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2022 Global Reliability and Prognostics and Health Management conference, PHM-Yantai 2022

2022 Global Reliability and Prognostics and Health Managemen...

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2022 Global Reliability and Prognostics and Health Management conference, PHM-Yantai 2022

ISBN: (纸本)9781665496315

The proceedings contain 267 papers. The topics discussed include: reliability growth model based on Bayesian parameter inference;adaptive maximum cyclostationarity blind deconvolution and its application on incipient fault diagnosis of bearing;a flexible battery capacity estimation method using a portion of charging curve;application of a method of identifying instantaneous shaft speed from spectrum in aeroengine vibration analysis;a monocular visual inertial odometer based on weighted median pre-integration;improved VMD method based on nonlinear Hoyer for abnormal impact interference;application exploration of digital twin in rail transit health management;a subsystem data based reliability acceptance test plan derivation method;fault feature extraction of rolling-element bearing based on neighborhood rough set;safety design and analysis method of single event effects for civil aircraft;auxiliary detection method of illegal actions in football teaching based on binocular vision;robust motion pose capture method of aerobics based on visual simulation imaging;and application of sparse representation method based on K-SVD-ADMM in anomaly detection of satellite telemetry.

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Understanding Plant Leaf Diseases: A Review, analysis, exploration of Techniques and Technologies

Understanding Plant Leaf Diseases: A Review, Analysis, Explo...

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visual Analytics and data visualization (ICVADV), International conference on

作者： Thirumalasetty Nagalakshmi M Sambasivudu Thota Siva Ratna Sai Dept. of CSE Malla Reddy College of Engineering and Technology (A) Telangana India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Modern agriculture relies on plant leaf disease detection to reduce crop losses and guarantee food security. This study takes a look at the three main types of methods used to identify plant leaf diseases: conventional methods, techniques based on Machine Learning (ML), and methodology based on Deep Learning (DL). Although they provide essential insights, the scalability and precision of traditional methods-which depend on chemical analysis, image processing, and human inspection-are severely lacking. ML techniques, which make use of algorithms such as Random Forests (RF) and Support Vector Machines (SVM), automate feature-based categorization, which improves detection. Automated disease identification with high accuracy has recently been made possible by DL, which uses sophisticated methods like semantic segmentation, object recognition, and transfer learning in conjunction with architectures like Convolutional Neural Networks (CNNs). This study examines all of these approaches in detail, comparing and contrasting their advantages, disadvantages, and possible uses. Emerging developments, such as eXplainable Artificial Intelligence (XAI) and Internet of Things (IoT)-based systems, are also highlighted in the discussion, with an emphasis on how they will influence smart and sustainable agriculture in the future.

关键词： Support vector machines Deep learning Reviews Explainable AI Semantic segmentation Plants (biology) Transfer learning Food security Object recognition Diseases

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Collaboration Spotting X - A visual Network exploration Tool 8

Collaboration Spotting X - A Visual Network Exploration Tool

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8th International conference on Social Network analysis, Management and Security (SNAMS)

作者： Bobic, Aleksandar Le Goff, Jean-Marie Guetl, Christian CERN IPT Dept Geneva Switzerland Graz Univ Technol CoDiS Lab ISDS Graz Austria

ISBN: (纸本)9781665494953

Due to many technological advancements, the amount of connected data drastically increased in the last decade. The analysis of this data and the insights it generates show great potential for supporting decision making processes in various industries and aspects of our lives. Multiple visual analytics solutions have been proposed to gain further insights into such data and gain explainable results. However, the majority of existing solutions are either closed sourced, not available or no longer developed. To mitigate the issues above and based on findings from expert interviews conducted using an existing tool, this paper introduces Collaboration Spotting X, a new network-based interactive visual analytics and information retrieval tool prototype. This prototype enables users to explore connected network datasets such as social network data and bibliometric data using multiple visual cues and interactions. Furthermore, to gain an insight into how this prototype is perceived by users and identify further improvements, a preliminary study with a class of 37 computer science graduate students is described. The study findings show that the students perceive Collaboration Spotting X as a useful tool that helps them complete tasks through visualisation and interaction. Additionally, multiple aspects were identified that might have caused users to experience in addition to positive emotions also some negative emotions during usage. These aspects might have also contributed to a lower usability score. Finally, multiple improvement directions have been identified, which will be implemented in future developments.

关键词： visual Analytics visual Network Analytics Network visualisation visual Analytics Tool visual Network Analytics Tool Social Network visualisation Information Retrieval

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TweetVis: a visual approach to explore Twitter conversations over time

TweetVis: a visual approach to explore Twitter conversations...

