DC distribution of PV systems has spread back especially in the residential sector as a variety of electronic appliances became locally available in the market. The compatibility of household appliances with the best voltage-level in a DC environment is the field that still in the research phase and has not yet made a practically extensive appearance. This paper mainly discusses this issue by providing a review of the concerning research efforts, identifying the gaps in the existing knowledge. The work explains the electrical diagrams of the recently produced appliances, classifying them to get an understanding of how each one consumes energy. It includes exploiting the recent dependence of the commercial appliances on power electronics to improve the efficiency of the existing DC distribution systems by extrapolating new architectures. The proposed topology has a DC distribution environment with two levels of voltage for all appliances. Appliances performances have been evaluated by calculating the energy transfer efficiency. The outcomes of this work can help in designing more efficient DC power distribution networks with minimal energy converters and establishing standardizations for DC microgrids.
A photodetector converts optical signals to detectable electrical signals. Lately, self-powered photodetectors have been widely studied because of their advantages in device miniaturization and low power consumption, which make them preferable in various applications, especially those related to green technology and flexible electronics. Since self-powered photodetectors do not have an external power supply at zero bias, it is important to ensure that the built-in potential in the device produces a sufficiently thick depletion region that efficiently sweeps the carriers across the junction, resulting in detectable electrical signals even at very low-optical power signals. Therefore, two-dimensional (2D) materials are explored as an alternative to silicon-based active regions in the photodetector. In addition, plasmonic effects coupled with self-powered photodetectors will further enhance light absorption and scattering, which contribute to the improvement of the device's photocurrent generation. Hence, this review focuses on the employment of 2D materials such as graphene and molybdenum disulfide (MoS2) with the insertion of hexagonal boron nitride (h-BN) and plasmonic nanoparticles. All these approaches have shown performance improvement of photodetectors for self-powering applications. A comprehensive analysis encompassing 2D material characterization, theoretical and numerical modelling, device physics, fabrication and characterization of photodetectors with graphene/MoS2 and graphene/h-BN/MoS2 heterostructures with plasmonic effect is presented with potential leads to new research opportunities.
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.
At the 90-nm node, the rate of transistor miniaturization slows down due to challenges in overcoming the increased leakage current (Ioff). The invention of high-k/metal gate technology at the 45-nm technology node was an enormous step forward in extending Moore's Law. The need to satisfy performance requirements and to overcome the limitations of planar bulk transistor to scales below 22 nm led to the development of fully depleted silicon-on-insulator (FDSOI) and fin field-effect transistor (FinFET) technologies. The 28-nm wafer planar process is the most cost-effective, and scaling towards the sub-10 nm technology node involves the complex integration of new materials (Ge, III-V, graphene) and new device architectures. To date, planar transistors still command >50% of the transistor market and applications. This work aims to downscale a planar PMOS to a 14-nm gate length using La2O3 as the high-k dielectric material. The device was virtually fabricated and electrically characterized using SILVACO. Taguchi L9 and L27 were employed to study the process parameters' variability and interaction effects to optimize the process parameters to achieve the required output. The results obtained from simulation using the SILVACO tool show good agreement with the nominal values of PMOS threshold voltage (Vth) of -0.289 V ± 12.7% and Ioff of less than 10-7 A/µm, as projected by the International Technology Roadmap for Semiconductors (ITRS). Careful control of SiO2 formation at the Si interface and rapid annealing processing are required to achieve La2O3 thermal stability at the target equivalent oxide thickness (EOT). The effects of process variations on Vth, Ion and Ioff were investigated. The improved voltage scaling resulting from the lower Vth value is associated with the increased Ioff due to the improved drain-induced barrier lowering as the gate length decreases. The performance of the 14-nm planar bulk PMOS is comparable to the performance of the FDSOI and FinFET technologies at the same gate length. The comparisons made with ITRS, the International Roadmap for Devices and Systems (IRDS), and the simulated and experimental data show good agreement and thus prove the validity of the developed model for PMOSs. Based on the results demonstrated, planar PMOSs could be a feasible alternative to FDSOI and FinFET in balancing the trade-off between performance and cost in the 14-nm process.
