Statistical Process Monitoring Using Advanced Data Driven And Deep Learning Approaches

Statistical Process Monitoring Using Advanced Data Driven And Deep Learning Approaches Book PDF
✏Book Title : Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches
✏Author : Fouzi Harrou
✏Publisher : Elsevier
✏Release Date : 2020-07-03
✏Pages : 328
✏ISBN : 9780128193662
✏Available Language : English, Spanish, And French

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✏Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches Book Summary : Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches tackles multivariate challenges in process monitoring by merging the advantages of univariate and traditional multivariate techniques to enhance their performance and widen their practical applicability. The book proceeds with merging the desirable properties of shallow learning approaches – such as a one-class support vector machine and k-nearest neighbours and unsupervised deep learning approaches – to develop more sophisticated and efficient monitoring techniques. Finally, the developed approaches are applied to monitor many processes, such as waste-water treatment plants, detection of obstacles in driving environments for autonomous robots and vehicles, robot swarm, chemical processes (continuous stirred tank reactor, plug flow rector, and distillation columns), ozone pollution, road traffic congestion, and solar photovoltaic systems. Uses a data-driven based approach to fault detection and attribution Provides an in-depth understanding of fault detection and attribution in complex and multivariate systems Familiarises you with the most suitable data-driven based techniques including multivariate statistical techniques and deep learning-based methods Includes case studies and comparison of different methods

Unsupervised Process Monitoring And Fault Diagnosis With Machine Learning Methods Book PDF
✏Book Title : Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
✏Author : Chris Aldrich
✏Publisher : Springer Science & Business Media
✏Release Date : 2013-06-15
✏Pages : 374
✏ISBN : 9781447151852
✏Available Language : English, Spanish, And French

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✏Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods Book Summary : This unique text/reference describes in detail the latest advances in unsupervised process monitoring and fault diagnosis with machine learning methods. Abundant case studies throughout the text demonstrate the efficacy of each method in real-world settings. The broad coverage examines such cutting-edge topics as the use of information theory to enhance unsupervised learning in tree-based methods, the extension of kernel methods to multiple kernel learning for feature extraction from data, and the incremental training of multilayer perceptrons to construct deep architectures for enhanced data projections. Topics and features: discusses machine learning frameworks based on artificial neural networks, statistical learning theory and kernel-based methods, and tree-based methods; examines the application of machine learning to steady state and dynamic operations, with a focus on unsupervised learning; describes the use of spectral methods in process fault diagnosis.

Multivariate Statistical Process Control Book PDF
✏Book Title : Multivariate Statistical Process Control
✏Author : Zhiqiang Ge
✏Publisher : Springer Science & Business Media
✏Release Date : 2012-11-28
✏Pages : 194
✏ISBN : 9781447145134
✏Available Language : English, Spanish, And French

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✏Multivariate Statistical Process Control Book Summary : Given their key position in the process control industry, process monitoring techniques have been extensively investigated by industrial practitioners and academic control researchers. Multivariate statistical process control (MSPC) is one of the most popular data-based methods for process monitoring and is widely used in various industrial areas. Effective routines for process monitoring can help operators run industrial processes efficiently at the same time as maintaining high product quality. Multivariate Statistical Process Control reviews the developments and improvements that have been made to MSPC over the last decade, and goes on to propose a series of new MSPC-based approaches for complex process monitoring. These new methods are demonstrated in several case studies from the chemical, biological, and semiconductor industrial areas. Control and process engineers, and academic researchers in the process monitoring, process control and fault detection and isolation (FDI) disciplines will be interested in this book. It can also be used to provide supplementary material and industrial insight for graduate and advanced undergraduate students, and graduate engineers. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Performance Assessment For Process Monitoring And Fault Detection Methods Book PDF
✏Book Title : Performance Assessment for Process Monitoring and Fault Detection Methods
✏Author : Kai Zhang
✏Publisher : Springer
✏Release Date : 2016-10-04
✏Pages : 153
✏ISBN : 9783658159719
✏Available Language : English, Spanish, And French

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✏Performance Assessment for Process Monitoring and Fault Detection Methods Book Summary : The objective of Kai Zhang and his research is to assess the existing process monitoring and fault detection (PM-FD) methods. His aim is to provide suggestions and guidance for choosing appropriate PM-FD methods, because the performance assessment study for PM-FD methods has become an area of interest in both academics and industry. The author first compares basic FD statistics, and then assesses different PM-FD methods to monitor the key performance indicators of static processes, steady-state dynamic processes and general dynamic processes including transient states. He validates the theoretical developments using both benchmark and real industrial processes.

