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检索条件"任意字段=Conference on Visual Data Exploration and Analysis V"

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Interactive Parallel Coordinates visualization Platform for Survey data

Interactive Parallel Coordinates Visualization Platform for ...

引用

International conference on Artificial Intelligence and data Sciences (AiDAS)

作者： M. Bakri Afrina N.A. M. Hamiz Kolej Pengajian Perkomputeran Information dan Matematik (KPPIM) UiTM Cawangan Melaka Kampus Jasin Melaka Malaysia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

Survey data often encompasses many variables, making it challenging to identify complex relationships. Parallel coordinates visualization offers a powerful solution for exploring these relationships. We propose a web-based application that leverages parallel coordinates to enhance survey data analysis. This user-friendly tool overcomes limitations of traditional methods by enabling interactive exploration of high-dimensional data. The application adheres to the waterfall model for development, ensuring thorough testing and achieving a high System Usability Scale (SUS) score of 86.8%. By providing deeper insights through visual exploration, this application empowers researchers to make more informed decisions. However, well-structured and pre-processed data remains essential for accurate visualizations. Overall, our web-based application with parallel coordinates visualization offers an innovative and efficient approach to survey data analysis.

关键词： Surveys visualization data analysis Accuracy Decision making data visualization data mining Usability Artificial intelligence Testing

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Standardizing LiDAR Surveying Practices for Accurate and Consistent data Collection: Colorizing Point Clouds

Standardizing LiDAR Surveying Practices for Accurate and Con...

引用

International conference on Sustainable Infrastructure: Innovation, Opportunities and Challenges, SIIOC 2023

作者： vishwas, A.S. Abhijith, P.A. Umesh, Pruthviraj Gangadharan, K.v. Rai, Sharan Department of Mechanical Engineering National Institute of Technology Karnataka Surathkal Mangaluru575025 India Department of Water Resources and Ocean Engineering National Institute of Technology Karnataka Surathkal Mangaluru575025 India

ISBN: (纸本)9789819748433

This paper discusses the importance of standardizing LiDAR surveying practices to ensure accurate and consistent data collection and analysis. Light Detection and Ranging (LiDAR) is a remote sensing technology that uses laser pulses to measure distances and create 3D maps of the environment. With the increasing use of LiDAR in various industries, it is crucial to establish standardized procedures for data collection, processing, and reporting. The paper reviews current practices and identifies key areas that require standardization, including data acquisition parameters, quality control. The benefits of standardization include improved data quality and coloring the point cloud which improves visual interpretation. The paper discusses a simplified software algorithm for merging the point cloud and the panoramic image, which uses open-source hardware and software which provides scope for further modification and improvement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： data accuracy

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Navigation Sensor Technology Assessment Capability for data-Driven Systems analysis

Navigation Sensor Technology Assessment Capability for Data-...

引用

Accelerating Space Commerce, exploration, and New Discovery conference, ASCEND 2023

作者： Lee, Esther Tartabini, Paul v. Starr, Brett R. Friz, Paul D. Karlgaard, Christopher D. Samareh, Jamshid Vehicle Analysis Branch. NASA Langley Research Center HamptonVA23681 United States Analytical Mechanics Associates Inc. NASA Langley Research Center HamptonVA23681 United States

ISBN: (数字)9781624107054

ISBN: (纸本)9781624107054

The capability to assess the mission performance of novel navigation technologies from a systems-level approach and to provide quantitative results is crucial. With growing interest and innovations from NASA’s commercial partners, data-driven results that quantify the technological impact will guide research developments and facilitate stakeholder decision-making while requiring less time and resources. This paper presents a method to evaluate various sensor combinations and their impact on the overall system performance using an existing six degrees-of-freedom, physics-based engineering simulation for a government reference lunar lander as a testbed. Selected navigation technologies over various technology readiness levels, including Inertial Measurement Units, Navigation Doppler Lidar, and radar altimeter-radar velocimeter, were studied in this paper. Results using this testbed to perform sensitivity studies and to provide quantitative assessments are reported. One key finding is that there are diminishing returns for reducing sensor errors. The most influential sensor parameter to landing success are vehicle configuration and mission dependent. Results from this method could be used by stakeholders to make data-driven systems-based decisions and by technology developers as guidance for the specific parameter improvements that will have the most significant impact on mission success. This capability could be further used to assess alternative scenarios should one type of technology become unavailable or to re-assess initial assumptions and identify appropriate requirements from a system perspective. © 2023, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Testbeds

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LiDAR Integration with ROS for SLAM Mediated Autonomous Path exploration 1

引用

International conference on Advanced Computing and Intelligent Technologies, ICACIT 2022

作者： Chaudhuri, Rapti Deb, Suman National Institute of Technology Agartala Agartala India

