Hybrid bootstrap-based approach with binary artificial bee colony and particle swarm optimization in Taguchi's T-Method

Taguchi's T-Method is one of the Mahalanobis Taguchi System (MTS)-ruled prediction techniques that has been established specifically but not limited to small, multivariate sample data. When evaluating data using a system such as the Taguchi's T-Method, bias issues often appear due to inconsistencies induced by model complexity, variations between parameters that are not thoroughly configured, and generalization aspects. In Taguchi's T-Method, the unit space determination is too reliant on the characteristics of the dependent variables with no appropriate procedures designed. Similarly, the least square-proportional coefficient is well known not to be robust to the effect of the outliers, which indirectly affects the accuracy of the weightage of SNR that relies on the model-fit accuracy. The small effect of the outliers in the data analysis may influence the overall performance of the predictive model unless more development is incorporated into the current framework. In this research, the mechanism of improved unit space determination was explicitly designed by implementing the minimum-based error with the leave-one-out method, which was further enhanced by embedding strategies that aim to minimize the impact of variance within each parameter estimator using the leave-one-out bootstrap (LOOB) and 0.632 estimates approaches. The complexity aspect of the prediction model was further enhanced by removing features that did not provide valuable information on the overall prediction. In order to accomplish this, a matrix called Orthogonal Array (OA) was used within the existing Taguchi's T-Method. However, OA's fixed-scheme matrix, as well as its drawback in coping with the high-dimensionality factor, leads to a sub- optimal solution. On the other hand, the usage of SNR, decibel (dB) as its objective function proved to be a reliable measure. The architecture of a Hybrid Binary Artificial Bee Colony and Particle Swarm Optimization (Hybrid Binary ABC-PSO), including the Binary Bitwise ABC (BitABC) and Probability Binary PSO (PBPSO), has been developed as a novel search engine that helps to cater the limitation of OA. The SNR (dB) and mean absolute error (MAE) were the main part of the performance measure used in this research. The generalization aspect was a fundamental addition incorporated into this research to control the effect of overfitting in the analysis. The proposed enhanced parameter estimators with feature selection optimization in this analysis had been tested on 10 case studies and had improved predictive accuracy by an average of 46.21% depending on the cases. The average standard deviation of MAE, which describes the variability impact of the optimized method in all 10 case studies, displayed an improved trend relative to the Taguchi’s T-Method. The need for standardization and a robust approach to outliers is recommended for future research. This study proved that the developed architecture of Hybrid Binary ABC-PSO with Bootstrap and minimum-based error using leave-one-out as the proposed parameter estimators enhanced techniques in the methodology of Taguchi's T-Method by effectively improving its prediction accuracy

INTELLIGENT MODELLING OF GRADIENT FLEXIBLE PLATE STRUCTURE UTILISING HYBRID EVOLUTIONARY ALGORITHM

The gradient flexible plate structure has been widely used in engineering industries. However, the gradient flexible plate is susceptible to vibrational disturbances and affecting its durability and performance over time. Hence, the unwanted vibration needs to be controlled and can be accomplished by developing an accurate model. Despite that, the accurate model is hard to be obtained especially in estimating the model parameters. Thus, the research presents the development of dynamic modelling for gradient flexible plate structure (GFPS). A slanted GFPS with orientation angle of 30° and all edges clamped was developed and fabricated to represent the actual dynamics of the system. Then, data acquisition and instrumentation system were integrated to the rig to collect the input-output vibration data. The research utilised parametric system identification based on autoregressive with exogenous input (ARX) model structure. First, evolutionary algorithms, namely particle swarm optimisation (PSO) and grey wolf optimisation (GWO) were used in developing GFPS dynamic model and their performances were compared. It was discovered that GWO model outperformed PSO model. However, the computational time of GWO is slower compared to PSO. Thus, a hybrid of grey wolf and particle swarm optimisation (GWO-PSO) were proposed to further improve the system modelling. It was found out that the hybrid GWO-PSO model outperformed PSO and GWO models by achieving the lowest mean squared error, correlation up to 95 % confidence level, and good stability. The obtained GWO-PSO models which is model order 2 and model order 4 were verified by using proportional-integral-derivative (PID) based controller. Their performances were measured in terms of model robustness based on vibration suppression. The final result confirmed that model order 2 of GWO-PSO is the optimum model to represent GFPS system modelling with 71.08% vibration attenuation

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out