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In this study the problem of fitting shape primitives to point cloud scenes was tackled as a parameter optimisation procedure, and solved using the popular bees algorithm. Tested on three sets of clean and differently blurred point cloud models, the bees algorithm obtained performances comparable to those obtained using the state-of-the-art random sample consensus (RANSAC) method, and superior to those obtained by an evolutionary algorithm. Shape fitting times were compatible with real-time application. The main advantage of the bees algorithm over standard methods is that it doesn't rely on ad hoc assumptions about the nature of the point cloud model like RANSAC approximation tolerance.

This article describes the Bees Algorithm in standard formulation and presents two applications to real-world continuous optimisation engineering problems. In the first case, the Bees Algorithm is employed to train three artificial neural networks (ANNs) to model the inverse kinematics of the joints of a three-link manipulator. In the second case, the Bees Algorithm is used to optimise the parameters of a linear model used to approximate the torque output for an electro-hydraulic load system. In both cases, the Bees Algorithm outperformed the state-of-the-art in the literature, proving to be an effective optimisation technique for engineering systems.

This study presents a new neural network approach to identify the presence and type of obstruction in pipes from measurements of passive acoustic emissions. Inserts were used in a fluid re-circulation loop to simulate different types of blockage at various flow rates within the turbulent regime, generating patterns of acoustic emissions. The data were pre-processed using Fourier analysis, and two candidate sets of statistical descriptors were extracted for each measurement. The first set used average and spread of the Fourier transform amplitudes, the second used data binning to obtain a concise representation of the spectrum of amplitudes. Experimental evidence showed the second set of descriptors was the most suitable to train the neural network to recognize with accuracy the presence and type of blockage. The obtained results compare favourably with the literature, indicating that the approach provides a tool to enhance process monitoring in water supply systems, in particular early detection of upstream blockages.