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Colombian Caribbean conference (C3), IEEE

作者： José Llumiquinga Erick Cuenca Arnaud Sallaberry Yachay Tech University Urcuquí Ecuador LIRMM University of Montpellier CNRS AMIS Paul-Valéry University of Montpellier Montpellier France

Information visualization is a field of visual data analysis that focuses on working with abstract information. It also incorporates interactions that make it easier for users to assimilate and understand the information presented quickly. This work focuses on developing TweetVis, a visualization tool that follows the fundamentals of Twitter data analysis. In particular, TweetVis makes it possible to explore Twitter conversations over time, as these conversations exert significant influence in the social and political spheres, underlining the importance of their analysis and exploration. To access Twitter conversations, use is made of the official Twitter API. Once the data is collected, it goes through a processing process that turns it into a graph, where the nodes represent the tweets, and the links denote the interactions between them. TweetVis allows users to explore conversations generated from a tweet interactively over time. Finally, this tool’s effectiveness is tested in a real case, analyzing the tweet generated by a public figure.

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QVis: A visual Analytics Tool for Exploring Noise and Errors in Quantum Computing Systems

QVis: A Visual Analytics Tool for Exploring Noise and Errors...

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Quantum Computing and Engineering (QCE), IEEE International conference on

作者： Chad A. Steed Junghoon Chae Samudra Dasgupta Travis S. Humble Oak Ridge National Laboratory Oak Ridge USA

We present the preliminary design and results of QVis, a visual analytics tool for exploring quantum device performance data. QV is helps uncover temporal and multivariate variations in noise properties of quantum devices. We describe the implementations of these methods as well as applications to the analysis of a 127-qubit data set derived from the IBM washington processor over a 16-month period. Both human-interactive and semi-automated analytic methods are included to address requirements for visual exploration, thresholding, and clustering techniques. Our application of QVis to real-world scenarios demonstrates the ability to reveal noteworthy patterns in the behavior of the critical characterization metrics.

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visualization Tool for Comparative analysis of Seabird Movement data

Visualization Tool for Comparative Analysis of Seabird Movem...

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International conference on Information visualisation (iv)

作者： Tomoya Onuki Kazuo Misue University of Tsukuba Ibaraki Japan

ISBN: (纸本)9781665490085

Seabirds have varied movement patterns depending on their group, nesting site, or season. Therefore, seabird experts need to compare movement data under various conditions for conservation and elucidation of ecology. visualization of movement data helps intuitive analysis. However, it is not easy for seabird experts to design suitable visual representations for comparison. The purpose of our study is to develop a tool to support the visualization process for comparison of seabird movement. There are three components to visualization techniques for comparison: Juxtaposition, Superposition, and Explicit Encodings. In our study, we aim to support comparative analysis under complex conditions using flexible Small Multiples based on juxtaposition and superposition. We designed visual representations for the comparative analysis of seabird movement data. We implemented such visual representations into a tool and confirmed its effectiveness for comparative analysis of seabird movement data.

关键词： visualization data visualization Encoding Ecology

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Vision mamba mender 24

Vision mamba mender

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Proceedings of the 38th International conference on Neural Information Processing Systems

作者： Jiacong Hu Anda Cao Zunlei Feng Shengxuming Zhang Yi Wang Lingxiang Jia Mingli Song College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University College of Computer Science and Technology Zhejiang University School of Software Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security School of Software Technology Zhejiang University College of Computer Science and Technology Zhejiang University and State Key Laboratory of Blockchain and Data Security Zhejiang University and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security

ISBN: (纸本)9798331314385

Mamba, a state-space model with selective mechanisms and hardware-aware architecture, has demonstrated outstanding performance in long sequence modeling tasks, particularly garnering widespread exploration and application in the field of computer vision. While existing works have mixed opinions of its application in visual tasks, the exploration of its internal workings and the optimization of its performance remain urgent and worthy research questions given its status as a novel model. Existing optimizations of the Mamba model, especially when applied in the visual domain, have primarily relied on predefined methods such as improving scanning mechanisms or integrating other architectures, often requiring strong priors and extensive trial and error. In contrast to these approaches, this paper proposes the Vision Mamba Mender, a systematic approach for understanding the workings of Mamba, identifying flaws within, and subsequently optimizing model performance. Specifically, we present methods for predictive correlation analysis of Mamba's hidden states from both internal and external perspectives, along with corresponding definitions of correlation scores, aimed at understanding the workings of Mamba in visual recognition tasks and identifying flaws therein. Additionally, tailored repair methods are proposed for identified external and internal state flaws to eliminate them and optimize model performance. Extensive experiments validate the efficacy of the proposed methods on prevalent Mamba architectures, significantly enhancing Mamba's performance. For more information, please visit https://***/.

