Efficiently classifying sheep breeds through image analysis is pivotal in modern animal husbandry, influencing critical management and breeding decisions. This study delves into automating this process by harnessing C...
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The development of autonomous vehicles, especially in the context of self-driving cars, depends heavily on object detection and classification. In order to provide safe and effective navigation, this process entails r...
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Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emi...
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Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of *** this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,***,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train *** introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is *** improves physical interpretability of ***,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of ***,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is ***,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction *** results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.
The rapid growth of social media has made influencer marketing a vital strategy for brands to engage with their target audience. Identifying the right influencers who can effectively promote products and drive engagem...
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Software-defined vehicles (SDVs) are an emerging technology in the automotive industry, changing from software to hardware. This paper discusses the total restructuring of the automobile at different levels, including...
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The main aim of this paper is to investigate the impact of gate radius variation and dielectric material change on Gate-All-Around (GAA) MOSFETs in terms of gate controllability and performance in-depth. This study ca...
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The concept of distributed energy, where different energy sources are combined in remote locations, forms the basis of today's power systems overall energy production logic. Furthermore, advancements in power elec...
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The concept of distributed energy, where different energy sources are combined in remote locations, forms the basis of today's power systems overall energy production logic. Furthermore, advancements in power electronic infrastructures have emphasized their increased utilization within power systems. In particular, the transition from current source converters (CSC) technology to voltage source converters (VSC) technology has made it easier to integrate power grids with different characteristics into existing power systems. High voltage direct current (HVDC) transmission applications also play a significant role in this integration. In these increasingly complex power systems with various infrastructures and applications, maintaining a sustainable, secure, economical, and environmentally-friendly balance between supply and demand becomes more challenging using classical approaches. In this study, a metaheuristic algorithm is proposed for solving the power flow problems in hybrid AC/DC power systems that include VSC-based, Multi-Terminal HVDC grids. The proposed algorithm is an enhanced version of the symbiotic organisms search (SOS) algorithm and is named di-SOS (diversity improved SOS with Parazite RFDB) algorithm. To demonstrate the effectiveness of the developed algorithm, comparisons were made with SOS algorithm variants and 15 different metaheuristic algorithms found in the literature using various test functions. Nonparametric Wilcoxon signed-rank tests and Friedman tests were performed the compared algorithms and in the comparison between SOS algorithm variants, the di_1-SOS variant of the di_SOS algorithm performed the best with an algorithm score of 2.245. In the comparison with the other 15 metaheuristic algorithms, the di_1-SOS algorithm ranked first with a ranking score of 4.525, demonstrating its success in solving classical test functions. Finally, the algorithm was employed to address power flow problems concerns within hybrid AC/DC power systems, emp
A brain tumor is a disease that is caused by the abnormal growth of cells inside the brain or central spinal canal. It originates from the brain or, may spread to the brain from the other parts of the human body. Whil...
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The maternal health includes the health conditions of women in three stages that are during pregnancy, childbirth and the postpartum time. We can also say that maternal health is the health of women at the time during...
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Recycling is one of the new methods for efficient waste management. The way recycling is now done, which requires people to deliver vast amounts of rubbish to recycling centers, can be annoying and demotivating. The r...
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