Abstract: Systems and methods for input evaluations through model inversion are described herein. In certain embodiments, a system includes a memory configured to store a model of a physical process, wherein the model receives input values and provide output values, wherein the input values represent potential input parameters for the physical process and the output values represent potential measures of process outputs. The system also includes an interface capable of receiving desired output values for the physical process. Further, the system includes processors executing computer executable instructions associated with an application that cause the processors to receive the desired output values; test a plurality of values for at least one of the input values; identify a combination of the input values that is associated with the desired output values; and provide the combination for output through the interface.

Abstract: Approaches for estimating and optimizing performance of an industrial asset are described. In an example, a set of values of an operational parameter of the industrial asset installed within a networked environment are obtained. Thereafter, the set of values is analyzed to identify a trend within the operational parameter values. The identified trend, along with the set of values, is then used as input to a machine learning model to estimate the value of a performance indicator for the asset. In another example, a desired performance indicator value may be obtained and used in the estimation model to determine optimal operational parameter values to achieve the specified performance. In an example, the estimation model is trained using historical data comprising operational parameters and corresponding performance indicators. Based on the estimated values, the estimation system may generate recommendations for optimizing asset performance or implementing necessary adjustments to achieve desired outcomes.

Abstract: Systems and methods for generating additional samples for industrial processes are described herein. In certain embodiments, a system includes a memory configured to store a model associated with a physical process, wherein the model is configured to receive one or more input values and provide one or more output values, wherein the input values represent potential input parameters for the physical process and the output values represent potential measures of process information. The system also includes one or more processors executing computer executable instructions that cause the processors to receive the input values; generate the output values by executing the model using the input values as model inputs; provide the output values to replace one or more measures based on samples acquired from the physical process; and analyze the physical process based on the output values.

Abstract: The present disclosure relates to a system and method for improved monitoring compliance within an enterprise based on inputs received from stakeholders. The present invention provides a system for generation of compliance model and updation of compliance model based in changes in regulatory compliances. The generation model is configured to generate a first set of labels and a first set of data points. Further, the first set of labels are validated by one or more said user to generate a second set of labels for additional data points based on validation of first set of labels. The generation of first and second set of label generates training ready data from the first and second set of labels for training a data model. Further, the present invention provides for updating of compliance model based on update in regulatory compliance rules.

Abstract: A method and system to achieve heating and cooling temperature within one or more zones when occupied is disclosed. The method comprises training, via at least one processor, a machine learning (ML) model based at least on historical temperature data and input conditions of one or more zones; receiving temperature data from one or more sensors and one or more input conditions of the one or more zones for a predefined time period in real-time; determining a threshold time period to achieve heating or cooling temperature within the one or more zones when occupied using the trained ML model, based at least on the received temperature data and input conditions of the one or more zones for the predefined time period; and adjusting the one or more temperature set points to achieve heating or cooling temperature within the one or more zones when occupied.

Abstract: A method of an onboard device may include receiving component data related to a component of the vehicle during an operation of the vehicle; determining that the component data satisfies a first threshold during the operation of the vehicle; generating a condition indicator that indicates that the component data satisfies the first threshold, based on determining that the component data satisfies the first threshold during the operation of the vehicle; and transmitting the condition indicator to a remote server via a network during the operation of the vehicle. The remote server may determine whether a number of condition indicators, including the condition indicator, received from the onboard device of the vehicle satisfies a second threshold, and selectively generate an alert associated with the component of the vehicle based on determining whether the number of condition indicators received.

Abstract: A method and system to predict occupancy status is disclosed. The method comprises receiving, via at least one processor, occupancy data of one or more zones via a plurality of sensors for a first period of time; determining occupancy trends for each zone for the first period of time using a trained machine learning (ML) model; mapping the determined occupancy trends for each zone with fluctuations in occupancy of each zone in real-time and booking status of each zone; and predicting occupancy of each zone for a second period of time and a threshold time for each zone to heat or cool at one or more temperature set points using the trained ML model based at least on the mapping. Thereafter, the method comprises adjusting the one or more temperature set points for each zone at the threshold time based at least on the prediction.

Abstract: A method may include receiving sensor data from a sensor associated with a component of a vehicle; determining a component scalar of the component using the sensor data and a digital twin model of the component; detecting an abnormality of the sensor associated with the component of the vehicle; based on detecting the abnormality of the sensor, determining an estimated component scalar of the component using the component scalar; and providing the estimated component scalar to the digital twin model of the component.

Abstract: Methods, apparatuses, and computer program products for generating predictions using machine learning model(s) are provided. For example, a computer-implemented method may include receiving raw historical data identifying historical value(s) corresponding to attribute value(s) of a set of ordered attribute values and transforming the raw historical data into parameter expanded data by, for each particular attribute value of the set of ordered attribute values, aggregating each of the historical value(s) that corresponds to the particular attribute value and each of the historical value(s) that corresponds to an attribute value that is greater than the particular attribute value. The machine learning model(s) are trained based on the parameter expanded data, which trained machine learning model(s) are used to generate the predictions.

Abstract: A method for optimizing aggregate values for a request for quotation is provided. The method includes: receiving data associated with a request for quotation for one or more items, the request for quotation including a respective quantity associated with each of the one or more items; assigning, using a first trained machine learning model, one or more aggregate values to the request for quotation; assigning, using a second trained machine learning model, a respective win probability for each of the one or more aggregate values; transmitting the one or more aggregate values and the one or more win probabilities associated with each of the one or more aggregate values to an electronic database; and transmitting the one or more aggregate values and the one or more win probabilities associated with each of the one or more aggregate values to a graphical user interface.

Abstract: A method may include receiving sensor data from a sensor associated with a component of a vehicle; determining a component scalar of the component using the sensor data and a digital twin model of the component; detecting an abnormality of the sensor associated with the component of the vehicle; based on detecting the abnormality of the sensor, determining an estimated component scalar of the component using the component scalar; and providing the estimated component scalar to the digital twin model of the component.

Abstract: A method of an onboard device may include receiving component data related to a component of the vehicle during an operation of the vehicle; determining that the component data satisfies a first threshold during the operation of the vehicle; generating a condition indicator that indicates that the component data satisfies the first threshold, based on determining that the component data satisfies the first threshold during the operation of the vehicle; and transmitting the condition indicator to a remote server via a network during the operation of the vehicle. The remote server may determine whether a number of condition indicators, including the condition indicator, received from the onboard device of the vehicle satisfies a second threshold, and selectively generate an alert associated with the component of the vehicle based on determining whether the number of condition indicators received.

Abstract: The disclosure relates to a water treatment system which may include at least one reverse osmosis cartridge, at least one filtration cartridge, at least one pump and an enclosure.