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Cognitive Task Analysis: Techniques Applied to Airborne Weapons Training

Proceedings of the Human Factors and Ergonomics Society Annual Meeting 1989年第19期33卷 1358 - 1362页

作者： Michele Terranova Thomas L. Seamster Cathrine E. Snyder Inga E. Treitler Oak Ridge National Laboratory Oak Ridge Tennessee Carlow Associates Incorporated Fairfax Virginia Data Systems Research and Development Martin Marietta Energy Systems Oak Ridge Tennessee University of Tennessee Knoxville Tennessee

This is an introduction to cognitive task analysis as it may be used in Naval Air systems Command (NAVAIR) training development. The focus of a cognitive task analysis is human knowledge, and its methods of analysis are those developed by cognitive psychologists. This paper explains the role that cognitive task analysis can play in the development of advanced training systems and presents the findings from a preliminary cognitive task analysis of airborne weapons operators. Cognitive task analysis is a collection of powerful techniques that are quantitative, computational, and rigorous. The techniques are currently not in wide use in the training community, so examples of this methodology are presented along with the results.

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3D Ultrasound Tracking of The Left Ventricle Using One-Step Forward Prediction and data Fusion of Collaborative Trackers

3D Ultrasound Tracking of The Left Ventricle Using One-Step ...

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26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2008), vol.5

作者： Lin Yang Bogdan Georgescu Yefeng Zheng Peter Meer Dorin Comaniciu Integrated Data Systems Siemens AG Corporate Research and Development Princeton NJ USA Electrical and Computer Engineering Rutgers University Piscataway NJ USA

Tracking the left ventricle (LV) in 3D ultrasound data is a challenging task because of the poor image quality and speed requirements. Many previous algorithms applied standard 2D tracking methods to tackle the 3D problem. However, the performance is limited due to increased data size, landmarks ambiguity, signal drop-out or non-rigid deformation. In this paper we present a robust, fast and accurate 3D LV tracking algorithm. We propose a novel one-step forward prediction to generate the motion prior using motion manifold learning, and introduce two collaborative trackers to achieve both temporal consistency and failure recovery. Compared with tracking by detection and 3D optical flow, our algorithm provides the best results and sub-voxel accuracy. The new tracking algorithm is completely automatic and computationally efficient. It requires less than 1.5 seconds to process a 3D volume which contains 4,925,440 voxels.

关键词： Ultrasonic imaging Collaboration Robustness Tracking data systems Image quality Shape Heart Cardiovascular diseases Image segmentation

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Applying amorphous CoNbZr shield to improve the dielectric-breakdown voltages of the gap layers of narrow-gap read heads

Applying amorphous CoNbZr shield to improve the dielectric-b...

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International Conference on Magnetics (INTERMAG)

作者： K. Hoshino S. Odai M. Hatatani STR branch Office Central Research and Development Laboratory Hitachi Limited Kanagawa Japan Data Storage Systems Division Hitachi and Limited Kanagawa Japan

In this paper, the effect of the lower shield materials on the breakdown voltage of the lower insulating gap layer has been investigated. A hybrid shield CoNbZr layer on NiFe shield was applied ton the read , and the ... 详细信息

关键词： Amorphous materials Voltage Magnetic heads Dielectric measurements Electrodes Dielectrics and electrical insulation Dielectric substrates Testing Disk recording Hard disks

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Climate reference network stations: Location, location, location...

Climate reference network stations: Location, location, loca...

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Combined Preprints: 84th American Meteorological Society (AMS) Annual Meeting

作者： Helfert, Michael R. Changery, Michael J. Easterling, David R. Janis, Michael J. Baker, Samuel M. Summer, Buford M. Graybeal, Daniel Y. Hubbard, Kenneth G. Redmond, Kelly T. May, Edwin L. NOAA National Climatic Data Center Asheville NC Southeast Regional Climate Center Department of Natural Resources Columbia SC Northeast Regional Climate Center Cornell University Ithaca NY High Plains Regional Climate Center University of Nebraska Lincoln NB Western Regional Climate Center Desert Research Institute Reno NV Short and Associates Inc. NOAA Office of Systems Development Washington DC Short and Associates Inc. NOAA Office of Systems Development Asheville NC National Climatic Data Center 151 Patton Avenue Asheville NC 28801

