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IEEE International conference on Intelligence and Security Informatics, ISI

作者： Steven R. Haynes Jonathan A. Singel College of Information Sciences & Technology Pennslyvania State University University Park PA USA

This paper describes a conceptual model and set of tools for understanding and supporting knowledge reachback during weapons of mass destruction (WMD) events. The model and tool set are products of a case study and design research carried out with the defense threat reduction agency (DTRA), the United States government organization with primary responsibility for reduction of the global threat from WMDs. The conceptual model provides a useful abstraction for those working to support reachback in situations related to WMDs, emergency response, crisis management, and other complex domains. Development of the conceptual model is grounded in an analysis of WMD scenarios and the intense knowledge communication demands that arise from coordination of inter-organizational responses to these events. The conceptual model and scenario analysis suggest a number of approaches to supporting reachback with advanced information systems and technologies.

关键词： Personnel Knowledge management Educational institutions Weapons Predictive models US Government Crisis management Information analysis Management information systems data visualization

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Contact angles and liquid loss behavior of high index fluids

Contact angles and liquid loss behavior of high index fluids

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European Mask and Lithography conference

作者： Paul M. Harder Timothy A. Shedd Multiphase Flow and Visualization and Analysis Laboratory Dept. of Mechanical Engineering University of Wisconsin-Madison 1500 Engineering Drive Madison WI U.S.A. 53706-1609

ISBN: (纸本)9780819466556

To achieve the numerical aperture required for the next generation of immersion lithography, water may be replaced with a high index liquid as the immersion fluid. Due to their low surface tension to viscosity ratios, candidate high index fluids have an increased tendency to lose liquid from the under-lens region during scanning. Since any residual liquid left on the wafer is a potential defect mechanism, the conversion to high index fluids may drastically reduce scanning speeds and chip throughput. The mechanism for liquid loss strongly depends on the behavior of the three-phase contact line, so this work focuses on the experimental study of the static and dynamic contact behavior of five high index fluids. Contact angle and critical liquid loss velocity data is compared to the critical velocity model we previously developed, and the liquid loss behavior is discussed.

关键词： liquids Fluid contact angle Indexes Surface Tension numerical aperture critical velocity scanning CONTACT Contact Information Wafer Dosage Form

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Assessing flexible models and rule extraction from censored survival data

Assessing flexible models and rule extraction from censored ...

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International Joint conference on Neural Networks (IJCNN)

作者： Paulo J.G. Lisboa Elia M. Biganzoli Azzam F. Taktak Terence A. Etchells Ian H. Jarman M.S. Hane Aung Federico Ambrogi School of Computing and Mathematical Sciences Liverpool John Moores University UK Unit of Medical Statistics and Biometry Istituto Nazionale Tumori Milan Italy Department of Clinical Engineering Royal Liverpool University Hospital UK

The evaluation of generic non-linear models for censored data needs to address the two complementary requirements in the software development life-cycle, of validation and verification. The former involves making a rigorous assessment of predictive accuracy in prognostic modelling and the latter is interpreted in this paper as comprising two different stages, namely model selection and rule-based interpretation of the composition of prognostic risk groups. With reference to prognostic performance is survival modelling the well-known ROC framework has recently been extended to a threshold independent, time-dependent performance index to quantify the predictive accuracy of censored data models, termed the C' index, which is briefly described. The rule-based framework for direct validation of risk group allocation against expert domain knowledge, uses low-order Boolean rules to approximate the response surfaces generated by analytical inference models. In the case of censored data, this approach serves to characterise the allocation of patients into risk groups generated by a risk staging index. Furthermore, the low-order rules define low-dimensional sub-spaces where individual data points can be directly visualised in relation to the decision boundaries for their risk group. Taken together, the quantitative performance index, Boolean explanatory rules and direct visualisation of the data, define a consistent and transparent validation framework based on triangulation of information. This information can be included in decision support systems.

关键词： data mining Predictive models Accuracy Performance analysis data visualization Programming data models Response surface methodology Risk analysis Analytical models

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Multi-platform Airplane Health Management

Multi-platform Airplane Health Management

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IEEE conference on Aerospace

作者： Greg J. Clark John L. Vian Michael E. West Vassilis L. Syrmos William F. Randolph William J. Hardman Matthew A. Shawver Geoff J. Hanson The Boeing Company Phantom Works University of Hawaii NAVAIR Referentia Systems Inc.

