According to the widespread advance in technological industry, there has been an essen-tial need of high quality and speed methods for data transmission. In many field such as Electrical Engineering, Data Science, Com...
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According to the widespread advance in technological industry, there has been an essen-tial need of high quality and speed methods for data transmission. In many field such as Electrical Engineering, Data Science, Computer Engineering, and Information and Communication Technol-ogy efficient transfer for acquired data are leaded to a great improvement in ultimate performance. Hence traditional way of transmitting data is strongly dependent on the distances among the devices;present work applies Frequency Control and First-order low-pass filtering control model to optimize data transmission. The trends of current results are in good agreement with previous literature. Then the relation between data transmission and output powers are discuss. After that, the effect of distance on data transformation for laboratory equipment is explained. Finally, it was observed that a vital way for resources management is controlling the way for data transformation.(c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).
The multi-level inverter (MLI) is advantageous for DC to AC voltage conversion in power distribution networks. The low-power applications require square and quasi-square waveforms, whereas the sinusoidal waveform is r...
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The multi-level inverter (MLI) is advantageous for DC to AC voltage conversion in power distribution networks. The low-power applications require square and quasi-square waveforms, whereas the sinusoidal waveform is required in high-power applications. The pure sinusoidal waveform of an inverter is obtained by improving the number of levels in the inverter. Hence, more switches and DC sources are used in the inverters to produce multi-levels at the output. The large number of switches and DC sources increases harmonics in the system. The modulation techniques are used with the MLI topologies to alleviate these issues. Recently, modulation techniques based on optimization algorithms have been used to minimize Total Harmonic Distortion (THD). This paper reviews optimization algorithm-based modulation techniques in the recent literature. This review aims to provide an effective solution for enhancing THD in MLI. Different MLI types are initially explained, and then the recent algorithms are explained for THD minimization.
The biggest challenge of this article is how to maximize the rest time of intermittent controllers. This paper mainly uses intermittent quantized controller (IQC) to examine asymptotic synchronization between fraction...
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The biggest challenge of this article is how to maximize the rest time of intermittent controllers. This paper mainly uses intermittent quantized controller (IQC) to examine asymptotic synchronization between fractional-order neural networks (FONNs). Firstly, by utilizing the advantages of intermittent properties, a novel lemma with asymptotic stability inequalities is proposed. Secondly, combining intermittent properties with quantization technique, two different categories of aperiodically intermittent quantized controllers (AIQCs) are designed to ensure asymptotic convergence of FONNs. Due to the certain correlation between control interval, rest interval, and convergence rate parameters, thus, optimization algorithm becomes particularly important in maximizing rest time as much as possible. Thirdly, by constructing Lyapunov functions, several useful conditions are established for the asymptotic synchronization of FONNs. Finally, the rationality of the proposed theoretical analysis is confirmed by two numerical examples.
This article proposes a method for obtaining the refractive index and thickness of thin films based on reflectivity at different angles combined with optimization algorithms. This method has high computational accurac...
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This article proposes a method for obtaining the refractive index and thickness of thin films based on reflectivity at different angles combined with optimization algorithms. This method has high computational accuracy for both high absorption films (such as aluminum films, gold films, etc.) and low absorption films (such as MgF2 films). Since this study did not adopt the inversion scheme of refractive index dispersion equation fitting, it can be used for optical constant inversion in the case of unknown composition of coating materials. The results indicate that this method can simultaneously obtain high-precision information on the complex refractive index and film thickness of the coating material on the substrate. The calculation example achieved a thickness inversion error of 0.165 % for a 2nm metal aluminum film layer and 0.3303 % for a 150 nm MgF2 transparent film layer. Therefore, this study may provide further guidance for high absorption and measurement of the complex refractive index and thickness of transparent films.
To address the measurement accuracy degradation of triaxial magnetometers caused by manufacturing errors and environmental interference, and the limited robustness of traditional calibration methods, this study propos...
