Millimeter-wave amplifiers are the crucial component in 5G wireless communication systems, operating at frequencies between 24 GHz and 100 GHz. They are used to amplify weak signals, compensating forpropagation losses...
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Aiming at the recognition and classification problem of micro assembly parts in industrial production, the Yolov8 object detection algorithm was used as the base model to improve the C2f class in the backbone. And app...
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The existing attribute reductions are carried out using equivalence relations under a complete information system,and there is less research on attribute reductions of incomplete information systems with new theoretic...
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The existing attribute reductions are carried out using equivalence relations under a complete information system,and there is less research on attribute reductions of incomplete information systems with new theoretical models such as multi-granularity decision rough *** address the above shortcomings,this paper first makes up a pessimistic-optimistic multi-granularity decision rough set model based on tolerance relations in incomplete information *** concepts of attribute importance and approximate distribution quality are introduced into the model to form an attribute reduction algorithm under incomplete information ***,due to the NPhard problem of attribute reduction,in order to further ensure the accuracy of the reduction result,this paper proposes a pessimistic-optimistic multi-granularity reduction algorithm under quantum particle swarm *** results on multipleattribute data proved that the algorithm proposed in this paper can effectively attribute reduction in the decision table with missing *** the same time,the algorithm of this paper has the role of iterative optimization search,ensuring the accuracy of the reduction results and increasing the applicability of multi-granularity decision rough sets.
To address the low-voltage, high-current requirements in hydrogen production applications, a virtual 48-pulse three-phase rectifier is proposed, and achieves the equivalent performance of four parallel 12-pulse rectif...
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This paper presents the design and simulation of a plasmonic refractive index (RI) sensor and a temperature sensor. The suggested design comprises a MIM waveguide integrated with a grill-shaped rectangular cavity. Sim...
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The development of high-performance optically transparent radio frequency(RF)radiators is limited by the intrinsic loss issue of transparent conductive films(TCFs).Instead of pursuing expensive endeavors to improve th...
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The development of high-performance optically transparent radio frequency(RF)radiators is limited by the intrinsic loss issue of transparent conductive films(TCFs).Instead of pursuing expensive endeavors to improve the TCFs'electrical properties,this study introduces an innovative approach that leverages leaky-wave mode manipulation to mitigate the TCFs'attenuating effect and maximize the RF *** finding reveals that the precise control of the mode confinement on glass-coated TCFs can create a low-attenuation window for leaky-wave propagation,where the total attenuation caused by TCF dissipation and wave leakage is effectively *** observed low-attenuation leaky-wave state on lossy TCFs originates from the delicate balance between wave leakage and TCF dissipation,attained at a particular glass cladding *** leveraging the substantially extended radiation aperture achieved under suppressed wave attenuation,this study develops an optically transparent antenna with an enhanced endfire realized gain exceeding 15 dBi and a radiation efficiency of 66%,which is validated to offer competitive transmission performance for advancing ubiquitous wireless communication and sensing applications.
In this work, we introduce an innovative approach to efficiently reduce the model size for transformer architectures. Our methodology includes low rank adaptation and knowledge distillation, leveraging a ViT-Base mode...
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The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries an...
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The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other ***,it is important to construct a digital twin ***,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted *** this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)***,we fuse the spatial-temporal graph based on the interrelationship of spatial ***,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding ***,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)*** module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like *** dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted *** on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.
Regularized system identification has become the research frontier of system identification in the past *** related core subject is to study the convergence properties of various hyper-parameter estimators as the samp...
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Regularized system identification has become the research frontier of system identification in the past *** related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to *** this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been ***,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is *** general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance ***,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter *** this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting ***,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.
Load scheduling plays a vital role in the home energy management systems. The main objective of this load scheduling is to balance the power demand and supply power without degrading the performance of the loads and c...
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