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Self Learning Anomaly Detection In Industrial Production
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Book Synopsis Self-learning Anomaly Detection in Industrial Production by : Meshram, Ankush
Download or read book Self-learning Anomaly Detection in Industrial Production written by Meshram, Ankush and published by KIT Scientific Publishing. This book was released on 2023-06-19 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Configuring an anomaly-based Network Intrusion Detection System for cybersecurity of an industrial system in the absence of information on networking infrastructure and programmed deterministic industrial process is challenging. Within the research work, different self-learning frameworks to analyze passively captured network traces from PROFINET-based industrial system for protocol-based and process behavior-based anomaly detection are developed, and evaluated on a real-world industrial system.
Book Synopsis Control Charts and Machine Learning for Anomaly Detection in Manufacturing by : Kim Phuc Tran
Download or read book Control Charts and Machine Learning for Anomaly Detection in Manufacturing written by Kim Phuc Tran and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the latest research on advanced control charts and new machine learning approaches to detect abnormalities in the smart manufacturing process. By approaching anomaly detection using both statistics and machine learning, the book promotes interdisciplinary cooperation between the research communities, to jointly develop new anomaly detection approaches that are more suitable for the 4.0 Industrial Revolution. The book provides ready-to-use algorithms and parameter sheets, enabling readers to design advanced control charts and machine learning-based approaches for anomaly detection in manufacturing. Case studies are introduced in each chapter to help practitioners easily apply these tools to real-world manufacturing processes. The book is of interest to researchers, industrial experts, and postgraduate students in the fields of industrial engineering, automation, statistical learning, and manufacturing industries.
Book Synopsis Proceedings of the 2018 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory by : Beyerer, Jürgen
Download or read book Proceedings of the 2018 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory written by Beyerer, Jürgen and published by KIT Scientific Publishing. This book was released on 2019-07-12 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Outlier Analysis by : Charu C. Aggarwal
Download or read book Outlier Analysis written by Charu C. Aggarwal and published by Springer. This book was released on 2016-12-10 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories: Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data. Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner. The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.
Book Synopsis Machine Learning for Automated Anomaly Detection in Semiconductor Manufacturing by : Michael Daniel DeLaus
Download or read book Machine Learning for Automated Anomaly Detection in Semiconductor Manufacturing written by Michael Daniel DeLaus and published by . This book was released on 2019 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the realm of semiconductor manufacturing, detecting anomalies during manufacturing processes is crucial. However, current methods of anomaly detection often rely on simple excursion detection methods, and manual inspection of machine sensor data to determine the cause of a problem. In order to improve semiconductor production line quality, machine learning tools can be developed for more thorough and accurate anomaly detection. Previous work on applying machine learning to anomaly detection focused on building reference cycles, and using clustering and time series forecasting to detect anomalous wafer cycles. We seek to improve upon these techniques and apply them to related domains of semiconductor manufacturing. The main focus is to develop a process for automated anomaly detection by combining the previously used methods of cluster analysis and time series forecasting and prediction. We also explore detecting anomalies across multiple semiconductor manufacturing machines and recipes.
Book Synopsis Machine Learning, Optimization, and Data Science by : Giuseppe Nicosia
Download or read book Machine Learning, Optimization, and Data Science written by Giuseppe Nicosia and published by Springer Nature. This book was released on 2023-03-08 with total page 639 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set, LNCS 13810 and 13811, constitutes the refereed proceedings of the 8th International Conference on Machine Learning, Optimization, and Data Science, LOD 2022, together with the papers of the Second Symposium on Artificial Intelligence and Neuroscience, ACAIN 2022. The total of 84 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 226 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, neuroscience, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.
Book Synopsis Multimodal Panoptic Segmentation of 3D Point Clouds by : Dürr, Fabian
Download or read book Multimodal Panoptic Segmentation of 3D Point Clouds written by Dürr, Fabian and published by KIT Scientific Publishing. This book was released on 2023-10-09 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: The understanding and interpretation of complex 3D environments is a key challenge of autonomous driving. Lidar sensors and their recorded point clouds are particularly interesting for this challenge since they provide accurate 3D information about the environment. This work presents a multimodal approach based on deep learning for panoptic segmentation of 3D point clouds. It builds upon and combines the three key aspects multi view architecture, temporal feature fusion, and deep sensor fusion.
Book Synopsis Proceedings of the 2022 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory by : Beyerer, Jürgen
Download or read book Proceedings of the 2022 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory written by Beyerer, Jürgen and published by KIT Scientific Publishing. This book was released on 2023-07-05 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: In August 2022, Fraunhofer IOSB and IES of KIT held a joint workshop in a Schwarzwaldhaus near Triberg. Doctoral students presented research reports and discussed various topics like computer vision, optical metrology, network security, usage control, and machine learning. This book compiles the workshop's results and ideas, offering a comprehensive overview of the research program of IES and Fraunhofer IOSB.
