Nnmodeling

  nnmodeling: Comprehensive Membrane Science and Engineering Enrico Drioli, Lidietta Giorno, 2010-07-09 Comprehensive Membrane Science and Engineering, Four Volume Set covers all aspects of membrane science and technology - from basic phenomena to the most advanced applications and future perspectives. Modern membrane engineering is critical to the development of process-intensification strategies and to the stimulation of industrial growth. The work presents researchers and industrial managers with an indispensable tool toward achieving these aims. Covers membrane science theory and economics, as well as applications ranging from chemical purification and natural gas enrichment to potable water Includes contributions and case studies from internationally recognized experts and from up-and-coming researchers working in this multi-billion dollar field Takes a unique, multidisciplinary approach that stimulates research in hybrid technologies for current (and future) life-saving applications (artificial organs, drug delivery)
  nnmodeling: Algorithms and Architectures for Parallel Processing Yongxuan Lai, Tian Wang, Min Jiang, Guangquan Xu, Wei Liang, Aniello Castiglione, 2022-02-22 The three volume set LNCS 13155, 13156, and 13157 constitutes the refereed proceedings of the 21st International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2021, which was held online during December 3-5, 2021. The total of 145 full papers included in these proceedings were carefully reviewed and selected from 403 submissions. They cover the many dimensions of parallel algorithms and architectures including fundamental theoretical approaches, practical experimental projects, and commercial components and systems. The papers were organized in topical sections as follows: Part I, LNCS 13155: Deep learning models and applications; software systems and efficient algorithms; edge computing and edge intelligence; service dependability and security algorithms; data science; Part II, LNCS 13156: Software systems and efficient algorithms; parallel and distributed algorithms and applications; data science; edge computing and edge intelligence; blockchain systems; deept learning models and applications; IoT; Part III, LNCS 13157: Blockchain systems; data science; distributed and network-based computing; edge computing and edge intelligence; service dependability and security algorithms; software systems and efficient algorithms.
  nnmodeling: Mastering PyTorch Ashish Ranjan Jha, 2024-05-31 Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples Updated for PyTorch 2.x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networks Purchase of the print or Kindle book includes a free eBook in PDF format Key Features Understand how to use PyTorch to build advanced neural network models Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks Book DescriptionPyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learn Implement text, vision, and music generation models using PyTorch Build a deep Q-network (DQN) model in PyTorch Deploy PyTorch models on mobile devices (Android and iOS) Become well versed in rapid prototyping using PyTorch with fastai Perform neural architecture search effectively using AutoML Easily interpret machine learning models using Captum Design ResNets, LSTMs, and graph neural networks (GNNs) Create language and vision transformer models using Hugging Face Who this book is for This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.
  nnmodeling: Soft Computing in Data Science Bee Wah Yap, Azlinah Hj Mohamed, Michael W. Berry, 2018-12-10 This book constitutes the refereed proceedings of the 4th International Conference on Soft Computing in Data Science, SCDS 2018, held in Bangkok, Thailand, in August 2018. The 30 revised full papers presented were carefully reviewed and selected from 75 submissions. The papers are organized in topical sections on machine and deep learning, image processing, financial and fuzzy mathematics, optimization algorithms, data and text analytics, data visualization.
  nnmodeling: Safety and Reliability – Safe Societies in a Changing World Stein Haugen, Anne Barros, Coen van Gulijk, Trond Kongsvik, Jan Erik Vinnem, 2018-06-15 Safety and Reliability – Safe Societies in a Changing World collects the papers presented at the 28th European Safety and Reliability Conference, ESREL 2018 in Trondheim, Norway, June 17-21, 2018. The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk management Safety and Reliability – Safe Societies in a Changing World will be invaluable to academics and professionals working in a wide range of industrial and governmental sectors: offshore oil and gas, nuclear engineering, aeronautics and aerospace, marine transport and engineering, railways, road transport, automotive engineering, civil engineering, critical infrastructures, electrical and electronic engineering, energy production and distribution, environmental engineering, information technology and telecommunications, insurance and finance, manufacturing, marine transport, mechanical engineering, security and protection, and policy making.
