Search

Nowadays most of the time humans spend in daily life on mobile phones but the thing that tends to get worried about is battery consumption as they run out quickly. We could not make important jobs such as emergency calls or send important documents when our phone battery runs out. This study represents vibration based sound energy is harvested to charge mobile phones using six piezoelectric sound transducers (PSTs) which converts sound energy to electrical energy. This paper proposed designing a low voltage sound energy harvesting circuit (SEHC) using PSTs. PSTs with SEHC is considered as an alternative power source to charge the smart phone battery, especially for remote area where power is unavailable. The SEHC consists of PSTs, non-controlled bridge rectifier circuit, filter, switch-mode open loop boost converter, charger circuit, and storage device at the output terminal. The complete SEHC has designed and simulated using Matlab Simulink software. The PSTs have been analyzed with different frequency and the output voltage of PSTs utilized as an input source to design SEHC. Finally, the results are generated at an output of 7.8 V DC from an input 1.05 V AC at 314 Hz through a SEHC using the PSTs, which suitable to charge smartphone battery efficiently.

Three-phase induction motors (TIMs) are widely used for machines in industrial operations. As an accurate and robust controller, fuzzy logic controller (FLC) is crucial in designing TIMs control systems. The performance of FLC highly depends on the membership function (MF) variables, which are evaluated by heuristic approaches, leading to a high processing time. To address these issues, optimisation algorithms for TIMs have received increasing interest among researchers and industrialists. Here, we present an advanced and efficient quantum-inspired lightning search algorithm (QLSA) to avoid exhaustive conventional heuristic procedures when obtaining MFs. The accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, damping capability and minimisation of statistical errors under diverse speeds and loads. The performance of the proposed QLSAF speed controller is validated through experiments. Test results under different conditions show consistent speed responses and stator currents with the simulation results., Though optimization algorithms for fuzzy logic controller (FIC)-based three-phase induction motor (TIM) systems are attractive for improving efficiency, existing methods have limited search capability. Here, the authors report a quantum-inspired lightning search algorithm with enhanced performance.

Because of the cost and complexity of implementing an optical paper sorting system, the demand for an intelligent system for waste paper sorting has increased. This research focused on the development of a smart intelligent system (SIS) for recyclable waste paper sorting. The basis for selecting the regions of interests (ROIs) is the margin area of a paper object image because almost all printed documents keep the margin area intact. The paper grade is identified using a proximity search. The SIS with the HSI colour space offered maximum success rates of 99 %, 82 % and 89 %, while with the RGB model, the classification success rates were 94 %, 93 % and 98 % for white paper, old newsprint paper and old corrugated cardboard, respectively. The SIS is clearly superior to other prevailing techniques because of the faster decision making and lower cost of implementation.

State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.

Ocular imaging has developed rapidly and plays a critical role in clinical care and ocular disease management. Development of image processing technologies pertinent to ocular diseases has paved the way for automated diagnostic systems including detection techniques using deep learning (DL) approaches. The prevalence of an abnormal tissue layer in the conjunctiva, known as pterygium eye disease, is increasing due to lack of awareness. Despite the non-cancerous/benign nature of pterygium, a clinical diagnosis from an ophthalmologist is still required to prevent the pterygium tissues from extending into the pupil, which would result in blurred vision. However, current diagnostic methods are mostly dependent on human expertise. Automated detection can potentially serve as an assistive method to reduce diagnosis time by applying a DL approach. Considering the lack of comprehensive research work on pterygium detection using DL, we propose a new architecture consisting of an improved CNN-based trained network named VggNet16-wbn that is derived from VggNet16, a pre-trained CNN algorithm. This paper presents an overview of the DL as a core approach to the transfer learning (TL) concept, as well as current efforts towards automated ocular detection approaches. A new architecture of a CNN-based trained network was proposed based on a network assessment from six CNN pre-trained networks to detect pterygium. This work consists of two main modules, namely, data acquisition and DCNN classification. The proposed trained network, VggNet16-wbn, shows the best performance with 99.22% accuracy, 98.45% sensitivity, and a perfect score on specificity and area under the curve metrics. This work has high potential for creating a pterygium screening system that can be used as a baseline for fully automated detection using a DL approach.

This paper develops a state-of-charge (SOC) estimation model for a lithium-ion battery using an improved extreme learning machine (ELM) algorithm. ELM is suitable for an SOC estimation since the ELM algorithm has fast estimation speed, good generalization performance, and high accuracy. However, the performance of ELM is highly dependent on training accuracy and the number of neurons in a hidden layer. Hence, a gravitational search algorithm (GSA) is applied to improve the ELM computational intelligence by searching for the optimal value hidden layer neurons. The optimal ELM-based GSA model does not require internal battery knowledge and mathematical model for an SOC estimation. The model robustness is validated at different temperatures using different electric vehicle drive cycles. The performance of the ELM-GSA model is verified with two popular neural network methods: back-propagation neural network (BPNN) and radial basis function neural network (RBFNN). The results are evaluated using different error rates and computation costs. The results demonstrate that the ELM-based GSA model offers a higher accuracy and lower SOC error rate than those of BPNN-based GSA and RBFNN-based GSA models. Furthermore, a detailed comparative study between the proposed model and existing SOC strategies is conducted, which also demonstrates the superiority of the proposed model.

Solar photovoltaic (PV) is one of the prominent sustainable energy sources which shares a greater percentage of the energy generated from renewable resources. As the need for solar energy has risen tremendously in the last few decades, monitoring technologies have received considerable attention in relation to performance enhancement. Recently, the solar PV monitoring system has been integrated with a wireless platform that comprises data acquisition from various sensors and nodes through wireless data transmission. However, several issues could affect the performance of solar PV monitoring, such as large data management, signal interference, long-range data transmission, and security. Therefore, this paper comprehensively reviews the progress of several solar PV-based monitoring technologies focusing on various data processing modules and data transmission protocols. Each module and transmission protocol-based monitoring technology is investigated with regard to type, design, implementations, specifications, and limitations. The critical discussion and analysis are carried out with respect to configurations, parameters monitored, software, platform, achievements, and suggestions. Moreover, various key issues and challenges are explored to identify the existing research gaps. Finally, this review delivers selective proposals for future research works. All the highlighted insights of this review will hopefully lead to increased efforts toward the enhancement of the monitoring technologies in future sustainable solar PV applications.

