Abstract: A method of inspecting a component using an image retrieval module includes storing an inspection image file in a memory and identifying a region of interest in the inspection image file. The method further includes accessing a database storing image files and determining feature vectors associated with the image files. The method also includes determining a hash code for each image file based on the feature vectors and classifying a subset of image files as relevant based on the hash codes. The method further includes sorting the subset of image files based on the feature vectors and generating search results based on the sorted subset of image files. The image retrieval module includes a convolutional neural network configured to learn from the determination of the feature vectors and increase the accuracy of the image retrieval module in classifying the image files.

Abstract: A method of inspecting a component includes storing at least one inspection image file in a memory and receiving a search request associated with the at least one inspection image file. The method also includes accessing a database including a plurality of image files, comparing the hash code of the at least one inspection image file to the hash code of each image file of the plurality of image files, and identifying a first subset of image files based on the hash code comparison. The method also includes comparing the feature data of the at least one inspection image file to the feature data of each image file of the first subset of image files and classifying a second subset of image files as relevant based on the feature data comparison. The method further includes generating search results based on the second subset of image files.

Abstract: A method of inspecting a component using an image retrieval module includes storing an inspection image file in a memory and identifying a region of interest in the inspection image file. The method further includes accessing a database storing image files and determining feature vectors associated with the image files. The method also includes determining a hash code for each image file based on the feature vectors and classifying a subset of image files as relevant based on the hash codes. The method further includes sorting the subset of image files based on the feature vectors and generating search results based on the sorted subset of image files. The image retrieval module includes a convolutional neural network configured to learn from the determination of the feature vectors and increase the accuracy of the image retrieval module in classifying the image files.

Abstract: A method of inspecting a component includes storing at least one inspection image file in a memory and receiving a search request associated with the at least one inspection image file. The method also includes accessing a database including a plurality of image files, comparing the hash code of the at least one inspection image file to the hash code of each image file of the plurality of image files, and identifying a first subset of image files based on the hash code comparison. The method also includes comparing the feature data of the at least one inspection image file to the feature data of each image file of the first subset of image files and classifying a second subset of image files as relevant based on the feature data comparison. The method further includes generating search results based on the second subset of image files.

Abstract: A method of inspecting a component using an image inspection controller that includes a processor communicatively coupled to a memory includes classifying each sample image in a first database as a first sample or a second sample using a classification module, extracting at least one class generic feature from each first sample to generate a plurality of class generic features, and extracting at least one class specific feature from each second sample to generate a plurality of class specific features. The method further includes combining the class generic features and the class specific features to generate a plurality of supplemental images. The method further includes storing the sample images and the supplemental images in a second database, classifying each sample image and each supplemental image, capturing at least one image of the component using a camera, and identifying at least one feature of the component in the at least one image of the component using the classification module.

Abstract: A method of inspecting a component using an image inspection controller that includes a processor communicatively coupled to a memory includes classifying each sample image in a first database as a first sample or a second sample using a classification module, extracting at least one class generic feature from each first sample to generate a plurality of class generic features, and extracting at least one class specific feature from each second sample to generate a plurality of class specific features. The method further includes combining the class generic features and the class specific features to generate a plurality of supplemental images. The method further includes storing the sample images and the supplemental images in a second database, classifying each sample image and each supplemental image, capturing at least one image of the component using a camera, and identifying at least one feature of the component in the at least one image of the component using the classification module.

Abstract: A system thinker application receives a first issue, a first resolution to the first issue, and a first plurality of skills. The system thinker application searches a system environment electronic profile for a second issue, a second resolution to the second issue, and a second plurality of skills, wherein the system environment electronic profile contains a plurality of component profiles, and wherein the plurality of component profiles contain a second issue, a second resolution to the second issue, and a second plurality of skills. The system thinker application determines if the first issue, the first resolution to the first issue, and any one of the first plurality of skills are similar to any one of the second issue, the second resolution to the second issue, and any one of the second plurality of skills. The system thinker application adds skills to the system environment electronic profile and the component profile.

Abstract: The system thinker application receives a first issue, a first resolution to the first issue, and a first plurality of skills. The system thinker application searches a system environment electronic profile for a second issue, a second resolution to the second issue, and a second plurality of skills, wherein the system environment electronic profile contains a plurality of component profiles, and wherein the plurality of component profiles contain a second issue, a second resolution to the second issue, and a second plurality of skills. The system thinker application determines if the first issue, the first resolution to the first issue, and any one of the first plurality of skills are similar to any one of the second issue, the second resolution to the second issue, and any one of the second plurality of skills. The system thinker application adds skills to the system environment electronic profile and the component profile.

Abstract: A mechanism for creating links and services on the Internet to reduce the bottlenecks at service providers by associating code with links to make them intelligent and a special protocol between the links and the back end servers to maintain scalableness and performance. Scalableness is supported by moving much of the functionality of searching onto the client side.