An AI automated self-organising, feature imitating approach for point cloud data reduction

This paper presents a novel AI based self-organising data reduction technique which combines feature detection and topological learning to reduce the memory footprint of point cloud data in an adaptive way, reducing point density in featureless parts of the point cloud whilst maintaining sufficient points to preserve details of interest to the engineer. As a case study, this is applied to a 3D LiDAR scan of a masonry bridge with localised details such as anchors, cracks and patches of vegetation. The process comprises 4 stages: For each point, the distance to its neighbouring point is calculated using a nearestneighbours search. Afterwards, 3D point cloud data is projected onto a 2D plane using principal component analysis. Next, the variables are mapped into the feature space where they are grouped using a clustering algorithm. Clusters correspond to specific physical features within the point cloud. Points within these clusters are assigned a feature weight that dictates their level of bias compared to points that do not belong to an engineering feature. Finally, a weighted self-organising map (SOM) algorithm is applied to learn the topology of the original point cloud, with a specific focus on feature points. This algorithm is a type of artificial neural network that uses competitive learning to generate an output map iteratively adjusted to represent the input. An advantage of the SOM is that the output map can be used to define the position of vertices in a finite element mesh. The results show that the standard SOM was able to reduce the data size by over 90%. However, such a reduction in data size risks loss of smaller engineering features. Upon application of a feature weight, the method was able to produce the same data reduction with up to 35% decrease in quantization error compared to a standard SOM. The research is of timely importance. It reduces the computational cost of point cloud visualisation and improves the reverse engineering process, enabling conversion of point clouds into CFD and FE meshes. The work will benefit academics, researchers and engineers in developing their models

Fixed modelling parameters. For sources see the main text.

<p>Fixed modelling parameters. For sources see the main text.</p

Reference positions of the joint centres in the model.

<p>Reference positions of the joint centres in the model.</p

Chart showing the performance characteristics of asynchronous versus synchronous genetic algorithm implementations on varying numbers of CPU cores.

<p>Chart showing the performance characteristics of asynchronous versus synchronous genetic algorithm implementations on varying numbers of CPU cores.</p

Thermal topology optimisation of a plasma facing component for use in next-generation fusion reactors

The ITER tokamak, the experimental fusion reactor designed to be the first to produce net energy, has had a monoblock concept selected for use as a plasma facing component in the divertor region. This design currently consists of a CuCrZr cooling pipe surrounded by a copper interlayer and embedded in a tungsten armour plate. Additive manufacturing may facilitate a geometry capable of greater efficiency through the introduction of greater design freedom whilst maintaining compatibility with the monoblock concept. This is achieved through the addition of high conductivity material to the armour domain surrounding the coolant pipe. Finite element simulation of the heat transfer system combined with a topology optimisation methodology has been used to find the optimal distribution of high thermal conductivity material (such as Cu) for three thermal objectives: minimising temperature and thermal gradient, and maximising conductive heat flux. The topology optimisation relies on a density-based approach which makes use of the globally convergent method of moving asymptotes technique [1]. The optimised geometries have been tested for both steady state operation and transient heat flux events for both a symmetric, flat monoblock design and an asymmetric component designed to minimise leading edges. In high heat flux transient events, the optimisation resulted in temperature reductions of over 200K and reduced thermal gradients. These techniques may be used to help protect divertor components from damage in future devices

Diagram showing how the minimum ankle torque required to support an animal can be calculated.

<p>Diagram showing how the minimum ankle torque required to support an animal can be calculated.</p

Charts showing the frequency distributions of the (extension/fibre length) ratio for a variety of muscles and vertebrate species.

<p>Charts showing the frequency distributions of the (extension/fibre length) ratio for a variety of muscles and vertebrate species.</p

Orthographic views of the limb bones, muscle paths, wrapping cylinders, joint axes and contact points used in the model.

<p>The scale bar is 1-tracer (<a href="http://www.povray.org" target="_blank">www.povray.org</a>).</p

Simulated trackways generated by spatially summing the impulse between the foot contacts and the substrate.

<p>Simulated trackways generated by spatially summing the impulse between the foot contacts and the substrate.</p

Muscle properties for each of the joint range of motion conditions.

<p>Muscle properties for each of the joint range of motion conditions.</p