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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. [ABSTRACT FROM AUTHOR]

Integrating long-term observational data analysis with numerical simulations of dam operations provides an effective approach to dam safety evaluation. However, analytical results are often subject to errors due to challenges in accurately surveying and modeling the foundation, as well as temporal changes in foundation properties. This paper proposes a concrete dam displacement separation model that distinguishes between deformation caused by foundation restraint and that induced by external loads. By combining this model with intelligent optimization techniques and long-term observational data, we can identify the actual mechanical parameters of the dam and conduct structural health assessments. The proposed model accommodates multiple degrees of freedom and is applicable to both two- and three-dimensional dam modeling. Consequently, it is well-suited for parameter identification and health diagnosis of concrete gravity and arch dams with extensive observational data. The efficacy of this diagnostic model has been validated through computational case studies and practical engineering applications. [ABSTRACT FROM AUTHOR]

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.

Introduction: Sleep 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, timeconsuming 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. Methods: To 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. Results: The 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%. Discussion: All 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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

It is crucial for flood discharge structure vibration safety evaluations to filter low-frequency noise, separate dense-frequency components and obtain high-frequency component accurately from vibration signals. Variational mode decomposition, a novel signal adaptive decomposition method, effectively processes flood discharge structures. However, the mode number and quadratic penalty item uncertainty in variational mode decomposition directly affects the vibration signal decomposition. Therefore, an improved variational mode decomposition method for vibration signal processing is proposed in this study. The proposed method adaptively determines the mode number based on singular entropy and frequency stability to completely separate the structural vibration components (including dense-frequency components and high-frequency components) and noise components from the vibration signal. Next, an objective quadratic penalty item function based on sample entropy and mutual information is proposed to quantify the mode mixing between the structural vibration components. Finally, a particle swarm optimisation algorithm based on beetle antenna search is proposed to optimise the quadratic penalty item, which overcomes the shortcomings of traditional algorithms and suppresses the mode mixing between the structural vibration components. The validity and feasibility of the proposed method was verified by the simulation signal and was applied to a sluice prototype project. The results showed that the method effectively filtered noise, greatly improved the vibration response signal-to-noise ratio and obtained the structural vibration component time history signal, which provides a foundation for flood discharge structure vibration safety evaluation and health monitoring. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

While the concept of swarm intelligence was introduced in 1980s, the first swarm optimisation algorithm was introduced a decade later, in 1992. In this paper, nineteen representative original swarm optimisation algorithms are analysed to extract their common features and design a taxonomy for swarm optimisation. We use twenty-nine benchmark problems to compare the performance of these nineteen algorithms in the form they were first introduced in the literature against five state-of-the-art swarm algorithms. This comparison reveals the advancements made in this field over three decades. It reveals that, while the state-of-the-art swarm optimisation algorithms are indeed competitive in terms of the quality of solutions they find, their complexities have evolved to be more computationally demanding when compared to the nineteen original algorithms of swarm optimisation. The investigation suggests that there is an urge to continue to design swarm optimisation algorithms that are simpler, while maintaining their current competitive performance. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

This paper presents the application of a reinforcement learning (RL) algorithm, concretely Q-Learning, as the core of a decision support system (DSS) for a steelmaking subprocess, the Composition Adjustment by Sealed Argon-bubbling with Oxygen Blowing (CAS-OB) from the SSAB Raahe steel plant. Since many CAS-OB actions are selected based on operator experience, this research aims to develop a DSS to assist the operator in taking the proper decisions during the process, especially less experienced operators. The DSS is intended to supports the operators in real-time during the process to facilitate their work and optimise the process, improving material and energy efficiency, thus increasing the operation's sustainability. The objective is that the algorithm learns the process based only on raw data from the CAS-OB historical database, and on rewards set according to the objectives. Finally, the DSS was tested and validated by a developer engineer from the CAS-OB steelmaking plant. The results show that the algorithm successfully learns the process, recommending the same actions as those taken by the operator 69.23% of the time. The algorithm also suggests a better option in 30.76% of the remaining cases. Thanks to the DSS, the heat rejection due to wrong composition is reduced by 4%, and temperature accuracy is increased to 83.33%. These improvements resulted in an estimated reduction of 2% in CO2 emissions, 0.5% in energy consumption and 1.5% in costs. Additionally, actions taken based on the operator's experience are incorporated into the DSS knowledge, facilitating the integration of operators with lower experience in the process. [ABSTRACT FROM AUTHOR]

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.

