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The rising demand for electrical energy, coupled with the running down of conventional energy sources, has prompted vast research into renewable energy sources (RES) for power generation. Wind and solar photovoltaic energies are among the most critical resources, despite their susceptibility to fluctuating climatic conditions. Power electronics have been helpful in the integration of renewable energy sources (RESs) with grid, and multilevel inverters (MLIs) have arisen as critical components for the effective utilization of energy from RES and the enhancement of power quality. This paper provides an extensive overview of the latest developments in MLI topologies and how they have been applied in different industries. Optimal MLI designs for various applications will be identified by a thorough investigation of performance parameters including component count, total harmonic distortion (THD), and overall efficiency. Additionally, the paper explores the role of MLIs in diverse sectors including RES, microgrids, and residential applications. By synthesizing insights from recent literature and performance evaluations, this review aims to provide valuable guidance for researchers and practitioners in selecting the most suitable MLI topology for specific applications.

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.

This paper investigates the efficacy of an optimized fuzzy logic controller for real-time swing-up control and stabilization to a rigidly coupled twin-arm inverted pendulum system. The proposed fuzzy controller utilizes Lyapunov criteria for controller design to ensure system stability. The membership functions are further optimized based on the entropy function. The controller design is based on the black-box approach, eliminating the need for an accurate mathematical model of the system. The experimental results shows an improvement in the transient and steady-state response of the controlled system as compared to other state-of-the-art controllers. The proposed controller exhibits a small settling time of 4.0 s and reaches the stable swing-up position within 5 oscillations. Various error indices are evaluated that validates an overall improvement in the performance of the system.

Light-Duty Electric Vehicle (LDEV) with a distributed drive powertrain provides several potential advantages in terms of flexibility, controllability and responsiveness over conventional powertrains. The precise distribution of driving and braking torque of such configuration is crucially vital for improving the overall performance and efficiency of the vehicles. This paper proposes a new torque allocation (TA) model emphasizing the wheel load variation due to the passenger occupancy payload. A light-duty vehicle model is developed along with the occupancy payload arrangement and a dynamic tire-road friction estimation method for the control system for wheel slip. This proposed TA algorithm uses offline optimization to derive the necessary transmissible torque to the driving wheels. Unlike conventional optimization, it adopts a set of predefined distribution coefficients. Therefore it can execute in a real time platform without high computation power and additional hardware requirements. The efficiency of the model is analyzed using Indian Urban Drive Cycle (IN-UDC). A comparative analysis using a traditional torque allocation model highlights the contributions of this novel torque allocation. The results obtained from various simulations demonstrate the effectiveness of the proposed new TA algorithm.

The purpose of microgrids is to improve system flexibility and resilience during normal and emergency conditions. The ceaseless load growth mandates to increase microgrid’s capacity, thereby improving the system flexibility and resilience. However, capacity expansion requires significant investments, making it essential to identify the optimal capacity of energy resources. The methodologies proposed in the literature identifies the microgrid’s capacity with an assumption of investments with a single installment. This way of theoretical approach leads to unrealistic solutions. Besides, microgrid’s participation in a flexible market will enhance its performance both in commercial and technical aspects. Therefore, this paper proposes a realistic framework with the concept “expansion through time” inspired by “Real Options Theory.” This framework includes practical parameters like resource & load uncertainty, physical space required to install, revenue generated by resources, and maximum demand penalty, on top of electrical parameters; constrained with significant return in investments to improve the overall savings. In addition, this paper proposes a market participation model for microgrid, which defines a bidding process with two components, such as regular and flexible portions under both normal and extreme conditions. This study considers renewable-based energy resources like solar-photovoltaic plants (SPPs) and battery energy storage systems (BESSs) as microgrids’ energy resources. The system chosen for testing the efficacy of the proposed framework is a real-world active-microgrid of Malta College of Arts, Science and Technology (MCAST), located on an island.

