Typhoon is one of the major natural disasters in Taiwan. The solar water heater system installed in the house roof has the most serious damage caused by typhoon invasion. Because the wind blows into the backward of th...
详细信息
Typhoon is one of the major natural disasters in Taiwan. The solar water heater system installed in the house roof has the most serious damage caused by typhoon invasion. Because the wind blows into the backward of the solar water heater system, the heat collecter is damaged by the stronger wind pressure gradient induced between the upper and lower. This study was using CFD (Computational Fluid Dynamics) method to investigate the flowfield over a suspended and ground solar water heater during the wind speed about 30m/s, and compared the results with the experimental data for validation. Simulations of 60% solar water collector model with different tilt angles and 40% model with different guiding plate were also done in this study to investigate the effect of the guide plate. The result shows that the 3D flowfield effect is clear, and 2D model can not simulated the flowfield. The heat collector erected away from the ground can be reduced to the uplift, and the separation bubbles are induced behind the bucket of the heat collector surface. The cornner vortices generated and interacted from the collector-side, and the separation bubbles is extending in spanwise. The larger tilt angle of the solar collector causes the larger pressure difference in the leading edge. The guiding plate avoids the inflow directly hit the bottom of the solar collector, and it can efficiently reduce the leading edge pressure difference. Nevertheless, when the cross-sectional area of the guiding plate increase, the uplift force acted on heat collector decreases and the drag force would be increased. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Kunming University of Science and Technology
Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features di...
详细信息
ISBN:
(纸本)9780819489647
Feature extraction is a critical component of medical image analysis. Many computer-aided diagnosis approaches employ hand-designed, heuristic lesion extracted features. An alternative approach is to learn features directly from images. In this preliminary study, we explored the use of Adaptive Deconvolutional Networks (ADN) for learning high-level features in diagnostic breast mass lesion images with potential application to computer-aided diagnosis (CADx) and content-based image retrieval (CBIR). ADNs (Zeiler, et. al., 2011), are recently-proposed unsupervised, generative hierarchical models that decompose images via convolution sparse coding and max pooling. We trained the ADNs to learn multiple layers of representation for two breast image data sets on two different modalities (739 full field digital mammography (FFDM) and 2393 ultrasound images). Feature map calculations were accelerated by use of GPUs. Following Zeiler et. al., we applied the Spatial Pyramid Matching (SPM) kernel (Lazebnik, et. al., 2006) on the inferred feature maps and combined this with a linear support vector machine (SVM) classifier for the task of binary classification between cancer and non-cancer breast mass lesions. Non-linear, local structure preserving dimension reduction, Elastic Embedding (Carreira-Perpinan, 2010), was then used to visualize the SPM kernel output in 2D and qualitatively inspect image relationships learned. Performance was found to be competitive with current CADx schemes that use human-designed features, e. g., achieving a 0.632+ bootstrap AUC (by case) of 0.83 [0.78, 0.89] for an ultrasound image set (1125 cases).
Terahertz (THz) sensing has been developed over the past three decades for concealed weapons detection, medical imaging, and non-destructive evaluation;however methods for THz image exploitation have not been well rep...
详细信息
ISBN:
(纸本)9781467327916
Terahertz (THz) sensing has been developed over the past three decades for concealed weapons detection, medical imaging, and non-destructive evaluation;however methods for THz image exploitation have not been well reported. We test a multiscale image fusion algorithm for the 2011 IEEE National Aerospace and Electronics Conf. (NAECON) Grand Challenge which consists of Terahertz (THz) and visual images. The study consists of image characterization (signals distribution), image processing (data fusion), and image analysis (edge detection). We found that THz image characterization did not necessarily follow a distinct Gaussian distribution, THz imagery fusion with visual data supported target detection, and that image analysis enhanced target assessment. For" the initial experiment, we assess the target segmentation through edge detection, image fusion results, and image fusion quality assessment. The preliminary image exploitation and fusion results can further develop THz collection over clothing-obscured concealed weapons imaging, parameter optimization, and targeting evaluation.
Visual representations of time-series are useful for tasks such as identifying trends, patterns and anomalies in the data. Many techniques have been devised to make these visual representations more scalable, enabling...