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 learning using 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, it has been observed that the suggested algorithm outperforms the others. [ABSTRACT FROM AUTHOR]

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.

In this study, the system with fractional power nonlinearity is introduced into the theory of aperiodic stochastic resonance (ASR). The fractional exponent is a key parameter and its effect on the ASR phenomenon excited by aperiodic binary signal is investigated in this system. Compared to the classical bistable system, the system with fractional power nonlinearity shows better performance. It can adjust not only the noise intensity but also the fractional exponent to enhance weak signal. In the field of signal transmission, pure aperiodic binary signal is usually submerged in the noise and the signal is unknown. Thus, an effective method is proposed based on ASR and moving average. By the method, the unknown aperiodic binary signal can be recovered in the noise background. To improve the efficiency of the signal recovery, the adaptive ASR is realised with the help of adaptive particle swarm optimisation (APSO) algorithm to optimise the parameters. The method may provide some reference to the engineering field. [ABSTRACT FROM AUTHOR]

Open‐circuit voltage decay (OCVD) is a method to characterise minority carrier effective lifetime (τeff). It is non‐destructive, simple and low‐cost. It has been mainly used in silicon p‐n junctions. τeff is not only a very important parameter to optimise device design but also to supervise process steps. It is not the only parameter we can obtain by OCVD. Due to the intrinsic space charge region capacitance of a p‐n junction, the doping level of the lowest‐doped region (Nl) and built‐in potential (Vbi) are extractable. Moreover, it is also possible to obtain the shunt resistance (Rsh) value when it has a significant effect on the p‐n junction behaviour. The authors first applied the well‐established one‐diode model in a transient regime to simulate OCVD signal. In a second step, they used an optimisation algorithm to fit the experimental curve of a silicon diode to extract τeff, Nl, Vbi and Rsh. These values were compared to those obtained from C–V and I–V. Results are promising and demonstrate for the first time, the flexibility of the OCVD method. It opens up the perspective for the development of add‐on features of the method and for measuring short lifetime. [ABSTRACT FROM AUTHOR]

Image compression is an intensively studied subject in computer vision. The deep generative model, especially generative adversarial networks (GANs), is a popular new direction for this subject. In this study, the authors propose a new compression method based on a generative model and focus on its application by GANs. The decoder in the proposed method is modified from the GAN generator model, which can produce visually real‐like synthetic images. It is one of the two models in GANs, which is trained through a two‐players' contest game. The encoder is an optimisation algorithm called backpropagation‐to‐the‐input, which derives from an image inpainting algorithm based on generative models. In the proposed method, the authors turn the encoding process into an optimisation task to search for optimal encoded representations. Compared with traditional methods, the proposed method can compress images from certain domains into extremely small and shape‐fixed encoded space but still retain better visual representations. It is easy and convenient to apply without any retraining or additional modification to the generative models. [ABSTRACT FROM AUTHOR]

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.

Introduction: Sleep 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., Methods: To 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., Results: The 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%., Discussion: All 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Xiong, Wang, He, Wang, Liu, Sun, Zhang and Zhang.)

The growing demand for the cloud community market towards attracting and sustaining the incoming and the available cloud users is addressed actively to meet the competitive environment. There is a good scope for improving the provider capabilities in the cloud in order to satisfy the users with attractive benefits. The study introduces an effective virtual machine (VM) migration strategy using an optimisation algorithm in such a way to facilitate the user selection of the providers based on their budgetary requirements in running their own platforms. The constraints associated with the selection of the provider include cost, revenue, and resource, which are altogether confined as an elective factor. The optimisation algorithm employed for the VM migration is referred to as Taylor series‐based salp swarm algorithm (Taylor‐SSA) that is the integration of the Taylor series with SSA. The evaluation of the method is progressed using three setups by varying the number of providers and users. The cost, the revenue, and the resource of the proposed method are analysed and concluded that the proposed method acquired a minimal cost, maximal resource gain and revenue. [ABSTRACT FROM AUTHOR]

