Visualization Techniques of a CFD++ Data Set of a Spinning Smart Munition

2004 ◽  
Author(s):  
Richard C. Angelini ◽  
Jubaraj Sahu
Keyword(s):  
Data Set ◽  
Visualization Techniques

scholarly journals COMBINED VISUAL EXPLORATION OF 2D GROUND RADAR AND 3D POINT CLOUD DATA FOR ROAD ENVIRONMENTS

2018 ◽  
Vol XLII-4/W10 ◽  
pp. 231-236
Author(s):  
J. Wolf ◽  
S. Discher ◽  
L. Masopust ◽  
S. Schulz ◽  
R. Richter ◽  
...  
Keyword(s):  
Point Cloud ◽  
Radar Data ◽  
Visual Exploration ◽  
Data Sources ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Data Set ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Visualization Techniques

<p><strong>Abstract.</strong> Ground-penetrating 2D radar scans are captured in road environments for examination of pavement condition and below-ground variations such as lowerings and developing pot-holes. 3D point clouds captured above ground provide a precise digital representation of the road’s surface and the surrounding environment. If both data sources are captured for the same area, a combined visualization is a valuable tool for infrastructure maintenance tasks. This paper presents visualization techniques developed for the combined visual exploration of the data captured in road environments. Main challenges are the positioning of the ground radar data within the 3D environment and the reduction of occlusion for individual data sets. By projecting the measured ground radar data onto the precise trajectory of the scan, it can be displayed within the context of the 3D point cloud representation of the road environment. We show that customizable overlay, filtering, and cropping techniques enable insightful data exploration. A 3D renderer combines both data sources. To enable an inspection of areas of interest, ground radar data can be elevated above ground level for better visibility. An interactive lens approach enables to visualize data sources that are currently occluded by others. The visualization techniques prove to be a valuable tool for ground layer anomaly inspection and were evaluated in a real-world data set. The combination of 2D ground radar scans with 3D point cloud data improves data interpretation by giving context information (e.g., about manholes in the street) that can be directly accessed during evaluation.</p>

scholarly journals A Novel Interaction-based Methodology Towards Explainable AI with Better Understanding of Pneumonia Chest X-ray Images

2021 ◽  
Author(s):  
Shaw-Hwa Lo ◽  
Yiqiao Yin
Keyword(s):  
State Of The Art ◽  
Image Data ◽  
Prediction Performance ◽  
Data Set ◽  
X Ray ◽  
Real World Application ◽  
Explainable Ai ◽  
Chest X Ray ◽  
High Prediction ◽  
Visualization Techniques

Abstract In the field of eXplainable AI (XAI), robust “black-box” algorithms such as Convolutional Neural Networks (CNNs) are known for making high prediction performance. However, the ability to explain and interpret these algorithms still require innovation in the understanding of influential and, more importantly, ex-plainable features that directly or indirectly impact the performance of predictivity. A number of methods existing in literature focus on visualization techniques but the concepts of explainability and interpretability still require rigorous definition. In view of the above needs, this paper proposes an interaction-based methodology – Influence Score (I-score) – to screen out the noisy and non-informative variables in the images hence it nourishes an environment with explainable and interpretable features that are directly associated to feature predictiv-ity. We apply the proposed method on a real world application in Pneumonia Chest X-ray Image data set and produced state-of-the-art results. We demonstrate how to apply the proposed approach for more general big data problems by improving the explainability and in-terpretability without sacrificing the prediction performance. The contribution of this paper opens a novel angle that moves the community closer to the future pipelines of XAI problems.

scholarly journals Data-visualization and new literacies

2019 ◽  
Vol 7 (12) ◽  
pp. 165-182
Author(s):  
Eduardo De Moura Almeida
Keyword(s):  
Data Visualization ◽  
Qualitative Methodology ◽  
Music Videos ◽  
Objective Method ◽  
Statistical Distributions ◽  
Video Editing ◽  
Quantitative Methodology ◽  
Data Set ◽  
Verbal Language ◽  
Visualization Techniques

This article introduces audiovisual data-set, and data-visualization methods developed to analyze video remixes, i.e., Anime Music Videos (AMVs). It mainly focuses on reviewing the methodology that has supported our Ph.D. thesis which aims to integrate a qualitative methodology according to Bakhtin's theories of genre and architectonics, and a quantitative methodology based on media visualization techniques and statistical distributions of formal and stylistic choices. More specifically, it aimed to reflect upon on audiovisual productions belonging to the site http://www.animemusicvideos.org/. Community whose goal is the elaboration and distribution of Japanese animations remixes called Anime Music Videos (AMVs). We propose to integrate an enunciative-discursive perspective, formulated for verbal language with an analysis objective method of multimodal objects. Hence we suggest a mixed research design (quantitative and qualitative) based on statistical distributions and description and analysis of our data visualization techniques, aiming to reveal relationships and patterns in our collection of audiovisual data, so as to provide productive transitions between the concepts proposed by Bakhtin and his Circle, and analysis of editing and video editing tools.