Advances In Production Management Systems Production Management For Data Driven Intelligent Collaborative And Sustainable Manufacturing Book PDF
✏Book Title : Advances in Production Management Systems Production Management for Data Driven Intelligent Collaborative and Sustainable Manufacturing
✏Author : Ilkyeong Moon
✏Publisher : Springer
✏Release Date : 2018-08-24
✏Pages : 570
✏ISBN : 9783319997049
✏Available Language : English, Spanish, And French

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✏Advances in Production Management Systems Production Management for Data Driven Intelligent Collaborative and Sustainable Manufacturing Book Summary : The two-volume set IFIP AICT 535 and 536 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2018, held in Seoul, South Korea, in August 2018. The 129 revised full papers presented were carefully reviewed and selected from 149 submissions. They are organized in the following topical sections: lean and green manufacturing; operations management in engineer-to-order manufacturing; product-service systems, customer-driven innovation and value co-creation; collaborative networks; smart production for mass customization; global supply chain management; knowledge based production planning and control; knowledge based engineering; intelligent diagnostics and maintenance solutions for smart manufacturing; service engineering based on smart manufacturing capabilities; smart city interoperability and cross-platform implementation; manufacturing performance management in smart factories; industry 4.0 - digital twin; industry 4.0 - smart factory; and industry 4.0 - collaborative cyber-physical production and human systems.

📒Advanced Process Data Analytics ✍ Weike Sun (Ph. D.)

Advanced Process Data Analytics Book PDF
✏Book Title : Advanced Process Data Analytics
✏Author : Weike Sun (Ph. D.)
✏Publisher :
✏Release Date : 2020
✏Pages : 498
✏ISBN : OCLC:1193320018
✏Available Language : English, Spanish, And French

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✏Advanced Process Data Analytics Book Summary : Process data analytics is the application of statistics and related mathematical tools to data in order to understand, develop, and improve manufacturing processes. There have been growing opportunities in process data analytics because of advances in machine learning and technologies for data collection and storage. However, challenges are encountered because of the complexities of manufacturing processes, which often require advanced analytical methods. In this thesis, two areas of application are considered. One is the construction of predictive models that are useful for process design, optimization, and control. The other area of application is process monitoring to improve process efficiency and safety. In the first area of study, a robust and automated approach for method selection and model construction is developed for predictive modeling. Two common challenges when building data-driven process models are addressed: the high diversity in data quality and how to select from a wide variety of methods. The proposed approach combines best practices with data interrogation to facilitate consistent application and continuous improvement of tools and decision making. The second area of study focuses on process monitoring for complex manufacturing systems, which includes fault detection, identification, and classification. Four sets of algorithms are developed to address limitations of traditional monitoring methods. The first set provides the optimal strategy for Gaussian linear processes, including deep understanding of the process monitoring structure and optimal fault detection based on a probabilistic formulation. The second set aims at building a self-learning fault detection system for changing normal operating conditions. The third set is developed based on information-theoretic learning to address limitations of second-order statistical learning for both fault detection and classification. The fourth set tackles the problem of nonlinear and dynamic process monitoring. The proposed methodologies and algorithms are tested on several case studies where the value of advanced process data analytics is demonstrated.

Financial Signal Processing And Machine Learning Book PDF
✏Book Title : Financial Signal Processing and Machine Learning
✏Author : Ali N. Akansu
✏Publisher : John Wiley & Sons
✏Release Date : 2016-05-31
✏Pages : 320
✏ISBN : 9781118745670
✏Available Language : English, Spanish, And French

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✏Financial Signal Processing and Machine Learning Book Summary : The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analysis through temporal-causal modeling, and large-scale copula-based approaches. Key features: Highlights signal processing and machine learning as key approaches to quantitative finance. Offers advanced mathematical tools for high-dimensional portfolio construction, monitoring, and post-trade analysis problems. Presents portfolio theory, sparse learning and compressed sensing, sparsity methods for investment portfolios. including eigen-portfolios, model return, momentum, mean reversion and non-Gaussian data-driven risk measures with real-world applications of these techniques. Includes contributions from leading researchers and practitioners in both the signal and information processing communities, and the quantitative finance community.

