Abstract: Autonomous vehicle model training and validation using low-discrepancy sequences may include: generating a low-discrepancy sequence in a multidimensional space comprising a plurality of multidimensional points; mapping each sample of a plurality of samples of a data corpus to a corresponding entry in the low-discrepancy sequence, wherein each sample of the plurality of samples comprises one or more environmental descriptors for an environment relative to a vehicle and one or more state descriptors describing a state of the vehicle; selecting, from the data corpus, a training data set by selecting, for each multidimensional point of the low-discrepancy sequence having one or more mapped samples, a mapped sample for inclusion in the training data set; and training one or more models used to generate autonomous driving decisions of an autonomous vehicle based on the selected training data set.

Abstract: Autonomous vehicle model training and validation using low-discrepancy sequences may include: generating a low-discrepancy sequence in a multidimensional space comprising a plurality of multidimensional points; mapping each sample of a plurality of samples of a data corpus to a corresponding entry in the low-discrepancy sequence, wherein each sample of the plurality of samples comprises one or more environmental descriptors for an environment relative to a vehicle and one or more state descriptors describing a state of the vehicle; selecting, from the data corpus, a training data set by selecting, for each multidimensional point of the low-discrepancy sequence having one or more mapped samples, a mapped sample for inclusion in the training data set; and training one or more models used to generate autonomous driving decisions of an autonomous vehicle based on the selected training data set.

Abstract: Methods, systems, and computer storage media for providing an optimal control configuration for a material processing system are provided. In operation, a material processing engine accesses causal graph input data. Causal graph input data includes input data of a continuous flow process. Based on the causal graph and the input data, a causal graph that aligns with do-calculus manipulations—associated with determining identifiable causal relationships corresponding to input materials of the continuous flow process—is generated. The causal graph is parsed based on the do-calculus manipulations to determine valid conditioning sets associated with estimating a causal impact on an optimization target. Based on the valid conditioning sets, an optimal control configuration comprising optimal control variable values is generated.

Abstract: A hover-capable flying machine such as a drone includes a robotic arm extending from the body, and an instrumentality for balancing the machine in response to disturbances such as those caused by picking up and dropping of the payload by the extended robotic arm. In embodiments, the end of the arm is equipped with a balancing rotor assembly that may provide lift sufficient to counteract the weight of the payload and/or of the arm. In embodiments, the machine's power pack is shifted in response to the disturbances. The power pack may be moved, for example, on a rail within and/or extending beyond the machine in a direction generally opposite to the extended arm. The power pack may also be built into a bandolier-like device that can be rolled-in and rolled out, thus changing the center of gravity of the machine.

Abstract: An execution environment for executing an event-oriented control program of a programmable logic controller, wherein the control program comprises a plurality of functional modules to be successively executed, is configured to execute the functional modules in an event-controlled manner on the occurrence of activation events that are each associated with the individual functional modules. In this respect, the individual activation events of the functional modules to be successively executed form an event chain. The execution environment comprises a monitoring module for monitoring an execution duration of the functional modules of the event chain, wherein the monitoring module is configured to check the execution duration for an exceeding of a predefined time duration.

Abstract: The present disclosure provides a method for anomaly classification for an industrial control system (ICS) communication network based on statistical learning and deep learning. This method designs LSTM deep learning structure parameters and performs modeling analysis based on a large amount of traffic data during normal operation of the ICS communication network; based on real-time communication traffic data thresholds generated by a SARIMA online statistical learning model, designs correlated algorithms to analyze a numerical relationship between background traffic and real-time traffic; and classifies ICS communication network anomalies according to an ICS network anomaly classification algorithm.

Abstract: A method, computer system, and a computer program product for anomaly detection is provided. The present invention may include converting business process logs into a graphical data structure. The present invention may include generating an optimized graph encoding for anomaly detection using an unsupervised machine learning model. The present invention may include computing an anomaly score for each activity of the business process log using a process aware metric based on feature representation. The present invention may include labeling each of the one or more data points with a high anomaly score.