Bacteria- or virus-infected chicken is conventionally detected by manual observation and confirmed by a laboratory test, which may lead to late detection, significant economic loss, and threaten human health. This paper reports on the development of an innovative technique to detect bacteria- or virus-infected chickens based on the optical chromaticity of the chicken comb. The chromaticity of the infected and healthy chicken comb was extracted and analyzed with International Commission on Illumination (CIE) XYZ color space. Logistic Regression, Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and Decision Trees have been developed to detect infected chickens using the chromaticity data. Based on the X and Z chromaticity data from the chromaticity analysis, the color of the infected chicken's comb converged from red to green and yellow to blue. The development of the algorithms shows that Logistic Regression, SVM with Linear and Polynomial kernels performed the best with 95% accuracy, followed by SVM-RBF kernel, and KNN with 93% accuracy, Decision Tree with 90% accuracy, and lastly, SVM-Sigmoidal kernel with 83% accuracy. The iteration of the probability threshold parameter for Logistic Regression models has shown that the model can detect all infected chickens with 100% sensitivity and 95% accuracy at the probability threshold of 0.54. These works have shown that, despite using only the optical chromaticity of the chicken comb as the input data, the developed models (95% accuracy) have performed exceptionally well, compared to other reported results (99.469% accuracy) which utilize more sophisticated input data such as morphological and mobility features. This work has demonstrated a new feature for bacteria- or virus-infected chicken detection and contributes to the development of modern technology in agriculture applications.
Accurate spectral irradiance measurement in the near-infrared range is significant for the design and characterization of photodetector and photovoltaic cells. Approximation method is commonly used to solve for the input power using estimated spectral irradiance, where the dependency on wavelength and temperature remains uncertain. This study aims to determine the power spectrum at different radiation temperatures using a single pixel photodetector, taking into consideration factors such as transmission spectra of alumina radiator, CaF2 collimating lens, responsivity, and measured photocurrent information of photodetectors. Utilizing predictive mathematical model, five commercial photodetectors, including Silicon, Germanium, In0.53Ga0.47As, In0.73Ga0.27As, and In0.83Ga0.17As were used to solve for the power densities as a function of wavelengths at radiation temperatures of 1000 °C and 1500 °C. The spectral irradiance of photodetectors was determined with a percentage difference of <4.9 %, presenting an accurate power density estimation for the spectrum at a wide range of radiation temperatures. Power irradiance data obtained were validated in the narrow wavelength range with 1000 nm, 1400 nm, 1500 nm, and 2000 nm bandpass filters. The reported work demonstrates a simple and efficient way which could contribute to develop a cost-effective method of measuring and determining the spectrum irradiances of objects at different radiation temperatures. This predictive analysis method hopefully intensifies the progress of efforts to reduce the reliance on complex optoelectronic instruments in accurately solving power irradiance information.
The lack of control in voltage overshoot, transient response, and steady state error are major issues that are frequently encountered in a grid-connected photovoltaic (PV) system, resulting in poor power quality performance and damages to the overall power system. This paper presents the performance of a control strategy for an inverter in a three-phase grid-connected PV system. The system consists of a PV panel, a boost converter, a DC link, an inverter, and a resistor-inductor (RL) filter and is connected to the utility grid through a voltage source inverter. The main objective of the proposed strategy is to improve the power quality performance of the three-phase grid-connected inverter system by optimising the proportional-integral (PI) controller. Such a strategy aims to reduce the DC link input voltage fluctuation, decrease the harmonics, and stabilise the output current, voltage, frequency, and power flow. The particle swarm optimisation (PSO) technique was implemented to tune the PI controller parameters by minimising the error of the voltage regulator and current controller schemes in the inverter system. The system model and control strategies were implemented using MATLAB/Simulink environment (Version 2020A) Simscape-Power system toolbox. Results show that the proposed strategy outperformed other reported research works with total harmonic distortion (THD) at a grid voltage and current of 0.29% and 2.72%, respectively, and a transient response time of 0.1853s. Compared to conventional systems, the PI controller with PSO-based optimization provides less voltage overshoot by 11.1% while reducing the time to reach equilibrium state by 32.6%. The consideration of additional input parameters and the optimization of input parameters were identified to be the two main factors that contribute to the significant improvements in power quality control. Therefore, the proposed strategy effectively enhances the power quality of the utility grid, and such an enhancement contributes to the efficient and smooth integration of the PV system.
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.
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.
Generally, waste heat is redundantly released into the surrounding by anthropogenic activities without strategized planning. Consequently, urban heat islands and global warming chronically increases over time. Thermophotovoltaic (TPV) systems can be potentially deployed to harvest waste heat and recuperate energy to tackle this global issue with supplementary generation of electrical energy. This paper presents a critical review on two dominant types of semiconductor materials, namely gallium antimonide (GaSb) and indium gallium arsenide (InGaAs), as the potential candidates for TPV cells. The advantages and drawbacks of non-epitaxy and epitaxy growth methods are well-discussed based on different semiconductor materials. In addition, this paper critically examines and summarizes the electrical cell performance of TPV cells made of GaSb, InGaAs and other narrow bandgap semiconductor materials. The cell conversion efficiency improvement in terms of structural design and architectural optimization are also comprehensively analyzed and discussed. Lastly, the practical applications, current issues and challenges of TPV cells are critically reviewed and concluded with recommendations for future research. The highlighted insights of this review will contribute to the increase in effort towards development of future TPV systems with improved cell conversion efficiency.
