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

共 5168 条记录，以下是911-920 订阅

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visual exploration and Planning of the Automated Material Handling System for Smart Factory in the Immersive Environment

Visual Exploration and Planning of the Automated Material Ha...

引用

virtual Reality and 3D User Interfaces Abstracts and Workshops (vRW), IEEE conference on

作者： Yifei Zhang Yougao Li Xiangyang Xue Jianfeng Feng Siming Chen Fudan University Shanghai Jiao Tong University Ji Hua Laboratory School of Data Science Fudan University

The combination of the digital twin and the smart factory has been a popular research direction in recent years. Automated Material Handling System (AMHS) is an important scene of smart factory, while most traditional simulation AMHS analysis tools only provide results or simple 2D displays. We introduce a visual exploration system that combines virtual reality (vR) with AMHS to allow users to observe and analyze the performance of the system in real-time, and we provide a series of egocentric interactions to help users analyze and interfere with the performance of the system. The interactions include ray interaction, task scheduling interaction, and path planning interaction, We demonstrate how to explore AMHS using visualization and interaction in our system through case studies.

关键词： visualization Solid modeling Three-dimensional displays Two dimensional displays Materials handling virtual reality User interfaces

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Satellite Image Denoising Using Optimized Pulse Coupling Network

Satellite Image Denoising Using Optimized Pulse Coupling Net...

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IEEE International conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS)

作者： Rajesh v P. Sivakumar Department of ECE Kalasalingam Academy of Research and Education Krishnan Koil Tamil Nadu

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Satellite image denoising is of utmost importance in various applications weather monitoring, flood control and crop monitoring focusing on enhancing the visual quality of affected images. Denoising is a contemporary trend in the application of image processing, yet encounters numerous restrictions attributed to environmental conditions. In recent times, there has been extensive exploration of DL (deep learning) methods for the purpose of noise removal in satellite images. This work presents a DL based optimization model for Satellite image denoising. That is the DL model PCN (pulse coupling network) with AF (artificial flora) optimizer is used for parameter enhancement in PCN. PSNR and SSIM values achieved are 44.8 and 0.89 respectivelyon the FloodNet database. Thus, the analysis yielded superior performance and achieved better quality images.

关键词： Couplings Measurement visualization Noise reduction Noise Market research Satellite images

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Feasibility Evaluation of Warm Solvent Assisted Gravity Drainage Process in Low-Carbon Developing Super-Heavy Oil or Oil Sands Project 84

Feasibility Evaluation of Warm Solvent Assisted Gravity Drai...

引用

2023 SPE EuropEC - Europe Energy conference featured at the 84th EAGE Annual conference and Exhibition, EURO 2023

作者： Liang, Guangyue Xie, Qian Liu, Yang Liu, Shangqi Xia, Zhaohui Bao, Yu Zhou, Jiuning Research Institute of Petroleum Exploration and Development CNPC China

ISBN: (纸本)9781613999912

It is very difficult to realize good economy returns using conventional SAGD process in many oil sands projects due to large CPF investment, massive steam injection, expensive surface diluent adding and increasing carbon emission tax. By contrast, warm solvent assisted gravity drainage process (WSAGD) is a promising low-carbon technology to deal with these SAGD challenges. This paper conducted feasibility evaluation by combined with Nsolv Best pilot analysis and a series of physical simulations. From 2014 to 2017, WSAGD pilot was successfully carried out by injecting butane at 60℃ in Suncor Dover oil sands. Its reservoir geological characteristics, physical properties, development technology policy and production performance were systematically analyzed. Combined with 4D seismic interpretation, RST and observation well data, the size and growth rate of solvent chamber were monitored and analyzed. Considering great uncertainty in numerical simulations influenced by many factors including grid size, solvent diffusion coefficient, interfacial tension and capillary force, a series of experimental tests and physical simulations were conducted. The behavior of viscosity reduction, interfacial tension reduction and microscopic oil displacement related to different solvents were systematically tested including propane, butane, pentane and hexane. Particularly, the performance of SAGD and WSAGD process were evaluated by 2D and 3D visual physical simulations. In Nsolv Best pilot, the target reservoir is low pressure, thin and shallow buried. The oil rate reached 250-300 barrels per day under 300 m horizontal section, and API degree of produced oil was upgraded to 13-16 from original 8. After 3 years of tests, the width of solvent chamber is 40-60m, lateral and vertical 1.56 m and 0.96 m per month, and horizontal conformance is 67%. The experiments results show that viscosity reduction trend will flatten out when the solvent concentration exceeds 10 vol% due to partial asphalt

关键词： Solvents

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exploration of the Fusion of FAST Algorithm and SIFT Algorithm in Image Feature Extraction

Exploration of the Fusion of FAST Algorithm and SIFT Algorit...

