The application of satellite information refers to the utilization of data generated by operational satellites that have been launched into orbit. Due to the recent rapid increase in the number of satellites in space,...
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The application of satellite information refers to the utilization of data generated by operational satellites that have been launched into orbit. Due to the recent rapid increase in the number of satellites in space, satellite information is growing exponentially. This growth underscores the necessity of finding effective ways to utilize this wealth of satellite data. In particular, it is anticipated that the number of individually developed national satellites will continue to rise, making it imperative to establish efficient methods for the fusion and application of satellite information through the configuration of satellite constellations and integrated operations of national satellites. In this paper, we propose a method to improve temporal resolution by employing a genetic algorithm for efficient fusion and application of satellite information. Additionally, we present a method for configuring national satellite constellations by adjusting the revisit times of operational satellites using the previously mentioned method. To mitigate the potential shortening of the lifespan of currently operational satellites due to fuel consumption, we altered the orbits of future satellites slated for launch. We also made adjustments to the Right Ascension of the Ascending Node (RAAN), a parameter that has minimal impact on payload. We conducted separate analyses for Electro Optical (EO) and Synthetic Aperture Radar (SAR) satellites, each with different imaging schedules. Our findings confirmed that it is possible to configure constellations of national satellites by modifying the revisit characteristics of both types of satellites.
This paper presents a hybrid approach that combines a genetic algorithm (GA)-optimized type-2 fuzzy logic controller (T2FLC) with a fractional-order technique for enhanced control of a microgrid system. The T2FLC appr...
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This paper presents a hybrid approach that combines a genetic algorithm (GA)-optimized type-2 fuzzy logic controller (T2FLC) with a fractional-order technique for enhanced control of a microgrid system. The T2FLC approach is employed to handle the inherent uncertainties in the microgrid due to fluctuating renewable energy inputs and varying loads. The GA optimizes the parameters of the designed FO-T2FLC approach, ensuring optimal performance under different operational conditions. This developed strategy is a modification and development of the traditional approach, as it is characterized by rapid dynamic response, high durability, distinctive performance, ease of application, and inexpensive. Also, this designed strategy does not depend on the mathematical model of the studied system, which gives satisfactory results if the system parameters change. The microgrid system on the direct current side features a photovoltaic array with battery storage. In contrast, the alternating current section comprises a multi-functional voltage source inverter integrated with a shunt active power filter. This setup delivers energy to the connected loads and the network. To manage the system effectively;traditional power control methods (direct power control and space vector modulation) are used for the alternating current section. Additionally, the proposed regulator control the direct current bus voltage loop, regulate the reactive and active power loops of the network, and compensate for the total harmonic distortion in the source streams. It also injects the required active power into the network to enhance the competence of the power network. In this work, the efficiency of the proposed FO-T2FLC-GA approach is verified using MATLAB, comparing it to the T2FLC-GA approach and some existing strategies such as third-order sliding mode control. The results obtained highlight the effectiveness and strength of the FO-T2FLC-GA approach in improving power quality and reducing the total
This paper aims to develop an efficient scheduling approach based on genetic algorithms to optimize energy consumption and maximize the operational lifetime of Wireless cial for prolonging the operational lifespan of ...
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This paper aims to develop an efficient scheduling approach based on genetic algorithms to optimize energy consumption and maximize the operational lifetime of Wireless cial for prolonging the operational lifespan of wireless sensor networks (WSNs) that include a substantial number of sensors. Simultaneously activating all sensors results in a fast depletion of energy, thus diminishing the overall lifespan of the network. To address this issue, it is necessary to schedule sensor activity in an effective manner. This task, known as the maximum coverage set scheduling (MCSS) problem, is highly complex and has been demonstrated to be NP-hard. This article presents a customized genetic algorithm designed to tackle the MCSS problem, aiming to improve the longevity of Wireless Sensor Networks (WSNs). Our methodology effectively detects and enhances combinations of coverage sets and their corresponding schedules. The program incorporates key criteria such as the detection ranges of individual sensors, their energy levels, and activity durations to optimize the overall energy efficiency and operational sustainability of the network. The performance of the suggested algorithm is assessed through simulations and compared to that of the Greedy algorithm and the Pattern search algorithm. The results indicate that our genetic algorithm not only maximizes network lifetime but also enhances the efficiency and efficacy of solving the MCSS problem. This represents a significant improvement in managing the energy consumption in WSNs.
