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Statistical Process Monitoring Using Advanced Data Driven And Deep Learning Approaches

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Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches

Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches Book
Author : Fouzi Harrou,Ying Sun,Amanda S. Hering,Muddu Madakyaru,abdelkader Dairi
Publisher : Elsevier
Release : 2020-07-03
ISBN : 0128193662
Language : En, Es, Fr & De

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Book Description :

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

Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches

Statistical Process Monitoring Using Advanced Data Driven and Deep Learning Approaches Book
Author : Fouzi Harrou,Ying Sun,Amanda S. Hering,Muddu Madakyaru,abdelkader Dairi
Publisher : Elsevier
Release : 2020-07-29
ISBN : 0128193654
Language : En, Es, Fr & De

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Book Description :

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

Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods Book
Author : Chris Aldrich,Lidia Auret
Publisher : Springer Science & Business Media
Release : 2013-06-15
ISBN : 1447151852
Language : En, Es, Fr & De

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Book Description :

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.

Data Driven Decision Making Under Uncertainty for Intelligent Life cycle Control of the Built Environment

Data Driven Decision Making Under Uncertainty for Intelligent Life cycle Control of the Built Environment Book
Author : Charalampos Andriotis
Publisher : Unknown
Release : 2019
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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.

The International Journal Advanced Manufacturing Technology

The International Journal  Advanced Manufacturing Technology Book
Author : Anonim
Publisher : Unknown
Release : 1987
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download The International Journal Advanced Manufacturing Technology book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Financial Signal Processing and Machine Learning

Financial Signal Processing and Machine Learning Book
Author : Ali N. Akansu,Sanjeev R. Kulkarni,Dmitry M. Malioutov
Publisher : John Wiley & Sons
Release : 2016-05-31
ISBN : 1118745671
Language : En, Es, Fr & De

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Book Description :

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.

Chemical Abstracts

Chemical Abstracts Book
Author : Anonim
Publisher : Unknown
Release : 2002
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Chemical Abstracts book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Mastering Scala Machine Learning

Mastering Scala Machine Learning Book
Author : Alexander Kozlov
Publisher : Packt Publishing
Release : 2016-06-29
ISBN : 9781785880889
Language : En, Es, Fr & De

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Book Description :

Advance your skills in efficient data analysis and data processing using the powerful tools of Scala, Spark, and HadoopAbout 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 maintainabilityWho This Book Is ForMastering 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 ScalaIn DetailSince 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.

Electrical Electronics Abstracts

Electrical   Electronics Abstracts Book
Author : Anonim
Publisher : Unknown
Release : 1997
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Electrical Electronics Abstracts book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Data Analytics Applied to the Mining Industry

Data Analytics Applied to the Mining Industry Book
Author : Ali Soofastaei
Publisher : CRC Press
Release : 2020-11-13
ISBN : 0429781768
Language : En, Es, Fr & De

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Book Description :

Data Analytics Applied to the Mining Industry describes the key challenges facing the mining sector as it transforms into a digital industry able to fully exploit process automation, remote operation centers, autonomous equipment and the opportunities offered by the industrial internet of things. It provides guidelines on how data needs to be collected, stored and managed to enable the different advanced data analytics methods to be applied effectively in practice, through use of case studies, and worked examples. Aimed at graduate students, researchers, and professionals in the industry of mining engineering, this book: Explains how to implement advanced data analytics through case studies and examples in mining engineering Provides approaches and methods to improve data-driven decision making Explains a concise overview of the state of the art for Mining Executives and Managers Highlights and describes critical opportunity areas for mining optimization Brings experience and learning in digital transformation from adjacent sectors

General Catalog University of California Santa Cruz

General Catalog    University of California  Santa Cruz Book
Author : University of California, Santa Cruz
Publisher : Unknown
Release : 2006
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download General Catalog University of California Santa Cruz book written by University of California, Santa Cruz, available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

International Aerospace Abstracts

International Aerospace Abstracts Book
Author : Anonim
Publisher : Unknown
Release : 1998
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download International Aerospace Abstracts book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