Abstract: A virtual metrology method using a convolutional neural network (CNN) is provided. In this method, a dynamic time warping (DTW) algorithm is used to delete unsimilar sets of process data, and adjust the sets of process data to be of the same length, thereby enabling the CNN to be used for virtual metrology. A virtual metrology model of the embodiments of the present invention includes several CNN models and a conjecture model, in which plural inputs of the CNN model are sets of time sequence data of respective parameters, and plural outputs of the CNN models are inputs to the conjecture model.

Abstract: The present invention describes a method for gauging energy used for producing a unit of mass or volume of compressed gas (Specific Energy Consumption) in relation to a common output flow in a multiple compressor system, said method comprising: —from a first compressor, constructing an ideal specific energy consumption curve in the first compressor as a function of the output flow of the first compressor; and —from a first compressor and a second compressor, constructing a combined ideal specific energy consumption specific energy consumption curve in the first compressor and the second compressor as a function of the combined output flow of the first compressor and the second compressor, wherein the method involves constructing one or several ideal specific energy consumption curve(s) for multiple combined compressors, in any combination(s), and wherein the method involves creating a theoretical operation model for the multiple compressor system.

Abstract: A computer-implemented method for monitoring a communication system includes identifying a set of signals that need to be transmitted over the communication system for proper functioning of the control and/or surveillance; for each signal from the identified set of signals, identifying one or more resources of the communication system that are needed for transmission of this signal; obtaining information that is indicative of the operational state of the identified resources; and evaluating, from the obtained information, at least one remedial activity which, when performed on at least one resource, and/or on the control and/or surveillance, is likely to improve, and/or to restore, the reliability of the control and/or surveillance.

Abstract: A control system configured to serve one or more energy loads in a building comprises equipment configured to consume, produce, or store one or more resources including electricity, water, natural gas, steam, hot thermal energy, cold thermal energy, or electrical energy. The control system includes an asset allocator configured to receive an input model that describes a physical layout of the equipment and create a net list that defines connections between the equipment using the input model. The asset allocator is configured to discover one or more systems of interconnected equipment and one or more groups of equipment using the net list, formulate an optimization problem using the systems and groups of equipment, and operate the equipment according to the optimization problem.

Abstract: A system for commissioning a model, comprising one or more processing circuits configured to identify a first plurality of data points in the building, automatically tag at least a portion of the first plurality of data points with one or more first tags using context data extracted from and/or associated with the data points, the one or more entities comprising one or more of building equipment, building spaces, people, or events, identify at least one of the first plurality of data points for manual review and generate one or more suggested tags for the at least one data point, receive feedback from the manual review, and receive a second plurality of data points in the building and automatically tag at least a portion of the second plurality of data points with one or more second tags using the feedback from the manual review.

Abstract: Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Such assessment may be performed to determine the robustness of autonomous systems, including the use of virtual assessment of software components within a simulated environment. To this end, a server may retrieve one or more routines associated with autonomous operation. The server may also generate a set of test data associated with test conditions. The server may also execute an emulator that virtually simulates autonomous environment. The test data may be presented to the routines executing in the emulator to generate output data. The server may then analyze the output data to determine a quality metric.

Abstract: A system that includes artificial intelligence (AI) configured to identify text and images within an industrial reference. Example industrial references include electrical drawings and P&IDs. The system includes a method for training artificial intelligence model to recognize text characters and strings in addition to industrial images using a limited sample set. The use of a limited sample set improves computer performance by relying on a smaller dataset to train the model.

