检索结果-内蒙古大学图书馆

Framework for integrating project-based learning, experience and practice in professional graduate education for engineering leaders in industry leading to the professional engineering doctorate and fellow levels

Framework for integrating project-based learning, experience...

引用

2005 ASEE Annual Conference and Exposition: The Changing Landscape of engineering and Technology Education in a Global World

作者： Keating, Donald A. Stanford, Thomas G. Dunlap, Duane D. McHenry, Albert L. Deloatch, Eugene M. Lee, Peter Y. Depew, Dennis R. Bertoline, Gary R. Dyrenfurth, Michael J. Tricamo, Stephen J. Palmer, Harvey J. Davis, Isadore T. Morrison, Ray E. Tidwell, Joseph P. Gonzalez-Landis, Kathleen O'Brien, Joe Snellenberger, Jay M. Quick, David H. Olson, Roger N. Coulson, Lynda M. University of South Carolina United States Western Carolina University United States Arizona State University East United States Morgan State University United States California Polytechnic State University United States Purdue University United States New Jersey Institute of Technology United States Rochester Institute of Technology United States Raytheon Missile Systems United States Lockheed-Martin Company United States Boeing Company United States Engineering Partnership-Arizona United States Hewlett-Packard United States Rolls-Royce Corporation United States ASEE-Graduate Studies Division United States Department of Chemical Engineering University of South Carolina United States Department of Engineering and Technology Western Carolina University United States College of Technology Andapplied Sciences Arizona State University East United States School of Engineering Morgan State University United States College of Engineering California State Polytechnic University United States College of Technology Purdue University United States Discovery Resources Envision Center Department of Computer Graphics College of Technology Purdue University United States Department of Industrial and Manufacturing Engineering Kate Gleason College of Engineering Rochester Institute of Technology United States Engineering University Relations Raytheon Missile Systems Tucson AZ United States ASEE Corporate Members Council Lockheed Martin Aero Technical Institute and Engineering Workforce Development Lockheed Martin Aeronautics Company Fort Worth TX United States ASEE-Continuing Professional Development Division Southwest Regional University United States Engineering and Technical Education Boeing Company Meza AZ United States Hewlett-Packard Company Palo Alto CA United States Rolls-Royce Corporation Indianapolis IN United States R and D Customer Requirements Allison Advanced Development Company Rolls-Royce Corporation Indianapolis IN United States Lockheed Martin Aero Technica

This is the third of four papers prepared for a special panel session of the National Collaborative Task Force on engineering Graduate Education Reform. The paper formulates a very creative approach and framework for postgraduate professional education that fosters the continuous development of the U.S. engineering workforce in industry concurrently with engineering practice for technological innovation. The framework integrates innovative project-based learning, progressive experience, self-directed learning, and graduate studies concurrently with engineering practice. The framework is specifically designed to foster growth beyond the professional master's level, leading to the professional Doctor of engineering and engineering Fellow for senior career development of engineering leaders. The intent is to build clusters of postgraduate professional education across the country in partnership with industry that strengthens U.S. innovative capability for continuous technology development and competitiveness.

关键词： engineering education

来源：评论

学校读者我要写书评

暂无评论

CG-SENSE revisited: Results from the first ISMRM reproducibility challenge

arXiv

引用

arXiv 2020年

作者： Maier, Oliver Baete, Steven H. Fyrdahl, Alexander Hammernik, Kerstin Harrevelt, Seb Kasper, Lars Karakuzu, Agah Loecher, Michael Patzig, Franz Tian, Ye Wang, Ke Gallichan, Daniel Uecker, Martin Knoll, Florian Institute of Medical Engineering Graz University of Technology Graz Austria Center for Biomedical Imaging New York University School of Medicine New YorkNY United States Department of Clinical Physiology Karolinska University Hospital Karolinska Institutet Stockholm Sweden Department of Computing Imperial College London London United Kingdom Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands Institute for Biomedical Engineering ETH Zurich University of Zurich Zurich Switzerland Translational Neuromodeling Unit Institute for Biomedical Engineering University of Zurich ETH Zurich Zurich Switzerland NeuroPoly Lab Institute of Biomedical Engineering Polytechnique Montréal Montréal Canada Department of Radiology Stanford University StanfordCA United States Department of Radiology and Imaging Sciences University of Utah Salt Lake CityUT United States Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los AngelesCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Cardiff University Brain Research Imaging Centre Cardiff United Kingdom Institute for Diagnostic and Interventional Radiology University Medical Center Göttingen Göttingen Germany Germany University of Göttingen Germany University of Göttingen Germany