The color of transformer oil can be one of the first indicators determining the quality of the transformer oil and the condition of the power transformer. The current method of determining the color index (CI) of transformer oil utilizes a color comparator based on the American Society for Testing and Materials (ASTM) D1500 standard, which requires a human observer, leading to human error and a limited number of samples tested per day. This paper reports on the utilization of ultra violet-blue laser at 405- and 450-nm wavelengths to measure the CI of transformer oil. In total, 20 transformer oil samples with CI ranging from 0.5 to 7.5 were measured at optical pathlengths of 10 and 1 mm. A linear regression model was developed to determine the color index of the transformer oil. The equation was validated and verified by measuring the output power of a new batch of transformer oil samples. Data obtained from the measurements were able to quantify the CI accurately with root-mean-square errors (RMSEs) of 0.2229 for 405 nm and 0.4129 for 450 nm. This approach shows the commercialization potential of a low-cost portable device that can be used on-site for the monitoring of power transformers.
The optimization of thermophotovoltaic (TPV) cell efficiency is essential since it leads to a significant increase in the output power. Typically, the optimization of In0.53Ga0.47As TPV cell has been limited to single variable such as the emitter thickness, while the effects of the variation in other design variables are assumed to be negligible. The reported efficiencies of In0.53Ga0.47As TPV cell mostly remain
The essential oil of Backhousia citriodora, commonly known as lemon myrtle oil, possesses various beneficial properties due to its richness in bioactive compounds. This study aimed to characterize the chemical profile of the essential oil isolated from leaves of Backhousia citriodora (BCEO) and its biological properties, including antioxidant, antibacterial, and antibiofilm activities. Using gas chromatography-mass spectrometry, 21 compounds were identified in BCEO, representing 98.50% of the total oil content. The isomers of citral, geranial (52.13%), and neral (37.65%) were detected as the main constituents. The evaluation of DPPH radical scavenging activity and ferric reducing antioxidant power showed that BCEO exhibited strong antioxidant activity at IC50 of 42.57 μg/mL and EC50 of 20.03 μg/mL, respectively. The antibacterial activity results showed that BCEO exhibited stronger antibacterial activity against Gram-positive bacteria (Staphylococcus aureus and Staphylococcus epidermidis) than against Gram-negative bacteria (Escherichia coli and Klebsiella pneumoniae). For the agar disk diffusion method, S. epidermidis was the most sensitive to BCEO with an inhibition zone diameter of 50.17 mm, followed by S. aureus (31.13 mm), E. coli (20.33 mm), and K. pneumoniae (12.67 mm). The results from the microdilution method showed that BCEO exhibited the highest activity against S. epidermidis and S. aureus, with the minimal inhibitory concentration (MIC) value of 6.25 μL/mL. BCEO acts as a potent antibiofilm agent with dual actions, inhibiting (85.10% to 96.44%) and eradicating (70.92% to 90.73%) of the biofilms formed by the four tested bacteria strains, compared with streptomycin (biofilm inhibition, 67.65% to 94.29% and biofilm eradication, 49.97% to 89.73%). This study highlights that BCEO can potentially be a natural antioxidant agent, antibacterial agent, and antibiofilm agent that could be applied in the pharmaceutical and food industries. To the best of the authors' knowledge, this is the first report, on the antibiofilm activity of BCEO against four common nosocomial pathogens.
Monitoring the condition of transformer oil is considered to be one of the preventive maintenance measures and it is very critical in ensuring the safety as well as optimal performance of the equipment. Various oil properties and contents in oil can be monitored such as acidity, furanic compounds and color. The current method is used to determine the color index (CI) of transformer oil produces an error of 0.5 in measurement, has high risk of human handling error, additional expense such as sampling and transportations, and limited samples can be measured per day due to safety and health reasons. Therefore, this work proposes the determination of CI of transformer oil using ultraviolet-to-visible (UV-Vis) spectroscopy. Results show a good correlation between the CI of transformer oil and the absorbance spectral responses of oils from 300 nm to 700 nm. Modeled equations were developed to relate the CI of the oil with the cutoff wavelength and absorbance, and with the area under the curve from 360 nm to 600 nm. These equations were verified with another set of oil samples. The equation that describes the relationship between cutoff wavelength, absorbance and CI of the oil shows higher accuracy with root mean square error (RMSE) of 0.1961.
