Paradoxes are poems of science and philosophy that collectively allow us to address broad multidisciplinary issues within a microcosm. A true paradox is a source of creativity and a concise expression that delivers a profound idea and provokes a wild and endless imagination. The study of paradoxes leads to ultimate clarity and, at the same time, indisputably challenges your mind. Paradoxes in Scientific Inference analyzes paradoxes from many different perspectives: statistics, mathematics, philosophy, science, artificial intelligence, and more. The book elaborates on findings and reaches new and exciting conclusions. It challenges your knowledge, intuition, and conventional wisdom, compelling you to adjust your way of thinking. Ultimately, you will learn effective scientific inference through studying the paradoxes.

General Purpose Simulation System (GPSS) is a special computer programming language primarily used to simulate what can be classified as discrete systems. A discrete system is one where, at any given instant in time, a countable number of things can take place. The basic operation of a mine itself can be considered such a system. Discrete Simulation and Animation for Mining Engineers explains how to model mining systems using GPSS/H (R) and PROOF (R) by Wolverine Software Corporation. Employing a unique approach that encourages engagement from the start, the text discusses animation first, and then slowly introduces simulation language. As each new topic is covered, an animation is provided to illustrate the key concepts.Leveraging valuable insight gained from the author's extensive experience modeling mines around the world, the book: Describes how to apply discrete system simulation to mines Shows how to make those simulations come alive with animation Includes real-world examples and exercises that hone practical problem-solving skills Written by a mining engineer for mining engineers and students of mining, Discrete Simulation and Animation for Mining Engineers offers a comprehensive yet accessible treatment of mine simulation and animation useful in increasing the efficiency of industrial mining processes.

The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations making them especially useful in applications where the complexity of data or tasks makes the design of such functions by hand impractical.Exploring Neural Networks with C# presents the important properties of neural networks while keeping the complex mathematics to a minimum. Explaining how to build and use neural networks, it presents complicated information about neural networks structure, functioning, and learning in a manner that is easy to understand.Taking a "learn by doing" approach, the book is filled with illustrations to guide you through the mystery of neural networks. Examples of experiments are provided in the text to encourage individual research. Online access to C# programs is also provided to help you discover the properties of neural networks.Following the procedures and using the programs included with the book will allow you to learn how to work with neural networks and evaluate your progress. You can download the programs as both executable applications and C# source code from http://home.agh.edu.pl/~tad//index.php?page=programyandlang=en

Advances in Metaheuristics: Applications in Engineering Systems provides details on current approaches utilized in engineering optimization. It gives a comprehensive background on metaheuristic applications, focusing on main engineering sectors such as energy, process, and materials. It discusses topics such as algorithmic enhancements and performance measurement approaches, and provides insights into the implementation of metaheuristic strategies to multi-objective optimization problems. With this book, readers can learn to solve real-world engineering optimization problems effectively using the appropriate techniques from emerging fields including evolutionary and swarm intelligence, mathematical programming, and multi-objective optimization. The ten chapters of this book are divided into three parts. The first part discusses three industrial applications in the energy sector. The second focusses on process optimization and considers three engineering applications: optimization of a three-phase separator, process plant, and a pre-treatment process. The third and final part of this book covers industrial applications in material engineering, with a particular focus on sand mould-systems. It also includes discussions on the potential improvement of algorithmic characteristics via strategic algorithmic enhancements. This book helps fill the existing gap in literature on the implementation of metaheuristics in engineering applications and real-world engineering systems. It will be an important resource for engineers and decision-makers selecting and implementing metaheuristics to solve specific engineering problems.

Experiential Learning presents an evolving form of education that fundamentally involves "learning by doing" and having students reflect on the work. The book discusses these recent developments pertaining to the use of experiential learning in engineering education. Covering a range of innovations in experiential learning, the book explores development in laboratories, in-class and problem-based learning, project work, and society-based aspects, including indigenous elements in the curriculum. It includes case studies and examples sourced from institutions around the world. Focuses on recent and practical aspects of implementing experiential learning to help improve engineering education. Offers an examination of the undergraduate experience, which leads to professional certification. Includes a chapter on lessons in other professional education areas, such as medicine and health care, business, and social work. A broad readership will find value in this book, including faculty who teach undergraduate engineering courses; engineering education researchers; industry partners that provide co-op experience; and developers of training modules for practicing engineers.