Globally, the research on battery technology in electric vehicle applications is advancing tremendously to address the carbon emissions and global warming issues. The effectiveness of electric vehicles depends on the accurate assessment of key parameters as well as proper functionality and diagnosis of the battery storage system. However, poor monitoring and safety strategies of the battery storage system can lead to critical issues such as battery overcharging, over-discharging, overheating, cell unbalancing, thermal runaway, and fire hazards. To address these concerns, an effective battery management system plays a crucial role in enhancing battery performance including precise monitoring, charging-discharging control, heat management, battery safety, and protection. The goal of this paper is to deliver a comprehensive review of different intelligent approaches and control schemes of the battery management system in electric vehicle applications. In line with that, the review evaluates the intelligent algorithms in battery state estimation concerning their features, structure, configuration, accuracy, advantages, and disadvantages. Moreover, the review explores the various controllers in battery heating, cooling, equalization, and protection highlighting categories, characteristics, targets, achievements, benefits, and shortcomings. The key issues and challenges in terms of computation complexity, execution problems along with various internal and external factors are identified. Finally, future opportunities and directions are delivered to design an efficient intelligent algorithm and controller toward the development of an advanced battery management system for future sustainable electric vehicle applications.

Electric vehicles are receiving widespread attention around the world due to their improved performance and zero carbon emissions. The effectiveness of electric vehicles depends on proper interfacing between energy storage systems and power electronics converters. However, the power delivered by energy storage systems illustrates unstable, unregulated and substantial voltage drops. To overcome these limitations, electric vehicle converters, controllers and modulation schemes are necessary to achieve a secured and reliable power transfer from energy storage systems to the electric motor. Nonetheless, electric vehicle converters and controllers have shortcomings including a large number of components, high current stress, high switching loss, slow dynamic response and computational complexity. Therefore, this review presents a detailed investigation of different electric vehicle converters highlighting topology, features, components, operation, strengths and weaknesses. Moreover, this review explores the various types of electric vehicle converter controllers and modulation techniques concerning functional capabilities, operation, benefits and drawbacks. Besides, the significance of optimization algorithms in electric vehicle converters is illustrated along with their objective functions, executions and various factors. Furthermore, this review explores the key issues and challenges of electric vehicle converters, controllers and optimizations to identify future research gaps. Finally, important and specific suggestions are delivered toward the development of an efficient converter for future sustainable electric vehicle applications.

This paper presents a hybrid optimization approach for the enhancement of performance of a piezoelectric energy harvesting system (PEHS). The existing PEHS shows substantial power loss during hardware implementation. To overcome the problem, this study proposes a hybrid optimization technique to improve the PEHS efficiency. In addition, the converter design as well as controller technique are enhanced and simulated in a MATLAB/Simulink platform. The controller technique of the proposed structure is connected to the converter prototype through the dSPACE DS1104 board (dSPACE, Paderborn, Germany). To enhance the proportional-integral voltage controller (PIVC) based on hybrid optimization method, a massive enhancement in reducing the output error is done in terms of power efficiency, power loss, rising time and settling time. The results show that the overall PEHS converter efficiency is about 85% based on the simulation and experimental implementations.

The increase in population and rapid industrial development of a country, especially in urban areas, contributes to various urbanization issues such as housing, food demands, education, health, poverty, etc. An alternative method of farming utilizing minimized land areas with large crop produce is highly sought. This farming method is expected to be operated at a lower cost with easy monitoring of essential plantation necessities such as light, water level, temperature, and humidity. This paper discusses the design and development of an automatic monitoring of an indoor vertical hydroponic system. Three main components will be designed and developed to realize the system. Firstly, an indoor hydroponic module with IoT technology. Secondly, a dashboard for monitoring and managing the operation of the system. Finally, a two-factor authentication mechanism to enhance the security elements of the system. Preliminary results obtained showed promising progress and opportunity for the designed system.

This study examined the educational effects in strengthening programming skills among university’s undergraduate engineering students via integration of a robotics project and an experiential learning approach. In this study, a robotics project was conducted to close the gap of students’ difficulty in relating the theoretical concepts of programming and real-world problems. Hence, an experiential learning approach using the Kolb model was proposed to investigate the problem. In this project, students were split into groups whereby they were asked to develop codes for controlling the navigation of a wheeled mobile robot. They were responsible for managing their group’s activities, conducting laboratory tests, producing technical reports and preparing a video presentation. The statistical analysis performed on the students’ summative assessments of a programming course revealed a remarkable improvement in their problem-solving skills and ability to provide programming solutions to a real-world problem.

The development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state of charge (SOC) estimation in lithium-ion batteries. Nevertheless, SOC accuracy is subject to the suitable value of the hyperparameters selection of the TDNN algorithm. Hence, the TDNN algorithm is optimized by the improved firefly algorithm (iFA) to determine the optimal number of input time delay (UTD) and hidden neurons (HNs). This work investigates the performance of lithium nickel manganese cobalt oxide (LiNiMnCoO2) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2) toward SOC estimation under two experimental test conditions: the static discharge test (SDT) and hybrid pulse power characterization (HPPC) test. Also, the accuracy of the proposed method is evaluated under different EV drive cycles and temperature settings. The results show that iFA-based TDNN achieves precise SOC estimation results with a root mean square error (RMSE) below 1%. Besides, the effectiveness and robustness of the proposed approach are validated against uncertainties including noise impacts and aging influences.

© 2020 Elsevier Ltd Developing an efficient, cost-effective and environmentally friendly solution for solid waste collection (SWC) and transportation system remains a major challenge for municipalities. Waste collection encompasses the largest part of the total budget in current waste management systems. SWC is a crosscutting issue that can be directly or indirectly linked to 10 of the 17 United Nations’ sustainable development goals (SDGs). This study aims to develop an SWC route optimisation model to improve collection efficiency, save collection costs and reduce emissions by considering fixed routing optimisation (FRO) with static data and variable routing optimisation (VRO) with real-time data. To realise the optimisation, a mixed-integer linear programming model utilising FRO and VRO was developed. Results show that VRO improved the collection efficiency by 26.08 % when the minimum filled-up level for collection was 70 %. Moreover, VRO achieved 44.44 % cost savings and 17.60 % carbon emission reduction at 70 % filled level. The proposed system achieved the targeted goals and demonstrated the feasibility of an optimisation model for the waste management sector to build a sustainable smart city. The findings of this study can be used to strengthen efforts towards the achievement of the SDGs related to solid waste collection and management.

