Throughout this paper,let(Ω,ι,μ)be a probability space,D the collection of allleft-continuous distribution functions,and D^+={F(0)=0|F∈D},and L(Ω)the collec-tion of all random variables which is a.s.finite on Ω,...Throughout this paper,let(Ω,ι,μ)be a probability space,D the collection of allleft-continuous distribution functions,and D^+={F(0)=0|F∈D},and L(Ω)the collec-tion of all random variables which is a.s.finite on Ω,and L^+={ξ≥0 a.s.|ξ∈L(Ω)}.For random metric (normed) spaces,see [1]or[2].Theorem 1 Let(M,d)be a complete metric space f:M→M,a contract mappingwith contract coefficient α∈[0,1),L(Ω,m)the collection of all M-valued random vari-展开更多
Motivated by the effort to understand the mathematical structure underlying the Teukolsky equations in a Kerr metric background, a homogeneous integral equation related to the prolate spheroidal function is studied. F...Motivated by the effort to understand the mathematical structure underlying the Teukolsky equations in a Kerr metric background, a homogeneous integral equation related to the prolate spheroidal function is studied. From the consideration of the Fredholm determinant of the integral equation, a family of generalized error function is defined, with which the Fredholm determinant of the sinc kernel is also evaluated. An analytic solution of a special ease of the fifth Painlev~ transcendent is then worked out explicitly.展开更多
The Quadric Error Metrics(QEM)algorithm is a widely used method for mesh simplification;however,it often struggles to preserve high-frequency geometric details,leading to the loss of salient features.To address this l...The Quadric Error Metrics(QEM)algorithm is a widely used method for mesh simplification;however,it often struggles to preserve high-frequency geometric details,leading to the loss of salient features.To address this limitation,we propose the Salient Feature Sampling Points-based QEM(SFSP-QEM)—also referred to as the Deep Learning-Based Salient Feature-Preserving Algorithm for Mesh Simplification—which incorporates a Salient Feature-Preserving Point Sampler(SFSP).This module leverages deep learning techniques to prioritize the preservation of key geometric features during simplification.Experimental results demonstrate that SFSP-QEM significantly outperforms traditional QEM in preserving geometric details.Specifically,for general models from the Stanford 3D Scanning Repository,which represent typical mesh structures used in mesh simplification benchmarks,the Hausdorff distance of simplified models using SFSP-QEM is reduced by an average of 46.58% compared to those simplified using traditional QEM.In customized models such as the Zigong Lantern used in cultural heritage preservation,SFSP-QEM achieves an average reduction of 28.99% in Hausdorff distance.Moreover,the running time of this method is only 6%longer than that of traditional QEM while significantly improving the preservation of geometric details.These results demonstrate that SFSP-QEMis particularly effective for applications requiring high-fidelity simplification while retaining critical features.展开更多
Modern computer graphics applications usually require high resolution object models for realistic rendering. However, it is expensive and difficult to deform such models in real time. In order to reduce the computatio...Modern computer graphics applications usually require high resolution object models for realistic rendering. However, it is expensive and difficult to deform such models in real time. In order to reduce the computational cost during deformations, a dense model is often manipulated through a simplified structure, called cage, which envelops the model. However, cages are usually built interactively by users, which is tedious and time-consuming. In this paper, we introduce a novel method that can build cages automatically for both 2D polygons and 3D triangular meshes. The method consists of two steps： 1） simplifying the input model with quadric error metrics and quadratic programming to build a coarse cage; 2） removing the self-intersections of the coarse cage with Delaunay partitions. With this new method, a user can build a cage to envelop an input model either entirely or partially with the approximate vertex number the user specifies. Experimental results show that, compared to other cage building methods with the same number of vertex, cages built by our method are more similar to the input models. Thus, the dense models can be manipulated with higher accuracy through our cages.展开更多
multi-resolution TIN model is an important issue in the contexts of visu-alization,virtual reality(VR),and geographic information systems(GIS).This paper proposes a new method for constructing multi-resolution TIN mod...multi-resolution TIN model is an important issue in the contexts of visu-alization,virtual reality(VR),and geographic information systems(GIS).This paper proposes a new method for constructing multi-resolution TIN models with multi-scale topographic features preservation.The proposed method is driven by a half-edge collapse operation in a greedy framework and employs a new quadric error metric to efficiently measure geometric errors.We define topographic features in a multi-scale manner using a center-surround operator on Gaussian-weighted mean curvatures.Experimental results demonstrate that the proposed method performs better than previous methods in terms of topographic features preservation,and is able to achieve multi-resolution TIN models with a higher accuracy.展开更多