Now the most used texbook for introductory cryptography courses in both mathematics and computer science, the Third Edition builds upon previous editions by offering several new sections, topics, and exercises. The authors present the core principles of modern cryptography, with emphasis on formal definitions, rigorous proofs of security.

This monograph introduces breakthrough control algorithms for partial differential equation models with moving boundaries, the study of which is known as the Stefan problem. The algorithms can be used to improve the performance of various processes with phase changes, such as additive manufacturing. Using the authors' innovative design solutions, readers will also be equipped to apply estimation algorithms for real-world phase change dynamics, from polar ice to lithium-ion batteries. A historical treatment of the Stefan problem opens the book, situating readers in the larger context of the area. Following this, the chapters are organized into two parts. The first presents the design method and analysis of the boundary control and estimation algorithms. Part two then explores a number of applications, such as 3D printing via screw extrusion and laser sintering, and also discusses the experimental verifications conducted. A number of open problems and provided as well, offering readers multiple paths to explore in future research. Materials Phase Change PDE Control & Estimation is ideal for researchers and graduate students working on control and dynamical systems, and particularly those studying partial differential equations and moving boundaries. It will also appeal to industrial engineers and graduate students in engineering who are interested in this area.

This book aims to develop professional and practical microcontroller applications in the ARM-MDK environment with Texas Instruments MSP432P401R LaunchPad kits. It introduces ARM Cortex-M4 MCU by highlighting the most important elements, including: registers, pipelines, memory, and I/O ports. With the updated MSP432P401R Evaluation Board (EVB), MSP-EXP432P401R, this MCU provides various control functions with multiple peripherals to enable users to develop and build various modern control projects with rich control strategies. Micro-controller programming is approached with basic and straightforward programming codes to reduce learning curves, and furthermore to enable students to build embedded applications in more efficient and interesting ways. For authentic examples, 37 Class programming projects are built into the book that use MSP432P401R MCU. Additionally, approximately 40 Lab programming projects with MSP432P401R MCU are included to be assigned as homework.

Carefully separating the essential from the ornamental, Essentials of Control Techniques and Theory presents the nuts and bolts for designing a successful controller. It discusses the theory required to support the art of designing a working controller as well as the various aspects to convince a client, employer, or examiner of your expertise. A Compelling Account of the Basics of Control Theory Control solutions for practicing engineers Using the author's own Javascript On-Line Learning Interactive Environment for Simulation (Jollies), the text relies on computer-based graphical analysis methods, such as Nyquist, Nichols, root locus, and phase-plane, to illustrate how useful computer simulation can be for analyzing both linear and nonlinear systems. It explains step-by-step the design and modeling of various control systems, including discrete time systems and an inverted pendulum. Along with offering many web-based simulations, the book shows how mathematics, such as vectors, matrices, and the differential equations that govern state variables, can help us understand the concepts that underpin the controller's effects. From frequency domain analysis to time-domain state-space representation, this book covers many aspects of classical and modern control theory. It presents important methods for designing and analyzing linear systems and controllers.

Using real-life examples to illustrate the performance of learning algorithms and instructing readers how to apply them to practical applications, this work offers a comprehensive treatment of subspace learning algorithms for neural networks. The authors summarize a decade of high quality research offering a host of practical applications. They demonstrate ways to extend the use of algorithms to fields such as encryption communication, data mining, computer vision, and signal and image processing to name just a few. The brilliance of the work lies with how it coherently builds a theoretical understanding of the convergence behavior of subspace learning algorithms through a summary of chaotic behaviors.

