Narrow Results

In this paper, the steady and transient heat transfer behaviours of multidirectional (1D/2D/3D) functionally graded composite plates are investigated. Also, two different porosity types, i.e., even and uneven distributions, are considered along with material gradation. Here, the spatial/temperature-dependent effective material properties of these highly heterogeneous composites with porosity are evaluated using the extended Voigt’s homogenisation scheme via multi-variable power-law functions and further verified with the representative volume element (RVE) scheme. In continuation, generalised mathematical models are developed by including material nonlinearity using the cubic-polynomial-based temperature-dependent constitutive model. The Poisson’s and Laplace’s equations are utilised for steady and transient heat transfer problems, respectively, and the weak form is derived using the Galerkin method. Further, coupled finite element method (FEM)–finite difference method (FDM) is adopted via Picard’s successive iteration and Crank–Nicolson technique to compute the nonlinear transient temperature. The proposed model is verified by comparing it with the previously reported results. In addition, the effects of heterogeneity, porosity, power-law indices and boundary conditions on the steady/transient heat transfer responses of multidirectional functionally graded plates are examined and discussed in detail. At last, the optimum material distributions are obtained using the response surface methodology (RSM) by minimising the heat transfer responses.

In this paper, we present a novel approach of implementing a combination methodology to find appropriate neural network architecture and weights using an evolutionary least square based algorithm (GALS).¹ This paper focuses on aspects such as the heuristics of updating weights using an evolutionary least square based algorithm, finding the number of hidden neurons for a two layer feed forward neural network, the stopping criterion for the algorithm and finally some comparisons of the results with other existing methods for searching optimal or near optimal solution in the multidimensional complex search space comprising the architecture and the weight variables. We explain how the weight updating algorithm using evolutionary least square based approach can be combined with the growing architecture model to find the optimum number of hidden neurons. We also discuss the issues of finding a probabilistic solution space as a starting point for the least square method and address the problems involving fitness breaking. We apply the proposed approach to XOR problem, 10 bit odd parity problem and many real-world benchmark data sets such as handwriting data set from CEDAR, breast cancer and heart disease data sets from UCI ML repository. The comparative results based on classification accuracy and the time complexity are discussed.

This work is an ongoing investigation of attitude control ( rad rotation) as well as vibration suppression of a very flexible structure. A nominal fuzzy logic controller was designed and was used as a basis for design of an optimised fuzzy controller. Optimisation algorithm and procedure are explained. Results show a clear improvement in performance of the controller in comparison to that of the nominal system.

The work presented in this paper deals with the description of an analytic modeling of the neuromusculoskeletal system responsible for the finger movement. This simulation task is complex due to the interacting processes (physiological and biomechanical) represented by muscles, joints and bones. In this study, we focused on the presentation of a complete model for the finger motion decomposed in quasi-static positions. In fact, this model can be considered as a preliminary step before dynamic modeling. The proposed model is composed of several compartments: biomechanical finger model, mechanical muscle model and muscle/neural activation model. The main objective of this study is to estimate, by inverse procedure, the muscle forces, muscle activations and neural activations that are responsible for generating a given finger movement decomposed in successive quasi-static positions. The anatomical model contains six muscles which control the decomposed movement of the three joints of the system. To estimate the model unknowns, an optimization technique is proposed for improving robustness to initial conditions and physiological reliability. After, an experimental protocol for recording surface electromyogram (sEMG) data, from three extrinsic muscles, according to specific finger positions is applied on five subjects to evaluate the model reliability.

From analysis of the obtained results, both in simulation and experiment, the presented model seems to be able to mimic, in a realistic way, the finger movement decomposed in quasi-static positions. Finally, results, model limitations and further developments are discussed.

The clustering is an important data analysis technique. However, clustering high-dimensional data like documents needs more effort in order to extract the richness relevant information hidden in the multidimensionality space. Recently, document clustering algorithms based on metaheuristics have demonstrated their efficiency to explore the search area and to achieve the global best solution rather than the local one. However, most of these algorithms are not practical and suffer from some limitations, including the requirement of the knowledge of the number of clusters in advance, they are neither incremental nor extensible and the documents are indexed by high-dimensional and sparse matrix. In order to overcome these limitations, we propose in this paper, a new dynamic and incremental approach (CS_LSI) for document clustering based on the recent cuckoo search (CS) optimization and latent semantic indexing (LSI). Conducted Experiments on four well-known high-dimensional text datasets show the efficiency of LSI model to reduce the dimensionality space with more precision and less computational time. Also, the proposed CS_LSI determines the number of clusters automatically by employing a new proposed index, focused on significant distance measure. This later is also used in the incremental mode and to detect the outlier documents by maintaining a more coherent clusters. Furthermore, comparison with conventional document clustering algorithms shows the superiority of CS_LSI to achieve a high quality of clustering.

