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Keras 2.x Projects: 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras
by Giuseppe CiaburroDemonstrate fundamentals of Deep Learning and neural network methodologies using Keras 2.x Key Features Experimental projects showcasing the implementation of high-performance deep learning models with Keras. Use-cases across reinforcement learning, natural language processing, GANs and computer vision. Build strong fundamentals of Keras in the area of deep learning and artificial intelligence. Book Description Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more. By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems. What you will learn Apply regression methods to your data and understand how the regression algorithm works Understand the basic concepts of classification methods and how to implement them in the Keras environment Import and organize data for neural network classification analysis Learn about the role of rectified linear units in the Keras network architecture Implement a recurrent neural network to classify the sentiment of sentences from movie reviews Set the embedding layer and the tensor sizes of a network Who this book is for If you are a data scientist, machine learning engineer, deep learning practitioner or an AI engineer who wants to build speedy intelligent applications with minimal lines of codes, then this book is the best fit for you. Sound knowledge of machine learning and basic familiarity with Keras library would be useful.
Keras Deep Learning Cookbook: Over 30 recipes for implementing deep neural networks in Python
by Rajdeep Dua Manpreet Singh GhotraLeverage the power of deep learning and Keras to develop smarter and more efficient data modelsKey FeaturesUnderstand different neural networks and their implementation using KerasExplore recipes for training and fine-tuning your neural network modelsPut your deep learning knowledge to practice with real-world use-cases, tips, and tricksBook DescriptionKeras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy.The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learningWhat you will learnInstall and configure Keras in TensorFlowMaster neural network programming using the Keras library Understand the different Keras layers Use Keras to implement simple feed-forward neural networks, CNNs and RNNsWork with various datasets and models used for image and text classificationDevelop text summarization and reinforcement learning models using KerasWho this book is forKeras Deep Learning Cookbook is for you if you are a data scientist or machine learning expert who wants to find practical solutions to common problems encountered while training deep learning models. A basic understanding of Python and some experience in machine learning and neural networks is required for this book.
Keras Reinforcement Learning Projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents
by Giuseppe CiaburroA practical guide to mastering reinforcement learning algorithms using KerasKey FeaturesBuild projects across robotics, gaming, and finance fields, putting reinforcement learning (RL) into actionGet to grips with Keras and practice on real-world unstructured datasetsUncover advanced deep learning algorithms such as Monte Carlo, Markov Decision, and Q-learningBook DescriptionReinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library.The book begins with getting you up and running with the concepts of reinforcement learning using Keras. You’ll learn how to simulate a random walk using Markov chains and select the best portfolio using dynamic programming (DP) and Python. You’ll also explore projects such as forecasting stock prices using Monte Carlo methods, delivering vehicle routing application using Temporal Distance (TD) learning algorithms, and balancing a Rotating Mechanical System using Markov decision processes.Once you’ve understood the basics, you’ll move on to Modeling of a Segway, running a robot control system using deep reinforcement learning, and building a handwritten digit recognition model in Python using an image dataset. Finally, you’ll excel in playing the board game Go with the help of Q-Learning and reinforcement learning algorithms.By the end of this book, you’ll not only have developed hands-on training on concepts, algorithms, and techniques of reinforcement learning but also be all set to explore the world of AI.What you will learnPractice the Markov decision process in prediction and betting evaluationsImplement Monte Carlo methods to forecast environment behaviorsExplore TD learning algorithms to manage warehouse operationsConstruct a Deep Q-Network using Python and Keras to control robot movementsApply reinforcement concepts to build a handwritten digit recognition model using an image datasetAddress a game theory problem using Q-Learning and OpenAI GymWho this book is forKeras Reinforcement Learning Projects is for you if you are data scientist, machine learning developer, or AI engineer who wants to understand the fundamentals of reinforcement learning by developing practical projects. Sound knowledge of machine learning and basic familiarity with Keras is useful to get the most out of this book
Keras to Kubernetes: The Journey of a Machine Learning Model to Production
by Dattaraj RaoBuild a Keras model to scale and deploy on a Kubernetes cluster We have seen an exponential growth in the use of Artificial Intelligence (AI) over last few years. AI is becoming the new electricity and is touching every industry from retail to manufacturing to healthcare to entertainment. Within AI, we're seeing a particular growth in Machine Learning (ML) and Deep Learning (DL) applications. ML is all about learning relationships from labeled (Supervised) or unlabeled data (Unsupervised). DL has many layers of learning and can extract patterns from unstructured data like images, video, audio, etc. Keras to Kubernetes: The Journey of a Machine Learning Model to Production takes you through real-world examples of building DL models in Keras for recognizing product logos in images and extracting sentiment from text. You will then take that trained model and package it as a web application container before learning how to deploy this model at scale on a Kubernetes cluster. You will understand the different practical steps involved in real-world ML implementations which go beyond the algorithms. • Find hands-on learning examples • Learn to uses Keras and Kubernetes to deploy Machine Learning models • Discover new ways to collect and manage your image and text data with Machine Learning • Reuse examples as-is to deploy your models • Understand the ML model development lifecycle and deployment to production If you're ready to learn about one of the most popular DL frameworks and build production applications with it, you've come to the right place!
