Search

Biologically inspired controllers demonstrate great success in several applications, mainly in situations that present disturbances and uncertainties in the dynamics of the system. In recent times, several works have appeared in the area of emotional learning which occurs in the human brain, thus allowing to the emergence of new theories and applications in control engineering. In control engineering, it is possible to highlight the BELBIC (Brain Emotional Learning-Based Intelligent Controller). However, the design and commissioning of this type of controller still represents a major challenge for researchers, since it is necessary to determine some characteristic signals to this system (stimuli), which can vary from application to application. This work presents a methodology for the construction of architectures for BELBIC stimulus signals, using as a basis the DRL (Deep Reinforcement Learning) techniques. The DRL allows extracting characteristic patterns from the dynamics of systems which, perhaps, may have high dimensionality and possibly nonlinear dynamics, as is the case of most problems involving real-world dynamic systems. The resulting controller model is validated by applying an inverted pendulum dynamic system in order to demonstrate a new approach to the architectures of the BELBIC that allows to achieve a greater generalization in its application, as well as providing a viable alternative to the traditional models in use. [ABSTRACT FROM AUTHOR]

Aim of the study - To compare the thickness of subcutaneous, preperitoneal and visceral adipose tissue measured by ultrasonography (US) and relate them to the value of Body Mass Index (BMI). Methods - Weight, height and the abdominal perimeter were determined in 218 volunteers (177 females and 41 males, aged between 18 and 33 years, with a body mass index between 20.03 and 37.27kg/m²), later submitted to abdominal ultrasonography. Further, four lifestyle questions were answered by the volunteers. Results - The US allowed to quantify and classify objectively and reproducibly subcutaneous adipose tissue, preperitoneal and visceral, for p<0.01. Pearson's correlation (p<0.01) did not show inter-observer variability in US measurements of subcutaneous adipose tissue (r=0.9871), preperitoneal (r=0.9003), and visceral (r=0.9407). A strong linear correlation between BMI with subcutaneous adipose tissue (r=0.64) and with preperitoneal (r=0.56) was identified. It was verified that the US can classify the genus based on the thickness of the intra-abdominal adipose tissue, abdominal perimeter and BMI with a total accuracy of 86.69%. Conclusions - US shows to be an objective and capable method in the characterization and differentiation of intra-abdominal adipose tissue. The combined use of biometric (except weight and height) and US data allows a correct estimation of BMI. Future studies are needed to understand the usefulness of the Deep Learning frameworks in the automatic detection of different types of abdominal adipose tissue, thus guaranteeing the possibility of the US becoming a quick and preventive method for assessing obesity. [ABSTRACT FROM AUTHOR]