The development of an artificial organic networks toolkit for LabVIEW

Abstract

Two of the most challenging problems that scientists and researchers face when they want to experiment with new cutting-edge algorithms are the time-consuming for encoding and the difficulties for linking them with other technologies and devices. In that sense, this article introduces the artificial organic networks toolkit for LabVIEW™ (AON-TL) from the implementation point of view. The toolkit is based on the framework provided by the artificial organic networks technique, giving it the potential to add new algorithms in the future based on this technique. Moreover, the toolkit inherits both the rapid prototyping and the easy-to-use characteristics of the LabVIEW™ software (e.g., graphical programming, transparent usage of other softwares and devices, built-in programming event-driven for user interfaces), to make it simple for the end-user. In fact, the article describes the global architecture of the toolkit, with particular emphasis in the software implementation of the so-called artificial hydrocarbon networks algorithm. Lastly, the article includes two case studies for engineering purposes (i.e., sensor characterization) and chemistry applications (i.e., blood-brain barrier partitioning data model) to show the usage of the toolkit and the potential scalability of the artificial organic networks technique.

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks

“…To this end, artificial organic networks, as well as artificial hydrocarbon networks, allow [23,25]: Modularity and organization of information, inheritance of packaging information, and structural stability of data packages. A detailed description of the artificial organic networks framework can be found in [23].…”

“…Different compounds have been defined in literature [23], and the simplest of those is the saturated and linear chain of molecules like in (2); where, the line symbol represents a simple bond between two molecules. In fact, if there are n CH-molecules, then the compound will have two $C{H}_3$ and $(n-2)$ $C{H}_2$ molecules [25,26]. Then, a function $\psi \in \mathbb{R}$ is associated to the behavior of the artificial hydrocarbon compound, e.g., the piecewise function [23,27], as expressed in (3); where, $L_t$ represents the t -th bound that limits the action of a CH-molecule over the input space by transforming the bounds into centers $M_{c,j}$.…”

“…A gradient descent method based on the energy of the adjacent molecules ($E_{j-1}$ and $E_j$) is used to calculate the distances as in (5); where, $0<\eta <1$ is the learning rate parameter [23,25]. For implementability, the energy of molecules is computed using a loss function [23,25]. In this work, the least squares estimates (LSE) was used to compute the energy of molecules.…”

“…Formally, an artificial hydrocarbon network is a mixture of artificial hydrocarbon compounds (see Figure 1) each one computed using a chemical-based heuristic rule, expressed in the so-called AHN-algorithm [23,25]. Throughout this work, an artificial hydrocarbon network considers one compound, such that $c=1$ and $S\left(x\right)={\psi }_1\left(x\right)$.…”

Design of Experimental Platform for Intelligent Car

“…The simulation platform can improve the efficiency of the control algorithm and reduce the manpower and material cost, especially for the limited experimental space. Therefore, this paper presents a intelligent car simulation system based on LabVIEW software [4,5]. In order to optimize the control algorithm, different types of runways are often produced in the process of intelligent car speed regulation.…”