  nnmodeling: Industrial Engineering, Machine Design And Automation (Iemda 2014) - Proceedings Of The 2014 Congress & Computer Science And Application (Ccsa 2014) - Proceedings Of The 2nd Congress Shihong Qin, Xiaolong Li, 2015-03-30 This proceedings put together 68 selected articles from the joint conferences of 2014 Congress on Industrial Engineering, Machine Design and Automation (IEMDA2014) and the 2nd Congress on Computer Science and Application (CCSA2014), held in Sanya, China during December 12 - 14, 2014. The conference program of IEMDA 2014 focused on areas of Industrial Engineering, Machine Design and Automation, while the CCSA 2014 program provided the platform for Computer Science and Applications.Collected together the latest research results and applications on industrial engineering, machine design, automation, and computer science and other related Engineering topics. All submitted papers to this proceedings were subjected to strict peer-reviewing by 2-4 expert referees, to ensure that all articles selected are of highest standard and are relevance to the conference.
  nnmodeling: Road and Off-Road Vehicle System Dynamics Handbook Gianpiero Mastinu, Manfred Ploechl, 2014-01-06 Featuring contributions from industry leaders in their respective fields, this volume presents comprehensive, authoritative coverage of all the major issues involved in road vehicle dynamic behavior. It begins with a short history of road and off-road vehicle dynamics followed by thorough, detailed state-of-the-art chapters on modeling, analysis and optimization in vehicle system dynamics, vehicle concepts and aerodynamics, pneumatic tires and contact wheel-road/off-road, modeling vehicle subsystems, vehicle dynamics and active safety, man-vehicle interaction, intelligent vehicle systems, and road accident reconstruction and passive safety.
  nnmodeling: Time Domain Methods in Electrodynamics Peter Russer, Uwe Siart, 2008-09-26 This book consists of contributions given in honor of Wolfgang J.R. Hoefer. Space and time discretizing time domain methods for electromagnetic full-wave simulation have emerged as key numerical methods in computational electromagnetics. Time domain methods are versatile and can be applied to the solution of a wide range of electromagnetic field problems. Computing the response of an electromagnetic structure to an impulsive excitation localized in space and time provides a comprehensive characterization of the electromagnetic properties of the structure in a wide frequency range. The most important methods are the Finite Difference Time Domain (FDTD) and the Transmission Line Matrix (TLM) methods. The contributions represent the state of the art in dealing with time domain methods in modern engineering electrodynamics for electromagnetic modeling in general, the Transmission Line Matrix (TLM) method, the application of network concepts to electromagnetic field modeling, circuit and system applications and, finally, with broadband devices, systems and measurement techniques.
  nnmodeling: From Statistical Physics to Statistical Inference and Back P. Grassberger, J.P. Nadal, 2012-12-06 Physicists, when modelling physical systems with a large number of degrees of freedom, and statisticians, when performing data analysis, have developed their own concepts and methods for making the `best' inference. But are these methods equivalent, or not? What is the state of the art in making inferences? The physicists want answers. More: neural computation demands a clearer understanding of how neural systems make inferences; the theory of chaotic nonlinear systems as applied to time series analysis could profit from the experience already booked by the statisticians; and finally, there is a long-standing conjecture that some of the puzzles of quantum mechanics are due to our incomplete understanding of how we make inferences. Matter enough to stimulate the writing of such a book as the present one. But other considerations also arise, such as the maximum entropy method and Bayesian inference, information theory and the minimum description length. Finally, it is pointed out that an understanding of human inference may require input from psychologists. This lively debate, which is of acute current interest, is well summarized in the present work.
  nnmodeling: Social Safety and Security Joannes Chliaoutakis, 2020-01-23 Social safety in social environments entails the sense of feeling safe with other people. Thus, social safety and security are very important to our life. Although research in this area has made significant progress in the past few years, there are still many social problems that require attention and further development in order to secure peace of mind. There is a very clear and accurate understanding and judgment of the overall situation of social security in the current and future period. This Special Issue focuses on a number of contemporary issues in social safety and security. The objective of this book is to rapidly disseminate the latest research and knowledge in this important area.