引用

Electronic Information Engineering and Computer Science (EIECS), 2021 International conference on

作者： Jingyi Zuo Xianghua Ma School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In the field of computer vision, image feature extraction is the bridge connecting low-level image data with high-level visual tasks such as image matching and object recognition. The SIFT algorithm occupies an important position in feature extraction due to its outstanding stability and uniqueness, but its computational complexity is relatively high, limiting its application in scenarios with high real-time requirements, and the algorithm runs relatively slowly. In contrast, the FAST algorithm is known for its efficiency, but it does not provide a method for calculating feature descriptors and lacks invariance to scale and rotation changes, only suitable for translational image changes. Therefore, this paper explores the fusion of the FAST algorithm and the SIFT algorithm, aiming to combine their strengths.

关键词： visualization Systematics Limiting Image matching Focusing Feature extraction Stability analysis Real-time systems Object recognition Optimization

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An Improved Inception v3 Deep learning model for Cardiovascular Disease Prediction 5

An Improved Inception V3 Deep learning model for Cardiovascu...

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5th International conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Sowjanya, Lingineni Lakshmi Kowshya, Kodali Roshini, Manduri Sai Krishna, Bogireddy venkatrama Phani Kumar, S. Kolli, venkata Krishna Kishore Vfstr Deemed to be University Department of Cse Guntur India

ISBN: (纸本)9798331522100

Accurate prediction of cardiovascular disease risk is essential for improving healthcare outcomes and resource management. This study uses a cardiovascular disease dataset and applies deep learning models to forecast disease risk in specific patient populations. In our approach, data preprocessing encompassed normalization and partitioning of the dataset into training and test subsets. We utilized the Inception v3 model, conducting training over ten epochs for data analysis. We also assessed alternative models to broaden our evaluation - specifically, vGG 16, vGG 19, ResNet 34, and ResNet 50. These models were analyzed based on essential performance metrics, such as accuracy, precision, recall, and F1 score. Inception v3 achieved the highest accuracy at 98%, followed by vGG 16 at 86%, vGG 19 at 80%, ResNet 34 at 78%, and ResNet 50 at 68%. We further enhanced our analysis with visual tools such as histograms and heat maps to identify key trends and correlations in the data. These insights provided a clearer understanding of significant risk factors and potential prevention strategies. Our findings demonstrate that deep learning models, particularly Inception v3, are highly effective for cardiovascular disease prediction, contributing to improved healthcare strategies and decision-making. © 2025 IEEE.

关键词： Risk perception

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An Improved Inception v3 Deep learning model for Cardiovascular Disease Prediction

An Improved Inception V3 Deep learning model for Cardiovascu...

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Emerging Systems and Intelligent Computing (ESIC), International conference on

作者： Lingineni Lakshmi Sowjanya Kodali Kowshya Manduri Sai Roshini Bogireddy Krishna venkatrama Phani Kumar S venkata Krishna Kishore Kolli Department of CSE VFSTR Deemed to be University Guntur India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Accurate prediction of cardiovascular disease risk is essential for improving healthcare outcomes and resource management. This study uses a cardiovascular disease dataset and applies deep learning models to forecast disease risk in specific patient populations. In our approach, data preprocessing encompassed normalization and partitioning of the dataset into training and test subsets. We utilized the Inception v3 model, conducting training over ten epochs for data analysis. We also assessed alternative models to broaden our evaluation—specifically, vGG 16, vGG 19, ResNet 34, and ResNet 50. These models were analyzed based on essential performance metrics, such as accuracy, precision, recall, and F1 score. Inception v3 achieved the highest accuracy at 98%, followed by vGG 16 at 86%, vGG 19 at 80%, ResNet 34 at 78%, and ResNet 50 at 68%. We further enhanced our analysis with visual tools such as histograms and heat maps to identify key trends and correlations in the data. These insights provided a clearer understanding of significant risk factors and potential prevention strategies. Our findings demonstrate that deep learning models, particularly Inception v3, are highly effective for cardiovascular disease prediction, contributing to improved healthcare strategies and decision-making.

关键词： Deep learning Training Analytical models visualization Accuracy Computational modeling Medical services Predictive models Cardiovascular diseases Resource management

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Implementing Agile Task Management System: Leveraging Web Development and Power BI for Enhanced Decision-Making

Implementing Agile Task Management System: Leveraging Web De...

引用

Self Sustainable Artificial Intelligence Systems (ICSSAS), International conference on

作者： M. Jaishree v. Anupriya M Sukitha Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Developing a Task management system can benefit organizations in several ways which offers a comprehensive solution that enhances productivity, improves collaboration, provides visibility, supports Agile practices, ensures accountability, and allows for seamless integration and scalability. This helps organizations manage their tasks and projects more efficiently, ultimately leading to better outcomes and increased success. The Agile Task Management system can be developed by combining both the front-end and back-end along with a database for storing and organizing the company project details like the status, project timeline, and project team details like their mail IDs and the particular projects they belong to. The task management web application can be developed in various languages and frameworks for managing and aggregating projects with specific employees. This research study focuses on two main areas: developing a task management web application and creating a dashboard using Power BI for in-depth analysis of the organization’s project dataset. Power BI plays a crucial role in data analytics by integrating data from multiple sources, enabling interactive exploration, and facilitating collaborative decision-making through its powerful visualization and reporting tools. The discussion includes using HTML, JavaScript, Ajax, and Bootstrap for the front end, PHP for the backend, and MySQL for the database to create a comprehensive Task Management web application. Additionally, it covers the development of a simple task management system with a Python Flask application using SQL Alchemy and PyQt5 for GUI creation with an SQLite database. Additionally, the system is designed to handle complete project-related datasets by incorporating Power BI Desktop to create interactive dashboards for visualizing project data which is a separate part of the task management system.

