Abstract: Methods for ascertaining a control strategy for a technical system using a Bayesian optimization method. The control strategy is created based on model parameters of a control model and is executable. The method includes providing a quality function whose shape corresponds to a regression function and that evaluates a quality of a controlling of the technical system based on model parameters; carrying out a Bayesian optimization method based on the quality function in order to iteratively ascertain a model parameter set having model parameters within a model parameter domain that indicates the permissible value ranges for the model parameters; and determining the model parameter domain for at least one of the model parameters as a function of an associated maximum a posteriori estimated value of the quality function.

Abstract: The purpose of the present invention is to provide a plant operation optimization assistance device, a plant operation optimization control device, and a method, which enable a reduction in computational load. The plant operation optimization assistance device is characterized by comprising: an input unit for inputting, as an input signal, an operation amount signal assigned to a plant and a process signal detected at the plant; a sensitivity estimation unit for taking, as a sensitivity signal, a time change amount of the process signal with respect to the operation amount signal; and a signal classification unit for sorting operational states of the plant from the input signal and giving an operational state signal, and also extracting, as a state-specific high-sensitivity signal, a sensitivity signal indicating high-sensitivity, from among sensitivity signals, for each of the sorted operational states.

Abstract: A method includes receiving data, flagging outliers in the received data, each of the outliers representing extreme data values in the received data that stand out greatly from an overall pattern of data values at a given scale in the received data, storing the flagged outliers in an outlier file, removing the flagged outliers from the received data, curating the flagged data points with respect to various neighborhood kernel sizes, and further curating data points with small perturbations using contextual custom regression techniques as a second order refinement process.

Abstract: Various embodiments of the present technology generally relate to integrating machine learning models into industrial automation environments. More specifically, embodiments of the present technology include systems and methods for implementing machine learning models within industrial control code to improve performance, increase productivity, and add capability to existing control programs. In an embodiment, a system comprises a screen capture component configured to capture images of a human-machine interface in an industrial automation environment, wherein the one or more images include at least one visual depiction of data collected from an industrial device. The system further comprises an input component configured to provide the images to a machine learning model configured to analyze an operating condition of the industrial device.

Abstract: The present disclosure relates to a fan system and a fan driving method. The fan system includes a first fan and a second fan, which are used to operate according to a first driving signal and a second driving signal, respectively, and to generate a first rotating speed signal and a second rotating speed signal. The first rotating signal has a first phase, and the second rotating signal has a second phase. The second fan is electrically connected to the first fan to receive the first rotating speed signal, and obtain a detected phase difference between the first phase and the second phase. The second fan is further configured to selectively increase or decrease the duty cycle of the second driving signal according to the detected phase difference, so that the second phase is adjusted to maintain a preset phase difference with the first phase.

Abstract: An assistance system for assisting the planning of automation systems includes a configuration database that has configuration datasets of automation systems, where a respective configuration dataset in each case has the configuration data of a predefined automation system, includes a vectorization component for structuring and adjusting configuration datasets, where the vectorization component is configured to convert the configuration datasets of the configuration database into vectorized configuration datasets, and includes an AI component for processing the vectorized configuration datasets using artificial intelligence, where the processing of the vectorized configuration datasets by the artificial intelligence (AI) component entails utilization of a neural network that has a deep learning architecture.

Abstract: Disclosed is a large-scale dynamic double-effect scheduling method for a flexible job shop based on genetic programming (GP). In a GP implementation process of the present disclosure, in addition to features related to workpieces, processing robots and automated guided vehicles (AGVs), such as processing time of working procedures and buffer area waiting time, features of a ratio of processing time to transfer time and a ratio of processing energy consumption to transfer energy consumption are also added in a terminal set to cause job shop environment features to be more comprehensively applied to a scheduling rule design, thereby automatically designing a more consistent and accurate scheduling rule. A double-effect scheduling rule of the present disclosure overcomes the shortcomings of slow convergence speed, difficulty in rapid corresponding changes and unfit scheduling rules, and can quickly produce a more efficient and energy-saving production scheduling scheme.

