trajectory if the infeasible search algorithm does not determine a feasible control trajectory, and controlling the controlled process by application of the feasible control trajectory.

Abstract: An electronic apparatus including at least one interface; and at least one processor configured to: register a first user account with a first server configured to provide a first service, through the at least one interface, identify a second server corresponding to an external apparatus connectable through the at least one interface, wherein the second server is configured to provide a second service different from the first service, register the first user account with the second server through the at least one interface to link the first user account to a second user account corresponding to the second service, and perform a linkage service operation based on the first service and the second service, using the first user account and the second user account.

Abstract: A control device and a monitoring system of a manufacturing device, capable of enabling an external device to acquire data easily without grasping a configuration of the control device having a plurality of function control units. A control device of a manufacturing device includes a display function control unit and a control function control unit. The display function control unit includes a data model generation unit, and the control function control unit includes a data model generation unit. The display function control unit includes: a data model acquiring unit that acquires a data model generated by the data model generation unit of the control function control unit; and a data model integration unit that integrates a data model generated by the data model generation unit of the display function control unit with a data model of the control function control unit acquired by the data model acquiring unit.

Abstract: A compact data structure generation engine can be used to generate a compact data structure that represents performance data for high-scale networks. The compact data structure representing the performance data can be used to monitor the operation performed on or by a computer system to identify potentially anomalous conditions. In response, a corrective action can be taken to address the issue. This can be useful, for example, in improving the efficiency, effectiveness, and reliability of the computer system during operation.

Abstract: The present disclosure relates to a scalable experimental workflow that uses a culture system to maintain a steady state in a biological system, and techniques for identifying values for parameters in a in silico model based on experimental data obtained from the biological system. Particularly, aspects of the present disclosure are directed to obtaining measurement data for one or more characteristics of a biological system developed in a culture system, where the measurement data is indicative of each of the one or more characteristics at a physiological steady state where growth of the biological system is occurring at a substantially constant growth rate, determining a value for a parameter of a model of the biological system based on an growth formula, the measurement data, and the substantially constant growth rate, and parametrizing the model with at least the value determined for the parameter.

Abstract: A component mounting system includes multiple component mounting lines where multiple component mounters and a feeder storage are aligned along a board conveyance direction, a feeder exchange device configured to move along the board conveyance direction to exchange the feeder between the component mounter and the feeder storage of the component mounting line in charge, and a memory device. The feeder exchange device is configured to store an in-process feeder in the feeder storage of the component mounting line in charge, when it is determined, based on production information and feeder information, that there is the in-process feeder that was used in the component mounting line in charge and is scheduled to be used in another component mounting line.

Abstract: Disclosed herein are related to a system, a method, and a non-transitory computer readable storing instructions for operating an energy plant comprised of heating, ventilation and air conditioning (HVAC) devices. In one aspect, the system generates gradient data indicating a gradient of operating performance of the energy plant with respect to values of a plurality of control variables of HVAC devices. The system determines, from the plurality of control variables, a reduced group of control variables of the HVAC devices based on the gradient data. The system determines a set of values of the reduced group of control variables. The system operates the energy plant according to the determined set of values of the reduced group of control variables.

Abstract: An update system may be used to update referenced data objects that are used by multiple applications. In some cases incorrect data may be entered and later corrected. Data consistency techniques are described herein to help avoid use of incorrect data before the data is corrected. A communication from the update system may include updated master data objects and an indication that there are further updates queued. A flag may be set for each of the updated master data objects as they are stored in a database. Then when a request to access those objects is received, the request may be denied when the flag set, thereby preventing access to potentially incorrect or outdated data.

Abstract: A system includes a first asset disposed in an industrial environment configured to perform one or more operations, a second asset disposed in the industrial environment, and a server device communicatively coupled to the first asset and the second asset. The server device is configured to receive a first set of stream-based data from the first asset, receive a second set of stream-based data from the second asset, wherein the first set of stream-based data and the second set of stream-based data are received in real time or near real time, determine whether the one or more operations are within a threshold based on a comparison of the first set of stream-based data with respect to the second set of stream-based data, and send a command to the first asset or the second asset in response to the one or more operations being outside the threshold.