Kerberos: The Definitive Guide
by Jason GarmanKerberos, the single sign-on authentication system originally developed at MIT, deserves its name. It's a faithful watchdog that keeps intruders out of your networks. But it has been equally fierce to system administrators, for whom the complexity of Kerberos is legendary.Single sign-on is the holy grail of network administration, and Kerberos is the only game in town. Microsoft, by integrating Kerberos into Active Directory in Windows 2000 and 2003, has extended the reach of Kerberos to all networks large or small. Kerberos makes your network more secure and more convenient for users by providing a single authentication system that works across the entire network. One username; one password; one login is all you need.Fortunately, help for administrators is on the way. Kerberos: The Definitive Guide shows you how to implement Kerberos for secure authentication. In addition to covering the basic principles behind cryptographic authentication, it covers everything from basic installation to advanced topics like cross-realm authentication, defending against attacks on Kerberos, and troubleshooting.In addition to covering Microsoft's Active Directory implementation, Kerberos: The Definitive Guide covers both major implementations of Kerberos for Unix and Linux: MIT and Heimdal. It shows you how to set up Mac OS X as a Kerberos client. The book also covers both versions of the Kerberos protocol that are still in use: Kerberos 4 (now obsolete) and Kerberos 5, paying special attention to the integration between the different protocols, and between Unix and Windows implementations.If you've been avoiding Kerberos because it's confusing and poorly documented, it's time to get on board! This book shows you how to put Kerberos authentication to work on your Windows and Unix systems.
Kernel Learning Algorithms for Face Recognition
by Jeng-Shyang Pan Jun-Bao Li Shu-Chuan ChuKernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its newest applications.
Kernel Methods and Machine Learning
by S. Y. KungOffering a fundamental basis in kernel-based learning theory, this book covers both statistical and algebraic principles. It provides over 30 major theorems for kernel-based supervised and unsupervised learning models. The first of the theorems establishes a condition, arguably necessary and sufficient, for the kernelization of learning models. In addition, several other theorems are devoted to proving mathematical equivalence between seemingly unrelated models. With over 25 closed-form and iterative algorithms, the book provides a step-by-step guide to algorithmic procedures and analysing which factors to consider in tackling a given problem, enabling readers to improve specifically designed learning algorithms, build models for new applications and develop efficient techniques suitable for green machine learning technologies. Numerous real-world examples and over 200 problems, several of which are Matlab-based simulation exercises, make this an essential resource for graduate students and professionals in computer science, electrical and biomedical engineering. Solutions to problems are provided online for instructors.