The United States Climate Reference Network (USCRN) goal is to document long-term climate behavior in accordance with the 1999 Climate Monitoring Principles certified by the National research Council. USCRN scientific success is based upon frontend careful and rigorous selection of appropriate instrument monitoring sites. The primary variables to be monitored are the air temperature and the precipitation. The secondary variables of solar radiation, wind velocity, and IR (skin) temperature are specificaily designed to support the precision and understanding of the primary instrument measurements. The Phase 1 plan is for about 100 USCRN stations to be deployed across the United States by the end of 2006. A secondary grid of 75 stations has been envisioned for Alaska, Hawaii, and perhaps Puerto Rico and the Virgin Islands. 49 USCRN stations are now operating. Two experimental stations are also operating in severe environments in Alaska. Criteria for USCRN sites targeted for operations during the next 50-100 years are very exacting. They exceed WMO standards for climate monitoring and station siting. This paper discusses primary climate station siting criteria: proper instrument and site exposures;land use and land tenure stability;avoidance of built-up areas and areas of future development;avoidance of agriculture, irrigated areas, and large artificial water bodies;regional climate representativiness with respect to temperature and precipitation;avoidance of localized microclimates, and consistent site maintenance and instrument recalibrations. Examples are given of sites and locations sensitive to climate change and of sites representing special circumstances and environments.

关键词： Weather information services

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Appearance modeling under geometric context

Appearance modeling under geometric context

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International Conference on Computer Vision (ICCV)

作者： Jian Li S.K. Zhou R. Chellappa Center for Automation Research & ECE Department University of Maryland College Park MD USA Integrated Data Systems Department Siemens AG Corporate Research and Development Princeton NJ USA

We propose a unified framework based on a general definition of geometric transform (GeT) for modeling appearance. GeT represents the appearance by applying designed functionals over certain geometric sets. We show that image warping, Radon transform, trace transform, etc. are special cases of our definition. Moreover, three different types of GeTs are designed to handle deformation, articulation and occlusion and applied to fingerprinting the appearance inside a contour. They include the contour-driven GeT, the feature curve based GeT and selecting functionals to model the appearance inside the convex hull of the contour. A multi-resolution representation that combines both shape and appearance information is also proposed. We apply our approach to image synthesis and object recognition. The proposed approach produces promising results when applied to fingerprinting the appearance of human and body parts despite the challenges due to articulated motion and deformations

关键词： Solid modeling Context modeling Active appearance model Biological system modeling Shape Humans Fingerprint recognition Automation data systems Educational institutions

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Integrated Detection, Tracking and Recognition for IR Video-Based Vehicle Classification

Integrated Detection, Tracking and Recognition for IR Video-...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xue Mei S.K. Zhou Hao Wu Center for Automation Research and ECE Department University of Maryland College Park MD USA Integrated Data Systems Department Siemens AG Corporate Research and Development Princeton NJ USA

We present an approach for vehicle classification in IR video sequences by integrating detection, tracking and recognition. The method has two steps. First, the moving target is automatically detected using a detection algorithm. Next, we perform simultaneous tracking and recognition using an appearance-model based particle filter. The tracking result is evaluated at each frame. Low confidence in tracking performance initiates a new cycle of detection, tracking and classification. We demonstrate the robustness of the proposed method using outdoor IR video sequences

关键词： Infrared detectors Vehicle detection Vehicles Target tracking Video sequences Detection algorithms Image segmentation Analysis of variance Object detection Robustness

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Biomedical data and AI

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Science China Life Sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Modeling and Simulation for the Spread of Covid-19 in an Indian City: A Case Study 22

Modeling and Simulation for the Spread of Covid-19 in an Ind...