A system that is capable of assessing spine osteoarthritis conditions which affect a significant portion of the elderly population could be very valuable to radiologists, researchers of arthritis and musculoskeletal diseases, and educators. To this end, there is very limited research published in the literature concerning the degradation assessment of spinal intervertebral disc space narrowing (DSN). Thus, this paper intends to develop a system that focuses on assessing the degradation of disk space narrowing (DSN) to assist in radiologist’s decision-making in the characterization of cervical and lumbar images. A novel experiment based on our previous research (Aouache et al. 2009; Aouache et al. Biomed Eng Online 14(1):6, 2015) was conducted by integrating clustering and retrieval platforms to achieve this objective. Two shape boundary, 9-points, and B-spline have been used as the foundation for DSN model construction using active shape model. The segmented DSNs have then indexed via region and contour-based features descriptor. For better efficiency, clustering using a vocabulary tree model (VTM) is constructed to identify correct DSN cluster and build multi-clusters subsets for faster and robust retrieval research process. Our system achieved an accuracy of average retrieval rate (ARR) more than 90% and 88% for cervical and lumbar data set accordingly. We expect the proposed system will assist in decision-making and uses by radiologists or researchers for further investigation.

In this paper, we propose an automatic Active Contour Model (ACM) using corneal centroid information for corneal segmentation. The algorithm was validated on 259 of Anterior Segment Photographed Images (ASPIs) that were captured using a smartphone. The proposed system comprises of four components; a) data collection, b) pre-processing, c) corneal segmentation using proposed automated centroid-based ACM, and (d) feature extraction. There were five corneal shape parameters employed, namely Horizontal Visible Iris Diameter (HVID), Vertical Visible Iris Diameter (VVID), eccentricity, asphericity and solidity. Each of these parameters has its own significant features in measuring the corneal shape quantitatively, which could potentially assist the ophthalmologists during a screening process. Using all ASPIs, the findings show that this method is competent in segmenting 231 corneal regions while Circular Hough Transform method only manage to segment 165 regions successfully. These segmentation results demonstrate a bright potential towards the development of intelligent classification system for ocular diseases.

Fossil fuels and other conventional energy sources used to generate electricity are finite. Therefore, alternative energy sources should be pursued to meet present and future energy demands. The photovoltaic (PV) is a promising renewable energy source, especially for the remote areas. The PV is a DC power source that needs to be converted into usable AC power using an inverter. However, its nonlinearity and output fluctuation pose challenges in the design of PV based inverter. In this paper, a PV inverter controller system with the fundamentals of a fuzzy logic controller (FLC) and its applications and execution are reviewed. The different fuzzy controllers, inverter control algorithms, and switching techniques are studied. The findings indicate that the fuzzy logic controls have been gaining attention in the area of power control engineering, especially in inverter controller design for PV applications and generation. The FLC has a flexible and intelligent design, expedient user interface, easy computation and learning system, and combinations of different control algorithms. The FLC is also verifiable for completeness, redundancy, and consistency. However, finding the boundaries of membership functions and other rules of FLC requires manual tuning, long computation time, and considerable effort. This paper comprehensively reviews the FLC-based inverter control system to minimize PV output fluctuations, which cause inverter issues related to output harmonics, power factor, switching schemes, losses, and system implementation. The inverter system and its control strategy for future PV applications and generation require further research and development. Consequently, this review focuses on many factors and challenges and provides recommendations for designing capable and efficient inverter control systems for converting PV power to usable AC power. All the highlighted insights of this review will hopefully lead to increased efforts toward the development of the advanced inverter control systems for PV applications for AC loads and the utility grid.

This paper introduces a novel optimal schedule controller to manage renewable energy resources (RESs) in virtual power plant (VPP) using binary particle swarm optimization (BPSO) algorithm. It is crucial to minimize the costs giving priority for sustainable resources use instead of purchasing from the national grid. The effectiveness of the proposed approach is examined by the IEEE 14 bus system containing microgrids (MGs) integrated with RESs in the form of VPP. Real load demand recorded is used to model and simulate the test case studies of the system for 24 h in Perlis, Malaysia. Moreover, weather data collected from the Malaysian Meteorological Department such as wind, solar, fuel, and battery status data are used in the BPSO to find the best ON and OFF schedules. The results found that the developed BPSO algorithm is robust in reducing energy consumption and emissions of the VPP. This study contributes to the development of an optimization algorithm for an optimal scheduling controller of MG integrated VPP in order to reduce carbon emissions and manage sustainable energy. Finally, a comparative analysis of the optimal algorithms over conventional justifies the use of RESs integration and validates the developed BPSO for sustainable energy management and emissions reduction.

In recent years, light detection and ranging (LiDAR) technology has gained huge popularity in various applications such as navigation, robotics, remote sensing, and advanced driving assistance systems (ADAS). This popularity is mainly due to the improvements in LiDAR performance in terms of range detection, accuracy, power consumption, as well as physical features such as dimension and weight. Although a number of literatures on LiDAR technology have been published earlier, not many has been reported on the state-of-the-art LiDAR scanning mechanisms. The aim of this article is to review the scanning mechanisms employed in LiDAR technology from past research works to the current commercial products. The review highlights four commonly used mechanisms in LiDAR systems: Opto-mechanical, electromechanical, micro-electromechanical systems (MEMS), and solid-state scanning. The study reveals that electro-mechanical scanning is the most prominent technology in use today. The commercially available 1D time of flight (TOF) LiDAR instrument is currently the most attractive option for conversion from 1D to 3D LiDAR system, provided that low scanning rate is not an issue. As for applications with low size, weight, and power (SWaP) requirements, MEMS scanning is found to be the better alternative. MEMS scanning is by far the more matured technology compared to solid-state scanning and is currently given great emphasis to increase its robustness for fulfilling the requirements of ADAS applications. Finally, solid-state LiDAR systems are expected to fill in the gap in ADAS applications despite the low technology readiness in comparison to MEMS scanners. However, since solid-state scanning is believed to have superior robustness, field of view (FOV), and scanning rate potential, great efforts are given by both academics and industries to further develop this technology.

