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Epilepsy is a long-term neurological condition that disrupts brain function in people of all ages, epilepsy is a condition that is analysed through the brain signals via electroencephalogram (EEG) signal. To analyse epilepsy using spatial and temporal data, various machine-learning-based techniques are used. However, most of the techniques suffer from inaccuracy issues in dealing with the dynamic and raw EEG signal. In this research, an intelligent societal optimisation-driven classifier is introduced based on convolutional neural networks (CNN) for epileptic seizure prediction using EEG signals. To boost predictive accuracy, we extract frequency band features from the EEG signal utilising wavelet decomposition. The frequency band features form the feature vector, is provided smart societal optimisation- CNN such that the prediction performance is enhanced through the optimal tuning of the CNN with the smart societal optimisation. Smart societal optimisation is proposed by integrating the behaviour of the Lobos wolf and the Moggie. The smart societal optimisation-based CNN attains 87.673% accuracy, 84.949% sensitivity91.274%specificity for the K-Fold-10 for CHB-MIT scalp EEG database.

IntroductionSleep apnoea syndrome (SAS) is a serious sleep disorder and early detection of sleep apnoea not only reduces treatment costs but also saves lives. Conventional polysomnography (PSG) is widely regarded as the gold standard diagnostic tool for sleep apnoea. However, this method is expensive, time-consuming and inherently disruptive to sleep. Recent studies have pointed out that ECG analysis is a simple and effective diagnostic method for sleep apnea, which can effectively provide physicians with an aid to diagnosis and reduce patients’ suffering.MethodsTo this end, in this paper proposes a LightGBM hybrid model based on ECG signals for efficient detection of sleep apnea. Firstly, the improved Isolated Forest algorithm is introduced to remove abnormal data and solve the data sample imbalance problem. Secondly, the parameters of LightGBM algorithm are optimised by the improved TPE (Tree-structured Parzen Estimator) algorithm to determine the best parameter configuration of the model. Finally, the fusion model TPE_OptGBM is used to detect sleep apnoea. In the experimental phase, we validated the model based on the sleep apnoea ECG database provided by Phillips-University of Marburg, Germany.ResultsThe experimental results show that the model proposed in this paper achieves an accuracy of 95.08%, a precision of 94.80%, a recall of 97.51%, and an F1 value of 96.14%.DiscussionAll of these evaluation indicators are better than the current mainstream models, which is expected to assist the doctor’s diagnostic process and provide a better medical experience for patients.

Abstract This research work aims to study photovoltaic systems that generate energy for self-consumption using different traditional technologies, such as silicon, and emerging technologies, like nanowires and quantum. The photovoltaic system without batteries was implemented in a residential property in three different places, in Portugal. According to Portuguese Law, the sale of surplus energy to the grid is possible but the respective value for its selling is not defined. To evaluate the project viability, two different analyses are considered: with and without the sale of surplus energy to the grid. Results show that if there is no sale of excess energy produced to the grid, the project is not economically viable considering the four different technologies. Otherwise, using traditional technologies, the project is economically viable, presenting a payback time lower than 10 years. This shows that the introduction of nanostructures in solar cells is not yet a good solution in the application of solar systems namely with the current law. Furthermore, independently of the used technology, the current Portuguese law seems to difficult the investment return, which should not be the way to encourage the use of renewable sources.

Abstract The use of low earth orbit (LEO) satellites to enhance the performance of global navigation satellite system navigation and positioning services has become a popular research topic. In this study, NSGA‐III optimisation algorithm was used to design two hybrid configurations of 177 and 186 LEO constellations for enhancing the BeiDou Satellite Navigation System (BDS). Under the enhanced effect of optimisation constellation, the global average geometric dilution of precision (GDOP) of BDS was reduced to 0.8 ± 0.1, and the maximum GDOP was reduced from 2.4 to less than 1.1 (54.2% reduction). In order to verify the contribution of the two constellations to the convergence time and positioning accuracy of BDS precise point positioning (PPP), a LEO enhanced BDS PPP simulation experiment was carried out using International GNSS Service data from five stations. The results show that after 10 min of static positioning, both LEO constellations improved the positioning accuracy of BDS from the decimetre level to less than 5 cm. The maximum improvement for 177 and 186 LEO was 95.0% and 96.9%, respectively. Additionally, the convergence time for 177 and 186 LEO reduced to less than 3.5 and 3 min, and the maximum improvement was 93.5% and 95.2%, respectively. Overall, both constellations can improve the positioning accuracy and convergence time of BDS PPP.

