检索结果-内蒙古大学图书馆

Utilization of machine learning and neural networks to optimize the enclosure angle, magnetic field, and radiation parameter for mixed convection of hybrid nanofluid flow next to assess environmental impact

引用

Engineering Analysis with Boundary Elements 2023年 146卷 252-262页

作者： Tao Hai Hayder A. Dhahad Masood Ashraf Ali Vishal Goyal Sattam Fahad Almojil Abdulaziz Ibrahim Almohana Abdulrhman Fahmi Alali Khaled Twfiq Almoalimi Farah Qasim Ahmed Alyousuf School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou 558000 China Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Shah Alam Selangor 40450 Malaysia Mechanical Engineering Department University of Technology Baghdad Iraq Department of Industrial Engineering College of Engineering Prince Sattam bin Abdulaziz University Alkharj 16273 Saudi Arabia Department of Electronics and Communication Engineering GLA University Mathura India Department of Civil Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq

This paper examines the mixed convective heat transfer (HTR) of nanofluid (NFD) flow in a rectangular enclosure with the upper moving wall numerically. The lower wall has a high temperature and a number of semi-circular obstacles with the same temperature are installed on it. The upper moving wall has a low temperature and the other two walls are insulated. The enclosure can change from horizontal to vertical. Radiation HTR is considered in the enclosure and there is a magnetic field (MGF) that can change the angle from horizontal to vertical affecting the NFD. This study is carried out for different angles of the enclosure and MGF from horizontal to vertical for radiation parameters (RDP) of 0 to 3 and a constant MGF with Hartmann number of 20 and Richardson number of 10. The aim is to estimate the Nusselt number (Nu), entropy generation (ETG), and Bejan number (Be). The SIMPLE algorithm is utilized using FORTRAN software, and optimization is done using artificial intelligence to find the maximum and minimum output values. The results demonstrate that the maximum value of Nu and Bes corresponds to the MGF angle and enclosure angle of 90°. The minimum value of the Nu and the maximum amount of ETG corresponds to the horizontal MGF and horizontal enclosure when the RDP is 1.5. An increment in the RDP enhances the amount of Nu. The maximum amount of ETG, i.e. 12.87, corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. corresponds to the enclosure with an angle of 45° for the horizontal MGF and the absence of RDP. It was also found that most environmental impacts, and hence values for different environmental factors, arise from the production of nanoparticles; thus, it is a significant contributor to environmental impacts.

关键词： Machine learning Entropy generation Numerical calculations Convection Nanofluid

来源：评论

学校读者我要写书评

暂无评论

Waveform optimization for MIMO joint communication and radio sensing systems with training overhead

arXiv

引用

arXiv 2020年

作者： Yuan, Xin Feng, Zhiyong Andrew Zhang, J. Ni, Wei Liu, Ren Ping Wei, Zhiqing Xu, Changqiao Key Laboratory of the Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications China Global Big Data Technologies Center University of Technology Sydney Australia Key Laboratory of the Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications China Global Big Data Technologies Center University of Technology Sydney Australia Commonwealth Scientific and Industrial Research Organization Australia State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China

In this paper, we study optimal waveform design to maximize mutual information (MI) for a joint communication and (radio) sensing (JCAS, a.k.a., radar-communication) multi-input multi-output (MIMO) downlink system. We consider a typical packet-based signal structure which includes training and data symbols. We first derive the conditional MI for both sensing and communication under correlated channels by considering the training overhead and channel estimation error (CEE). Then, we derive a lower bound for the channel estimation error and optimize the power allocation between the training and data symbols to minimize the CEE. Based on the optimal power allocation, we provide optimal waveform design methods for three scenarios, including maximizing MI for communication only and for sensing only, and maximizing a weighted sum MI for both communication and sensing. We also present extensive simulation results that provide insights on waveform design and validate the effectiveness of the proposed designs. Copyright © 2020, The Authors. All rights reserved.