Electric vehicles (EVs) have become increasingly popular due to zero carbon emission, reduction of fossil fuel reserve, comfortable and light transport. However, EVs employing lithium-ion battery are facing difficulties in terms of predicting accurate health and remaining useful life states due to various internal and external factors. Currently, very few papers are addressed to summarize the state of health (SOH) and remaining useful life (RUL) estimation approaches. In this regard, the goal of this paper is to comprehensively review the different estimation models to predict SOH, and RUL in a comparative manner. The results identify the classifications, characteristics and evaluation processes with advantages and disadvantages for EV applications. The review also investigates the issues and challenges with possible solutions. Furthermore, the review provides some selective proposals for the further technological development of SOH, and RUL estimation for lithium-ion batteries. All the highlights insight this review will hopefully lead to the increasing efforts towards the development of the advanced SOH and RUL methods for future EV uses.

A video-based fall detection system was presented; which consists of data acquisition, image processing, feature extraction, feature selection, classification and finite state machine. A two-dimensional human posture image was represented by 12 features extracted from the generalisation of a silhouette shape to a quadrilateral. The corresponding feature vectors for three groups of human pose were statistically analysed by using a non-parametric Kruskal Wallis test to assess the different significance level between them. From the statistical test, non-significant features were discarded. Four selected kernel-based Support Vector Machine: linear, quadratics, cubic and Radial Basis Function classifiers were trained to classify three human posture groups. Among four classifiers, the last one performed the best in terms of performance matric on testing set. The classifier outperformed others with high achievement ofaverage sensitivity, precision and F-score of 99.19%, 99.25% and 99.22%, respectively. Such pose classification model output was further used in a simple finite state machine to trigger the falling event alarms. The fall detection system was tested on different fall video sets and able to detect the presence offalling events in a frame sequence of videos with accuracy of 97.32% and low computional time.

High demand on voltage regulator (VR) currently requires VR manufacturers to improve their time-to-market, particularly for new product development. To fulfill the output stability requirement, VR manufacturers characterize the VR in terms of the equivalent series resistance (ESR) of the output capacitor because the ESR variation affects the VR output stability. The VR characterization outcome suggests a stable range of ESR, which is indicated in the ESR tunnel graph in the VR datasheet. However, current practice in industry manually characterizes VR, thereby increasing the manufacturing time and cost. Therefore, an efficient method based on multilayer neural network has been developed to obtain the ESR tunnel graph. The results show that this method able to reduce the VR characterization time by approximately 53% and achieved critical ESR prediction error less than 5%. This work demonstrated an efficient and effective approach for VR characterization in terms of ESR.

This paper presents a quantum lightning search algorithm (QLSA)-based optimization technique for controlling speed of the induction motor (IM) drive. The developed QLSA is implemented in fuzzy logic controller to generate suitable input and output fuzzy membership function for IM drive speed controller. The main objective of this paper is to develop QLSA-based fuzzy (QLSAF) speed controller to minimise the mean absolute error in order to improve the performance of the IM drive with changes in speed and mechanical load. The QLSAF-based speed controller is implemented in simulation model in the MATLAB/Simulink environment and the prototype is fabricated and experimentally tested in a fully integrated DSP for controlling the IM drive system. The experimental results of the developed QLSAF speed controller are compared with the simulation results under different performance conditions. Several experimental results show that there are good agreement of the controller parameters, SVPWM signals, and different types of speed responses and stator currents with the simulation results, which are verified and validated the performance of the proposed QLSAF speed controller. Also, the proposed QLSAF speed controller outperforms other studies with settling time in simulation and in experimental implementation, which validates the controller performance as well.

State of charge (SOC) is one of the crucial parameters in a lithium-ion battery. The accurate estimation of SOC guarantees the safe and efficient operation of a specific application. However, SOC estimation with high accuracy is a serious concern to the automobile engineer due to the battery nonlinear characteristics and complex electrochemical reactions. This paper presents an improved nonlinear autoregressive with exogenous input (NARX)-based neural network (NARXNN) algorithm for an accurate and robust SOC estimation of lithium-ion battery which is effective and computationally rich for controlling dynamic system and predicting time series. However, the accuracy of recurrent NARXNN depends on the amount of input order, output order, and hidden layer neurons. The unique contribution of the improved recurrent NARXNN-based SOC estimation is developed using lighting search algorithm (LSA) for finding the best value of input delays, feedback delays, and hidden layer neurons. The contributions are summarized as: 1) the computational capability of NARXNN model which does not require battery model and parameters rather only needs current, voltage, and temperature sensors; 2) the effectiveness of LSA which is verified with particle swarm optimization; 3) the adaptability, efficiency, and robustness of the model which are evaluated using FUDS and US06 drive cycles at varying temperatures conditions; and 4) the performance of the proposed model which is compared with back propagation neural network and radial basis function neural network optimized by LSA using different error statistical terms and computational time. Furthermore, a comparative analysis of SOC estimation in proposed method and existing techniques is presented for validation of NARXNN performance. The results prove that the proposed NARXNN model achieves higher accuracy with less computational time than other existing SOC algorithms under different temperature conditions and electric vehicle drive cycles.

The state of charge (SOC) is a critical evaluation index of battery residual capacity. The significance of an accurate SOC estimation is great for a lithium-ion battery to ensure its safe operation and to prevent from over-charging or over-discharging. However, to estimate an accurate capacity of SOC of the lithium-ion battery has become a major concern for the electric vehicle (EV) industry. Therefore, numerous researches are being conducted to address the challenges and to enhance the battery performance. The main objective of this paper is to develop an accurate SOC estimation approach for a lithium-ion battery by improving back-propagation neural network (BPNN) capability using backtracking search algorithm (BSA). BSA optimization is utilized to improve the accuracy and robustness of BPNN model by finding the optimal value of hidden layer neurons and learning rate. In this paper, Dynamic Stress Test and Federal Urban Driving Schedule drive profiles are applied for testing the model at three different temperatures. The obtained results of the BPNN based BSA model are compared with the radial basis function neural network, generalized regression neural network and extreme learning machine model using statistical error values of root mean square error, mean absolute error, mean absolute percentage error, and SOC error to check and validate the model performance. The obtained results show that the BPNN based BSA model outperforms other neural network models in estimating SOC with high accuracy under different EV profiles and temperatures.