Due to the high penetration of grid-connected photovoltaic (GCPV) systems, the network operators are regularly updating the grid codes to ensure that the operation of GCPV systems will assist in maintaining grid stability. Among these, low-voltage-ride-through (LVRT) is an essential attribute of PV inverters that allows them to remain connected with the grid during short-term disturbances in the grid voltage. Hence, PV inverters are equipped with control strategies that secure their smooth operation through this ride-through period as per the specified grid code. However, during the injection of reactive power under LVRT condition, various challenges have been observed, such as inverter overcurrent, unbalance phase voltages at the point of common coupling (PCC), overvoltage in healthy phases, oscillations in active, reactive power and dc-link voltage, distortion in injected currents and poor dynamic response of the system. Several strategies are found in the literature to overcome these challenges associated with LVRT. This paper critically reviews the recent challenges and the associated strategies under LVRT conditions in GCPV inverters. The drawbacks associated with the conventional current control strategies are investigated in MATLAB/Simulink environment. The advanced LVRT control strategies are categorized and analyzed under different types of grid faults. The work categorizes the state-of-the-art LVRT techniques on the basis of the synchronization methods, current injection techniques and dc-link voltage control strategies. It is found that state-of-the-art control strategies like OVSS/OCCIDGS provides improved voltage support and current limitation, which results in smooth LVRT operation by injecting currents of enhanced power quality.

Parameter estimation of photovoltaic modules is an essential step to observe, analyze, and optimize the performance of solar power systems. An efficient optimization approach is needed to obtain the finest value of unknown parameters. Herewith, this article proposes a novel opposition-based tunicate swarm algorithm for parameter estimation. The proposed algorithm is developed based on the exploration and exploitation components of the tunicate swarm algorithm. The opposition-based learning mechanism is employed to improve the diversification of the search space to provide a precise solution. The parameters of three types of photovoltaic modules (two polycrystalline and one monocrystalline) are estimated using the proposed algorithm. The estimated parameters show good agreement with the measured data for three modules at different irradiance levels. Performance of the developed opposition-based tunicate swarm algorithm is compared with other predefined algorithms in terms of robustness, statistical, and convergence analysis. The root mean square error values are minimum ( $6.83\times 10 ^{-4}$ , $2.06\times 10 ^{-4}$ , and $4.48\times 10 ^{-6}$ ) compared to the tunicate swarm algorithm and other predefined algorithms. Proposed algorithm decreases the function cost by 30.11%, 97.65%, and 99.80% for the SS2018 module, SolarexMSX-60 module, and Leibold solar module, respectively, as compared to the basic tunicate swarm algorithm. The statistical results and convergence speed depicts the outstanding performance of the anticipated approach. Furthermore, the Friedman ranking tests confirm the competence and reliability of the developed approach.

Extreme bursts of radiation from space result in rapid increases in the concentration of radiocarbon in the atmosphere. Such rises, known as Miyake Events, can be detected through the measurement of radiocarbon in dendrochronological archives. The identification of Miyake Events is important because radiation impacts of this magnitude pose an existential threat to satellite communications and aeronautical avionics and may even be detrimental to human health. However, at present, radiocarbon measurements on tree-ring archives are generally only available at decadal resolution, which smooths out the effect of a possible radiation burst. The Miyake Events discovered so far, in tree-rings from the years 3372-3371 BCE, 774-775 CE, and 993-994 CE, have essentially been found by chance, but there may be more. In this paper, we use signal processing techniques, in particular COSFIRE, to train filters with data on annual changes in radiocarbon (Δ14C) around those dates. Then, we evaluate the trained filters and attempt to detect similar Miyake Events in the past. The method that we propose is promising, since it identifies the known Miyake Events at a relatively low false positive rate. Using the findings of this paper, we propose a list of 26 calendar years that our system persistently indicates are Miyake Event-like. We are currently examining a short-list of five of the newly identified dates and intend to perform single-year radiocarbon measurements over them. Signal processing techniques, such as COSFIRE filters, can be used as guidance tools since they are able to identify similar patterns of interest, even if they vary in time or in amplitude.