Kernel Methods for Machine Learning with Math and Python: 100 Exercises for Building Logic
by Joe SuzukiThe most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as follows:The content is written in an easy-to-follow and self-contained style.The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book.The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels.Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used.Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed.This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
Kernel Methods for Machine Learning with Math and R: 100 Exercises for Building Logic
by Joe SuzukiThe most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building R programs. The book’s main features are as follows:The content is written in an easy-to-follow and self-contained style.The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book.The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels.Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used.Once readers have a basic understanding of the functional analysis topics covered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed.This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
Kernel Methods for Pattern Analysis
by John Shawe-Taylor Nello CristianiniKernel methods provide a powerful and unified framework for pattern discovery, motivating algorithms that can act on general types of data (e. g. strings, vectors or text) and look for general types of relations (e. g. rankings, classifications, regressions, clusters). The application areas range from neural networks and pattern recognition to machine learning and data mining. This book, developed from lectures and tutorials, fulfils two major roles: firstly it provides practitioners with a large toolkit of algorithms, kernels and solutions ready to use for standard pattern discovery problems in fields such as bioinformatics, text analysis, image analysis. Secondly it provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
Kernel Ridge Regression in Clinical Research
by Aeilko H. Zwinderman Ton J. CleophasIBM (international business machines) has published in its SPSS statistical software 2022 update a very important novel regression method entitled Kernel Ridge Regression (KRR). It is an extension of the currently available regression methods, and is suitable for pattern recognition in high dimensional data, particularly, when alternative methods fail. Its theoretical advantages are plenty and include thekernel trick for reduced arithmetic complexity,estimation of uncertainty by Gaussians unlike histograms,corrected data-overfit by ridge regularization,availability of 8 alternative kernel density models for datafit.A very exciting and wide array of preliminary KRR research has already been published by major disciplines (like studies in quantum mechanics and nuclear physics, studies of molecular affinity / dynamics, atomisation energy studies, but also forecasting economics studies, IoT (internet of things) studies for e-networks, plant stress response studies, big data streaming studies, etc). In contrast, it is virtually unused in clinical research. This edition is the first textbook and tutorial of kernel ridge regressions for medical and healthcare students as well as recollection / update bench, and help desk for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional regression analyses. Step by step analyses of over 20 data files stored at Supplementary Files at Springer Interlink are included for self-assessment. We should add that the authors are well qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015) and Professor Cleophas is past-president of the American College of Angiology (2000-2002). From their expertise they should be able to make adequate selections of modern KRR methods for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 24 years and their research can be characterized as a continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.
Kernelization: Theory of Parameterized Preprocessing
by Fedor V. Fomin Daniel Lokshtanov Saket Saurabh Meirav ZehaviPreprocessing, or data reduction, is a standard technique for simplifying and speeding up computation. Written by a team of experts in the field, this book introduces a rapidly developing area of preprocessing analysis known as kernelization. The authors provide an overview of basic methods and important results, with accessible explanations of the most recent advances in the area, such as meta-kernelization, representative sets, polynomial lower bounds, and lossy kernelization. The text is divided into four parts, which cover the different theoretical aspects of the area: upper bounds, meta-theorems, lower bounds, and beyond kernelization. The methods are demonstrated through extensive examples using a single data set. Written to be self-contained, the book only requires a basic background in algorithmics and will be of use to professionals, researchers and graduate students in theoretical computer science, optimization, combinatorics, and related fields.
Key 5G Physical Layer Technologies: Enabling Mobile and Fixed Wireless Access
by Douglas H. MoraisThis book covers the key technologies associated with the physical transmission of data on fifth generation (5G) mobile systems. Following an overview of these technologies, a high-level description of 3GPP’s mobile communications standard (5G NR) is given and it is shown how the key technologies presented earlier facilitate the transmission of control data and very high-speed user data. In the final chapter, an overview and the physical layer aspects of 5G NR enabled Fixed Wireless Access (FWA) networks is presented. This book is intended for those practicing engineers and graduate and upper undergraduate engineering students who have an interest in 3GPP’s 5G enabled mobile and or FWA networks and want to acquire, where missing, the necessary technology background in order to understand 3GPP’s physical layer specifications and operation.Provides a comprehensive covering of key 3GPP 5G NR physical layer technologies, presented in a clear, tractable fashion, with sufficient mathematics to make it technically coherent;Addresses all key 5G NR technologies, including digital modulation, LDPC and Polar coding, multicarrier based multiple access techniques, and multiple antenna techniques including MIMO and beamforming;Presents an overview of 5G NR Radio Access Network (RAN) architecture and a detailed understanding of how user and control data is transported in the physical layer by the application of the technologies presented;Provides an overview and addresses physical layer aspects of 5G NR enabled Fixed Wireless Access networks.
