Iot Streams For Data Driven Predictive Maintenance And Iot Edge And Mobile For Embedded Machine Learning
DOWNLOAD
Download Iot Streams For Data Driven Predictive Maintenance And Iot Edge And Mobile For Embedded Machine Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Iot Streams For Data Driven Predictive Maintenance And Iot Edge And Mobile For Embedded Machine Learning book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Iot Streams For Data Driven Predictive Maintenance And Iot Edge And Mobile For Embedded Machine Learning
DOWNLOAD
Author : Joao Gama
language : en
Publisher: Springer Nature
Release Date : 2021-01-09
Iot Streams For Data Driven Predictive Maintenance And Iot Edge And Mobile For Embedded Machine Learning written by Joao Gama and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-09 with Computers categories.
This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020 and held in September 2020. Due to the COVID-19 pandemic the workshops were held online. The 21 full papers and 3 short papers presented in this volume were thoroughly reviewed and selected from 35 submissions and are organized according to the workshops and their topics: IoT Streams 2020: Stream Learning; Feature Learning; ITEM 2020: Unsupervised Machine Learning; Hardware; Methods; Quantization.
Analog Circuits For Machine Learning Current Voltage Temperature Sensors And High Speed Communication
DOWNLOAD
Author : Pieter Harpe
language : en
Publisher: Springer Nature
Release Date : 2022-03-24
Analog Circuits For Machine Learning Current Voltage Temperature Sensors And High Speed Communication written by Pieter Harpe and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-24 with Technology & Engineering categories.
This book is based on the 18 tutorials presented during the 29th workshop on Advances in Analog Circuit Design. Expert designers present readers with information about a variety of topics at the frontier of analog circuit design, with specific contributions focusing on analog circuits for machine learning, current/voltage/temperature sensors, and high-speed communication via wireless, wireline, or optical links. This book serves as a valuable reference to the state-of-the-art, for anyone involved in analog circuit research and development.
Neuroscience Computing Performance And Benchmarks Why It Matters To Neuroscience How Fast We Can Compute
DOWNLOAD
Author : Felix Schürmann
language : en
Publisher: Frontiers Media SA
Release Date : 2023-04-26
Neuroscience Computing Performance And Benchmarks Why It Matters To Neuroscience How Fast We Can Compute written by Felix Schürmann and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-26 with Science categories.
Combining Expert Knowledge And Deep Learning With Case Based Reasoning For Predictive Maintenance
DOWNLOAD
Author : Patrick Klein
language : en
Publisher: Springer Nature
Release Date : 2025-05-12
Combining Expert Knowledge And Deep Learning With Case Based Reasoning For Predictive Maintenance written by Patrick Klein and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-12 with Mathematics categories.
If a manufacturing company's main goal is to sell products profitably, protecting production systems from defects is essential and has led to vast documentation and expert knowledge. Industry 4.0 has facilitated access to sensor and operational data across the shop floor, enabling data-driven models that detect faults and predict failures, which are crucial for predictive maintenance to minimize unplanned downtimes and costs. Commonly, a universally applicable machine learning (ML) approach is used without explicitly integrating prior knowledge from sources beyond training data, risking incorrect rediscovery or neglecting already existing knowledge. Integrating expert knowledge with ML can address the scarcity of failure examples and avoid the learning of spurious correlations, though it poses technical challenges when combining Semantic Web-based knowledge graphs with neural networks (NNs) for time series data. For his research, a physical smart factory model with condition monitoring sensors and a knowledge graph was developed. This setup generated the required data for exploring the integration of expert knowledge with (Siamese) NNs for similarity-based fault detection, anomaly detection, and automation of root cause analysis. Patrick Klein applied symbolic and sub-symbolic AI techniques, demonstrating that integrating expert knowledge with NNs enhances prediction performance and confidence in them while reducing the number of learnable parameters and failure examples.
Intelligent Systems And Applications
DOWNLOAD
Author : Kohei Arai
language : en
Publisher: Springer Nature
Release Date : 2024-07-31
Intelligent Systems And Applications written by Kohei Arai and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-31 with Computers categories.
This volume is a collection of meticulously crafted, insightful, and state-of-the-art papers presented at the Intelligent Systems Conference 2024, held in Amsterdam, The Netherlands, on 5-6 September 2024. The conference received an overwhelming response, with a total of 535 submissions. After a rigorous double-blind peer review process, 181 papers were selected for presentation. These papers span a wide range of scientific topics, including Artificial Intelligence, Computer Vision, Robotics, Intelligent Systems, and more. We hope that readers find this volume both interesting and valuable. Furthermore, we expect that the conference and its proceedings will inspire further research and technological advancements in these critical areas of study. Thank you for engaging with this collection of works from the Intelligent Systems Conference 2024. Your interest and support contribute significantly to the ongoing progress and innovation in the field of intelligent systems.
Low Power Computer Vision
DOWNLOAD
Author : George K. Thiruvathukal
language : en
Publisher: CRC Press
Release Date : 2022-02-22
Low Power Computer Vision written by George K. Thiruvathukal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-22 with Computers categories.
Energy efficiency is critical for running computer vision on battery-powered systems, such as mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the methods that have won the annual IEEE Low-Power Computer Vision Challenges since 2015. The winners share their solutions and provide insight on how to improve the efficiency of machine learning systems.
Efficient Execution Of Irregular Dataflow Graphs
DOWNLOAD
Author : Nimish Shah
language : en
Publisher: Springer Nature
Release Date : 2023-08-14
Efficient Execution Of Irregular Dataflow Graphs written by Nimish Shah and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-14 with Technology & Engineering categories.