Enhance Oil And Gas Exploration With Data Driven Geophysical And Petrophysical Models Book PDF
✏Book Title : Enhance Oil and Gas Exploration with Data Driven Geophysical and Petrophysical Models
✏Author : Keith R. Holdaway
✏Publisher : John Wiley & Sons
✏Release Date : 2017-10-09
✏Pages : 368
✏ISBN : 9781119215103
✏Available Language : English, Spanish, And French

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✏Enhance Oil and Gas Exploration with Data Driven Geophysical and Petrophysical Models Book Summary : Leverage Big Data analytics methodologies to add value to geophysical and petrophysical exploration data Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models demonstrates a new approach to geophysics and petrophysics data analysis using the latest methods drawn from Big Data. Written by two geophysicists with a combined 30 years in the industry, this book shows you how to leverage continually maturing computational intelligence to gain deeper insight from specific exploration data. Case studies illustrate the value propositions of this alternative analytical workflow, and in-depth discussion addresses the many Big Data issues in geophysics and petrophysics. From data collection and context through real-world everyday applications, this book provides an essential resource for anyone involved in oil and gas exploration. Recent and continual advances in machine learning are driving a rapid increase in empirical modeling capabilities. This book shows you how these new tools and methodologies can enhance geophysical and petrophysical data analysis, increasing the value of your exploration data. Apply data-driven modeling concepts in a geophysical and petrophysical context Learn how to get more information out of models and simulations Add value to everyday tasks with the appropriate Big Data application Adjust methodology to suit diverse geophysical and petrophysical contexts Data-driven modeling focuses on analyzing the total data within a system, with the goal of uncovering connections between input and output without definitive knowledge of the system's physical behavior. This multi-faceted approach pushes the boundaries of conventional modeling, and brings diverse fields of study together to apply new information and technology in new and more valuable ways. Enhance Oil & Gas Exploration with Data-Driven Geophysical and Petrophysical Models takes you beyond traditional deterministic interpretation to the future of exploration data analysis.

Mastering Scala Machine Learning Book PDF
✏Book Title : Mastering Scala Machine Learning
✏Author : Alex Kozlov
✏Publisher : Packt Publishing Ltd
✏Release Date : 2016-06-28
✏Pages : 310
✏ISBN : 9781785885266
✏Available Language : English, Spanish, And French

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✏Mastering Scala Machine Learning Book Summary : Advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and Hadoop About This Book This is a primer on functional-programming-style techniques to help you efficiently process and analyze all of your data Get acquainted with the best and newest tools available such as Scala, Spark, Parquet and MLlib for machine learning Learn the best practices to incorporate new Big Data machine learning in your data-driven enterprise to gain future scalability and maintainability Who This Book Is For Mastering Scala Machine Learning is intended for enthusiasts who want to plunge into the new pool of emerging techniques for machine learning. Some familiarity with standard statistical techniques is required. What You Will Learn Sharpen your functional programming skills in Scala using REPL Apply standard and advanced machine learning techniques using Scala Get acquainted with Big Data technologies and grasp why we need a functional approach to Big Data Discover new data structures, algorithms, approaches, and habits that will allow you to work effectively with large amounts of data Understand the principles of supervised and unsupervised learning in machine learning Work with unstructured data and serialize it using Kryo, Protobuf, Avro, and AvroParquet Construct reliable and robust data pipelines and manage data in a data-driven enterprise Implement scalable model monitoring and alerts with Scala In Detail Since the advent of object-oriented programming, new technologies related to Big Data are constantly popping up on the market. One such technology is Scala, which is considered to be a successor to Java in the area of Big Data by many, like Java was to C/C++ in the area of distributed programing. This book aims to take your knowledge to next level and help you impart that knowledge to build advanced applications such as social media mining, intelligent news portals, and more. After a quick refresher on functional programming concepts using REPL, you will see some practical examples of setting up the development environment and tinkering with data. We will then explore working with Spark and MLlib using k-means and decision trees. Most of the data that we produce today is unstructured and raw, and you will learn to tackle this type of data with advanced topics such as regression, classification, integration, and working with graph algorithms. Finally, you will discover at how to use Scala to perform complex concept analysis, to monitor model performance, and to build a model repository. By the end of this book, you will have gained expertise in performing Scala machine learning and will be able to build complex machine learning projects using Scala. Style and approach This hands-on guide dives straight into implementing Scala for machine learning without delving much into mathematical proofs or validations. There are ample code examples and tricks that will help you sail through using the standard techniques and libraries. This book provides practical examples from the field on how to correctly tackle data analysis problems, particularly for modern Big Data datasets.

