检索结果-南通市图书馆

The relationship between four—dimensional θ=π Yang—Mills theory and the two—dimensional Wess —Zumino—Novikov—Witten model

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Physics B 2002年第8期11卷 785-789页

作者：寇谡鹏 DepartmentofPhysics BeijingNormalUniversityBeijing100875China

Used the dimensional reduction in the sense of Parisi and Sourlas, the gauge fixing term of the four-dimensionalYang-Mills field without the theta term is reduced to a two-dimensional principal chiral model. By adding the θ term(θ = π), the two-dimensional principal chiral model changes into the two-dimensional level 1 Wess-Zumino-Novikov-Witten model. The non-trivial fixed point indicates that Yang-Mills theory at θ = π is a critical theory without massgap and confinement.

关键词： Yang-Mills field dimensional reduction Wess-Zumino-Novikov-Witten model

Cross-dimensional Video Super-Resolution Exploring Features Interactions and Dependencies

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Cross-Dimensional Video Super-Resolution Exploring Features ...

作者： Yacouba Coulibaly 中国石油大学(北京)

学位级别：硕士

Video super-resolution（VSR）is a deep learning-based video restoration task that aims to improve video *** a series of low-resolution（LR）input frames,the goal of VSR is to map the corresponding central high-resolution（HR）frame by exploring the temporal connection between consecutive *** convolution neural network（CNN）,due to its high efficiency in computer vision tasks,has been widely used in ***,given the input frames,these methods try to align the reference frame with the supporting frames using optical *** motion estimation and motion compensation（MEMC）using optical flow is computationally expensive and fails to guarantee proper high resolution（HR）.This study presents a novel approach to video super-resolution（VSR）that addresses the challenges of computational complexity and time consumption in existing *** focuses on extracting shared information from the reference frame and its neighboring frames and how different features across spatial,temporal,and channel dimensions interact and depend on each other to improve video *** achieve this,the proposed approach first adopts a three-step learning pipeline:efficient fast learning with the fast-learning block（3D-EFL）.The 3D-EFL introduces 3D depthwise separable convolution to learn feature representation from input frames with few computational *** second step consists of a triplet attention learning strategy that investigates the interactions between spatial and channel dimensions and exploits these interactions to enhance video *** final step consists of increasing the spatial dimension of the feature and high-resolving the reference ***,it adopts low-residual and high-residual learning to connect these steps,alleviating the gradient vanishing problem and allowing more gradient *** proposed approach not only reduces computational complexity but also provides an accurate high-resolution（HR）output without the need for inter-frame mot

关键词： deep learning video super-resolution attention neural network cross-dimensional learning dimensional reduction

Homogenization of Periodically Heterogeneous Thin Beams

同方学位论文库评论

在线全文

同方学位论文库

学校读者我要写书评

暂无评论

Chinese Annals of Mathematics,Series B 2018年第3期39卷 397-426页

作者： Georges GRISO Bernadette MIARA Laboratoire J.-L.Lions-CNRS Bote courrier 187Universite Pierre et Marie Curie4 Place Jussieu75005 ParisFrance Universite Paris-Est 5 boulevard DescartesChamps-sur-Marne77454 Marne-la-Vallee Cedex2France

Consider an elastic thin three-dimensional body made of a periodic distribution of elastic *** both the thickness of the beam and the size of the heterogeneities tend simultaneously to zero the authors obtain three di... 详细信息

关键词： Bernoulli-Navier model Beam Korn's inequalities dimensional reduction Homogenization

A New Hybrid Feature Selection Method Using T-test and Fitness Function

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第9期68卷 3997-4016页

作者： Husam Ali Abdulmohsin Hala Bahjat Abdul Wahab Abdul Mohssen Jaber Abdul Hossen Department of Computer Science Faculty of ScienceUniversity of BaghdadBaghdadIraq Faculty of Computer Science Technology UniversityBaghdadIraq Department of Computer Science Al-Turath University CollegeBaghdadIraq

Feature selection(FS)(or feature dimensional reduction,or feature optimization)is an essential process in pattern recognition and machine learning because of its enhanced classification speed and accuracy and reduced system *** reduces the number of features extracted in the feature extraction phase by reducing highly correlated features,retaining features with high information gain,and removing features with no weights in *** this work,an FS filter-type statistical method is designed and implemented,utilizing a t-test to decrease the convergence between feature subsets by calculating the quality of performance value(QoPV).The approach utilizes the well-designed fitness function to calculate the strength of recognition value(SoRV).The two values are used to rank all features according to the final weight(FW)calculated for each feature subset using a function that prioritizes feature subsets with high SoRV *** FW is assigned to each feature subset,and those with FWs less than a predefined threshold are removed from the feature subset *** are implemented on three datasets:Ryerson Audio-Visual Database of Emotional Speech and Song,Berlin,and Surrey Audio-Visual Expressed *** performance of the F-test and F-score FS methods are compared to those of the proposed *** are also conducted on a system before and after deploying the FS *** demonstrate the comparative efficiency of the proposed *** complexity of the system is calculated based on the time overhead required before and after *** show that the proposed method can reduce system complexity.

