Download or read book Rapid Remaining useful life Prediction of Li ion Batteries Using Image based Machine Learning written by Jonathan Couture and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increased integration of lithium-ion batteries in our everyday lives, accurate and reliable battery management systems have become an imperative aspect of the well-being of our everyday electronics. This thesis proposes the use of novel machine learning methodologies to predict the remaining-useful-life (RUL) of lithium-ion batteries reliably, accurately, and swiftly. Firstly, a method that prides itself on being publicly available, and which can be easily implemented alongside existing methodology, is proposed to increase the prediction accuracy of the conventional health indicator methodology by 6.72% by using images of data curves as inputs. Subsequently, a more in-depth machine learning model is presented which managed to considerably outperform the current literature in terms of speed, accuracy, and reliability, achieving an RUL prediction accuracy of 90.85%. These proposed methodologies have a wide range of applications, from fault diagnostics, state-of-charge, and state-of-health prediction, to other, more complex, regression applications.

Download or read book Microgrid Energy Management written by and published by . This book was released on 2021-08-25 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Battery Management Algorithm for Electric Vehicles written by Rui Xiong and published by Springer Nature. This book was released on 2019-09-23 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically introduces readers to the core algorithms of battery management system (BMS) for electric vehicles. These algorithms cover most of the technical bottlenecks encountered in BMS applications, including battery system modeling, state of charge (SOC) and state of health (SOH) estimation, state of power (SOP) estimation, remaining useful life (RUL) prediction, heating at low temperature, and optimization of charging. The book not only presents these algorithms, but also discusses their background, as well as related experimental and hardware developments. The concise figures and program codes provided make the calculation process easy to follow and apply, while the results obtained are presented in a comparative way, allowing readers to intuitively grasp the characteristics of different algorithms. Given its scope, the book is intended for researchers, senior undergraduate and graduate students, as well as engineers in the fields of electric vehicles and energy storage.

Download or read book Cost Estimation written by Gregory K. Mislick and published by John Wiley & Sons. This book was released on 2015-05-04 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents an accessible approach to the cost estimation tools, concepts, and techniques needed to support analytical and cost decisions Written with an easy-to-understand approach, Cost Estimation: Methods and Tools provides comprehensive coverage of the quantitative techniques needed by professional cost estimators and for those wanting to learn about this vibrant career field. Featuring the underlying mathematical and analytical principles of cost estimation, the book focuses on the tools and methods used to predict the research and development, production, and operating and support costs for successful cost estimation in industrial, business, and manufacturing processes. The book begins with a detailed historical perspective and key terms of the cost estimating field in order to develop the necessary background prior to implementing the presented quantitative methods. The book proceeds to fundamental cost estimation methods utilized in the field of cost estimation, including working with inflation indices, regression analysis, learning curves, analogies, cost factors, and wrap rates. With a step-by-step introduction to the practicality of cost estimation and the available resources for obtaining relevant data, Cost Estimation: Methods and Tools also features: Various cost estimating tools, concepts, and techniques needed to support business decisions Multiple questions at the end of each chapter to help readers obtain a deeper understanding of the discussed methods and techniques An overview of the software used in cost estimation, as well as an introduction to the application of risk and uncertainty analysis A Foreword from Dr. Douglas A. Brook, a professor in the Graduate School of Business and Public Policy at the Naval Postgraduate School, who spent many years working in the Department of Defense acquisition environment Cost Estimation: Methods and Tools is an excellent reference for academics and practitioners in decision science, operations research, operations management, business, and systems and industrial engineering, as well as a useful guide in support of professional cost estimation training and certification courses for practitioners. The book is also appropriate for graduate-level courses in operations research, operations management, engineering economics, and manufacturing and/or production processes.

Download or read book Machine Learning written by Andreas Lindholm and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning"--

Download or read book Remaining Useful Life Prediction of Lithium Ion Batteries Based on Piecewise Random Coefficient Models written by 邱姿綺 and published by . This book was released on 2020 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Nonlinear Modeling written by Johan A. K. Suykens and published by Springer Science & Business Media. This book was released on 1998-06-30 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of eight contributions presents advanced black-box techniques for nonlinear modeling. The methods discussed include neural nets and related model structures for nonlinear system identification, enhanced multi-stream Kalman filter training for recurrent networks, the support vector method of function estimation, parametric density estimation for the classification of acoustic feature vectors in speech recognition, wavelet based modeling of nonlinear systems, nonlinear identification based on fuzzy models, statistical learning in control and matrix theory, and nonlinear time- series analysis. The volume concludes with the results of a time- series prediction competition held at a July 1998 workshop in Belgium. Annotation copyrighted by Book News, Inc., Portland, OR.

Download or read book Applied Deep Learning with Python written by Alex Galea and published by Packt Publishing Ltd. This book was released on 2018-08-31 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on guide to deep learning that’s filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who don’t have a data science background Covers the key foundational concepts you’ll need to know when building deep learning systems Full of step-by-step exercises and activities to help build the skills that you need for the real-world Book Description Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. We’ll explore a variety of approaches to classification like support vector networks, random decision forests and k-nearest neighbours to build out your understanding before we move into more complex territory. It’s okay if these terms seem overwhelming; we’ll show you how to put them to work. We’ll build upon our classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. It’s after this that we start building out our keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. By guiding you through a trained neural network, we’ll explore common deep learning network architectures (convolutional, recurrent, generative adversarial) and branch out into deep reinforcement learning before we dive into model optimization and evaluation. We’ll do all of this whilst working on a production-ready web application that combines Tensorflow and Keras to produce a meaningful user-friendly result, leaving you with all the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively. What you will learn Discover how you can assemble and clean your very own datasets Develop a tailored machine learning classification strategy Build, train and enhance your own models to solve unique problems Work with production-ready frameworks like Tensorflow and Keras Explain how neural networks operate in clear and simple terms Understand how to deploy your predictions to the web Who this book is for If you're a Python programmer stepping into the world of data science, this is the ideal way to get started.