ISBN: (数字)9789811929809

ISBN: (纸本)9789811929793

Path exploration is a significant problem domain of point-to-point robot navigation. Even the availability of robust sensors has created evolving computational challenges for mobile robot navigation (MRN) specially in GPS denied indoor environments (IE). This paper presents the theoretic and experimental analysis of path exploration challenges and possibilities in an indoor environment by systematic integration of LiDAR with ROS platform for constructing assessable SLAM. The algorithmic solutions have been tested with customized differential drive structure robot platform (CUBOT). This also studies the mapping of the concerned trajectory and environment localization. Consumer grade 2D RP LiDAR is used here as the percept for the edging obstacle periphery, and the associated tangential data is used to construct the localization and mapping. Significant analysis is done on visual representation of LiDAR data in ROS platform for performance evaluation of the path exploration and planning algorithms. This evaluated result would clearly serve as reference to researchers for selecting appropriate SLAM algorithm with an associated memory representation of already explored path. It would also facilitate further combination with optimized global and local path planning techniques to achieve a desired goal by autonomous mobile robot in constrained indoor environment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mapping

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Does Object Grounding Really Reduce Hallucination of Large vision-Language Models?

Does Object Grounding Really Reduce Hallucination of Large V...

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2024 conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Geigle, Gregor Timofte, Radu Glavaš, Goran WüNLP Germany Computer Vision Lab CAIDAS University of Würzburg Germany

ISBN: (纸本)9798891761643

Large vision-language models (LvLMs) have recently dramatically pushed the state of the art in image captioning and many image understanding tasks (e.g., visual question answering). LvLMs, however, often hallucinate and produce captions that mention concepts that cannot be found in the image. These hallucinations erode the trustworthiness of LvLMs and are arguably among the main obstacles to their ubiquitous adoption. Recent work suggests that addition of grounding objectives-those that explicitly align image regions or objects to text spans-reduces the amount of LvLM hallucination. Although intuitive, this claim is not empirically justified as the reduction effects have been established, we argue, with flawed evaluation protocols that (i) rely on data (i.e., MSCOCO) that has been extensively used in LvLM training and (ii) measure hallucination via question answering rather than open-ended caption generation. In this work, in contrast, we offer the first systematic analysis of the effect of fine-grained object grounding on LvLM hallucination under an evaluation protocol that more realistically captures LvLM hallucination in open generation. Our extensive experiments over three backbone LLMs reveal that grounding objectives have little to no effect on object hallucination in open caption generation. © 2024 Association for Computational Linguistics.

关键词： visual languages

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DYNAMIC SOURCE LOCALIZATION AND FUNCTIONAL CONNECTIvITY ESTIMATION WITH STATE-SPACE MODELS: PRELIMINARY FEASIBILITY analysis

DYNAMIC SOURCE LOCALIZATION AND FUNCTIONAL CONNECTIVITY ESTI...

引用

IEEE International conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Sanchez-Bornot, Jose M. Sotero, Roberto C. Coyle, Damien Ulster Univ Sch Comp Engn & Intelligent Syst Intelligent Syst Res Ctr Magee Campus Derry BT48 7JL Londonderry North Ireland Univ Calgary Dept Radiol Calgary AB Canada Univ Calgary Hotchkiss Brain Inst Calgary AB Canada Univ Bath Bath Inst Augmented Human Bath BA2 7AY Avon England

ISBN: (纸本)9798350302615

Dynamic imaging of source and functional connectivity (FC) using electroencephalographic (EEG) signals is essential for understanding the brain and cognition with sufficiently affordable technology to be widely applicable for studying changes associated with healthy ageing and the progression of neuropathology. We present an application for group analysis of recently developed state-space models and algorithms for simultaneously estimating the large-scale EEG inverse and FC problems. This approach reduces estimation bias and facilitates a detailed exploration and investigation of neuronal dynamics compared to current techniques. We present feasibility analyses for simulated and real EEG event-related data. The latter analysis uses a sixteen subjects EEG (Wakeman and Henson's) database, with signals recorded during a face-processing task. We implement a state-space methodology efficiently using an alternating least squares (ALS) algorithm. This application to neuroimaging analysis may be critical to reliably capture the brain dynamics despite interindividual variability, as demonstrated by the results presented.

关键词： State-space models EEG/MEG eventrelated source imaging Dynamic functional connectivity Feasibility analysis Machine learning

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Reform of Smart Exhibition English Guiding from the Perspective of New Media based on data Association analysis Model

Reform of Smart Exhibition English Guiding from the Perspect...

引用

6th International conference on Computing Methodologies and Communication, ICCMC 2022

作者： Cai, Yan Bao, Xianghua Maanshan Technical College Anhui Province Maanshan 243000 China Science and Technology Informatization Detachment of Ma'anshan Public Security Bureau Anhui 243000 China

ISBN: (纸本)9781665410281

Taking the exhibition English curriculum reform of the college as an example, from the aspects of course positioning, course objectives, teaching content design, and course evaluation and assessment, explore the teaching mode of exhibition English curriculum based on data association analysis model, and use multi-dimensional information data space, data development, The relationship between data analysis and data association analysis is used to discuss the visual presentation design, and follow the research ideas of "data collection/data statistics/analysis/import/preprocessing/data mining"to construct the intelligent exhibition English guidance reform under the new media technology. © 2022 IEEE.

