检索结果-内蒙古大学图书馆

SSRN 2024年

作者： Jiang, Hao Man, Zongzhe Guo, Zhenfei Feng, Wenwen Yang, Zhiyuan Lei, Zhenkun Bai, Ruixiang Yan, Shutian Cheng, Bo Northeast Forestry University Harbin150040 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110167 China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Dalian University of Technology Dalian116024 China AVIC Material Testing and Evaluation Center AVIC Manufacturing Technology Institute Beijing100024 China College of Automotive and Mechanical Engineering Changsha University of Science & Technology Hunan Province Changsha410114 China

Laser cladding is an advanced processing technology in the fields of additive manufacturing and surface modification. During the laser-induced melting and deposition process, the material experiences rapid solidification and cooling, resulting in the generation of residual stresses. In this paper, a data-driven approach is proposed to optimize the numerical model of laser cladding. This approach considers the temperature characteristics and distribution of residual stresses during the thermo-mechanical coupling process. The study investigates the role of the double ellipsoid heat source in the formation of the molten pool and the generation of residual stresses. To optimize the double ellipsoid heat source model, the residual stress distribution, measured using the contour method, is combined with the molten pool morphology obtained from the temperature effects of laser cladding. The Levenberg-Marquardt optimization method is used for iterative correction. Additionally, parallel computing is employed for calculating the Jacobian matrix, reducing computation time during iterations. The effectiveness of this method is validated through experimental data. Moreover, this method is not limited to specific specimen size and shape, as it accurately predicts the characteristics of residual stress distribution. Thus, it provides a reliable means for simulating the generation mechanism of residual stresses in titanium alloy laser cladding. © 2024, The Authors. All rights reserved.

关键词： Laser cladding

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CHANCES AND CHALLENGES OF CHATGPT AND SIMILAR MODELS FOR EDUCATION IN M&S

TechRxiv

引用

TechRxiv 2023年

作者： Scherer, William T. Tolk, Andreas Loper, Margaret L. Barry, Philip Rabadi, Ghaith Yilmaz, Levent The MITRE Corporation Modeling and Analysis Innovation Center 1001 Research Park Blvd #200 CharlottesvilleVA22911 United States Georgia Tech Research Institute Georgia Institute of Technology AtlantaGA30308 United States University of Virgina Department of Systems and Information Engineering CharlottesvilleVA22904 United States L3Harris Corporation Mission Analysis Group 2235 Monroe Street HerndonVA20171 United States University of Central Florida School of Modeling Simulation and Training 3100 Technology Pkwy OrlandoFL32826 United States Auburn University Samuel Ginn College of Engineering Computer Science and Software Engineering AuburnAL36849 United States

The position paper summarizes the inputs of a set of experts from academia and industry presenting their view on chances and challenges of using ChatGPT within the Modelling and Simulation education. They also address the need to evaluate continuous education as well as education of faculty members to address scholastic challenges and opportunities while meeting the expectation of industry. © 2023, CC BY.

关键词： Modeling languages

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Calling Cards: A Customizable Platform to Longitudinally Record Protein-DNA Interactions Over Time in Cells and Tissues (vol 3, e883, 2023)

CURRENT PROTOCOLS

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CURRENT PROTOCOLS 2023年第10期3卷 e883页

作者： Yen, Allen Mateusiak, Chase Sarafinovska, Simona Gachechiladze, Mariam A. Guo, Juanru Chen, Xuhua Moudgil, Arnav Cammack, Alexander J. Hoisington-Lopez, Jessica Crosby, MariaLynn Brent, Michael R. Mitra, Robi D. Dougherty, Joseph D. Department of Genetics Washington University School of Medicine Saint Louis Missouri Department of Psychiatry Washington University School of Medicine Saint Louis Missouri Contribution: Conceptualization Data curation Formal analysis Funding acquisition Investigation Methodology Project administration Software Supervision Validation Visualization Writing - original draft Writing - review & editing Edison Family Center for Genome Sciences and Systems Biology Washington University School of Medicine Saint Louis Missouri Contribution: Conceptualization Data curation Formal analysis Methodology Software Visualization Writing - original draft Contribution: Formal analysis Funding acquisition Investigation Visualization Writing - original draft Writing - review & editing Contribution: Formal analysis Funding acquisition Investigation Visualization Writing - review & editing Contribution: Data curation Methodology Software Writing - review & editing Contribution: Methodology Writing - review & editing Current address: Department of Ophthalmology Stanford University School of Medicine Stanford California Contribution: Conceptualization Methodology Software Validation Department of Neurology Washington University School of Medicine Saint Louis Missouri Current address: Department of Neurodegenerative Diseases University College London London United Kingdom Contribution: Conceptualization Methodology Validation Contribution: Investigation Writing - review & editing Department of Computer Science and Engineering Washington University Saint Louis Missouri Contribution: Funding acquisition Contribution: Conceptualization Funding acquisition Project administration Resources Software Supervision Writing - review & editing Intellectual and Developmental Disabilities Research Center Washington University School of Medicine Saint Louis Missouri Contribution: Conceptualization Funding acquisition Project administration Resources Supervision Writing - origin