  nnmodeling: Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering Kim, Dookie, Sekhar Roy, Sanjiban, Länsivaara, Tim, Deo, Ravinesh, Samui, Pijush, 2018-06-15 The disciplines of science and engineering rely heavily on the forecasting of prospective constraints for concepts that have not yet been proven to exist, especially in areas such as artificial intelligence. Obtaining quality solutions to the problems presented becomes increasingly difficult due to the number of steps required to sift through the possible solutions, and the ability to solve such problems relies on the recognition of patterns and the categorization of data into specific sets. Predictive modeling and optimization methods allow unknown events to be categorized based on statistics and classifiers input by researchers. The Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering is a critical reference source that provides comprehensive information on the use of optimization techniques and predictive models to solve real-life engineering and science problems. Through discussions on techniques such as robust design optimization, water level prediction, and the prediction of human actions, this publication identifies solutions to developing problems and new solutions for existing problems, making this publication a valuable resource for engineers, researchers, graduate students, and other professionals.
  nnmodeling: Artificial Intelligence Methods in the Environmental Sciences Sue Ellen Haupt, Antonello Pasini, Caren Marzban, 2008-11-28 How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.
  nnmodeling: Stability Analysis of Neural Networks Grienggrai Rajchakit, Praveen Agarwal, Sriraman Ramalingam, 2021-12-05 This book discusses recent research on the stability of various neural networks with constrained signals. It investigates stability problems for delayed dynamical systems where the main purpose of the research is to reduce the conservativeness of the stability criteria. The book mainly focuses on the qualitative stability analysis of continuous-time as well as discrete-time neural networks with delays by presenting the theoretical development and real-life applications in these research areas. The discussed stability concept is in the sense of Lyapunov, and, naturally, the proof method is based on the Lyapunov stability theory. The present book will serve as a guide to enable the reader in pursuing the study of further topics in greater depth and is a valuable reference for young researcher and scientists.
  nnmodeling: Complexity and Complex Thermo-Economic Systems Stanislaw Sieniutycz, 2019-11-24 Complexity and Complex Thermoeconomic Systems describes the properties of complexity and complex thermo-economic systems as the consequence of formulations, definitions, tools, solutions and results consistent with the best performance of a system. Applying to complex systems contemporary advanced techniques, such as static optimization, optimal control, and neural networks, this book treats the systems theory as a science of general laws for functional integrities. It also provides a platform for the discussion of various definitions of complexity, complex hierarchical structures, self-organization examples, special references, and historical issues. This book is a valuable reference for scientists, engineers and graduated students in chemical, mechanical, and environmental engineering, as well as those in physics, ecology and biology, helping them better understand the complex thermodynamic systems and enhance their technical skills in research. - Provides a lucid presentation of the dynamical properties of thermoeconomic systems - Includes original graphical material that illustrates the properties of complex systems - Written by a first-class expert in the field of advanced methods in thermodynamics
  nnmodeling: Cyber Security Intelligence and Analytics Zheng Xu, Saed Alrabaee, Octavio Loyola-González, Niken Dwi Wahyu Cahyani, Nurul Hidayah Ab Rahman, 2023-04-29 This book provides the proceedings of the 5th International Conference on Cyber Security Intelligence and Analytics. The 5th International Conference on Cyber Security Intelligence and Analytics (CSIA 2023) is an international conference dedicated to promoting novel theoretical and applied research advances in the interdisciplinary agenda of cyber security, particularly focusing on threat intelligence and analytics and countering cybercrime. Cyber security experts, including those in data analytics, incident response and digital forensics, need to be able to rapidly detect, analyze and defend against a diverse range of cyber threats in near real-time conditions. We are organizing the CSIA 2023 at Radisson Blu Shanghai Pudong Jinqiao Hotel. It will feature a technical program of refereed papers selected by the international program committee, keynote address.
  nnmodeling: Thermal Food Processing Da-Wen Sun, 2012-05-16 Thermal processing remains one of the most important processes in the food industry. Now in its second edition, Thermal Food Processing: New Technologies and Quality Issues continues to explore the latest developments in the field. Assembling the work of a worldwide panel of experts, this volume highlights topics vital to the food industry today an
  nnmodeling: Progress in Systems Engineering Henry Selvaraj, Dawid Zydek, Grzegorz Chmaj, 2014-08-12 This collection of proceedings from the International Conference on Systems Engineering, Las Vegas, 2014 is orientated toward systems engineering, including topics like aero-space, power systems, industrial automation and robotics, systems theory, control theory, artificial intelligence, signal processing, decision support, pattern recognition and machine learning, information and communication technologies, image processing, and computer vision as well as its applications. The volume’s main focus is on models, algorithms, and software tools that facilitate efficient and convenient utilization of modern achievements in systems engineering.