Waste collection widely depends on the route optimization problem that involves a large amount of expenditure in terms of capital, labor, and variable operational costs. Thus, the more waste collection route is optimized, the more reduction in different costs and environmental effect will be. This study proposes a modified particle swarm optimization (PSO) algorithm in a capacitated vehicle-routing problem (CVRP) model to determine the best waste collection and route optimization solutions. In this study, threshold waste level (TWL) and scheduling concepts are applied in the PSO-based CVRP model under different datasets. The obtained results from different datasets show that the proposed algorithmic CVRP model provides the best waste collection and route optimization in terms of travel distance, total waste, waste collection efficiency, and tightness at 70-75% of TWL. The obtained results for 1 week scheduling show that 70% of TWL performs better than all node consideration in terms of collected waste, distance, tightness, efficiency, fuel consumption, and cost. The proposed optimized model can serve as a valuable tool for waste collection and route optimization toward reducing socioeconomic and environmental impacts.

Induction motor drive is widely used in many load applications in approximately 60% of the total industrial load. However, the dynamic configuration of the induction motor, especially a three-phase induction motor (TIM) is a nonlinear system which is not easy to explain theoretically due to sudden changes in load or speed. Therefore, the development of variable speed drive with a robust switching technique and controller design is compulsory for improving the performance efficiency of the TIM. An advanced switching strategy is used for controlling voltage, frequency, and reducing harmonic output signals while an efficient controller is required to regulate currents, speed, torque, and rotor flux. Nevertheless, the development of such controllers and switching methods need the extensive mathematical model and complex online computation. This paper presents a critical review of the different switching techniques, switching pattern of voltage space vectors and switching time to solve the existing problems and enhance the performance of the TIM. Also, the paper describes the different intelligent controller techniques in terms of scalar control and vector control. Consequently, a comprehensive review on conventional controllers such as proportional integral derivative and artificial intelligence controllers such as an artificial neural network, adaptive neural fuzzy inference system, and fuzzy logic control are explained on their structure, algorithm, and mathematical model. Furthermore, the different integrated circuits of the TIM drive controller are reviewed along with benefits and limitations, current issues and challenges, and conclude with recommendations. All the highlighted insights of this review will hopefully lead to increased efforts toward the development of the advanced switching techniques and controllers for the future induction motor drive.

Induction motor (IM) drives, specifically the three-phase IMs, are a nonlinear system that are difficult to explain theoretically because of their sudden changes in load or speed conditions. Thus, an advanced controller is needed to enhance IM performance. Among numerous control techniques, fuzzy logic controller (FLC) has increasing popularity in designing complex IM control system due to their simplicity and adaptability. However, the performance of FLCs depends on rules and membership functions (MFs), which are determined by a trial-and-error procedure. The main objective of this paper is to present a critical review on the control and optimization techniques for solving the problems and enhancing the performance of IM drives. A detailed study on the control of variable speed drive, such as scalar and vector, is investigated. The scalar control functions of speed and V/f control are explained in an open- and closed-loop IM drive. The operation, advantages, and limitations of the direct and indirect field-oriented controls of vector control are also demonstrated in controlling the IM drive. A comprehensive review of the different types of optimization techniques for IM drive applications is highlighted. The rigorous review indicates that existing optimization algorithms in conventional controller and FLC can be used for IM drive. However, some problems still exist in achieving the best MF and suitable parameters for IM drive control. The objective of this review also highlights several factors, challenges, and problems of the conventional controller and FLC of the IM drive. Accordingly, the review provides some suggestions on the optimized control for the research and development of future IM drives. All the highlighted insights and recommendations of this review will hopefully lead to increasing efforts toward the development of advanced IM drive controllers for future applications.

A variety of rechargeable batteries are now available in world markets for powering electric vehicles (EVs). The lithium-ion (Li-ion) battery is considered the best among all battery types and cells because of its superior characteristics and performance. The positive environmental impacts and recycling potential of lithium batteries have influenced the development of new research for improving Li-ion battery technologies. However, the cost reduction, safe operation, and mitigation of negative ecological impacts are now a common concern for advancement. This paper provides a comprehensive study on the state of the art of Li-ion batteries including the fundamentals, structures, and overall performance evaluations of different types of lithium batteries. A study on a battery management system for Li-ion battery storage in EV applications is demonstrated, which includes a cell condition monitoring, charge, and discharge control, states estimation, protection and equalization, temperature control and heat management, battery fault diagnosis, and assessment aimed at enhancing the overall performance of the system. It is observed that the Li-ion batteries are becoming very popular in vehicle applications due to price reductions and lightweight with high power density. However, the management of the charging and discharging processes, CO2 and greenhouse gases emissions, health effects, and recycling and refurbishing processes have still not been resolved satisfactorily. Consequently, this review focuses on the many factors, challenges, and problems and provides recommendations for sustainable battery manufacturing for future EVs. This review will hopefully lead to increasing efforts toward the development of an advanced Li-ion battery in terms of economics, longevity, specific power, energy density, safety, and performance in vehicle applications.

Due to increasing concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels, the electric vehicles (EVs) receive massive popularity due to their performances and efficiencies in recent decades. EVs have already been widely accepted in the automotive industries considering the most promising replacements in reducing CO2 emissions and global environmental issues. Lithium-ion batteries have attained huge attention in EVs application due to their lucrative features such as lightweight, fast charging, high energy density, low self-discharge and long lifespan. This paper comprehensively reviews the lithium-ion battery state of charge (SOC) estimation and its management system towards the sustainable future EV applications. The significance of battery management system (BMS) employing lithium-ion batteries is presented, which can guarantee a reliable and safe operation and assess the battery SOC. The review identifies that the SOC is a crucial parameter as it signifies the remaining available energy in a battery that provides an idea about charging/discharging strategies and protect the battery from overcharging/over discharging. It is also observed that the SOC of the existing lithium-ion batteries have a good contribution to run the EVs safely and efficiently with their charging/discharging capabilities. However, they still have some challenges due to their complex electro-chemical reactions, performance degradation and lack of accuracy towards the enhancement of battery performance and life. The classification of the estimation methodologies to estimate SOC focusing with the estimation model/algorithm, benefits, drawbacks and estimation error are extensively reviewed. The review highlights many factors and challenges with possible recommendations for the development of BMS and estimation of SOC in next-generation EV applications. All the highlighted insights of this review will widen the increasing efforts towards the development of the advanced SOC estimation method and energy management system of lithium-ion battery for the future high-tech EV applications.