This book constitutes the proceedings of the 1st International Conference on Systems and Information Sciences (ICCIS), held in Manta, Ecuador, from July 27 to 29, 2020, and was jointly organized by Universidad Laica Eloy Alfaro de Manabi "ULEAM", in collaboration with GDEON. ICCIS aims to bring together systems and information sciences researchers and developers from academia and industry around the world to discuss cutting-edge research. The book covers the following topics: AI, Expert Systems and Big Data Analytics Cloud, IoT and Distributed Computing Communications Database System and Application Financial Technologies (FinTech), Economics and Business Engineering m-Learning and e-Learning Security Software Engineering Web Information Systems and Applications General Track

Fashion Retail Supply Chain Management: A Systems Optimization Approach is a comprehensive reference source that provides the state-of-the-art findings on many important emerging research issues related to retail supply chain management and optimization problems. The book takes an explicit systems approach, and discusses retailled fashion supply chain coordination mechanisms and consumer market informationdriven fashion retail supply chain models, as well as suggesting future research avenues. This volume will be of interest not only to those involved in the fashion industry, but also to academics and practitioners in the wider fields of business, manufacturing engineering, systems engineering and supply chain management.

The emergence of fuzzy logic and its applications has dramatically changed the face of industrial control engineering. Over the last two decades, fuzzy logic has allowed control engineers to meet and overcome the challenges of developing effective controllers for increasingly complex systems with poorly defined dynamics. Today's engineers need a working knowledge of the principles and techniques of fuzzy logic-Intelligent Control provides it.

The author first introduces the traditional control techniques and contrasts them with intelligent control. He then presents several methods of representing and processing knowledge and introduces fuzzy logic as one such method. He highlights the advantages of fuzzy logic over other techniques, indicates its limitations, and describes in detail a hierarchical control structure appropriate for use in intelligent control systems. He introduces a variety of applications, most in the areas of robotics and mechatronics but with others including air conditioning and process/production control. One appendix provides discussion of some advanced analytical concepts of fuzzy logic, another describes a commercially available software system for developing fuzzy logic application.

Intelligent Control is filled with worked examples, exercises, problems, and references. No prior knowledge of the subject nor advanced mathematics are needed to comprehend much of the book, making it well-suited as a senior undergraduate or first-year graduate text and a convenient reference tool for practicing professionals.

Broadly defined as the science and technology of systems responding to neural processes in the brain, neuroadaptive systems (NASs) has become a rapidly developing area of study. One of the first books available in this emerging area, Neuroadaptive Systems: Theory and Applications synthesizes knowledge about human behavior, cognition, neural processing, and technology and how it can be used to optimize the design, development, modeling, simulation, and applications of complex neuro-based systems. Balancing coverage of theory and applications, the book examines the general aims of NASs and how neurogenomics can be applied in training applications. It includes important results and findings gathered from approximately two decades of brain computer interaction research. But more than this, the book details the underlying rationale for using NASs compared to other kinds of human-machine systems and raises questions and concerns about budding neuro-scientific areas that gives insight into the way humans may interact with neuro-technological systems in the future. With contributions from international professionals and researchers, this book presents state-of-the-art developments in neuroscience, human factors, and brain activity measurement. Packed with models, case studies, research results, and illustrations, it discusses approaches to understanding the functions of neuronal networks, and then explores challenges and applications of neuroadaptive systems. It provides tools for future development and the theory to support it.

Fast fashion is an industrial trend that refers to the concept of shortening lead time (production, distribution) and offering new products to the market as fast as possible. Despite an abundance of research results, there is no comprehensive reference source that covers the state-of-the-art findings on both theoretical modeling and empirical research on fast fashion systems. This edited volume consists of three sections - review and exploratory studies, analytical models, and empirical research - made up of many interesting contributions in the respective domain. The result is a well-balanced handbook which includes both theoretical results (from various perspectives) and empirical findings. This volume will be of interest not only to those involved in the fashion industry, but also to academics and practitioners in the wider fields of business, manufacturing engineering, systems engineering and supply chain management.