Abstract: A control device includes a processor that controls a printing manufacturing process including printing processing and post-processing in plural types of printing manufacture, in which the processor is configured to: according to an occurrence of an event in which the printing processing has ended but printing manufacture to be performed again is required, acquire information about a type of a sheet from printing manufacture information about printing manufacture having a delivery date; predict a time required for each processing of the printing manufacturing process in the printing manufacture; calculate an end time of reprinting manufacture, in which the printing manufacture to be performed again is required, in a case where the reprinting manufacture is incorporated so as to be continuous with other printing manufacture in which the same sheet as the reprinting manufacture is used; and perform control to determine whether or not to incorporate the reprinting manufacture so that the reprinting manufacture i

Abstract: An apparatus, methods and systems for data collection related to an oil and gas process and disclosed. A system may include a multi-sensor acquisition component including a plurality of inputs and a plurality of outputs, a sensor data storage profile circuit structured to determine a data storage profile including a data storage plan for the plurality of inputs, a sensor communication circuit structured to interpret a plurality of inputs, a sensor data storage implementation circuit structured to store at least a portion of the inputs in response to the data storage profile, a data analysis circuit structured to analyze the plurality of inputs and determine a data quality parameter, and a data response circuit structured to adjust at least one of the data storage profile and the data collection routine in response to the data quality parameter.

Abstract: A data center infrastructure monitoring system and method that includes a plurality of geographically distributed data centers, each data center comprising a plurality of mechanical assets. A data center gateway system communicatively coupled to one or more data centers of the plurality of data centers is configured to determine a subset of the plurality of mechanical assets of the data center that provide cooling to equipment of a customer of the data center, and output, for display, a graphical depiction of the subset of the plurality of mechanical assets of the data center that provide cooling to equipment of the customer in the data center.

Abstract: Techniques for operating a sensor are provided. An example method according to these techniques includes sensing, at the sensor, changes in intensity of a magnetic field of a magnet affixed to a monitored asset to produce sensor data, wherein the monitored asset is disposed in a non-metallic liquid or solid medium, and wherein the sensor is disposed outside of the non-metallic medium; The method also includes analyzing, at the sensor, the sensor data to produce feature information indicative of vibration of the monitored asset. The method also includes providing the feature information to a predictive algorithm to generate prognosis information indicating an occurrence of a known condition of the monitored asset.

Abstract: A system and method for controlling a drilling tool inside a wellbore makes use of simulated annealing and Bayesian optimization to determine optimum controllable drilling parameters. In some aspects, a computing device generates sampled exploration points using simulated annealing and runs a Bayesian optimization using a loss function and the exploration points to optimize at least one controllable drilling parameter to achieve a predicted value for a selected drilling parameter. In some examples, the selected drilling parameter is rate-of-penetration (ROP) and in some examples, the controllable drilling parameters include such parameters as rotational speed (RPM) and weight-on-bit (WOB). In some examples, the computing device applies the controllable drilling parameter(s) to the drilling tool to achieve the predicted value for the selected drilling parameter and provide real-time, closed-loop control and automation in drilling.

Abstract: Duty cycles of pulse width modulation (“PWM”) pulses are determined by measurements taken with respect to an internally generated clock signal. One of these measurements calculates, in a continuous dynamic manner, a ratio of the number of cycles of the internally generated clock signal to one or more cycles of a PWM clock signal utilized as a time base for generation of the PWM pulses. This clock ratio measurement designates how many cycles of the internally generated clock signal will be used to designate a first portion of a duty cycle for each PWM pulse. Another measurement is utilized to determine a fractional portion of a cycle of the internally generated clock signal that will be used to designate a second portion of the duty cycle for each PWM pulse.

Abstract: Systems, devices, and methods of measuring directional latency and congestion in a communication network. A Uni-Directional Latency Determination Unit is connected in a communication network, located between an end-user device and a server. It monitors packets transported between the end-user device and the server, and it estimates a uni-directional latency of packet transport from the end-user device to the server or from the server to the end-user device. It utilizes a Transmission Control Protocol (TCP) Header and Timestamp Analyzer, to perform an analysis of data contained in timestamps of TCP packet headers of transported packets; and particularly, it analyzes data contained in a TSval field of such TCP packet headers. Congestion mitigation operations are accordingly deployed or activated.

Abstract: In exemplary aspects of optimizing data centers, historical data corresponding to a data center is collected. The data center includes a plurality of systems. A data center representation is generated. The data center representation can be one or more of a schematic and a collection of data from among the historical data. The data center representation is encoded into a neural network model. The neural network model is trained using at least a portion of the historical data. The trained model is deployed using a first set of inputs, causing the model to generate one or more output values for managing or optimizing the data center with respect to design and risk aspects.