For space manufacturers, additive manufacturing promises to dramatically reduce weight and costs by means of integral designs achieved through part consolidation. However, integrated designs hinder the ability to change and service components over time – actually increasing costs – which is instead enabled by highly modular designs. Finding the optimal trade-off between integral and modular designs in additive manufacturing is of critical importance. In this article, a product modularisation methodology is proposed for supporting such trade-offs. The methodology is based on combining function modelling with optimisation algorithms. It evaluates product design concepts with respect to product adaptability, component interface costs, manufacturing costs and cost of post-processing activities. The developed product modularisation methodology is derived from data collected through a series of workshops with industrial practitioners from three different manufacturer companies of space products. The implementation of the methodology is demonstrated in a case study featuring the redesign of a satellite antenna. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A prominent issue in microgrid (MG) cost optimisation is the consideration of variable parameters such as demand, wind speed, sun radiation intensity etc. This study deals with mitigating substantial deviation between study results and actual MG operation indices. The deviation is the result of ignoring the effects of power electronics devices in the optimisation algorithm. A new optimisation model is proposed to involve the main converter's restrictions in the optimisation process. For connecting renewable sources with uncertain outputs such as photovoltaic, wind, and also loads to a MG, using power electronic converters is inevitable. This study proposes an operation cost optimisation development by foreseeing the effects and limitations of power electronic devices, disregarding of which would undoubtedly lead to a notable deviation in optimisation results. [ABSTRACT FROM AUTHOR]

Due to the integration of the renewable generation and the distributed load that inherently uncertain and unpredictable, developing an efficient distributed management structure of such a complex system remains a challenging issue. Most of the existing works on the demand‐side management concentrate on the centralised methods or need a proper initialisation process. This study proposed a demand‐side management strategy that can solve the optimisation problem in a distributed manner without initialisation. The objective of the designed demand management system is to maximise the social welfare of a smart grid by controlling the active power economically. The proposed optimisation strategy that generates the optimal power references uses the neighbouring information while considering the local feasible constraints by using a projection operation. Furthermore, the optimisation algorithm is initialisation free, which avoids any initialisation process when plugging‐in new customers or plugging‐out power units, such as demand loads, battery energy storage systems and distributed generators. The proposed strategy only uses the neighbouring information, so that the proposed approach is scalable and potentially applicable to large‐scale smart grids. The effectiveness and scalability of the proposed algorithm are established and verified through case studies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study is concerned with the problem of finite‐time H∞ filter design for a class of Itô stochastic systems with Markovian switching and distributed time‐varying delays. Firstly, a partially mode‐dependent filter is designed to accommodate to unreliable network transmission. The attention is focused on deriving sufficient conditions for the filtering error system to ensure the finite‐time boundedness and to satisfy a prescribed H∞ disturbance attenuation. Then based on stochastic functional theory, the existence of H∞ filter is presented by solving existing linear matrix inequalities optimisation problems. Furthermore, the result is extended to the case where the mode information is completely transmitted. Finally, a numerical example is provided to show the effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

[Display omitted] This paper presents the parameter identification procedure of carbon nanotubes (CNTs) reinforced interphase in fuzzy fibre reinforced polymer (FFRP). The procedure was completed with ANSYS Workbench 19.2 software by combining Mechanical ADPL and Goal-Driven Optimisation. Firstly, a three-phase representative volume element (RVE) containing carbon fibre, CNTs reinforced interphase and epoxy resin was developed as a collection of Mechanical APDL commands. This RVE model was simulated to evaluate the elastic constants of FFRP lamina. CNTs reinforced interphase was characterised by transversely isotropic model. Interphase properties were parametrised and became input parameters in the Goal-Driven Optimisation. FFRP lamina elastic constants were set as the output parameters. Multi-objective Genetic Algorithm (MOGA) was used to identify the interphase properties, so that the output FFRP lamina elastic constants match the objective and constraints. The optimisation algorithm converged after 585 evaluations. Five potential candidate point, which met required objectives and constraints, were found. The identified interphase properties agreed well with the literature (an average percentage error of around 2%). This inverse procedure shows the potential to identify the interphase properties in nano-engineered composites, which are extremely difficult to measure experimentally. [ABSTRACT FROM AUTHOR]

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.