关键词： Feature selection dimensional reduction feature optimization pattern recognition classification t-test

A KdV-Type Wronskian Formulation to Generalized KP, BKP and Jimbo–Miwa Equations

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Communications in Theoretical Physics 2017年第7期67卷 1-5页

作者： Li Cheng Yi Zhang Normal School Jinhua Polytechnic Jinhua 321007 China Department of Mathematics Zhejiang Normal University Jinhua 321004 China

The purpose of this paper is to introduce a class of generaJized nonlinear evolution equations, which can be widely applied to describing a variety of phenomena in nonlinear physical science. A KdV-type Wronskian formulation is constructed by employing the Wronskian conditions of the KdV equation. Applications are made for the （3＋1）- dimensional generalized KP, BKP and Jimbo-Miwa equations, thereby presenting their Wronskian sufficient conditions. An N-soliton solution in terms of Wronskian determinant is obtained. Under a dimensional reduction, our results yield Wronskian solutions of the KdV equation.

关键词： generalized KP BKP and Jimbo-Miwa equations the KdV equation Wronskian formulation dimensional reduction

Inhomogeneous Incompressible Navier-Stokes Equations on Thin Domains

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Communications in Mathematics and Statistics 2020年第2期8卷 239-253页

作者： Yongzhong Sun Shifang Wang Department of Mathematics Nanjing UniversityNanjing 210093People’s Republic of China

We consider the inhomogeneous incompressible Navier-Stokes equation on thin domains T^(2)×∈T,∈→*** is shown that the weak solutions on T^(2)×∈T converge to the strong/weak solutions of the 2D inhomogeneous incom... 详细信息

关键词： Inhomogeneous incompressible Navier-Stokes equation Thin domain limit dimensional reduction Relative energy

Outlier Detection Algorithm Based on Iterative Clustering

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Donghua University(English Edition) 2015年第4期32卷 554-558页

作者：古平罗辛杨瑞龙张程 Department of Computer Science and Technology Chongqing University

A novel approach for outlier detection with iterative clustering( ICOD) in diverse subspaces is proposed. The proposed methodology comprises two phases,iterative clustering and outlier factor computation. During the clustering phase, multiple clusterings are detected alternatively based on an optimization procedure that incorporates terms for cluster quality and novelty relative to existing solution. Once new clusters are detected,outlier factors can be estimated from a new definition for outliers( cluster based outlier), which provides importance to the local data behavior. Experiment shows that the proposed algorithm can detect outliers which exist in different clusterings effectively even in high dimensional data sets.

关键词： clustering outlier detection dimensional reduction

A Novel Subspace Outlier Detection Approach in High dimensional Data Sets

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

A Novel Subspace Outlier Detection Approach in High Dimensio...

Dimension reduction Based on Sampling

2010 3rd International Conference on Computer and Electrical Engineering(ICCEE 2010)

作者： Jinsong Leng School of Computer and Security Edith Cowan University

Many real applications are required to detect outliers in high dimensional data sets. The major difficulty of mining outliers lies on the fact that outliers are often embedded in subspaces. No efficient methods are available in general for subspace-based outlier detection. Most existing subspace-based outlier detection methods identify outliers by searching for abnormal sparse density units in subspaces. In this paper, we present a novel approach for finding outliers in the ’interesting’ subspaces. The interesting subspaces are strongly correlated with `good’ clusters. This approach aims to group the meaningful subspaces and then identify outliers in the projected subspaces. In doing so, an extension to the subspace-based clustering algorithm is proposed so as to find the ’good’ subspaces, and then outliers are identified in the projected subspaces using some classical outlier detection techniques such as distance-based and density-based algorithms. Comprehensive case studies are conducted using various types of subspace clustering and outlier detection algorithms. The experimental results demonstrate that the proposed method can detect outliers effectively and efficiently in high dimensional data sets.

关键词： Data Mining Subspace Clustering Outlier Detection dimensional reduction

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

国际计算机前沿大会会议论文集 2023年第1期 207-220页

作者： Zhuping Li Donghua Yang Mengmeng Li Haifeng Guo Tiansheng Ye Hongzhi Wang Harbin Institute of Technology HarbinChina ConDB BeijingChina

Dimension reduction provides a powerful means of reducing the number of random variables under ***,there were many similar tuples in large datasets,and before reducing the dimension of the dataset,we removed some similar tuples to retain the main information of the dataset while accelerating the dimension ***,we propose a dimension reduction technique based on biased sampling,a new procedure that incorporates features of both dimensional reduction and biased sampling to obtain a computationally efficient means of reducing the number of random variables under *** this paper,we choose Principal Components Analysis(PCA)as the main dimensional reduction algorithm to study,and we show how this approach works.

关键词： PCA dimensional reduction biased sampling