Calling Cards is a platform technology to record a cumulative history of transient protein-DNA interactions in the genome of genetically targeted cell types. The record of these interactions is recovered by next-generation sequencing. Compared with other genomic assays, readouts of which provide a snapshot at the time of harvest, Calling Cards enables correlation of historical molecular states to eventual outcomes or phenotypes. To achieve this, Calling Cards uses the piggyBac transposase to insert self-reporting transposon “Calling Cards” into the genome, leaving permanent marks at interaction sites. Calling Cards can be deployed in a variety of in vitro and in vivo biological systems to study gene regulatory networks involved in development, aging, and disease. Out of the box, it assesses enhancer usage but can be adapted to profile-specific transcription factor (TF) binding with custom TF-piggyBac fusion proteins. The Calling Cards workflow has five main stages: delivery of Calling Cards reagents, sample preparation, library preparation, sequencing, and data analysis. Here, we first present a comprehensive guide for experimental design, reagent selection, and optional customization of the platform to study additional TFs. Then, we provide an updated protocol for the five steps, using reagents that improve throughput and decrease costs, including an overview of a newly deployed computational pipeline. This protocol is designed for users with basic molecular biology experience to process samples into sequencing libraries in 2 days. Familiarity with bioinformatic analysis and command line tools is required to set up the pipeline in a high-performance computing environment and to conduct downstream analyses. © 2023 Wiley Periodicals LLC. This article was corrected on 16 October 2023. See the end of the full text for details. Basic Protocol 1 : Preparation and delivery of Calling Cards reagents Support Protocol 1 : Next-generation sequencing quantification of barcode

关键词： calling cards epigenetics enhancer usage gene regulation transcription factor binding

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Distributed nonlinear model predictive control and metric learning for heterogeneous vehicle platooning with cut-in/cut-out maneuvers

arXiv

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arXiv 2020年

作者： Basiri, Mohammad Hossein Ghojogh, Benyamin Azad, Nasser L. Fischmeister, Sebastian Karray, Fakhri Crowley, Mark The Department of Systems Design Engineering University of Waterloo ON Canada The Department of Electrical and Computer Engineering University of Waterloo ON Canada The Smart Hybrid and Electric Vehicles Systems Lab The Real-Time Embedded Software Lab The Machine Learning Lab The Centre for Pattern Analysis and Machine Intelligence

Vehicle platooning has been shown to be quite fruitful in the transportation industry to enhance fuel economy, road throughput, and driving comfort. Model Predictive Control (MPC) is widely used in literature for platoon control to achieve certain objectives, such as safely reducing the distance among consecutive vehicles while following the leader vehicle. In this paper, we propose a Distributed Nonlinear MPC (DNMPC), based upon an existing approach, to control a heterogeneous dynamic platoon with unidirectional topologies, handling possible cut-in/cut-out maneuvers. The introduced method addresses a collision-free driving experience while tracking the desired speed profile and maintaining a safe desired gap among the vehicles. The time of convergence in the dynamic platooning is derived based on the time of cut-in and/or cut-out maneuvers. In addition, we analyze the improvement level of driving comfort, fuel economy, and absolute and relative convergence of the method by using distributed metric learning and distributed optimization with Alternating Direction Method of Multipliers (ADMM). Simulation results on a dynamic platoon with cut-in and cut-out maneuvers and with different unidirectional topologies show the effectiveness of the introduced method. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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Preface