  nnmodeling: Modelling, Simulation and Control of Thermal Energy Systems Kwang Y. Lee, Damian Flynn, Hui Xie, Li Sun, 2020-11-03 Faced with an ever-growing resource scarcity and environmental regulations, the last 30 years have witnessed the rapid development of various renewable power sources, such as wind, tidal, and solar power generation. The variable and uncertain nature of these resources is well-known, while the utilization of power electronic converters presents new challenges for the stability of the power grid. Consequently, various control and operational strategies have been proposed and implemented by the industry and research community, with a growing requirement for flexibility and load regulation placed on conventional thermal power generation. Against this background, the modelling and control of conventional thermal engines, such as those based on diesel and gasoline, are experiencing serious obstacles when facing increasing environmental concerns. Efficient control that can fulfill the requirements of high efficiency, low pollution, and long durability is an emerging requirement. The modelling, simulation, and control of thermal energy systems are key to providing innovative and effective solutions. Through applying detailed dynamic modelling, a thorough understanding of the thermal conversion mechanism(s) can be achieved, based on which advanced control strategies can be designed to improve the performance of the thermal energy system, both in economic and environmental terms. Simulation studies and test beds are also of great significance for these research activities prior to proceeding to field tests. This Special Issue will contribute a practical and comprehensive forum for exchanging novel research ideas or empirical practices that bridge the modelling, simulation, and control of thermal energy systems. Papers that analyze particular aspects of thermal energy systems, involving, for example, conventional power plants, innovative thermal power generation, various thermal engines, thermal energy storage, and fundamental heat transfer management, on the basis of one or more of the following topics, are invited in this Special Issue: • Power plant modelling, simulation, and control; • Thermal engines; • Thermal energy control in building energy systems; • Combined heat and power (CHP) generation; • Thermal energy storage systems; • Improving thermal comfort technologies; • Optimization of complex thermal systems; • Modelling and control of thermal networks; • Thermal management of fuel cell systems; • Thermal control of solar utilization; • Heat pump control; • Heat exchanger control.
  nnmodeling: Advances in Neural Networks - ISNN 2006 Jun Wang, Zhang Yi, Jacek M. Zurada, Bao-Liang Lu, Yin Hujun, 2006-05-11 This is Volume III of a three volume set constituting the refereed proceedings of the Third International Symposium on Neural Networks, ISNN 2006. 616 revised papers are organized in topical sections on neurobiological analysis, theoretical analysis, neurodynamic optimization, learning algorithms, model design, kernel methods, data preprocessing, pattern classification, computer vision, image and signal processing, system modeling, robotic systems, transportation systems, communication networks, information security, fault detection, financial analysis, bioinformatics, biomedical and industrial applications, and more.
  nnmodeling: MICAI 2005: Advances in Artificial Intelligence Alexander Gelbukh, Hugo Terashima, 2005-11-04 This book constitutes the refereed proceedings of the 4th Mexican International Conference on Artificial Intelligence, MICAI 2005, held in Monterrey, Mexico, in November 2005. The 120 revised full papers presented were carefully reviewed and selected from 423 submissions. The papers are organized in topical sections on knowledge representation and management, logic and constraint programming, uncertainty reasoning, multiagent systems and distributed AI, computer vision and pattern recognition, machine learning and data mining, evolutionary computation and genetic algorithms, neural networks, natural language processing, intelligent interfaces and speech processing, bioinformatics and medical applications, robotics, modeling and intelligent control, and intelligent tutoring systems.
  nnmodeling: Artificial Neural Networks - ICANN 2010 Konstantinos Diamantaras, Wlodek Duch, Lazaros S. Iliadis, 2010-09-13 th This volume is part of the three-volume proceedings of the 20 International Conference on Arti?cial Neural Networks (ICANN 2010) that was held in Th- saloniki, Greece during September 15–18, 2010. ICANN is an annual meeting sponsored by the European Neural Network Society (ENNS) in cooperation with the International Neural Network So- ety (INNS) and the Japanese Neural Network Society (JNNS). This series of conferences has been held annually since 1991 in Europe, covering the ?eld of neurocomputing, learning systems and other related areas. As in the past 19 events, ICANN 2010 provided a distinguished, lively and interdisciplinary discussion forum for researches and scientists from around the globe. Ito?eredagoodchanceto discussthe latestadvancesofresearchandalso all the developments and applications in the area of Arti?cial Neural Networks (ANNs). ANNs provide an information processing structure inspired by biolo- cal nervous systems and they consist of a large number of highly interconnected processing elements (neurons). Each neuron is a simple processor with a limited computing capacity typically restricted to a rule for combining input signals (utilizing an activation function) in order to calculate the output one. Output signalsmaybesenttootherunitsalongconnectionsknownasweightsthatexcite or inhibit the signal being communicated. ANNs have the ability “to learn” by example (a large volume of cases) through several iterations without requiring a priori ?xed knowledge of the relationships between process parameters.
