Disclosed is a methodology for determination and prediction of heat exchanger fouling, such as polymer fouling in the circulation loop that forms part of the heat exchanger system. The buildup of a polymer or other undesired material deposit in the heat exchanger provides a distinctive temperature signature (thermal gradient) on the surface of the heat exchanger asset, which is visualized using a thermographic camera. Coupling images (thermograms) from the camera with a machine learning algorithm identifies fouling and, with knowledge of the historical data of the asset and operating and ambient conditions, enables prediction of future fouling. The thermal images provide several types, or orders, of temperature information that are indicative of locations vulnerable to fouling. In one case, the method uses machine learning applied to time-based temperature change/gradient information to detect hidden polymer fouling in areas that form part of the heat exchanger asset.

According to one embodiment, a generation device acquires a plurality of images of a temperature distribution of a conveyance object. The images are generated by a thermal camera. The generation device sets a plurality of measurement areas in each of the images along a conveyance direction of the conveyance object. The generation device generates time-series data of temperature change over time for each of the plurality of measurement areas. The generation device generates a temperature profile of temperature change over time of the conveyance object by using a portion of temperatures extracted from the time-series data of the plurality of measurement areas.

An apparatus, comprising: at least one processing circuitry, and at least one memory for storing instructions to be executed by the processing circuitry, wherein the at least one memory and the instructions are configured to, with the at least one processing circuitry, cause the apparatus at least to: input display data obtained from a first terminal endpoint device located in a first three-dimensional environment into a deep neural network model for terminal endpoint device pose estimation, the display data comprising at least image data of a captured image of at least part of the first three-dimensional environment acquired by the first terminal endpoint device at a first point of time and sensory data indicative of at least a motion vector of a movement of the first terminal endpoint device in the three-dimensional environment acquired by the first terminal endpoint device at a second point of time, the deep neural network model being trained with, as model input, training image data of a captured training image of at least part of a three-dimensional training environment acquired by a training terminal endpoint device located in the three-dimensional training environment and training sensory data indicative of at least a motion vector of a movement of the training terminal endpoint device in the three-dimensional training environment and, as model output, training poses of the training terminal endpoint device in the three-dimensional training environment, and obtain from the deep neural network model for terminal endpoint device pose estimation, based on the input display data, a first estimated pose of the first terminal endpoint device in the first three-dimensional environment.

An apparatus, comprising: at least one processing circuitry, and at least one memory for storing instructions to be executed by the processing circuitry, wherein the at least one memory and the instructions are configured to, with the at least one processing circuitry, cause the apparatus at least to: input display data obtained from a first terminal endpoint device located in a first three-dimensional environment into a deep neural network model for terminal endpoint device pose estimation, the display data comprising at least image data of a captured image of at least part of the first three-dimensional environment acquired by the first terminal endpoint device at a first point of time and sensory data indicative of at least a motion vector of a movement of the first terminal endpoint device in the three-dimensional environment acquired by the first terminal endpoint device at a second point of time, the deep neural network model being trained with, as model input, training image data of a captured training image of at least part of a three-dimensional training environment acquired by a training terminal endpoint device located in the three-dimensional training environment and training sensory data indicative of at least a motion vector of a movement of the training terminal endpoint device in the three-dimensional training environment and, as model output, training poses of the training terminal endpoint device in the three-dimensional training environment, and obtain from the deep neural network model for terminal endpoint device pose estimation, based on the input display data, a first estimated pose of the first terminal endpoint device in the first three-dimensional environment.

Various techniques are disclosed to provide for improved human body temperature detection using thermal images of an inner canthus. In one example, a method includes capturing a thermal image of a human being using a thermal imager. The method also includes determining an uncompensated temperature measurement associated with an inner canthus of a face of the human being using corresponding pixels of the thermal image. The method also includes determining a correction term as a function of a distance between the thermal imager and the human being. The method also includes applying the correction term to the uncompensated temperature measurement to provide a corrected temperature measurement associated with the inner canthus to compensate for attenuation associated with the distance. Additional methods and systems are also provided.

Various techniques are disclosed to provide for improved human body temperature detection using thermal images of an inner canthus. In one example, a method includes capturing a thermal image of a human being using a thermal imager. The method also includes determining an uncompensated temperature measurement associated with an inner canthus of a face of the human being using corresponding pixels of the thermal image. The method also includes determining a correction term as a function of a distance between the thermal imager and the human being. The method also includes applying the correction term to the uncompensated temperature measurement to provide a corrected temperature measurement associated with the inner canthus to compensate for attenuation associated with the distance. Additional methods and systems are also provided.

A method and a system method for real-time wide-field dynamic temperature sensing of an object, the method comprising producing wide-field illumination to upconverting nanoparticles at the object plane, collecting a light emitted by the upconverting nanoparticles, dividing a collected light into a reflected component and a transmitted component; imaging the reflected component into a first image, imaging the transmitted component into a second image; processing the images; and reconstruction of the object from resulting proceed images.

A method and a system method for real-time wide-field dynamic temperature sensing of an object, the method comprising producing wide-field illumination to upconverting nanoparticles at the object plane, collecting a light emitted by the upconverting nanoparticles, dividing a collected light into a reflected component and a transmitted component; imaging the reflected component into a first image, imaging the transmitted component into a second image; processing the images; and reconstruction of the object from resulting proceed images.

Systems and methods are described for securely monitoring a shipping container for an environmental anomaly using elements of a wireless node network of sensor-based ID nodes disposed within the container and a command node associated with the container. The method has the command node identifying which of the ID nodes are confirmed as trusted sensors based upon a security credential specific to each of the ID nodes; monitoring only the confirmed ID nodes for sensor data broadcast those ID nodes; detecting the anomaly based upon the sensor data from at least one of the confirmed ID nodes; automatically generating an alert notification related to the detected environmental anomaly for the shipping container; and transmitting the alert notification to the external transceiver to initiate a mediation response related to the detected environmental anomaly.

A temperature measuring device using a thermal imaging camera according to an embodiment of the present invention may comprise: a first operation module for obtaining, for the thermal imaging camera, a curve of temperature difference versus output code difference where the X axis represents the output code difference and the Y axis represents the temperature difference indicated by a plurality of measured values; a second operation module for obtaining a function of temperature difference versus output code difference, the function curve-fitted by using the curve of temperature difference versus output code difference; and a third operation module for measuring the temperature of an object by applying the curve-fitted function of temperature difference versus output code difference.