The Integrated Airplane Health Management (IAHM) program is a Navy research activity led by The Boeing Company with support from the University of Hawaii, Referentia Systems Incorporated, and Impact Technologies, LLC. The program focuses on interoperable multi-platform aircraft health management data handling and analysis methods applicable to military aircraft and commercial air transportation. A primary goal of the program is to significantly increase aircraft system reliability, safety, maintainability, availability, and affordability, resulting in improved mission performance and operational capabilities. As such, improved aircraft data accessibility and interoperability is viewed as a key enabler. The program consists of three phases leading to a proof-of-concept demonstration to show technical feasibility and potential payoffs for both military aircraft and commercial airlines. Software data loading translators have been developed and are being used to populate a new MIMOSA-based multi-platform health management database that can interface with common algorithms to support condition-based maintenance practices. In particular, this paper provides an overview of the concept and the efforts of the IAHM team in accommodating a variety of representative aircraft health data from F/A-18, C-17, and 737 platforms.12 A graphical user interface has been developed to access the IAHM database for data visualization, new diagnostic and prognostic algorithm research and development, and also for processing new datasets with the health management algorithms being developed. The resulting IAHM database schema, data interface methods, and algorithms are applicable to new, derivative, and current aircraft.

关键词： Airplanes Military aircraft Visual databases Technology management data handling data analysis Air transportation Reliability Air safety Maintenance

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Climatic variation of the structure of maximum daily temperatures in Spain: A combined statistical and computational intelligence approach.

Climatic variation of the structure of maximum daily tempera...

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International Joint conference on Neural Networks (IJCNN)

作者： Julio J. Valdes Antonio Pou Institute for Information Technology National Research Council Canada Ottawa ONT Canada Department of Ecology Faculty of Sciences Autonomous University of Madrid Madrid Spain

Two blocks (1904-1921 and 1990-2007) of daily maximum temperature data from seventeen Spanish meteorological stations exhibit a multimodal empirical distribution function (EDF). Most of the stations show important differences in their EDF for each one of the considered periods of time, a fact that reveals the complexity of climatic changes within the accepted general warming trend of the Iberian Peninsula. As a tentative approach to understand the underlying structure of data, each EDF has been decomposed on two normal distributed functions. The parameters describing these functions for each station and for each time period have been space-optimized and visualized using classical optimization and genetic programming. The changes in the geographical distribution of the classes derived from the analysis point towards a recent greater role of Mediterranean climates, spreading its influence to the interior of the Peninsula. The general picture, however, is much more complex than a linear warming and a number of stations even show negative trends. This study is considered to be a preliminary methodological exploration of future procedures destined to close the gap between data driven analysis and what models based upon first principles may tell.

关键词： Temperature distribution Computational intelligence data analysis Earth Instruments Neural networks Meteorology Distribution functions data visualization Genetic programming

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A network algorithm to discover sequential patterns 07

A network algorithm to discover sequential patterns

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Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence

作者： Luís Cavique ESCS-IPL Portugal

ISBN: (纸本)9783540770008

This paper addresses the discovery of sequential patterns in very large databases. Most of the existing algorithms use lattice structures in the space search that are very demanding computationally. The output of these algorithms generates a large number of rules. The aim of this work is to create a swift algorithm for the discovery of sequential patterns with a low time complexity. In this work, we also want to define tools that allow us to simplify the work of the final user, by offering a new visualization of the sequences, while bypassing the analysis of thousands of association rules.

关键词： data mining graph theory frequent sequence mining

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SciDAC visualization and analytics center for enabling technology

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Journal of Physics: conference Series 2007年第1期78卷

作者： Bethel, E. Wes Johnson, Chris Joy, Ken Ahern, Sean Pascucci, Valerio Childs, Hank Cohen, Jonathan Duchaineau, Mark Hamann, Bernd Hansen, Charles Laney, Dan Lindstrom, Peter Meredith, Jeremy Ostrouchov, George Parker, Steven Silva, Claudio Sanderson, Allen Tricoche, Xavier Computing Sciences Division Lawrence Berkeley National Laboratory University of California Berkeley CA 94720-8139 United States Scientific Computing and Imaging Institute University of Utah Salt Lake City UT 84112 72 S. Central Campus Drive United States Institute for Data Analysis and Visualization University of California Davis CA 95616-8562 One Shields Avenue United States National Center for Computational Sciences Oak Ridge National Laboratory MS-6016 Oak Ridge TN 37831 One Bethel Valley Road United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore CA 94551 P.O. Box 808 L-561 United States

The visualization and Analytics Center for Enabling Technologies (VACET) focuses on leveraging scientific visualization and analytics software technology as an enabling technology for increasing scientific productivity and insight. Advances in computational technology have resulted in an "information big bang," which in turn has created a significant data understanding challenge. This challenge is widely acknowledged to be one of the primary bottlenecks in contemporary science. The vision of VACET is to adapt, extend, create when necessary, and deploy visual data analysis solutions that are responsive to the needs of DOE's computational and experimental scientists. Our center is engineered to be directly responsive to those needs and to deliver solutions for use in DOE's large open computing facilities. The research and development directly target data understanding problems provided by our scientific application stakeholders. VACET draws from a diverse set of visualization technology ranging from production quality applications and application frameworks to state-of-the-art algorithms for visualization, analysis, analytics, data manipulation, and data management. © 2007 IOP Publishing Ltd.