  nnmodeling: Computational Science and Its Applications -- ICCSA 2012 Beniamino Murgante, Osvaldo Gervasi, Sanjay Misra, Nadia Nedjah, Ana Maria Alves Coutinho Rocha, David Taniar, Bernady O. Apduhan, 2012-06-16 The four-volume set LNCS 7333-7336 constitutes the refereed proceedings of the 12th International Conference on Computational Science and Its Applications, ICCSA 2012, held in Salvador de Bahia, Brazil, in June 2012. The four volumes contain papers presented in the following workshops: 7333 - advances in high performance algorithms and applications (AHPAA); bioinspired computing and applications (BIOCA); computational geometry and applicatons (CGA); chemistry and materials sciences and technologies (CMST); cities, technologies and planning (CTP); 7334 - econometrics and multidimensional evaluation in the urban environment (EMEUE); geographical analysis, urban modeling, spatial statistics (Geo-An-Mod); 7335 - optimization techniques and applications (OTA); mobile communications (MC); mobile-computing, sensind and actuation for cyber physical systems (MSA4CPS); remote sensing (RS); 7336 - software engineering processes and applications (SEPA); software quality (SQ); security and privacy in computational sciences (SPCS); soft computing and data engineering (SCDE). The topics of the fully refereed papers are structured according to the four major conference themes: 7333 - computational methods, algorithms and scientific application; 7334 - geometric modelling, graphics and visualization; 7335 - information systems and technologies; 7336 - high performance computing and networks.
  nnmodeling: Business Forecasting Michael Gilliland, Len Tashman, Udo Sglavo, 2021-05-11 Discover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field In Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning accomplished authors Michael Gilliland, Len Tashman, and Udo Sglavo deliver relevant and timely insights from some of the most important and influential authors in the field of forecasting. You'll learn about the role played by machine learning and AI in the forecasting process and discover brand-new research, case studies, and thoughtful discussions covering an array of practical topics. The book offers multiple perspectives on issues like monitoring forecast performance, forecasting process, communication and accountability for forecasts, and the use of big data in forecasting. You will find: Discussions on deep learning in forecasting, including current trends and challenges Explorations of neural network-based forecasting strategies A treatment of the future of artificial intelligence in business forecasting Analyses of forecasting methods, including modeling, selection, and monitoring In addition to the Foreword by renowned researchers Spyros Makridakis and Fotios Petropoulos, the book also includes 16 opinion/editorial Afterwords by a diverse range of top academics, consultants, vendors, and industry practitioners, each providing their own unique vision of the issues, current state, and future direction of business forecasting. Perfect for financial controllers, chief financial officers, business analysts, forecast analysts, and demand planners, Business Forecasting will also earn a place in the libraries of other executives and managers who seek a one-stop resource to help them critically assess and improve their own organization's forecasting efforts.
  nnmodeling: River Basin Management IV C. A. Brebbia, K. L. Katsifarakis, 2007 In recent years, significant advances have been made in the overall management of riverine systems, including advances in hydraulic and hydrologic modelling, environmental protection and flood forecasting. Containing papers presented at the Fourth International Conference on River Basin Management this book addresses the latest developments in these fields. Featured topics include: Hydraulics and Hydrology; Integrated Watershed Planning; River and Watershed Management; Water Quality Modelling; Flood Risk; Ecological Perspective; MIS, GIS and Remote Sensing; Sediment Transport; Environmental Impact; Hydrological Impact and Case Studies.
