Anomaly detection is a crucial aspect of embedded applications. However, limited computational power, evolving environments or lack of training data are difficulties that can limit anomaly detection algorithms. Anomaly detection can be performed by one class classification algorithms to remove the need for anomalous data in the training set. This p...
Published in Computers in Biology and Medicine
While the advanced diagnostic tools and healthcare management protocols have been struggling to contain the COVID-19 pandemic, the spread of the contagious viral pathogen before the symptom onset acted as the Achilles’ heel. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely used for COVID-19 diagnosis, they are hardl...
Published in ISA transactions
Despite the increased sensor-based data collection in Industry 4.0, the practical use of this data is still in its infancy. In contrast, academic literature provides several approaches to detect machine failures but, in most cases, relies on simulations and vast amounts of training data. Since it is often not practical to collect such amounts of da...
Near out-of-distribution detection (OOD) aims at discriminating semantically similar data points without the supervision required for classification. This paper puts forward an OOD use case for radar targets detection extensible to other kinds of sensors and detection scenarios. We emphasize the relevance of OOD and its specific supervision require...
Despite fruitful achievements made by unsupervised machine learning-based anomaly detection for network intrusion detection systems, they are still prone to the issue of high false alarm rates, and it is still difficult to reach very high recalls. In 2020, Leichtnam et al. proposed Sec2graph, an unsupervised approach applied to security objects gra...
Missing data is a recurrent problem in remote sensing, mainly due to cloud coverage for multispectral images and acquisition problems. This can be a critical issue for crop monitoring, especially for applications relying on machine learning techniques, which generally assume that the feature matrix does not have missing values. This paper proposes ...
Enthusiasm for Machine Learning is spreading to nearly all fields such as transportation, energy, medicine, banking or insurance as the ubiquity of sensors through IoT makes more and more data at disposal with an ever finer granularity. The abundance of new applications for monitoring of complex infrastructures (e.g. aircrafts, energy networks) tog...
Published in Ultrasonics
Non-destructive testing is a group of methods for evaluating the integrity of components. Among them, ultrasonic inspection stands out due to its ability to visualize both shallow and deep sections of the material in the search for flaws. Testing of the critical components can be a tiring and time-consuming task. Therefore, human experts in analyzi...
Published in ISA transactions
The reliability of gas turbine diagnostics clearly relies on reliable measurements. However, raw data reliability can be corrupted by label noise issues, as for instance an erroneous association between data and the respective unit of measure. Such issue, rarely investigated in the literature, is named Unit of Measure Inconsistency (UMI). Machine L...