New, significant scientific discoveries in laser and photonic technologies, systems perspectives, and integrated design approaches can improve even further the impact in critical areas of challenge. Yet this knowledge is dispersed across several disciplines and research arenas. Laser and Photonic Systems: Design and Integration brings together a multidisciplinary group of experts to increase understanding of the ways in which systems perspectives may influence laser and photonic innovations and application integration. By bringing together chapters from leading scientists and technologists, industrial and systems engineers, and managers, the book stimulates new thinking that would bring a systems, network, and system-of-systems perspective to bear on laser and photonic systems applications. The chapters challenge you to explore opportunities for revolutionary and broader advancements. The authors emphasize the identification of emerging research and application frontiers where there are promising contributions to lasers, optics, and photonics applications in fields such as manufacturing, healthcare, security, and communications. The book contains insights from leading researchers, inventors, implementers, and innovators. It explains a variety of techniques, models, and technologies proven to work with laser and photonic systems, their development, design, and integration. Such systems are of growing interest to many organizations, given their promise and potential solutions of grand societal challenges. Lastly, the book helps you leverage the knowledge into exciting new frontiers of successful solutions.

In areas such as military, security, aerospace, and disaster management, the need for performance optimization and interoperability among heterogeneous systems is increasingly important. Model-driven engineering, a paradigm in which the model becomes the actual software, offers a promising approach toward systems of systems (SoS) engineering. However, model-driven engineering has largely been unachieved in complex dynamical systems and netcentric SoS, partly because modeling and simulation (M&S) frameworks are stove-piped and not designed for SoS composability. Addressing this gap, Netcentric System of Systems Engineering with DEVS Unified Process presents a methodology for realizing the model-driven engineering vision and netcentric SoS using DEVS Unified Process (DUNIP). The authors draw on their experience with Discrete Event Systems Specification (DEVS) formalism, System Entity Structure (SES) theory, and applying model-driven engineering in the context of a netcentric SoS. They describe formal model-driven engineering methods for netcentric M&S using standards-based approaches to develop and test complex dynamic models with DUNIP. The book is organized into five sections: Section I introduces undergraduate students and novices to the world of DEVS. It covers systems and SoS M&S as well as DEVS formalism, software, modeling language, and DUNIP. It also assesses DUNIP with the requirements of the Department of Defense's (DoD) Open Unified Technical Framework (OpenUTF) for netcentric Test and Evaluation (T&E). Section II delves into M&S-based systems engineering for graduate students, advanced practitioners, and industry professionals. It provides methodologies to apply M&S principles to SoS design and reviews the development of executable architectures based on a framework such as the Department of Defense Architecture Framework (DoDAF). It also describes an approach for building netcentric knowledge-based contingency-driven systems. Section III guides graduate students, advanced DEVS users, and industry professionals who are interested in building DEVS virtual machines and netcentric SoS. It discusses modeling standardization, the deployment of models and simulators in a netcentric environment, event-driven architectures, and more. Section IV explores real-world case studies that realize many of the concepts defined in the previous chapters. Section V outlines the next steps and looks at how the modeling of netcentric complex adaptive systems can be attempted using DEVS concepts. It touches on the boundaries of DEVS formalism and the future work needed to utilize advanced concepts like weak and strong emergence, self-organization, scale-free systems, run-time modularity, and event interoperability. This groundbreaking work details how DUNIP offers a well-structured, platform-independent methodology for the modeling and simulation of netcentric system of systems.

• Utilizes case studies from different disciplines and sectors within engineering and other related technical areas to demonstrate how to go from data, to insight, and to decision making • Introduces various approaches to build models that exploits different algorithms • Discusses predictive models that can be built through machine learning and used to mine patterns from large datasets • Explores the augmentation of technical and mathematical materials with explanatory worked examples • Includes a glossary, lecture notes, self-assessments, and worked-out practice exercises

This book investigates the fundaments, standards, and protocols of Cyber-Physical Systems (CPS) in the industrial transformation environment. It facilitates a fusion of both technologies in the creation of reliable and robust applications. Cyber-Physical Systems for Industrial Transformation: Fundamentals, Standards, and Protocols explores emerging technologies such as artificial intelligence, data science, blockchain, robotic process automation, virtual reality, edge computing, and 5G technology to highlight current and future opportunities to transition CPS to become more robust and reliable. The book showcases the real-time sensing, processing, and actuation software and discusses fault-tolerant and cybersecurity as well. This book brings together undergraduates, postgraduates, academics, researchers, and industry individuals that are interested in exploring new ideas, techniques, and tools related to CPS and Industry 4.0.

