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This research work emphasizes on the development of a unique high-gain BOCUK DC-DC converter for Switched Mode Power Supply (SMPS) applications. The high-gain BOOST-CUK (BOCUK) DC-DC converter combines the benefits of both Boost and Cuk converters for attaining instantaneous line current shaping and load regulation. The proposed BOCUK converter provides a non-inverted output and improves the voltage gain and efficiency. The modes of operation of the high-gain BOCUK DC-DC Converter in a Continuous Conduction Mode (CCM) with parasitic elements in the circuit are examined and a state-space model is constructed. The second-order model is derived using the Modified Hankel Matrix Method (HMM) to make the controller design easier. Dual-loop Control employing an inner Linear Quadratic Regulator (LQR) current Controller and an outer Atom Search Optimization (ASO)-tuned Proportional Integral (PI) controller is used to analyze the performance indices of the high-gain converter and fluctuations in line, load and setpoint are investigated. Simulation results prove that the LQR controller is robust in tracking the load voltage and provides excellent line current shaping with low % Total Harmonic Distortion (THD) and nearly Unity Power Factor (UPF). The hardware model for 200W is built and implemented using the TMS320F28027F microcontroller to verify the simulation results.

This research work emphasizes on the development of a unique high-gain BOCUK DC-DC converter for Switched Mode Power Supply (SMPS) applications. The high-gain BOOST-CUK (BOCUK) DC-DC converter combines the benefits of both Boost and Cuk converters for attaining instantaneous line current shaping and load regulation. The proposed BOCUK converter provides a non-inverted output and improves the voltage gain and efficiency. The modes of operation of the high-gain BOCUK DC-DC Converter in a Continuous Conduction Mode (CCM) with parasitic elements in the circuit are examined and a state-space model is constructed. The second-order model is derived using the Modified Hankel Matrix Method (HMM) to make the controller design easier. Dual-loop Control employing an inner Linear Quadratic Regulator (LQR) current Controller and an outer Atom Search Optimization (ASO)-tuned Proportional Integral (PI) controller is used to analyze the performance indices of the high-gain converter and fluctuations in line, load and setpoint are investigated. Simulation results prove that the LQR controller is robust in tracking the load voltage and provides excellent line current shaping with low % Total Harmonic Distortion (THD) and nearly Unity Power Factor (UPF). The hardware model for 200W is built and implemented using the TMS320F28027F microcontroller to verify the simulation results. [ABSTRACT FROM AUTHOR]

No one denies the importance of renewable energy sources in modern power systems in terms of sustainability and environmental conservation. However, due to their reliance on environmental change, they are unreliable systems. This paper uses a Unified Power Flow Controller (UPFC) to enhance the reliability and performance of grid-tied renewable energy systems. This system consists of two renewable sources, namely photovoltaic cells (PV) and wind turbines (WTs). The UPFC was selected for its unique advantage in both active and reactive power control. The UPFC is controlled with an optimized Fractional Order Proportional–Integral–Derivative (FOPID) controller. The parameters of this controller were tuned using an Atomic Search Optimization (ASO) algorithm. Simulation results confirm the efficiency of the suggested controller in supporting the reliability and performance of the hybrid power system during some disturbance events including voltage sag, swell, and unbalanced loading. In addition, power quality can be improved through reducing the total harmonic distortion. It is worth mentioning that two maximum point tracking techniques had been included for the PV and WT systems separately. MATLAB/SIMULINK 2021a software was used to model the system. [ABSTRACT FROM AUTHOR]

In multilevel inverters (MLI), output voltage waveform consists of dominant low order harmonics, which needs to be minimized. Simultaneously, good control over the fundamental voltage for desirable operation is needed. In this paper, an atom search optimization (ASO) based selective harmonic elimination (SHE) method is proposed for a variable dc bus based reduced component count (RCC) MLI. ASO is a population-based metaheuristic algorithm, which mathematically models the motion of atoms in nature to accurately determine the optimum firing angle of the switches by solving SHE fitness function. The proposed ASO SHE method outperforms recent metaheuristics based SHE methods such as bee algorithm, imperialistic colonial algorithm (ICA), firefly, particle swarm optimization (PSO), and teaching learning-based optimization (TLBO) in solving SHE problem for 11-level multilevel inverter. Detailed simulation case studies are presented to effectively demonstrate the performance of the proposed ASO SHE method on a stand-alone photovoltaic (PV) based RCC MLI subjected to sudden changes in irradiance, load and dc-link capacitor voltage. The experimental results on a PV based variable dc bus multilevel inverter validate the excellent performance of ASO SHE method in minimizing the total harmonic distortion (THD) and dominant order harmonics under sudden change in operating conditions.

