Quantum power system state estimation(QPSSE)offers an inspiring direction for tackling the challenge of state estimation through quantum ***,the current bottlenecks originate from the scarcity of practical and scalabl...
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Quantum power system state estimation(QPSSE)offers an inspiring direction for tackling the challenge of state estimation through quantum ***,the current bottlenecks originate from the scarcity of practical and scalable QPSSE methodologies in the noisy intermediate-scale quantum(NISQ)*** paper devises a NISQ−QPSSE algorithm that facilitates state estimation on real NISQ *** new contributions include:(1)A variational quantum circuit(VQC)-based QPSSE formulation that empowers QPSSE analysis utilizing shallow-depth quantum circuits;(2)A variational quantum linear solver(VQLS)-based QPSSE solver integrating QPSSE iterations with VQC optimization;(3)An advanced NISQ-compatible QPSSE methodology for tackling the measurement and coefficient matrix issues on real quantum computers;(4)A noise-resilient method to alleviate the detrimental effects of noise *** encouraging test results on the simulator and real-scale systems affirm the precision,universal-ity,and scalability of our QPSSE algorithm and demonstrate the vast potential of QPSSE in the thriving NISQ era.
Estimating the crowd count and density of highly dense scenes witnessed in Muslim gatherings at religious sites in Makkah and Madinah is critical for developing control strategies and organizing such a large ***,since...
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Estimating the crowd count and density of highly dense scenes witnessed in Muslim gatherings at religious sites in Makkah and Madinah is critical for developing control strategies and organizing such a large ***,since the crowd images in this case can range from low density to high density,detection-based approaches are hard to apply for crowd ***,deep learning-based regression has become the prominent approach for crowd counting problems,where a density-map is estimated,and its integral is further computed to acquire the final count *** this paper,we put forward a novel multi-scale network(named 2U-Net)for crowd counting in sparse and dense *** proposed framework,which employs the U-Net architecture,is straightforward to implement,computationally efficient,and has single-step *** layers are used to retrieve the pooling layers’erased information and learn hierarchically pixelwise spatial *** helps in obtaining feature values,retaining spatial locations,and maximizing data integrity to avoid data *** addition,a modified attention unit is introduced and integrated into the proposed 2UNet model to focus on specific crowd *** proposed model concentrates on balancing the number of model parameters,model size,computational cost,and counting accuracy compared with other works,which may involve acquiring one criterion at the expense of other *** on five challenging datasets for density estimation and crowd counting have shown that the proposed model is very effective and outperforms comparable mainstream ***,it counts very well in both sparse and congested crowd *** 2U-Net model has the lowest MAE in both parts(Part A and Part B)of the ShanghaiTech,UCSD,and Mall benchmarks,with 63.3,7.4,1.5,and 1.6,***,it obtains the lowest MSE in the ShanghaiTech-Part B,UCSD,and Mall benchmarks with 12.0,1.9,and 2.1,respectively.
Artificial Intelligence, including machine learning and deep convolutional neural networks (DCNNs), relies on complex algorithms and neural networks to process and analyze data. DCNNs for visual recognition often requ...
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The transition to sustainable energy systems has highlighted the critical need for efficient sizing of renewable energy resources in microgrids. In particular, designing photovoltaic (PV) and battery systems to meet r...
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Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of *** biomedical corpus contains numerous complex long sentences and overlapping relational trip...
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Extracting valuable information frombiomedical texts is one of the current research hotspots of concern to a wide range of *** biomedical corpus contains numerous complex long sentences and overlapping relational triples,making most generalized domain joint modeling methods difficult to apply effectively in this *** a complex semantic environment in biomedical texts,in this paper,we propose a novel perspective to perform joint entity and relation extraction;existing studies divide the relation triples into several steps or ***,the three elements in the relation triples are interdependent and inseparable,so we regard joint extraction as a tripartite classification *** the same time,fromthe perspective of triple classification,we design amulti-granularity 2D convolution to refine the word pair table and better utilize the dependencies between biomedical word ***,we use a biaffine predictor to assist in predicting the labels of word pairs for relation *** model(MCTPL)Multi-granularity Convolutional Tokens Pairs of Labeling better utilizes the elements of triples and improves the ability to extract overlapping triples compared to previous ***,we evaluated our model on two publicly accessible *** experimental results show that our model’s ability to extract relation triples on the CPI dataset improves the F1 score by 2.34%compared to the current optimal *** the DDI dataset,the F1 value improves the F1 value by 1.68%compared to the current optimal *** model achieved state-of-the-art performance compared to other baseline models in biomedical text entity relation extraction.
Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution *...
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Promoting the high penetration of renewable energies like photovoltaic(PV)systems has become an urgent issue for expanding modern power grids and has accomplished several challenges compared to existing distribution *** study measures the effectiveness of the Puma optimizer(PO)algorithm in parameter estimation of PSC(perovskite solar cells)dynamic models with hysteresis consideration considering the electric field effects on *** models used in this study will incorporate hysteresis effects to capture the time-dependent behavior of PSCs *** PO optimizes the proposed modified triple diode model(TDM)with a variable voltage capacitor and resistances(VVCARs)considering the hysteresis *** suggested PO algorithm contrasts with other wellknown optimizers from the literature to demonstrate its *** results emphasize that the PO realizes a lower RMSE(Root mean square errors),which proves its capability and efficacy in parameter extraction for the *** statistical results emphasize the efficiency and supremacy of the proposed PO compared to the other well-known competing *** convergence rates show good,fast,and stable convergence rates with lower RMSE via PO compared to the other five competitive ***,the lowermean realized via the PO optimizer is illustrated by the box plot for all optimizers.
Road safety is a critical concern worldwide, with millions of lives lost and countless injuries sustained in traffic accidents annually. To address this pressing issue, a costeffective and reliable solution is propose...
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Current and future trends in seismic acquisition point towards higher geophone densities (forecasted to be 1M nodes per survey). The geophones’ high operating precision and a sampling rate of a few milliseconds leads...
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As the self-driving technology is getting mature for public transportation applications, the safety concern of onboard passengers has become an important issue. It is essential to identify inappropriate or hazardous b...
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Graphics processing units (GPUs) have been increasingly used to solve a range of compute-intensive and data-parallel scientific computing problems that can be perfectly parallelized for performance speedups. Particula...
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