Abstract: A method for testing an updated version of an application for vehicles. In the method, input data for the application, which are used for a present version of the application on at least one vehicle, and output data of the present version of the application are received by a vehicle-external computing system, from the at least one vehicle via a wireless data link. The updated version of the application is executed on the vehicle-external computing system using the received input data. Output data of the updated version of the application are compared with the received output data of the present version of the application. An evaluation of the updated version of the application being carried out, based on the comparison.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for performing interactive digital image editing operations utilizing machine learning models and a feature backpropagation refinement layer. For example, the disclosed systems perform interactive digital image editing operations by incorporating a feature backpropagation refinement layer within a non-interactive machine learning model that utilizes a consistency loss to adjust the feature backpropagation refinement layer according to one or more user interactions. In some embodiments, the disclosed systems utilize a feature backpropagation refinement layer that includes a bias sublayer for localizing changes to a digital image and a convolutional sublayer for channel-wise scale and feature combinations across channels. In some cases, the disclosed systems utilize a consistency loss that facilitates localized modifications to a digital image based on distances of various pixels or features from a user interaction.

Abstract: A system and method for crowd-sourced environmental system control and building maintenance includes a server for providing selective access to building occupants and managers. Users are permitted to generate building reports in the form of (i) thermal reports using a thermal report module, and/or (ii) maintenance reports using a maintenance report module. The reports are each geo-located to locations within the building, and are then captured, stored, and aggregated at the server. The aggregated reports are sorted according to their geo-locations and comfort rules are used to (i) permit a manager at a client computer to access the server to respond, and/or (ii) automatically respond and assign a response status to particular reports. An inspection checklist interface is generated and populated to display a list of preventative maintenance tasks, each of the tasks being user-selectable to designate completion, with the updated status of the reports being stored at the server.

Abstract: A hover-capable flying machine such as a drone includes a robotic arm extending from the body, and an instrumentality for balancing the machine in response to disturbances such as those caused by picking up and dropping of the payload by the extended robotic arm. In embodiments, the end of the arm is equipped with a balancing rotor assembly that may provide lift sufficient to counteract the weight of the payload and/or of the arm. In embodiments, the machine's power pack is shifted in response to the disturbances. The power pack may be moved, for example, on a rail within and/or extending beyond the machine in a direction generally opposite to the extended arm. The power pack may also be built into a bandolier-like device that can be rolled-in and rolled out, thus changing the center of gravity of the machine.

Abstract: The disclosure provides an environment in which it is possible to switch an algorithm involved in an abnormality detection process. A control device: calculates a feature quantity from a state value acquired from a monitored object; uses a learning model on the basis of the calculated feature quantity to execute one of a plurality of types of algorithms for calculating a value indicating the probability that an abnormality is occurring in the monitored object; determines, on the basis of the calculated value, whether the abnormality is occurring; and switches, in accordance with a condition defined in advance, the one algorithm that is executed.

Abstract: A method for maintaining a physical asset based on recommendations generated by analyzing operational data and a composite model of a plurality of models representing the physical asset includes constructing, by a computing device, using a port-reduced static condensation reduced basis element approximation of at least a portion of a partial differential equation, the composite model. The computing device analyzes an error indicator associated with at least one model within the composite model to determine that the error indicator exceeds a tolerance level and increases a number of basis functions in the port-reduced static condensation reduced basis element approximation accordingly. The computing device receives first operational data associated with at least one region of the physical asset and updates the composite model. The computing device provides a recommendation for maintaining the physical asset, based upon the updated composite model.

Abstract: Provided is a method for the computer-assisted control of a technical system, in particular in a plant for generating energy, to achieve a predetermined technical behavior of the technical system, wherein an operating data set for controlling the system is provided. A system model for describing the mode of operation of the technical system is provided, wherein on the basis of the operating data set and on the basis of the system model, an optimization data set is determined by an optimization method. Based on the optimization data set, relevant parameters of the technical system that allow a more advantageous control of the technical system than other parameters of the technical system are selected using a selection method, wherein with the selected relevant parameters, a control method for the technical system is determined, wherein the technical system is controlled with the aid of the control method.

Abstract: The novel use of separate programs to control a parallel execution of a plurality of large scale linear program solvers executing sequentially on separate cores is disclosed. An embodiment detailing the system's use in optimizing a utility company's assets is also disclosed. As upgrades or better programs become available, in some embodiments each program in the system can be replaced without affecting the structure of the other programs. In some embodiments, the use of cloud based architecture allows for improved data collection and system execution as all that is needed to process data at a faster speed is to “rent” the additional physical or virtual cores from the provider.