Predictive Maintenance In Dynamic Systems Book PDF
✏Book Title : Predictive Maintenance in Dynamic Systems
✏Author : Edwin Lughofer
✏Publisher : Springer
✏Release Date : 2019-02-28
✏Pages : 567
✏ISBN : 9783030056452
✏Available Language : English, Spanish, And French

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✏Predictive Maintenance in Dynamic Systems Book Summary : This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power.

Signal Processing And Machine Learning For Biomedical Big Data Book PDF
✏Book Title : Signal Processing and Machine Learning for Biomedical Big Data
✏Author : Ervin Sejdic
✏Publisher : CRC Press
✏Release Date : 2018-07-04
✏Pages : 606
✏ISBN : 9781351061216
✏Available Language : English, Spanish, And French

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✏Signal Processing and Machine Learning for Biomedical Big Data Book Summary : This will be a comprehensive, multi-contributed reference work that will detail the latest research and developments in biomedical signal processing related to big data medical analysis. It will describe signal processing, machine learning, and parallel computing strategies to revolutionize the world of medical analytics and diagnosis as presented by world class researchers and experts in this important field. The chapters will desribe tools that can be used by biomedical and clinical practitioners as well as industry professionals. It will give signal processing researchers a glimpse into the issues faced with Big Medical Data.

The International Journal Advanced Manufacturing Technology Book PDF
✏Book Title : The International Journal Advanced Manufacturing Technology
✏Author :
✏Publisher :
✏Release Date : 1987
✏Pages :
✏ISBN : UCAL:B4310895
✏Available Language : English, Spanish, And French

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✏The International Journal Advanced Manufacturing Technology Book Summary :

Data Driven Decision Making Under Uncertainty For Intelligent Life Cycle Control Of The Built Environment Book PDF
✏Book Title : Data Driven Decision Making Under Uncertainty for Intelligent Life cycle Control of the Built Environment
✏Author : Charalampos Andriotis
✏Publisher :
✏Release Date : 2019
✏Pages :
✏ISBN : OCLC:1117333600
✏Available Language : English, Spanish, And French