The use of Distributed Generations in conjunction is expected to increase in the coming years. This research explores how to optimise Distributed Generations planning in distribution systems for load models for rating...
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The use of Distributed Generations in conjunction is expected to increase in the coming years. This research explores how to optimise Distributed Generations planning in distribution systems for load models for rating and placement determination using a multi-tasking genetic algorithm. The purpose of this research is to reduce rean and reactive power losses from a system perspective. From the aspect of the analysis, the coordinated control of various varieties of Distributed Generations are taken into consideration. Different forms of DGs include solar, photovoltaics, diesel engines, synchronous condensers, and doubly-fed induction generators. In this analysis, planning for improving system performance such as reducing of real power loss and reactive power loss with enhancement of voltage profile. The worth of the planned strategy is illustrated in the 37-bus distribution test network. Researchers, industrialists, scientists, and anyone working in the smart grid with DGs will find this paper quite valuable.
Pests and diseases pose significant threats to crop safety and accessibility. Deep learning integration with traditional pest management fosters sustainable agriculture, minimizes chemical pesticide use, and facilitat...
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To address the shortcomings of traditional genetic algorithm (GA) in multi-agent path planning, such as prolonged planning time, slow convergence, and solution instability, this paper proposes an Asynchronous genetic ...
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To address the shortcomings of traditional genetic algorithm (GA) in multi-agent path planning, such as prolonged planning time, slow convergence, and solution instability, this paper proposes an Asynchronous genetic algorithm (AGA) to solve multi-agent path planning problems effectively. To enhance the real-time performance and computational efficiency of Multi-Agent Systems (MAS) in path planning, the AGA incorporates an Equal-Size Clustering algorithm (ESCA) based on the K-means clustering method. The ESCA divides the primary task evenly into a series of subtasks, thereby reducing the gene length in the subsequent GA process. The algorithm then employs GA to solve each subtask sequentially. To evaluate the effectiveness of the proposed method, a simulation program was designed to perform path planning for 100 trajectories, and the results were compared with those of State-Of-The-Art (SOTA) methods. The simulation results demonstrate that, although the solutions provided by AGA are suboptimal, it exhibits significant advantages in terms of execution speed and solution stability compared to other algorithms.
In precise fuel circuit systems, the filtration of particulate impurities seriously affects the efficiency and service life of various components. For filtration process intensification of high-pressure fuel laser per...
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In precise fuel circuit systems, the filtration of particulate impurities seriously affects the efficiency and service life of various components. For filtration process intensification of high-pressure fuel laser perforated filters, the two-phase flow characteristics in filters is studied. The size, position and number of filtration holes are taken as optimization variables, and take the filtration efficiency and flow pressure drop as optimization objectives. Computational fluid dynamics (CFD) is used to simulate the two-phase motion of continuous phase and discrete particles in a periodic unit. Artificial neural networks (ANN) are utilized for objectives prediction, and the NSGA-II genetic algorithm is employed for multi-objective optimization, resulting in the Pareto front solution set. Furtherly, the reasonable solution is selected by introducing TOPSIS to ensure that two optimization indexes are relatively smaller and balanced. The optimized filter element scheme allows the filter to have a pressure drop of less than 3.2 MPa under high pressure and a filtration efficiency of over 80% for spherical particle impurities with a diameter of 5 μm or more.
The indoor thermal environment and air quality are critical components of urban living, making the energy efficiency and performance optimization of air conditioning and mechanical ventilation (ACMV) systems especiall...