Abstract: A continuous-time recurrent neural network (CTRNN) is described that exploits the nonlinear dynamics of micro-electro-mechanical system (MEMS) devices to model a neuron in accordance with a neuron rate model that is the basis for dynamic field theory. Each MEMS device in the CTRNN is configured to simulate a neuron population by exploiting the characteristics of bi-stability and hysteresis inherent in certain MEMS device structures. In an embodiment, the MEMS device is a microbeam or cantilevered microbeam device that is excited with an alternating current (AC) voltage at or near an electrical resonance frequency associated with the MEMS device. In another embodiment, the MEMS device is an arched microbeam device that is excited with a direct current voltage and exhibits snap-through behavior due to the physical design of the structure. A CTRNN can be implemented using a number of MEMS devices that are interconnected, the connections associated with varying connection coefficients.

Abstract: The invention relates to a method for generating a control system for a target system, wherein: operational data is received; a first neural model component is trained with the received operational data for generating a prediction on a state of the target system based on the received operational data; a second neural model component is trained with the operational data for generating a regularizer for use in inverting the first neural model component; and the control system is generated by inverting the first neural model component by optimization and arranging to apply the regularizer generated with the second neural model component in the optimization. The invention relates also to a system and a computer program product.

Abstract: Systems and methods are provided to move the solving of multi-physics engineering simulations away from specific CAE, or combination CAD and CAE, applications. In one embodiment, an Application Programming Interface (API) is provided that can be integrated into any device, system, application, or software workflow. The API exposes a series of functions or modules that a user can use to create a simulation file that includes parameters such as a model for the simulation, physics for the simulation, timings for the simulation, and other parameters. The simulation file may then be executed on one or more nodes of a cloud-based computer cluster, and the results of executing the simulation can be provided back to the user. The user may then visualize the results using their preferred device, software, application, or workflow.

Abstract: A system and a method of managing a manufacturing process includes receiving production data relating to the manufacturing process and determining an operational mode associated with the manufacturing process using historical, multivariate senor data. The method may further determine a recommended action to affect production based on the determined operational mode. The operational mode may be based on at least one of: a level of operation in a continuous flow process relating to a joint set of process variables, a representation of a joint dynamic of the set of process variables over a predefined length, and a joint configuration of an uptime/downtime of a plurality of units comprising a process flow.

Abstract: Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Such assessment may be performed to determine the robustness of autonomous systems, including the use of virtual assessment of software components within a simulated environment. To this end, a server may retrieve one or more routines associated with autonomous operation. The server may also generate a set of test data associated with test conditions. The server may also execute an emulator that virtually simulates autonomous environment. The test data may be presented to the routines executing in the emulator to generate output data. The server may then analyze the output data to determine a quality metric.

Abstract: A system, method, and apparatus is provided for production processes to coordinate the utilization of the operational zone for process variables and automate the execution of the target settings to maximize the production goals without requiring human intervention.

Abstract: The inhibition device includes a micro-controller configured with a triggering condition including a number of intervals and, for each interval, a corresponding duration and a corresponding threshold. Each interval is a range specifying how much the vehicle's acceleration pedal has changed its position in terms of percentages of a pedal stroke. Each duration specifies the fastest time duration allowable for the acceleration pedal to attain a corresponding interval of pedal position change. The micro-controller converts progress signals of the acceleration pedal to corresponding percentages, obtains a difference DEF between the successive percentages, records a time duration RES between successive progress signals, and calculates DEF/RES=X. When X is greater than or equal to a threshold of a corresponding interval, the micro-controller sends an idle signal to the vehicle's engine control unit or intercepts the progress signals to prevent them from reaching the engine control unit.

Abstract: Method for transferring data from a device to a data management means, switching unit, device and system The invention relates to a method for transferring data (16) from a device (6) to a data management means (4). In order to reduce the amount of data transferred and according to the method of the invention, using modeling data (26, 42) present in the device (6) a model (30, 44) described by the modeling data (26, 42) is determined. Using the model (30, 44), the data that is actually to be transferred to the data management means (4) is selected from a volume of data (40) provided for transfer. The device (6) transfers the selected data to the data management means (4).