  nnmodeling: Composite Materials Technology S.M. Sapuan, Iqbal Mohammed Mujtaba, 2009-12-23 Artificial neural networks (ANN) can provide new insight into the study of composite materials and can normally be combined with other artificial intelligence tools such as expert system, genetic algorithm, and fuzzy logic. Because research on this field is very new, there is only a limited amount of published literature on the subject.Compiling in
  nnmodeling: Machine Learning: Concepts, Methodologies, Tools and Applications Management Association, Information Resources, 2011-07-31 This reference offers a wide-ranging selection of key research in a complex field of study,discussing topics ranging from using machine learning to improve the effectiveness of agents and multi-agent systems to developing machine learning software for high frequency trading in financial markets--Provided by publishe
  nnmodeling: Verilog® Quickstart James M. Lee, 2012-12-06 Welcome to the world of Verilog! Once you read this book, you will join the ranks of the many successful engineers who use Verilog. I have been using Verilog since 1986 and teaching Verilog since 1987. I have seen many different Verilog courses and many approaches to learning Verilog. This book generally follows the outline of the Verilog class that I teach at the University of California, Santa Cruz, Extension. This book does not take a cookie-cutter approach to learning Verilog, nor is it a completely theoretical book. Instead, what we will do is go through some of the formal Verilog syntax and definitions, and then show practical uses. Once we cover most of the constructs of the language, we will look at how style affects the constructs you choose while modeling your design. This is not a complete and exhaustive reference on Verilog. If you want a Verilog reference, I suggest one of the Open Verilog International (OVI) reference manuals.
  nnmodeling: The 8th International Conference on Computer Engineering and Networks (CENet2018) Qi Liu, Mustafa Mısır, Xin Wang, Weiping Liu, 2019-04-15 This book examines innovation in the fields of computer engineering and networking, and explores important, state-of-the-art developments in areas such as artificial intelligence, machine learning, information analysis and communication. It gathers papers presented at the 8th International Conference on Computer Engineering and Networks (CENet2018), held in Shanghai, China on August 17–19, 2018. • Explores emerging topics in computer engineering and networking, along with their applications • Discusses how to improve productivity by using the latest advanced technologies • Examines innovation in the fields of computer engineering and networking
  nnmodeling: High-Throughput Screening in Chemical Catalysis Alfred Hagemeyer, Peter Strasser, Anthony F. Volpe, Jr., 2006-03-06 In this first book to present every important aspect of this fascinating and developing field, the three editors A. Hagemeyer, P. Strasser and A. F. Volpe Jr. from Symyx Technologies have chosen a perfect mixture of distinguished, international authors from both academia and industry. Each chapter is devoted to a major topic - high-throughput experimentation methodologies, integrated combinatorial synthesis and screening workflow, and applications to chemical catalysts with an emphasis on heterogeneous catalysis, olefin polymerization and electrocatalysis for fuel cells. An indispensable source for everyone working in the field.
  nnmodeling: Intelligent Robotic Systems Witold Jacak, 2005-12-27 Here is a comprehensive presentation of methodology for the design and synthesis of an intelligent complex robotic system, connecting formal tools from discrete system theory, artificial intelligence, neural network, and fuzzy logic. The necessary methods for solving real time action planning, coordination and control problems are described. A notable chapter presents a new approach to intelligent robotic agent control acting in a realworld environment based on a lifelong learning approach combining cognitive and reactive capabilities. Another key feature is the homogeneous description of all solutions and methods based on system theory formalism.
  nnmodeling: Multi-disciplinary Trends in Artificial Intelligence Chattrakul Sombattheera, Kim Loi Nguyen, Rajeev Wankar, Tho Quan, 2013-01-13 This volume constitutes the refereed proceedings of the 6th Multi-disciplinary International Workshop On Artificial Intelligence, MIWAI 2012, held in Ho Chi Minh City, Vietnam, in December 2012. The 29 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections in AI-GIS for climate change, computer vision, decision theory, e-commerce and AI, multiagent planning and learning, game theory, industrial applications of AI, multiagent systems and evolving intelligence, robotics and Web services.
  nnmodeling: Advances in Cardiac Signal Processing U. Rajendra Acharya, 2007-04-25 This book provides a comprehensive review of progress in the acquisition and extraction of electrocardiogram signals. The coverage is extensive, from a review of filtering techniques to measurement of heart rate variability, to aortic pressure measurement, to strategies for assessing contractile effort of the left ventricle and more. The book concludes by assessing the future of cardiac signal processing, leading to next generation research which directly impact cardiac health care.