Real-time hardware-in-the-loop-(HIL) simulation integration is now a fundamental component of the power electronics control design cycle. This integration is required to test the efficacy of controller implementations. Even though hardware-in-the-loop-(HIL) tools use FPGA devices with computing power that is rapidly evolving, developers constantly need to balance the ease of deploying models with acceptable accuracy. This study introduces a methodology for implementing a full-bridge inverter and buck converter utilising the associate-discrete-circuit-(ADC) model, which is optimised for real-time simulator applications. Additionally, this work introduces a new approach for choosing ADC parameter values by using the artificial-bee-colony-(ABC) algorithm, the firefly algorithm (FFA), and the genetic algorithm (GA). The implementation of the ADC-based model enables the development of a consistent architecture in simulation, regardless of the states of the switches. The simulation results demonstrate the efficacy of the proposed methodology in selecting optimal parameters for an ADC-switch-based full-bridge inverter and buck converter. These results indicate a reduction in overshoot and settling time observed in both the output voltage and current of the chosen topologies.

Recently, metaheuristics (MHs) have become increasingly popular in real-world engineering applications such as in the design of airframes structures and aeroelastic designs owing to its simple, flexible, and efficient nature. In this study, a novel hybrid algorithm is termed as Ensemble of Genetic algorithm, Grey wolf optimizer, Water cycle algorithm and Population base increment learningusing Weighted sum (E-GGWP-W), based on the successive archive methodology of the weighted population has been proposed to solve the aircraft composite wing design problem. Four distinguished algorithms viz. a Genetic algorithm (GA), a Grey wolf optimizer (GWO), a Water cycle algorithm (WCA), and Population base increment learning (PBIL) were used as ingredients of the proposed algorithm. The considered wing design problem is posed for overall weight minimization subject to several aeroelastic and structural constraints along with multiple discrete design variables to ascertain its viability for real-world applications. The algorithms are validated through the standard test functions of the CEC-RW-2020 test suite and composite Goland wing aeroelastic optimization. To check the performance, the proposed algorithm is contrasted with eight well established and newly developed MHs. Finally, a statistical analysis is done by performing Friedman’s rank test and allocating respective ranks to the algorithms. Based on the outcome, ithas been observed that the suggested algorithm outperforms the others.

Satellite remote sensing provides a unique opportunity for calibrating land surface models due to their direct measurements of various hydrological variables as well as extensive spatial and temporal coverage. This study aims to apply terrestrial water storage (TWS) estimated from the gravity recovery and climate experiment (GRACE) mission as well as soil moisture products from advanced microwave scanning radiometer–earth observing system (AMSR-E) to calibrate a land surface model using multi-objective evolutionary algorithms. For this purpose, the non-dominated sorting genetic algorithm (NSGA) is used to improve the model’s parameters. The calibration is carried out for the period of two years 2003 and 2010 (calibration period) in Australia, and the impact is further monitored over 2011 (forecasting period). A new combined objective function based on the observations’ uncertainty is developed to efficiently improve the model parameters for a consistent and reliable forecasting skill. According to the evaluation of the results against independent measurements, it is found that the calibrated model parameters lead to better model simulations both in the calibration and forecasting period.

In a fuel cell electric vehicle (FCEV) powered by a metal hydride tank, the performance of the tank is an indicator of the overall health status, which is used to predict its behaviour and make appropriate energy management decisions. The aim of this paper is to investigate how to evaluate the effects of charge/discharge cycles on the performance of a commercial automotive metal hydride hydrogen storage system applied to a real FCEV. For this purpose, a mathematical model is proposed based on uncertain physical parameters that are identified using the stochastic particle swarm optimisation (PSO) algorithm combined with experimental measurements. The variation of these parameters allows an assessment of the degradation level of the tank’s performance on both the quantitative and qualitative aspects. Simulated results derived from the proposed model and experimental measurements were in good agreement, with a maximum relative error of less than 2%. The validated model was used to establish the correlations between the observed degradations in a hydride tank recovered from a real FCEV. The results obtained show that it is possible to predict tank degradations by developing laws of variation of these parameters as a function of the real conditions of the use of the FCEV (number of charging/discharging cycles, pressures, mass flow rates, temperatures).

One of the main goals of any power grid is sustainability. The given study proposes a new method, which aims to reduce users’ anxiety especially at slow charging stations and improve the smart charging model to increase the benefits for the electric vehicles’ owners, which in turn will increase the grid stability. The issue under consideration is modelled as an optimisation problem to minimise the cost of charging. This approach levels the load effectively throughout the day by providing power to charge EVs’ batteries during the off-peak hours and drawing it from the EVs’ batteries during peak-demand hours of the day. In order to minimise the costs associated with EVs’ charging in the given optimisation problem, an improved version of an intelligent algorithm is developed. In order to evaluate the effectiveness of the proposed technique, it is implemented on several standard models with various loads, as well as compared with other optimisation methods. The superiority and efficiency of the proposed method are demonstrated, by analysing the obtained results and comparing them with the ones produced by the competitor techniques.