详细信息
Visual representations of time-series are useful for tasks such as identifying trends, patterns and anomalies in the data. Many techniques have been devised to make these visual representations more scalable, enabling the simultaneous display of multiple variables, as well as the multi-scale display of time-series of very high resolution or that span long time periods. There has been comparatively little research on how to support the more elaborate tasks associated with the exploratory visual analysis of time-series, e. g., visualizing derived values, identifying correlations, or discovering anomalies beyond obvious outliers. Such tasks typically require deriving new time-series from the original data, trying different functions and parameters in an iterative manner. We introduce a novel visualization technique called ChronoLenses, aimed at supporting users in such exploratory tasks. ChronoLenses perform on-the-fly transformation of the data points in their focus area, tightly integrating visual analysis with user actions, and enabling the progressive construction of advanced visual analysis pipelines.
We present a visualization framework for exploring and analyzing data sets from biomechanical and neuromuscular simulations. These data sets describe versatile information related to the different stages of a motion a...
详细信息
We present a visualization framework for exploring and analyzing data sets from biomechanical and neuromuscular simulations. These data sets describe versatile information related to the different stages of a motion analysis. In studying these data using a 3D visualization approach, interactive exploring is important, especially for supporting spatial analysis. Moreover, as these data contain many various but related elements, numerical analysis of neuromuscular simulations is complicated. visualization techniques enhance the analysis process, thus improving the effectiveness of the experiments. Our approach allows convenient definitions of relationships between numerical data sets and 3D objects. Scientific simulation data sets appropriate for this style of analysis are present everywhere motion analysis is performed and are predominant in many clinical works. In this paper, we outline the functionalities of the framework as well as applications embedded within the OpenSim simulation platform. These functionalities form an effective approach specifically designed for the investigation of neuromuscular simulations. This claim is supported by evaluation experiments where the framework was used to analyze gaits and crouch motions.
The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e. g., being of sparse or dense structure...
详细信息
The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e. g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization - a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown - right in the place where they are positioned, thus supporting the user's overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks.
Understanding how topics evolve in text data is an important and challenging task. Although much work has been devoted to topic analysis, the study of topic evolution has largely been limited to individual topics. In ...
详细信息
Understanding how topics evolve in text data is an important and challenging task. Although much work has been devoted to topic analysis, the study of topic evolution has largely been limited to individual topics. In this paper, we introduce TextFlow, a seamless integration of visualization and topic mining techniques, for analyzing various evolution patterns that emerge from multiple topics. We first extend an existing analysis technique to extract three-level features: the topic evolution trend, the critical event, and the keyword correlation. Then a coherent visualization that consists of three new visual components is designed to convey complex relationships between them. Through interaction, the topic mining model and visualization can communicate with each other to help users refine the analysis result and gain insights into the data progressively. Finally, two case studies are conducted to demonstrate the effectiveness and usefulness of TextFlow in helping users understand the major topic evolution patterns in time-varying text data.
Research in the field of complex fluids such as polymer solutions, particulate suspensions and foams studies how the flow of fluids with different material parameters changes as a result of various constraints. Surfac...
详细信息
Research in the field of complex fluids such as polymer solutions, particulate suspensions and foams studies how the flow of fluids with different material parameters changes as a result of various constraints. Surface Evolver, the standard solver software used to generate foam simulations, provides large, complex, time-dependent data sets with hundreds or thousands of individual bubbles and thousands of time steps. However this software has limited visualization capabilities, and no foam specific visualization software exists. We describe the foam research application area where, we believe, visualization has an important role to play. We present a novel application that provides various techniques for visualization, exploration and analysis of time-dependent 2D foam simulation data. We show new features in foam simulation data and new insights into foam behavior discovered using our application.
In modeling and analysis of longitudinal social networks, visual exploration is used in particular to complement and inform other methods. The most common graphical representations for this purpose appear to be animat...
详细信息
In modeling and analysis of longitudinal social networks, visual exploration is used in particular to complement and inform other methods. The most common graphical representations for this purpose appear to be animations and small multiples of intermediate states, depending on the type of media available. We present an alternative approach based on matrix representation of gestaltlines (a combination of Tufte's sparklines with glyphs based on gestalt theory). As a result, we obtain static, compact, yet data-rich diagrams that support specifically the exploration of evolving dyadic relations and persistent group structure, although at the expense of cross-sectional network views and indirect linkages.
暂无评论