In the last two decades,renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic,industrial,and agriculture sectors.Solar forecasting plays a vital role i...In the last two decades,renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic,industrial,and agriculture sectors.Solar forecasting plays a vital role in smooth operation,scheduling,and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants.Numerous models and techniques have been developed in short,mid and long-term solar forecasting.This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature,by mainly focusing on investigating the influence of meteorological variables,time horizon,climatic zone,pre-processing techniques,air pollution,and sample size on the complexity and accuracy of the model.To make the paper reader-friendly,it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication,time resolution,input parameters,forecasted parameters,error metrics,and performance.The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities.Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data.Besides,it also discusses the diverse key constituents that affect the accuracy of a model.It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.展开更多
As one of the most promising paradigms of integrated circuit design,the approximate circuit has aroused widespread concern in the scientific community.It takes advantage of the inherent error tolerance of some applica...As one of the most promising paradigms of integrated circuit design,the approximate circuit has aroused widespread concern in the scientific community.It takes advantage of the inherent error tolerance of some applications and relaxes the accuracy for reductions in area and power consumption.This paper aims to provide a comprehensive survey of reliability issues related to approximate circuits,which covers three concerns:error characteristic analysis,reliability and test,and reliable design involving approximate circuits.The error characteristic analysis is used to compare the outputs of the approximate circuit with those of its precise counterpart,which can help to find the most appropriate approximate design for a specific application in the large design space.With the approximate design getting close to physical realization,manufacturing defects and operational faults are inevitable;therefore,the reliability prediction and vulnerability test become increasingly important.However,the research on approximate circuit reliability and test is insufficient and needs more attention.Furtherly,although there is some existing work combining the approximate design with fault tolerant techniques,the reliability-enhancement approaches for approximate circuits are lacking.展开更多
文摘Throughout this paper,let(Ω,ι,μ)be a probability space,D the collection of allleft-continuous distribution functions,and D^+={F(0)=0|F∈D},and L(Ω)the collec-tion of all random variables which is a.s.finite on Ω,and L^+={ξ≥0 a.s.|ξ∈L(Ω)}.For random metric (normed) spaces,see [1]or[2].Theorem 1 Let(M,d)be a complete metric space f:M→M,a contract mappingwith contract coefficient α∈[0,1),L(Ω,m)the collection of all M-valued random vari-
基金Supported by the National Natural Science Foundation of China under Grant Nos 11171329,11203003 and 11373013
文摘Motivated by the effort to understand the mathematical structure underlying the Teukolsky equations in a Kerr metric background, a homogeneous integral equation related to the prolate spheroidal function is studied. From the consideration of the Fredholm determinant of the integral equation, a family of generalized error function is defined, with which the Fredholm determinant of the sinc kernel is also evaluated. An analytic solution of a special ease of the fifth Painlev~ transcendent is then worked out explicitly.
基金Our research was funded by the Sichuan Key Provincial Research Base of Intelligent Tourism(No.ZHZJ23-02)supported by the Scientific Research and Innovation Team Program of Sichuan University of Science and Engineering(No.SUSE652A006)+1 种基金Additional support was provided by the National Cultural and Tourism Science and Technology Innovation Research andDevelopment Project(No.202417)the Lantern Culture and Crafts Innovation Key Laboratory Project of the Sichuan ProvincialDepartment of Culture and Tourism(No.SCWLCD-A02).
文摘The Quadric Error Metrics(QEM)algorithm is a widely used method for mesh simplification;however,it often struggles to preserve high-frequency geometric details,leading to the loss of salient features.To address this limitation,we propose the Salient Feature Sampling Points-based QEM(SFSP-QEM)—also referred to as the Deep Learning-Based Salient Feature-Preserving Algorithm for Mesh Simplification—which incorporates a Salient Feature-Preserving Point Sampler(SFSP).This module leverages deep learning techniques to prioritize the preservation of key geometric features during simplification.Experimental results demonstrate that SFSP-QEM significantly outperforms traditional QEM in preserving geometric details.Specifically,for general models from the Stanford 3D Scanning Repository,which represent typical mesh structures used in mesh simplification benchmarks,the Hausdorff distance of simplified models using SFSP-QEM is reduced by an average of 46.58% compared to those simplified using traditional QEM.In customized models such as the Zigong Lantern used in cultural heritage preservation,SFSP-QEM achieves an average reduction of 28.99% in Hausdorff distance.Moreover,the running time of this method is only 6%longer than that of traditional QEM while significantly improving the preservation of geometric details.These results demonstrate that SFSP-QEMis particularly effective for applications requiring high-fidelity simplification while retaining critical features.