This study addresses the pose estimation problem of an aircraft runway using visual observations in a landing approach scenario. The authors utilised the fact that the geodetic coordinates of most runways are known precisely with highly visible markers. Thus, the runway observations can increase the level of situational awareness during the landing approach, providing additional redundancy of navigation and less reliance on global positioning system. A novel pose optimisation algorithm is proposed utilising unit dual quaternion for the runway corner observations obtained from a monocular camera. The estimated runway pose is further fused with an inertial navigation system in an extended Kalman filter. An open-source flight simulator is used to collect and process the visual and flight dataset during the landing approach, demonstrating reliable runway pose estimates and the improved inertial navigation solution.

For a non-linear anisotropic permanent magnet synchronous machine (PMSM), a prediction model for model predictive control (MPC) considering effects like cross-coupling and saturation is developed in a straight forward procedure. The objective of the designed MPC is either tracking of reference currents or torque tracking. Both approaches use the projected fast gradient method (PFGM) as optimisation algorithm. The latter approach makes look-up-tables for current references obsolete and additionally minimises winding losses. This two approaches are compared in a simulation study with a state of the art PI controller.

The interaction between renewable energy generation and high-voltage direct current (HVDC) transmission system becomes an essential cause of the subsynchronous oscillation (SSO) problem. In this study, the permanent magnet synchronous generator-based wind farm connected to HVDC transmission system is studied. The susceptivity analysis is carried out to find the main influence factors of the SSO problem. Besides, a parameter design method for subsynchronous damping controller is proposed. The configuration and the performance indicator are discussed, and then the optimisation algorithm is developed based on hybrid genetic algorithm and electromagnetic transient simulation. Finally, the proposed design method is validated by simulation in PSCAD/EMTDC.

This study introduces an active distribution network with high penetration of inverter-based distributed generations (IBDGs) to improve the steady state, dynamic and transient performance of the distribution network. The size and location of the IBDGs, capacitor bank, on-load tap changers position and voltage regulator set points have been optimised to minimise the system losses, improve voltage profile and maximise the dynamic reactive power reserve. In addition to that, the adaptive master/slave roles are deployed in planning the active distribution network to achieve the best utilisation of each device based on its capability and response time in steady state, dynamic and transient operation. Indeed, the optimisation algorithm allows the IBDGs to operate in an inductive mode of operation during steady state resulted in maximising the dynamic reactive power reserve. Consequently, the IBDGs react with large dynamic margin in response to grid fault leading to enhanced voltage recovery and improved transient response. A comprehensive system optimisation and transient analysis are carried out to demonstrate the performance of the distribution network for enhancing the voltage profile, increasing the penetration level of renewable energy distributed generation, IBDGs, and improving the transient recovery in response to severe grid fault.

It is generally difficult to obtain accurate optimisation functions during parameters matching and optimisation process of the hybrid system, especially in the optimisation variable field containing both a continuous set and a discontinuous set and when the control strategy has an important influence on the system performance. To aim at the fuel economy of the parallel hydraulic hybrid system, a global parameter matching and control strategy based on the simulation model were proposed. The AMESim simulation model as the judgment module of the optimisation algorithm was used to match and optimise the main parameters and control strategies in the hybrid system. The communication of underlying parameters was developed by the VBA in EXCEL platform. Parameters match was implemented on a truck crane chassis in practice. The test results indicated that the rate of fuel saving reached 15.2%, which was generally consistent with the theoretical analysis. The parameters were met the energy-saving requirements of hybrid vehicles.

At present, there is a little research on the workspace of the hybrid mechanism, and the further development of the engraving plotter is hindered due to the limitation of the workspace. In order to clearly understand the workspace of the hybrid engraving plotter, and verify whether the workspace meets the industrial production needs, the five degrees of freedom hybrid engraving plotter is analysed and studied. First, the structure of the engraving plotter is analysed and simplified, and then the direct equation is deduced by the analytic method. Second, according to the engraving plotter direct solution equation, the boundary of workspace is determined and the volume is calculated by using the optimisation algorithm. In order to understand the change of workspace in different z planes, the workspace of the parallel part is intercepted by some planes, which are parallel to XOY; thus, the authors know that the cut plane area of the workspace increases with the increase of Z values. Finally, the influence of the mechanism parameters is discussed on the dexterity. These theoretical knowledge will make important research significance for guiding engineering practice.

This paper presents a review of the basic concepts of swarm intelligence and some views regarding the future of re-search in this area aimed at establishing a starting point for future work in different engineering fields. A bibliogra-phic search of the most updated databases regarding classic articles on the subject and the most recent applications and results was used for constructing this review, especially in the areas of automatic control, signal and image pro-cessing and robotics. The main concepts were selected and organised in chronological order. A taxonomy was ob-tained for evolutionary computing techniques, a clear differentiation between swarm intelligence and other evolutio-nary algorithms and an overview of the different techniques and applications.

In the previous issue of Business Management an optimisation algorithm for multimodel production on a paced production line was presented. This publication deals with the application of the algorithm to an actual production facility excluding the scheduling phase. The applicability of the algorithm is tested in respect of determining an optimal cycle time for the multi-model production line, optimal batch sizes and balancing the production line for three product types.