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Author: Jun Zhao
Publisher: Springer
ISBN: 3319940511
Category : Computers
Languages : en
Pages : 443
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Book Description
This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.
Author: Jun Zhao
Publisher: Springer
ISBN: 3319940511
Category : Computers
Languages : en
Pages : 443
Get Book
Book Description
This book presents modeling methods and algorithms for data-driven prediction and forecasting of practical industrial process by employing machine learning and statistics methodologies. Related case studies, especially on energy systems in the steel industry are also addressed and analyzed. The case studies in this volume are entirely rooted in both classical data-driven prediction problems and industrial practice requirements. Detailed figures and tables demonstrate the effectiveness and generalization of the methods addressed, and the classifications of the addressed prediction problems come from practical industrial demands, rather than from academic categories. As such, readers will learn the corresponding approaches for resolving their industrial technical problems. Although the contents of this book and its case studies come from the steel industry, these techniques can be also used for other process industries. This book appeals to students, researchers, and professionals within the machine learning and data analysis and mining communities.
Author: Weidong Li
Publisher: Springer Nature
ISBN: 3030668495
Category : Technology & Engineering
Languages : en
Pages : 218
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Book Description
This book reports innovative deep learning and big data analytics technologies for smart manufacturing applications. In this book, theoretical foundations, as well as the state-of-the-art and practical implementations for the relevant technologies, are covered. This book details the relevant applied research conducted by the authors in some important manufacturing applications, including intelligent prognosis on manufacturing processes, sustainable manufacturing and human-robot cooperation. Industrial case studies included in this book illustrate the design details of the algorithms and methodologies for the applications, in a bid to provide useful references to readers. Smart manufacturing aims to take advantage of advanced information and artificial intelligent technologies to enable flexibility in physical manufacturing processes to address increasingly dynamic markets. In recent years, the development of innovative deep learning and big data analytics algorithms is dramatic. Meanwhile, the algorithms and technologies have been widely applied to facilitate various manufacturing applications. It is essential to make a timely update on this subject considering its importance and rapid progress. This book offers a valuable resource for researchers in the smart manufacturing communities, as well as practicing engineers and decision makers in industry and all those interested in smart manufacturing and Industry 4.0.
Author: Zhiwen Chen
Publisher: Springer
ISBN: 3658167564
Category : Technology & Engineering
Languages : en
Pages : 112
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Book Description
Zhiwen Chen aims to develop advanced fault detection (FD) methods for the monitoring of industrial processes. With the ever increasing demands on reliability and safety in industrial processes, fault detection has become an important issue. Although the model-based fault detection theory has been well studied in the past decades, its applications are limited to large-scale industrial processes because it is difficult to build accurate models. Furthermore, motivated by the limitations of existing data-driven FD methods, novel canonical correlation analysis (CCA) and projection-based methods are proposed from the perspectives of process input and output data, less engineering effort and wide application scope. For performance evaluation of FD methods, a new index is also developed.
Author: Jing Wang
Publisher: Springer Nature
ISBN: 9811680442
Category : Technology & Engineering
Languages : en
Pages : 277
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Book Description
This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book.
Author: Chao Shang
Publisher: Springer
ISBN: 9811066779
Category : Technology & Engineering
Languages : en
Pages : 143
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Book Description
This thesis develops a systematic, data-based dynamic modeling framework for industrial processes in keeping with the slowness principle. Using said framework as a point of departure, it then proposes novel strategies for dealing with control monitoring and quality prediction problems in industrial production contexts. The thesis reveals the slowly varying nature of industrial production processes under feedback control, and integrates it with process data analytics to offer powerful prior knowledge that gives rise to statistical methods tailored to industrial data. It addresses several issues of immediate interest in industrial practice, including process monitoring, control performance assessment and diagnosis, monitoring system design, and product quality prediction. In particular, it proposes a holistic and pragmatic design framework for industrial monitoring systems, which delivers effective elimination of false alarms, as well as intelligent self-running by fully utilizing the information underlying the data. One of the strengths of this thesis is its integration of insights from statistics, machine learning, control theory and engineering to provide a new scheme for industrial process modeling in the era of big data.
Author: De-Nian Yang
Publisher: Springer Nature
ISBN: 9819722594
Category :
Languages : en
Pages : 448
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Book Description
Author: John MacIntyre
Publisher: Springer
ISBN: 3030199096
Category : Computers
Languages : en
Pages : 244
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Book Description
This book constitutes the refereed proceedings of two International Workshops held as parallel events of the 15th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2019, in Hersonissos, Crete, Greece, in May 2019: the 8th Mining Humanistic Data Workshop, MHDW 2019, and the 4th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2019. The 6 full papers and 4 short papers presented at MHDW 2019 were carefully reviewed and selected from 13 submissions; out of the 14 papers submitted to 5G-PINE 2019, 6 were accepted as full papers and 1 as short paper. The MHDW papers focus on the application of innovative as well as existing data matching, fusion and mining and knowledge discovery and management techniques (such as decision rules, decision trees, association rules, ontologies and alignments, clustering, filtering, learning, classifier systems, neural networks, support vector machines, preprocessing, post processing, feature selection, visualization techniques) to data derived from all areas of humanistic sciences, e.g., linguistic, historical, behavioral, psychological, artistic, musical, educational, social, and ubiquitous computing and bioinformatics. The papers presented at 5G-PINE focus on several innovative findings coming directly from modern European research in the area of modern 5G telecommunications infrastructures and related innovative services and cover a wide variety of technical and business aspects promoting options for growth and development.
Author: Kanak Kalita
Publisher:
ISBN: 9781799872092
Category : Electronic books
Languages : en
Pages : 298
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Book Description
"This book is a compilation of chapters on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization, offering both soft computing approaches and machining processes"--
Author: Adwitiya Sinha
Publisher: CRC Press
ISBN: 0429671776
Category : Computers
Languages : en
Pages : 234
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Book Description
About the Book The book provides details of applying intelligent mining techniques for extracting and pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing. Salient Features of the Book Exhaustive coverage of Data Analysis using R Real-life healthcare models for: Visually Impaired Disease Diagnosis and Treatment options Applications of Big Data and Deep Learning in Healthcare Drug Discovery Complete guide to learn the knowledge discovery process, build versatile real life healthcare applications Compare and analyze recent healthcare technologies and trends Target Audience This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions.
Author: Jingzheng Ren
Publisher: Elsevier
ISBN: 012821743X
Category : Technology & Engineering
Languages : en
Pages : 542
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Book Description
Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis Gives direction to future development trends of AI technologies in chemical and process engineering
