With the expansion of the scale of renewable energy units connected to the power system, the problems of volatility and instability brought about by them are becoming more and more prominent. Compressed air energy sto...
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Background and AimsSoil salinization is a major cause of land degradation and ecological damage. Traditional soil salinity monitoring techniques are limited in coverage and scalability, while remote sensing offers bro...
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Background and AimsSoil salinization is a major cause of land degradation and ecological damage. Traditional soil salinity monitoring techniques are limited in coverage and scalability, while remote sensing offers broader applicability and efficiency. This study addresses spatiotemporal variations in soil salt content (SSC) inversion across crop types in Tongliao City, Inner Mongolia, China, using an innovative integration of multi-temporal data and crop cover types, improving remote sensing monitoring *** sampling data and Sentinel-2 images from June to September in 2021 and 2022 were utilized. The deep learning U-net model classified key crops, including sunflower (33%), beet (12%), and maize (55%), and analyzed the effects of crop coverage on SSC across multiple time series. Six spectral variables were selected using the SVR-RFE model (R2 = 0.994, MAE = 0.016). SSC prediction models were developed using three machine learning methods (DBO-RF, PSO-SVM, BO-BP) and a deep learning method (Transformer).ResultsConsidering crop coverage variations improved the sensitivity of spectral variables to SSC response, enhancing predictive accuracy and model stability. Crop classification showed that the salinity index (SIs) correlated more strongly with SSC than the vegetation index (VIs), with SI6 having the highest correlation coefficient of 0.50. The Transformer model, using multi-time series data, outperformed other algorithms, achieving an average R2 of 0.71. The SSC inversion map from the Transformer model closely matched field survey *** research provides a novel approach to soil salinity prediction using satellite remote sensing, offering a scalable solution for monitoring salinization and valuable insights for environmental management and agricultural planning.
This paper considers an N-pursuer-M-evader scenario involving L virtual targets. The virtual targets serve as an intermediary target for the pursuers, allowing the pursuers to delay their final assignment to the evade...
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This paper considers an N-pursuer-M-evader scenario involving L virtual targets. The virtual targets serve as an intermediary target for the pursuers, allowing the pursuers to delay their final assignment to the evaders. However, upon reaching the virtual target, the pursuers must decide which evader to capture. It is assumed that there are more pursuers than evaders and that the pursuers are faster than the evaders. The objective is two-part: first, assign each pursuer to a virtual target and evader such that the pursuer team's energy is minimized, and, second, choose the virtual targets' locations for this minimization problem. The approach taken is to consider the Apollonius geometry between each pursuer's virtual target location and each evader. Using the constructed Apollonius circles, the pursuer's travel distance and maneuver at a virtual target are obtained. These metrics serve as a gauge for the total energy required to capture a particular evader and are used to solve the joint virtual target selection and pursuer-evader assignment problem. This paper provides a mathematical definition of this problem, the solution approach taken, and an example. Following the example, a Monte Carlo analysis is performed, demonstrating the efficacy of the algorithm and its suitability for real-time applications.
High power density and efficiency are key factors for automotive traction machines. One possible way to reach these goals is to increase the slot filling factor. As yet, most research has either been focused on optimi...
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ISBN:
(纸本)9781509029099
High power density and efficiency are key factors for automotive traction machines. One possible way to reach these goals is to increase the slot filling factor. As yet, most research has either been focused on optimizing the slot geometry with a given magnet wire diameter or on finding the optimal diameter for a given geometry. Oftentimes the submitted results lead either to a magnetically suboptimal stator geometry or the suggested winding pattern and geometries are not producible. The introduction of the needle winding technology, as an alternative to the insertion technology for the manufacturing of stators of automotive traction machines, enabled a defined wire placement in the slot. To use the full benefit of this advantage an optimal and producible winding layout is necessary. Therefore, in this article new optimization algorithms are proposed and compared to algorithms found in literature with regard to reachable slot filling factors and producibility. In a case study, the best performing algorithm was used to obtain an optimal combination of wire diameter and slot geometry to maximize the filling factor. With the proposed algorithm feasible winding patterns and slot geometries with an optimized filling factor can be obtained.
Small parasitic Hemipteran insects known as bedbugs(Cimicidae)feed on warm-blooded mammal’s *** most famous member of this family is the Cimex lectularius or common *** current paper proposes a novel swarm intelligen...