关键词： Waveform analysis

来源：评论

学校读者我要写书评

暂无评论

SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain

arXiv

引用

arXiv 2022年

作者： Ren, Pu Rao, Chengping Chen, Su Wang, Jian-Xun Sun, Hao Liu, Yang Department of Civil and Environmental Engineering Northeastern University BostonMA02115 United States Department of Mechanical and Industrial Engineering Northeastern University BostonMA02115 United States Key Laboratory of Urban Security and Disaster Engineering The Ministry of Education Beijing University of Technology Beijing100124 China Department of Aerospace and Mechanical Engineering University of Notre Dame Notre DameIN46556 United States Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing100872 China School of Engineering Sciences University of Chinese Academy of Sciences Beijing101408 China

Recently, there has been an increasing interest in leveraging physics-informed neural networks (PINNs) for modeling dynamical systems. However, very limited studies have been conducted along this horizon on seismic wave modeling tasks. A critical challenge is that these geophysical problems are typically defined in large domains (i.e., semi-infinite), which leads to high computational cost. In this paper, we present a new PINN model for seismic wave modeling in semi-infinite domain without the need of labeled data. In specific, the absorbing boundary condition is introduced into the network as a soft regularizer for handling truncated boundaries. To scale up, we consider a sequential training strategy via temporal domain decomposition to improve the scalability of the network and solution accuracy. Moreover, we design a novel surrogate modeling strategy to account for parametric loading, which estimates the wave propagation in semi-infinite domain given the seismic loading at different locations. Various numerical experiments have been implemented to evaluate the performance of the proposed PINN model in the context of forward modeling of seismic wave propagation. In particular, we define diverse material distributions to test the versatility of this approach. The results demonstrate excellent solution accuracy under distinctive scenarios. Copyright © 2022, The Authors. All rights reserved.

关键词： Wave propagation

来源：评论

学校读者我要写书评

暂无评论

Z2-ZNCC: ZigZag Scanning based Zero-means Normalized Cross Correlation for Fast and Accurate Stereo Matching on Embedded GPU

Z2-ZNCC: ZigZag Scanning based Zero-means Normalized Cross C...

引用

IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Qiong Chang Aolong Zha Weimin Wang Xin Liu Masaki Onishi Tsutomu Maruyama Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Tokyo Japan AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory Tokyo Japan Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Japan

ISBN: (数字)9781728197104

ISBN: (纸本)9781728197111

Mobile stereo matching systems are becoming more important in many applications such as auto-driving and autonomous robots. However, to maintain its low power consumption, mobile platforms have only limited hardware resources. Accurate stereo matching methods require a high computational complexity, and it is difficult to maintain both acceptable accuracy and processing speed on the mobile platforms. To solve this trade-off, in this paper, we propose a novel acceleration approach for a well-known matching algorithm Zero-means Normalized Cross Correlation (ZNCC), and show its effectiveness on a Jetson TX2 embedded GPU. By combining our new approach, Z 2 - ZNCC, with the Semi-Global Matching (SGM) algorithm, our system achieves a low error rate of 7.76% while keeping 28 fps for 1242×375 pixels images with the maximum disparity of 128 on the KITTI 2015 dataset. This performance is higher than previous state-of-the-art system on the same hardware platform.

关键词： Correlation Power demand Graphics processing units Hardware System-on-chip Acceleration Field programmable gate arrays

来源：评论

学校读者我要写书评

暂无评论

Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

引用

Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

来源：评论

学校读者我要写书评

暂无评论

Generalized quantum teleportation of shared quantum secret with quantum walks

arXiv

引用

arXiv 2020年

作者： Li, Hengji Li, Jian Chen, Xiubo Information Security Center State Key Laboratory Networking and Switching Technology Beijing University of Posts Telecommunications Beijing100876 China School of Artificial Intelligence Beijing University of Posts Telecommunications Beijing100876 China Center for Quantum Information Research ZaoZhuang University ZaoZhuang Shandong277160 China GuiZhou University Guizhou Provincial Key Laboratory of Public Big Data GuiyangGuizhou550025 China

Very recently, Lee et al. proposed the first secure quantum teleporation protocol, where quantum information shared by an arbitrary number of senders can be transferred to another arbitrary number of receivers. Here, by introducing quantum walks, a novel secure (n, m) quantum teleportation of shared quantum secret between n senders and m receivers is presented. Firstly, two kinds of (n, 2) teleportation schemes are proposed by n-walker quantum walks on the line, the first walker of which is driven by three coins, respectively, based on two kinds of coin operators: the homogeneous coins and the position-dependent coins. Secondly, by increasing the amount of the coins of the first walker to m + 1, the previous (n, 2) scheme can be generalized to (n, m) teleportation scheme. Then, we give the proof of the information security of our proposed scheme, in which neither any single nor subparties of senders and receivers can fully access the secret quantum information. Moreover, the projective measurements are needed, instead of the joint Bell measurements that are necessary in Lee et al.’s protocol. Our work can also be extended further to QWs on the cycle. This work provides an additional relevant instance of the richness of quantum walks for quantum information processing tasks and thus opens the wider application purpose of quantum walks. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum optics