Epilepsy is a complex process and the prediction of seizure is uncertain. Currently, many researchers are exploring different optimization methods to detect the epileptic seizure. Atomic search optimization (ASO) shows a better search capability by using interaction force and the constraint force but it still some deficiency from a local optimum and a low search efficiency. To overcome these demerits, a new enhanced search ability based ASO named as ESAASO is proposed. In this study, inertia weight, levy flight and ranking strategies are integrated into ASO to improve the search performance. The proposed method has been studied on CHB-MIT scalp EEG database. There are 13 number of features are extracted by using TQWT and features are selected using genetic algorithm (GA). The proposed detection method has been computed using twelve well known optimization algorithms with LSSVM classifier. We have obtained average accuracy, sensitivity, selectivity, specificity, average detection rate, G-Mean and area under curve (AUC) values as 98.37%, 91.11%, 91.67%, 91.46%, 91.28%, 91.28% and 0.992 respectively using 10-fold cross-validation method. The proposed algorithm is found outperform as compared to conventional ASO for seizure and non-seizure detection. The proposed method can be experimented using short term EEG datasets in future.

No one denies the importance of renewable energy sources in modern power systems in terms of sustainability and environmental conservation. However, due to their reliance on environmental change, they are unreliable systems. This paper uses a Unified Power Flow Controller (UPFC) to enhance the reliability and performance of grid-tied renewable energy systems. This system consists of two renewable sources, namely photovoltaic cells (PV) and wind turbines (WTs). The UPFC was selected for its unique advantage in both active and reactive power control. The UPFC is controlled with an optimized Fractional Order Proportional–Integral–Derivative (FOPID) controller. The parameters of this controller were tuned using an Atomic Search Optimization (ASO) algorithm. Simulation results confirm the efficiency of the suggested controller in supporting the reliability and performance of the hybrid power system during some disturbance events including voltage sag, swell, and unbalanced loading. In addition, power quality can be improved through reducing the total harmonic distortion. It is worth mentioning that two maximum point tracking techniques had been included for the PV and WT systems separately. MATLAB/SIMULINK 2021a software was used to model the system.

A multilevel drip irrigation system generally uses a first-level pivot as the water source. The water is delivered to each head pivot in the field drip irrigation subsystems through the main water distribution pipe network system (MWDPNS). Subsequently, it is irrigated to farmland through field drip irrigation pipe network subsystems (FDIPNS). This paper focused on the lowest total investment of the pipe network to establish a model for simultaneous optimization of the pipe network layout and pipe diameter selection in a multilevel gravity drip irrigation system, and the method and steps based on atom search optimization are proposed. First, all the FDIPNS are optimized; then, the MWDPNS is optimized with the optimization results of each subsystem as the basic design parameters. For the MWDPNS, the available water diversion nodes other than the water demand nodes can be added and selected. By comprehensively considering the topological and hydraulic constraints, the topology design and pipe diameter selection are optimized simultaneously when the selection of the water distribution nodes is uncertain. For the FDIPNS, the field width is regarded as a linear piecewise function of the field length, and the constraints such as the pipeline layout, flow velocity, discharge, pressure, and rotational irrigation scheme are considered. This model can be applied to most irregular fields. Taking a two-level gravity drip irrigation project in Xinjiang, China as an example, the effectiveness of the proposed model and algorithm was assessed. The results demonstrate that the proposed optimization method has a good application value for the pipe network design of multilevel gravity drip irrigation systems. [ABSTRACT FROM AUTHOR]