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2023年 178卷 v-viii页

作者： Faure, Emil Danchenko, Olena Bondarenko, Maksym Tryus, Yurii Bazilo, Constantine Zaspa, Grygoriy Department of Information Security and Computer Engineering Cherkasy State Technological University Cherkasy Ukraine Department of Computer science and Systems Analysis Cherkasy State Technological University Cherkasy Ukraine Department of Instrumentation Mechatronics and Computerized Technologies Cherkasy State Technological University Cherkasy Ukraine Department of Software of Automated Systems Cherkasy State Technological University Cherkasy Ukraine

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An Interdisciplinary Survey on Information Flows in Supply Chains

arXiv

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arXiv 2023年

作者： Pennekamp, Jan Matzutt, Roman Klinkmüller, Christopher Bader, Lennart Serror, Martin Wagner, Eric Malik, Sidra Spib, Maria Rahn, Jessica Gürpinar, Tan Vlad, Eduard Leemans, Sander J.J. Kanhere, Salil S. Stich, Volker Wehrle, Klaus RWTH Aachen University Ahornstrasse 55 Aachen North Rhine-Westphalia 52074 Germany BPMotion 10 Gore Ave KirraweeNSW2232 Australia Cyber Analysis & Defense Fraunhofer FKIE Fraunhofer-straße 20 North Rhine-Westphalia Wachtberg53343 Germany Software Systems Research Group Data61 CSIRO Level 5/13 Garden St EveleighNSW2015 Australia Institute for Industrial Management RWTH Aachen University Campus-Boulevard 55 North Rhine-Westphalia Aachen52074 Germany Supply Chain Development and Strategy Fraunhofer IML Joseph-von-Fraunhofer-Straße 2-4 North Rhine-Westphalia Dortmund44227 Germany School of Computer Science and Engineering University of New South Wales Engineering Rd SydneyNSW2052 Australia

Supply chains form the backbone of modern economies and therefore require reliable information flows. In practice, however, supply chains face severe technical challenges, especially regarding security and privacy. In this work, we consolidate studies from supply chain management, information systems, and computer science from 2010–2021 in an interdisciplinary meta-survey to make this topic holistically accessible to interdisciplinary research. In particular, we identify a significant potential for computer scientists to remedy technical challenges and improve the robustness of information flows. We subsequently present a concise information flow-focused taxonomy for supply chains before discussing future research directions to provide possible entry points. Copyright © 2023, The Authors. All rights reserved.

关键词： Supply chain management

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Preface

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2024年 222卷 v-viii页

作者： Faure, Emil Tryus, Yurii Vartiainen, Tero Danchenko, Olena Bondarenko, Maksym Bazilo, Constantine Zaspa, Grygoriy Department of Information Security and Computer Engineering Cherkasy State Technological University Cherkasy Ukraine Department of Computer Science and Systems Analysis Cherkasy State Technological University Cherkasy Ukraine Department of Computing Sciences University of Vaasa Vaasa Finland Department of Instrumentation Mechatronics and Computerized Technologies Cherkasy State Technological University Cherkasy Ukraine Department of Software of Automated System Cherkasy State Technological University Cherkasy Ukraine

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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Quantifying the quality of indoor maps 4

Quantifying the quality of indoor maps

引用

4th ISPRS Geospatial Week 2019

作者： Assali, M. Pipelidis, G. Podolskiy, V. Iwaszczuk, D. Heinen, L. Gerndt, M. Dept. of Computer Architecture and Parallel Systems Dept. of Informatics Technical University of Munich Munich Germany Software and Systems Engineering Research Group Dept. of Informatics Technical University of Munich Munich Germany Remote Sensing and Image Analysis Group Dept. of Civil and Environmental Engineering Science Technische Universität Darmstadt Germany BMW Group IT Munich Germany

Indoor maps are required for multiple applications, such as, navigation, building maintenance and robotics. One of common methods for map generation is laser scanning. In such maps, not only geometry of the map is of interest, but also its quality. This study aims at developing methods for real-time generation of indoor maps using features extracted from pointclouds obtained by a robot with their simultaneous quality assessment. We investigate, how this quality can be quantified for feature-based maps. First, we introduce a method for modeling 2D maps into 3D models that enable their usage for localization. Second, we review and evaluate a number of algorithms that can enable us to address features in a map. Hence, we enable the generation of objects from a pointcloud that has been sensed. Finally, we study several aspects of the map quality and we formalize them into metrics that can be applied to quantify their quality. © Authors 2019.

关键词： Robots

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Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

Local and global structure-aware entropy regularized mean te...

引用

23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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