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✏Data Driven Decision Making Under Uncertainty for Intelligent Life cycle Control of the Built Environment Book Summary : This dissertation provides novel frameworks for data-driven probabilistic performance-based assessments and optimal or near-optimal stochastic control strategies for structural, infrastructural and other engineering systems. The goal of this research is to enable efficient and robust structural performance predictions and optimized decisions over the entire operating life of systems, by developing advanced statistical learning models, machine learning formulations and Artificial Intelligence (AI) algorithms, in order to contribute to a future of smart and sustainable infrastructure. To this end, the developed approaches build upon and extend well-established statistical modeling frameworks, infuse intelligence to structural informatics through newly introduced schemes for structural data mining and processing, provide comprehensive solutions to challenging life-cycle objectives, and support complex decisions in previously intractable sequential decision-making problems through novel AI-aided algorithms and theoretical concepts.Efficient assessment of various societal, environmental and economic losses necessitates adept statistical and learning models, able to consistently capture longitudinal dependencies in data and translate multivariate information in structural condition and performance metrics. This dissertation addresses this need, within a softmax regression fragility analysis framework that avoids fragility function crossing inconsistencies and scales well in high-dimensional intensity measure spaces with multiple structural states. Moreover, softmax-based fragility functions are generalized by advanced statistical learning and deep learning formulations that employ Dynamic Bayesian Networks (DBNs), in the form of Dependent Markov Models (DMMs) and Dependent Hidden Markov Models (DHMMs), as well as Recurrent Neural Network (RNN) architectures. The above considerably extend and generalize the framework of probabilistic performance engineering, with theoretically consistent multi-state multi-variate fragility functions, which also have multi-step predictive capabilities in time. The hidden spaces of DHMMs and RNNs are shown to be able to encode noisy input to noisy output sequences through structured hidden spaces. It turns out that the Markovian properties of these spaces can portray damage-consistent dynamics, whereas they are directly pertinent to the input required in advanced decision frameworks that employ Markovian processes for decision-making either under full, partial, or mixed observability assumptions.Hidden Markov models equipped with costs and control actions can provide a theoretically neat and computationally robust framework for sequential decision-making problems under uncertainty, through Partially Observable Markov Decision Processes (POMDPs). This research casts stochastic control problems for determination of optimal or near-optimal life-cycle maintenance and inspection strategies within the premises of POMDPs. Specialized formulations of full or mixed observability are also developed, through Markov Decision Processes (MDPs) or Mixed Observability Markov Decision Processes (MOMDPs), respectively. Along these lines, this research enables decision-support systems which can operate in stochastic engineering environments with uncertain action outcomes and noisy real-time observations, having global optimality guarantees as a result of the relevant underlying dynamic programming formulations introduced and, in many cases, well-defined performance bounds. In the same vein, the Value of Information (VoI) and the Value of Structural Health Monitoring (VoSHM) are quantified and a straightforward definition for the expected life-cycle gains of different observational and monitoring options is established and evaluated. Formulating VoI and VoSHM within the framework of POMDPs, the estimates of these metrics depict value gaps between the optimal life-cycle strategies of the examined options, thus also being able to provide bounds on the respective gains.For small- to medium-scale systems, solutions to the life-cycle optimization problems are derived by point-based solution schemes which provide efficient exploration heuristics, value function updates over the POMDP belief-space, vector compression techniques and convergence properties. For large-scale multi-component engineering systems that form large state and action spaces, such point-based schemes are however impractical as they require explicit prior information of the system dynamics model. To this end, the Deep Centralized Multi-agent Actor Critic (DCMAC) is developed herein and implemented in the solution procedure. DCMAC is an efficient off-policy actor-critic Deep Reinforcement Learning (DRL) algorithm with experience replay. DCMAC alleviates the curse of dimensionality related to state, observation and actions spaces of multi-component systems through deep network approximators and a factorized representation of the actor. DCMAC interacts directly with the simulator, thus avoiding the need for full and explicit model-based knowledge of the system dynamics, and operates in the POMDP belief space, by encoding sequences of actions and observations in belief vectors through Bayesian updates. Overall, DCMAC is able to efficiently tackle the state and action space scalability issues, as well as the potential model unavailability at the system level, all of which often make the decision problems of large multi-component systems hard to solve, if not intractable, by conventional machine learning schemes and other life-cycle optimization methodologies.All developed methods and frameworks are rigorously evaluated in relevant numerical applications and their strengths, limitations and broader capabilities are highlighted and discussed. Results demonstrate the effectiveness of the proposed models, solution procedures and algorithmic schemes, in enabling efficient data-driven probabilistic predictions and structural informatics, as well as comprehensive optimal or near-optimal stochastic control strategies for engineering systems. Overall, the originally developed statistical and machine learning models, in conjunction with the dedicated AI-aided algorithms, can ensure advanced and sophisticated solutions and open numerous new scientific paths towards smart cities, intelligent infrastructure, and autonomous control of the built environment.