  nnmodeling: Handbook of Materials Modeling Sidney Yip, 2007-11-17 The first reference of its kind in the rapidly emerging field of computational approachs to materials research, this is a compendium of perspective-providing and topical articles written to inform students and non-specialists of the current status and capabilities of modelling and simulation. From the standpoint of methodology, the development follows a multiscale approach with emphasis on electronic-structure, atomistic, and mesoscale methods, as well as mathematical analysis and rate processes. Basic models are treated across traditional disciplines, not only in the discussion of methods but also in chapters on crystal defects, microstructure, fluids, polymers and soft matter. Written by authors who are actively participating in the current development, this collection of 150 articles has the breadth and depth to be a major contributor toward defining the field of computational materials. In addition, there are 40 commentaries by highly respected researchers, presenting various views that should interest the future generations of the community. Subject Editors: Martin Bazant, MIT; Bruce Boghosian, Tufts University; Richard Catlow, Royal Institution; Long-Qing Chen, Pennsylvania State University; William Curtin, Brown University; Tomas Diaz de la Rubia, Lawrence Livermore National Laboratory; Nicolas Hadjiconstantinou, MIT; Mark F. Horstemeyer, Mississippi State University; Efthimios Kaxiras, Harvard University; L. Mahadevan, Harvard University; Dimitrios Maroudas, University of Massachusetts; Nicola Marzari, MIT; Horia Metiu, University of California Santa Barbara; Gregory C. Rutledge, MIT; David J. Srolovitz, Princeton University; Bernhardt L. Trout, MIT; Dieter Wolf, Argonne National Laboratory.
  nnmodeling: Data Science Robert Stahlbock, Hamid R. Arabnia, 2025-04-16 This book constitutes the proceedings of the 20th International Conference on Data Science, ICDATA 2024, held as part of the 2024 World Congress in Computer Science, Computer Engineering and Applied Computing, in Las Vegas, USA, during July 22 to July 25, 2024. This proceedings book includes 39 papers selected from a total of 243 submissions. They are organized in topical sections as follows: Artificial intelligence, data science, and neural networks; natural language processing, large language modelc, generative AI; data science, data analytics, and applications; prediction and forecasting and security applications; and poster papers.
  nnmodeling: Deep Learning with Python Nikhil Ketkar, 2017-04-18 Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python alsointroduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
  nnmodeling: Intelligent Information Systems and Knowledge Management for Energy: Applications for Decision Support, Usage, and Environmental Protection Metaxiotis, Kostas, 2009-08-31 This book analyzes the need for a holistic approach for the construction and engineering of cities and societies--Provided by publisher.
  nnmodeling: Biology and Computation H. Gutfreund, G. Toulouse, 1994 This book provides a comprehensive review of the works in the rapidly evolving field of neural networks and brain studies. Its purpose is two-fold: to help physicists entering this field to get a broader view of the context of the domain, and to help scientists of other disciplines to reach a better understanding of the physicists' contributions within a context of perspectives they can relate to.Included in the volume are 68 carefully selected, high quality reprints to provide the volume with both breadth and depth. It is organized into 5 sections and 22 chapters, both the sections and chapters being preceded by introductory comments by the editors.
  nnmodeling: ART-based Fuzzy Classifiers Elena Sapojnikova, 2004
  nnmodeling: Computer and Computing Technologies in Agriculture Daoliang Li, Yingyi Chen, 2012-01-12 The three-volume set IFIP AICT 368-370 constitutes the refereed post-conference proceedings of the 5th IFIP TC 5, SIG 5.1 International Conference on Computer and Computing Technologies in Agriculture, CCTA 2011, held in Beijing, China, in October 2011. The 189 revised papers presented were carefully selected from numerous submissions. They cover a wide range of interesting theories and applications of information technology in agriculture, including simulation models and decision-support systems for agricultural production, agricultural product quality testing, traceability and e-commerce technology, the application of information and communication technology in agriculture, and universal information service technology and service systems development in rural areas. The 59 papers included in the third volume focus on simulation, optimization, monitoring, and control technology.
  nnmodeling: The The Reinforcement Learning Workshop Alessandro Palmas, Emanuele Ghelfi, Dr. Alexandra Galina Petre, Mayur Kulkarni, Anand N.S., Quan Nguyen, Aritra Sen, Anthony So, Saikat Basak, 2020-08-18 Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide Key FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook Description Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you’ll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you’ll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is for If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.
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