American Doctoral Dissertations

American Doctoral Dissertations Book
Author : Anonim
Publisher : Unknown
Release : 2000
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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Index to Theses with Abstracts Accepted for Higher Degrees by the Universities of Great Britain and Ireland and the Council for National Academic Awards

Index to Theses with Abstracts Accepted for Higher Degrees by the Universities of Great Britain and Ireland and the Council for National Academic Awards Book
Author : Anonim
Publisher : Unknown
Release : 2007
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Index to Theses with Abstracts Accepted for Higher Degrees by the Universities of Great Britain and Ireland and the Council for National Academic Awards book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Fault Detection Supervision and Safety for Technical Processes SAFEPROCESS 91

Fault Detection  Supervision  and Safety for Technical Processes  SAFEPROCESS  91  Book
Author : International Federation of Automatic Control
Publisher : Pergamon
Release : 1992
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Hardbound. These Proceedings provide a general overview as well as detailed information on the developing field of reliability and safety of technical processes in automatically controlled processes. The plenary papers present the state-of-the-art and an overview in the areas of aircraft and nuclear power stations, because these safety-critical system domains possess the most highly developed fault management and supervision schemes. Additional plenary papers covered the recent developments in analytical redundancy. In total there are 95 papers presented in these Proceedings.

Government Reports Announcements Index

Government Reports Announcements   Index Book
Author : Anonim
Publisher : Unknown
Release : 1995
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download Government Reports Announcements Index book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Conference Papers Index

Conference Papers Index Book
Author : Anonim
Publisher : Unknown
Release : 1988
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Monthly. Papers presented at recent meeting held all over the world by scientific, technical, engineering and medical groups. Sources are meeting programs and abstract publications, as well as questionnaires. Arranged under 17 subject sections, 7 of direct interest to the life scientist. Full programs of meetings listed under sections. Entry gives citation number, paper title, name, mailing address, and any ordering number assigned. Quarterly and annual indexes to subjects, authors, and programs (not available in monthly issues).

Documentation Abstracts

Documentation Abstracts Book
Author : Anonim
Publisher : Unknown
Release : 1999
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

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CAD CAM Abstracts

CAD CAM Abstracts Book
Author : Anonim
Publisher : Unknown
Release : 1992
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

Download CAD CAM Abstracts book written by , available in PDF, EPUB, and Kindle, or read full book online anywhere and anytime. Compatible with any devices.

Cyber Physical Power Systems State Estimation

Cyber Physical Power Systems State Estimation Book
Author : Arturo Bretas,Newton Bretas,Joao B.A. London Jr,Breno Carvalho
Publisher : Elsevier
Release : 2021-06-01
ISBN : 0323903223
Language : En, Es, Fr & De

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Book Description :

Cyber-Physical Power System State Estimation updates classic state estimation tools to enable real-time operations and optimize reliability in modern electric power systems. The work introduces and contextualizes the core concepts and classic approaches to state estimation modeling. It builds on these classic approaches with a suite of data-driven models and non-synchronized measurement tools to reflect current measurement trends required by increasingly more sophisticated grids. Chapters outline core definitions, concepts and the network analysis procedures involved in the real-time operation of EPS. Specific sections introduce power flow problem in EPS, highlighting network component modeling and power flow equations for state estimation before addressing quasi static state estimation in electrical power systems using Weighted Least Squares (WLS) classical and alternatives formulations. Particularities of the state estimation process in distribution systems are also considered. Finally, the work goes on to address observability analysis, measurement redundancy and the processing of gross errors through the analysis of WLS static state estimator residuals. Develops advanced approaches to smart grid real-time monitoring through quasi-static model state estimation and non-synchronized measurements system models Presents a novel, extended optimization, physics-based model which identifies and corrects for measurement error presently egregiously discounted in classic models Demonstrates how to embed cyber-physical security into smart grids for real-time monitoring Introduces new approaches to calculate power flow in distribution systems and for estimating distribution system states Incorporates machine-learning based approaches to complement the state estimation process, including pattern recognition-based solutions, principal component analysis and support vector machines