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Unsteady Flow Field Prediction: Understanding the Dynamics in an Intuitive Physics Way 26

Unsteady Flow Field Prediction: Understanding the Dynamics i...

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26th International conference on Pattern Recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

作者： Qu, Xiyao Liu, Zijing An, Wei Lyu, Hongqiang Liu, Xuejun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Key Lab Aerodynam Noise Control Mianyang Sichuan Peoples R China State Key Lab Aerodynam Mianyang Sichuan Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn Nanjing Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

The prediction of unsteady flow field is a challenge in the area of fluid dynamics due to the spatiotemporally dynamic behavior. Given high-dimensional spatial information of the unsteady flow field for a number of previous time steps, the subsequent multi-step or even multi-cycle information is to be obtained. Inspired by human intuitive physics, we investigate a purely visual approach for unsteady flow field prediction directly from images which are obtained by mapping physical quantities to the color space, and propose a novel stochastic latent variable model based on deep neural networks. Our model is evaluated on two numerical simulation datasets of different complexities in terms of conditions and scopes of the flow field. The results show that trained with two-cycle data, over ten-cycle future flow fields are accurately predicted by our model on the low-complexity dataset. For the high-complexity one, our model also gives accurate multi-cycle predictions after trained with data of more cycles.

关键词： Deep learning visualization Fluid dynamics Time series analysis Stochastic processes Predictive models data models

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Comparison of bathymetry estimated from multibeam echosounder and optical data 26

Comparison of bathymetry estimated from multibeam echosounde...

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Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVI conference 2024

作者： Maltese, Antonino Presti, Valeria Lo Nocera, Giovanni Andrea Sulli, Attilio Dipartimento di Ingegneria Università degli Studi di Palermo Palermo Italy Dipartimento di Scienze della Terra e del Mare Università degli Studi di Palermo Palermo Italy Piazza Marina 61 Palermo Italy

ISBN: (纸本)9781510680906

Accurate bathymetry estimation is crucial for various marine and coastal applications, including resource management and environmental protection. This study presents a comparison of bathymetry estimated using optical satellite imagery, on sandy bottom, versus multibeam bathymetric data. The study area was the Gulf of Sciacca, on the southern coast of Sicily Island (Italy). High-resolution PlanetScope SuperDove satellite images were processed using a simplified version of the original radiative transfer equation from Jain and Miller (1977) was used to estimate water depth. The model was calibrated and then validated using multibeam data. Multibeam data were acquired using a Reason 8125 multibeam echosounder. Bathymetry was estimated from optical data from the shoreline down to ∼25 m water depth;while approaching this depth, the reflectance assumed an asymptotic value. The results show a strong correlation between the depths estimated from satellite images and those obtained from multibeam data, with a root mean square error lower than 11% with a confidence interval of 95%. However, some discrepancies were observed in areas in mixed pixels or reflectance surface effects. © 2024 SPIE.

关键词： Bathymetry

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analysis on Cutting Down of Stock Market Outbreak of Covid-19

Analysis on Cutting Down of Stock Market Outbreak of Covid-1...

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International conference on Smart Systems and Inventive Technology (ICSSIT)

作者： C. Susmitha L. Nikhil M. Kavitha Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

Human life was affected worldwide during the rough years of COVID-19, including the economic and social disruption that affected tens of millions of people. This research comes to a clear conclusion about which sectors are most affected, how it indirectly affects many other sectors, such as the financial stock exchange, and how it increases the risk of extreme poverty. This paper has arrived at a general conclusion about the stock market in the difficult years of 2019-21 of COVID-19. Different visualization techniques using some of the most popular libraries like Seaborn, Matplotlib, NumPy, and Pandas are used to know the accuracy and main content of a large, combined dataset. Before performing the visualization techniques, this study will combine datasets with different attributes and perform cleaning and integration methods on the combined dataset. The purpose of the project is to shed light on the stock market through an analysis of the daily closing prices of the market. The reason to take this project is for the overall growth of the economy and hope it will help many businesses developers.

关键词： COVID-19 data analysis Profitability data visualization Market research Time measurement Libraries

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Visual analysis of Goal-Leading Phases in Soccer 21

Visual Analysis of Goal-Leading Phases in Soccer

引用

14th Biannual conference of the Italian-SIGCHI-Chapter (CHIItaly)

作者： Buono, Paolo Ceriani, Miguel Costabile, Maria F. Valdivia, Paola Univ Bari Bari Italy Telecom Paris Palaiseau France

ISBN: (纸本)9781450389778

This work focuses on the use of a specific type of visualization to analyze soccer matches and highlight individual players' goal contributions. An analytic approach in team sports is nowadays considered crucial by both practitioners and other interested parties such as fans or betters. In soccer, the analysis of sequences of passes and shoots can help the analysts understanding some aspects of the performance of players and teams. We show how, through a compact representation of n-ary temporal relationship, users can get a comprehensive view of the evolution of the passes leading to a goal in several matches. The dataset considered in this work contains data from the first 26 weeks of the season 2019-2020 of the Italian major soccer league.