Key 5G Physical Layer Technologies: Enabling Mobile and Fixed Wireless Access
by Douglas H. Morais This updated book, reconfigured as a textbook, covers the key technologies associated with the physical transmission of data on 5G mobile systems. Following an updated overview of these technologies, the author provides a high-level description of 3GPP’s mobile communications standard (5G NR) and shows how the key technologies presented earlier facilitate the transmission of very high-speed user data and control data and can provide very low latency for use cases where this is important. In the final chapter, an overview and the physical layer aspects of 5G NR enabled Fixed Wireless Access (FWA) networks is presented. Material in the first edition addressed mainly the key physical layer technologies and features associated with 3GPP release 15, the first release to support 5G. This edition adds descriptions of some of the technological advancements supported in release 16, including integrated access and backhaul (IAB), sidelink communication, NR positioning, operation in unlicensed bands, and multiple transmission points transmission. This textbook is intended for graduate and upper undergraduate engineering students and practicing engineers who have an interest in 3GPP’s 5G enabled mobile and or FWA networks and want to acquire, where missing, the necessary technology background in order to understand 3GPP’s physical layer specifications and operation. The author provides working problems and helpful examples throughout the text.
Key 5G/5G-Advanced Physical Layer Technologies: Enabling Mobile and Fixed Wireless Access
by Douglas H. MoraisThis third edition of this text covers the key technologies associated with the physical transmission of data on 5G mobile systems. Following an updated overview of these technologies, the author provides a high-level description of 3GPP’s mobile communications standard (5G/5G-Advanced) and shows how the key technologies presented earlier facilitate the transmission of very high-speed user data and control data and can provide very low latency for use cases where this is important. In the final chapter, an updated overview and the physical layer aspects of 5G NR enabled Fixed Wireless Access (FWA) networks is presented. Material in the second edition addressed mainly the key physical layer technologies and features associated with 3GPP Release 15, the first release to support 5G, and Release 16. This edition adds descriptions of some of the technological advancements supported in Releases 17 and 18, the latter being designated by 3GPP as 5G-Advanced. In addition to numerous enhancements of existing features, these releases include new features such as support for 1024-QAM in the downlink in the FR1 band, Reduced Capability (RedCAP) devices, Network Controlled repeaters, operation in the 6 GHz band and above 52.6 GHz, support for broadcast/multicast services, and Non-terrestrial Networks (NTNs). Additionally, a look ahead at some of the planned features and enhancements of Release 19 is provided. This textbook is intended for graduate and upper undergraduate engineering students and practicing engineers and technicians who have an interest in 3GPP’s 5G enabled mobile and or FWA networks and want to acquire, where missing, the necessary technology background in order to understand 3GPP’s physical layer specifications and operation. Provided are working problems and helpful examples throughout the text.
Key Digital Trends Shaping the Future of Information and Management Science: Proceedings of 5th International Conference on Information Systems and Management Science (ISMS) 2022 (Lecture Notes in Networks and Systems #671)
by Sanjay Misra Deepak Singh Lalit Garg Nishtha Kesswani Joseph G. Vella Imene Brigui Dilip Singh SisodiaThis book (proceedings of ISMS 2022) is intended to be used as a reference by students and researchers who collect scientific and technical contributions with respect to models, tools, technologies and applications in the field of information systems and management science. This textbook shows how to exploit information systems in a technology-rich management field. The book introduces concepts, principles, methods, and procedures that will be valuable to students and scholars in thinking about existing organization systems, proposing new systems, and working with management professionals in implementing new information systems.