📒Computing Information Directory ✍ Darlene Myers Hildebrandt

Computing Information Directory Book PDF
✏Book Title : Computing Information Directory
✏Author : Darlene Myers Hildebrandt
✏Publisher :
✏Release Date : 1994
✏Pages :
✏ISBN : UOM:39015026530629
✏Available Language : English, Spanish, And French

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✏Computing Information Directory Book Summary :

Big Data And Networks Technologies Book PDF
✏Book Title : Big Data and Networks Technologies
✏Author : Yousef Farhaoui
✏Publisher : Springer
✏Release Date : 2019-07-17
✏Pages : 372
✏ISBN : 9783030236724
✏Available Language : English, Spanish, And French

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✏Big Data and Networks Technologies Book Summary : This book reviews the state of the art in big data analysis and networks technologies. It addresses a range of issues that pertain to: signal processing, probability models, machine learning, data mining, databases, data engineering, pattern recognition, visualization, predictive analytics, data warehousing, data compression, computer programming, smart cities, networks technologies, etc. Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. In turn, data science inspires novel techniques and theories drawn from mathematics, statistics, information theory, computer science, and the social sciences. All papers presented here are the product of extensive field research involving applications and techniques related to data analysis in general, and to big data and networks technologies in particular. Given its scope, the book will appeal to advanced undergraduate and graduate students, postdoctoral researchers, lecturers and industrial researchers, as well general readers interested in big data analysis and networks technologies.

11th International Symposium On Process Systems Engineering Pse2012 Book PDF
✏Book Title : 11th International Symposium on Process Systems Engineering PSE2012
✏Author :
✏Publisher : Elsevier
✏Release Date : 2012-12-31
✏Pages : 1800
✏ISBN : 9780444595089
✏Available Language : English, Spanish, And French

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✏11th International Symposium on Process Systems Engineering PSE2012 Book Summary : While the PSE community continues its focus on understanding, synthesizing, modeling, designing, simulating, analyzing, diagnosing, operating, controlling, managing, and optimizing a host of chemical and related industries using the systems approach, the boundaries of PSE research have expanded considerably over the years. While early PSE research was largely concerned with individual units and plants, the current research spans wide ranges of scales in size (molecules to processing units to plants to global multinational enterprises to global supply chain networks; biological cells to ecological webs) and time (instantaneous molecular interactions to months of plant operation to years of strategic planning). The changes and challenges brought about by increasing globalization and the the common global issues of energy, sustainability, and environment provide the motivation for the theme of PSE2012: Process Systems Engineering and Decision Support for the Flat World. Each theme includes an invited chapter based on the plenary presentation by an eminent academic or industrial researcher Reports on the state-of-the-art advances in the various fields of process systems engineering Addresses common global problems and the research being done to solve them

Data Driven Technology For Engineering Systems Health Management Book PDF
✏Book Title : Data Driven Technology for Engineering Systems Health Management
✏Author : Gang Niu
✏Publisher : Springer
✏Release Date : 2016-07-27
✏Pages : 357
✏ISBN : 9789811020322
✏Available Language : English, Spanish, And French

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✏Data Driven Technology for Engineering Systems Health Management Book Summary : This book introduces condition-based maintenance (CBM)/data-driven prognostics and health management (PHM) in detail, first explaining the PHM design approach from a systems engineering perspective, then summarizing and elaborating on the data-driven methodology for feature construction, as well as feature-based fault diagnosis and prognosis. The book includes a wealth of illustrations and tables to help explain the algorithms, as well as practical examples showing how to use this tool to solve situations for which analytic solutions are poorly suited. It equips readers to apply the concepts discussed in order to analyze and solve a variety of problems in PHM system design, feature construction, fault diagnosis and prognosis.

Mechatronics And Information Technology Book PDF
✏Book Title : Mechatronics and Information Technology
✏Author : Qing Kai Han
✏Publisher : Trans Tech Publications Ltd
✏Release Date : 2011-12-22
✏Pages : 1100
✏ISBN : 9783038138280
✏Available Language : English, Spanish, And French

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✏Mechatronics and Information Technology Book Summary : Volume is indexed by Thomson Reuters CPCI-S (WoS). These are the proceedings of the 2011 International Conference on Mechatronics and Information Technology (ICMIT 2011), which was held on August 16-19th, 2011, in Shenyang, Liaoning Province, P.R. China. The primary aim of ICMIT 2011 was to share ideas and to discuss new techniques and applications in mechatronics and information technology in order to speed the development of advanced equipment manufacture, within the conference theme of “mechatronics and information technology for advanced equipment manufacture”. The topics covered by ICMIT 2011 included: Control Theory and Applications, Magnetic Resonance Imaging, Actuators and Mechanisms, Communication and Network Systems, Smart Materials and Structures, Ubiquitous Applications, Welfare Engineering, Sensors and Signal/Image Processing, Biomedical Engineering, Embedded Systems, Robotics, Human Interfaces, Mechatronics and MEMS, Information Technology, Intelligent Control and Systems, Condition Monitoring/Fault Diagnosis, Applied Electromagnetics and Mechanics and Power Electronics.