关键词： data visualization hypergraphs sports

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Text Query based Traffic Video Event Retrieval with Global-Local Fusion Embedding

Text Query based Traffic Video Event Retrieval with Global-L...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Thang-Long Nguyen-Ho Minh-Khoi Pham Tien-Phat Nguyen Hai-Dang Nguyen Do, Minh N. Nguyen, Tam, V Minh-Triet Tran Univ Sci Ho Chi Minh City Vietnam Viet Nam Natl Univ Ho Chi Minh City Vietnam John Neumann Inst Ho Chi Minh City Vietnam Univ Illinois Urbana IL USA Univ Dayton Dayton OH 45469 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Retrieving event videos based on textual description is a promising research topic in the fast-growing data field. However, traffic data increases every day, so it is essential to need intelligent traffic system management in conjunction with humans to speed up the search. We propose a multi-module system that delivers accurate results that meet objectives, including explainability and scalability at the same time. Our solution considers neighbors entities related to the mentioned object to represent an event by rule-based, which can represent an event by the relationship of multiple objects. In our proposed retrieval method, we add our modified model of Alibaba solution with the post-processing techniques from HCMUS method in AI City Challenge 2021 to boost the explainability of the obtained results. As the traffic data is vehicle-centric, we apply two language and image modules to analyze the input data and obtain the global properties of the context and the internal attributes of the vehicle. We introduce a one-on-one dual training strategy for each representation vector to optimize the interior features for the query. Finally, a refinement module gathers previous results to enhance the final retrieval result. We benchmarked our approach on the data of the AI City Challenge 2022 and obtained the competitive results at an MMR of 0.3611. We were ranked in the top 4 on 50% of the test set and in the top 5 on the full set.

关键词： Training visualization Target tracking Image color analysis Scalability Urban areas Training data

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t-SNE Based on Sobol Sequence Initialized Exchange Market Algorithm 10

t-SNE Based on Sobol Sequence Initialized Exchange Market Al...

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10th IEEE Joint International Information Technology and Artificial Intelligence conference, ITAIC 2022

作者： Wu, Chun Wang, Bingzhe Yang, Zan Nai, Wei Xing, Yidan Wang, Zihao Lin, Yukai Tongji Zhejiang College Department of Electronic and Information Engineering Jiaxing China Tongji Zhejiang College Faculty of Science Jiaxing China

ISBN: (数字)9781665422079

ISBN: (纸本)9781665422079

T-distributed stochastic neighbor embedding (t-SNE) is a nonlinear dimensionality reduction method in manifold learning in the field of artificial intelligence (AI). It is very suitable for the visualization of high-dimensional data. It has now been widely used in image processing, natural language processing, speech processing, and gene data analysis. However, t-SNE also has some problems, for example, the solution process depends on gradient information, which is prone to fall into local optimum. At present, many scholars have studied t-SNE and have tried to employ t-SNE to solve specific problems of different industries, but most of them have not done related work on the optimization of its solution process. In this paper, a Sobol sequence initialized exchange market algorithm (SSEMA) has been proposed to replace the gradient dependent solution method in the original t-SNE, which can find a more reliable global optimal solution by comparing with just considering about gradient information. © 2022 IEEE.

关键词： data visualization

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Covid-19 in Saudi Arabia: A pandemic data descriptive analysis and visualization 2021

Covid-19 in Saudi Arabia: A pandemic data descriptive analys...

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7th International conference on Arab Women in Computing, ArabWIC 2021

作者： Ghouzali, Sanaa Bayoumi, Sahar Larabi-Marie-Sainte, Souad Shaheen, Sara Department of Information Technology College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Department of Information Technology Institute of Graduate Studies and Research Alexandria University Alexandria Egypt Computer Science Department College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

ISBN: (纸本)9781450384186

At the end of 2019, humans have been contracted by a new coronavirus, known as Covid-19, that caused a pneumonia outbreak in the Wuhan city in China. As this virus expanded globally outside China, the World Health Organization declared the outbreak as a global pandemic in March 2020. The paper aims to investigate the severity of the outbreak in different regions of Saudi Arabia and the impact of the lockdown through data visualization of different epidemiological metrics of Covid-19, such as the spread growth rate and the case fatality rate. The data visualization of the pandemic spread, and fatality was provided with Tableau using the real-time data. © 2021 Association for Computing Machinery.