The muscle grading of livestock is a primary component of valuation in the meat industry. In pigs, the muscularity of a live animal is traditionally estimated by visual and tactile inspection from an experienced assessor. In addition to being a time-consuming process, scoring of this kind suffers from inconsistencies inherent to the subjectivity of human assessment. On the other hand, accurate, computer-driven methods for carcass composition estimation, such as magnetic resonance imaging (MRI) and computed tomography scans (CT-scans), are expensive and cumbersome to both the animals and their handlers. In this paper, we propose a method that is fast, inexpensive, and non-invasive for estimating the muscularity of live pigs, using RGB-D computer vision and machine learning. We used morphological features extracted from the depth images of pigs to train a classifier that estimates the muscle scores that are likely to be given by a human assessor. The depth images were obtained from a Kinect v1 camera which was placed over an aisle through which the pigs passed freely. The data came from 3246 pigs, each having 20 depth images, and a muscle score from 1 to 7 (reduced later to 5 scores) assigned by an experienced assessor. The classification based on morphological features of the pig's body shape-using a gradient boosted classifier-resulted in a mean absolute error of 0.65 in tenfold cross-validation. Notably, the majority of the errors corresponded to pigs being classified as having muscle scores adjacent to the groundtruth labels given by the assessor. According to the end users of this application, the proposed approach could be used to replace expert assessors at the farm.

Glaucoma is a chronic progressive optic neuropathy that causes visual impairment or blindness if left untreated. It is crucial to diagnose it at an early stage in order to enable treatment. Fundus photography is a viable option for population-based screening. A fundus photograph enables the observation of the excavation of the optic disk—the hallmark of glaucoma. The excavation is quantified as a vertical cup-to-disk ratio (VCDR). The manual assessment of retinal fundus images is, however, time-consuming and costly. Thus, an automated system is necessary to assist human observers. We propose a computer-aided diagnosis system, which consists of the localization of the optic disk, the determination of the height of the optic disk and the cup, and the computation of the VCDR. We evaluated the performance of our approach on eight publicly available datasets, which have, in total, 1712 retinal fundus images. We compared the obtained VCDR values with those provided by an experienced ophthalmologist and achieved a weighted VCDR mean difference of 0.11. The system provides a reliable estimation of the height of the optic disk and the cup in terms of the relative height error (RHE = 0.08 and 0.09, respectively). The Bland–Altman analysis showed that the system achieves a good agreement with the manual annotations, especially for large VCDRs which indicate pathology.

In the above article [1], reference [22] was missing. The citation should be given in Section II: “System Modelling, A. Vehicle Structure and Occupancy Payload Model” after the line “The reaction force (or normal force) experienced by each wheel can be obtained by defining the position of CG using Eq. 1” as well as after the line “This essentially require a TA model to deal with the torque non-uniformity.”

The popularity and the appeal of systems which are able to automatically determine the gender from face images are growing rapidly. Such a great interest arises from the wide variety of applications, especially in the fields of retail and video surveillance. In recent years, there have been several attempts to address this challenge, but a definitive solution has not yet been found. In this paper, we propose a novel approach that fuses domain-specific and trainable features to recognize the gender from face images. In particular, we use the SURF descriptors extracted from 51 facial landmarks related to eyes, nose, and mouth as domain-dependent features, and the COSFIRE filters as trainable features. The proposed approach turns out to be very robust with respect to the well-known face variations, including different poses, expressions, and illumination conditions. It achieves state-of-the-art recognition rates on the GENDER-FERET (94.7%) and on the labeled faces in the wild (99.4%) data sets, which are two of the most popular benchmarks for gender recognition. We further evaluated the method on a new data set acquired in real scenarios, the UNISA-Public, recently made publicly available. It consists of 206 training (144 male, 62 female) and 200 test (139 male, 61 female) images that are acquired with a real-time indoor camera capturing people in regular walking motion. Such experiment has the aim to assess the capability of the algorithm to deal with face images extracted from videos, which are definitely more challenging than the still images available in the standard data sets. Also for this data set, we achieved a high recognition rate of 91.5%, that confirms the generalization capabilities of the proposed approach. Of the two types of features, the trainable COSFIRE filters are the most effective and, given their trainable character, they can be applied in any visual pattern recognition problem.