检索结果-内蒙古大学图书馆

Biomedical engineering International Conference (BMEiCON)

作者： Tzu-Hsueh Tsai Hsin-Chia Chen Hao Chiao Yang Yu-Chieh Chao Jyh-Miin Lin Chih-Ching Chen Hing-Chiu Chang Chin-Kuo Chang Wei-Hsuan Yu Feng-Nan Hwang Martin Graves Department of Medical Imaging Kaohsiung Medical University Hospital Graduate Institute of Clinical Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Diagnostic Imaging Madou Sin-Lau Hospital Tainan Taiwan Robinson College University of Cambridge Cambridge UK Department of Finance Chung Yuan Christian University Taoyuan City Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong Hong Kong Institute of Psychiatry Psychology and Neuroscience Kings College London London UK Global Health Program College of Public Health National Taiwan University Taipei Taiwan Department of Mathematics National Central University Taoyuan City Taiwan NHS Foundation Trust University of Cambridge Cambridge UK

ISBN: (纸本)9781665489041

Recent studies have suggested that the boundary between data-driven deep-learning non-Cartesian magnetic resonance imaging (MRI) reconstruction methods and conventional optimization-based, iterative reconstruction methods is becoming blurred. For instance, the unrolled iterative reconstruction method can be regarded as a trainable neural network. Another example is that the Moore-Penrose pseudoinverse plays a central role in finding the predefined solution to many imaging processes. However, the application of pseudoinverse in MRI reconstruction was obstructed in clinical imaging, mostly due to the excessive storage required for singular vectors. Since the spatial encoding of MRI is fully determined by the known k-space trajectory, the generalized inverse can be ”iteratively learning in a data-free fashion”, which leads to surprising but realizable properties. To compare our method with other conventional methods, numerical simulations were performed using in vivo MRI. The proposed method leads to nearly equivalent image quality with a much shorter run-time (only 0.68%) than the conjugate gradient (CG) method. We discuss the potential impact of the generalized inverse as a feasible reconstruction method for non-Cartesian MRI.

关键词： Image quality In vivo Magnetic resonance imaging Neural networks Transforms Reconstruction algorithms Numerical simulation

来源：评论

学校读者我要写书评

暂无评论

Nonrigid registration using Gaussian processes and local likelihood estimation

arXiv

引用

arXiv 2020年

作者： Wiens, Ashton Kleiber, William Nychka, Douglas Barnhart, Katherine R. Department of Applied Mathematics University of Colorado BoulderCO United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO United States Cooperative Institute for Research in Environmental Sciences University of Colorado BoulderCO United States Department of Geological Sciences University of Colorado BoulderCO United States US Geological Survey Landslide Hazards Program 1711 Illinois St. GoldenCO80401 United States

Surface registration, the task of aligning several multidimensional point sets, is a necessary task in many scientific fields. In this work, a novel statistical approach is developed to solve the problem of nonrigid registration. While the application of an affine transformation results in rigid registration, using a general nonlinear function to achieve nonrigid registration is necessary when the point sets require deformations that change over space. The use of a local likelihood-based approach using windowed Gaussian processes provides a flexible way to accurately estimate the nonrigid deformation. This strategy also makes registration of massive data sets feasible by splitting the data into many subsets. The estimation results yield spatially-varying local rigid registration parameters. Gaussian process surface models are then fit to the parameter fields, allowing prediction of the transformation parameters at unestimated locations, specifically at observation locations in the unregistered data set. Applying these transformations results in a global, nonrigid registration. A penalty on the transformation parameters is included in the likelihood objective function. Combined with smoothing of the local estimates from the surface models, the nonrigid registration model can prevent the problem of overfitting. The efficacy of the nonrigid registration method is tested in two simulation studies, varying the number of windows and number of points, as well as the type of deformation. The nonrigid method is applied to a pair of massive remote sensing elevation data sets exhibiting complex geological terrain, with improved accuracy and uncertainty quantification in a cross validation study versus two rigid registration methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

来源：评论

学校读者我要写书评

暂无评论

A new estimator for Weibull distribution parameters: Comprehensive comparative study for Weibull Distribution

arXiv

引用

arXiv 2019年

作者： Sadani, Sahar Abdollahnezhad, Kamel Teimouri, Mahdi Ranjbar, Vahid Department of Statistics Faculty of Science Golestan University Gorgan Department of Mathematics and Statistics Faculty of Science and Engineering Gonbad Kavous University Gonbad Kavous Iran

Weibull distribution has received a wide range of applications in engineering and science. The utility and usefulness of an estimator is highly subject to the field of practitioner's study. In practice users looking for their desired estimator under different setting of param-eters and sample sizes. In this paper we focus on two topics. Firstly, we propose U-statistics for the Weibull distribution parameters. The consistency and asymptotically normality of the introduced U-statistics are proved theoretically and by simulations. Several of methods have been proposed for estimating the parameters of Weibull distribution in the literature. These methods include: The generalized least square type 1, the generalized least square type 2, the L-moments, the Logarithmic moments, the maximum likelihood estimation, the method of moments, the percentile method, the weighted least square, and weighted maxi-mum likelihood estimation. Secondary, due to lack of a comprehensive comparison between the Weibull distribution parameters estimators, a comprehensive comparison study is made between our proposed U-statistics and above nine estimators. Based on simulations, it turns out that the our proposed U-statistics show the best performance in terms of bias for estimating the shape and scale parameters when the sample size is large. Copyright © 2019, The Authors. All rights reserved.