scholarly journals Visual Analytics in Delirium Management

2021 ◽  
Author(s):  
Werner O. Hackl ◽  
Michael Netzer ◽  
Renate Nantschev ◽  
Michael Schaller ◽  
Elske Ammenwerth
Keyword(s):  
Data Mining ◽  
Hierarchical Clustering ◽  
Visual Analytics ◽  
Time Course ◽  
Safety Issue ◽  
Current Data ◽  
Data Set ◽  
Patient Safety Issue ◽  
Delirium Management ◽  
Visualization Techniques

Background: Delirium is a patient safety issue that often occurs within the population of elderly people. As delirium may be characterized by fluctuating progress, the aim of this work is to find methods to visualize the occurrence of delirium over time in different patient stays in gerontopsychatric settings. Methods: We analyzed current data mining visualization techniques for clinical research using a delirium data set collected in a gerontopsychatric setting. Results: We identified heatmaps and dendrograms resulting from hierarchical clustering as a suitable visualization method. Conclusion: Heat maps with hierarchical clustering are a suitable data mining tool or visualization technique to study delirium cases in the time course of patient stays.

scholarly journals A novel interaction-based methodology towards explainable AI with better understanding of Pneumonia Chest X-ray Images

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Shaw-Hwa Lo ◽  
Yiqiao Yin
Keyword(s):  
Interactive Effect ◽  
Image Data ◽  
Prediction Performance ◽  
Data Set ◽  
X Ray ◽  
Real World Application ◽  
Explainable Ai ◽  
Chest X Ray ◽  
High Prediction ◽  
Visualization Techniques

AbstractIn the field of eXplainable AI (XAI), robust “blackbox” algorithms such as Convolutional Neural Networks (CNNs) are known for making high prediction performance. However, the ability to explain and interpret these algorithms still require innovation in the understanding of influential and, more importantly, explainable features that directly or indirectly impact the performance of predictivity. A number of methods existing in literature focus on visualization techniques but the concepts of explainability and interpretability still require rigorous definition. In view of the above needs, this paper proposes an interaction-based methodology–Influence score (I-score)—to screen out the noisy and non-informative variables in the images hence it nourishes an environment with explainable and interpretable features that are directly associated to feature predictivity. The selected features with high I-score values can be considered as a group of variables with interactive effect, hence the proposed name interaction-based methodology. We apply the proposed method on a real world application in Pneumonia Chest X-ray Image data set and produced state-of-the-art results. We demonstrate how to apply the proposed approach for more general big data problems by improving the explainability and interpretability without sacrificing the prediction performance. The contribution of this paper opens a novel angle that moves the community closer to the future pipelines of XAI problems. In investigation of Pneumonia Chest X-ray Image data, the proposed method achieves 99.7% Area-Under-Curve (AUC) using less than 20,000 parameters while its peers such as VGG16 and its upgraded versions require at least millions of parameters to achieve on-par performance. Using I-score selected explainable features allows reduction of over 98% of parameters while delivering same or even better prediction results.

How are we doing in tribal libraries?

2017 ◽  
Vol 38 (1) ◽  
pp. 20-44 ◽  
Author(s):  
Hsuanwei Michelle Chen ◽  
Tawa Ducheneaux
Keyword(s):  
Information Visualization ◽  
Public Library ◽  
Legal Service ◽  
Data Set ◽  
Content Type ◽  
Tribal Populations ◽  
Report Data ◽  
Urban Libraries ◽  
Reported Data ◽  
Visualization Techniques

Purpose The purpose of this paper is to investigate the operation and management as well as the activities of tribal libraries in general, providing insights and implications in five areas: general operations and management, staffing and human resource management, financial operations, service and program management, and technology-related activities, using Oglala Lakota College (OLC) Library as a case study. Design/methodology/approach This paper uses information visualization techniques to create visual displays of report data collected from OLC Library. Visualizations were created using Tableau software to provide a quantitative, analytical, and evidence-based view of how tribal libraries operate and are managed. Findings Tribal populations can be well served despite limited funding and staff resources, providing academic and public library services on par with urban libraries. Research limitations/implications Drawing a story from the data proved to be difficult because a bias had been created by the legal service area that most tables of the state data set used to compare reported data. How tribal libraries translate value also posed another challenge. Because the research was conducted in a single tribal library, further research in different, expanded settings and contexts is suggested. Originality/value This study is one of the first to investigate tribal library activities by exploring report data and quantitatively using information visualization techniques.

