The first step in the design phase of the Brushless Direct Current(BLDC)motor is the formulation of the mathematical framework and is often used due to its analytical ***,the BLDC motor design problem is considered to...
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The first step in the design phase of the Brushless Direct Current(BLDC)motor is the formulation of the mathematical framework and is often used due to its analytical ***,the BLDC motor design problem is considered to be an optimization *** this paper,the analytical model of the BLDC motor is presented,and it is considered to be a basis for emphasizing the optimization *** analytical model used for the experimentation has 78 non-linear equations,two objective functions,five design variables,and six non-linear constraints,so the BLDC motor design problem is considered as highly non-linear in electromagnetic ***-objective optimization becomes the forefront of the current research to obtain the global best solution using metaheuristic *** bio-inspired multi-objective grey wolf optimizer(MOGWO)is presented in this paper,and it is formulated based on Pareto optimality,dominance,and archiving *** performance of theMOGWO is verified on standard multi-objective unconstraint benchmark functions and applied to the BLDC motor design *** results proved that the proposedMOGWO algorithm could handle nonlinear constraints in electromagnetic optimization *** performance comparison in terms of Generational Distance,inversion GD,Hypervolume-matrix,scattered-matrix,and coverage metrics proves that the MOGWO algorithm can provide the best solution compared to other selected *** source code of this paper is backed up with extra online support at https://***/mysite and https://***/matlabcentral/fileexchange/75259-multiobjective-non-sorted-grey-wolf-mogwo-nsgwo.
The operating cost of multi-stack fuel cell (FC) hybrid electric vehicles (HEVs) is notably affected by the energy management strategy (EMS). For this purpose, this study proposes a predictive hierarchical (PH)-EMS to...
The operating cost of multi-stack fuel cell (FC) hybrid electric vehicles (HEVs) is notably affected by the energy management strategy (EMS). For this purpose, this study proposes a predictive hierarchical (PH)-EMS to decrease the multi-stack FC-HEV operating cost. The PH-EMS consists of two levels. The first level is a Sugeno-type fuzzy logic (FL)-EMS that determines the number of active FCs for participation in an optimization level based on the vehicle's future velocity. The second level is the model predictive control (MPC) approach, which distributes the optimal power among FCs and the battery based on the number of active FCs and predicted velocity in the prediction horizon. To evaluate the effectiveness of the proposed EMS, the results are compared to a rule-based (RB) EMS. The results indicate that the total operating cost of the PH-EMS is 55.504% lower compared to RB-EMS.
In recent years, data-driven design approaches based on generative machine learning (ML) have been applied in the field of metasurface design. Even though generative machine learning has demonstrated remarkable capabi...
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ISBN:
(数字)9798350379051
ISBN:
(纸本)9798350379068
In recent years, data-driven design approaches based on generative machine learning (ML) have been applied in the field of metasurface design. Even though generative machine learning has demonstrated remarkable capabilities in metasurface synthesis, training procedures to obtain an appropriate model remain a challenge. In this work, we present the deployment of conditional generative adversarial networks (cGAN) to design reflective standard unit cells for the use of 5G millimeter wave (mm Wave) passive metasurface reflectors. A total of 48 different configurations of cGAN were trained over a range of hyperparameters selected under proper guidelines used in the literature. The design performance of each cGAN configuration is measured by a mean absolute error (MAE) between a simulated reflection phase of a generated unit cell and a target reflection phase. The test results show that cGAN s trained by updating the discriminator one step for each single generator update tend to offer lower MAEs compared to cGANs trained by updating the discriminator three iterations for one generator update. The optimal cGAN among 48 hyperparameter configurations can generate unit cells with reflection phase resembling the desired target one.
The theory of Ambiguous-Intuitionistic-Fuzzy-Sets (IFS) termed as AIFS is a better way to deal with uncertain/vagueness information as compared to IFS. Considering the novelty of AIFS, this paper presents one of the a...
The theory of Ambiguous-Intuitionistic-Fuzzy-Sets (IFS) termed as AIFS is a better way to deal with uncertain/vagueness information as compared to IFS. Considering the novelty of AIFS, this paper presents one of the applications of AIFS to the Renewable-Energy (RE) Systems (RESs). This is achieved by taking the foundation development of Ambiguous-Intuitionistic-Fuzzy-Hybrid-Averaging-Operator referred by the AFHA operator. The incorporation of the AFHA operator into RESs is a viable approach to tackle complex difficulties. This research examines the practical uses, theoretical basis, and operational consequences of implementing AFHA operator in renewable energy systems. It also demonstrates the effectiveness of AFHA operator in addressing various operational situations in RESs. A comparative study is also included in this study to show and observe the validity of the obtained results.
Rapid advancements are being made in autonomous systems for three-dimensional (3D) object identification, which is essential for sensory components. This review paper analyzes cutting-edge 3D object recognition techni...
