The present work demonstrates the design and implementation of a human-safe, portable, noninvasive device capable of predicting type 2 diabetes, using electrical bioimpedance and biometric features to train an artificial learning machine using an active learning algorithm based on population selection. In addition, there is an API with a graphical interface that allows the prediction and storage of data when the characteristics of the person are sent. The results obtained show an accuracy higher than 90% with statistical significance (
p
< 0.05). The Kappa coefficient values were higher than 0.9, showing that the device has a good predictive capacity which would allow the screening process of type 2 diabetes. This development contributes to preventive medicine and makes it possible to determine at a low cost, comfortably, without medical preparation, and in less than 2 minutes whether a person has type 2 diabetes.
Electromagnetic Transients Program make extensive use of transmission line models for the simulation of electromagnetic transients. This paper proposes a circuit representation of the modal transformation, more specifically Clarke's matrix. The arrangement of ideal transformers we propose allows modal transformation to be directly implemented in software such as Alternative Transient Program -Electromagnetic Transients Program. We combined the proposed circuit with single-phase transmission line models that consider frequency independent and frequency dependent parameters to represent transposed three-phase transmission lines. The main advantage of the proposed approach is that it allows the implementation of new transmission line models without depending on models provided in applications. To show this capability, we included the frequency dependence of soil parameters in the simulations. Results show that the proposed model is accurate both in the frequency domain and in the time domain.
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