关键词： Exhibitions

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visual analysis of Cyclic Time Series with Semantic Zoom

Visual Analysis of Cyclic Time Series with Semantic Zoom

引用

International conference on Information visualisation (Iv)

作者： Patrick Louis Belgin Mutlu Josef Suschnigg Tobias Schreck Pro2Future GmbH Graz Austria Graz University of Technology Graz Austria

ISBN: (数字)9798350380163

ISBN: (纸本)9798350380170

visual analysis (vA) tasks often involve exploring large and complex multi-dimensional datasets to identify trends and anomalies. However, the challenge lies in displaying all the data and maintaining the desired level of detail within the limited screen space. In this paper, we propose a solution that incorporates multiple visualizations and semantic zooming to address this compromise. Our visualization tool focuses on cycle-dependent data, showcasing time series with repetitive behavior. Through semantic zooming, cyclic time series data can be displayed in large quantities and high levels of detail without the need for multiple views. Our proposed tool includes three independent visualizations: line plots, horizon graphs, and adaptive heatmaps. By offering different visualization options, we aim to provide a rich and flexible analytical experience that response to the different user needs and encourages comprehensive data exploration. The tool accommodates both novice and expert users, allowing for intuitive analysis as well as advanced techniques for detailed examination. Our approach follows the mantra of “overview first, zoom and filter, then details-on-demand” facilitating rapid detection and exploration of patterns and trends. In this paper, we present the detailed design, interaction capabilities with semantic zoom, and the results of a user study that demonstrate the effectiveness and usefulness of our proposed tool.

关键词： Heating systems visualization Semantics Time series analysis data visualization Market research Usability

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Leveraging Content Based Image Retrieval Using data Mining for Efficient Image exploration 1

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4th International conference on Mobile Radio Communications and 5G Networks, MRCN 2023

作者： Kaur, Jaspreet Gupta, Divya Singh, Amrinder Shah, Syed Hassan Ahmed Department of Computer Science and Engineering Chandigarh University Mohali 140413 India University Institute of Teachers Training and Research ChandigarhUniversity Punjab Mohali India California State University FullertonCA United States

ISBN: (数字)9789819707003

ISBN: (纸本)9789819706990

A content-based image Retrieval (CBIR) has become an essential tool for managing and searching large-scale images. However, the accuracy and performance of CBIR systems can be improved by combining data mining techniques. Content-based retrieval (CBR) uses the properties and characteristics of the content itself to search for and retrieve information from a big database instead of depending on text or metadata. CBR is very helpful in research, where it is necessary to swiftly and effectively examine vast amounts of data. According to the results, data mining techniques can considerably increase the retrieval process accuracy and effectiveness. Similar photos can be grouped together using clustering algorithms, common patterns of visual features can be found using association rule mining, and images can be classified using classification techniques. In order to create a system for content-based image retrieval and processing, we studied the retrieval of images from huge databases using a variety of feature extraction and matching techniques. The demand for CBIR development came as a result of the sharp rise in image database volumes and their widespread use in several applications. The description of basic feature extraction methods including texture, color, and form is provided in this study. Once these features are retrieved and then used for comparing photos based on similarity. This study suggests a cutting-edge system design for CBIR system that integrates content-based picture and color analysis with data mining methods. This work is intended to develop a segmentation module for the CBIR system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature extraction

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Advancements in Heart Disease Classification: Leveraging Deep Learning Techniques for ECG analysis 15

Advancements in Heart Disease Classification: Leveraging Dee...

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15th International conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Manimaran, v. Shanthi, N. Aravindhraj, N. Aatarsh, K.M. Adharshini, G. Gokul, P. Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350370249

Heart disease, a global health burden, demands early and accurate detection. While vast medical datasets exist, extracting crucial diagnostic patterns remains a challenge. This study investigates the potential of deep learning architectures to improve heart disease diagnosis accuracy. While traditional machine learning approaches have shown promise, this research delves deeper. It explores Convolutional Neural Networks (CNNs) and pre-trained models like vGG19, ResNet50, and DenseNet201 to identify patterns within medical data. The proposed framework leverages deep learning models to analyze diverse medical records, potentially including electrocardiograms (ECGs) and imaging data. These models automatically extract significant features, which subsequently build a classification model for heart disease detection. The study aims to compare the performance of deep learning models against traditional methods using metrics like accuracy, sensitivity, and specificity. By harnessing the power of deep learning for both feature extraction and classification, this research strives to contribute to the development of more robust and accurate diagnostic tools for heart disease. © 2024 IEEE.

关键词： Deep learning Densely Connected Convolutional Networks Heart disease classification Residual Neural Networks visual Geometry Group

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