Key Digital Trends in Artificial Intelligence and Robotics: Proceedings of 4th International Conference on Deep Learning, Artificial Intelligence and Robotics, (ICDLAIR) 2022 - Progress in Algorithms and Applications of Deep Learning (Lecture Notes in Networks and Systems #670)
by Alfredo Vaccaro Luigi Troiano Nishtha Kesswani Irene Díaz Rodriguez Imene Brigui David Pastor-EscuredoThe book (proceedings of the 4th International Conference on Deep Learning, Artificial Intelligence and Robotics (ICDLAIR) 2022) introduces key topics from artificial intelligence algorithms and programming organisations and explains how they contribute to health care, manufacturing, law, finance, retail, real estate, accountancy, digital marketing, and various other fields. Although artificial intelligence (AI) has generated a lot of hype over the past ten years, these consequences on how we live, work, and play are still in their infancy and will likely have a significant impact in the future. The supremacy of AI in areas like speech and picture recognition, navigational apps, personal assistants for smartphones, ride-sharing apps, and many other areas is already well established. The book is primarily meant for academics, researchers, and engineers who want to employ AI applications to address real-world issues. The authors hope that businesses and technology creators will also find it appealing to utilise in industry.
Key Technologies for On-Demand 6G Network Services (Wireless Networks)
by Jing Wang Qi Qi Jianxin Liao Bo He Jingyu WangThis book delves into the confluence of AI and the transformative potential it holds for the future of 6G network services. It uncovers how the integration of AI technologies as well as redefines the landscape of network management and control. This book also offers a new paradigm for delivering on-demand services that are immersive, personalized and of ultimate performance. A detailed exploration of AI-driven network management systems is presenting in this book, discussing the development of knowledge-defined networking, the construction of all-scenario on-demand service systems and the critical role of network management and control in achieving 6G’s vision. This book begins by examining the historical evolution of communication networks and the pivotal shift towards technology-driven demands in the 6G era. It outlines the book’s coverage of the foundational theories, wireless technologies as well as network architectures that will underpin the next generation of mobile services. Further, this book provides a comprehensive analysis of the key technologies required to support 6G on-demand services, such as trusted and autonomous access control, intelligent resource allocation and service capability coordination. It discusses the challenges and opportunities in developing a network that is not only high-performing but also adaptable to a wide range of applications, from personal use to industrial and agricultural production, and public services. This book targets advanced-level students and researchers working in this field serving as both a technical guide and a visionary outlook on the role of AI in shaping 6G networks. It also offers insights into the research, development, and potential applications of AI in network services, making it an invaluable resource for professionals, who understand or contribute to the advancement of 6G technologies.
Key Technologies of High Frequency Wireless Communications
by Kai Yang Neng Ye Jianguo Li Jianxiong Pan Yujie LinThis book provides solutions to some of the issues present in the physical layer of current high-frequency wireless communication systems. It reviews the development history of high-frequency wireless communication systems, pinpoints certain existing problems in contemporary high-frequency communication systems, and proposes solutions. The 6th Generation Mobile Networks (6G) is based on terrestrial cellular networks and integrates satellite communication, drone communication, and marine communication to form an integrated air-ground-sea network, providing comprehensive coverage, high speed, high security, and multifunctional communication solutions. High-frequency wireless communication, represented by millimeter-wave and terahertz communications, offers a wide available spectrum and high transmission rates, making it a highly promising broadband wireless access technology in 6G. To achieve higher transmission rates (Tbps level), lower transmission latency (millisecond level), higher security performance (physical layer security), and stronger hardware integration (communication-sensing integration), high-frequency wireless transmission faces many challenges due to characteristics such as short wavelengths, high path loss, and weak components. These challenges include: Large link attenuation and poor coverage in high-frequency wireless communication systems, resulting in low spectrum efficiency for edge users.Low output power, poor linearity, and low efficiency of high-frequency power amplifiers, making it difficult to achieve long-distance transmission of wideband signals.High sidelobe energy in high-frequency multi-user secure transmission, leading to unfocused spatial regions and low transmission efficiency.Independent design functions and high information processing delay in high-frequency communication and sensing, causing wastage of spectrum resources and hardware resources.To address these challenges, the author has conducted innovative work aimed at improving the spectrum efficiency, power amplifier efficiency, transmission efficiency, and processing efficiency of high-frequency wireless transmission. The research findings have been published in high-impact journals such as IEEE Transactions on Vehicular Technology, Microwave Theory and Techniques, Broadcasting, IEEE Sensors Journal, and IEEE Wireless Communications Letters. Based on these foundations, this book is dedicated to discussing efficient transmission technology for high-frequency wireless communications with a focus on 6G. The book addresses fields such as signal processing, spectrum management, and high-efficiency sustainable communications. It is highly recommended for academic researchers, students, and engineers in wireless communication, terahertz communication, and electronic information fields.