Electrical Electronics Abstracts Book PDF
✏Book Title : Electrical Electronics Abstracts
✏Author :
✏Publisher :
✏Release Date : 1997
✏Pages :
✏ISBN : OSU:32435059588608
✏Available Language : English, Spanish, And French

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✏Electrical Electronics Abstracts Book Summary :

Intelligent And Fuzzy Techniques Smart And Innovative Solutions Book PDF
✏Book Title : Intelligent and Fuzzy Techniques Smart and Innovative Solutions
✏Author : Cengiz Kahraman
✏Publisher : Springer Nature
✏Release Date : 2020-07-10
✏Pages : 1705
✏ISBN : 9783030511562
✏Available Language : English, Spanish, And French

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✏Intelligent and Fuzzy Techniques Smart and Innovative Solutions Book Summary : This book gathers the most recent developments in fuzzy & intelligence systems and real complex systems presented at INFUS 2020, held in Istanbul on July 21–23, 2020. The INFUS conferences are a well-established international research forum to advance the foundations and applications of intelligent and fuzzy systems, computational intelligence, and soft computing, highlighting studies on fuzzy & intelligence systems and real complex systems at universities and international research institutions. Covering a range of topics, including the theory and applications of fuzzy set extensions such as intuitionistic fuzzy sets, hesitant fuzzy sets, spherical fuzzy sets, and fuzzy decision-making; machine learning; risk assessment; heuristics; and clustering, the book is a valuable resource for academics, M.Sc. and Ph.D. students, as well as managers and engineers in industry and the service sectors.

Conference Record Book PDF
✏Book Title : Conference Record
✏Author :
✏Publisher :
✏Release Date : 1989
✏Pages :
✏ISBN : CORNELL:31924050367113
✏Available Language : English, Spanish, And French

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✏Conference Record Book Summary :

Globecom 89 Book PDF
✏Book Title : GLOBECOM 89
✏Author :
✏Publisher :
✏Release Date : 1989
✏Pages : 1975
✏ISBN : UCSD:31822003880614
✏Available Language : English, Spanish, And French

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✏GLOBECOM 89 Book Summary :

15th International Conference On Soft Computing Models In Industrial And Environmental Applications Soco 2020  Book PDF
✏Book Title : 15th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2020
✏Author : Álvaro Herrero
✏Publisher : Springer Nature
✏Release Date : 2020-08-28
✏Pages : 876
✏ISBN : 9783030578022
✏Available Language : English, Spanish, And French

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✏15th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2020 Book Summary : This book contains accepted papers presented at SOCO 2020 conference held in the beautiful and historic city of Burgos (Spain), in September 2020. Soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate, and analyze very complex issues and phenomena. After a through peer-review process, the SOCO 2020 International Program Committee selected 83 papers which are published in these conference proceedings and represents an acceptance rate of 35%. Due to the COVID-19 outbreak, the SOCO 2020 edition was blended, combining on-site and on-line participation. In this relevant edition a special emphasis was put on the organization of special sessions. Eleven special session were organized related to relevant topics such as: Soft Computing Applications in Precision Agriculture, Manufacturing and Management Systems, Management of Industrial and Environmental Enterprises, Logistics and Transportation Systems, Robotics and Autonomous Vehicles, Computer Vision, Laser-Based Sensing and Measurement and other topics such as Forecasting Industrial Time Series, IoT, Big Data and Cyber Physical Systems, Non-linear Dynamical Systems and Fluid Dynamics, Modeling and Control systems The selection of papers was extremely rigorous in order to maintain the high quality of SOCO conference editions and we would like to thank the members of the Program Committees for their hard work in the reviewing process. This is a crucial process to the creation of a high standard conference and the SOCO conference would not exist without their help.