Purpose The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. Methods The task of the challenge was to reconstruct radially acquired multi-coil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). Results Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. Discussion and Conclusion While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, e.g., density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient meta-data accompanying open data sets. Defining reproducibility quantitatively turned out to be non-trivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection

arXiv

引用

arXiv 2017年

作者： Häne, Christian Heng, Lionel Lee, Gim Hee Fraundorfer, Friedrich Furgale, Paul Sattler, Torsten Pollefeys, Marc Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States Department of Computer Science ETH Zürich Universitätstrasse 6 Zürich8092 Switzerland Information Division DSO National Laboratories 12 Science Park Drive Singapore118225 Singapore Department of Computer Science National University of Singapore 13 Computing Drive Singapore117417 Singapore Institute for Computer Graphics & Vision Graz University of Technology Inffeldgasse 16 GrazA-8010 Austria Department of Mechanical and Process Engineering ETH Zürich Leonhardstrasse 21 Zürich8092 Switzerland Microsoft One Microsoft Way RedmondWA98052 United States

Cameras are a crucial exteroceptive sensor for self-driving cars as they are low-cost and small, provide appearance information about the environment, and work in various weather conditions. They can be used for multiple purposes such as visual navigation and obstacle detection. We can use a surround multi-camera system to cover the full 360-degree field-of-view around the car. In this way, we avoid blind spots which can otherwise lead to accidents. To minimize the number of cameras needed for surround perception, we utilize fisheye cameras. Consequently, standard vision pipelines for 3D mapping, visual localization, obstacle detection, etc. need to be adapted to take full advantage of the availability of multiple cameras rather than treat each camera individually. In addition, processing of fisheye images has to be supported. In this paper, we describe the camera calibration and subsequent processing pipeline for multi-fisheye-camera systems developed as part of the V-Charge project. This project seeks to enable automated valet parking for self-driving cars. Our pipeline is able to precisely calibrate multi-camera systems, build sparse 3D maps for visual navigation, visually localize the car with respect to these maps, generate accurate dense maps, as well as detect obstacles based on real-time depth map extraction. Copyright © 2017, The Authors. All rights reserved.

关键词： Calibration

来源：评论

学校读者我要写书评

暂无评论

In-depth assessment of an interactive graph-based approach for the segmentation for pancreatic metastasis in ultrasound acquisitions of the liver with two specialists in internal medicine

arXiv

引用

arXiv 2018年

作者： Egger, Jan Schmalstieg, Dieter Bettac, Lucas Hänle, Mark Hann, Alexander Chen, Xiaojun Gräter, Tilmann Egger, Jan Zoller, Wolfram G. Hann, Alexander Institute for Computer Graphics Vision Graz University of Technology Graz Austria Dept. of Internal Medicine I Ulm University Ulm Germany School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China Dept. of Diagnostic and Interventional Radiology Ulm University Ulm Germany BioTechMed-Graz Graz Austria Dept. of Internal Medicine and Gastroenterology Katharinenhospital Stuttgart Germany

The manual outlining of hepatic metastasis in (US) ultrasound acquisitions from patients suffering from pancreatic cancer is common practice. However, such pure manual measurements are often very time consuming, and the results repeatedly differ between the raters. In this contribution, we study the in-depth assessment of an interactive graph-based approach for the segmentation for pancreatic metastasis in US images of the liver with two specialists in Internal Medicine. Thereby, evaluating the approach with over one hundred different acquisitions of metastases. The two physicians or the algorithm had never assessed the acquisitions before the evaluation. In summary, the physicians first performed a pure manual outlining followed by an algorithmic segmentation over one month later. As a result, the experts satisfied in up to ninety percent of algorithmic segmentation results. Furthermore, the algorithmic segmentation was much faster than manual outlining and achieved a median Dice Similarity Coefficient (DSC) of over eighty percent. Ultimately, the algorithm enables a fast and accurate segmentation of liver metastasis in clinical US images, which can support the manual outlining in daily practice. Copyright © 2018, The Authors. All rights reserved.