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AirLens: Multi-Level visual exploration of Air Quality Evolution in Urban Agglomerations

AirLens: Multi-Level Visual Exploration of Air Quality Evolu...

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24th Eurographics/IEEE VGTC conference on visualization (EuroVis)

作者： Qu, Dezhan Lv, Cheng Lin, Yiming Zhang, Huijie Wang, Rong Northeast Normal Univ Sch Informat Sci & Technol Changchun Peoples R China Jilin Univ Key Lab Intelligent Informat Proc Changchun Peoples R China

The precise prevention and control of air pollution is a great challenge faced by environmental experts in recent years. Understanding the air quality evolution in the urban agglomeration is important for coordinated control of air pollution. However, the complex pollutant interactions between different cities lead to the collaborative evolution of air quality. The existing statistical and machine learning methods cannot well support the comprehensive analysis of the dynamic air quality evolution. In this study, we propose AirLens, an interactive visual analytics system that can help domain experts explore and understand the air quality evolution in the urban agglomeration from multiple levels and multiple aspects. To facilitate the cognition of the complex multivariate spatiotemporal data, we first propose a multi-run clustering strategy with a novel glyph design for summarizing and understanding the typical pollutant patterns effectively. On this basis, the system supports the multi-level exploration of air quality evolution, namely, the overall level, stage level and detail level. Frequent pattern mining, city community extraction and useful filters are integrated into the system for discovering significant information comprehensively. The case study and positive feedback from domain experts demonstrate the effectiveness and usability of AirLens.

关键词： Feedback

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Impact analysis of Different Production History Input Methods on Test Well Interpretation Results for Africa Low Productivity Swabbing Wells 1

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13th International Field exploration and Development conference, IFEDC 2023

作者： Wang, Rui-Feng Qu, Dong-Meng Zhao, Guo-Liang Jinlin-Zhang Ren, Bai-Chuan Zimo-Lin RIPED CNPC Beijing China Beijing ShineTech Oil Technology Ltd. Beijing China

ISBN: (数字)9789819704798

ISBN: (纸本)9789819704781

Some blocks in Africa use swabbing for well testing due to low productivity characteristics. As such, the bottom hole flowing pressure does not decrease continuously, but changes in a jaggies pattern with the alternation of swabbing and recovery, which creates great uncertainties in the production history input during well test interpretation and thus affects the accuracy of results. There are three common production history input methods: a. Average production method: the entire production history is averaged and input;b. Swabbing cycle method: the production of each swabbing cycle is input separately;c. Swabbing section and slug flow section method: Input the swabbing section and slug flow section separately for each swabbing cycle. After employing actual well testing data in several Africa blocks, the impact of those methods on well test interpretation has been analyzed with following insights (i) the starting point of the PBU and the rate before PBU have significant effects on the interpretation results;(ii) the impact of three production history input methods on permeability results is negligible (iii) the skin factor obtained by the Swabbing section and slug flow section method is generally appropriate, but the workload of this method is significantly heavier;(iv) use Swabbing section and slug flow section method for the 3–5 swabbing sections before the PBU, while the previous production section uses the Average production method input, can greatly reduce the workload while ensuring the accuracy of the interpretation. Based on the above insights, well test interpretation was conducted on the data of multiple swabbing wells in several blocks in Africa. The practice shows that the interpretation accuracy can be improved without significantly increasing workload, thus making the interpretation results matched better with the actual field characteristics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Well testing

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A visual analytics approach for anomaly detection from a novel traffic light data

A visual analytics approach for anomaly detection from a nov...

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2021 conference on visualization and data analysis, VDA 2021

作者： Turner, Glenn Chen, Guoning Zhang, Yunpeng University of Houston Department of Computer Science United States Boise State University Dakota State University United States University of Melbourne Australia Imperial College London United Kingdom

Traffic signals are part of our critical infrastructure and protecting their integrity is a serious concern. Security flaws in traffic signal systems have been documented and effective detection of exploitation of these flaws remains a challenge. In this paper we present a visual analytics approach to look for anomalies in traffic signal data (i.e., abnormal traffic light patterns) that may indicate a compromise of the system. To our knowledge it is a first time a visual analytics approach is applied for the processing and exploration of traffic signal data. This system supports level-of-detail exploration with various visualization techniques. data cleaning and a number of preprocessing techniques for the extraction of summary information (e.g., traffic signal cycles) of the data are also performed before the visualization and data exploration. Our system successfully reveals the errors in the input data that would be difficult to capture with simple plots alone. In addition, our system captures some abnormal signal patterns that may indicate intrusions into the system. In summary, this work offers a new and effective way to study attacks or intrusions to traffic signal control systems via the visual analysis of traffic light signal patterns. © 2021, Society for Imaging Science and Technology.

关键词： Traffic signals

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