Wayne Fueling Systems LLC

Vehicle Service Group, LLC

The Heil Co.

Bodhayan Dev

Prem Swaroop

Atish Kamble

Girish Juneja

Sreedhar Patnala

Michael Johannes

In one aspect, data characterizing a fuel storage facility can be received from a sensor in operable communication with the fuel storage facility. An estimate of meter drift of a flow meter of a fuel dispenser in fluid communication with the fuel storage facility can be determined based on the received data. The estimate of meter drift can be determined based on at least one predictive model that predicts whether a calibration parameter characterizing a calibration of the flow meter has deviated from a predetermined flow meter calibration parameter. The estimate of meter drift can be provided.

Real-time determination of meter drift via loss qualification and quantification

Systems and methods for detecting hail damage on a vehicle are described including, receiving an image of at least a section of a vehicle. Detecting a plurality of hail damage including, detecting a plurality of damaged areas distributed over the entire section of the vehicle, and differentiating the plurality of damaged areas from one or more areas of noise, processing the received image to classify one or more sections of the vehicle as one or more panels of the vehicle bodywork, and using the detected areas of damage, the classification of the seriousness of the damage and the classification of one or more panels to compute a panel damage density estimate.

Damage detection using machine learning

Data characterizing a fuel storage facility can be received from one or more of a plurality of sensors disposed in the fuel storage facility. A fuel leak prediction for the fuel storage facility can be determined by a server, based on the received data, and further based on at least one predictive model that predicts whether a fuel leak exists in the fuel storage facility. The fuel leak prediction can be provided by the server. Related apparatus, systems, methods, techniques, and articles are also described.

Fuel leak determination via predictive modeling

Among other things, the techniques described herein include a method for receiving a plurality of images of one or more containers while the one or more containers are being emptied, the plurality of images comprising a training set of images and a validation set of images; labeling each image of the plurality of images as including either an overfilled container or a not-overfilled container; processing each image of the plurality of images to reduce bias of a machine learning model; training, and based on the labeling, the machine learning model using the plurality of images; and optimizing the machine learning model by performing learning against the validation set, the optimized machine learning model being used to generate a prediction for a new image of a container, the prediction indicating whether the container in the new image was overfilled prior to the new container being emptied.

Identifying overfilled containers

Methods, devices, apparatus, systems and computer-readable storage media for assessing damages on vehicles are provided. In one aspect, a method includes: accessing an image of a vehicle showing shapes each indicating at least one damage area on the vehicle; providing the image as input to a first model to identify the shapes in the image and obtaining shape data describing a position of each shape identified in the image; providing the image as input to a second model to identify one or more panels of the vehicle and obtaining panel data describing a position of each panel identified in the image; automatically correlating the one or more shapes and the one or more panels based on the shape data and the panel data to determine a number of shapes present on each panel; and generating a damage assessment report describing the number of shapes present on each panel.

Assessing damages on vehicles

In one aspect, fuel inventory data characterizing a level of fuel stored at a fuel storage facility can be received from a sensor in operable communication with the fuel storage facility. Flow meter data characterizing an amount of fuel dispensed from the fuel storage facility by a fuel dispenser nozzle can be received from a flow meter in fluid communication with the fuel dispenser nozzle and the fuel storage facility. Fuel transaction data characterizing the dispensing of fuel by the fuel dispenser nozzle and the dispensing of the fuel by one or more additional fuel dispenser nozzles in fluid communication with the fuel storage facility can be received. An estimate of meter drift characterizing a change in calibration of the flow meter can be determined based on the fuel inventory data, the flow meter data, and the fuel transaction data. The estimate of meter drift can be provided.

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Marlborough, MA, United States of America

Bodhayan Dev