关键词： Graphic methods

来源：评论

学校读者我要写书评

暂无评论

Interactive outlining of pancreatic cancer liver metastases in ultrasound images

arXiv

引用

arXiv 2017年

作者： Egger, Jan Schmalstieg, Dieter Chen, Xiaojun Zoller, Wolfram G. Hann, Alexander Institute for Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 Graz8010 Austria BioTechMed Krenngasse 37/1 Graz8010 Austria Shanghai Jiao Tong University School of Mechanical Engineering 800 Dong Chuan Road Shanghai200240 China Department of Internal Medicine and Gastroenterology Katharinenhospital Stuttgart Germany Department of Internal Medicine I Ulm University Ulm Germany

Ultrasound (US) is the most commonly used liver imaging modality worldwide. Due to its low cost, it is increasingly used in the follow-up of cancer patients with metastases localized in the liver. In this contribution, we present the results of an interactive segmentation approach for liver metastases in US acquisitions. A (semi-) automatic segmentation is still very challenging because of the low image quality and the low contrast between the metastasis and the surrounding liver tissue. Thus, the state of the art in clinical practice is still manual measurement and outlining of the metastases in the US images. We tackle the problem by providing an interactive segmentation approach providing real-time feedback of the segmentation results. The approach has been evaluated with typical US acquisitions from the clinical routine, and the datasets consisted of pancreatic cancer metastases. Even for difficult cases, satisfying segmentations results could be achieved because of the interactive real-time behavior of the approach. In total, 40 clinical images have been evaluated with our method by comparing the results against manual ground truth segmentations. This evaluation yielded to an average Dice Score of 85% and an average Hausdorff Distance of 13 pixels. Copyright © 2017, The Authors. All rights reserved.

关键词： Pathology

来源：评论

学校读者我要写书评

暂无评论

Navigating the Artificial Intelligence Revolution in NeuroOncology: A Multidisciplinary Viewpoint

SSRN

引用

SSRN 2024年

作者： Saxena, Sanjay Panda, Soumyaranjan Tiwari, Ekta Singh, Rajesh Fouda, Mostafa M. Kalra, Mannudeep K. Kotecha, Ketan Saba, Luca Suri, Jasjit S. Department of Computer Science & Engineering International Institute of Information Technology Odisha Bhubaneswar India Department of Computer Science & Information Technology Mahatma Gandhi Central University Bihar India Department of Computer & Engineering Visvesvaraya National Institute of Technology Maharashtra Nagpur India Department of Research and Innovation Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Department of Electrical and Computer Engineering Idaho State University PocatelloID83209 United States Department of Radiology Massachusetts General Hospital BostonMA United States Symbiosis centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune India Cagliari Italy Department of ECE Idaho State University PocatelloID83209 United States Department of CE Graphics Era Deemed to be University Dehradun248002 India University Center for Research & Development Chandigarh University Mohali India Pune India Stroke Diagnostic and Monitoring Division AtheroPoint RosevilleCA95661 United States Harvard Medical School United States University Hospital of Cagliari Italy

