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检索条件"任意字段=Conference on Visualization and Data Analysis 2004"

共 481 条记录，以下是151-160 订阅

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BEX - A novel wafer yield distribution model and analysis

BEX - A novel wafer yield distribution model and analysis

引用

15th Annual IEEE/SEMI Advanced Semiconductor Manufacturing conference and Workshop (ASMC 2004)

作者： Melzner, H Darnhofer, T Infineon Technol Munich Germany

ISBN: (纸本)0780383125

Baseline Excursion analysis (BEX) provides a statistical method to analyze wafer yield distributions. A novel graphical visualization scheme has been developed that supports inspection, analysis, and comparison of yield distributions much more effectively than standard yield histograms. In a first step, wafer yield is transformed to defect density by an appropriate model such as Poisson or Stapper model. Total defect density is subsequently modeled as the sum of Baseline and Excursion defect density components. Parameters of both distributions are extracted from a curve fit to the data. Key figures such as Baseline limited yield and Excursion limited yield are estimated and allow to tailor, focus and control yield improvement activities.

关键词： yield analysis yield modeling yield distribution defect density baseline excursions

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visualization of multivariate image data using image fusion and perceptually optimized color scales based on sRGB

Visualization of multivariate image data using image fusion ...

引用

Medical Imaging 2004 conference

作者： Saalbach, A Twellmann, T Nattkemper, TW White, MJ Khazen, M Leach, MO Univ Bielefeld Appl Neuroinformat Grp D-33615 Bielefeld Germany

ISBN: (纸本)0819452807

Due to the rapid progress in medical imaging technology, analysis of multivariate image data is receiving increased interest. However. their visual exploration is a challenging task since it requires the integration of information from many different sources which usually cannot be perceived at once by an observer. Image fusion techniques are commonly used to obtain information from multivariate image data, while psychophysical aspects of data visualization are usually not considered. visualization is typically achieved by means of device derived color scales. With respect to psychophysical aspects of visualization, more sophisticated color mapping techniques based on device independent (and perceptually uniform) color spaces like CIELUV have been proposed. Nevertheless, the benefit of these techniques is limited by the fact that they require complex color space transformations to account for device characteristics and viewing conditions. In this paper we present a new framework for the visualization of multivariate image data using image fusion and color mapping techniques. In order to overcome problems of consistent image presentations and color space transformations, we propose perceptually optimized color scales based on CIELUV in combination with sRGB (IEC 61966-2-1) color specification. In contrast to color definitions based purely on CIELUV, sRGB data can be used directly under reasonable conditions, without complex transformations and additional information. In the experimental section we demonstrate the advantages of our approach in an application of these techniques to the visualization of DCE-MRI images from breast cancer research.

关键词： multivariate image data image fusion perceptually optimized color scales sRGB

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Web-based collaborative 3D information visualization tool

Web-based collaborative 3D information visualization tool

引用

Proceedings - Eighth International conference on Information Visualisation, IV 2004

作者： Meiguins, Bianchi Serique De Souza Jr., Rosevaldo Dias De Garcia, Marcelo Brito Gonçalves, Aruanda Simões Departamento de Informática Centro de Exatas e Naturais Universidade Federal do Pará C.P. 8619 CEP 66075-900Belém - PA Brazil Prog. de Pos-Graduacao Depto. de Engenharia Elétrica Universidade Federal do Pará C.P. 8619 CEP 66075-900 -Belém - PA Brazil

The amount of data that must be collected for one to decide on a course of action, nowadays and in any field, is nothing if extraordinary. The three-dimensional graphic representation of data is a powerful tool for its analysis. And the possibility for interaction and browsing throughout these data makes this analysis much more interesting and productive. Nevertheless, one doesn't usually make a decision alone, and most tools are of a single-user nature, which jeopardizes the decision-making process, especially if those involved are distant from each other, geographically speaking. With a collaborative information visualization environment, it is possible to debate over the same data and change them if necessary, in real time, diminishing time and avoiding mistakes in the decision-making process. Therefore, this paper proposes a Web-based collaborative tool for data visualization using Java, EAI (External Authoring Interface) and VRML (Virtual Reality Modeling Language).

关键词： World Wide Web

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CircleView - A new approach for visualizing time-related multidimensional data sets 04

CircleView - A new approach for visualizing time-related mul...