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The indoor thermal environment and air quality are critical components of urban living, making the energy efficiency and performance optimization of air conditioning and mechanical ventilation (ACMV) systems especially important. Active chilled beam systems, recognized for their energy-saving potential, have garnered significant attention. However, while existing investigations have focused primarily on design and control strategies, there has been a lack of in-depth exploration into the structural optimization of heat exchangers within active chilled beams. This investigation utilized computational fluid dynamics (CFD) simulations to examine the effects of fin spacing, tube spacing, and tube shapes on both pressure drop and heat transfer efficiency in heat exchangers. Subsequently, a further analysis was conducted to evaluate how these structural parameters impact the overall cooling capacity of chilled beams. By integrating neural networks and genetic algorithms, the investigation achieved a balance between pressure drop and heat transfer efficiency, resulting in optimal structural parameters to improve the cooling performance of active chilled beams. The results demonstrated that the cooling performance of the chilled beam system with the optimized heat exchanger was significantly improved, reaching a heat transfer rate per unit projected area of 4533.9 W/m2, with a cooling performance enhancement of 30.6 %. Under temperature differentials between the heat exchanger and air ranging from 6 K to 22 K, the cooling capacity increased by 26.4-30.6 %.
The present study focuses on the numerical investigation of nano-enhanced phase change material (Ne-PCM)-based heat pipes designed for electronic cooling applications. It uses both paraffin wax and n-eicosane as phase...
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The present study focuses on the numerical investigation of nano-enhanced phase change material (Ne-PCM)-based heat pipes designed for electronic cooling applications. It uses both paraffin wax and n-eicosane as phase change materials (PCMs) that are combined with copper oxide (CuO) nanoparticles at different concentrations of 1%, 3%, 5%, and 7%. The heat input to the heat pipe ranges from 10 to 50 W in an increment of 10 W to simulate realistic operating conditions. The idea is to predict the heat pipe's thermal performance at various combinations of nanoparticles and PCMs and compare the same to the baseline case of deionized (DI) water (without PCM). The results show a constant drop in the evaporator temperature for the Ne-PCM-assisted heat pipes. Paraffin wax and n-eicosane exhibit maximum reductions of 2.86% and 1.94%, respectively, in evaporator wall temperature compared to using conventional DI water (without PCM). The thermal resistance of the heat pipe also decreases consistently with increasing the heat input for all cases, with the most significant reduction of 33.11% and 16.63% for paraffin wax-CuO- and n-eicosane-CuO-assisted heat pipes, respectively. The maximum evaporator heat transfer coefficients recorded are 257.79 W/m(2)K, 353 W/m(2)K, and 265.18 W/m(2)K for heat pipes using DI water (without PCM), paraffin wax-CuO, and n-eicosane-CuO, respectively. The nanoparticles act as a thermal conductivity enhancer and bring down the heat pipe's evaporator temperature with the addition of PCMs. Thus, the effective thermal conductivity of the Ne-PCM-based heat pipe is notably higher compared to heat pipes using DI water (without PCM). To understand the complex thermal behavior of the Ne-PCM-based heat pipes and to better predict their thermal performance, a predictive model has been developed using an artificial neural network (ANN). This model drives the genetic algorithm (GA) that considers the multivariable interaction of PCMs and nanoparticle concentrati
The advances in manufacturing technologies have enabled the production of Voronoi structures. The main advantage of these complex designs is their lightweight and enhanced mechanical properties, such as high buckling ...
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The advances in manufacturing technologies have enabled the production of Voronoi structures. The main advantage of these complex designs is their lightweight and enhanced mechanical properties, such as high buckling resistance. Some applications of Voronoi structures are in reducing the weight of automotive and aerospace parts and developing biomedical implants. Metaheuristics are being used to optimize these structures while improving their mechanical properties. Hence, in this study, a systematic review is conducted to identify the trends and gaps in the use of the genetic algorithm to optimize two and three-dimensional Voronoi structures. The results mapped seven application domains and suggest that future research should combine manufacturability and optimization constraints, such as additive manufacturing restrictions. In addition, alternative materials (e.g., ceramics and metals) could be used to create specimens for the mechanical tests, and other approaches to finite element simulation are required to speed up the optimization process. The quantitative analysis suggests that this is an emerging topic, with few researchers in local groups cooperating to develop the field.
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