This comprehensive review presents the role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in neuro-oncology, focussing their groundbreaking impact on the field. It gives a historical overview of AI's evolution in area of neuro-oncology, representing its progress from basic data management to high-level predictive analytics that now significantly makes personalized treatment ***, This study examines various ML based methods that emphasize brain tumor classification, genetic profiling, and individualized care plans proposals, which basically reflects the shift towards precision medicine. This paper also highlights, developments in DL particularly in the development of neuro-imaging analysis, which has significantly guided surgical planning and radiation therapy for improved patient outcomes. Through various case studies, the paper presents recent breakthroughs where AI interventions have noticeably enhanced the treatment of neuro-oncological conditions. Moreover, It also addresses the ethical considerations encompassing the use of automated decision-making tools, emphasizing the need for ethical governance in AI deployment. By integrating multidisciplinary perspectives, the study underscores the collaborative effort between clinicians, data scientists, and ethicists, which is very crucial for the ethical and effective implementation of AI in current clinical practice. The main aim of discussion is to equip neurooncologists/sergeons and doctors with understandings into sustainable AI adoption which ensures that AI's path in neuro-oncology aligns with both clinical needs for personalized medicine and ethical *** paper concludes by outlining considered methods and frameworks for the future integration of AI in neuro-oncology, predicting trends, and preparing to meet the challenges ahead. © 2024, The Authors. All rights reserved.

关键词： Personalized medicine

来源：评论

学校读者我要写书评

暂无评论

An efficient and effective automatic image annotation using positive and negative example images

引用

ICIC Express Letters 2011年第8 B期5卷 2927-2932页

作者： Song, Haiyu Li, Xiongfei Wang, Pengjie Bao, Cuizhu Dong, Yuying Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University No. 2699 Qianjin Avenue Changchun 130012 China Institute of Computer Graphics and Image Processing Dalian Nationalities University No. 18 Liaohe Road Eco. and Tech. Dev. Zone Dalian 116600 China College of Computer Science and Engineering Dalian Nationalities University No. 18 Liaohe Road Eco. and Tech. Dev. Zone Dalian 116600 China College of Life Science Dalian Nationalities University No. 18 Liaohe Road Eco. and Tech. Dev. Zone Dalian 116600 China

Automatic image annotation is an active topic and difficult task in computer vision domain, which has attracted more and more researchers' attention. Many approaches have been proposed to automatically annotate images. To improve the annotation quality, most of them have profound principlessophisticated modelsand high time complexities. How to efficiently annotate image is becoming one key issue in the field of computer vision. We propose a very efficient and effective approach called keyword vector classification approach, whose model is very simple. Experiments on Corel5K dataset show that the annotation performance of the our proposed approach is almost four times better than translation model and twice better than CMRM (in terms of precision, recall and F1) while the time cost of ours surprisingly decreases. In addition, to our knowledge, our proposed approach outperforms most of existing ones in terms of N+. © 2011 ISSN 1881-803X.

关键词： Image retrieval

来源：评论

学校读者我要写书评

暂无评论

Economic impact for integrating constructivism, project-based learning and practice into high quality professional graduate education for engineers in industry to enhance corporate advantage and U.S. competitiveness in the global economy

Economic impact for integrating constructivism, project-base...

引用

2005 ASEE Annual Conference and Exposition: The Changing Landscape of engineering and Technology Education in a Global World

作者： Snellenberger, J.M. Quick, D.H. Tidwell, J.P. O'Brien, J. Davis, T.I. McHenry, A.L. Bardo, J.W. Dunlap, D.D. Deloatch, E.M. Lee, P.Y. Palmer, H.J. Tricamo, S.J. Depew, D.R. Bertoline, G.R. Dyrenfurth, M.J. Keating, D.A. Stanford, T.G. Rolls-Royce Corporation United States Boeing Company United States Hewlett-Packard United States Raytheon Missile Systems United States Arizona State University East United States Western Carolina University United States Morgan State University United States California Polytechnic State University United States Rochester Institute of Technology United States New Jersey Institute of Technology United States Purdue University United States Rolls-Royce Corporation Indianapolis IN United States ASEE Corporate Members Council United States Allison Advanced Development Company Rolls-Royce Corporation Indianapolis IN United States Boeing Company Meza AN United States Hewlett-Packard Company Palo Alto CA United States Raytheon Missile Systems Tucson AZ United States College of Technology and Applied Sciences Arizona State University East United States Department of Engineering and Technology Western Carolina University United States ASEE Graduate Studies Division School of Engineering Morgan State University United States ASEE College of Engineering California State Polytechnic University United States Kate Gleason College of Engineering Rochester Institute of Technology United States Department of Industrial and Manufacturing Engineering College of Technology Purdue University United States Discovery Resources Envision Center Department of Computer Graphics College of Technology Purdue University United States Department of Graduate Studies College of Technology Purdue University United States Department of Mechanical Engineering University of South Carolina United States Department of Chemical Engineering University of South Carolina United States