Radiative cooling, a passive cooling technology, functions by reflecting the majority of solar radiation (within the solar spectrum of 0.3-2.5 μm) and emitting thermal radiation (within the atmospheric windows of 8-13 μm and 16-20 μm). Predominantly, synthetic polymers are effectively utilized for radiative cooling while posing potential environmental hazards due to their complex components, toxicity, or nonbiodegradation. Bacterial cellulose, a natural and renewable biopolymer, stands out due to its environmentally friendly, scalability, high purity, and significant infrared emissivity. In this work, we developed a bacterial cellulose-based composite film (BCF) with a cross-linked network structure by a facile agitation spraying method to achieve enhanced and sustainable radiative cooling performance. The BCF exhibited superior optical properties and environmental tolerance, with a notable infrared emissivity of 94.6%. As a result, the thermal emitter demonstrates a substantial subambient cooling capacity (11:00 to 13:00, maximum drop of 7.15 °C, average drop of 4.85 °C; 22:00 to 2:00, maximum drop of 2.7 °C, average drop of 2.32 °C). Additionally, the BCF maintained stable emissivity after 240 h of continuous UV irradiation. Furthermore, BCF can effectively preserve the freshness of fruits under intense solar irradiation. Hence, BCF with high radiative cooling performance presents a broad application prospect in building energy conservation, solar cells efficiency enhancement, and food transportation packaging.
For most natural or naturally-derived liquid products, their color reflects on their quality and occasionally affects customer preferences. To date, there are a few subjective and objective methods for color measurement which are currently utilized by various industries. Researchers are also improving these methods and inventing new methods, as color is proven to have the ability to provide various information on the condition and quality of the liquid. However, a review on the methods, especially for amber-colored liquid, has not been conducted yet. This paper presents a comprehensive review on the subjective and objective methods for color measurement of amber-colored liquids. The pros and cons of the measurement methods, the effects of the color on customer preferences, and the international industry standards on color measurements are reviewed and discussed. In addition, this study elaborates on the issues and challenges related to the color measurement techniques as well as recommendations for future research. This review demonstrates that the existing color measurement technique can determine the color according to the standards and color scales. However, the efforts toward minimizing the complexity of the hardware while maximizing the signal processing through advanced computation are still lacking. Therefore, through this critical review, this review can hopefully intensify the efforts toward finding an optimized method or technique for color measurement of liquids and thus expedite the development of a portable device that can measure color accurately.
Accurate state of charge (SOC) estimation of lithium-ion (Li-ion) batteries is crucial in prolonging cell lifespan and ensuring its safe operation for electric vehicle applications. In this article, we propose the deep learning-based transformer model trained with self-supervised learning (SSL) for end-to-end SOC estimation without the requirements of feature engineering or adaptive filtering. We demonstrate that with the SSL framework, the proposed deep learning transformer model achieves the lowest root-mean-square-error (RMSE) of 0.90% and a mean-absolute-error (MAE) of 0.44% at constant ambient temperature, and RMSE of 1.19% and a MAE of 0.7% at varying ambient temperature. With SSL, the proposed model can be trained with as few as 5 epochs using only 20% of the total training data and still achieves less than 1.9% RMSE on the test data. Finally, we also demonstrate that the learning weights during the SSL training can be transferred to a new Li-ion cell with different chemistry and still achieve on-par performance compared to the models trained from scratch on the new cell.
Since the year 2020, coronavirus disease 2019 (COVID-19) has emerged as the dominant topic of discussion in the public and research domains. Intensive research has been carried out on several aspects of COVID-19, including vaccines, its transmission mechanism, detection of COVID-19 infection, and its infection rate and factors. The awareness of the public related to the COVID-19 infection factors enables the public to adhere to the standard operating procedures, while a full elucidation on the correlation of different factors to the infection rate facilitates effective measures to minimize the risk of COVID-19 infection by policy makers and enforcers. Hence, this paper aims to provide a comprehensive and analytical review of different factors affecting the COVID-19 infection rate. Furthermore, this review analyses factors which directly and indirectly affect the COVID-19 infection risk, such as physical distance, ventilation, face masks, meteorological factor, socioeconomic factor, vaccination, host factor, SARS-CoV-2 variants, and the availability of COVID-19 testing. Critical analysis was performed for the different factors by providing quantitative and qualitative studies. Lastly, the challenges of correlating each infection risk factor to the predicted risk of COVID-19 infection are discussed, and recommendations for further research works and interventions are outlined.