Meta-Heuristics Optimization Algorithms in Engineering Business Economics and Finance: Pandian Vasant
Meta-heuristic and Evolutionary Algorithms for Engineering Optimization: Omid Bozorg-Haddad/ Mohammad Solgi/ Hugo A. Loáiciga
Online Requirements Engineering at runtime. An overview of the different supporting frameworks:1. Auflage. Bekim Meta
Software-Engineering als kreativer Prozess:Meta-Kriterien fürr die Entwicklung von Informatiklösungen Erwin Müller
AGENT-ORIENTED ENGINEERING OF COMPLEX SYSTEMS:Meta-Models Processes Environment and Layers Ambra Molesini
A Meta-methodology for Collaborative Networked Organisations:Creating Directly Applicable Methods for Enterprise Engineering Projects Ovidiu Noran
Enriching Reverse Engineering with Feature Analysis:Introducing the Dynamix Feature Meta-Model Orla Greevy
This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. Part I makes key concepts in statistics readily clear. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. Part III provides insight into meta-statistics (statistics of statistics) and demonstrates why experiments often do not replicate. Finally, the textbook shows how complex statistics can be avoided by using clever experimental design. Both non-scientists and students in Biology, Biomedicine and Engineering will benefit from the book by learning the statistical basis of scientific claims and by discovering ways to evaluate the quality of scientific reports in academic journals and news outlets.
Max Hoffmann describes the realization of a framework that enables autonomous decision-making in industrial manufacturing processes by means of multi-agent systems and the OPC UA meta-modeling standard. The integration of communication patterns and SOA with grown manufacturing systems enables an upgrade of legacy environments in terms of Industry 4.0 related technologies. The added value of the derived solutions are validated through an industrial use case and verified by the development of a demonstrator that includes elements of self-optimization through Machine Learning and communication with high-level planning systems such as ERP. About the Author: Dr.-Ing. Max Hoffmann is a scientific researcher at the Institute of Information Management in Mechanical Engineering, RWTH Aachen University, Germany, and leads the group ´´Industrial Big Data´´. His research emphasizes on production optimization by means of data integration through interoperability and communication standards for industrial manufacturing and integrated analysis by using Machine Learning and stream-based information processing.