引用

Working conference on Advanced Visual Interfaces, AVI 2004

作者： Keim, Daniel A. Schneidewind, Jörn Sips, Mike University of Konstanz Germany

ISBN: (纸本)1581138679

This paper introduces a new approach for visualizing multidimensional time-referenced data sets, called Circle View. The Circle View technique is a combination of hierarchical visualization techniques, such as treemaps [6], and circular layout techniques such as Pie Charts and Circle Segments [2]. The main goal is to compare continuous data changing their characteristics over time in order to identify patterns, exceptions and similarities in the data. To achieve this goal Circle View is a intuitive and easy to understand visualization interface to enable the user very fast to acquire the information needed. This is an important feature for fast changing visualization caused by time related data streams. Circle View supports the visualization of the changing characteristics over time, to allow the user the observation of changes in the data. Additionally it provides user interaction and drill down mechanism depending on user demands for a effective exploratory data analysis. There is also the capability of exploring correlations and exceptions in the data by using similarity and ordering algorithms. © 2004 ACM.

关键词： visualization

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Using visual exploratory data analysis to find bias in option pricing models

Using visual exploratory data analysis to find bias in optio...

引用

8th International conference on Information Visualisation

作者： Lajbcygier, P Monash Univ Sch Business Syst Clayton Vic 3168 Australia

ISBN: (纸本)0769521770

Options are amongst the most heavily transacted financial instruments in the world. This paper examines how the methods of visual exploratory tools, espoused by Cleveland[1] can be used to analyze the residuals from conventional option pricing models [2, 3]. Until recently, these models were believed to be unbiased [4, 5]. With the aid of visual exploratory tools we see that options on the All Ordinaries share price index trading on the Sydney Futures exchange have persistent, systematic and significant bias. This is the first time that various statistically oriented visual exploratory tools have been used to analyze option residuals. We find that the analysis motivates the use of alternative option pricing methods.

关键词： Industrial economics

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The 4D cluster visualization project

The 4D cluster visualization project

引用

Medical Imaging 2004 conference

作者： Redmond, M Brodsky, E Hu, YH Grist, T Schulte, M Block, W Univ Wisconsin Madison WI 53792 USA

ISBN: (纸本)0819452807

It is becoming increasingly common to image time-resolved flow patterns through the vascular system in all three spatial dimensions using non-invasive methods. The capability to generate four-dimensional (4D) (x, y, z and time) vascular flow data is growing in several modalities. Vastly undersampled Isotropic PRojection (VIPR) is one such method using high-resolution, fast Magnetic Resonance Imaging (MRI) of the vasculature system during intravenous contrast injection. VIPR currently produces 4D data sets of twenty to forty frames of 256(3) voxels each, and stronger magnets will allow higher resolution time series that generate gigabytes of data. Real-time visualization and analysis of 4D data can quickly overwhelm the memory and processing capabilities of desktop workstations. 4D Cluster visualization (4DCV) offers a straightforward, scalable approach to interactively display and manipulate 4D, reconstructed, VIPR data sets. 4DCV exploits the inherently parallel nature of 4D frame data to interactively manipulate and render individual 3D data frames simultaneously across all nodes of a visualization cluster. An interactive animation is produced in real-time by reading back the 2D rendered results, to a central animation console where the image sequence is assembled into a continuous stream for display. Basic 4DCV can be extended to allow rendering of multiple frames on one node, compression of image streams for serving remote clinical workstations, and local archival storage of 3D data frames at the cluster nodes for quick retrieval of medical exams. 4D Cluster visualization concepts can also be extended to distributed and Grid implementations.

关键词： medical imaging 4D visualization cluster visualization parallel rendering cluster animation

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visualization techniques and automation of cluster analysis with self-organizing maps for decision support in customer segmentation

Visualization techniques and automation of cluster analysis ...

引用

International conference on Artificial Intelligence/International conference on Machine Learning, Models, Technologies and Applications

作者： Richartz, J Market Intelligence Department Ethicon Endo-Surgery (Europe) GmbH Germany

ISBN: (纸本)1932415335

Self Organizing Maps (SOM) used as a method of cluster analysis can be a powerful tool to understand customer behavior in the context of customer relationship management. Decision makers ask for easy to use applications, which are able to support data mining analysis in particular as well as the whole process of knowledge discovery in databases. Artificial neural networks compete with traditional statistical methods in fields of usability, performance and validity. In this article new approaches to measuring and evaluating the power of Kohonen maps for cluster analysis will be presented. The underlying application allows the user to run an automated cluster analysis together with automated quality calculation. In order to review the solution determined by the system, new visualization techniques have been developed.

