Emerging non-volatile memory technologies provides a solution to break through the limitations of the vonNeumann architecture and using these technologies to develop logic-in-memory circuits. In this paper, we propose...
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Lunar domes have always been one of the important windows to understand lunar volcanic activity, however traditional identification methods for geological domes are expensive, so this study attempts to establish an au...
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To expedite the characteristic mode analysis (CMA) of electromagnetic target structures, this paper combines frequency- and material-independent reactions (FMIR) with characteristic mode analysis using the volume-surf...
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A well-designed curriculum is essential to ensure that students can maintain a healthy academic life, whether at school, college, or university. Such a curriculum would help students balance their academic work and ex...
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In this work, we propose a hybrid method to fast solve the optimization of array located on the PEC carrier inside the dielectric radome with parameters variation. By constructing a reusable low-rank reduced-order mod...
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Mobile Edge Computing (MEC) has become an indispensable way to reduce the execution delay of devices. However, for some devices located far away from the MEC server, the transmission delay of communication with MEC is...
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In this work we consider a scheme for unsourced random access (uRA) in cell free (CF) wireless networks, which is conceptually reminiscent of the 2-step RACH scheme defined in 3GPP for cellular networks. During the de...
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Skin tones come in a diverse range of shades and are often necessary for various computer vision tasks. While skin detection is a well-studied focus, skin tone classification is not. Most works also use the Fitzpatric...
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Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable gro...
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Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.
Quasigroups have various applications in mathematics, computerscience, and cryptography. In coding theory and cryptography they have been used in error-correcting codes, error-detection codes, to construct key exchan...
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