A soldering quality inspection method and a soldering quality inspection apparatus are provided. The soldering quality inspection method includes: acquiring an inspection image; calculating, by a processing device, a dyed area percentage of an area of a part of a soldering region in the inspection image that is dyed by a dye ink relative to an area of the soldering region, and determining whether the dyed area percentage is greater than a predetermined dyed percentage. When the dyed area percentage is determined to be equal to or less than the predetermined dyed percentage, a position under inspection is determined to be of good soldering quality, and a corresponding inspection result information is generated. When the dyed area percentage is determined to be greater than the predetermined dyed percentage, the position under inspection is determined to be of poor soldering quality, and the corresponding inspection result information is generated.

A system for monitoring a switchgear includes an infrared camera; a processing unit; and an output unit. The infrared camera acquires a first infrared image having a first number of pixels, and the processing unit determines a pixel in the first infrared image with a maximum temperature. The processing unit utilizes a second number less than the first number to determine a temperature interval for the first infrared image equal to a difference between the maximum temperature in the first infrared image and a threshold temperature in the first infrared image. The processing unit is configured to determine that a hot spot exists in the switchgear using the temperature interval for the first infrared image.

A method for detecting defects in a formed metal part includes locating one or more regions of interest in a synthetic image of a part manufactured by a forming process. The synthetic image is informed based on a physics-based simulation of the forming process. The regions of interest indicate a high risk of having a defect from the forming process. A set of training images including real images of actual manufactured parts are registered with the synthetic image. The regions of interest are overlaid on each training image, to extract patches from the training images that correspond to high-risk regions. An anomaly detection model is trained on the patches extracted from the training images to detect a defect in a formed metal part from an acquired image of the formed metal part, by detecting an anomaly in a patch extracted from the acquired image that corresponds to a high-risk region.

An inventive approach is disclosed to integrate Digital Image Correlation (DIC) with the Acoustic Emission method that may be used for structural health monitoring and assessment of critical structural components in civil, mechanical, and aerospace industries. The inventive approach relies on passively recording acoustic emission across the specimen being tested and activating the DIC cameras automatically to measure deformation on the specimen's surface. The resulting acousto-optic system can be used to determine damage initiation, progressive damage development, identify critical regions and make lifetime predictions of the tested specimen.

The present invention relates to a method of determining stock in an inventory. The method comprises obtaining one or more images comprising one or more objects. Further, estimating a three dimensional (3D) location of a Stock Keeping Unit (SKU) marker associated with each of one or more visible objects. Furthermore, determining a stacking pattern of the one or more objects for each level on the pallet using one of the 3D location of SKU marker and a learning model. Thereafter, detecting at least one of presence or absence of one or more undetected objects at each level based on the stacking pattern and the 3D location of the SKU marker. Finally, determining the stock in the inventory based on the presence or the absence of the one or more undetected objects and the one or more visible objects.

A method for determining the authenticity of an item, the method comprising: receiving, by an item, a seed; storing the seed in a block of non-volatile memory in the supply item follower component; calculating, by the item follower component, an output of a cryptographic function with the input based on the seed and storing the output in the block of non-volatile memory; iteratively calculating, by the item, the outputs of the cryptographic function wherein for each iteration the input for the cryptographic function is based on the seed and all previous outputs, and for each iteration storing the output in the block of non-volatile memory; and determining the authenticity of the item based on a selected output of the cryptographic function of the item, the selected output being one of the outputs stored in the block of non-volatile memory.

Disclosed herein are examples of defect detection techniques for inspecting semiconductor devices, such as CMOS image sensors, during the manufacturing process. The defects can include common defects, such as scratches, dirt, etc., as well as low-contrast defects, such as watermarks. The detection technique may use a supervised machine learning network.

Embodiments of present invention provide a method for checking integrity of a device selection process. The method includes placing multiple devices in a device tray that has multiple cells arranged in a matrix of M-rows and N-columns; separating the multiple devices into a first group and a second group; causing a machine to memorize locations of at least the first group; removing the second group from the device tray; after the removing, causing the machine to capture an image of devices remaining in the device tray and identify locations of the remaining devices based upon the image; comparing locations so identified with locations of the first group of devices memorized by the machine; and taking a corrective action when a discrepancy is found between the locations identified and locations memorized. An apparatus for performing the above method is also provided.

A deterioration estimation method according to one embodiment is a deterioration estimation method for estimating deterioration of a blade of an optical fiber cutter that cuts an optical fiber. The deterioration estimation method includes a process of determining a state of an end face of the optical fiber cut by the blade and a process of estimating from a determination result for the end face whether or not the blade is deteriorated.

Determination of presence or absence of a defect having irregular position, size, shape, and/or the like in an image are made automatically. An inspection device includes: an inspection image obtaining section that obtains an inspection image used to determine presence or absence of an internal defect in an inspection target; and a defect presence/absence determining section that determines presence or absence of a defect with use of a restored image generated by inputting the inspection image into a generative model constructed by machine learning that uses, as training data, an image of an inspection target in which a defect is absent, the generative model being constructed so as to generate a new image having a similar feature to that of an image input into the generative model.