来源：评论

学校读者我要写书评

暂无评论

Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

引用

The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

来源：评论

学校读者我要写书评

暂无评论

Recombination of repeat elements generates somatic complexity in human genomes

引用

四川生理科学杂志 2022年第7期44卷 1291-1291页

作者： Giovanni Pascarella RIKEN Center for Integrative Medical Sciences (IMS) Yokohama 230-0045 Japan Institute for Protein Research Osaka University Osaka 565-0871 Japan Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Kashiwa 277-8562 Chiba Japan Department of Medicine University of California San Diego La Jolla CA 92093 USA Department of Cell and Molecular Biology Karolinska Institutet Stockholm 171 77 Sweden Stockholm University Frescativägen 114 19 Stockholm Sweden Department of Medical Biochemistry and Biophysics Karolinska Institutet Stockholm 171 77 Sweden Science for Life Laboratory Tomtebodavägen 23 171 65 Solna Sweden Department of Neuropathology Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology (TMGHIG) Tokyo 173-0015 Japan Central RNA Laboratory and Department of Neuroscience and Brain Technologies Istituto Italiano di Tecnologia (IIT) Genova 16163 Italy Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) Tokyo 135-0064 Japan Graduate School of Frontier Sciences University of Tokyo Chiba 277-8562 Japan Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) AIST Tokyo 135-0064 Japan Human Technopole Milan 20157 Italy

Non-allelic recombination between homologous repetitive elements contributes to evolution and human genetic ***,we combine short-and long-DNA read sequencing of repeat elements with a new bioinformatics pipeline to show that somatic recombination of Alu and L1 elements is widespread in the human *** analysis uncovers tissue-specific non-allelic homologous recombination hallmarks;moreover,we find that centromeres and cancer-associated genes are enriched for retroelements that may act as recombination *** compare recombination profiles in human-induced pluripotent stem cells and differentiated neurons and find that the neuron-specific recombination of repeat elements accompanies chromatin changes during cell-fate determination.

关键词： determination. elements companies

来源：评论

学校读者我要写书评

暂无评论

Neural networks for protein structure and function prediction and dynamic analysis

引用

Biophysical reviews 2020年第2期12卷 569-573页

作者： Yuko Tsuchiya Kentaro Tomii Artificial Intelligence Research Center (AIRC) Tokyo Japan. Biotechnology Research Institute for Drug Discovery Tokyo Japan. Artificial Intelligence Research Center (AIRC) Tokyo Japan. k-tomii@aist.go.jp. Biotechnology Research Institute for Drug Discovery Tokyo Japan. k-tomii@aist.go.jp. Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL) National Institute of Advanced Industrial Science and Technology (AIST) 2-4-7 Aomi Koto-ku Tokyo 135-0064 Japan. k-tomii@aist.go.jp.

Hardware and software advancements along with the accumulation of large amounts of data in recent years have together spurred a remarkable growth in the application of neural networks to various scientific fields. Machine learning based on neural networks with multiple (hidden) layers is becoming an extremely powerful approach for analyzing data. With the accumulation of large amounts of protein data such as structural and functional assay data, the effects of such approaches within the field of protein informatics are increasing. Here, we introduce our recent studies based on applications of neural networks for protein structure and function prediction and dynamic analysis involving: (i) inter-residue contact prediction based on a multiple sequence alignment (MSA) of amino acid sequences, (ii) prediction of protein-compound interaction using assay data, and (iii) detection of protein allostery from trajectories of molecular dynamic (MD) simulation.

关键词： Contact prediction Deep learning Neural networks Protein allostery Protein-compound interaction

来源：评论

学校读者我要写书评

暂无评论

Erratum to: Evotuning protocols for Transformer-based variant effect prediction on multi-domain proteins

引用

Briefings in bioinformatics 2021年第6期22卷 bbab287页

作者： Hideki Yamaguchi Yutaka Saito Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Kashiwa Chiba 277-8561 Japan. Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) Koto-ku Tokyo 135-0064 Japan. AIST-Waseda University Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) Shinjuku-ku Tokyo 169-8555 Japan.

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：