关键词： Self organizing maps

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ClusterMap: Labeling clusters in large datasets via visualization

ClusterMap: Labeling clusters in large datasets via visualiz...

引用

CIKM 2004: Proceedings of the Thirteenth ACM conference on Information and Knowledge Management

作者： Chen, Keke Liu, Ling College of Computing Georgia Institute of Technology 801 Atlantic Dr. Atlanta GA 30329

With the rapid increase of data in many areas, clustering on large datasets has become an important problem in data analysis. Since cluster analysis is a highly iterative process, cluster analysis on large datasets prefers short iteration on a relatively small representative set. Thus, a two-phase framework "sampling/summarization - iterative cluster analysis" is often applied in practice. Since the clustering result only labels the small representative set, there are problems with extending the result to the entire large dataset, which are almost ignored by the traditional clustering research. This extending is often named as labeling process. Labeling irregular shaped clusters, distinguishing outliers and extending cluster boundary are the main problems in this stage. We address these problems and propose a visualization-based approach to dealing with them precisely. This approach partially involves human into the process of defining and refining the structure "ClusterMap". Based on this structure, the ClusterMap algorithm scans the large dataset to adapt the boundary extension and generate the cluster labels for the entire dataset. Experimental result shows that ClusterMap can preserve cluster quality considerably with low computational cost, compared to the distance-comparison- based labeling algorithms. Copyright 2004 ACM.

关键词： data acquisition

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Spatial-temporal traffic data analysis based on global data management using MAS

Spatial-temporal traffic data analysis based on global data ...

引用

7th IEEE International conference on Intelligent Transportation Systems

作者： Zhang, HS Zhang, Y Li, ZH Hu, DC Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

The spatial-temporal traffic data analysis based on global data management is a newly developed and crucial approach to help traffic managers having the global view of urban traffic status in the level of road network, which is very clearly useful in traffic control and route guidance. The multiagent systems are used in traffic data management with full consideration of the characteristics of traffic data and the cooperation and workflow among them. In software implementation of data management, the agent-based common object request broker architecture is adopted taking the distributed urban traffic data in the large area under network environments into account. Based on the global traffic data, the approach of visualized spatial-temporal analysis is then induced. The similarity of traffic data is analyzed first for each link and its profile is achieved to undertake the primary processing of urban traffic data. Furthermore, analysis results are shown on the basis of the geographic information systems for transportation. The two types of visualization, pseudocolor and contour maps, are adopted in the demonstration to display the traffic status graphically and its changing frames. Among the applications in some big cities in China, the case of urban traffic analysis for Beijing is studied to demonstrate the implementation of the approach.

关键词： data management multiagent systems (MASs) spatial-temporal data analysis visualization

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aTEDS: Airborne Turbine Engine Diagnostic System

aTEDS: Airborne Turbine Engine Diagnostic System

引用

IEEE Aerospace conference

作者： Grabill, P Seale, J Brotherton, T Intelligent Automat Corp Poway CA 92064 USA

ISBN: (纸本)0780381556

There is a significant need for technologies that improve flight safety and reduce maintenance and support costs for aircraft turbine engines. The C17 aircraft is being used extensively in the fight against terror in Iraq and other parts of the world. Engines are being stressed with both high operation temperatures and a severe short take off and landing environment. The current C17 / F117 engines do not have an engine vibration monitoring system. If high vibration conditions occur the flight crew can not easily identify the problem engine. IAC is developing an airborne Turbine Engine Diagnostics System (aTEDS) for the C17 / F117 engine. aTEDS is designed to automatically collect, process, and monitor vibration data collected from the F117 engine. In addition to the on-board system aTEDS includes a laptop PC ground based diagnostic system to aid in data collection, visualization, analysis and fault isolation. Presented in the paper is a description of the aTEDS system and preliminary results from collection and processing of T1 C17 flight data.

关键词： aerospace engines military computing military aircraft aircraft testing aerospace computing vibrations fault diagnosis data acquisition software packages airborne turbine engine diagnostic system flight safety maintenance reduction support cost reduction C17 engines F117 engines short take off environment landing environment engine vibration monitoring system onboard system laptop PC ground based diagnostic system data visualization data analysis fault isolation T1 C17 flight data processing T1 C17 flight data collection Software packages flight safety fault isolation aerospace computing aircraft testing military computing military aircraft data visualisation Fault diagnosis Engines Aerospace engines short take off and landing flying personnel TURBINE ENGINE

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