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Small parasitic Hemipteran insects known as bedbugs(Cimicidae)feed on warm-blooded mammal’s *** most famous member of this family is the Cimex lectularius or common *** current paper proposes a novel swarm intelligence optimization algorithm called the Bedbug Meta-Heuristic algorithm(BMHA).The primary inspiration for the bedbug algorithm comes from the static and dynamic swarming behaviors of bedbugs in *** two main stages of optimization algorithms,exploration,and exploitation,are designed by modeling bedbug social interaction to search for *** proposed algorithm is benchmarked qualitatively and quantitatively using many test functions including *** results of evaluating BMHA prove that this algorithm can improve the initial random population for a given optimization problem to converge towards global optimization and provide highly competitive results compared to other well-known optimization *** results also prove the new algorithm's performance in solving real optimization problems in unknown search *** achieve this,the proposed algorithm has been used to select the features of fake news in a semi-supervised manner,the results of which show the good performance of the proposed algorithm in solving problems.
With the massive demand for spectrum resources due to the massive increase of wireless devices, it was necessary to manage the scarcity of radio spectrum resources. Cognitive Radio is a technology for efficiently usin...
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ISBN:
(纸本)9781665426329
With the massive demand for spectrum resources due to the massive increase of wireless devices, it was necessary to manage the scarcity of radio spectrum resources. Cognitive Radio is a technology for efficiently using the available spectrum resources in a wireless communication system. However, with the help of using various optimization algorithms, Cognitive Radio can manage and utilize the spectrum of resources more efficiently. This paper gives an overview of the state-of-art research that utilizes many optimization algorithms for different purposes such as sensing, allocating, sharing, and mobilizing the spectrum for better utilization and improving the throughput, convergence speed, delay, and minimization the interference. The main algorithms enclosed in this paper are Genetic algorithm, Particle Swarm optimization, Ant Colony optimization, and Artificial Bee Colony optimization algorithm.
Thanks to the presence of artificial intelligence methods, the diagnosis of patients can be done quickly and accurately. This article introduces a new diagnostic system (DS) that includes three main layers called the ...
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Thanks to the presence of artificial intelligence methods, the diagnosis of patients can be done quickly and accurately. This article introduces a new diagnostic system (DS) that includes three main layers called the rejection layer (RL), selection layer (SL), and diagnostic layer (DL) to accurately diagnose cases suffering from various diseases. In RL, outliers can be removed using the genetic algorithm (GA). At the same time, the best features can be selected by using a new feature selection method called the hybrid feature selection approach (HFSA) in SL. In the next step, the filtered data is passed to the naive Bayes (NB) classifier in DL to give accurate diagnoses. In this work, the main contribution is represented in introducing HFSA as a new selection approach that is composed of two main stages;fast stage (FS) and accurate stage (AS). In FS, chi-square, as a filtering methodology, is applied to quickly select the best features while Hybrid optimization algorithm (HOA), as a wrapper methodology, is applied in AS to accurately select features. It is concluded that HFSA is better than other selection methods based on experimental results because HFSA can enable three different classifiers called NB, K-nearest neighbors (KNN), and artificial neural network (ANN) to provide the maximum accuracy, precision, and recall values and the minimum error value. Additionally, experimental results proved that DS, including GA as an outlier rejection method, HFSA as feature selection, and NB as diagnostic mode, outperformed other diagnosis models.
Feature spaces optimization plays a very important role in object recognition and categorization. After analyzing of several fashionable local features at present, some optimization algorithms based on the information...
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ISBN:
(纸本)9783037851036
Feature spaces optimization plays a very important role in object recognition and categorization. After analyzing of several fashionable local features at present, some optimization algorithms based on the information theory are proposed. In this paper, we describe the approaches to recognize generic objects using these features which have been optimized. As baselines for comparison, we also implemented some additional recognition systems using other optimization algorithms. The performance analysis on the obtained experimental results demonstrates that the proposed optimization algorithms are effective and efficient.
To address the challenge of the "curse of dimensionality" in aerodynamic design optimization of compressors, this study introduces an innovative optimization technique suitable for compressor airfoil design....
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To address the challenge of the "curse of dimensionality" in aerodynamic design optimization of compressors, this study introduces an innovative optimization technique suitable for compressor airfoil design. This technique, rooted in a hybrid mechanism-data-driven approach, seamlessly integrates a hierarchical parameterization method, based on elliptic topological deformation, into a multitasking evolutionary algorithm framework. This integration deviates from the conventional approach of treating parameterization methods and optimization algorithms as distinct elements. The proposed method positions airfoil parameterization as its core, constructing two tasks within the optimization algorithm. It leverages the critical influence of the parameterization method on the aerodynamic performance landscape of the airfoils and the intrinsic qualities of the hierarchical parameterization method in the design space. The multitasking evolutionary optimization framework facilitates effective information exchange between tasks, significantly boosting optimization efficiency. In comparison to standard data-driven multitasking evolutionary algorithms, the proposed method achieves superior optimized solutions with merely 11 x D aerodynamic performance evaluations, where D denotes the number of design variables.
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