Development Of Robust Sensors Research Articles

Technologies for Risk Mitigation and Support of Impaired Drivers

This editorial serves as an extended introduction to the Special Issue on Technologies for Risk Mitigation and Support of Impaired Drivers. It gives the context to recent advances in assisted and automated driving and the new challenges that arise when modern technology meets human users. The Special Issue focuses on the development of robust sensors and detection algorithms for driver state monitoring of fatigue, stress, and inattention, and on the development of personalized multimodal, user-oriented, and adaptive information, warning, actuation, and handover strategies. A summary of more recent developments serves as a motivation for each article that follows.

Micromagnetic materials characterization using machine learning

Abstract Micromagnetic materials characterization is a nondestructive means of predicting mechanical properties and stress of steel and iron products. The method is based on the circumstance that both mechanical and magnetic behaviour relate to microstructure over similar interaction mechanisms, which leads to characteristic correlations between mechanical and magnetic properties of ferromagnetic materials. The prediction of mechanical properties or stress from micromagnetic parameters represents an inverse problem commonly addressed by regression and classification approaches. Challenges for the industrial application of micromagnetic methods lie in the development of robust sensors, definition of significant features, and implementation of powerful machine learning algorithms for a reliable quantitative target value prediction by processing of the micromagnetic features. This contribution briefly explains the background of micromagnetics, describes the typical challenges experienced in practice and provides insight into latest progress in the application of machine learning to micromagnetic data.

PARAFAC Decomposition of Three‐Way Kinetic‐Spectrophotometric Spectral Matrices Based on Phosphomolymbdenum Blue Complex Chemistry for Nitrite Determination in Water and Meat Samples

A three‐way analytical methodology experimentally based on kinetic‐spectrophotometric and parallel factor analysis (PARAFAC) chemometrics analysis was assessed for the quantification of nitrite in water and meat samples. This method is based on the reduction of phosphomolybdic acid to phosphomolymbdenum blue complex by sodium sulfide. The obtained phosphomolymbdenum blue complex is oxidized by the addition of nitrite and this causes a reduction in intensity of the blue color. Three‐way data matrices were generated by acquisition of ultraviolet‐visible (UV‐Vis) spectra (600–900 nm) as a function of the time and of different relative concentration of the nitrite (0.10–2.10 µg mL−1). The PARAFAC trilinear model, without restrictions, was used in the data analysis. A full decomposition of the data matrices was obtained (spectra, concentration, and time profile). It was shown that kinetic methods coupled to three‐way chemometrics analytical methods can be used for the development of robust sensors for the analysis of nitrite in water and meat samples. The accuracy of the method, evaluated through the root mean square error of prediction (RMSEP), was 0.0515 and 0.1181 for nitrite by PARAFAC and partise least squares (PLS) models respectively. The results with the PARAFAC model are better than those of the PLS model, according to results, it being possible to recover the spectra and kinetic profiles, as well as the initial concentration of nitrite with good accuracy.