Cellular Helmet Liner Design through Bio-inspired Structures and Topology Optimization of Compliant Mechanism Lattices

The continuous development of sport technologies constantly demands advancements in protective headgear to reduce the risk of head injuries. This article introduces new cellular helmet liner designs through two approaches. The first approach is the study of energy-absorbing biological materials. The second approach is the study of lattices comprised of force-diverting compliant mechanisms. On the one hand, bio-inspired liners are generated through the study of biological, hierarchical materials. An emphasis is given on structures in nature that serve similar concussion-reducing functions as a helmet liner. Inspiration is drawn from organic and skeletal structures. On the other hand, compliant mechanism lattice (CML)-based liners use topology optimization to synthesize rubber cellular unit cells with effective positive and negative Poisson's ratios. Three lattices are designed using different cellular unit cell arrangements, namely, all positive, all negative, and alternating effective Poisson's ratios. The proposed cellular (bio-inspired and CML-based) liners are embedded between two polycarbonate shells, thereby, replacing the traditional expanded polypropylene foam liner used in standard sport helmets. The cellular liners are analyzed through a series of 2D extruded ballistic impact simulations to determine the best performing liner topology and its corresponding rubber hardness. The cellular design with the best performance is compared against an expanded polypropylene foam liner in a 3D simulation to appraise its protection capabilities and verify that the 2D extruded design simulations scale to an effective 3D design

Extending the SACOC algorithm through the Nystrom method for dense manifold data analysis

Data analysis has become an important field over the last decades. The growing amount of data demands new analytical methodologies in order to extract relevant knowledge. Clustering is one of the most competitive techniques in this context.Using a dataset as a starting point, these techniques aim to blindly group the data by similarity. Among the different areas, manifold identification is currently gaining importance. Spectral-based methods, which are the mostly used methodologies in this area, are however sensitive to metric parameters and noise. In order to solve these problems, new bio-inspired techniques have been combined with different heuristics to perform the clustering solutions and stability, specially for dense datasets. Ant Colony Optimization (ACO) is one of these new bio-inspired methodologies. This paper presents an extension of a previous algorithm named Spectral-based ACO Clustering (SACOC). SACOC is a spectral-based clustering methodology used for manifold identification. This work is focused on improving this algorithm through the Nystrom extension. The new algorithm, named SACON, is able to deal with Dense Data problems.We have evaluated the performance of this new approach comparing it with online clustering algorithms and the Nystrom extension of the Spectral Clustering algorithm using several datasets

A NOVEL APPROACH BASED ON ELEPHANT HERDING OPTIMIZATION FOR CONSTRAINED OPTIMIZATION PROBLEMS

Many real-world problems can be formulated as an optimization problem and they have some constraints generally. To overcome these constraints, bio-inspired algorithms are adapted to constrained optimization using constraint handling methods and some modifications. In this study, a new approach is developed to solve constrained optimization problems with elephant herding optimization algorithm which is a newly-emerging optimization technique. Besides the basic EHO, two EHO variants (EHO-NoB and GL-EHO) are adapted to constrained optimization with this approach. The well-known thirteen constrained benchmark functions are used to analysis the performances of algorithms. Experimental results show that the GL-EHO has a better performance than the basic EHO and other algorithms. In addition, the results of GL-EHO are comparable level with the result of another EHO variant in the literature

Verifying P Systems with Costs by Using Priced-Timed Maude

We consider P systems that assigns storage costs per step to membranes, and execution costs to rules. We present an abstract syntax of the new class of membrane systems, and then deal with costs by extending the operational semantics of P systems with promoters, inhibitors and registers.We use Priced-Timed Maude to implement the P systems with costs. By using such a rewriting engine which corresponds to the semantics of membrane systems with costs, we are able to prove the operational correctness of this implementation. Based on such an operational correspondence, we can analyze properly the evolutions of the P systems with costs, and verify several reachability properties, including the cost of computations that reach a given membrane con guration. This approach opens the way to various optimization problems related to membrane systems, problems making sense in a bio-inspired model which now can be veri ed by using a complex software platform