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In this study, a new control approach is introduced for active/reactive power control in modernized microgrids (MMGs). The dynamics of MMG are considered to be unknown and a fuzzy reference tracking linear quadratic regulator (FRT-LQR) is designed. To tackle the effect of uncertainties and faults such as short-Circuit, weak connection, unbalanced grids, an optimal H ∞ -based deep learned control (OHDLC) is presented. The main contributions are: (1) The dynamics are unknown, and are online identified by the restricted Boltzmann machines (RBMs). (2) The parameters in hidden layers are tuned by the unsupervised contrastive divergence (UCD) algorithm, and the parameters in the output layers are tuned by the designed Lyapunov based learning rules that ensure the stability. (3) The designed H ∞ -based supervisor compensates the perturbations. (4) Several simulations, comparisons, and real-time examination as Hardware-in-the Loop (HiL) setup verify the applicability of the suggested control method. A comparison between the suggested approach and related controllers shows that the designed controller is more robust and accurate. In the suggested method, besides the fact that the deep learning approach improves the accuracy, the designed H ∞ -based supervisor also enhances the robustness. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) and mobile edge computing (MEC) architectures are common in real-time application scenarios for improving the reliability of service responses. Energy conservation (EC) and energy harvesting (EH) are significant concerns in such architectures due to the self-sustainable devices and resource-constraint edge nodes. The density of the users and service requirements are further reasons for energy conservation and the need for energy harvesting in these scenarios. This article proposes decisive task scheduling for energy conservation (DTS-EC). The proposed energy conservation method relies on conditional decision-making through classification disseminations and energy slots for data handling. By classifying the energy requirements and the states of the mobile edge nodes, the allocation and queuing of data are determined, preventing overloaded nodes and dissemination. This process is recurrent for varying time slots, edge nodes, and tasks. The proposed method is found to achieve a high data dissemination rate (8.16%), less energy utilization (10.65%), and reduced latency (11.44%) at different time slots. [ABSTRACT FROM AUTHOR]

The chaotic systems have extensive applications in various branches of engineering problems such as financial problems, image processing, secure communications, and medical problems, among many others. In most applications, a synchronization needs to be made with another favorite chaotic system, or output trajectories track the desired signal. The dynamics of these systems are complicated, they are very sensitive to the initial conditions, and they exhibit a stochastic unpredictable behavior. In this study, a new robust type-3 fuzzy logic control (T3-FLC) is designed that can be applied for a large case of chaotic systems under faulty actuators and unknown perturbed dynamics. The dynamic uncertainties are estimated by the online learned type-3 fuzzy logic systems (T3-FLSs). The rules of T3-FLS are optimized by the Lyapunov theorem. The actuator nonlinearities are identified by a new method. The effects of approximation error (AE), dynamic perturbations and unknown time-varying control gains are tackled by the designed adaptive compensator. The designed compensator is constructed by online estimation of the upper bound of AE. By several simulations and comparison with the new FLS-based controllers, the better performance of the designed T3-FLC is shown. In addition, the performance of the designed controller is examined in a secure communication system. [ABSTRACT FROM AUTHOR]

In this paper, a new approach is presented for stabilizing and synchronizing financial chaotic systems. A new type-3 (T3) fuzzy-based system (FLS) with an online optimization scheme is designed to cope with chaotic behavior, high-level uncertainties, and unknown dynamics. An adaptive compensator also eliminates the effect of approximation errors (AEs) and perturbations. The stability of the dynamics of synchronization errors is guaranteed by the use of the Lyapunov method. Several simulations and comparisons demonstrate the superiority of the suggested control and synchronization scenarios. [ABSTRACT FROM AUTHOR]

This paper utilizes spatial econometric reenactments to examine the geographic effects of different types of environmentally friendly power on corban discharges. The example covers 31 nations in the Asia-Pacific district during the time frame 2000 to 2018. The spatial connection in the model was affirmed by symptomatic testing, and the spatial Durbin model was picked as the last model. Results show that Gross domestic product per capita, receptiveness to business sectors, unfamiliar direct venture, energy force, and urbanization critically affect CO2 emanations. In correlation, just wind and sunlight-based energy have added to a generous abatement in ozone harming substance emanations in nations over the long run. In contrast, hydropower, bioenergy, and geothermal energy discoveries have been irrelevant. A cross-sectional examination worldview delineated that nations with more elevated sunlight-based energy yield have higher CO2 outflows, while nations with lower levels have lower CO2 emanations. The presence of spatial impacts in the model gave off an impression of the negative consequences for homegrown CO2 outflows of Gross domestic product per capita and exchange transparency of adjoining nations. Furthermore, energy power and higher creation of sustainable power in adjoining nations will prompt lower homegrown CO2 outflows. [ABSTRACT FROM AUTHOR]