Key Technologies of Intelligentized Welding Manufacturing: The Spectral Diagnosis Technology for Pulsed Gas Tungsten Arc Welding of Aluminum Alloys (Transactions on Intelligent Welding Manufacturing)
by Shanben Chen Yiming HuangThis book presents the recent research results of the application of arc spectrum in the welding process. It sheds light on the fundamentals of monitoring welding quality using arc spectral information. By analyzing the topic both from a global and local perspective, it establishes a knowledge base of features characterizing welding statuses. Researchers, scientists and engineers in the field of intelligent welding can benefit from the book. As such, this book provides valuable knowledge, useful methods, and practical algorithms that are applicable in real-time detection of welding defects.
Key Technologies of Intelligentized Welding Manufacturing: Visual Sensing of Weld Pool Dynamic Characters and Defect Prediction of GTAW Process
by Jian Chen Zhili Feng Zongyao ChenThis book describes the application of vision-sensing technologies in welding processes, one of the key technologies in intelligent welding manufacturing. Gas tungsten arc welding (GTAW) is one of the main welding techniques and has a wide range of applications in the manufacturing industry. As such, the book also explores the application of AI technologies, such as vision sensing and machine learning, in GTAW process sensing and feature extraction and monitoring, and presents the state-of-the-art in computer vision, image processing and machine learning to detect welding defects using non-destructive methods in order to improve welding productivity. Featuring the latest research from ORNL (Oak Ridge National Laboratory) using digital image correlation technology, this book will appeal to researchers, scientists and engineers in the field of advanced manufacturing.
Keyboarding with Computer Applications: Lessons 1-150
by Jack E. Johnson Delores Sykes Cotton Carole Glosup Stanley Judith Chiri-MulkeyThis book will appeal to every student. All elements, including the courseware, textbook, and student software manuals are fully integrated to provide students with the total learning experience.
Keyboarding and Information Processing (Book One) (Sixth Edition)
by Jack P. Hoggatt Jon A. Shank Jerry W. Robinson Amanda Robinson Lee R. Beaumont T. James CrawfordIn today's world of people doing business anytime, anywhere from PC's and laptops, proper keyboarding skills are essential. While solid keyboarding skills never change, the applications and software do. That's why Century 21 Keyboarding not only teaches users the fundamentals, it also keeps them current with new technology-a reputation it's held for more than 75 years.
Keyboarding and Word Processing Essentials: Microsoft Word 2010 (18th Edition)
by Susie H. Vanhuss Connie M. Forde Donna L. WooThe Eighteenth Edition of KEYBOARDING AND WORD PROCESSING ESSENTIALS, LESSONS 1-55 uses proven techniques to help readers master the keyboarding and formatting skills they need for career success-from initial new-key learning to expertise in formatting business documents with Microsoft Word 2010. South-Western College Keyboarding offers a proven, time-tested approach that helps readers develop a strong foundation in basic keyboarding, steadily improve their skills, and rapidly become proficient in document formatting and business communication. Each lesson is clearly focused, well structured, and designed to provide step-by-step training and reinforcement to help readers quickly acquire and apply new skills. This one-book solution includes 55 lessons, documents, and software instructions within a space-saving easel-back format.
Keyboarding with Computer Applications
by Jack E. Johnson Delores Sykes Cotton Judith Chiri-Mulkey Carole G. StanleyKeyboarding, Word Processing, Spreadsheets, Desktop Publishing, Databases.