الصفحة 99
الصفحة 99
Magnetic Nanostructures
Addresses the exciting and rapidly developing topic of nanostructured magnetic materials. It combines modern topics in nanoscale magnetism with issues relating to the fabrication and characterization of magnetic nanostructures. The chapters describe a wide range of physical aspects, together with theoretical and experimental methods. "Magnetic Nanostructures" will be of interest to researchers and specialists both in academic and industrial research. Graduate students will also find in this book an accessible introduction to the essential issues.
Magnetic Functions Beyond the Spin-Hamiltonian
Using the spin-Hamiltonian formalism the magnetic parameters are introduced through the components of the Lambda-tensor involving only the matrix elements of the angular momentum operator. The energy levels for a variety of spins are generated and the modeling of the magnetization, the magnetic susceptibility and the heat capacity is done. Theoretical formulae necessary in performing the energy level calculations for a multi-term system are prepared with the help of the irreducible tensor operator approach. The goal of the programming lies in the fact that the entire relevant matrix elements (electron repulsion, crystal field, spin-orbit interaction, orbital-Zeeman, and spin-Zeeman operators) are evaluated in the basis set of free-atom terms. The modeling of the zero-field splitting is done at three levels of sophistication. The spin-Hamiltonian formalism offers simple formulae for the magnetic parameters by evaluating the matrix elements of the angular momentum operator in the basis set of the crystal-field terms. The magnetic functions for dn complexes are modeled for a wide range of the crystal-field strengths.
Magnetic Control of Tokamak Plasmas
The main topic of Magnetic Control of Tokamak Plasmas is the design of feedback control systems guaranteeing the stability of plasma equilibrium inside a tokamak and the regulation of the plasma position and shape during plasma pulses. Modelling and control details are presented, allowing the non-expert to understand the control problem. Starting from equations of magneto-hydro-dynamics, all the steps needed for the derivation of plasma state-space models are enumerated. The basics of electromagnetics are frequently recalled. The control problem is then described beginning with control of current and position – vertical and radial – and progressing to the more challenging shape control. The solutions proposed vary from simple PIDs to more sophisticated MIMO controllers.
Macrosocial Determinants of Population Health
Macrosocial Determinants of Population Health explores social factors such as culture, mass media, political systems, and migration that influence population health while systematically considering how we may best study these factors and use our knowledge from this study to guide public health interventions.Each section ends with Galea’s integrative chapters, bringing the observations and conclusions from the chapters into clear, usable focus. Macrosocial Determinants of Population Health is a work of major theoretical, empirical, and practical interest for disciplines as varied as public health, epidemiology, health promotion, sociology, and health policy. Its systematic field-building approach makes it as valuable to the public health provider as to the scholars and students studying the health of populations.
Machine Learning: ECML 2007 ; 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007, Proceedings
The two premier annual European conferences in the areas of machine learning and data mining have been collocated ever since the ?rst joint conference in Freiburg, 2001. The European Conference on Machine Learning (ECML) traces its origins to 1986, when the ?rst European Working Session on Learning was held in Orsay, France. The European Conference on Principles and Practice of KnowledgeDiscoveryinDatabases(PKDD) was?rstheldin1997inTrondheim, Norway.
Machine Learning: ECML 2006 ; 17th European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006, Proceedings
This book constitutes the refereed proceedings of the 17th European Conference on Machine Learning, ECML 2006, held in Berlin, Germany in September 2006, jointly with PKDD 2006. The 46 revised full papers and 36 revised short papers presented together with abstracts of 5 invited talks were carefully reviewed and selected from 564 papers submitted to both, ECML and PKDD. The papers present a wealth of new results in the area and address all current issues in machine learning.
Machine Learning Techniques for Multimedia : Case Studies on Organization and Retrieval
This book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain. Arising from the EU MUSCLE network, a program that drew together multidisciplinary teams with expertise in machine learning, pattern recognition, artificial intelligence, and image, video, text and crossmedia processing, the book first introduces the machine learning principles and techniques that are applied in multimedia data processing and analysis. The second part focuses on multimedia data processing applications, with chapters examining specific machine learning issues in domains .