关键词： Accuracy Decision making Standards organizations data visualization Collaboration Termination of employment visual databases Security Reliability Graphical user interfaces

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Interactive visualization of BioMedical data

Interactive Visualization of BioMedical Data

引用

IEEE International conference on Big data

作者： Mohammad Shafiqul Islam Rahat Ibn Rafiq Guenter Tusch College of Computing Grand Valley State University Michigan USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Public databases such as the NCBI Gene Expression Omnibus (GEO) house millions of experimental gene expression datasets invaluable for transcriptome meta-analysis, enabling researchers to identify genes, pathways, and biomarkers with enhanced accuracy. However, while these datasets offer great potential, finding relevant biological pathways in disease-related contexts remains challenging. To address this, we introduce a web-based visualization tool that utilizes PubMed abstracts to extract key biomedical entities, such as genes, pathways, diseases, species, and chemicals, and represent their relationships through an interactive graph. By leveraging PubMed’s annotated MeSH terms, our tool allows users to explore the literature more intuitively, bypassing traditional dataset search limitations and focusing on published results. The tool transforms complex query outputs into a graph-based network, providing a visual interface for navigating connections between biological entities. This interactive approach helps researchers to analyze relationships within large-scale biomedical data and uncover potential pathways for drug repurposing and disease understanding. We discuss the tool’s design using ***, its application in biomedical research, and the challenges faced when visualizing large datasets, offering new insights for improved data interpretation and exploration.

关键词： Drugs data visualization Biomarkers Biology Gene expression data mining Bioinformatics Chemicals Diseases Biomedical imaging

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Highly Efficient HDR Signal Processing for ASICs Using the Flexfloat data Type

Highly Efficient HDR Signal Processing for ASICs Using the F...

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Digital Signal Processing and its Applications (DSPA), International conference on

作者： Zinovchik O. Yu. Chesnokov v. N. Zaripov R. A. Moscow Institute of Physics and Technology

The on-going evolution of technology of capturing and generation video, sound, RF, etc. signals results in very HDR data to be stored and processed. The analysis of human perception of different signals is showing that the linear representation of the above mentioned signals in ASICs is highly inefficient (has redundant bits corresponding to high values of the signal). This results in excessive power consumption and silicon area. This paper is presenting a new datatype (FlexFloat), which is designed taking into account the behavior of Human visual System and allows highly efficient processing and storage for HDR data. The effectiveness of the proposed methodology is demonstrated on the example of HDR image processing.

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Seeing the Forest and the Trees: Solving visual Graph and Tree Based data Structure Problems using Large Multimodal Models 25

Seeing the Forest and the Trees: Solving Visual Graph and Tr...

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27th Australasian Computing Education conference-ACE-Annual

作者： Gutierrez, Sebastian Hou, Irene Lee, Jihye Angelikas, Kenneth Man, Owen Mettille, Sophia Prather, James Denny, Paul MacNeil, Stephen Temple Univ Philadelphia PA 19122 USA

ISBN: (纸本)9798400714252

Recent advancements in generative AI systems have raised concerns about academic integrity among educators. Beyond excelling at solving programming problems and text-based multiple-choice questions, recent research has also found that large multimodal models (LMMs) can solve Parsons problems based only on an image. However, such problems are still inherently text-based and rely on the capabilities of the models to convert the images of code blocks to their corresponding text. In this paper, we further investigate the capabilities of LMMs to solve graph and tree data structure problems based only on images. To achieve this, we computationally construct and evaluate a novel benchmark dataset comprising 9,072 samples of diverse graph and tree data structure tasks to assess the performance of the GPT-4o, GPT-4 with vision (GPT-4v), Gemini 1.5 Pro, Gemini 1.5 Flash, Gemini 1.0 Pro vision, and Claude 3 model families. GPT-4o and Gemini 1.5 Flash performed best on trees and graphs respectively. GPT-4o achieved 87.6% accuracy on tree samples, while Gemini 1.5 Pro, achieved 76.9% accuracy on graph samples. Our findings highlight the influence of structural and visual variations on model performance. This research not only introduces an LMM benchmark to facilitate replication and further exploration but also underscores the potential of LMMs in solving complex computing problems, with important implications for pedagogy and assessment practices.

关键词： Generative AI Academic Integrity Computing Education Large Multimodal Models LMMs Large Language Models LLMs

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