Abstract: A plurality of evaluation functions and a machine learning result of each of the evaluation functions are output so that a relation between the evaluation function and the learning result can be ascertained. An output device includes: an output unit that outputs a plurality of evaluation functions used by a machine learning device that performs machine learning of parameters of components of a servo control device that controls a servo motor that drives an axis of a machine tool, a robot, or an industrial machine and a machine learning result of each of the evaluation functions; and an information acquisition unit that acquires the machine learning result from at least one of the servo control device and the machine learning device.

Abstract: Aspects of the disclosure include, for example, obtaining input data. Further embodiments include a determination of a fast path prediction for a first time period according to the input data based on a fast path model. Embodiments include providing instructions to deliver information to a user device according to the fast path prediction. Additional embodiments include obtaining additional input data. Embodiments include a determination of a slow path prediction for the first time period according to the input data and the additional input data based on a slow path model, retraining the fast path model according to the input data and the fast path prediction, and training the slow path model according to the slow path prediction. Embodiments include a determination of a fast path negative impact metric and determination of a slow path negative impact metric. Other embodiments are disclosed.

Abstract: A controller for controlling a vapor compression system is provided. The controller is configured to control an operation of the VCS with different combinations of setpoints for different actuators of the VCS to estimate a cost of operation of the VCS for each of the different combinations of setpoints, and compute, using a Bayesian optimization of the combinations of setpoints and their corresponding estimated costs of operation, a probabilistic surrogate model, wherein the probabilistic surrogate model defines at least first two order moments of the cost of operation in the probabilistic mapping. The controller is further configured to select an optimal combination of setpoints having the largest likelihood of being a global minimum at the surrogate model according to an acquisition function of the first two order moments of the cost of operation.

Abstract: A computer-implemented method for learning a policy for selection of an associative topic, which can be used in a dialog system, is described. The method includes obtaining a policy base that indicates a topic transition from a source topic to a destination topic and a short-term reward for the topic transition, by analyzing data from a corpus. The short-term reward may be defined as probability of associating a positive response. The method also includes calculating an expected long-term reward for the topic transition using the short-term reward for the topic transition with taking into account a discounted reward for a subsequent topic transition. The method further includes generating a policy using the policy base and the expected long-term reward for the topic transition. The policy indicates selection of the destination topic for the source topic as an associative topic for a current topic.

Abstract: Autonomous vehicle operational management may include traversing, by an autonomous vehicle, a vehicle transportation network. Traversing the vehicle transportation network may include operating a scenario-specific operational control evaluation module instance, wherein the scenario-specific operational control evaluation module instance is an instance of a scenario-specific operational control evaluation module, wherein the scenario-specific operational control evaluation module implements a partially observable Markov decision process. Traversing the vehicle transportation network may include receiving a candidate vehicle control action from the scenario-specific operational control evaluation module instance, and traversing a portion of the vehicle transportation network based on the candidate vehicle control action.

Abstract: A model (8) is based on mathematical-physical equations. The model models the production of a particular output product (1) from at least one input product (2) supplied in each case to an installation in the raw materials industry on the basis of operation (B) of the installation. During production of the output products (1), the installation is controlled by a control device (5) in such a manner that particular actual operation (B) of the installation corresponds as far as possible to particular desired operation (B*) of the installation. The desired operation (B*) is determined by the control device (5) using the model (8) of the installation. The model (8) is parameterized according to a number of first model parameters (P1) for the purpose of modelling the installation.

Abstract: Automatic display unit backup during failures of one more display units through the utilization of graphic user interface objects defined for control transfer and reversion after resolution of the failures is provided herein. A system can comprise a memory operatively coupled to a processor that executes stored executable components comprising a first controller, a second controller, and a failure indication component that provides a first notification based on a first detection of a first failure at the second controller, and a second notification based on a second detection of a second failure at the first controller. Further, the executable components can comprise a control transfer component that automatically transfers control of a second display unit from the v controller to the first controller based on the first notification, or control of a first display unit from the first controller to the second controller based on the second notification.

