Using Multi-Layer Perceptrons to Enhance the Performance of Indoor RTLS

Accuracy in indoor Real-Time Locating Systems (RTLS) is still a problem requiring novel solutions. Wireless Sensor Networks are an alternative to develop RTLS aimed at indoor environments. However, there are some effects associated to the propagation of radio frequency waves, such as attenuation, diffraction, reflection and scattering that depends on the materials and the objects in the environment, especially indoors. These effects can lead to other undesired problems, such as multipath. When the ground is the main responsible for waves reflections, multipath can be modeled as the ground reflection effect. This paper presents a model for improving the accuracy of RTLS, focusing on the mitigation of the ground reflection effect and the estimation of the final position by using Neural Networks

Implementing a real-time locating system based on wireless sensor networks and artificial neural networks to mitigate the multipath effect

Wireless Sensor Networks comprise an ideal technology to develop Real-Time Locating Systems (RTLSs) aimed at indoor environments, where existing global navigation satellite systems do not work correctly due to the blockage of the satellite signals. In this regard, one of the main challenges is to deal with the problems that arise from the effects of the propagation of radio frequency waves, such as multipath. This paper presents an innovative mathematical model for improving the accuracy of RTLSs, focusing on the mitigation of the multipath effect by using Multi-Layer Perceptron Artificial Neural Networks. The model is used to implement a novel indoor Real-Time Locating System based on Wireless Sensor Networks

Data-Driven Mixed-Integer Optimization for Modular Process Intensification

High-fidelity computer simulations provide accurate information on complex physical systems. These often involve proprietary codes, if-then operators, or numerical integrators to describe phenomena that cannot be explicitly captured by physics-based algebraic equations. Consequently, the derivatives of the model are either absent or too complicated to compute; thus, the system cannot be directly optimized using derivative-based optimization solvers. Such problems are known as “black-box” systems since the constraints and the objective of the problem cannot be obtained as closed-form equations. One promising approach to optimize black-box systems is surrogate-based optimization. Surrogate-based optimization uses simulation data to construct low-fidelity approximation models. These models are optimized to find an optimal solution. We study several strategies for surrogate-based optimization for nonlinear and mixed-integer nonlinear black-box problems. First, we explore several types of surrogate models, ranging from simple subset selection for regression models to highly complex machine learning models. Second, we propose a novel surrogate-based optimization algorithm for black-box mixed-integer nonlinear programming problems. The algorithm systematically employs data-preprocessing techniques, surrogate model fitting, and optimization-based adaptive sampling to efficiently locate the optimal solution. Finally, a case study on modular carbon capture is presented. Simultaneous process optimization and adsorbent selection are performed to determine the optimal module design. An economic analysis is presented to determine the feasibility of a proposed modular facility.Ph.D