关键词： Weibull distribution

来源：评论

学校读者我要写书评

暂无评论

On Rijndael ByteSub Transformation

引用

Applied mathematics 2019年第3期10卷 113-118页

作者： W. Eltayeb Ahmed Mathematics and Statistics Department Faculty of Science Imam Mohammad Ibn Saud Islamic University Riyadh KSA Department of Basics and Engineering Sciences Faculty of Engineering University of Khartoum Khartoum Sudan

The first step in converting a plaintext to ciphertext by the famous Advanced Encryption Standard (AES), which is called Rijndael ByteSub Transformation, involves some operations: computing a multiplicative inverse, multiplying this multiplicative inverse by a specific matrix, and adding the result to a specific vector. The purpose of this research is to simplify these operations. This paper gives elegant techniques and presents the matrices multiplication as simple XOR operations, and the result is a simple, straightforward way finding the transformation.

关键词： Rijndael Cipher Advanced Encryption Standard Multiplicative Inverse XOR Operation

来源：评论

学校读者我要写书评

暂无评论

Power-law scaling for solid-state dewetting of thin films: an Onsager variational approach

arXiv

引用

arXiv 2020年

作者： Jiang, Wei Xu, Xianmin Bao, Weizhu Srolovitz, David J. School of Mathematics and Statistics Wuhan University Wuhan430072 China Hubei Key Laboratory of Computational Science Wuhan University Wuhan430072 China LSEC ICMSEC Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Department of Mathematics National University of Singapore Singapore119076 Departments of Materials Science and Engineering City University of Hong Kong Hong KongSAR China

We examine the kinetics of surface diffusion-controlled, solid-state dewetting by consideration of the retraction of the contact in a semi-infinite solid thin film on a flat rigid substrate. The analysis is performed within the framework of the Onsager variational principle applied to surface diffusion-controlled morphology evolution. Based on this approach, we derive a simple, reduced-order model to quantitatively analyse the power-law scaling of the dewetting process. Using asymptotic analysis and numerical simulations for the reduced-order model, we find that the retraction distance grows as the 2/5 power of time and the height of the ridge, adjacent to the contact, grows as the 1/5 power of time for late time. While the asymptotic analysis focuses on late time and a relatively simple geometric model, the Onsager approach is applicable to all times and descriptions of the morphology of arbitrary complexity. Copyright © 2020, The Authors. All rights reserved.

关键词： Asymptotic analysis

来源：评论

学校读者我要写书评

暂无评论

A Modern Method for Constructing the S-Box of Advanced Encryption Standard

引用

Applied mathematics 2019年第4期10卷 234-244页

The substitution table (S-Box) of Advanced Encryption Standard (AES) and its properties are key elements in cryptanalysis ciphering. We aim here to propose a straightforward method for the non-linear transformation of AES S-Box construction. The method reduces the steps needed to compute the multiplicative inverse, and computes the matrices multiplication used in this transformation, without a need to use the characteristic matrix, and the result is a modern method constructing the S-Box.

关键词： Advanced Encryption Standard S-Box Extended Euclidean Algorithm Greatest Common Divisor XOR Operation

来源：评论

学校读者我要写书评

暂无评论

Convection in a mushy-layer along a heated wall

arXiv

引用

arXiv 2021年

作者： Boury, Samuel Meyer, Colin R. Vasil, Geoffrey M. Wells, Andrew J. Courant Institute of Mathematical Sciences New York University New YorkNY10012 United States Thayer School of Engineering Dartmouth College 14 Engineering Drive HanoverNH03755 United States School of Mathematics and Statistics University of Sydney SydneyNSW2006 Australia Atmospheric Oceanic and Planetary Physics Department of Physics Clarendon Laboratory University of Oxford Parks Road OxfordOX1 3PU United Kingdom

Motivated by the mushy zones of sea ice, volcanoes, and icy moons of the outer solar system, we perform a theoretical and numerical study of boundary-layer convection along a vertical heated wall in a bounded ideal mushy region. The mush is comprised of a porous and reactive binary alloy with a mixture of saline liquid in a solid matrix, and is studied in the near-eutectic approximation. Here we demonstrate the existence of four regions and study their behavior asymptotically. Starting from the bottom of the wall, the four regions are (i) an isotropic corner region;(ii) a buoyancy dominated vertical boundary layer;(iii) an isotropic connection region;and (iv) a horizontal boundary layer at the top boundary with strong gradients of pressure and buoyancy. Scalings from numerical simulations are consistent with the theoretical predictions. Close to the heated wall, the convection in the mushy layer is similar to a rising buoyant plume abruptly stopped at the top, leading to increased pressure and temperature in the upper region, whose impact is discussed as an efficient melting mechanism. © 2021, CC BY.