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Rapid advancements are being made in autonomous systems for three-dimensional (3D) object identification, which is essential for sensory components. This review paper analyzes cutting-edge 3D object recognition techniques, specifically investigating the integration of Lidar and camera sensors. It also contrasts these techniques with more affordable alternatives, such as utilizing only a camera or combining a camera with Radar. The text emphasizes the limitations of existing techniques, which include significant expenses and practical difficulties, such as the need for real-time data processing and the integration of multiple sensors. The analysis highlights the necessity of implementing creative approaches to tackle these challenges and suggests areas of research to improve the precision of sensors, optimize the integration of data, and develop cost-effective technologies. With this thorough evaluation, our goal is to provide useful insights into the intricacies of 3D object identification, hence promoting future progress in the autonomy of intelligent systems. This work contributes to the continuing discussion on improving the capabilities of autonomous systems by addressing current constraints and investigating potential future opportunities.
Vehicular Internet of Things (V-IoT) applications have seen a rise in recent times, increasing the need for robust machine learning (ML) models trained on diverse and high-quality datasets. Curating high-quality data ...
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An innovative system for safe medical image segmentation in multi-center collaborations, the Multi-Center Privacy-Preserving Network is introduced in this research. Reduce data transmission traffic while improving dat...
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ISBN:
(数字)9798350388916
ISBN:
(纸本)9798350388923
An innovative system for safe medical image segmentation in multi-center collaborations, the Multi-Center Privacy-Preserving Network is introduced in this research. Reduce data transmission traffic while improving data privacy protection with our methodology’s novel method for multi-center collaborative learning. Our solution relies on a single encrypted data transfer, as opposed to federated learning’s round-by-round model data communication among the central server as well as local servers. The suggested CNN is a three-layer architecture that includes networks for encryption, segmentation, and decryption. Consequently, we provide a method for secure and adaptable cloud-based medical picture segmentation using a convolutional neural network. This research demonstrates that three cloud-based servers can train the algorithm using user-provided photos; nevertheless, they are unable to access comprehensive data on the model’s variables and user input. The experiment verifies that the technique, when put into effect, can guarantee the system’s security and efficiency while simultaneously reducing the user’s compute and storage loads. We use an encryption network to transform the raw picture data into ciphertext, and then we present a refined U-Net to separate the encrypted image data. The last step is to use a decryption network to get the segmentation mask. Computable image encryption is made possible by this design, which allows for ciphertext-based picture segmentation. We test our method on three datasets, one of which is a CTPA dataset, and two of which are cardiac MRI datasets. We show that MP-Net may safely use data from many sources to build a more accurate and comprehensive segmentation model.
Drug discovery and development is a time-consuming and cost-intensive process. computer-aided drug design can speed up the timeline and reduce costs by decreasing the number of necessary biochemical experiments. The n...
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Drug discovery and development is a time-consuming and cost-intensive process. computer-aided drug design can speed up the timeline and reduce costs by decreasing the number of necessary biochemical experiments. The number of studies using quantum computing to solve problems in drug development has been increasing in recent years. In this review, we briefly introduce the main steps in drug discovery and development and how computers help to find potential drug candidates. Recent studies of quantum computing in drug development based on the structure of target proteins are listed chronologically. They include protein structure prediction, molecular docking, quantum simulation, and quantitative structure-activity relationship (QSAR) models. Current quantum devices are still susceptible to noise and error but are well suited for hybrid quantum-classical algorithms. The quantum advantage is demonstrated on hybrid systems and quantum-inspired devices such as quantum annealers. We hope to see more applications of quantum computing in the field of drug discovery and development.
Finite control set model predictive control (FCS-MPC) is one of the widely used control techniques for multilevel inverters (MLI) where multiple control objectives can be included in the cost function to be minimized ...
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ISBN:
(数字)9798350318265
ISBN:
(纸本)9798350318272
Finite control set model predictive control (FCS-MPC) is one of the widely used control techniques for multilevel inverters (MLI) where multiple control objectives can be included in the cost function to be minimized at each time step. The priorities of the control objectives are determined by weighting factors that can either be configured with constant values that are optimal across a spectrum of operating conditions or dynamically adjusted based on the variations in the operating conditions through an auto-tuning mechanism. This paper proposes a reinforcement learning (RL) based algorithm to auto-tune weighting factors in FCS-MPC for multilevel inverters (MLIs) under varying operating conditions. The designed auto-tuning agent is trained and tested on single-phase grid-connected 9-level crossover switches cell (CSC9) MLI. Simulation results for the CSC9 inverter are curried out to demonstrate the efficacy of the proposed design in decreasing the total harmonic distortion (THD) of the generated current and minimizing capacitor voltage error. Comparison between the proposed solution and the cases with constant weighting factors and alternative auto-tuning methods is provided.
Millimeter wave (mmWave) wireless technology is primarily considered for low latency communication in fifth-generation mobile technology (5G) and has the potential to revolutionize industrial automation and manufactur...
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