Abstract: Methods and systems for visualizing, analyzing, archiving and securing computer and internet of things (IoT) data networks are disclosed. The system includes a data collection device (sensor), preprocessing unit, analysis unit containing at least the Koopman mode analysis unit, and a postprocessing unit. The methods include Koopman mode analysis, support vector machines or deep learning used to compute the baseline, detect and rank known and unknown threats to the system, visualize and archive them. The methods also include creating and representing an Artificial Intelligence (AI) determined risk level indicators; using combined intel and notice alert severities with the AI risk level indicators to rank the alerts; using the AI indicators to create zero day risks; an AI Button to show the AI indicators and ranked alerts on a computer screen; and graphic user interfaces (GUI) to intuitively represent and interact with the AI indicators and ranked alerts.

Abstract: Methods and systems for visualizing, analyzing, archiving and securing computer and internet of things (IoT) data networks are disclosed. The system includes a data collection device (sensor), preprocessing unit, analysis unit containing at least the Koopman mode analysis unit, and a postprocessing unit. The methods include Koopman mode analysis, support vector machines or deep learning used to compute the baseline, detect and rank known and unknown threats to the system, visualize and archive them.

Abstract: Methods and systems for visualizing, analyzing, archiving and securing computer and internet of things (IoT) data networks are disclosed. The system includes a data collection device (sensor), preprocessing unit, analysis unit containing at least the Koopman mode analysis unit, and a postprocessing unit. The methods include Koopman mode analysis, support vector machines or deep learning used to compute the baseline, detect and rank known and unknown threats to the system, visualize and archive them. The methods also include creating and representing an Artificial Intelligence (AI) determined risk level indicators; using combined intel and notice alert severities with the AI risk level indicators to rank the alerts; using the AI indicators to create zero day risks; an AI Button to show the AI indicators and ranked alerts on a computer screen; and graphic user interfaces (GUI) to intuitively represent and interact with the AI indicators and ranked alerts.

Abstract: Methods and systems for visualizing, analyzing, archiving and securing computer and internet of things (IoT) data networks are disclosed. The system includes a data collection device (sensor), preprocessing unit, analysis unit containing at least the Koopman mode analysis unit, and a postprocessing unit. The methods include Koopman mode analysis, support vector machines or deep learning used to compute the baseline, detect and rank known and unknown threats to the system, visualize and archive them.

Abstract: A system and method for stability monitoring, analysis, and control of energy systems, such as electric power systems, is provided. Specifically, the system and method described herein use a Koopman mode analysis to represent the operation of individual components in an energy system and/or to determine and rank a level of stability of the individual components in the energy system. The system and method described herein also use a Koopman mode analysis to determine optimal and/or safe times to release energy stored in storage systems.

Abstract: Methods and systems for visualizing, analyzing, archiving and securing computer and internet of things (IoT) data networks are disclosed. The system includes a data collection device (sensor), preprocessing unit, analysis unit containing at least the Koopman mode analysis unit, and a postprocessing unit. The methods include Koopman mode analysis, support vector machines or deep learning used to compute the baseline, detect and rank known and unknown threats to the system, visualize and archive them.

Abstract: Methods and systems for visualizing, analyzing, archiving and securing computer and internet of things (IoT) data networks are disclosed. The system includes a data collection device (sensor), preprocessing unit, analysis unit containing at least the Koopman mode analysis unit, and a postprocessing unit. The methods include Koopman mode analysis, support vector machines or deep learning used to compute the baseline, detect and rank known and unknown threats to the system, visualize and archive them.

Abstract: A system and method for stability monitoring, analysis, and control of energy systems, such as electric power systems, is provided. Specifically, the system and method described herein use a Koopman mode analysis to represent the operation of individual components in an energy system and/or to determine and rank a level of stability of the individual components in the energy system. The system and method described herein also use a Koopman mode analysis to determine optimal and/or safe times to release energy stored in storage systems.

Abstract: Certain embodiments relate to systems and methods for monitoring and improving the efficiency by which systems maintain building environments. A building management system may analyze values observed by sensors and other devices and determine whether the observed values fall within a range. If the observed values do not fall within the range and the building's control system has not taken appropriate action to ensure that the observed values do fall within the range, the building may generate a floor plan specifying where the failure occurred. A building ranking system may rank a plurality of buildings based upon their relative efficiency. The building ranking system may determine each building's efficiency based on a set of parameters related to the building's energy usage that are normalized for weather, occupancy, and other external parameters.