This paper presents an enhanced machine learning based state of charge (SOC) estimation method for a lithium-ion battery using a deep recurrent neural network (DRNN) algorithm. DRNN is suitable for SOC evaluation due to strong computation intelligence and self-learning capabilities. Nevertheless, the performance of DRNN is constrained due to the training accuracy and duration which entirely depends on the appropriate selection of hyper-parameters including hidden layer and hidden neurons. Therefore, firefly algorithm (FA) is employed to find the optimal number for hyper-parameters of DRNN networks. The optimized DRNN based FA algorithm for SOC estimation does not require extensive knowledge about battery chemistry, electrochemical battery model and added filter, rather only needs battery test bench to measure current and voltage. The developed model is tested using two different types of lithium-ion batteries namely lithium nickel manganese cobalt oxide (LiNiMnCoO 2 ) and lithium nickel cobalt aluminum oxide (LiNiCoAlO 2 ). The proposed model is validated by two experimental tests; one with static discharge test and other with pulse discharge test at room temperature. The experimental results indicate the superiority of the DRNN based FA method in comparison with the back-propagation neural network (BPNN) and radial basis function neural network (RBFNN).

Pterygium is one of the eye diseases that are prevalent among the workers who are frequently exposed to sunlight such as farmers, construction workers and fishermen. It is an eye disease that can be screened easily by health practitioners. However, most of the previously mentioned workers are rarely aware of the pterygium existence. Hence, health practitioners usually visit them to perform basic health checkup that includes eye disease screening. Due to many tests that need to be performed for each checkup cycle, a modern tool is required to make the screening process faster. As a result, this paper discusses a compact convolutional neural networks (CNN) approach to pterygium classification using transfer learning. Its architecture consists of only two layers of CNN and two layers of fully connected (FC) components. Rectified linear unit is used as the activation function except for the last FC layer, which utilizes Softmax function. The weights and biases are transferred from the first two layers of VGG-M architecture. Three regularization methods were experimented that include local response normalization, batch normalization and dropout. The best network configuration in term of accuracy with 0.9833 value is obtained by using local response normalization with dropout. On the other hand, the best area under the curve performance of 0.9865 is achieved by using batch normalization and dropout. Therefore, the developed compact system has managed to classify well the pterygium image taken from the frontal view.

Most passenger and heavy vehicles today are equipped with passive stabiliser bars (also called passive anti-roll bar [ARB]). However, these conventional passive stabiliser bars cannot satisfy the requirements of ride comfort and handling stability and are unable to adjust the roll stiffness of suspensions in real time. Vehicles are prone to rolling when they are cornering at high speeds. To overcome the drawbacks of passive ARB systems, several schemes with possible active intervention into the vehicle dynamics have been proposed. One of these schemes uses an active ARB, that is, a pair of hydraulic actuators that generate a stabilizing moment to balance the overturning moment. This study aims to present a brief overview of an active ARB system based on modelling and controller designs that have been proposed by various researchers. Prior to the main discussion, certain background information on the active ARB (AARB) system will be presented. This study was conducted to further improve the performance of the existing controller in the AARB system.

The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.

Anterior Osteoporosis (AOs) in the cervical vertebrae is an osteoporosis complication and a common condition of vertebral irregularity caused by a decrease in bone density and strength, which can lead to fragile bone and fractures. Consequently, it is crucial to detect the AOs irregularity early so that appropriate pharmacological intervention can be done to reduce further complications. To do so via a computer approach, an efficient method that can provide high classification rate is required. Basing on the fuzzy logic theory, this article affords a new method for AOs (classes and severity) classification of the cervical radiography by designing a fuzzy decision tree (FDT) model. The method involves two main processed, namely i) segmentation process which employs the active shape model (ASM) based on the 9-anatomical points representation (9-APR) to segment the cervical vertebra shape boundary (C-VSB) and ii) fuzzy based feature extraction and classifier development, known as FDT method. The fuzzy set along with its membership functions are derived from the resulting C-VSB segment. It operates by extracting a specific angle descriptor (horizontal, vertical, and corner) as crisp input to the fuzzification inter-face to produce reasonable key indexing to the fuzzy interface system. Then, the defuzzification interface converts it into a crisp output that adequately represents the degree of AOs class and severity as appraisal values. The resulting fuzzy then acts as input to a basic concept of if-then rules called FDT to recognise and distinguish between vertebrae presented with/without AOs. Receiver operating characteristic (ROC) and area under curve (AUC) index evaluation methods are examined to offer quantitative evaluation between the medical ground truth versus FDT classifier predicted results. Results obtained on a set of 400 cervical vertebrae images indicate superb classification rate (R > 90 %) which suggest that the proposed FDT as an appropriate solution to AOs classification process for reliable vertebral fracture diagnosis. In summary, the findings confirmed the effectiveness of FDT as an excellent classifier to recognize and differentiate AOs classes and severity thus, able to provide important basis for pathology.

Developing an accurate model for discharge estimation techniques of the ungauged river basin is a crucial challenge in water resource management especially in under-development regions. This article is a thorough review of the historical improvement stages of this topic to understand previous challenges that faced researchers, the shortfalls of methods and techniques, how researchers prevailed and what deficiencies still require solutions. This revision focuses on data-driven approaches and GIS-based methods that have improved the accuracy of estimation of hydrological variables, considering their advantages and disadvantages. Past studies used artificial intelligence and geo-statistical methods to forecast the runoff at ungauged river basins, and mapping the spatial distribution has been considered in this study. A recommendation for future research on the potential of a hybrid model utilizing both approaches is proposed and described.

The management of river water quality is one the most significant environmental challenges. Water quality index (WQI) describes several water quality variables at a certain aquatic environment and time. Classically, WQI is commonly computed using the traditional methods which involved lengthy computation, consume timing and occasionally associated with accidental errors during subindex calculation. Thus, providing an accurate prediction model for WQI is highly required. Recently, the artificial neural networks (ANNs) have been examined for similar prediction applications and exhibited a remarkable ability to capture the nonlinearity pattern between predictors and predictand. In the current research, two different ANN algorithms, namely radial basis function neural network (RBFNN) and back propagation neural networks models, have been applied to examine and mimic the relationship of WQI with the water quality variables in a tropical environment (Malaysia). The input variables categorized into two different architectures and have been inspected. In addition, comprehensive analysis for the performance evaluation and the sensitivity analysis of the variables have been conducted. The results achieved are positively promising with high performance accuracy belonging to RBFNN model for both scenarios. Furthermore, the proposed approach offers an effective alternative to compute and predict WQI, to the fact that WQI manual calculation methods involved lengthy computations, transformations, use of various subindex formulae for each value of the constituent water quality variables, and consuming time.

