Value Engineering in Artificial Intelligence
Author: Nardine Osman
Publisher: Springer Nature
Published:
Total Pages: 272
ISBN-13: 3031582020
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Value Engineering
Author: Surender Kumar
Publisher: Alpha Science Int'l Ltd.
Published: 2004-11
Total Pages: 258
ISBN-13: 9788173195785
DOWNLOAD EBOOKThe first decade of 21st century witnessed several changes, world wide, in technology management, restructuring and down sizing global trade and competition, international quality standards, information exchange, lean manufacturing and virtual enterprises etc. In this age of globalization, the survival of any industry mainly depends on its cost of production and quality of its products. With the rapid growth of competition and shrinking product life cycle value engineering has become an essential tool for attaining a competitive edge. This volume provides a logistic view of value engineering. The chapters written by experts in their respective fields are organized into different sections covering. Basic concepts of value engineering Information Technology and Value Engineering Systems Situational Case Studies / Industrial Examples Role of value engineering in profit improvement and effectiveness.
Artificial Intelligence in Process Engineering
Author: Michael Mavrovouniotis
Publisher: Elsevier
Published: 2012-12-02
Total Pages: 383
ISBN-13: 0323153143
DOWNLOAD EBOOKArtificial Intelligence in Process Engineering aims to present a diverse sample of Artificial Intelligence (AI) applications in process engineering. The book contains contributions, selected by the editors based on educational value and diversity of AI methods and process engineering application domains. Topics discussed in the text include the use of qualitative reasoning for modeling and simulation of chemical systems; the use of qualitative models in discrete event simulation to analyze malfunctions in processing systems; and the diagnosis of faults in processes that are controlled by Programmable Logic Controllers. There are also debates on the issue of quantitative versus qualitative information. The control of batch processes, a design of a system that synthesizes bioseparation processes, and process design in the domain of chemical (rather than biochemical) systems are likewise covered in the text. This publication will be of value to industrial engineers and process engineers and researchers.
Artificial Intelligence in Construction Engineering and Management
Author: Limao Zhang
Publisher: Springer Nature
Published: 2021-06-18
Total Pages: 271
ISBN-13: 9811628424
DOWNLOAD EBOOKThis book highlights the latest technologies and applications of Artificial Intelligence (AI) in the domain of construction engineering and management. The construction industry worldwide has been a late bloomer to adopting digital technology, where construction projects are predominantly managed with a heavy reliance on the knowledge and experience of construction professionals. AI works by combining large amounts of data with fast, iterative processing, and intelligent algorithms (e.g., neural networks, process mining, and deep learning), allowing the computer to learn automatically from patterns or features in the data. It provides a wide range of solutions to address many challenging construction problems, such as knowledge discovery, risk estimates, root cause analysis, damage assessment and prediction, and defect detection. A tremendous transformation has taken place in the past years with the emerging applications of AI. This enables industrial participants to operate projects more efficiently and safely, not only increasing the automation and productivity in construction but also enhancing the competitiveness globally.
Systems Engineering and Artificial Intelligence
Author: William F. Lawless
Publisher: Springer Nature
Published: 2021-11-02
Total Pages: 566
ISBN-13: 3030772837
DOWNLOAD EBOOKThis book provides a broad overview of the benefits from a Systems Engineering design philosophy in architecting complex systems composed of artificial intelligence (AI), machine learning (ML) and humans situated in chaotic environments. The major topics include emergence, verification and validation of systems using AI/ML and human systems integration to develop robust and effective human-machine teams—where the machines may have varying degrees of autonomy due to the sophistication of their embedded AI/ML. The chapters not only describe what has been learned, but also raise questions that must be answered to further advance the general Science of Autonomy. The science of how humans and machines operate as a team requires insights from, among others, disciplines such as the social sciences, national and international jurisprudence, ethics and policy, and sociology and psychology. The social sciences inform how context is constructed, how trust is affected when humans and machines depend upon each other and how human-machine teams need a shared language of explanation. National and international jurisprudence determine legal responsibilities of non-trivial human-machine failures, ethical standards shape global policy, and sociology provides a basis for understanding team norms across cultures. Insights from psychology may help us to understand the negative impact on humans if AI/ML based machines begin to outperform their human teammates and consequently diminish their value or importance. This book invites professionals and the curious alike to witness a new frontier open as the Science of Autonomy emerges.
Artificial Intelligence for Sustainable Value Creation
Author: Pagani, Margherita
Publisher: Edward Elgar Publishing
Published: 2021-09-07
Total Pages: 200
ISBN-13: 1839104392
DOWNLOAD EBOOKArtificial Intelligence for Sustainable Value Creation provides a detailed and insightful exploration of both the possibilities and the challenges that accompany widespread Artificial Intelligence
Data-Driven Science and Engineering
Author: Steven L. Brunton
Publisher: Cambridge University Press
Published: 2022-05-05
Total Pages: 615
ISBN-13: 1009098489
DOWNLOAD EBOOKA textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Artificial Intelligence in Design '91
Author: J. S. Gero
Publisher: Butterworth-Heinemann
Published: 2014-05-23
Total Pages: 953
ISBN-13: 1483164802
DOWNLOAD EBOOKArtificial Intelligence in Design '91 is a collection of 47 papers from the First International Conference on Artificial Intelligence in Design held at Edinburgh in June 1991. The papers in this book are grouped into 13 headings, starting with a background of AI design systems and to which extent AI that results from being used as planning tool be applied to quality-oriented design processes in architecture. A constraint-driven approach to object-oriented design is also shown on real-world objects. The use of CADSYN in the structural design of buildings is examined, along with design-dependent knowledge and design-independent knowledge. Discussions on empowering designers with integrated design environments are given whereby design objects may be retrieved from catalogues without requiring users to form queries. Mention is given to automated adjustment of parameter values frequently used in computer routine applications. The book also introduces the Computer Aided Design (CAD) as applied to architecture. Design representation using data models, non-monotonic reasoning in design, and the cognitive aspects of design using empirical studies are discussed. Topics of the industrial applications of AI in design, such as the needed steps to develop a successful AI-based tool, and a review of the Castlemain Project and telecommunication distribution networks follow. This book is suitable for programmers, computer science students, and architects and engineers who use computers in their line of work.
Applications of Artificial Intelligence in Process Systems Engineering
Author: Jingzheng Ren
Publisher: Elsevier
Published: 2021-06-05
Total Pages: 542
ISBN-13: 012821743X
DOWNLOAD EBOOKApplications 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
Machine Learning for Asset Managers
Author: Marcos M. López de Prado
Publisher: Cambridge University Press
Published: 2020-04-22
Total Pages: 152
ISBN-13: 1108879721
DOWNLOAD EBOOKSuccessful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to "learn" complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.