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Proceedings of the Winter Simulation Conference

作者： Aditya A. Paranjape Souvik Barat Anwesha Basu Rohan Salvi Supratim Ghosh Vinay Kulkarni Software Systems & Services TCS Research Tata Research Development and Design Centre Pune INDIA School of Information Sciences University of Illinois at Urbana-Champaign Champaign IL USA Decision & Data Sciences TCS Research Tata Research Development and Design Centre Pune INDIA

We present a case study on modeling and predicting the course of Covid-19 in the Indian city of Pune. The results presented in this paper are concerned primarily with the wave of infections triggered by the Delta variant during the period between February and June 2021. Our work demonstrates the necessity for bringing together compartmental stock-and-flow and agent-based models and the limitations of each approach when used individually. Some of the work presented here was carried out in the process of advising the local city administration and reflects the challenges associated with employing these models in a real-world environment with its uncertainties and time pressures. Our experience, described in the paper, also highlights the risks associated with forecasting the course of an epidemic with evolving variants.

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Using SVG to Model and Query Image Annotations and Their History

Using SVG to Model and Query Image Annotations and Their His...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Fusheng Wang Cornelius Rabsch Peiya Liu Integrated Data Systems Department Siemens AG Corporate Research and Development Princeton NJ USA Department of Mathematics and Computer Science University of Mannheim Mannheim Germany

Image annotation is becoming increasingly important for clinical applications and medical research. Especially, collaborative image annotation can harness the collective intelligence from distributed users. There are several challenges to model medical image annotations: i) Medical image annotation includes not only textual annotation but also graphical annotation, which can consist of multiple areas or layers; ii) To support collaborative annotation from multiple users, collaboration has to be supported and represented in a proper way; Hi) Both textual and graphical annotations should be searchable, e.g., support of spatial queries; iv) annotation history needs to be tracked and searchable in applications such as clinical trials; and v) To enable convenient annotation, and save cost, lightweight system should be provided. Most current medical image annotation applications are workstation based applications, and use proprietary methods to represent annotations, which make it difficult to search, share, and exchange annotations. Meanwhile, the Web is evolving quickly on supporting strong collaboration through several new technologies, including Ajax (Asynchronous JavaScript and XML) and SVG (Scalable Vector Graphics). Especially, SVG now becomes a standard language for vector graphics on the Web, which, indeed, is recently natively supported by latest Web browsers. This brings a new opportunity to support collaborative image annotations on the Web. In our work, we develop a comprehensive data model to support both textual and graphical annotations and their history on 2-D medical images, and implement the data model with the standard language SVG. This data model can support powerful queries on annotations including complex textual, spatial, collaboration, group, and history queries based on standard XML Query language XQuery. Besides, the approach can be used to support collaborative image annotations in a lightweight Web browser environment.

关键词： History Biomedical imaging Collaboration Collaborative work data models XML Graphics Clinical trials Costs Workstations

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Native Web Browser Enabled SVG-based Collaborative Multimedia Annotation for Medical Images

Native Web Browser Enabled SVG-based Collaborative Multimedi...

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International Conference on data Engineering

ISBN: (纸本)9781424418367;1424418364

Image annotation becomes increasingly important for clinical applications and medical research. In particular, collaborative image annotations can harness the collective intelligence from distributed experts. There are several challenges to support collaborative medical image annotations: i) Medical image annotation includes not only metadata annotation but also multimedia annotation, such as graphical annotation. The latter often requires a heavy-duty tool, which can be difficult to realize in a distributed environment; ii) Annotations need to be well modeled for easy exchange and support of queries, i.e., there is a gap between image annotation and content retrieval; and iii) An annotation platform is needed to provide authoring tools and the collaborative infrastructure. Meanwhile, the Web is evolving quickly on supporting interaction, participation and collaboration enabled by Web 2.0 technologies. Among them, scalable vector graphics (SVG) now becomes a standard language for vector graphics on the Web natively supported by latest Web browsers. In our work, we develop a collaborative image annotation platform, which provides: i) a flexible data model to support both metadata and multimedia annotations on 2-D medical images; ii) SVG based implementation of the data model that can support complex textual, spatial, and collaborative queries on annotations with XQuery; iii) a lightweight native Web browser enabled annotation authoring tool without any plugin needed; and iv) an architecture that provides authoring, storing, querying, and exchanging of annotations, and supports Web-based collaboration.

关键词： Collaboration Biomedical imaging Collaborative work Medical diagnostic imaging Graphics Collaborative tools data models Shape Java Animation

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