A theoretical framework has been modeled for real time monitoring of bin status.A physical architecture has been designed based on the theoretical model.Rule based intelligent sensing algorithms have been implemented.Prototype development of Smart Bin for automation of waste status data perception.Real time collection of bin status data by implementing Wireless Sensor Network. Due to the rising trend of urbanization along with overconsumption of non-recyclable resources, the volume of municipal solid waste is increasing every day. An efficient, cost effective and environment friendly solution for real time bin status monitoring, collection and transportation of municipal solid waste is still a major challenge to the local municipal authorities. This research proposes a novel model, architecture and intelligent sensing algorithm for real time solid waste bin monitoring system that would contribute to the solid waste collection optimization. The monitoring application is based on decision algorithms for sensing solid waste data in a wireless sensor network. The system is built on a three level architecture like smart bin, gateway and control station. The elementary concept is that, smart bins collect their status when any changes occur and transmit the status data to a server via an intermediate coordinator. A set of applications in server presents the updated bin status on real time. The field test performances show that the system can efficiently monitor real time bin status that makes it feasible to decide, which bin should collect and which should not. Thus the proposed system has achieved its goal to provide real time bin status information to the solid waste management operator. Later, this information can be used for collection route optimization to reduce collection costs and carbon emissions which in turn contribute to build green society.

Solid waste collection (SWC) has been one of the growing challenges throughout the world due to the development of urbanization and uncontrolled growth of the population. It is a complex process that is expensive and requires the elaborate logistics management and in-depth mathematical computation. Hence, there is an urgent necessity to develop an efficient waste collection process while saving time, fuel, labour, maintenance costs, and carbon emissions. This goal of this is paper is to deliver a comprehensive review of the SWC system concerning solid waste collection objectives, constraints, the different optimization approaches, and models, along with their strengths and weakness. The different types of optimization objectives and constraints on SWC are highlighted towards improving solid waste collection and minimizing the cost. Furthermore, this review has classified the available optimization methods into different categories and technologies to provide a clear understanding for the readers. Besides, future research gaps are identified through the investigation of various key issues and challenges of the existing methods. Moreover, the significant contributions of SWC in connection to sustainable development goals (SDGs) are explained thoroughly. Finally, the review delivers some important proposals that would be beneficial to the researchers and policymakers to design an efficient and robust SWC system toward achieving SDGs.

Global carbon emissions caused by fossil fuels and diesel-based vehicles have urged the necessity to move toward the development of electric vehicles and related battery storage systems. Lithium-ion batteries are the ideal candidate for electric vehicle due to their superior performance with regard to high energy density and long lifespan. The state of charge of lithium-ion batteries is one of the crucial evaluation indicators of the battery management system that confirms the extended battery life, better charging-discharging profiles, and safe driving of electric vehicles. However, the accuracy of the state of charge is influenced by several issues such as battery aging cycles, noise effects, and temperature impacts. Therefore, this review presents a detailed classification of the recent data-driven state of charge estimation highlighting algorithm, input features, configuration, execution process, strength, weakness and estimation error. This review critically investigates the various key implementation factors of the data-driven algorithms in terms of data preprocessing, hyperparameter adjustment, activation function, evaluation criteria, computational cost and robustness validation under uncertainties. In addition, the review explores the deficiencies of existing data-driven state of charge estimation algorithms to identify the gaps for future research. Finally, the review provides some effective future directions that would be beneficial to the automobile researchers and industrialists to design an accurate and robust state of charge estimation technique toward future sustainable electric vehicle applications.

The converters and their control strategies play an important role in converting, transmitting, and improving the performance of high-voltage direct current (HVDC) system. There are different types of converter and their control strategies being employed in the HVDC system, such as line-commutated converter and voltage source converter (VSC). However, the existing converter controllers have still some limitations on certain deficiencies in certain aspects such as in weak AC grid or at high voltage and power level. Thus, an advanced converter control strategy is very much important in order to ensure optimal power transfer with minimal loss and stable voltage. This paper presents a comprehensive review of the advanced control strategies to address the problems and enhance the performance of the VSC-based HVDC (VSC-HVDC) transmission system. A detailed study on the overview of VSC-HVDC and their converter classifications are investigated. The main contribution of this paper is to carry out the different types of VSC-HVDC control strategies in controlling voltage, current, power, and the control parameters of the HVDC transmission system. This paper also highlights several factors, challenges, and problems of the conventional VSC-HVDC controllers. Furthermore, this paper provides some suggestions for the advanced control for the future research and development of the HVDC system.

An accurate state of charge (SOC) estimation for lithium-ion battery has been an intensively researched subject in electric vehicle (EV) application towards the advancement of the sustainable transportation system. However, SOC estimation with high accuracy is challenging because of the complex internal characteristics of the lithium-ion battery which is changed by different environmental situations. This paper develops an accurate method for the state of charge (SOC) estimation of a lithium-ion battery using random forests (RFs) algorithm. However, the accuracy of RFs highly depends on the appropriate selection of trees and leaves per tree in a forest. Thus, this research develops an enhanced model with RFs based gravitational search algorithm (GSA). The aim of GSA is to find the best value of trees and leaves per tree. The robustness and accuracy of the proposed model are tested under different temperatures. The model training and validation are executed using federal urban driving schedule (FUDS). The effectiveness of the proposed method is compared with the conventional RFs and radial basis function neural network (RBFNN) and optimal RBFNN-GSA models using different statistical error terms and computational cost. The proposed RFs based GSA model offers higher robustness and accuracy in reducing RMSE by 55.4%, 67.4%, and MAE by 39.1% and 78.1% than conventional RFs and RBFNN based GSA model, respectively at 25°C.

This paper develops a state of charge (SOC) estimation model for a lithium-ion battery using an improved extreme learning machine (ELM) algorithm. ELM is suitable for SOC estimation since ELM algorithm has fast estimation speed, good generalization performance, and high accuracy. However, the performance of ELM is highly dependent on training accuracy and number of neurons in a hidden layer. Hence, gravitational search algorithm (GSA) is applied to improve the ELM computational intelligence by searching for the optimal value hidden layer neurons. The optimal ELM based GSA model does not require internal battery knowledge and mathematical model for SOC estimation. The model robustness is validated at different temperatures using different EV drive cycles. The performance of ELM-GSA model is verified with two popular neural network methods; back-propagation neural network (BPNN) and radial basis function neural network (RBFNN). The results are evaluated using different error rates and computation cost. The results demonstrate that the ELM-GSA model offers high accuracy and low SOC error rate than BPNN-GSA and RBFNN-GSA models. Furthermore, a detailed comparative study between the proposed model and existing SOC strategies is conducted which also demonstrates the superiority of the proposed model.

