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50th Annual Conference of the ieee Industrial Electronics Society, IECON 2024

作者： Kumar, Sanjay K.J. Nandini, K.L. Amritha Dharshan, S.P. Saran Sowmya, V. Bandaragoda, Tharindu Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India Centre for Data Analytics and Cognition La Trobe University Australia

ISBN: (纸本)9781665464543

Maintaining and inspecting infrastructure is crucial due to the safety hazards and economic costs of structural failures. Traditional methods are labor-intensive, time-consuming, and reactive. We propose an automated inspection system leveraging generative AI to enhance efficiency, predictive maintenance capabilities, and comprehensive data analysis. Our framework uses drone-based data acquisition with high-definition cameras and depth sensors, and a custom deep learning model, EyeNet, for precise crack detection. Generative AI techniques, including a Visual Question-Answering (VQA) model and an image-to-image model, are employed for detailed crack analysis and future crack pattern visualization, enabling proactive maintenance. The VQA model achieves an average Root Mean Square Error (RMSE) of 0.394 and an average Symmetric Mean Absolute Percentage Error (SMAPE) of 31.22%. A large Language Model generates comprehensive reports with visualizations, accessible via a dedicated website. Our system significantly improves the inspection process compared to traditional methods, setting a new benchmark by combining generative AI for detailed crack analysis and predictive maintenance capabilities, creating a comprehensive inspection system for civil infrastructure. © 2024 ieee.

关键词： Predictive maintenance

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A LATENT analysis OF A SUPER-RESOLVED SENTINEL-2 data CUBE FOR GREEN URBAN INFRASTRUCTURE HEALTH MONITORING

A LATENT ANALYSIS OF A SUPER-RESOLVED SENTINEL-2 DATA CUBE F...

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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Vaduva, Corina Faur, Daniela Grivei, Alexandru Vasilescu, Vlad Datcu, Mihai Univ Politehn Bucuresti Res Ctr Spatial Informat CEOSpaceTech Bucharest Romania German Aerosp Ctr DLR Earth Observat Ctr EOC Cologne Germany

ISBN: (纸本)9798350320107

In the context of accelerated urbanization, metropolitan green infrastructure is considered a strategic approach to ensure healthy and sustainable living environments. Earth Observation (EO) offers the right means for large scale and long term assessment and monitoring of such green areas and the entire urban environment. The methodology presented in this paper leverages one of the most common satellite missions for vegetation assessment, the Sentinel-2 mission, applies super-resolutions techniques to increase the image spatial resolution and quantifies the spectral radiation reflected by the ground in order to map the Earth's biophysical properties. By considering multiple acquisitions over the same area, time series of spectral indices are generated and processed using LDA, a generative model well known for hierarchical latent information extraction in both text and image analysis. The resulting temporal signature of each topic is further correlated with the evidence of environmental indicators to underline the vegetation vulnerability and specificity of the species. A use case centered for the Bucharest city in Romania, was included.

关键词： Earth Observation Super-resolved Sentinel-s image time series Latent Dirichlet Allocation Physics based analysis Vegetation health monitoring

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Handwriting for Efficient Text Entry in Industrial VR Applications: Influence of Board Orientation and Sensory Feedback on Performance

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第11期29卷 4438-4448页

作者： Fourrier, Nicolas Moreau, Guillaume Benaouicha, Mustapha Norm, Jean-Marie Segula Technol Naval & Energy Engn Res & Innovat U Rueil Malmaison France Ecole Cent Nantes AAU CRENAU UMR 1563 Nantes France LS2N PACCE UMR 6004 Nantes France STICC IMT Atlantique Lab UMR 6285 Brest France Univ Caen Normandy Cherbourg Univ Lab Appl Sci LUSAC Caen France Nantes Univ Ecole Cent Nantes AAU CRENAU UMR 1563 F-44000 Nantes France LS2N PACCE UMR 6004 F-44000 Nantes France

Text entry in Virtual Reality (VR) is becoming an increasingly important task as the availability of hardware increases and the range of VR applications widens. This is especially true for VR industrial applications where users need to input data frequently. large-scale industrial adoption of VR is still hampered by the productivity gap between entering data via a physical keyboard and VR data entry methods. data entry needs to be efficient, easy-to-use and to learn and not frustrating. In this paper, we present a new data entry method based on handwriting recognition (HWR). Users can input text by simply writing on a virtual surface. We conduct a user study to determine the best writing conditions when it comes to surface orientation and sensory feedback. This feedback consists of visual, haptic, and auditory cues. We find that using a slanted board with sensory feedback is best to maximize writing speeds and minimize physical demand. We also evaluate the performance of our method in terms of text entry speed, error rate, usability and workload. The results show that handwriting in VR has high entry speed, usability with little training compared to other controller-based virtual text entry techniques. The system could be further improved by reducing high error rates through the use of more efficient handwriting recognition tools. In fact, the total error rate is 9.28% in the best condition. After 40 phrases of training, participants reach an average of 14.5 WPM, while a group with high VR familiarity reach 16.16 WPM after the same training. The highest observed textual data entry speed is 21.11 WPM.