Abstract: A method for maintaining a physical asset based on recommendations generated by analyzing operational data and a composite model of a plurality of models representing the physical asset includes constructing, by a computing device, using a port-reduced static condensation reduced basis element approximation of at least a portion of a partial differential equation, the composite model. The computing device analyzes an error indicator associated with at least one model within the composite model to determine that the error indicator exceeds a tolerance level and increases a number of basis functions in the port-reduced static condensation reduced basis element approximation accordingly. The computing device receives first operational data associated with at least one region of the physical asset and updates the composite model. The computing device provides a recommendation for maintaining the physical asset, based upon the updated composite model.

Abstract: Systems and methods include reception of first data representing each of a plurality of calls transmitted between services of a service landscape, the first data representing each call comprising a respective value of an identifier, identification of a first plurality of the plurality of calls as comprising a same value of the identifier, determination, for each outbound call of the first plurality of calls, of a hypothesis associating the outbound call with a time delay relative to a preceding inbound call of the first plurality of calls, reception of second data representing each of a second plurality of calls transmitted between the services of the service landscape while a latency between two services of the service landscape is increased, the second data representing each of the second plurality of calls comprising a second same value of the identifier, determination of an expected timing of each outbound call of the second plurality of calls based on the hypotheses and the increased latency, comparison

Abstract: Disclosed herein are techniques for virtualizing the I/O of control modules that are to be implemented by a process controller in the runtime environment of a process plant. A configuration application identifies references to the I/O objects utilized by the control modules. In response, the configuration application generates virtual device signal tags (DSTs) to mimic the performance of the identified I/O objects. To facilitate testing and/or verification of the control module, e.g., during the commissioning of a back-end environment, the configuration application instantiates a virtual controller in a simulation environment. To generate the virtual controller, the configuration application replaces any references to the I/O objects with references to respective, generated virtual DSTs. Thus, by using the virtual DSTs as a proxy for the I/O to the field devices, the control module and/or controller may be tested prior to the field devices without the field environment being fully commissioned.

Abstract: A maintenance control program for use in maintaining mechanical and electro-mechanical equipment is provided. The maintenance control program includes a completed survey of the mechanical and electro-mechanical equipment. The completed survey is configured to document the physical characteristics of the mechanical and electro-mechanical equipment and an assessment of a plurality of metrics concerning the mechanical and electro-mechanical equipment. An algorithm is configured to apply values to the assessment of the plurality of metrics. A schedule of code required maintenance tasks is developed for the mechanical and electro-mechanical equipment, wherein an interval of the required maintenance tasks is determined by the algorithm based on the values applied to the assessment of the plurality of metrics.

Abstract: According to embodiments, a system, method and non-transitory computer-readable medium are provided to receive time series data associated with one or more sensors values of a piece of machinery at a first time period, perform a non-linear transformation on the time-series data to produce one or more nonlinear temporal embedding outputs, and projecting each of the nonlinear temporal embedding outputs to a different dimension space to identify at least one causal relationship in the nonlinear temporal embedding outputs. The nonlinear embeddings are further projected to the original dimension space to produce one or more causality learning outputs. Nonlinear dimensional reduction is performed on the one or more causality learning outputs to produce reduced dimension causality learning outputs. The learning outputs are mapped to one or more predicted outputs which include a prediction of one or more of the sensor values at a second time period.

Abstract: A vehicle control system for controlling driving of a vehicle reflecting an environment and a characteristic of a user, while suppressing increase in learning time, is provided. The vehicle control system includes classification means for classifying, by using one or more attributes selected from accumulation means for accumulating data including attributes relating to driving of a vehicle, driving properties included in the data, learning means for learning a model representing the driving property, for each of types that are a result of classification by the classification means, and control information determination means for determining, by using the model learned for the type associated with a value of the attribute at time of driving of a control target vehicle, control information for the driving.

