Structural changes in electrical energy systems are occurring rapidly. Due to the development of technology and the increase in energy consumption, the importance and impact of nanogrids in the energy system is increa...
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Structural changes in electrical energy systems are occurring rapidly. Due to the development of technology and the increase in energy consumption, the importance and impact of nanogrids in the energy system is increasing. It is very difficult to sustain nanogrids as offgrid. Therefore, this study thoroughly examines the most efficient controller designs for nanogrids that use fuel cells as their primary source of energy. In addition, dwarfmongoose Optimization (DMO), a new effective optimization technique, is used to determine controller gains in nanogrid systems for the first time in the literature. Controller gains for different objective functions such as ISE, IAE, ITSE and ITAE are determined using classical PID controllers and two degrees of freedom (2 DOF) PID controllers. Moreover, sensitivity analysis is carried out for both variable and constant load scenarios. The constant load is selected as 0.25 pu, and variable load is chosen from values ranging from 0.15 pu to 0.75 pu. In addition, the response of the system to parameter changes is examined by increasing or decreasing the time constants in all load cases. The system parameters are changed from - 20 % to +40 % in 0.05 intervals. It has been noted that the suggested controller topologies perform differently depending on the objective functions. Thus, a significant contribution has been made to researchers to indicate the effectiveness of various control structures in fuel cell powered nanogrids.
In this study, we propose a novel cloud-edge collaborative task assignment model for smart farms that consists of a cloud server, m edge servers, and n sensors. The edge servers rely solely on solar-generated energy, ...
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In this study, we propose a novel cloud-edge collaborative task assignment model for smart farms that consists of a cloud server, m edge servers, and n sensors. The edge servers rely solely on solar-generated energy, which is limited, whereas the cloud server has access to a limitless amount of energy supplied by the smart grid. Each entire task from a sensor is processed by either an edge server or the cloud server. We consider the task to be unsplittable. Building on the algorithm for the multimachine job scheduling problem, we develop a corresponding approximation algorithm. In addition, we propose a new discrete heuristic based on the dwarfmongoose optimization algorithmm, named the discrete dwarfmongoose optimization algorithm, and we utilize the proposed approximation algorithm to improve the convergence speed of this heuristic while yielding better solutions. In this study, we consider task sets with heavy tasks independently, where a heavy task is a task that requires many computing resources to process. If such tasks are assigned as ordinary tasks, the assignment results may be poor. Therefore, we propose a new method to solve this kind of problem.
Recycled aggregate concrete, also known as RAC or RCA, is becoming more and more popular as a building material due to its favorable environmental characteristics. However, the use of RAC is becoming more and more ham...
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Recycled aggregate concrete, also known as RAC or RCA, is becoming more and more popular as a building material due to its favorable environmental characteristics. However, the use of RAC is becoming more and more hampered by the uncertainty surrounding its fracture resistance. 3 distinct estimation methods were considered in the present study, named multi-layered perceptron neural network (MLP), support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS) in order to appraise the splitting tensile strength (STS) of fiber-reinforced (Steel fiber, Carbon fiber, Polypropylene fiber, Basalt fiber, Glass fiber, and Woolen fiber) RAC. The dwarfmongoose optimization algorithm (DMOA) was linked with MLP, SVR, and ANFIS to the identification of the best-performing combination of hyperparameters. It was clear from sensitivity analysis that C, RORCA, and W have a significant impact on the prediction of STS at 0.9432, 0.9431, and 0.9201. The MLPDM, SVRDM, and ANFDM algorithms provide significant promise for accurately predicting the STS of fiber reinforced RAC, as indicated by the findings. Throughout the training, validating, and testing phases, the ANFDM method showed outstanding dependability, with R2 values of 0.9877, 0.9669, and 0.9818. The value of OBJ metric shows that the smallest value is for ANFDM at 0.1028, then SVRDM at 0.1578, followed by MLPDM at 0.178.
A promising approach to enhancing sustainability within the construction industry is the development of recycled aggregate concrete (RAC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepacka...
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A promising approach to enhancing sustainability within the construction industry is the development of recycled aggregate concrete (RAC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$RAC$$\end{document}), which involves substituting natural aggregates with recycled materials. This innovative material not only reduces the environmental impact associated with the extraction and processing of natural aggregates but also promotes the circular economy by repurposing waste materials. Evaluating the frost durability of RAC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$RAC$$\end{document} through the Durability Factor (Df) is critical for several reasons in the realms of construction and civil engineering. This study investigates the frost durability of recycled aggregate concrete (RAC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$RAC$$\end{document}) by utilizing data mining techniques to predict the durability factor (Df) in cold regions. The necessity for this research arises from the growing need for sustainable construction practices, particularly through the use of recycled materials. We employed least square support vector regression (LSSVR\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$LSSVR$$\end{document}) and Random Forest (RF\documentclass[12pt]
Gesture recognition for Arabic speech translation includes developing advanced technologies that correctly translate body and hand movements corresponding to Arabic sign language (ArSL) into spoken Arabic. This levera...
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Gesture recognition for Arabic speech translation includes developing advanced technologies that correctly translate body and hand movements corresponding to Arabic sign language (ArSL) into spoken Arabic. This leverages machine learning and computer vision techniques in complex systems simulation platforms to scrutinize the gestures utilized in ArSL, detecting mild differences in facial expressions, hand shapes, and movements. Sign Language Recognition (SLR) is paramount in assisting communication for the Deaf and Hard-of-Hearing communities. It includes using vision-based methods and Surface Electromyography (sEMG) signals. The sEMG signal is crucial for recognizing hand gestures and capturing muscular activities in sign language. Researchers have comprehensively shown the capability of EMG signals to approach specific details, mainly in classifying hand gestures. This progression is a stimulating feature in extracting the interpretation and recognition of sign languages and investigating the phonology of signed language. Leveraging machine learning algorithms and signal processing techniques in complex systems simulation platforms, researchers aim to extract relevant traits from the sEMG signals that correspond to different ArSL gestures. This study introduces an Enhanced dwarf mongoose algorithm with a Deep Learning-Driven Arabic Sign Language Detection (EDMODL-ASLD) technique on sEMG data. In the initial phase, the presented EDMODL-ASLD model is subjected to data preprocessing to change the input sEMG data into an attuned format. In the next stage, feature extraction with fractal theories is used to gather relevant and nonredundant data from the EMG window to construct a feature vector. In this study, the absolute envelope (AE), energy (E), root-mean square (RMS), standard deviation (STD), and mean absolute value (MAV) are the five time-domain extracted features for the EMG window observation. Meanwhile, the dilated convolutional long short-term memory (ConvLST
In photovoltaic power generation systems, uneven light due to external shading reduces efficiency. In the case of uneven illumination, the traditional MPPT algorithm has problems such as slow convergence speed, low ac...
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