Machine Learning Refined : Foundations, Algorithms, and Applications
Provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology.
Machine Learning for Multimedia Content Analysis
Challenges in complexity and variability of multimedia data have led to revolutions in machine learning techniques. Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. A number of pixels in a digital image collectively conveys certain visual content to viewers. A TV video program consists of both audio and image streams that unfold the underlying story. To recognize the visual content of a digital image, or to understand the underlying story of a video program, we may need to label sets of pixels or groups of image and audio frames jointly.
Machine learning for data streams : With practical examples in MOA
The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA.
Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2018
Presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.
Machine Learning for Computer Scientists and Data Analysts: From an Applied Perspective
Describes traditional as well as advanced machine learning algorithms / Enables students to learn which algorithm is most appropriate for the data being handled / Includes numerous, practical case-studies; implementation codes in Python available for readers
Machine Learning for Audio, Image and Video Analysis : Theory and Applications
The book is organized in four parts: The first focuses on technical aspects, basic mathematical notions and elementary machine learning techniques. The second provides an extensive survey of most relevant machine learning techniques for media processing, while the third part focuses on applications and shows how techniques are applied in actual problems. The fourth part contains detailed appendices that provide notions about the main mathematical instruments used throughout the text
Machine learning approach for cloud data analytics in IoT
Covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications. Elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.
Machine Learning and Probabilistic Graphical Models for Decision Support Systems
Presents recent advancements in research, a review of new methods and techniques, and applications in decision support systems (DSS) with Machine Learning and Probabilistic Graphical Models, which are very effective techniques in gaining knowledge from Big Data and in interpreting decisions. It explores Bayesian network learning, Control Chart, Reinforcement Learning for multicriteria DSS, Anomaly Detection in Smart Manufacturing with Federated Learning, DSS in healthcare, DSS for supply chain management, etc. Researchers and practitioners alike will benefit from this book to enhance the understanding of machine learning, Probabilistic Graphical Models, and their uses in DSS in the context of decision making with uncertainty. The real-world case studies in various fields with guidance and recommendations for the practical applications of these studies are introduced in each chapter.
Machine Learning and Knowledge Extraction ; 5th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2021, Virtual Event, August 17–20, 2021, Proceedings
Constitutes the refereed proceedings of the 5th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2021, held in virtually in August 2021. The 20 full papers and 2 short papers presented were carefully reviewed and selected from 48 submissions. The cross-domain integration and appraisal of different fields provides an atmosphere to foster different perspectives and opinions; it will offer a platform for novel ideas and a fresh look on the methodologies to put these ideas into business for the benefit of humanity.
Machine Learning and Knowledge Discovery in Databases ; European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part II
Constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008.The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer.
Machine Learning and Knowledge Discovery in Databases ; European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I
Constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008.The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer.
Machine learning and its application to reacting flows: ml and combustion
These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges.
Machine Learning and Data Mining in Pattern Recognition ; 4th International Conference, MLDM 2005, Leipzig, Germany, July 9-11, 2005, Proceedings
Today, artificial intelligence deals with large amounts of data and knowledge and finds new information using machine learning and data mining. Machine learning and data mining are irreplaceable subjects and tools for the theory of pattern recognition and in applications of pattern recognition such as bioinformatics and data retrieval. This was the fourth edition of MLDM in Pattern Recognition which is the main event of Technical Committee 17 of the International Association for Pattern Recognition; it started out as a workshop and continued as a conference in 2003. Today, there are many international meetings which are titled “machine learning” and “data mining”, whose topics are text mining, knowledge discovery, and applications. This meeting from the first focused on aspects of machine learning and data mining in pattern recognition problems. We planned to reorganize classical and well-established pattern recognition paradigms from the viewpoints of machine learning and data mining. Though it was a challenging program in the late 1990s, the idea has inspired new starting points in pattern recognition and effects in other areas such as cognitive computer vision.