Book Synopsis Advanced Intelligent Computing Technology and Applications by : De-Shuang Huang
Download or read book Advanced Intelligent Computing Technology and Applications written by De-Shuang Huang and published by Springer Nature. This book was released on with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Anomaly Detection of Semiconductor Manufacturing Based on Machine Learning by :
Download or read book Anomaly Detection of Semiconductor Manufacturing Based on Machine Learning written by and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Machine Learning and Knowledge Discovery in Databases. Research Track by : Albert Bifet
Download or read book Machine Learning and Knowledge Discovery in Databases. Research Track written by Albert Bifet and published by Springer Nature. This book was released on with total page 509 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Control Charts and Machine Learning for Anomaly Detection in Manufacturing by : Kim Phuc Tran
Download or read book Control Charts and Machine Learning for Anomaly Detection in Manufacturing written by Kim Phuc Tran and published by Springer Nature. This book was released on 2021-08-29 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the latest research on advanced control charts and new machine learning approaches to detect abnormalities in the smart manufacturing process. By approaching anomaly detection using both statistics and machine learning, the book promotes interdisciplinary cooperation between the research communities, to jointly develop new anomaly detection approaches that are more suitable for the 4.0 Industrial Revolution. The book provides ready-to-use algorithms and parameter sheets, enabling readers to design advanced control charts and machine learning-based approaches for anomaly detection in manufacturing. Case studies are introduced in each chapter to help practitioners easily apply these tools to real-world manufacturing processes. The book is of interest to researchers, industrial experts, and postgraduate students in the fields of industrial engineering, automation, statistical learning, and manufacturing industries.
Book Synopsis Computer Vision – ECCV 2022 by : Shai Avidan
Download or read book Computer Vision – ECCV 2022 written by Shai Avidan and published by Springer Nature. This book was released on 2022-10-22 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Book Synopsis IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency by : Oliver Niggemann
Download or read book IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency written by Oliver Niggemann and published by Springer. This book was released on 2018-08-20 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access work presents selected results from the European research and innovation project IMPROVE which yielded novel data-based solutions to enhance machine reliability and efficiency in the fields of simulation and optimization, condition monitoring, alarm management, and quality prediction.
Book Synopsis Innovative Intelligent Industrial Production and Logistics by : Alexander Smirnov
Download or read book Innovative Intelligent Industrial Production and Logistics written by Alexander Smirnov and published by Springer Nature. This book was released on 2023-07-06 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume includes extended and revised versions of a set of selected papers from the First International Conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2020, held as virtual event in November 4-6, 2020 and Second International Conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2021, held as virtual event in October 25-27, 2021. The 9 full papers included in this book were carefully reviewed and selected from 44 submissions. They were organized in topical sections as follows: on kernel search based gaussian process anomaly detection; general architecture framework and general modelling framework.
Book Synopsis Model-Based Safety and Assessment by : Christel Seguin
Download or read book Model-Based Safety and Assessment written by Christel Seguin and published by Springer Nature. This book was released on 2022-09-08 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 8th International Symposium on Model-Based Safety and Assessment, IMBSA 2022, held in Munich, Germany, in September 2022. The 15 revised full papers and 3 short papers presented were carefully reviewed and selected from 27 initial submissions. The papers focus on model-based and automated ways of assessing safety and other attributes of dependability of complex systems. They are organized in topical sections on safety analysis automation, MBSA practices, causal models and failure modeling strategies, designing mitigations of faults and attacks, data based safety analysis, dynamic risk assessment.
Book Synopsis Deep Learning and XAI Techniques for Anomaly Detection by : Cher Simon
Download or read book Deep Learning and XAI Techniques for Anomaly Detection written by Cher Simon and published by Packt Publishing Ltd. This book was released on 2023-01-31 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create interpretable AI models for transparent and explainable anomaly detection with this hands-on guide Purchase of the print or Kindle book includes a free PDF eBook Key FeaturesBuild auditable XAI models for replicability and regulatory complianceDerive critical insights from transparent anomaly detection modelsStrike the right balance between model accuracy and interpretabilityBook Description Despite promising advances, the opaque nature of deep learning models makes it difficult to interpret them, which is a drawback in terms of their practical deployment and regulatory compliance. Deep Learning and XAI Techniques for Anomaly Detection shows you state-of-the-art methods that'll help you to understand and address these challenges. By leveraging the Explainable AI (XAI) and deep learning techniques described in this book, you'll discover how to successfully extract business-critical insights while ensuring fair and ethical analysis. This practical guide will provide you with tools and best practices to achieve transparency and interpretability with deep learning models, ultimately establishing trust in your anomaly detection applications. Throughout the chapters, you'll get equipped with XAI and anomaly detection knowledge that'll enable you to embark on a series of real-world projects. Whether you are building computer vision, natural language processing, or time series models, you'll learn how to quantify and assess their explainability. By the end of this deep learning book, you'll be able to build a variety of deep learning XAI models and perform validation to assess their explainability. What you will learnExplore deep learning frameworks for anomaly detectionMitigate bias to ensure unbiased and ethical analysisIncrease your privacy and regulatory compliance awarenessBuild deep learning anomaly detectors in several domainsCompare intrinsic and post hoc explainability methodsExamine backpropagation and perturbation methodsConduct model-agnostic and model-specific explainability techniquesEvaluate the explainability of your deep learning modelsWho this book is for This book is for anyone who aspires to explore explainable deep learning anomaly detection, tenured data scientists or ML practitioners looking for Explainable AI (XAI) best practices, or business leaders looking to make decisions on trade-off between performance and interpretability of anomaly detection applications. A basic understanding of deep learning and anomaly detection–related topics using Python is recommended to get the most out of this book.
