923 research outputs found
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
Quantum-inspired feature and parameter optimization of evolving spiking neural networks with a case study from ecological modelling
The paper introduces a framework and implementation of an integrated connectionist system, where the features and the parameters of an evolving spiking neural network are optimised together using a quantum representation of the features and a quantum inspired evolutionary algorithm for optimisation. The proposed model is applied on ecological data modeling problem demonstrating a significantly better classification accuracy than traditional neural network approaches and a more appropriate feature subset selected from a larger initial number of features. Results are compared to a naive Bayesian classifier
A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications
The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime
An Improved Bees Algorithm for Training Deep Recurrent Networks for Sentiment Classification
Recurrent neural networks (RNNs) are powerful tools for learning information from
temporal sequences. Designing an optimum deep RNN is difficult due to configuration and training
issues, such as vanishing and exploding gradients. In this paper, a novel metaheuristic optimisation
approach is proposed for training deep RNNs for the sentiment classification task. The approach
employs an enhanced Ternary Bees Algorithm (BA-3+), which operates for large dataset classification
problems by considering only three individual solutions in each iteration. BA-3+ combines the
collaborative search of three bees to find the optimal set of trainable parameters of the proposed deep
recurrent learning architecture. Local learning with exploitative search utilises the greedy selection
strategy. Stochastic gradient descent (SGD) learning with singular value decomposition (SVD) aims to
handle vanishing and exploding gradients of the decision parameters with the stabilisation strategy
of SVD. Global learning with explorative search achieves faster convergence without getting trapped
at local optima to find the optimal set of trainable parameters of the proposed deep recurrent learning
architecture. BA-3+ has been tested on the sentiment classification task to classify symmetric and
asymmetric distribution of the datasets from different domains, including Twitter, product reviews,
and movie reviews. Comparative results have been obtained for advanced deep language models and
Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. BA-3+ converged
to the global minimum faster than the DE and PSO algorithms, and it outperformed the SGD, DE,
and PSO algorithms for the Turkish and English datasets. The accuracy value and F1 measure have
improved at least with a 30–40% improvement than the standard SGD algorithm for all classification
datasets. Accuracy rates in the RNN model trained with BA-3+ ranged from 80% to 90%, while the
RNN trained with SGD was able to achieve between 50% and 60% for most datasets. The performance
of the RNN model with BA-3+ has as good as for Tree-LSTMs and Recursive Neural Tensor Networks
(RNTNs) language models, which achieved accuracy results of up to 90% for some datasets. The
improved accuracy and convergence results show that BA-3+ is an efficient, stable algorithm for the
complex classification task, and it can handle the vanishing and exploding gradients problem of
deep RNNs
Modelling and Analysis of Drosophila Early Visual System A Systems Engineering Approach
Over the past century or so Drosophila has been established as an ideal model organism to
study, among other things, neural computation and in particular sensory processing. In this
respect there are many features that make Drosophila an ideal model organism, especially
the fact that it offers a vast amount of genetic and experimental tools for manipulating
and interrogating neural circuits. Whilst comprehensive models of sensory processing in
Drosophila are not yet available, considerable progress has been made in recent years in
modelling the early stages of sensory processing. When it comes to visual processing,
accurate empirical and biophysical models of the R1-R6 photoreceptors were developed
and used to characterize nonlinear processing at photoreceptor level and to demonstrate that
R1-R6 photoreceptors encode phase congruency.
A limitation of the latest photoreceptor models is that these do not account explicitly for
the modulation of photoreceptor responses by the network of interneurones hosted in the
lamina. As a consequence, these models cannot describe in a unifying way the photoreceptor
response in the absence of the feedback from the downstream neurons and thus cannot be
used to elucidate the role of interneurones in photoreceptor adaptation.
In this thesis, electrophysiological photoreceptor recordings acquired in-vivo from wild-
type and histamine defficient mutant fruit flies are used to develop and validate new com-
prehensive models of R1-R6 photoreceptors, which not only predict the response of these
photoreceptors in wild-type and mutant fruit flies, over the entire environmental range of
light intensities but also characterize explicitly the contribution of lamina neurons to photore-
ceptor adaptation. As a consequence, the new models provide suitable building blocks for
assembling a complete model of the retina which takes into account the true connectivity
between photoreceptors and downstream interneurones.
A recent study has demonstrated that R1-R6 photoreceptors employ nonlinear processing
to selectively encode and enhance temporal phase congruency. It has been suggested that
this processing strategy achieves an optimal trade-off between the two competing goals of
minimizing distortion in decoding behaviourally relevant stimuli features and minimizing
the information rate, which ultimately enables more efficient downstream processing of
spatio-temporal visual stimuli for edge and motion detection.Using rigorous information theoretic tools, this thesis derives and analyzes the rate-distortion characteristics associated with the linear and nonlinear transformations performed
by photoreceptors on a stimulus generated by a signal source with a well defined distribution
Palm biomass supply management : a predictive analysis tool
The flourishing of oil palm industry has always been regarded as a double-edged sword. While it has significantly contributed to the economic growth, it is, nonetheless, disputably unsustainable as it is a land-intensive industry and causing disposal problems by leaving behind massive waste. To strengthening the industry’s competitive advantage and offsetting its drawbacks, this thesis presents a forward-looking framework – Biomass Supply Value Chain (BSVC)– to put emphasis on the value creation for the biomass industry. It aims to enhance the current biomass supply chain by harnessing the emerging technological advancement of artificial intelligence (AI), as well as by incorporating game theory to examine the strategic arrangement of the industry players. The proposed framework is capable of optimising the procurement process in the supply chain management: first, by identifying biomass properties for optimum biomass utilisation through the developed Biomass Characteristic Index (BCI); second, by applying AI into supply chain-related tasks for aiding better decision-making and problem-solving; and third, by adopting game theory in analysing strategic options, and providing appropriate strategies to minimise uncertainty and risk in procurement process. The “value” as suggested in the BSVC does not merely refer to a narrow economic sense, but is an all-encompassing value concerning non-monetary utility values, including sustainability, environmental preservation and the appreciation of the biomass industry
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