Study of Mechanical Response of Paper-Based Microfluidic System as a Potential Milk Tester.

Milk is considered a complete meal that requires supervision to determine its suitability for human consumption. The development of sustainable devices that evaluate food properties has gained importance due to the necessity of integrating these instruments into the production chain. However, the materials employed to develop it, such as polymers, semiconductors, and glass, lack sustainability and require specialized equipment to fabricate them. Different chemical techniques have been used to miniaturize these detection systems such as microfluidics, which have been used in milk component detection using colorimetry. In this work, a cantilever beam paper-based microfluidic system is proposed to evaluate differences in milk, according to nutritional information, using its electromechanical response. A 20-microliter milk drop is deposited in the system, which induces hygroexpansion and deflection due to liquid transport within the paper. Likewise, a conductive path is added on the beam top surface to supply a constant current that induces heat to evaporate the solution. According to the results obtained, it is possible to point out differences between trademarks with this microfluidic system. The novelty of this system relies on the paper electromechanical response that integrates the hygroexpansion-induced displacement, which can be used for further applications such as milk microtesters instead of colorimetric tests that use paper as a property-evaluation platform in combination with chemical reactions.

Power Disturbance Monitoring through Techniques for Novelty Detection on Wind Power and Photovoltaic Generation.

Novelty detection is a statistical method that verifies new or unknown data, determines whether these data are inliers (within the norm) or outliers (outside the norm), and can be used, for example, in developing classification strategies in machine learning systems for industrial applications. To this end, two types of energy that have evolved over time are solar photovoltaic and wind power generation. Some organizations around the world have developed energy quality standards to avoid known electric disturbances; however, their detection is still a challenge. In this work, several techniques for novelty detection are implemented to detect different electric anomalies (disturbances), which are k-nearest neighbors, Gaussian mixture models, one-class support vector machines, self-organizing maps, stacked autoencoders, and isolation forests. These techniques are applied to signals from real power quality environments of renewable energy systems such as solar photovoltaic and wind power generation. The power disturbances that will be analyzed are considered in the standard IEEE-1159, such as sag, oscillatory transient, flicker, and a condition outside the standard attributed to meteorological conditions. The contribution of the work consists of the development of a methodology based on six techniques for novelty detection of power disturbances, under known and unknown conditions, over real signals in the power quality assessment. The merit of the methodology is a set of techniques that allow to obtain the best performance of each one under different conditions, which constitutes an important contribution to the renewable energy systems.

State of Charge Estimation Model Based on Genetic Algorithms and Multivariate Linear Regression with Applications in Electric Vehicles.

Nowadays, the use of renewable, green/eco-friendly technologies is attracting the attention of researchers, with a view to overcoming recent challenges that must be faced to guarantee the availability of Electric Vehicles (EVs). Therefore, this work proposes a methodology based on Genetic Algorithms (GA) and multivariate regression for estimating and modeling the State of Charge (SOC) in Electric Vehicles. Indeed, the proposal considers the continuous monitoring of six load-related variables that have an influence on the SOC (State of Charge), specifically, the vehicle acceleration, vehicle speed, battery bank temperature, motor RPM, motor current, and motor temperature. Thus, these measurements are evaluated in a structure comprised of a Genetic Algorithm and a multivariate regression model in order to find those relevant signals that better model the State of Charge, as well as the Root Mean Square Error (RMSE). The proposed approach is validated under a real set of data acquired from a self-assembly Electric Vehicle, and the obtained results show a maximum accuracy of approximately 95.5%; thus, this proposed method can be applied as a reliable diagnostic tool in the automotive industry.