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Automated Classification of Visible and Near-Infrared Spectra Using Self-Organizing Maps

Automated Classification of Visible and Near-Infrared Spectr...

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IEEE conference on Aerospace

作者： Ted L. Roush Robert Hogan MS 245-3 NASA Ames Research Center Moffett Field CA USA MS 245-3 Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA USA

Existing and planned space missions to planetary and satellite surfaces produce increasing volumes of spectral data. Understanding the scientific content in this large data volume is a daunting task. Various statistical approaches are available to assess such data sets. We apply an automated classification scheme based on Kohonen Self-Organizing maps (SOM) developed originally for the thermal infrared (TIR) and extended here to the visible and near-infrared (VNIR). Available data from spectral libraries are used to train and test the classification in the VNIR. The library spectra are labeled in a hierarchical scheme with class, sub-class, and mineral group names. After training, the test spectra are presented to the SOM output layer and assigned membership to the appropriate cluster. These assignments are then evaluated to assess the robustness, scientific meaning and accuracy of the derived SOM classes as they relate to the spectral labels. We investigate the influence of particle size on our results by training and classifying three particle size separates. We find the SOM results are robust based upon the number of clusters determined from ten independent training/testing efforts. We find the SOM results are most scientifically meaningful for the grossest differences between materials, although some individual groups retain high accuracy even when the overall accuracy of the SOM is low.

关键词： Self organizing feature maps Testing Robustness Clustering algorithms NASA Libraries Humans Intelligent sensors Information analysis data visualization

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Aerial Lidar data Classification using AdaBoost

Aerial Lidar Data Classification using AdaBoost

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International conference on 3D Imaging, Modeling, Processing, visualization and Transmission (3DIMPVT)

作者： Suresh K. Lodha Darren M. Fitzpatrick David P. Helmbold School of Engineering University of California Santa Cruz CA USA

We use the AdaBoost algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, height variation, normal variation, lidar return intensity, and image intensity. We also use only lidar-derived features to organize the data into three classes (the road and grass classes are merged). We apply and test our results using ten regions taken from lidar data collected over an area of approximately eight square miles, obtaining higher than 92% accuracy. We also apply our classifier to our entire dataset, and present visual classification results both with and without uncertainty. We implement and experiment with several variations within the AdaBoost family of algorithms. We observe that our results are robust and stable over all the various tests and algorithmic variations. We also investigate features and values that are most critical in distinguishing between the classes. This insight is important in extending the results from one geographic region to another.

关键词： Laser radar Classification tree analysis Machine learning algorithms Roads Iterative algorithms Support vector machines Support vector machine classification Testing Uncertainty data engineering

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3D CARS image reconstruction and pattern recognition on SHG images

3D CARS image reconstruction and pattern recognition on SHG ...

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conference on Biophotonics - Photonic Solutions for Better Health Care III

作者： Medyukhina, Anna Vogler, Nadine Latka, Ines Dietzek, Benjamin Cicchi, Riccardo Pavone, Francesco S. Popp, Juergen Inst Photon Technol Albert Einstein Str 9 D-07745 Jena Germany Univ Jena Abbe Ctr Photon Inst Phys Chem D-07743 Jena Germany European Lab Non Linear Spect I-50019 Sesto Fiorentino Italy

ISBN: (纸本)9780819491190

Nonlinear optical imaging techniques based e.g. on coherent anti-Stokes Raman scattering (CARS) or second-harmonic generation (SHG) show great potential for in-vivo investigations of tissue. While the microspectroscopic imaging tools are established, automized data evaluation, i.e. image pattern recognition and automized image classification, of nonlinear optical images still bares great possibilities for future developments towards an objective clinical diagnosis. This contribution details the capability of nonlinear microscopy for both 3D visualization of human tissues and automated discrimination between healthy and diseased patterns using ex-vivo human skin samples. By means of CARS image alignment we show how to obtain a quasi-3D model of a skin biopsy, which allows us to trace the tissue structure in different projections. Furthermore, the potential of automated pattern and organization recognition to distinguish between healthy and keloidal skin tissue is discussed. A first classification algorithm employs the intrinsic geometrical features of collagen, which can be efficiently visualized by SHG microscopy. The shape of the collagen pattern allows conclusions about the physiological state of the skin, as the typical wavy collagen structure of healthy skin is disturbed e.g. in keloid formation. Based on the different collagen patterns a quantitative score characterizing the collagen waviness - and hence reflecting the physiological state of the tissue - is obtained. Further, two additional scoring methods for collagen organization, respectively based on a statistical analysis of the mutual organization of fibers and on FFT, are presented.

关键词： CARS SHG 3D collagen image analysis pattern recognition

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