Hybrid Functional-Neural Approach for Surface Reconstruction

ABSTRACT. This paper introduces a new hybrid functional-neural approach for surface reconstruction. Our approach is based on the combination of two powerful artificial intelligence paradigms: on one hand, we apply the popular Kohonen neural network to address the data parameterization problem. On the other hand, we introduce a new functional network, called NURBS functional network, whose topology is aimed at reproducing faithfully the functional structure of the NURBS surfaces. These neural and functional networks are applied in an iterative fashion for further surface refinement. The hybridization of these two networks provides us with a powerful computational approach to obtain a NURBS fitting surface to a set of irregularly sampled noisy data points within a prescribed error threshold. The method has been applied to two illustrative examples. The experimental results confirm the good performance of our approach.This research has been kindly supported by the Computer Science National Program of the Spanish Ministry of Economy and Competitiveness, Project ref. no. TIN2012-30768, Toho University (Funabashi, Japan), and the University of Cantabria (Santander, Spain)

Free-form curve design by neural networks.

This paper gives a new approach of the two dimensional scattered data manipulation. The standard approximation and interpolation methods which can only be used for non-scattered data will also be applicable for scattered input with the help of the neural network. The Kohonen network produces an ordering of the scattered input points and here the B-spline curve is used for the approximation and interpolation

Multiple 2D self organising map network for surface reconstruction of 3D unstructured data

Surface reconstruction is a challenging task in reverse engineering because it must represent the surface which is similar to the original object based on the data obtained. The data obtained are mostly in unstructured type whereby there is not enough information and incorrect surface will be obtained. Therefore, the data should be reorganised by finding the correct topology with minimum surface error. Previous studies showed that Self Organising Map (SOM) model, the conventional surface approximation approach with Non Uniform Rational B-Splines (NURBS) surfaces, and optimisation methods such as Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimisation (PSO) methods are widely implemented in solving the surface reconstruction. However, the model, approach and optimisation methods are still suffer from the unstructured data and accuracy problems. Therefore, the aims of this research are to propose Cube SOM (CSOM) model with multiple 2D SOM network in organising the unstructured surface data, and to propose optimised surface approximation approach in generating the NURBS surfaces. GA, DE and PSO methods are implemented to minimise the surface error by adjusting the NURBS control points. In order to test and validate the proposed model and approach, four primitive objects data and one medical image data are used. As to evaluate the performance of the proposed model and approach, three performance measurements have been used: Average Quantisation Error (AQE) and Number Of Vertices (NOV) for the CSOM model while surface error for the proposed optimised surface approximation approach. The accuracy of AQE for CSOM model has been improved to 64% and 66% when compared to 2D and 3D SOM respectively. The NOV for CSOM model has been reduced from 8000 to 2168 as compared to 3D SOM. The accuracy of surface error for the optimised surface approximation approach has been improved to 7% compared to the conventional approach. The proposed CSOM model and optimised surface approximation approach have successfully reconstructed surface of all five data with better performance based on three performance measurements used in the evaluation

Bridge Structural Condition Assessment using 3D Imaging

Objective, accurate, and fast assessment of bridge structural condition is critical to timely assess safety risks. Current practices for bridge condition assessment rely on visual observations and manual interpretation of reports and sketches prepared by inspectors in the field. Visual observation, manual reporting and interpretation has several drawbacks such as being labor intensive, subject to personal judgment and experience, and prone to error. Terrestrial laser scanners (TLS) are promising sensors to automatically identify structural condition indicators, such as cracks, displacements and deflected shapes, as they are able to provide high coverage and accuracy at long ranges. However, there is limited research conducted on employing TLS to detect cracks for bridge condition assessment, which mainly focused on manual detection and measurements of cracks, displacements or shape deflections from the laser scan point clouds. TLS is an advance 3D imaging technology that is used to rapidly measure the 3D coordinates of densely scanned points within a scene. The data gathered by a TLS is provided in the form of 3D point clouds with color and intensity data often associated with each point within the cloud. This paper proposes a novel adaptive wavelet neural network (WNN) based approach to automatically detect concrete cracks from TLS point clouds for bridge structural condition assessment. The adaptive WNN is designed to selforganize, self-adapt, and sequentially learn a compact reconstruction of the 3D point cloud. The architecture of the network is based on a single-layer neural network consisting of Mexican hat wavelet functions. The approach was tested on a cracked concrete specimen. The preliminary experimental results show that the proposed approach is promising as it enables detecting concrete cracks accurately from TLS point clouds. Using the proposed method for crack detection would enable automatic and remote assessment of bridge condition. This would, in turn, result in reducing costs associated with infrastructure management, and improving the overall quality of our infrastructure by enhancing maintenance operations

Multidimensional flow mapping for proportional valves

Inverse, multidimensional input-output flow mapping is very important for use of valves in precision motion control applications. Due to the highly nonlinear characteristic and uncertain model structure of the cartridge valves, it is hard to formulate the modelling of their flow mappings into simple parameter estimation problems. This contribution conducts a comprehensive analysis and validation of three- and four-dimensional input-output-mapping approaches for a proportional pilot operated seat valves. Therefore, a virtual and a physical test-rig setup are utilized for initial measurement, implementation and assessment. After modeling and validating the valve under consideration, as a function of flow, pressure and temperature different mapping methods are investigated. More specifically, state of the art approaches, deep-learning methods and a newly developed approach (extPoly) are examined. Especially ANNs and Polynomials show reasonable approximation results even for more than two inputs. However, the results are strongly dependent on the structure and distribution of the input data points. Besides identification effort, the invertibility was investigated

Curve reconstruction from scattered data by Kohonen network.

The aim of this paper is to extend the method of modelling scattered databy free-from surfaces presented in [7]. In that paper the Kohonen neural network was used forordering the data from the scattered points. After the ordering process the B-spline curve orsurface approximation and interpolation methods can be applicable also for scattered input points.In this paper we extend this method to solve this problem in case of infinite set of data gettingfrom a cloud of points. A given B–spline curve can also be reconstructed by our method with thehelp of the Kohonen network

Neural-network-directed alignment of optical systems using the laser-beam spatial filter as an example

This report describes an effort at NASA Lewis Research Center to use artificial neural networks to automate the alignment and control of optical measurement systems. Specifically, it addresses the use of commercially available neural network software and hardware to direct alignments of the common laser-beam-smoothing spatial filter. The report presents a general approach for designing alignment records and combining these into training sets to teach optical alignment functions to neural networks and discusses the use of these training sets to train several types of neural networks. Neural network configurations used include the adaptive resonance network, the back-propagation-trained network, and the counter-propagation network. This work shows that neural networks can be used to produce robust sequencers. These sequencers can learn by example to execute the step-by-step procedures of optical alignment and also can learn adaptively to correct for environmentally induced misalignment. The long-range objective is to use neural networks to automate the alignment and operation of optical measurement systems in remote, harsh, or dangerous aerospace environments. This work also shows that when neural networks are trained by a human operator, training sets should be recorded, training should be executed, and testing should be done in a manner that does not depend on intellectual judgments of the human operator