Abstract: A method for controlling building equipment includes generating building data relating to conditions in a building space using a generative artificial intelligence model. The method also includes determining whether the building data correspond to conditions in the building space that comply with one or more regulations or certification standards. The method includes, in response to determining that the building data correspond to conditions in the building space that comply with the one or more regulations or certification standards, using the building data to operate building equipment that serve the building space.

Abstract: A method for determining an operating condition of a liquefied natural gas plant (2) includes preparing a training model (88) generated by machine learning using training data in which operating conditions data including a composition of a feed gas, a composition of a mixed refrigerant, and an ambient temperature and operation result data including a production efficiency of a liquefied product containing liquefied natural gas and a heavy component of the feed gas are associated together; and determining, as one new operating condition, a composition of the mixed refrigerant that optimizes a production efficiency of the liquefied natural gas predicted by the training model (88) from a latest composition of the feed gas in the liquefied natural gas plant (2) and a latest ambient temperature.

Abstract: The aim of the invention is rapid automatic tuning the parameters of a digital proportional-integral-derivative (PID) controller by analog feedback of an actual value for automation of technological processes with programmable logic controllers (PLCs). The proposed invention is based on the use of nine tuning equations derived by reverse engineering of a PID controller. Adjusting the PID controller parameters Kp, Ki and Kd is performed in a closed control loop with negative feedback separately in time, i.e. independently of each other in iteration steps k for Kp, m for Ki and n for Kd (see FIG. 1). The adaptive tuning method is compact, independent of other methods and algorithms, mathematically balanced (i.e. minimal computational resource requirements), and easy to implement. Setting up a PID controller by this method does not require a preliminary evaluation of a controlled system and the creation of its mathematical model. This implies its universal applicability.

Abstract: A system determines that a first data element in a first message is linked to a first data field in a first mapped dataset. The system receives a request to determine to which data field in a second mapped dataset a second data element in a second message should be mapped. The system extracts a first set of features from the first message. The system determines that the first data element is located on a first location within the first message. The system extracts a second set of features from the second message. The system determines that the second data element is location on a second location within the second message. The system determines a difference between the first location and the second location. If the difference is less than a threshold distance, the system associates the second data element to the first data element in the second mapped dataset.

Abstract: A method according to one embodiment includes receiving real-time sensor data from a plurality of pump sensors, wherein each pump sensor of the plurality of pump sensors is configured to generate sensor data associated with at least one characteristic of the pump's operation, comparing the real-time sensor data to at least one threshold value, determining fault information in response to determining the real-time sensor data is outside of one or more of the at least one threshold value, determining a real-time operating point of the pump on a pump performance curve associated with the pump based on the real-time sensor data, displaying, on a graphical user interface of an administrative device, the real-time operating point of the pump on the pump performance curve, and displaying, on the graphical user interface of the administrative device, the fault information in real time.

Abstract: A method, computer system, and computer program product for explaining time series machine learning model are provided. The embodiment may include determining a first order difference in time series input data and historical training data. The embodiment may also include performing perturbation of time series input data based on the determined first order difference and the determined historical training data. The embodiment may further include computing closeness of the determined first order difference in the historical training data to the determined first order difference in the time series input data. The embodiment may also include generating a uniform random sample of first value input to a time series machine learning model. The embodiment may further include determining values of other inputs to the time series machine learning model based on the generated random sample and a random sample from the historical training data first order differences.

Abstract: A control system for controlling motion of elevators of a bank of elevators uses a neural network trained for an extended destination prediction of a person based on a partial trajectory of the person to produce a multinomial of the extended destination prediction. The multinomial has at least two dimensions including a first dimension of destinations of the person and a second dimension of time intervals of the person arriving at the destinations of the first dimension. The control system optimizes a schedule of the bank of elevators based on the multinomial, and further controls the bank of elevators according to the schedule.

Abstract: A method of providing an onboard assistant for a vehicle, comprising: receiving sensor data from sensors of the vehicle; transforming the sensor data using a trained machine learning model to provide transformed sensor data; inferring from the transformed sensor data that a special condition for the vehicle requires intervention; and contacting an operator of the vehicle to inform the operator of the special condition and receive instructions for handling the special condition.