关键词： Keyboards Training Handwriting recognition Error analysis Usability Task analysis Speech recognition Virtual reality handwriting text entry industry

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Proceedings - 2022 ieee 15th Pacific visualization symposium, PacificVis 2022

Proceedings - 2022 IEEE 15th Pacific Visualization Symposium...

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15th ieee Pacific visualization symposium, PacificVis 2022

ISBN: (纸本)9781665423359

The proceedings contain 24 papers. The topics discussed include: tuning automatic summarization for incident report visualization;virtual inspection of additively manufactured parts;visualization for neural-network-based person re-identification;detecting critical points in 2D scalar field ensembles using Bayesian inference;incorporating texture information into dimensionality reduction for high-dimensional images;spatio-temporal analysis of multi-agent scheduling behaviors on fixed-track networks;boundary-aware rectilinear grid: accurate approximation of unstructured dataset into rectilinear grid with solid boundary handling capabilities;browser-based hyperbolic visualization of graphs;virtual inspection of additively manufactured parts;and a machine-learning-aided visual analysis workflow for investigating air pollution data.

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From Dungeons to Dashboards: Integrating Game-Based Character Sheet in University Environment 7

From Dungeons to Dashboards: Integrating Game-Based Characte...

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7th International symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Sotirov, Milen Nikolova-Sotirova, Donika Georgieva, Todorka Petrova, Valentina Nikola Vaptsarov Naval Academy Department of Information Technologies Varna Bulgaria Technical University - Varna Varna Bulgaria Technical University - Varna Department of Communication Technique and Technologies Varna Bulgaria

ISBN: (纸本)9798350383065

This paper presents the fusion of game-inspired approaches and their application in educational environment, exploring the potential to enhance student engagement and feedback mechanisms. Drawing from role-playing games and video game dynamics, this study explores a method for development of real-time academic dashboards with game interfaces. Through game analysis approach the authors underscore the transformative potential of such integration in the academic environment. Survey findings highlight the advantages of immediate feedback and data visualization. The research establishes a connection between gaming and pedagogy which is imperative for the change of educational methodologies. The authors of this paper developed innovative method 'AcademicDash: Character Sheet' interface tool for Universities. This dashboard establishes a novel integration of game-inspired dynamics into the academic landscape. © 2023 ieee.

关键词： data visualization

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Reliable Online Log Parsing using large Language Models with Retrieval-Augmented Generation 35

Reliable Online Log Parsing using Large Language Models with...

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35th ieee International symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Ju, Hansae Hanyang University Major in Bio Artificial Intelligence Dept. of Applied Artificial Intelligence Ansan Korea Republic of

ISBN: (纸本)9798350367041

Software logs play a crucial role in the development and maintenance of large-scale software systems, recording essential runtime information. Log parsing, which converts log messages into structured templates, is fundamental for various automated analysis tasks such as anomaly detection and root cause analysis. Unlike traditional log parsing methods that struggle with accurately identifying variables, recent large Language Models (LLMs)-based approaches leverage their in-context learning capabilities to achieve more precise variable identification. Nonetheless, LLMs require significant computational resources and often struggle with unseen data, rasing concerns about their reliability in real-world online scenarios. This study introduces a novel approach inspired by Retrieval-Augmented Generation to enhance the reliability and efficiency of LLM-based log parsing. The proposed method involves simple validation of generated templates through regex matching, incorporating contextually similar examples from a continuously updated template database into the LLM prompt. Experimental results demonstrate significant improvements in parsing performance, robustness to unseen data, and cost efficiency. © 2024 ieee.

关键词： Structured Query Language

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AI-Enabling Workloads on large-Scale GPU-Accelerated System: Characterization, Opportunities, and Implications 28

AI-Enabling Workloads on Large-Scale GPU-Accelerated System:...

