Remote diagnostics Technology Offers

Cracow University of Technology posted this:

A comprehensive method to assess the technical condition of the pressure equipment of installation operated in the chemical and petrochemical industry, with the use of acoustic emission signal analysis, structure stress field and the degradation st The algorithm for the assessment of the technical condition of pressure equipment of chemical and petrochemical installations, subjected to the long-term operation, allows to determine the damage degree of the material and allows to predict the further development of degradation processes in a function of time. The proposed approach to the problem of safe operation of devices is fundamentally different from the solutions which are currently used in industrial practice. Currently, the supervision of these objects is based mainly on standard non-destructive testing methods. The main limitation of used methods is the lack of the possibility of both examining the object in its entire volume and testing the device in working conditions. Developed algorithm allows to effectively solve current problems in the diagnosis of chemical installations related to the assessment of their technical condition and determination of the conditions for further operation in real time. The algorithm is particularly recommended for monitoring devices in which adverse damages occur due to the long-term operation.

Servei de Gestió de la Innovació posted this:
Licensing Manager at Universitat Politècnica de Catalunya - UPC

We have developed a new algorithm for ordering anterior chamber OCT images in such a way that it is possible to classify them, in a fully unsupervised manner, in meaningful groups according to relevant features. We have tested the algorithm with a large set of images classified by two expert ophthalmologists, and with a larger set of annotated images. We have verified that the separation in the different classes defined by the ophthalmologists (closed, narrow, open, and wide open_ figure 6) is similar when using the manually extracted features, or when using the features that are returned by the unsupervised algorithm (View figures). Therefore, the abstract features generated by the algorithm provide novel tools for assessing OCT images of the anterior chamber. They can be used for direct classification of the images and, furthermore, they can be linked to established quantities used for characterizing diseased eyes (like chamber depth, iris-corneal angle) resulting in an automatic detection system. As the algorithm is fully unsupervised, it can be easily automated and set up in OCT imaging systems to aid technicians and doctors in an early diagnosis. The two main advantages of the algorithm demonstrated here over previous works are that it doesn’t need any ground truth or gold standard for training, and it does not rely on specific landmarks; thus, it can analyze images in which relevant landmarks are not visible or not easy to locate.
Image processing method for glaucoma detection and computer program