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 non-transitory computer-readable medium comprising computer-executable instructions that, when executed, cause a processor to perform operations including receiving a first dataset from a first automation component, the first dataset corresponds to raw data acquired by a first sensor; receiving a second dataset from a second automation component, the second dataset corresponds to raw data acquired by a second sensor; receiving data indicating an expected operation related to operations of an industrial automation system including the first and second automation components; determining a signature based on the first and second datasets and the data indicating the expected operation, wherein the signature indicates an unexpected operation as compared to the expected operation; performing a root cause analysis using the signature to determine a relationship indicating a first set of changes of the first dataset corresponding to a second set of changes in the data indicating the expected operation.

Abstract: A cleaning arrangement for separating a billet material from an extraneous plant matter in a sugarcane harvester includes a cleaning chamber defining an inlet for the billet material and the extraneous plant matter, a first outlet for the extraneous plant matter, and a second outlet for the billet material. A fan is positioned within the cleaning chamber and configured to generate an airflow to direct the extraneous plant matter toward the first outlet. A billet deflector is located between the inlet and the first outlet and configured to direct the billet material toward the second outlet. An actuation mechanism is coupled to the billet deflector and is controllable to adjust the position of the billet deflector. A sensor is configured to generate a signal. A controller is programmed to activate the actuation mechanism to adjust the position of the billet deflector based on the signal.

Abstract: A method of providing an onboard assistant for a vehicle, comprising: receiving sensor data from sensors of the vehicle; transforming the sensor data using a trained machine learning model to provide transformed sensor data; inferring from the transformed sensor data that a special condition for the vehicle requires intervention; and contacting an operator of the vehicle to inform the operator of the special condition and receive instructions for handling the special condition.

Abstract: An information processing apparatus includes a first sensor, a second sensor, and a central processing unit (CPU). The first sensor detects an object present in a first direction with respect to an autonomous mobile body. The second sensor detects the object present in a second direction with respect to the autonomous mobile body, by a system different from a system of the first sensor. The CPU controls an operation of the autonomous mobile body based on a detection result acquired by the first sensor and a detection result acquired by the second sensor.

Abstract: The techniques disclosed herein enable systems to integrate aging awareness into machine learning agents for management of control systems. To achieve this, the machine learning agent extracts a set of states and an aging model from the control system and derives an aging term from the aging model. Based on the states and the aging term, the machine learning agent determines a set of actions that are applied to the control system. Subsequently, the machine learning agent can extract a new set of states to analyze the efficacy of the set of actions. This is achieved through an optimality function that quantifies the success of the set of actions by calculating an optimality score. The calculation is based on both a current performance of the system and a future drift or degradation of system components. Through many iterations of the action set, the machine learning agent can optimize system operations.

Abstract: A distributed control system (DCS) of an industrial process plant includes a data center storing a plant information model that includes a description of physical components, the control framework, and the control network of the plant using a modeling language. A set of exposed APIs provides DCS applications access to the model, and to an optional generic framework of the data center which stores basic structures and functions from which the DCS may automatically generate other structures and functions to populate the model and to automatically create various applications and routines utilized during run-time operations of the DCS and plant. Upon initialization, the DCS may automatically sense the I/O types of its interface ports, detect communicatively connected physical components within the plant, and automatically populate the plant information model accordingly. The DCS may optionally automatically generate related control routines and/or I/O data delivery mechanisms, HMI routines, and the like.

Abstract: A method of monitoring main contact conditions of main contacts in an automatic transfer switch is provided. The automatic transfer switch is coupled to a plurality of electrical power sources and an electrical load. The method includes: measuring, by sensors, temperatures of a plurality of main contacts coupled to the plurality of power sources; determining if the measured temperatures indicate a temperature rise at one or more main contacts of the plurality of main contacts; in response to determining that the measured temperatures indicate a temperature rise at the one or more main contacts, comparing a current value of the temperature rise with stored temperature rise values that represent main contact damages; and determining an amount of main contact damage at the one or more main contacts based on the comparison.

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: 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: 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 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: 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: 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: 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 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: 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.