Machine learning algorithms traditionally rely on large datasets for high accuracy. However, advances in the field are now enabling the exploration of solutions in niche engineering areas with smaller datasets. This article reviews the challenges and solutions in working with small datasets, particularly in optoelectronics and biomedical engineering. In optoelectronics, small datasets are key for designing and validating photonic systems, as experiments with living tissues can be costly and complex. The article discusses optimizing photonic response simulations and system calibration using machine learning models that are effective with smaller datasets. In biomedical engineering, the focus is on 3D-printed tissue phantoms, which mimic living tissue properties for non-invasive validation of photonic devices in diagnostics. The study explores how small data techniques like transfer learning, bootstrapping, regularization, and K-fold cross-validation can improve interpretations from small datasets, enhance predictive capabilities, and address data scarcity issues.
Interferometric methods are characterized by high measurement accuracy, and the use of fiber optics allows to be used in hard-to-reach areas. Fiber optic interferometry offers a promising capability to operate in real-time monitoring of metal surface in interacting components. This study uses a fiber optic interferometer as an instrument to estimate the surface conditions and displacement. The proposed solution allows evaluating the distance of the fiber optic end from the material surface to determine the distance and surface condition. Measurements were made in the range of 10-500 μm with a step of 10 μm. Stainless steel samples after sliding friction test were measured. The proposed sensor makes it possible to evaluate the degrees of abrasion of the various surfaces of the interacting components in machines.
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