Abstract: Various embodiments relate to an electronic device for brain-inspired adaptive control of resolving the bias-variance tradeoff and a method thereof. The method may include estimating a prediction error baseline for an environment, based on a first prediction error of a low-variance intelligent system for the environment and a second prediction error of a low-bias intelligent system for the environment; and implementing an adaptive control system by combining the low-variance intelligent system and the low-bias intelligent system based on the estimated prediction error baseline.

Abstract: An operation analysis method for a natural gas plant includes: acquiring production amount data of a product per unit time and operation data of a plurality of controlled devices forming the natural gas plant in association with each other along a time series; determining whether or not a controlled device that violates the operation constraint is present at the time point of acquisition of the production amount data when the production amount data is less than the reference production amount, and performing data processing of associating an item subjected to the operation constraint to the production amount data; and determining, for each of the items subjected to the operation constraints, a length of a period with the production reduction in accordance with a magnitude of the production reduction amount.

Abstract: A substrate processing system includes a laser triangulation sensor configured to transmit and receive light through a window of an exterior wall of a substrate processing chamber. A controller is configured to: position the laser triangulation sensor such that the laser triangulation sensor transmits light onto a measurement feature arranged between a first surface of a substrate support and a second surface of a gas distribution device, where the second surface faces the first surface; and while the laser triangulation sensor transmits light onto the measurement feature, determine a first distance between the first and second surfaces based on a difference between: a second distance between the laser triangulation sensor and the first surface measured using the laser triangulation sensor; and a third distance between the laser triangulation sensor and the second surface measured using the laser triangulation sensor.

Abstract: Provided are energy device control systems for distributed grid subsystem that control a first power demand of a plurality of appliances. The control system comprises a graphical user interface configured to accept a user input indicative of a first demand and dynamic allocation flexibility associated with the a respective energy device; a communication interface configured to aggregate dynamic allocation values from a plurality of system nodes including at least the user input indicative of a first demand and the dynamic allocation flexibility; and at least one processor programmed to: generate a learning model for evaluating dynamic future allocation with future energy execution prediction, wherein the dynamic future allocation includes at least energy operational information based on a categorization of energy usage at a plurality of respective energy devices; and trigger energy generation on the energy grid at respective generator nodes according to the learning model and dynamic projections.

Abstract: A node in a blockchain network may receive an event initiation for a first digital corollary of a first physical asset, receive conditional data related to the first digital corollary, detecting, by the node, a link to a second digital corollary, prompt a simulation of the second digital corollary, receive an outcome of the simulation, and determine an executable action for the second digital corollary.

Abstract: A DAS system may include an OTDR, and acoustic-sensing optical fibers coupled to the OTDR. The acoustic-sensing optical fibers may have known relative positions within an acoustic wave transmitting medium. The DAS system may also include a processor cooperating with the OTDR to determine a propagation direction of an acoustic wave from an acoustic event in the acoustic wave transmitting medium based upon the known relative positions of the acoustic-sensing optical fibers.

Abstract: Dual inlet air diffuser for a heating ventilation and air conditioning (HVAC) system. The dual inlet air diffuser mixes two or more separate and/or distinct air streams into a homogenous or nearly homogenous mixture of air. The two or more separate air streams may have different temperatures and/or different humidity levels, and/or may be from different sources, such as having one air stream comprising ventilation air that includes filtered air from an outside, ambient air source and a second conditioned air stream from a heating, air conditioning, or other air treatment unit.

Abstract: A scalable industrial data ingestion and analysis architecture integrates and collects data from multiple diverse sources at one or more industrial facilities. Data sources can include plant-level industrial devices and higher-level business systems. The data can be integrated and collected from multiple sources at an on-premise edge or gateway device, which sends the data to event queues on the cloud platform. The data queues orchestrate and store the data on cloud storage, and an analytics layer performs business analytics or other types of analysis on the stored data to produce various outcomes. Similar analytic platforms can also be implemented at the device level, and analytic functions can be scaled between the device level and higher levels in accordance with the scope of a given analytic function.