Emerging Technologies In Data Mining And Information Security Book PDF
✏Book Title : Emerging Technologies in Data Mining and Information Security
✏Author : Ajith Abraham
✏Publisher : Springer
✏Release Date : 2018-12-12
✏Pages : 861
✏ISBN : 9789811319518
✏Available Language : English, Spanish, And French

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✏Emerging Technologies in Data Mining and Information Security Book Summary : This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2018) held at the University of Engineering & Management, Kolkata, India, on February 23–25, 2018. It comprises high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers, and case studies related to all the areas of data mining, machine learning, Internet of Things (IoT) and information security.

Technical Digest Book PDF
✏Book Title : Technical Digest
✏Author :
✏Publisher :
✏Release Date : 1992
✏Pages :
✏ISBN : UOM:39015030262946
✏Available Language : English, Spanish, And French

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✏Technical Digest Book Summary :

Cep Software Directory Book PDF
✏Book Title : CEP Software Directory
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✏Release Date : 1997
✏Pages :
✏ISBN : STANFORD:36105017841847
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Cad Cam Abstracts Book PDF
✏Book Title : CAD CAM Abstracts
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✏Release Date : 1992
✏Pages :
✏ISBN : UOM:39015023871588
✏Available Language : English, Spanish, And French

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✏CAD CAM Abstracts Book Summary :

Interdisciplinary Evolution Of The Machine Brain Book PDF
✏Book Title : Interdisciplinary Evolution of the Machine Brain
✏Author : Wenfeng Wang
✏Publisher : Springer
✏Release Date : 2021-02-11
✏Pages : 150
✏ISBN : 9813342439
✏Available Language : English, Spanish, And French

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✏Interdisciplinary Evolution of the Machine Brain Book Summary : This book seeks to interpret connections between the machine brain, mind and vision in an alternative way and promote future research into the Interdisciplinary Evolution of Machine Brain (IEMB). It gathers novel research on IEMB, and offers readers a step-by-step introduction to the theory and algorithms involved, including data-driven approaches in machine learning, monitoring and understanding visual environments, using process-based perception to expand insights, mechanical manufacturing for remote sensing, reconciled connections between the machine brain, mind and vision, and the interdisciplinary evolution of machine intelligence. This book is intended for researchers, graduate students and engineers in the fields of robotics, Artificial Intelligence and brain science, as well as anyone who wishes to learn the core theory, principles, methods, algorithms, and applications of IEMB.

Dissertation Abstracts International Book PDF
✏Book Title : Dissertation Abstracts International
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✏Release Date : 2004
✏Pages :
✏ISBN : UOM:39015057953336
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Computational Science Iccs 2005 Book PDF
✏Book Title : Computational Science ICCS 2005
✏Author : V.S. Sunderam
✏Publisher : Springer
✏Release Date : 2007-05-22
✏Pages : 1089
✏ISBN : 9783540321118
✏Available Language : English, Spanish, And French

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✏Computational Science ICCS 2005 Book Summary : The Fifth International Conference on Computational Science (ICCS 2005) held in Atlanta, Georgia, USA, May 22–25, 2005, continued in the tradition of p- vious conferences in the series: ICCS 2004 in Krakow, Poland; ICCS 2003 held simultaneously at two locations, in Melbourne, Australia and St. Petersburg, Russia; ICCS 2002 in Amsterdam, The Netherlands; and ICCS 2001 in San Francisco, California, USA. Computational science is rapidly maturing as a mainstream discipline. It is central to an ever-expanding variety of ?elds in which computational methods and tools enable new discoveries with greater accuracy and speed. ICCS 2005 wasorganizedasaforumforscientistsfromthecoredisciplinesofcomputational science and numerous application areas to discuss and exchange ideas, results, and future directions. ICCS participants included researchers from many app- cation domains, including those interested in advanced computational methods for physics, chemistry, life sciences, engineering, economics and ?nance, arts and humanities, as well as computer system vendors and software developers. The primary objectives of this conference were to discuss problems and solutions in allareas,toidentifynewissues,toshapefuturedirectionsofresearch,andtohelp users apply various advanced computational techniques. The event highlighted recent developments in algorithms, computational kernels, next generation c- puting systems, tools, advanced numerical methods, data-driven systems, and emerging application ?elds, such as complex systems, ?nance, bioinformatics, computational aspects of wireless and mobile networks, graphics, and hybrid computation.