Medical data classification is considered as a challenging and complex task in the field of medical informatics. Various medical data classification methods are developed in the existing research works, but to achieve higher performance in terms of classification accuracy result in a great challenge in the medical sector due to the presence of missing values, uncertainty and redundant attributes. Hence, an effective and optimal data classification method named Atom Taylor Bird Swarm Algorithm-based Deep Belief Network (Atom Taylor BSA-based DBN) is proposed in this research to perform incremental classification using medical data. The proposed Atom Taylor BSA is designed by integrating the atom search optimization (ASO) with the Taylor Bird Swarm algorithm (Taylor BSA), which is the development of the Taylor series with BSA. The DBN classifier effectively performs the incremental classification using medical data with its associated neurons and generates optimal result based on the fitness measure. Accordingly, the selected features enable the classifier to increase the performance of classification accuracy. However, the proposed Atom Taylor BSA-based DBN obtained better performance using the metrics, like specificity, sensitivity, and accuracy with the values of 0.8307, 0.9074, and 0.8804, using Hungarian dataset. [ABSTRACT FROM AUTHOR]

The large-scale penetration of intermittent Renewable Energy (RE) sources such as wind and solar power generation may cause a problem of frequency aberration of interconnected Hybrid Power System (HPS). This occurs when the load frequency control of interconnected system is unable to compensate the power balance between generation and load demand. Also owing to the enhancement of future transport, the Plug-in Electric Vehicle (PEV) plays a significant role to customer at demand side. Thus, the PEV can act as a power control to compensate the power balance in Renewable Energy integrated power system. This paper presents a physics inspired Atom Search Optimization (ASO) algorithm for tuning the parameters of Fractional Order Proportional-Integral-Derivative (FOPID) controller for Automatic Load Frequency control of HPS. In this proposed work, an attempt has been made to analyze the frequency stability of HPS using Matignon's theorem. The interconnected HPS consists of reheat thermal power system, RE sources such as wind and solar thermal power generation associated with energy storage devices namely aqua electrolyzer, fuel cell and electric vehicle. The gain and fractional terms of the controller were obtained by minimizing the Integral Time Absolute Error of interconnected system. The robustness of ASO-tuned FOPID controller is tested on two-area HPS that was modelled using MATLAB/Simulink. The results obtained were then compared with other fractional order and classical integer order controllers. From the simulation results, it is inferred that the proposed ASO-tuned FOPID controller gives superior transient and steady-state response compared with other controllers. Moreover, the self-adaptiveness and robustness of the controller was validated to account for the change in RE power generations and system parameters. Furthermore, the effectiveness of the method is proved by comparing its performance with the recent literature works. The real-time applicability of proffered controller is validated in hardware-in-the-loop simulation using Real Time Digital Simulator.

One of the best biometrics used for human verification and identification is iris recognition. The contrast of its unique characteristics differs from one candidate to another in which the iris pattern has numerous well-known features like uniqueness texture, stability and compactness representation for human identification. Among these facts, several approaches in these areas are localized, but there is still an abundant problems such as the low match rate of the score level and low accuracy. Therefore, a decision model should be essential for iris recognition frameworks. This paper proposes a iris recognition based on a decision model using a unified framework based on the integration of three detection schemes due to variation occurred in shading and position change using Smallest Univalue Segment Assimilating Nucleus (SUSAN), Generalized Hough Transform (GHT) and Viola–Jones (SUSANGHT-VJ) for eye detection, enhancing the dimmer and darker areas using fuzzy retinex method and normalizing the iris boundary using daugman's Rubber Sheet Model for segmentation. Also, the iris corner points is extracted using Gabor Wavelet Transform (GWT), the vector properties of the blurred texture features are quantized using Local Phase Quantization (LPQ) and the optimal decision model based on Atom Search Optimization (ASO) and Feed Forward Counter propagation Neural Network (FFCNN) for matching score level and classification task. Furthermore, the current framework prevents false matches and inappropriate iris input, thus making the iris match score framework more reliable. The evaluation of the proposed approach is trained and tested with the employed eye template of iris and face datasets. Therefore, the results depict that the proposed technique gives a high recognition rate of 99.9% on different datasets compared to existing methods. [ABSTRACT FROM AUTHOR]