Abstract: A topology configuration system receives a graphic representation comprising object images of information handling systems to be configured for operating as a complex collection of information handling systems, uses artificial intelligence and deep learning to analyze images on the graphic representation and determine specific information handling systems present in the complex collection of information handling systems. A requirement list is used to create a configuration job. Sets of user instructions are sent to a user to guide the user and sets of device instructions are sent to each information handling system so that the user can configure the plurality of information handling systems to operate as the complex collection of information handling systems. Validation packets may be sent to each information handling system to confirm the complex collection of information handling systems is set up quickly and with little or no troubleshooting.

Abstract: A control method according to the present disclosure is executed by a first server of servers in a system including one or more IoT devices and the servers. The method includes: obtaining first transaction data including malfunction information indicating that one of the IoT devices is malfunctioning, and time information indicating a time when the IoT device has obtained the malfunction information; transferring the obtained first transaction data to second servers different from the first server; executing, together with the second servers, a first consensus algorithm for an agreement on an authenticity of the first transaction data; and recording a block containing the first transaction data in a distributed ledger of the first server, when the authenticity of the first transaction data is verified by the first consensus algorithm.

Abstract: Techniques for predicting an amount of at least one biomaterial produced or consumed by a biological system in a bioreactor are provided. Process conditions and metabolite concentrations are measured for the biological system as a function of time. Metabolic rates for the biological system, including specific consumption rates of metabolites and specific production rates of metabolites are determined. The process conditions and the metabolic rates are provided to a hybrid system model configured to predict production of the biomaterial. The hybrid system model includes a kinetic growth model configured to estimate cell growth as a function of time and a metabolic condition model based on metabolite specific consumption or secretion rates and select process conditions, wherein the metabolic condition model is configured to classify the biological system into a metabolic state. An amount of the biomaterial based on the hybrid system model is predicted.

Abstract: Systems and methods for determining operation commands for a fixture within an area of a facility. One example system includes a processor configured to: receive, from a first fixture associated with an area within a facility, operational data related to an operation of the first fixture; receive, from a second fixture associated with the area within the facility, environmental data related to a condition of the area; process the operational data and the environmental data through a status model to determine a status for the area, the status model trained with operational data and environmental data previously received from the first fixture, the second fixture, and other fixtures associated with the facility; determine an operation command for a third fixture associated with the area within the facility based on the status; and provide the operation command to the third fixture for execution.

Abstract: A generator set monitoring and control system includes a generator set located in a first location, an on-site controller located near the first location, and a remote display, located in a second location. The remote display is configured to send instructions to at least one of the generator set and on-site controller, receive genset operation outputs from the on-site controller, and display genset operation outputs.

Abstract: Embodiments for using a mixed internet of things (IoT) device network to improve user satisfaction by a processor. A collaboration of data associated with a user captured from a plurality of types of IoT devices in the IoT network may be gathered. The collaboration of data may be used to determine a level of satisfaction of the user according to a knowledge domain describing a plurality of satisfaction factors and levels in the service based industry such that the collaboration of data is associated with one or more of the plurality of satisfaction factors and levels.

Abstract: Example implementations relate to calculating a weighted efficiency index. An example implementation includes fetching a public resource utilization metric and a public cloud resources cost. A private resource utilization metric is collected and a private cloud resources cost is calculated. A weighted efficiency index is calculated from the public cloud resources cost, the private cloud resources cost, or any combination thereof, and a weighted deviation from an ideal resource utilization percentage. The weighted efficiency index may be displayed on a user interface.

Abstract: Configuring distributed control in an industrial system comprises building an asset model representative of a process control installation of the industrial system and creating an asset library of distributed control assets according to a distributed control programming standard. The asset model includes modeled assets defined according to levels of a physical model standard and representing physical devices of the industrial system. The distributed control assets each have one or more predefined, built-in facets. One of the distributed control assets in the asset library is mapped to each of the modeled assets to configure the process control installation of the industrial system and generate an asset-based control application for providing distributed control of the industrial system. Additional aspects relate to auto-creation of control applications based on an information model, either through the use of machine learning or an asset configurator tool.

Abstract: The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.

Abstract: Systems and methods for alarm panel analysis include receiving a plurality of alarm events from the respective alarm panels monitoring corresponding buildings. An alarm analysis system may classify the alarm panels by identifying an alarm type for the alarm events. The alarm analysis system may determine a number of occurrences of each alarm type for the alarm panels, and generate a data point for a dataset for the alarm panels. The alarm analysis system may identify statistical dividers in the data points for the data set, which may be used for assigning a ranking for the alarm panels based on their location in relation to the statistical dividers. The alarm analysis system may construct and a monitoring dashboard which includes the rankings of the alarm panels, which may be rendered on a display to an end user.