In this study, the efficacy of adding nanoparticles of MWCNTs to the base fluid of paraffin on the thermal conductivity has been investigated. MWCNTs-paraffin samples were prepared at different mass fractions using two-step method. Due to the dependency of thermal conductivity on temperature, experiments were performed at temperatures of 25, 40, 55 and 70 °C. It was found that at any temperature and nanotubes concentration, the incorporation of nanotubes into the base fluid improves the thermal conductivity. Based on the results, at the temperature of 70 °C, the incorporation of nanotubes with a mass fraction of 5% causes up to 40.6% improvement in thermal conductivity. It can be concluded that at higher temperatures, the addition of nanotubes has more positive effects on the thermal conductivity. Also, the higher the mass fraction of nanotubes, the greater the sensitivity of thermal conductivity to temperature. [ABSTRACT FROM AUTHOR]

In this research, the average Nusselt number in tall and narrow enclosures in the presence of mixed convection of a water-based nanofluid (H2O–Cu–TiO2) is determined and compared. The effect of quantities such as Richardson number of 0.01–100, the volume fractions of 0–2% and geometric shape of the enclosure on average Nusselt number and maximum value of flow function is numerically investigated. The FVM and SIMPLER algorithm are used for resolving the nonlinear equations. According to numerical results, for all the Richardson number and two tall and narrow enclosures, the average Nusselt number rises with growing the nanoparticles volume fractions. Comparison of the tall and narrow enclosures shows that for obtaining the highest heat transfer, by growing the nanoparticles volume fractions under similar conditions, it is better to use tall enclosures for large Richardson number and narrow enclosures for small Richardson number. The highest enhancement of the average Nusselt number with increasing the nanoparticles volume fractions for narrow enclosures was 10.44% at the Ri = 0.01. Whereas in the tall enclosures, the highest increase in the average Nusselt number was 14.51% at Ri = 100. For all the Richardson number and two tall and narrow enclosures, the maximum flow function value of the nanoparticles increases with increasing volume fractions. This increase in small Richardson number is greater than in large Richardson number. [ABSTRACT FROM AUTHOR]

The insulin injection rate in type-I diabetic patients is a complex control problem. The mathematical dynamics for the insulin/glucose metabolism can be different for various patients who undertake different activities, have different lifestyles, and have other illnesses. In this study, a robust regulation system on the basis of generalized type-2 (GT2) fuzzy-logic systems (FLSs) is designed for the regulation of the blood glucose level. Unlike previous studies, the dynamics of glucose–insulin are unknown under high levels of uncertainty. The insulin-glucose metabolism has been identified online by GT2-FLSs, considering the stability criteria. The learning scheme was designed based on the Lyapunov approach. In other words, the GT2-FLSs are learned using adaptation rules that are concluded from the stability theorem. The effect of the dynamic estimation error and other perturbations, such as patient activeness, were eliminated through the designed adaptive fuzzy compensator. The adaptation laws for control parameters, GT2-FLS rule parameters, and the designed compensator were obtained by using the Lyapunov stability theorem. The feasibility and accuracy of the designed control scheme was examined on a modified Bergman model of some patients under different conditions. The simulation results confirm that the suggested controller has excellent performance under various conditions. [ABSTRACT FROM AUTHOR]

Seed protein content, controlled by quantitative genes, is a very important trait for soybean. To idenlify the soybean quantitative trait loci (QTL) of seed protein content, which provides a base for MAS breeding and elite genes - discovering, a cross was made between the elite cultivar Zhonghuang 13, which exhibits a comprehensive excellent performance and has the higher protein content and the variety Dongshan 69 which has the lower protein content. The BC2F2 and BC2F3 backcross populations were further developed to identify protein composite interval mapping (ICIM), two QTLs flanked by the marker interval Satt396 to Satt180 and Sct_001 to Satt654 were both detecled in the BC2F2 and BC2F3 families. In the BC2F2 families, 3 QTLs were found on A1, C1 and J linkage groups, which accounted for 10.00%, 7.07% and 9.23% phenotypic variance, respectively. In the BC2F3 families, 4 QTLs were identified on B1, C1, I and J linkage groups, which explained 17. 57%, 3. 46%, 8. 53% and 11. 80% phenotypic variance, respectively. Region Sct_001 to Satt654 may be a new interval associated wilh protein content QTL. [ABSTRACT FROM AUTHOR]

Economic efficient Autonomous Road Vehicles (ARVs) are invariably subjected to uncertainties and perturbations. Therefore, control of vehicle systems requires stability to withstand the effect of variations in the nominal performance. Lateral path-tracking is a substantial task of ARVs, especially in critical maneuvering and cornering with variable speed. In this study, a new controller on the basis of interval type-3 (T3) fuzzy logic system (FLSs) is designed. The main novelties and advantages are as follows. (1) The uncertainty is a main challenge in the path-following problem of ARVs. However, in the fuzzy-based approaches, the bounds of uncertainty are assumed to be known. However, in the our suggested approach, the bounds of uncertainties are also fuzzy sets and type-3 FLSs with online adaptation rules are suggested to handle the uncertainties. (2) The approximation errors (AEs) and perturbations are investigated and tackled by the compensators. (3) The bounds of estimation errors are also uncertain and are estimated by the suggested adaptation laws. (4) The stability is ensured under unknown dynamics, perturbations and critical maneuvers. (5) Comparison with the benchmarking techniques and conventional fuzzy approaches verifies that the suggested path-following scheme results in better maneuver performance. [ABSTRACT FROM AUTHOR]