基金Supported by the NSFC-Guangdong Joint Fund under Grant Nos. U0735001,U0835004,U0935004the National Basic Research 973 Program of China under Grant No. 2011CB302204
文摘Modern computer graphics applications usually require high resolution object models for realistic rendering. However, it is expensive and difficult to deform such models in real time. In order to reduce the computational cost during deformations, a dense model is often manipulated through a simplified structure, called cage, which envelops the model. However, cages are usually built interactively by users, which is tedious and time-consuming. In this paper, we introduce a novel method that can build cages automatically for both 2D polygons and 3D triangular meshes. The method consists of two steps： 1） simplifying the input model with quadric error metrics and quadratic programming to build a coarse cage; 2） removing the self-intersections of the coarse cage with Delaunay partitions. With this new method, a user can build a cage to envelop an input model either entirely or partially with the approximate vertex number the user specifies. Experimental results show that, compared to other cage building methods with the same number of vertex, cages built by our method are more similar to the input models. Thus, the dense models can be manipulated with higher accuracy through our cages.
基金the National Basic Research Program of China("973")(Grant No.2006CB705500)the National Natural Science Foundation of China(Grant No.40571134)+1 种基金the National Hi-Tech Research and Development Program of China(Grant Nos.2007AA12Z241,2007AA12Z212)Outstanding Scholar of Ministry of Education of China(Grant No.NCET-07-0643)
文摘multi-resolution TIN model is an important issue in the contexts of visu-alization,virtual reality(VR),and geographic information systems(GIS).This paper proposes a new method for constructing multi-resolution TIN models with multi-scale topographic features preservation.The proposed method is driven by a half-edge collapse operation in a greedy framework and employs a new quadric error metric to efficiently measure geometric errors.We define topographic features in a multi-scale manner using a center-surround operator on Gaussian-weighted mean curvatures.Experimental results demonstrate that the proposed method performs better than previous methods in terms of topographic features preservation,and is able to achieve multi-resolution TIN models with a higher accuracy.
文摘In the last two decades,renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic,industrial,and agriculture sectors.Solar forecasting plays a vital role in smooth operation,scheduling,and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants.Numerous models and techniques have been developed in short,mid and long-term solar forecasting.This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature,by mainly focusing on investigating the influence of meteorological variables,time horizon,climatic zone,pre-processing techniques,air pollution,and sample size on the complexity and accuracy of the model.To make the paper reader-friendly,it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication,time resolution,input parameters,forecasted parameters,error metrics,and performance.The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities.Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data.Besides,it also discusses the diverse key constituents that affect the accuracy of a model.It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.
基金supported by the Natural Science Foundation of Shanghai under Grant No.20ZR1455900the State Key Laboratory of Computer Architecture(Institute of Computing Technology,Chinese Academy of Sciences)under Grant No.CARCHA202005.
文摘As one of the most promising paradigms of integrated circuit design,the approximate circuit has aroused widespread concern in the scientific community.It takes advantage of the inherent error tolerance of some applications and relaxes the accuracy for reductions in area and power consumption.This paper aims to provide a comprehensive survey of reliability issues related to approximate circuits,which covers three concerns:error characteristic analysis,reliability and test,and reliable design involving approximate circuits.The error characteristic analysis is used to compare the outputs of the approximate circuit with those of its precise counterpart,which can help to find the most appropriate approximate design for a specific application in the large design space.With the approximate design getting close to physical realization,manufacturing defects and operational faults are inevitable;therefore,the reliability prediction and vulnerability test become increasingly important.However,the research on approximate circuit reliability and test is insufficient and needs more attention.Furtherly,although there is some existing work combining the approximate design with fault tolerant techniques,the reliability-enhancement approaches for approximate circuits are lacking.
