Optimal design of a quadratic parameter varying vehicle suspension system using contrast-based Fruit Fly Optimisation

In the UK, in 2014 almost fifty thousand motorists made claims about vehicle damages caused by potholes. Pothole damage mitigation has become so important that a number of car manufacturers have officially designated it as one of their priorities. The objective is to improve suspension shock performance without degrading road holding and ride comfort. In this study, it is shown that significant improvement in performance is achieved if a clipped quadratic parameter varying suspension is employed. Optimal design of the proposed system is challenging because of the multiple local minima causing global optimisation algorithms to get trapped at local minima, located far from the optimum solution. To this end an enhanced Fruit Fly Optimisation Algorithm − based on a recent study on how well a fruit fly’s tiny brain finds food − was developed. The new algorithm is first evaluated using standard and nonstandard benchmark tests and then applied to the computationally expensive suspension design problem. The proposed algorithm is simple to use, robust and well suited for the solution of highly nonlinear problems. For the suspension design problem new insight is gained, leading to optimum damping profiles as a function of excitation level and rattle space velocity

A NOVEL THREE DEGREE-OF-FREEDOMS OSCILLATION SYSTEM OF INSECT FLAPPING WINGS

We propose an oscillation system to replicate the dynamic behavior of flapping wings, inspired by insect flight muscles. In particular, we study the flight of the fruit fly Drosophila virilis . We model the wing as a rigid body with three degree-of-freedom, described by three Euler angles: the stroke angle, the rotation angle and the deviation angle. Insect flight muscles are separated into two types: power muscles and control muscles. One actuator and one torsional spring at the stroke angle act as the power muscles. Two torsional springs at the rotation angle and the deviation angle mimic the control muscles. A dynamic model, using a blade-element model and a quasi-steady model to calculate aerodynamic forces and moments, is set up for analysis of the system\u27s performance. Using non-dimensional analysis, we are able to identify the dynamic behavior of the system through four coefficients: stroke stiffness coefficient, rotation stiffness coefficient, deviation stiffness coefficient and input torque coefficient. We use the dynamic model to explore a large coefficients space of the oscillation system. We find that tuning deviation stiffness coefficient and rotation stiffness coefficient generates four different types of wing trajectories. Among them, the one with a high deviation stiffness coefficient and a mediate rotation stiffness coefficient produces high lift and high power loading. Its wing trajectory is quite similar to the wing trajectory in actual insects. Furthermore, a hybrid optimization algorithm (a genetic algorithm and a Nelder-Mead simplex algorithm) is implemented to find the optimal stiffness coefficients. Through these coefficients, the system minimizes power loading while still providing enough lift to maintain a time-averaged constant altitude over one stroke cycle. The results of this optimization indicate that the flapping wing with nonzero deviation achieves a better aerodynamic performance than the wing with zero deviation. The oscillatory property of this system does not only explain how insects use flight muscles to tune wing kinematics, but it also allows for design simplifications of the wing driving mechanism of flapping micro air vehicles

Sparse algorithms for decoding and identification of neural circuits

The brain, as an information processing machine, surpasses any man-made computational device, both in terms of its capabilities and its efficiency. Neuroscience research has made great strides since the foundational works of Cajal and Golgi. However, we still have very little understanding about the algorithmic underpinnings of the brain as an information processor. Identifying mechanistic models of the functional building blocks of the brain will have significant impact not just on neuroscience, but also on artificial computational systems. This provides the main motivation for the work presented in this thesis, summarily i) biologically-inspired algorithms that can be efficiently implemented in silico, ii) functional identification of the processing in certain types of neural circuits, and iii) a collaborative ecosystem for brain research in a model organism, towards the synergistic goal of understanding functional mechanisms employed by the brain. First, this thesis provides a highly parallelizable, biologically-inspired, motion detection algorithm that is based upon the temporal processing of the local (spatial) phase of a visual stimulus. The relation of the phase based motion detector to the widely studied Reichardt detector model, is discussed. Examples are provided comparing the performance of the proposed algorithm with the Reichardt detector as well as the optic flow algorithm, which is the workhorse for motion detection in computer vision. Further, it is shown through examples that the phase based motion detection model provides intuitive explanations for reverse-phi based illusory motion percepts. Then, tractable algorithms are presented for decoding with and identification of neural circuits, comprised of processing that can be described by a second-order Volterra kernel (quadratic filter). It is shown that the Reichardt detector, as well as models of cortical complex cells, can be described by this structure. Examples are provided for decoding of visual stimuli encoded by a population of Reichardt detector cells and complex cells, as well as their identification from observed spike times. Further, the phase based motion detection model is shown to be equivalent to a second-order Volterra kernel acting on two normalized inputs. Subsequently, a general model that computes the ratio of two non-linear functionals, each comprising linear (first order Volterra kernel) and quadratic (second-order Volterra kernel) filters, is proposed. It is shown that, even under these highly non-linear operations, a population of cells can encode stimuli faithfully using a number of measurements that are proportional to the bandwidth of the input stimulus. Tractable algorithms are devised to identify the divisive normalization model and examples of identification are provided for both simulated and biological data. Additionally, an extended framework, comprising parallel channels of divisively normalized cells each subjected to further divisive normalization from lateral feedback connections, is proposed. An algorithm is formulated for identifying all the components in this extended framework from controlled stimulus presentation and observed outputs samples. Finally, the thesis puts forward the Fruit Fly Brain Observatory (FFBO), an initiative to enable a collaborative ecosystem for fruit fly brain research. Key applications in FFBO, and the software and computational infrastructure enabling them, are described along with case studies

Characterization of mating behaviour of the female fruit fly using machine vision

Courtship behaviour is the means for the animals to select their partner for reproduction. The fruit fly, Drosophila melanogaster, exhibit a complex courtship behaviour. Nearly all studies of D. melanogaster courtship have focused exclusively on the male behaviour. Female pre-copulatory behaviour is often relegated to ‘accepting’ or ‘rejecting’ of mating, and how females interact with males remains largely unknown. The aim of this study is to quantify and describe the mating behaviour of the female D. melanogaster. D. melanogaster is a model system that offers many genetic tools and when coupled with the recent technologies for neuronal manipulation, mapping and behavioural characterization, it has the potential to reveal the neurons involved in a particular behaviour. We analyzed the behaviour of the wild-type (WT) female fly by collecting information of the flies’ position during courtship using a tracking system and by automatically detecting specific behaviours using an automatic classifier. We found that WT flies displayed courtship acts and mating responses differently depending on their geographical origin strains. The automatic classes were developed in a machine learning system, to allow a faster and reliable behavioural analysis. In future work, the automatic classes developed in this research will be key to continue the female behaviour characterization