This is the fourth of four papers prepared for a special panel session of the National Collaborative Task Force on engineering Graduate Education Reform focusing on new educational approaches and processes that better meet the development needs of the U.S. engineering workforce in industry to enhance global competitiveness. Further graduate development of the U.S. engineering workforce in industry is critical to the continuous improvement, invention, development, and innovation of new technology which is the engine for U.S. economic prosperity and competitiveness. This paper focuses on a unique model for workforce development that represents a significant advancement in professional graduate education extending through the professional masters, professional doctorate and fellow levels of engineering practice. This advancement in professional graduate education forms a new partnership for university-industry engagement for U.S. engineering workforce development that completes a missing piece of the process to better enable U.S. technological innovation which is long overdue. The impact of project-based learning on industrial innovation is evidenced. This model is designed specifically to support the on-site engineering process for continuous improvement and innovation in industry. It purposefully integrates postgraduate professional education for industry's employed engineers with on-site technology development projects that are chosen to be directly relevant to industry's continuous innovation needs and high-end engineering projects. The returns for enhanced corporate advantage generated through this unique model are measured as a matrix of increasing complexity of economic worth of on-site projects and of increasing human proficiency gained for leadership of technological innovation.

关键词： Learning systems

来源：评论

学校读者我要写书评

暂无评论

Development of a Realistic Crowd Simulation Environment for Fine-grained Validation of People Tracking Methods

arXiv

引用

arXiv 2023年

作者： Foszner, Pawel Szczęsna, Agnieszka Ciampi, Luca Messina, Nicola Cygan, Adam Bizon, Bartosz Cogiel, Michal Golba, Dominik Macioszek, Elżbieta Staniszewski, Michal Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Institute of Information Science and Technologies National Research Council Pisa Italy QSystems.pro sp. z o.o. Mochnackiego 34 Bytom41-907 Poland Blees sp. z o.o. Zygmunta Starego 24a/10 Gliwice44-100 Poland Department of Transport Systems Traffic Engineering and Logistics Faculty of Transport and Aviation Engineering Silesian University of Technology Katowice Poland

Generally, crowd datasets can be collected or generated from real or synthetic sources. Real data is generated by using infrastructure-based sensors (such as static cameras or other sensors). The use of simulation tools can significantly reduce the time required to generate scenario-specific crowd datasets, facilitate data-driven research, and next build functional machine learning models. The main goal of this work was to develop an extension of crowd simulation (named CrowdSim2) and prove its usability in the application of people-tracking algorithms. The simulator is developed using the very popular Unity 3D engine with particular emphasis on the aspects of realism in the environment, weather conditions, traffic, and the movement and models of individual agents. Finally, three methods of tracking were used to validate generated dataset: IOU-Tracker, Deep-Sort, and Deep-TAMA. © 2023, CC BY.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

CrowdSim2: an Open Synthetic Benchmark for Object Detectors

arXiv

引用

arXiv 2023年

Data scarcity has become one of the main obstacles to developing supervised models based on Artificial Intelligence in computer Vision. Indeed, Deep Learning-based models systematically struggle when applied in new scenarios never seen during training and may not be adequately tested in non-ordinary yet crucial real-world situations. This paper presents and publicly releases CrowdSim2, a new synthetic collection of images suitable for people and vehicle detection gathered from a simulator based on the Unity graphical engine. It consists of thousands of images gathered from various synthetic scenarios resembling the real world, where we varied some factors of interest, such as the weather conditions and the number of objects in the scenes. The labels are automatically collected and consist of bounding boxes that precisely localize objects belonging to the two object classes, leaving out humans from the annotation pipeline. We exploited this new benchmark as a testing ground for some state-of-the-art detectors, showing that our simulated scenarios can be a valuable tool for measuring their performances in a controlled environment. © 2023, CC BY.

关键词： Object detection

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：