Abstract: Certain embodiments relate to systems and methods for monitoring and improving the efficiency by which systems maintain building environments. A building management system may analyze values observed by sensors and other devices and determine whether the observed values fall within a range. If the observed values do not fall within the range and the building's control system has not taken appropriate action to ensure that the observed values do fall within the range, the building may generate a floor plan specifying where the failure occurred. A building ranking system may rank a plurality of buildings based upon their relative efficiency. The building ranking system may determine each building's efficiency based on a set of parameters related to the building's energy usage that are normalized for weather, occupancy, and other external parameters.

Abstract: Various systems and methods are provided that detect faults in data-based systems utilizing techniques that stem from the field of spectral analysis and artificial intelligence. For example, a data-based system can include one or more sensors associated with a subsystem that measure time-series data. A set of indicator functions can be established that define anomalous behavior within a subsystem. The systems and methods disclosed herein can, for each sensor, analyze the time-series data measured by the respective sensor in conjunction with one or more indicator functions to identify anomalous behavior associated with the respective sensor of the subsystem. A spectral analysis can then be performed on the analysis to generate spectral responses. Clustering techniques can be used to bin the spectral response values and the binned values can be compared with fault signatures to identify faults. Identified faults can then be displayed in a user interface.

Abstract: A system and method for stability monitoring, analysis, and control of energy systems, such as electric power systems, is provided. Specifically, the system and method described herein use a Koopman mode analysis to represent the operation of individual components in an energy system and/or to determine and rank a level of stability of the individual components in the energy system. The system and method described herein also use a Koopman mode analysis to determine optimal and/or safe times to release energy stored in storage systems.

Abstract: Systems and methods are disclosed for the generation of more accurate usage profiles for use in the modeling of existing buildings. Building sensor data associated with individual rooms in a building can be decomposed using wavelets to identify features that correspond to heat loads generated by occupant presence and/or equipment operation. From this information, the occupancy state of individual rooms can be estimated. The estimated occupancy state for the individual rooms in the building then can be used to generate a usage profile for the building. The usage profile can be used to estimate current and/or future energy usage or costs or to control equipment in the building at a current and/or future time.

Abstract: A system and method are provided for physical data rectification using regression models. For example, the physical data may be energy infrastructure sensor data. The system may perform an estimation of sensor data during periods of data dropout using a regression model. The system may assess the accuracy of regression models through the comparison of probability distribution functions of physical data estimated using the regression model and actual physical data.

Abstract: Systems and methods are disclosed for analyzing building sensor information and decomposing the information therein to a more manageable and more useful form. Certain embodiments integrate energy-based and spectral-based analysis methods with parameter sampling and uncertainty/sensitivity analysis to achieve a more comprehensive perspective of building behavior. The results of this analysis may be presented to a user via a plurality of visualizations and/or used to automatically adjust certain building operations. In certain embodiments, advanced spectral techniques, including Koopman-based operations, are employed to discern features from the collected building sensor data.

Abstract: Systems and methods are disclosed for analyzing building sensor information and decomposing the information therein to a more manageable and more useful form. Certain embodiments integrate energy-based and spectral-based analysis methods with parameter sampling and uncertainty/sensitivity analysis to achieve a more comprehensive perspective of building behavior. The results of this analysis may be presented to a user via a plurality of visualizations and/or used to automatically adjust certain building operations. In certain embodiments, advanced spectral techniques, including Koopman-based operations, are employed to discern features from the collected building sensor data.

Abstract: Particles are separated, concentrated, or mixed within a fluid by means of a fluid-containing cell having a longitudinal axis, a cross-sectional area generally perpendicular to the longitudinal axis, and at least one particle motivating force directionally interacting with at least one recurrent circulating fluid flow generally aligned with the longitudinal axis within the fluid containing cell.

Abstract: An apparatus, a method and a process to achieve manipulation of particles and/or solutions through the use of electrokinetic properties are disclosed. The manipulation is performed using a disposable device positioned on top of a stage for purposes of powering the electrodes. The fluidic solution is brought into contact with the active part of the device and then manipulated.

Abstract: An apparatus, a method and a process to achieve manipulation of particles and/or solutions through the use of electrokinetic properties are disclosed. The manipulation is performed using a disposable device positioned on top of a stage for purposes of powering the electrodes. The fluidic solution is brought into contact with the active part of the device and then manipulated.

Abstract: Systems and methods are disclosed for analyzing building sensor information and decomposing the information therein to a more manageable and more useful form. Certain embodiments integrate energy-based and spectral-based analysis methods with parameter sampling and uncertainty/sensitivity analysis to achieve a more comprehensive perspective of building behavior. The results of this analysis may be presented to a user via a plurality of visualizations and/or used to automatically adjust certain building operations. In certain embodiments, advanced spectral techniques, including Koopman-based operations, are employed to discern features from the collected building sensor data.