This book focuses on the acceleration of emerging irregular sparse workloads, posed by novel artificial intelligent (AI) models and sparse linear algebra. Specifically, the book outlines several co-optimized hardware-software solutions for a highly promising class of emerging sparse AI models called Probabilistic Circuit (PC) and a similar sparse matrix workload for triangular linear systems (SpTRSV). The authors describe optimizations for the entire stack, targeting applications, compilation, hardware architecture and silicon implementation, resulting in orders of magnitude higher performance and energy-efficiency compared to the existing state-of-the-art solutions. Thus, this book provides important building blocks for the upcoming generation of edge AI platforms.
Edge Ai Merging Iot And Machine Learning For Real Time Analytics
DOWNLOAD
Author : Dr. D. Srinivasa Rao
language : en
Publisher: Xoffencer International Book Publication house
Release Date : 2024-10-10
Edge Ai Merging Iot And Machine Learning For Real Time Analytics written by Dr. D. Srinivasa Rao and has been published by Xoffencer International Book Publication house this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-10 with Computers categories.
In order to provide real-time analytics directly at the edge of the network, edge artificial intelligence (AI) is a disruptive technique that combines the capabilities of Internet of Things (IoT) devices with the power of machine learning (ML). As a result of this paradigm shift away from conventional cloud-centric approaches, latency is reduced, privacy is improved, and operational efficiency is increased. Information is processed locally on devices. The Internet of Things (IoT) is experiencing exponential expansion, which presents a problem for centralized cloud processing due to the sheer amount of data created by sensors, cameras, and linked equipment of all kinds. By putting artificial intelligence closer to the source of the data, Edge AI makes it possible to make decisions more quickly and reduces the need for continual data transmission to the cloud, which in turn reduces the expenses associated with bandwidth and cloud storage. Innovation is fostered across a variety of sectors, including healthcare, smart cities, autonomous cars, and industrial automation, via the integration of the Internet of Things (IoT) and machine learning at the edge. Real-time analytics makes it possible to identify trends and irregularities, which in turn leads to improvements in accessibility and efficiency in areas such as tailored services, increased security, and predictive maintenance. Utilizing on-device machine learning models enables quick insights, which is essential in applications that are time-sensitive. This is also true as Internet of Things devices grow more sophisticated. Furthermore, the infrastructure for edge computing is capable of supporting dispersed systems, which not only ensures increased system resilience but also reduces the likelihood of downtime. Nevertheless, putting Edge AI into practice is not without its difficulties. The management of the computational needs of machine learning models on devices with limited resources, the maintenance of scalability, and the guarantee of security across dispersed nodes are all key concerns that need to be addressed. The development of lightweight machine learning models, hardware that has been optimized, and security mechanisms that have been improved are all essential components in promoting the widespread use of this technology. Furthermore, the continuing developments in 5G networks and edge computing frameworks promise to push the frontiers of edge artificial intelligence, which will offer up new opportunities for real-time, decentralized intelligence. In conclusion, Edge AI is able to bridge the gap between the increasing needs of Internet of Things ecosystems and the requirement for real-time insights that can be put into action. With the ability to facilitate decision-making processes that are quicker, more intelligent, and more secure, it has the potential to completely transform whole sectors. Artificial intelligence at the edge of the network will play a crucial part in determining the future of intelligent systems as technology continues to advance
Advanced Contemporary Control
DOWNLOAD
Author : Andrzej Bartoszewicz
language : en
Publisher: Springer Nature
Release Date : 2020-06-24
Advanced Contemporary Control written by Andrzej Bartoszewicz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-24 with Technology & Engineering categories.
This book presents the proceedings of the 20th Polish Control Conference. A triennial event that was first held in 1958, the conference successfully combines its long tradition with a modern approach to shed light on problems in control engineering, automation, robotics and a wide range of applications in these disciplines. The book presents new theoretical results concerning the steering of dynamical systems, as well as industrial case studies and worked solutions to real-world problems in contemporary engineering. It particularly focuses on the modelling, identification, analysis and design of automation systems; however, it also addresses the evaluation of their performance, efficiency and reliability. Other topics include fault-tolerant control in robotics, automated manufacturing, mechatronics and industrial systems. Moreover, it discusses data processing and transfer issues, covering a variety of methodologies, including model predictive, robust and adaptive techniques, as well as algebraic and geometric methods, and fractional order calculus approaches. The book also examines essential application areas, such as transportation and autonomous intelligent vehicle systems, robotic arms, mobile manipulators, cyber-physical systems, electric drives and both surface and underwater marine vessels. Lastly, it explores biological and medical applications of the control-theory-inspired methods.
Deep Learning Models On Cloud Platforms
DOWNLOAD
Author : Vijay Ramamoorthi
language : en
Publisher: RK Publication
Release Date : 2024-07-25
Deep Learning Models On Cloud Platforms written by Vijay Ramamoorthi and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-25 with Computers categories.
Deep Learning Models on Cloud Platforms provides an in-depth exploration of the integration of deep learning techniques with cloud computing environments. Architectures, and frameworks for developing and deploying deep learning models at scale. It addresses practical considerations, including data management, computational resources, and cost-efficiency, while highlighting popular cloud platforms like AWS, Google Cloud, and Azure. Through real-world examples and case studies, readers will gain insights into best practices for leveraging cloud infrastructure to enhance deep learning capabilities and drive innovation across various industries.