Abstract: A method of providing optimized settings of graphics parameters for a computer gaming application includes: consolidating data related to settings of graphics parameters for different computer hardware equipment and respective performance values; training a machine learning model based on the consolidated data; determining a weight for each setting of a graphics parameter, by the trained machine learning model; for each set of graphics parameters, predicting a performance value achievable by the computer gaining application when it is executed on a specific type of computer, by the trained machine learning model; assigning a priority value to each graphics parameter based on its contribution to the performance value; choosing or generating at least one set of graphics parameters providing an optimized performance value, based on the predicted performance value associated with each set of graphics parameters and the determined weight for each graphics parameter and/or based on the assigned priority value.

Abstract: Systems and methods for training and utilizing constrained generative models in accordance with embodiments of the invention are illustrated. One embodiment includes a method for training a constrained generative model. The method includes steps for receiving a set of data samples from a first distribution, identifying a set of constraints from a second distribution, and training a generative model based on the set of data samples and the set of constraints.

Abstract: A floating master controller system is provided that includes a plurality of hardware units, each capable of carrying out at least one mechanical function. Each hardware unit includes a processor configured via controller code included in a memory to cause the respective hardware unit to operate and carry out the at least one mechanical function. The controller code for each hardware unit includes a master controller (MC) application and at least one floating master controller (FMC) application. Each MC application is operably configured to function as an active master controller that controls the at least one mechanical function in each of the plurality of hardware units in the system and is operably configured to control the at least one mechanical function for its respective hardware unit responsive to tracking another one of the MC applications that is functioning as the active master controller in the system.

Abstract: Disclosed herein are systems and methods to detect a wide range of eardrum conditions by using visually-descriptive words of a tympanic membrane of a subject.

Abstract: A unit exchanging device for automatically exchanging a component supply unit between multiple component mounting devices in which multiple component supply units for supplying components are detachably installed includes: a gripping portion for gripping the component supply unit; a moving section for moving the gripping portion along a predetermined direction between a position for attaching/detaching the component supply unit to and from a component mounting machine; and a control section for controlling the gripping and release of the gripping of the component supply unit by the gripping portion and controlling the moving section so as to change the standby position in accordance with the gripping state of the component supply unit in the gripping portion.

Abstract: There is provided a system and method of assisting defect detection on a semiconductor specimen. The method includes obtaining a first map informative of multiple care areas (CAs) to be inspected on a die; creating a plurality of bounding rectangles (BRs) enclosing the multiple CAs; and compacting the plurality of BRs to a set of compacted rectangles to meet a predefined inspection capacity while attempting to minimize a non-CA area enclosed by the set of compacted rectangles, giving rise to a second map informative of the set of compacted rectangles. The compaction comprises constructing an R-tree structure representative of the plurality of BRs and compacted rectangles, and selecting a set of nodes from the R-tree structure based on the predefined inspection capacity and representative of the set of compacted rectangles. The second map is usable for filtering a defect map indicative of defect candidate distribution on the die.

Abstract: The present disclosure relates to a pressure measuring device using a textile sensor. In one embodiment the pressure measuring device includes: a textile sensor which exhibits conductivity; a power unit which supplies power to the pressure measuring device; and a control unit which is electrically connected to a textile sensor layer and the power unit, checks a resistance change value detected by the textile sensor by a force applied to the textile sensor, and determines the pressure value of the force applied to the textile sensor on the basis of the checked resistance change value, wherein the control unit checks a control command corresponding to the pressure value measured on the basis of the force applied to the textile sensor, and may transmit the control command to a pre-designated peripheral device. A function corresponding to the checked control command may be executed on the pre-designated peripheral device.

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 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 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 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: 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: 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: 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: 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: 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: 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: 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.