Download or read book Handbook on Battery Energy Storage System written by Asian Development Bank and published by Asian Development Bank. This book was released on 2018-12-01 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook serves as a guide to deploying battery energy storage technologies, specifically for distributed energy resources and flexibility resources. Battery energy storage technology is the most promising, rapidly developed technology as it provides higher efficiency and ease of control. With energy transition through decarbonization and decentralization, energy storage plays a significant role to enhance grid efficiency by alleviating volatility from demand and supply. Energy storage also contributes to the grid integration of renewable energy and promotion of microgrid.

Download or read book Flood Forecasting Using Machine Learning Methods written by Fi-John Chang and published by MDPI. This book was released on 2019-02-28 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.

Download or read book Damage Tolerance and Durability of Material Systems written by Kenneth L. Reifsnider and published by Wiley-Interscience. This book was released on 2002-04-24 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: A daring, original approach to understanding and predicting the mechanical behavior of materials "Damage is an abstraction . . . Strength is an observable, an independent variable that can be measured, with clear and familiar engineering definitions." -from the Preface to Damage Tolerance and Durability of Material Systems Long-term behavior is one of the most challenging and important aspects of material engineering. There is a great need for a useful conceptual or operational framework for measuring long-term behavior. As much a revolution in philosophy as an engineering text, Damage Tolerance and Durability of Material Systems postulates a new mechanistic philosophy and methodology for predicting the remaining strength and life of engineering material. This philosophy associates the local physical changes in material states and stress states caused by time-variable applied environments with global properties and performance. There are three fundamental issues associated with the mechanical behavior of engineering materials and structures: their stiffness, strength, and life. Treating these issues from the standpoint of technical difficulty, time, and cost for characterization, and relationship to safety, reliability, liability, and economy, the authors explore such topics as: * Damage tolerance and failure modes * Factors that determine composite strength * Micromechanical models of composite stiffness and strength * Stiffness evolution * Strength evolution during damage accumulation * Non-uniform stress states * Lifetime prediction With a robust selection of example applications and case studies, this book takes a step toward the fulfillment of a vision of a future in which the prediction of physical properties from first principles will make possible the creation and application of new materials and material systems at a remarkable cost savings.

Download or read book Remaining Useful Life Prediction of Lithium Ion Batteries Under Different Temperature and Discharge Current Based on Piecewise Random Coefficient Models written by and published by . This book was released on 2021 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book 2017 IEEE International Conference on Prognostics and Health Management ICPHM written by and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Proceedings of International Conference on Image Vision and Intelligent Systems 2022 ICIVIS 2022 written by Peng You and published by Springer Nature. This book was released on 2023-03-28 with total page 1066 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of the papers accepted by the ICIVIS 2022—The International Conference on Image, Vision and Intelligent Systems, held on August 15–17, 2022, in Jinan, China. The topics focus but are not limited to image, vision and intelligent systems. Each part can be used as an excellent reference by industry practitioners, university faculties, research fellows and undergraduates as well as graduate students who need to build a knowledge base of the most current advances and state of practice in the topics covered by this conference proceedings.

Download or read book Applied Deep Learning with Keras written by Ritesh Bhagwat and published by Packt Publishing Ltd. This book was released on 2019-04-24 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take your neural networks to a whole new level with the simplicity and modularity of Keras, the most commonly used high-level neural networks API. Key FeaturesSolve complex machine learning problems with precisionEvaluate, tweak, and improve your deep learning models and solutionsUse different types of neural networks to solve real-world problemsBook Description Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code. Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model. By the end of this book, you will have the skills you need to use Keras when building high-level deep neural networks. What you will learnUnderstand the difference between single-layer and multi-layer neural network modelsUse Keras to build simple logistic regression models, deep neural networks, recurrent neural networks, and convolutional neural networksApply L1, L2, and dropout regularization to improve the accuracy of your modelImplement cross-validate using Keras wrappers with scikit-learnUnderstand the limitations of model accuracyWho this book is for If you have basic knowledge of data science and machine learning and want to develop your skills and learn about artificial neural networks and deep learning, you will find this book useful. Prior experience of Python programming and experience with statistics and logistic regression will help you get the most out of this book. Although not necessary, some familiarity with the scikit-learn library will be an added bonus.

Download or read book TinyML written by Pete Warden and published by O'Reilly Media. This book was released on 2019-12-16 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

Download or read book Data Science and Intelligent Systems written by Radek Silhavy and published by Springer Nature. This book was released on 2021-11-16 with total page 1073 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the second part of refereed proceedings of the 5th Computational Methods in Systems and Software 2021 (CoMeSySo 2021) proceedings. The real-world problems related to data science and algorithm design related to systems and software engineering are presented in this papers. Furthermore, the basic research’ papers that describe novel approaches in the data science, algorithm design and in systems and software engineering are included. The CoMeSySo 2021 conference is breaking the barriers, being held online. CoMeSySo 2021 intends to provide an international forum for the discussion of the latest high-quality research results