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To address the measurement accuracy degradation of triaxial magnetometers caused by manufacturing errors and environmental interference, and the limited robustness of traditional calibration methods, this study proposes a Dynamic Hierarchical Elite-guided Particle Swarm optimization (DHEPSO)-based ellipsoid fitting algorithm. First, an error model for the triaxial magnetometers is established. Next, the DHEPSO algorithm is utilized to fit the ellipsoid parameters by integrating a dynamic hierarchical mechanism, elite guidance strategy, and adaptive inertia weight adjustment, thereby balancing global exploration and local exploitation to efficiently optimize the parameters. Finally, error compensation and precise calibration are achieved using the optimized parameters. The simulation results show that, compared to the Least Squares Method (LSM), it reduces the absolute distance between the simulated data and the ellipsoid by 63.10% and the post-calibration total magnetic field intensity standard deviation by 60% under outlier interference. Against the traditional PSO, TSLPSO, MPSO, and AWPSO, DHEPSO achieves total distance reductions of 48.52%, 47.74%, 56.71%, and 33.09%, respectively, with faster convergence. The statistical analysis of 60 trials confirms DHEPSO's stability, exhibiting lower median error and interquartile range. The results validate DHEPSO's high precision and robustness in high-noise environments, offering theoretical support for engineering applications.
The location of nodes is critical in underwater wireless sensor networks (UWSNs), which is an ocean monitoring platform. UWSNs are motivated by the popular usage of localization and play a major role in several techno...
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The location of nodes is critical in underwater wireless sensor networks (UWSNs), which is an ocean monitoring platform. UWSNs are motivated by the popular usage of localization and play a major role in several technologies that depend primarily on innovations and localization of these nodes. Underwater node localization is a critical technology that enables the deployment of a variety of underwater applications. In this study, the underwater nodes are divided into two levels. Firstly, a clock asynchronous localization system (LS-AC) for base layer's node localization is presented. In order to eradicate the original ranging strategy's dependence on active nodes and address the problem of energy consumption, LS-AC performs in-network situation-based monitoring by relying on asynchronous clocks. Secondly, we propose a backtracking search algorithm (OTKL-BSA) based on optimal topology and knowledge learning. It is used to address the issues associated with traditional algorithms' lack of diversity and the imbalance between exploration and exploitation. Thirdly, to solve the problems that the traditional gray wolf optimizer (GWO) is prone to falling into local optimal values and has a low search efficiency, this paper proposes a GWO scheme based on hunting step size (GWO-HSS). Finally, simulation results show that the proposed algorithm outperforms SLMP, MCL-MP, MP-PSO, and MGP in aspects of localization performance.
This paper aims to provide a comprehensive guide to the application of the inverse tech-nique to the miniaturized tests, on material parameter identification, without investigating the aspects related to the correct c...
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This paper aims to provide a comprehensive guide to the application of the inverse tech-nique to the miniaturized tests, on material parameter identification, without investigating the aspects related to the correct choice of a specific material model. Firstly, a brief introduction to the fundamental principles and procedures associated with the inverse method is given. In general, the strategy described relies on the coupling of the finite element (FE) modelling with an optimization scheme. The FE method allows for the eval-uation of the response of the material under various types of tests, test conditions, and complex constitutive equations, and avoids the use of approximation techniques for the interpretation of the experimental results. Then, on this based, an example of FE-based inverse processes using small punch creep tests is given to illustrate the process and capability of identifying the creep damage properties. Finally, several sensitive issues, related to the application of the inverse approach, are addressed.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).
As one of continuous concern all over the world, the problem of water quality may cause diseases and poisoning and even endanger people's lives. Therefore, the prediction of water quality is of great significance ...
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As one of continuous concern all over the world, the problem of water quality may cause diseases and poisoning and even endanger people's lives. Therefore, the prediction of water quality is of great significance to the efficient management of water resources. However, existing prediction algorithms not only require more operation time but also have low accuracy. In recent years, neural networks are widely used to predict water quality, and the computational power of individual neurons has attracted more and more attention. The main content of this research is to use a novel dendritic neuron model (DNM) to predict water quality. In DNM, dendrites combine synapses of different states instead of simple linear weighting, which has a better fitting ability compared with traditional neural networks. In addition, a recent optimization algorithm called AMSGrad (Adaptive Gradient Method) has been introduced to improve the performance of the Adam dendritic neuron model (ADNM). The performance of ADNM is compared with that of traditional neural networks, and the simulation results show that ADNM is better than traditional neural networks in mean square error, root mean square error and other indicators. Furthermore, the stability and accuracy of ADNM are better than those of other conventional models. Based on trained neural networks, policymakers and managers can use the model to predict the water quality. Real-time water quality level at the monitoring site can be presented so that measures can be taken to avoid diseases caused by water quality problems.
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