Abstract: A method for controlling an automated or autonomous locomotive device, including automatically ascertaining a deviation from a predefined trajectory, the deviation requiring a return of the locomotive device to the predefined trajectory; automatically calculating a jerk as an input variable, as a function of the deviation from the predefined trajectory; automatically calculating an unconstrained correcting variable for the return to the predefined trajectory, as a function of a weighted sum that includes a weighted summand of the input variable and a weighted summand of the state for the return path; automatically calculating a constrained correcting variable regarding the jerk; the unconstrained correcting variable being manipulated via a cascade that includes multiple stages having one saturation function per stage; integrating the constrained correcting variable, to obtain a constrained return trajectory to the predefined trajectory; automatically steering the locomotive device to the predefined trajectory

Abstract: The present disclosure belongs to the field of marine engineering, and in particular relates to a method and system for guaranteeing the safety of offshore oil well control equipment. The method comprises: identifying the state of a main structure of the offshore oil well control equipment, identifying the state of a hydraulic control unit of offshore oil well control equipment, identifying the state of an electronic control unit of offshore oil well control equipment, predicting the state of the offshore oil well control equipment and making a real-time decision based on existing information; and the system for guaranteeing the safety of the offshore oil well control equipment comprises a state identification subsystem of a main structure, a state identification subsystem of the hydraulic control unit, a state identification subsystem of the electronic control unit and a state prediction and real-time decision subsystem.

Abstract: A data collection system, comprising circuitry configured to: set a sharable public variable from among a plurality of variables of an industrial machine controlled by a control device; collect data on the industrial machine based on the public variable; and record the data in a first storage.

Abstract: Systems, methods, and computer readable storage mediums for performing sensor health monitoring are described. The method includes verifying data quality and suppressing alert generation using machine learning techniques to identify whether two anomalies generated by an asset monitoring system are related. The method can include receiving data characterizing measurement data acquired by a sensor coupled to an industrial asset. An anomalous data sample within the received data can be identified and removed from the anomalous data sample. A new sample of the removed data sample can be estimated using interpolation and the new sample can be assessed. Maintenance analysis can be performed based on the assessed, estimated new sample.

Abstract: A method and system for set point tracking control of an nth order integer control system. A first feedback compensated input signal is generated from an input signal, r(t), and a first negative feedback signal. A fractional order controller generates a set point modified signal from the first feedback compensated input signal. A second feedback compensated input signal is generated from the set point modified signal and a second negative feedback signal. A process variable is generated from the second feedback compensated input signal and applied to a non-linear plant which is affected by a disturbance which carries over into an output signal, y(t). An extended state observer receives the process variable and the output signal and generates the first second negative feedback and the second negative feedback signal to cancel the disturbance.

Abstract: Constructing a MARS prediction model using predictor variables at a first point in time within a time horizon, including directly-controllable variables of first physical characteristics of a system and that are associated with adjustable operational control settings for directly controlling the first physical characteristic, and including controllable variables of second physical characteristics that are affected by the first physical characteristics, recursively using the prediction model to define an optimization problem for later point in time within the time horizon, transforming the optimization problem into a MILP problem, and solving the MILP problem using an optimization engine to determine, for any given one of the directly-controllable variables and corresponding to at least one of the points in time, for adjusting, using the optimized value, the adjustable operational control setting corresponding to the given directly-controllable variable and thereby control the physical characteristic associate

Abstract: A building automation system (BAS) includes building equipment located within a building and a BAS network configured to facilitate communications between the building equipment. The building equipment operate to affect a variable state or condition within the building. The BAS includes a BAS-BIM integrator configured to receive BAS points from the BAS network and to integrate the BAS points with a building information model (BIM). The BIM includes a plurality of BIM objects representing the building equipment. The BAS includes an integrated BAS-BIM viewer configured to use the BIM with the integrated BAS points to generate a user interface. The user interface includes a graphical representation of the BIM objects and the BAS points integrated therewith.