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28th Annual ieee International symposium on High-Performance Computer Architecture (HPCA)

作者： Li, Baolin Arora, Rohin Samsi, Siddharth Patel, Tirthak Arcand, William Bestor, David Byun, Chansup Roy, Rohan Basu Bergeron, Bill Holodnak, John Houle, Michael Hubbell, Matthew Jones, Michael Kepner, Jeremy Klein, Anna Michaleas, Peter McDonald, Joseph Milechin, Lauren Mullen, Julie Prout, Andrew Price, Benjamin Reuther, Albert Rosa, Antonio Weiss, Matthew Yee, Charles Edelman, Daniel Vanterpool, Allan Cheng, Anson Gadepally, Vijay Tiwari, Devesh Northeastern Univ Boston MA 02115 USA MIT Lincoln Lab Cambridge MA 02139 USA US Air Force Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781665420273

Production high-performance computing (HPC) systems are adopting and integrating GPUs into their design to accommodate artificial intelligence (AI), machine learning, and data visualization workloads. To aid with the design and operations of new and existing GPU-based large-scale systems, we provide a detailed characterization of system operations, job characteristics, user behavior, and trends on a contemporary GPU-accelerated production HPC system. Our insights indicate that the pre-mature phases in modern AI workflow take up significant GPU hours while underutilizing GPUs, which opens up the opportunity for a multi-tier system. Finally, we provide various potential recommendations and areas for future investment for system architects, operators, and users.

关键词： Systems operation Graphics processing units data visualization Production Machine learning Computer architecture Market research

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Research and Implementation of 3D visualization Technology Based on GIM Model 3

Research and Implementation of 3D Visualization Technology B...

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3rd ieee International Conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Zhang, Su Zhang, Yaping Qi, Lizhong Sun, Xiaohu State Grid Economic and Technological Research Institute Co. LTD Beijing China

ISBN: (纸本)9781665472784

In this paper, GIM model / 3D display, virtual reality and information integration technology is combined to put forward a four-layer framework of urban power grid 3D spatial visualization information platform. Through data analysis, parameter modeling, model assembly, model optimization and attribute warehousing, the system converts GIM model into a general standard 3D model file FBX, which reduces the data amount of the model to a large extent. The reconstruction and optimization of GIM model are realized. The platform integrates engineering information, geographic information, digital models and documents throughout the whole process of engineering construction to complete the application of three-dimensional digital handover. Finally, the application of 3D model in the secondary system simulation analysis, online monitoring and fault diagnosis is realized with the actual 220kV smart substation as the pilot. The simulation results show that this technology can effectively improve the efficiency of three-dimensional design, operation and maintenance of secondary system. © 2023 ieee.

关键词： Smart power grids

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A Survey of Mixture Models for Scientific Literature 4

A Survey of Mixture Models for Scientific Literature

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4th International symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Deng, Yujie Liu, Qiyu Li, Xiangyu Zhang, Gexiang Zhu, Ming Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798331541729

The emergent abilities of large language models (LLMs) in knowledge understanding and problem reasoning has garnered considerable interest from both academic and industrial sectors. The data processing of LLMs has expanded from textual data to multimodal data, including images, formulas, videos, and charts, leading to a sharp increase in model parameters and computational power requirements. Mixture models are one of the solutions to these issues, but research on mixture models for the understanding of scientific literature data remains scarce. This paper outlines the application of LLMs in the field of knowledge engineering and analyzes the importance of multi-model data LLMs in the intelligence domain. Then, this paper first conducts a review analysis of research on mixture models in scientific literature understanding, analyzing respective research directions and related work from three aspects: multimodal data understanding, dynamic expert scheduling strategies, and expert domain-specific training. Finally, this paper summarizes the current state of research on mixture models for scientific literature and looks forward to specific research directions in this field. Scientific literature, as an important carrier of human knowledge, is the core key supporting innovation. Existing research mainly focuses on the general application of mixture models. For the first time, this paper conducts a review analysis of mixture model research specifically for the field of scientific literature. © 2024 ieee.

关键词： data assimilation

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Analyzing large-Scale Twitter Real Time Streaming data with Manifold Machine Learning Algorithms in Apache SPARK

Analyzing Large-Scale Twitter Real Time Streaming Data with ...

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2023 International Conference on data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Abhineswari, M. Priyadarshini, R. Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350348910

With the rapid growth of social media platforms, Twitter has emerged as a valuable source that provides real-time data on public opinion, sentiment, and trends through approximately 7500 tweets per second, enabling individuals, brand organizations, and public influencers to express thoughts, opinions, and updates in a concise manner. This research paper combines Apache Spark, machine learning techniques, and data visualization methods to analyze sentiment patterns in streaming Twitter data. The implementation leverages distributed computing capabilities and real-time streaming processing techniques to handle large amounts of data. Hashtag counting, noun counting, sentiment lexicon, and Jaccard similarity are used for the trend analysis of tweets, and Logistic regression, Naive Bayes, Multinomial Naive Bayes, Support Vector Machine (SVM), Random Forest, and Decision Trees are used to classify tweets. One of the interactive visualization tools, like Tableau, generates dynamic representations of sentiment trends and distributions across topics or time periods. The generation of response time is combined with the measurement of accuracy, precision, recall, and F1 score as key performance indicators, indicating the effectiveness and scalability of the proposed system. This research contributes to the future advancements of sentiment analysis techniques for streaming social media data and opens avenues for further research in this domain. © 2023 ieee.

关键词： data Analytics

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