Autonomous mobile robots require an efficient navigation system in order to navigate from one location to another location fast and safe without hitting static or dynamic obstacles. A light-detection-and-ranging (LiDAR) based autonomous robot navigation is a multi-component navigation system consists of various parameters to be configured. With such structure and sometimes involving conflicting parameters, the process of determining the best configuration for the system is a non-trivial task. This work presents an optimisation method using Genetic algorithm (GA) to configure such navigation system with tuned parameters automatically. The proposed method can optimise parameters of a few components in a navigation system concurrently. The representation of chromosome and fitness function of GA for this specific robotic problem are discussed. The experimental results from simulation and real hardware show that the optimised navigation system outperforms a manually-tuned navigation system of an indoor mobile robot in terms of navigation time.

For most operations in process, petroleum and power industries gas-liquid two phase flows occurs, so an accurate estimation of void fraction is vital because it affects the calculations of heat and mass transfer as well as hydrodynamics. Any inaccuracy in estimation may lead to drastic incidents along with heavy monetary loss. An effort has been made here to estimate the approximate void fraction of supersonic steam jet into the sub-cooled water. Electrical Resistance Tomography (ERT) has been used for the purpose along with the Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EIDORS) to generate the conductivity scans obtained by ERT setup. Before the experimentation, for further assurance on our void fraction estimations, the measurement system has been calibrated by securing scans of heated Teflon rod of diameter 6 mm, which is approximately having the same cross-sectional surface area as the steam jet have and reported in previous studies at the same hydrodynamic conditions. Images of supersonic steam jet in subcooled water have been processed by the help of EIDORS and image processing technique. The over estimation in void fraction of Teflon rod is ranging from 46.17 to 83.44% and when it is subtracted from the total void fraction of supersonic steam jet (46.51–83.79%) at steam inlet pressure of 1.5–3.0 bar and surrounding water temperature 30–60 °C respectively, the actual void fraction of Teflon rod comes out to be ranging from 0.34% to 0.35% of the total cross-sectional area of vessel. When these results are compared with the previous studies, a close agreement has been observed between these two sets of results.

Streamflow forecasting can have a significant economic impact, as this can help in water resources management and in providing protection from water scarcities and possible flood damage. Artificial neural network (ANN) had been successfully used as a tool to model various nonlinear relations, and the method is appropriate for modeling the complex nature of hydrological systems. They are relatively fast and flexible and are able to extract the relation between the inputs and outputs of a process without knowledge of the underlying physics. In this study, two types of ANN, namely feed-forward back-propagation neural network (FFNN) and radial basis function neural network (RBFNN), have been examined. Those models were developed for daily streamflow forecasting at Johor River, Malaysia, for the period (1999---2008). Comprehensive comparison analyses were carried out to evaluate the performance of the proposed static neural networks. The results demonstrate that RBFNN model is superior to the FFNN forecasting model, and RBFNN can be successfully applied and provides high accuracy and reliability for daily streamflow forecasting.

A microgrid (MG) is a local entity that consists of distributed energy resources (DERs) to achieve local power reliability and sustainable energy utilization. The MG concept or renewable energy technologies integrated with energy storage systems (ESS) have gained increasing interest and popularity because it can store energy at off-peak hours and supply energy at peak hours. However, existing ESS technology faces challenges in storing energy due to various issues, such as charging/discharging, safety, reliability, size, cost, life cycle, and overall management. Thus, an advanced ESS is required with regard to capacity, protection, control interface, energy management, and characteristics to enhance the performance of ESS in MG applications. This paper comprehensively reviews the types of ESS technologies, ESS structures along with their configurations, classifications, features, energy conversion, and evaluation process. Moreover, details on the advantages and disadvantages of ESS in MG applications have been analyzed based on the process of energy formations, material selection, power transfer mechanism, capacity, efficiency, and cycle period. Existing reviews critically demonstrate the current technologies for ESS in MG applications. However, the optimum management of ESSs for efficient MG operation remains a challenge in modern power system networks. This review also highlights the key factors, issues, and challenges with possible recommendations for the further development of ESS in future MG applications. All the highlighted insights of this review significantly contribute to the increasing effort toward the development of a cost-effective and efficient ESS model with a prolonged life cycle for sustainable MG implementation.

Image retrieval system (IRS) is a searching system that uses certain characteristics or context in an image. In the medical field, the IRS has been used to provide the needed correct images to the physicians while the diagnosis and treatment process is being conducted. In this paper, the research focus is on content-based IRS. In content-based IRS, two phases are discussed: feature extraction and selection, and the retrieval phase. The feature extraction technique is based on global shape descriptor (GSD), Hu moment invariant (Hu) and Fourier descriptor (FD) while the feature selection technique uses analysis of variance (ANOVA). The retrieval phase is implemented with the new feature vector formed and the performance is evaluated using four distance metrics namely Euclidean (E), Manhattan (M), Normalized Euclidean (NE) and Normalized Manhattan (NM). The experiment for CBIRS is performed on 100 MRI human spine images for each thoracic, lumbar and sacrum. The retrieval performances gave the best result for NM in retrieving the lumbar bones which the precision is up to 91.1%.

This paper presents an optimal state of energy (SOE) estimation strategy of a lithium-ion battery using the back-propagation neural network (BPNN). Two heuristic optmization techniques named backtracketing search algorithm (BSA) and particle swarm optimization (PSO) algorithm are applied to improve the accuracy of BPNN model. Optimization algorithms are developed to determine the optimal value of hidden layer neurons and learning rate of BPNN model. Three most influencing factors including current, voltage and temperature are considered as the inputs to the optimal BPNN model. Federal Urban Driving Schedule (FUDS) is used to check the model robustness at 0°C, 25°C and 45°C. The model performance is evaluated based on the root mean square error (RMSE) and mean absolute error (MAE). The results show that the proposed model obtains good accuracy with an absolute error of ±5%. The BPNN based BSA model improves the SOE estimation accuracy by reducing RMSE and MAE by 2.8% and 4.4% compared to BPNN based PSO model at 25°C.