关键词： data visualization

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Local Attention Pyramid for Scene Image Generation

Local Attention Pyramid for Scene Image Generation

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shim, Sang-Heon Hyun, Sangeek Bae, DaeHyun Heo, Jae-Pil Sungkyunkwan Univ Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we first investigate the class-wise visual quality imbalance problem of scene images generated by GANs. The tendency is empirically found that the class-wise visual qualities are highly correlated with the dominance of object classes in the training data in terms of their scales and appearance frequencies. Specifically, the synthesized qualities of small and less frequent object classes tend to be low. To address this, we propose a novel attention module, Local Attention Pyramid (LAP) module tailored for scene image synthesis, that encourages GANs to generate diverse object classes in a high quality by explicit spread of high attention scores to local regions, since objects in scene images are scattered over the entire images. Moreover, our LAP assigns attention scores in a multiple scale to reflect the scale diversity of various objects. The experimental evaluations on three different datasets show consistent improvements in Frechet Inception Distance (FID) and Frechet Segmentation Distance (FSD) over the state-of-the-art baselines. Furthermore, we apply our LAP module to various GANs methods to demonstrate a wide applicability of our LAP module.

关键词： Measurement Deep learning visualization Image segmentation Computer vision Image analysis Image synthesis

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Using Graph Theory for Clinical data Management 5th

Using Graph Theory for Clinical Data Management

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5th Workshop on Intelligent data - From data to Knowledge, DOING 2024, 3rd Workshop on Knowledge Graphs analysis on a Large Scale, K-GALS 2024, 6th Workshop on Modern Approaches in data Engineering and Information System Design, MADEISD 2024 and 3rd Workshop on Personalization and Recommender Systems, PERS 2024 held in conjunction with 28th European conference on Advances in databases and Information Systems, ADBIS 2024

作者： Lazzaro, Ilaria Department of Medical and Surgical Sciences University "Magna Græcia" Catanzaro88100 Italy Data Analytics Research Center University "Magna Græcia" Catanzaro88100 Italy

ISBN: (纸本)9783031704208

The increasing complexity of these data and their related interactions (e.g., interactions between biological systems, genes, proteins, and neurons) requires an appropriate approach, which is realized through network models capable of systematically analysing the relationships between entities within a multi-level interconnected system. Multilayer networks best represent this approach. The workflow begins with exploring the structural and functional aspects of a multilayer network, shedding light on advanced techniques for model analysis, management, and visualization. Although various approaches have been implemented, the study of these complex structures presents several open challenges. Through a synthesis of theoretical insights and empirical observations, a comprehensive understanding of how multilayer networks model and respond to various stimuli is provided;however, several limitations related to data accessibility and computational costs are highlighted. It is also considered, the application of multilayer networks in the field of neuroimaging, aiming to leverage the available data from neuroimaging studies to understand brain connectivity and function better. The final objective involves designing and developing methodologies that enable the graphical representation of multilayer networks, the research and analysis of the topological metrics that comprise the network, to facilitate the evolution of medicine in the realm of data-driven healthcare. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network theory (graphs)

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VS2N: Interactive Dynamic visualization and analysis Tool for Spiking Neural Networks 18

VS2N: Interactive Dynamic Visualization and Analysis Tool fo...

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18th International conference on Content-Based Multimedia Indexing (IEEE CBMI)

作者： Elbez, Hammouda Benhaoua, Mohammed Kamel Devienne, Philippe Boulet, Pierre Univ Mustapha Stambouli Mascara Comp Sci Dept Mascara Algeria Univ Lille CNRS Cent Lille UMR 9189 CRIStAL F-59000 Lille France Univ Oran1 LAPECI Lab Oran Algeria

ISBN: (纸本)9781665442206

Bio-inspired computing architectures enable ultra-low power consumption and massive parallelism using neuromorphic computing, which is apt to implement Spiking Neural Networks (SNN). Such architectures are particularly suitable for energy-constrained applications. A deeper understanding of Spiking Neural Networks (SNN) behavior during training is needed to improve these architectures. This paper presents VS2N, a web-based tool for interactive visualization and analysis of SNN activity over time. This simulator-independent tool offers a way to examine, analyze and validate different hypotheses about SNN activity. We present available analysis modules and use-cases of the tool as an example.

关键词： Spiking neural networks neuromorphic computing visualization analysis big data

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