Several studies have reported the improvement of system stability with a doubly‐fed induction generator (DFIG) based wind turbines. Due to the remote location of DFIG, the local signals measured may not contain the complete oscillation information of the power system. This study describes an optimisation enabled wide area damping control (WADC) for DFIG to mitigate both local and inter area oscillations. Wide area measurement systems (WAMSs) which include phasor measurement units and synchrophasors are used for a centralised controller for damping inter‐area and local oscillatory modes. The proposed damping controller parameters are optimised along with the local power system stabiliser settings for maximising the impact on system modal damping. The challenging shortcoming of WAMS based controller is the variable communication latency which can adversely affect the controller if not accounted in the controller design process. The proposed WADC algorithm addresses this issue and compensates for the delayed and time stamped error signals. The variable latencies are normalised in the optimisation algorithm in the design of controller parameters. The proposed algorithm is verified with transient simulation in PSCAD/EMTDC for the most severe three‐phase faults. A set of comprehensive case studies are performed to analyse the effectiveness of the proposed algorithm for the noisy, delayed communication signals. [ABSTRACT FROM AUTHOR]

Average dwell time (ADT) of switched systems is dependent on two parameters: the decay coefficient of the subsystems and the increase coefficient of the Lyapunov functions of different subsystems, namely, λ and μ, respectively. In the literature, the two parameters are prescribed because popular computation techniques cannot deal with bilinear conditions easily. A new method for the computation of ADT is presented in this study, where a suggested optimisation algorithm is proposed to obtain a loose ADT condition. The advantages of the method lie in twofold: (i) the parameters λ and μ do not need to be given, and (ii) the presented ADT conditions are tractable to solve. The method is also extended to mode‐dependent ADT (MDADT), ADT in asynchronous control, and ADT and MDADT of positive switched systems. [ABSTRACT FROM AUTHOR]

The amount of solar energy incidence on a photovoltaic (PV) panel depends on the PV tilt angles with respect to the horizon. It is thus crucial to investigate the optimum tilt angles to maximise the efficiency of PV panels and at the same time to increase the performance of solar energy systems. The objective of this study is to estimate the optimum tilt angle for PV panels in order to collect the maximum solar radiation for the city of Dhahran in Saudi Arabia. A newly developed optimisation algorithm called the vortex search algorithm is used to estimate the solar radiation on the tilted surface. Moreover, one year can be divided into different periods in the proposed approach, and the optimum angle can be obtained for each one of these periods separately. The horizontal solar data (i.e. direct, diffuse and global solar radiation) is used to estimate the optimum tilt angle. The results demonstrate that the solar radiation estimated using the optimum tilt angle is maximised compared with the one estimated on a horizontal surface. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) could benefit a lot from compressive sensing (CS). Inherent physical structure of sensors of WSNs (battery‐powered devices) demands computational‐efficient algorithms with no heavy burden on a small subset of the sensors, i.e. fusion sensors. This could be achieved by distributed algorithms in which computation is distributed among all sensor nodes. On this basis, in this study, the authors have proposed a distributed and cooperative sparse recovery algorithm in which each sensor decodes a sparse signal by running a recovery algorithm with the cooperation of its neighbours. The proposed algorithm has a general structure and can be adapted to many optimisation algorithms in the context of the CS. This algorithm is completely distributed and requires an acceptable computational complexity that is suitable for WSNs. A detailed proof of convergence behaviour of the proposed algorithm is also presented. The superiority of the proposed algorithm compared with similar methods in terms of recovery quality and convergence rate is confirmed through simulation. [ABSTRACT FROM AUTHOR]

This study presents an improved under‐frequency load shedding (UFLS) scheme that can detect power deficit during the shedding process and accordingly adjust the amount of load shedding. This is achieved by continuous monitoring of the overshooting signal of the second frequency derivative of the centre of inertia. Once detected, an equivalent system inertia constant is estimated in order to quantify the new power deficit. The scheme is also equipped with an optimisation algorithm to determine the best combination of loads that is close to the amount of power deficit, which minimises frequency overshoot/undershoot. The optimisation technique selected for this work is based on particle swarm optimisation. The performance of the proposed UFLS scheme was validated using a modified IEEE 33 bus with two mini‐hydro generators and one full converter wind turbine. The system and the proposed UFLS was modelled and simulated in PSCAD/EMTDC software. The results confirmed that the proposed scheme is capable of shedding loads with minimum undershoot/overshoot, and detect and estimate a new power deficit during load shedding. The results reported by the proposed scheme proved to be significantly better than those reported by conventional and adaptive load shedding schemes. [ABSTRACT FROM AUTHOR]