Interactive Data Visualization to Understand Data Better

2014 ◽  
Vol 4 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Zhecheng Zhu ◽  
Bee Hoon Heng ◽  
Kiok Liang Teow
Keyword(s):  
Case Studies ◽  
Healthcare System ◽  
Data Visualization ◽  
Healthcare Systems ◽  
Data Set ◽  
First Case ◽  
Interactive Data ◽  
Visual Impact ◽  
Visualization Techniques

This paper focuses on interactive data visualization techniques and their applications in healthcare systems. Interactive data visualization is a collection of techniques translating data from its numeric format to graphic presentation dynamically for easy understanding and visual impact. Compared to conventional static data visualization techniques, interactive data visualization techniques allow users to self-explore the entire data set by instant slice and dice, quick switching among multiple data sources. Adjustable granularity of interactive data visualization allows for both detailed micro information and aggregated macro information displayed in a single chart. Animated transition adds extra visual impact that describes how system transits from one state to another. When applied to healthcare system, interactive visualization techniques are useful in areas such as information integration, flow or trajectory presentation and location related visualization, etc. In this paper, three case studies are shared to illustrate how interactive data visualization techniques are applied to various aspects of healthcare systems. The first case study shows a pathway visualization representing longitudinal disease progression of a patient cohort. The second case study shows a dashboard profiling different patient cohorts from multiple perspectives. The third case study shows an interactive map illustrating patient geographical distribution at adjustable granularity. All three case studies illustrate that interactive data visualization techniques help quick information access, fast knowledge sharing and better decision making in healthcare system.

Mental Health Through Biofeedback Is Important to Analyze

2021 ◽  
pp. 402-423
Author(s):  
Rohit Rastogi ◽  
Devendra Kumar Chaturvedi ◽  
Mayank Gupta
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Decision Tree ◽  
Research Work ◽  
Tension Type Headache ◽  
Data Set ◽  
Sf 36 ◽  
Skin Response ◽  
Type Headache ◽  
Visualization Techniques

Many apps and analyzers based on machine learning have been designed to help and cure the stress issue. This chapter is based on an experiment that the authors performed at Research Labs and Scientific Spirituality Centers of Dev Sanskriti VishwaVidyalaya, Haridwar and Patanjali Research Foundations, Uttarakhand. In the research work, the correctness and accuracy have been studied and compared for two biofeedback devices named as electromyography (EMG) and galvanic skin response (GSR), which can operate in three modes: audio, visual and audio-visual with the help of data set of tension type headache (TTH) patients. The authors used some data visualization techniques that EMG (electromyography) in audio mode is best among all other modes, and in this experiment, they have used a data set of SF-36 and successfully clustered them into three clusters (i.e., low, medium, and high) using K-means algorithm. After clustering, they used classification algorithm to classify a user (depending upon the sum of all the weights of questions he had answered) into one of these three class. They have also implemented various algorithms for classifications and compared their accuracy out of which decision tree algorithm has given the best accuracy.

Application of Geographical Information System and Interactive Data Visualization in Healthcare Decision Making

2016 ◽  
Vol 1 (1) ◽  
pp. 49-58 ◽  
Author(s):  
Zhecheng Zhu
Keyword(s):  
Decision Making ◽  
Information System ◽  
Data Visualization ◽  
Spatial Information ◽  
Real Life ◽  
Geographical Information ◽  
Data Set ◽  
Interactive Data ◽  
Visual Impact ◽  
Visualization Techniques

This paper focuses on two techniques and their applications in healthcare systems: geographic information system (GIS) and interactive data visualization. GIS is a type of technique applied to manipulate, analyze and display spatial information. It is a useful tool tackling location related problems. GIS applications in healthcare include evaluation of accessibility to healthcare facilities, site planning of new healthcare services and analysis of risks and spreads of infectious diseases. Interactive data visualization is a collection of techniques translating data from its numeric format to graphic presentation dynamically for easy understanding and visual impact. Compared to conventional static data visualization techniques, interactive data visualization techniques allow user to self-explore the entire data set by instant slice and dice, quick switching among multiple data sources. Adjustable granularity of interactive data visualization allows for both detailed micro information and aggregated macro information displayed in a single chart. Animated transition adds extra visual impact that describes how system transits from one state to another. When applied to healthcare system, interactive visualization techniques are useful in areas such as information integration, flow or trajectory presentation and location related visualization, etc. One area both techniques intersect is location analysis. In this paper, real life case studies will be given to illustrate how these two techniques, when combined together, help in solving quantitative or qualitative location related problem, visualizing geographical information and accelerating decision making procedures.

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