In WSN, extending the network life is still a major problem that needs to be tackled. Cross-layer protocols are used to get around these problems. In this paper, a new cross-layer design routing model is presented using a clustering-based technique. The proposed model is proceeding with the optimal cluster-based routing model via a new algorithm. Initially during the network generation, the node’s energy is predicted by the deep learning model termed as DNN model on the basis of the distance between node and sink as the input. Subsequently, during the clustering process, the cluster head is optimally selected via a new optimisation algorithm named Self-Improved Shuffle Shepherd Optimisation (SISSO) Algorithm. The cluster head selection is done by considering the constraints including Link quality, Distance, Overhead, Energy and Delay. Finally, a Modified Kernel Least Mean Square (MKLMS)-based data aggregation process is to eliminate the redundant data transmission. The performance of the SISSO method is proven superior over other conventional approaches with regard to the alive node and network lifetime. In the alive node analysis of supernode, the proposed SISSO model achieves the maximal number of alive supernodes at 2,000 rounds (i.e. 0.67) than other conventional methods.

Expansion of renewable energy development causes concerns which traditional land-use planning may have limited capacity to address adequately. The complexity and multiplicity of scales, criteria and actors involved in decision-making processes requires a holistic approach that captures the variety in stakeholder interests. Reaching consensus across interests ensures democratic and cost-effective decision-making processes. The Consensus-based Siting (ConSite) tool suite was developed for optimal siting of onshore wind-power plants and routing of high-voltage power lines considering stakeholder interests. ConSite is based on the operational steps of spatial multi-criteria decision analysis using a bottom-up holistic approach. Its spatially explicit graphical user interface allows for a high level of stakeholder involvement and includes inherent capabilities of scenario modelling. ConSite thereby helps to structure decision problems, balance conflicting interests and identify relevant decision strategies based on risk assessment and trade-off analysis. ConSite visualises the spatial consequences of implementing various decision strategies and balancing site-specific conflict levels with energy production potential.

Computational Fluid Dynamics (CFD) is used to simulate the flow filed in a rotor-casing assembly for different elliptic rotor aspect ratios and inlet flow velocities. The flow was simulated with the rotor fixed at its extreme positions, i.e. vertical and horizontal arrangement. The flow field results were used to ascertain the changes in the efficiency of a rotor-casing assembly. This included: inlet pressure, maximum velocity, and maximum turbulence values. A more fundamental quantity, integrated entropy generation was also calculated. Entropy generation is based on the second law of thermodynamic and accounts for all types of irreversibilties within the assembly. Of the different traditional quantities calculated, only the inlet pressure results were inline with the entropy generation results.

Microstructured optical fibres have increased degrees of flexibility that allow tailoring of optical properties for various applications. Three different algorithms for calculating mode properties and structural or confinement loss in these fibres are compared. A number of biologically-inspired approaches to optimising the fibre design (hole size, shape and arrangement) within the context and constraints of a particular fabrication technology are presented.

A recently completed project [1-4] investigated the use of computationally expensive black-box models in the context of optimisation, sensitivity analysis and uncertainty evaluation. The work involved algorithm implementation and the use of simple problems with analytical solutions to allow algorithm performance to be compared, as well as two real-world case studies. This paper discusses the results of the project and outlines plans for the development of a user-friendly toolbox to support optimisation, sensitivity analysis and uncertainty evaluation using finite element models.

In this chapter, we consider the application of mathematical optimisation algorithms to the field of marketing management. We describe the background and fundamentals of three techniques, simulated annealing, tabu search and genetic algorithms and give, following a literature review, worked examples of relevant marketing areas to whcih each of these techniques can be applied, in particular sales territory alignment, segmentation and site location analysis. These indicate how each optimisation technique can be applied beneficially to market management optimisation problems.

Many commercial measurement processes are planned with little or no regard to optimality in terms of measurement time and the estimated uncertainty of measurement. This can be because the complexity of the planning problem makes optimality in a dynamic environment dificult to achieve, even with expert knowledge. This paper presents a novel approach to measurement planning using automated planning. Detailed information regarding the modelling and encoding of measurement processes are provided. The benefits of this approach are demonstrated through the results of applying it to machine tool calibration. A discussion is then formed around the development of future tools to further validate the approach.

In this chapter, we consider the application of mathematical optimisation algorithms to the field of marketing management. We describe the background and fundamentals of three techniques, simulated annealing (SA), tabu search (TS) and genetic algorithms and give, following a literature review, worked examples of relevant marketing areas to whcih each of these techniques can be applied, in particular sales territory alignment, segmentation and site location analysis. These indicate how each optimisation technique can be applied beneficially to market management optimisation problems.

With the advent of social media in our lives and the transformation of consumer behaviour through the impact of the internet technology, online brand–human interactions are crucial in the consumer decision-making process, as well as on corporate performance. In this research study, we have developed a model to predict brand engagement as measured in terms of the amount of earned attention by the brand from the consumer. The exogenous variables of the model comprise parameters related to online traffic, flow of consumer-initiated brand commentaries as measured by three different time frames and the quantity of brand mentions. To test and validate the research model, we have applied a Memetic Algorithm (MA) which is well tailored to the phenomenon of propagation and social contagion. In order to assess this evolutionary algorithm, we have used two alternative local search methods—a Specific Local Search Procedure (SLSP) and the Steepest Ascent (SA) heuristic.We have found that the MA outperforms both local search-based methods, and the SLSP outperforms the SA.