关键词： Boundary layers

来源：评论

学校读者我要写书评

暂无评论

Solving Lur'e equations through zeroing neural networks

引用

Information Sciences 2025年 718卷

作者： Yu He Xiaojuan Wang Yafei Tie Huanqi Yang Theodore E. Simos Spyridon D. Mourtas Vasilios N. Katsikis School of Computer Science and Artificial Intelligence Huanghuai University Zhumadian 463000 China Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots Henan 463000 China Henan Key Laboratory of Smart Lighting Zhumadian 46300 China Hangzhou Dianzi University School of Mechanical Engineering Er Hao Da Jie 1158 Xiasha Hangzhou 310018 China Center for Applied Mathematics and Bioinformatics Gulf University for Science and Technology West Mishref 32093 Kuwait Section of Mathematics Dept. of Civil Engineering Democritus Univ. of Thrace Xanthi 67100 Greece Department of Economics Division of Mathematics-Informatics and Statistics-Econometrics National and Kapodistrian University of Athens Sofokleous 1 Street 10559 Athens Greece Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems” Siberian Federal University Prosp. Svobodny 79 660041 Krasnoyarsk Russia

Solving Lur'e equations plays a critical role in addressing linear-quadratic optimal control (LQOC) problems, especially in cases where the control cost matrices are singular. This paper introduces, for the first time, two novel zeroing neural network (ZNN) models—ZNNLE and ZNNLE-LQOC—specifically designed to solve the Lur'e equation system and the LQOC problem, respectively. The proposed models extend the applicability of the ZNN methodology to these challenging scenarios by offering robust and efficient solutions to time-varying matrix equations. Theoretical analyses confirm the validity of both models, while numerical simulations and practical applications demonstrate their effectiveness. Moreover, a comparative study with an enhanced alternating-direction implicit (ADI) method highlights the superior performance of the ZNNLE-LQOC model in solving LQOC problems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Classification of defective product for smart factory through deep learning method

引用

AIP Conference Proceedings 2024年第1期2937卷

作者： R. Raffik Praveen Kumar Misra Chandra Sekhar Kolli V. V. Krishna Reddy Mohan Kumar Chandol Surendra Kumar Shukla Department of Mechatronics Engineering Kumaraguru College of Technology Coimbatore 641049 Tamilnadu India Department of Mathematics and Statistics Dr. Shakuntala Misra National Rehabilitation University Lucknow 226017. Uttar Pradesh India Department of Information Technology Aditya College of Engineering & Technology SurampalemEast Godavari District Andhra Pradesh 533437 India Department of Information Technology Lakireddy Balireddy College of Engineering(Autonomous) Affiliated to JNTUK L B Reddy Nagar Mylavaram Krishna District Andhra Pradesh 521230 India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur District Andhra Pradesh 522302 India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand 248002 India

The supply of defect-free, high-quality items is a critical factor to calculate for the long-term competitiveness of industries. Quality control is important in factories to form the item defect-free as well as to meet the needs of customers. With later advancements in deep learning and computer vision advances, it has ended up identifying different flaws from the images with near-human exactness. By introducing an insightful assembling framework, defects can be limited, and human expenses can be brought down to empower feasible development. The smart production line employs production equipment, functional testing equipment, and defect detection equipment. This paper is presented with a flaw identification system for application in smart factories based on deep learning. Training with deep learning method, EfficientDet to naturally identify deformity from ultrasonic pictures. The test results show that it was able to classify flawed items rapidly with high precision in a real-world manufacturing environment. The proposed model during 5-fold cross-validation accomplished 96% of mean accuracy was a noteworthy advancement compared to a few comparative strategies that were already utilized for this task. EfficientDet detected all the flaws present in the material during assessment and performance analysis showed it was comparatively good in detecting flaws.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Quantifying Network Similarity using Graph Cumulants

arXiv

引用

arXiv 2021年

作者： Bravo-Hermsdorff, Gecia GundersonGunderson, Lee M. Maugis, Pierre-André Priebe, Carey E. Department of Statistical Sciences University College London LondonWC1E 7H8 United Kingdom Gatsby Computational Neuroscience Unit University College London LondonW1T 4JG United Kingdom Google Research Zürich8002 Switzerland Department of Applied Mathematics and Statistics Whiting School of Engineering Johns Hopkins University BaltimoreMD21218 United States

How might one test the hypothesis that networks were sampled from the same distribution? Here, we compare two statistical tests that use subgraph counts to address this question. The first uses the empirical subgraph densities themselves as estimates of those of the underlying distribution. The second test uses a new approach that converts these subgraph densities into estimates of the graph cumulants of the distribution (without any increase in computational complexity). We demonstrate — via theory, simulation, and application to real data — the superior statistical power of using graph cumulants. In summary, when analyzing data using subgraph/motif densities, we suggest using the corresponding graph cumulants instead. © 2021, CC BY.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：