This book offers a valuable reference guide for researchers in distributed optimization and for senior undergraduate and graduate students alike. Focusing on the natures and functions of agents, communication networks and algorithms in the context of distributed optimization for networked control systems, this book introduces readers to the background of distributed optimization; recent developments in distributed algorithms for various types of underlying communication networks; the implementation of computation-efficient and communication-efficient strategies in the execution of distributed algorithms; and the frameworks of convergence analysis and performance evaluation. On this basis, the book then thoroughly studies 1) distributed constrained optimization and the random sleep scheme, from an agent perspective; 2) asynchronous broadcast-based algorithms, event-triggered communication, quantized communication, unbalanced directed networks, and time-varying networks, from a communication network perspective; and 3) accelerated algorithms and stochastic gradient algorithms, from an algorithm perspective. Finally, the applications of distributed optimization in large-scale statistical learning, wireless sensor networks, and for optimal energy management in smart grids are discussed.

Mechatronics: Electronics in Products and Processes identifies the concepts which underpin the mechatronic approach to engineering design and brings together its principle components - sensors and transducers, embedded microprocessors, actuators and drives - to explore their interrelationships. The text focuses primarily on hardware elements and the impact of system architecture. Modern technology is set in an historical background and each chapter comes with learning objectives and chapter outlines. The book includes numerous case studies illustrating the concepts applied in such areas as automatic cameras, aerospace parts manufacturing, fly-by-wire systems, and boat autopilot.

This book presents a collection of papers on recent advances in problems concerning dynamics, optimal control and optimization. In many chapters, computational techniques play a central role. Set-oriented techniques feature prominently throughout the book, yielding state-of-the-art algorithms for computing general invariant sets, constructing globally optimal controllers and solving multi-objective optimization problems.

The AVEC symposium is a leading international conference in the fields of vehicle dynamics and advanced vehicle control, bringing together scientists and engineers from academia and automotive industry. The first symposium was held in 1992 in Yokohama, Japan. Since then, biennial AVEC symposia have been established internationally and have considerably contributed to the progress of technology in automotive research and development. In 2016 the 13th International Symposium on Advanced Vehicle Control (AVEC'16) was held in Munich, Germany, from 13th to 16th of September 2016. The symposium was hosted by the Munich University of Applied Sciences. AVEC'16 puts a special focus on automatic driving, autonomous driving functions and driver assist systems, integrated control of interacting control systems, controlled suspension systems, active wheel torque distribution, and vehicle state and parameter estimation. 132 papers were presented at the symposium and are published in these proceedings as full paper contributions. The papers review the latest research developments and practical applications in highly relevant areas of vehicle control, and may serve as a reference for researchers and engineers.

Advances in Metaheuristics: Applications in Engineering Systems provides details on current approaches utilized in engineering optimization. It gives a comprehensive background on metaheuristic applications, focusing on main engineering sectors such as energy, process, and materials. It discusses topics such as algorithmic enhancements and performance measurement approaches, and provides insights into the implementation of metaheuristic strategies to multi-objective optimization problems. With this book, readers can learn to solve real-world engineering optimization problems effectively using the appropriate techniques from emerging fields including evolutionary and swarm intelligence, mathematical programming, and multi-objective optimization. The ten chapters of this book are divided into three parts. The first part discusses three industrial applications in the energy sector. The second focusses on process optimization and considers three engineering applications: optimization of a three-phase separator, process plant, and a pre-treatment process. The third and final part of this book covers industrial applications in material engineering, with a particular focus on sand mould-systems. It also includes discussions on the potential improvement of algorithmic characteristics via strategic algorithmic enhancements. This book helps fill the existing gap in literature on the implementation of metaheuristics in engineering applications and real-world engineering systems. It will be an important resource for engineers and decision-makers selecting and implementing metaheuristics to solve specific engineering problems.

Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system. The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem. Key Features: · Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems. · Explains machine learning concepts as they arise in real-world case studies. · Shows how to diagnose, understand and address problems with machine learning systems. · Full source code available, allowing models and results to be reproduced and explored. · Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.