Abstract: A network-based control method for power consumption of an application, a terminal device and a non-transitory computer-readable storage medium are disclosed. The network-based control method may include: identifying a target application having a frequent wakeup or heartbeat detection behavior in response to a freezing function being enabled; monitoring whether a peer server to which the target application is connected is accessible; and freezing the target application by the freezing function in response to the peer server being inaccessible.

Abstract: A network-based control method for power consumption of an application, a terminal device and a non-transitory computer-readable storage medium are disclosed. The network-based control method may include: identifying a target application having a frequent wakeup or heartbeat detection behavior in response to a freezing function being enabled; monitoring whether a peer server to which the target application is connected is accessible; and freezing the target application by the freezing function in response to the peer server being inaccessible.

Abstract: A food preparation system and method include: triggering image capturing of a camera to obtain one or more images of a food support platform while the food support platform supports a first food item; performing ingredient recognition for the first food item, including: classifying a feature tensor of a respective image in a general classifier to identify one or more first-level food ingredient categories corresponding to the first food item; and classifying the feature tensor of the respective image in a respective detailed classifier corresponding to each of the one or more first-level food ingredient categories to identify a corresponding second-level food ingredient category corresponding to the first food item, wherein the second-level food ingredient category is a sub-category of said each first-level food ingredient category; and, adjusting one or more heating units for heating the first food item in accordance with the ingredient recognition that has been performed.

Abstract: A method of performing automated object inspection includes obtaining a plurality of test images. For each of the plurality of test images, the method includes quantitatively determining a respective transformation from a predefined contour of the reference marker in the predefined common layout to a respective anchor contour corresponding to the reference marker as captured in the test image; and applying an inverse of the respective transformation to at least a portion of the test image to obtain a respective regularized version of the test image, such that the reference markers captured in the respective regularized versions of the plurality of test images share an image-independent location and orientation. The method further includes performing independent automated inspection on each of two or more sub-portions of the respective regularized versions of the test images.

Abstract: A method of performing automated object inspection includes obtaining a plurality of test images. For each of the plurality of test images, the method includes performing independent object inspection on each of two or more sub-portions of the test image. The method further includes segmenting the test image into at least a first sub-portion of the test image and a second sub-portion of the test image; performing object inspection on the first sub-portion of the test image using a first subset of information channels of the test image and a first model trained on a first set of training images containing the first component; and performing object inspection on the second sub-portion of the test image using a second subset of information channels of the test image, and a second model trained on a second set of training images containing the second component.

Abstract: A method of performing automated object inspection includes obtaining a plurality of test images. For each of the plurality of test images, the method includes performing independent object inspection on each of two or more sub-portions of the test image. The method further includes segmenting the test image into at least a first sub-portion of the test image and a second sub-portion of the test image; performing object inspection on the first sub-portion of the test image using a first subset of information channels of the test image and a first model trained on a first set of training images containing the first component; and performing object inspection on the second sub-portion of the test image using a second subset of information channels of the test image, and a second model trained on a second set of training images containing the second component.

Abstract: A method of performing automated object inspection includes obtaining a plurality of test images. For each of the plurality of test images, the method includes quantitatively determining a respective transformation from a predefined contour of the reference marker in the predefined common layout to a respective anchor contour corresponding to the reference marker as captured in the test image; and applying an inverse of the respective transformation to at least a portion of the test image to obtain a respective regularized version of the test image, such that the reference markers captured in the respective regularized versions of the plurality of test images share an image-independent location and orientation. The method further includes performing independent automated inspection on each of two or more sub-portions of the respective regularized versions of the test images.

Abstract: A food preparation system and method include: triggering image capturing of a camera to obtain one or more images of a food support platform while the food support platform supports a first food item; performing ingredient recognition for the first food item, including: classifying a feature tensor of a respective image in a general classifier to identify one or more first-level food ingredient categories corresponding to the first food item; and classifying the feature tensor of the respective image in a respective detailed classifier corresponding to each of the one or more first-level food ingredient categories to identify a corresponding second-level food ingredient category corresponding to the first food item, wherein the second-level food ingredient category is a sub-category of said each first-level food ingredient category; and, adjusting one or more heating units for heating the first food item in accordance with the ingredient recognition that has been performed.