Uwe Kruger – författare

The development and application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia and industry alike.  Initially developed for monitoring and fault diagnosis in complex systems, such techniques have been refined and applied in various engineering areas, for example mechanical and manufacturing, chemical, electrical and electronic, and power engineering.  The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity and adaptability for developing monitoring applications.  In contrast, competitive model, signal or knowledge based techniques showed their potential only whenever cost-benefit economics have justified the required effort in developing applications.Statistical Monitoring of Complex Multivariate Processes presents recent advances in statistics based process monitoring, explaining how these processes can now be used in areas such as mechanical and manufacturing engineering for example, in addition to the traditional chemical industry.This book: Contains a detailed theoretical background of the component technology.Brings together a large body of work to address the field’s drawbacks, and develops methods for their improvement.Details cross-disciplinary utilization, exemplified by examples in chemical, mechanical and manufacturing engineering.Presents real life industrial applications, outlining deficiencies in the methodology and how to address them.Includes numerous examples, tutorial questions and homework assignments in the form of individual and team-based projects, to enhance the learning experience.Features a supplementary website including Matlab algorithms and data sets.This book provides a timely reference text to the rapidly evolving area of multivariate statistical analysis for academics, advanced level students, and practitioners alike.

The development and application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia and industry alike.  Initially developed for monitoring and fault diagnosis in complex systems, such techniques have been refined and applied in various engineering areas, for example mechanical and manufacturing, chemical, electrical and electronic, and power engineering.  The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity and adaptability for developing monitoring applications.  In contrast, competitive model, signal or knowledge based techniques showed their potential only whenever cost-benefit economics have justified the required effort in developing applications.

Statistical Monitoring of Complex Multivariate Processes presents recent advances in statistics based process monitoring, explaining how these processes can now be used in areas such as mechanical and manufacturing engineering for example, in addition to the traditional chemical industry.

This book:

Contains a detailed theoretical background of the component technology. Brings together a large body of work to address the field’s drawbacks, and develops methods for their improvement. Details cross-disciplinary utilization, exemplified by examples in chemical, mechanical and manufacturing engineering. Presents real life industrial applications, outlining deficiencies in the methodology and how to address them. Includes numerous examples, tutorial questions and homework assignments in the form of individual and team-based projects, to enhance the learning experience. Features a supplementary website including Matlab algorithms and data sets.

This book provides a timely reference text to the rapidly evolving area of multivariate statistical analysis for academics, advanced level students, and practitioners alike.

The development and application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia and industry alike.  Initially developed for monitoring and fault diagnosis in complex systems, such techniques have been refined and applied in various engineering areas, for example mechanical and manufacturing, chemical, electrical and electronic, and power engineering.  The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity and adaptability for developing monitoring applications.  In contrast, competitive model, signal or knowledge based techniques showed their potential only whenever cost-benefit economics have justified the required effort in developing applications.

Statistical Monitoring of Complex Multivariate Processes presents recent advances in statistics based process monitoring, explaining how these processes can now be used in areas such as mechanical and manufacturing engineering for example, in addition to the traditional chemical industry.

This book:

Contains a detailed theoretical background of the component technology. Brings together a large body of work to address the field’s drawbacks, and develops methods for their improvement. Details cross-disciplinary utilization, exemplified by examples in chemical, mechanical and manufacturing engineering. Presents real life industrial applications, outlining deficiencies in the methodology and how to address them. Includes numerous examples, tutorial questions and homework assignments in the form of individual and team-based projects, to enhance the learning experience. Features a supplementary website including Matlab algorithms and data sets.

This book provides a timely reference text to the rapidly evolving area of multivariate statistical analysis for academics, advanced level students, and practitioners alike.

Apply modern AI techniques across science, engineering, and healthcare domains Modern Machine Learning and Transformers began on long walks on New York’s Vischer Ferry trail and became an accessible yet rigorous gateway to Modern AI. Two authors—one focused on multivariate statistics and interpretability, the other in industrial, neural-network-driven modeling—converge on a practical philosophy: build models you can trust. Across eleven mostly stand-alone chapters, intuition comes first, implementation follows, and deeper math lives in later sections and appendices. Regression and logistic regression appear as a Gauss-Legendre network, introducing weights, learning rates, and stochastic gradient descent early. A historically informed arc links classical regression and classification to backpropagation and today’s transformers. Case studies span science, engineering, healthcare analytics, and scientific computing, showing what works, what fails, and why. Readers will also find: Neural-network viewpoint early: regression and classification become trainable networks via gradient descent and principled learning rates.Concept-first pedagogy: intuitive explanations, geometric insights, then rigorous derivations in later sections and appendices when needed.Self-contained chapters allow building flexible learning outcomes; companion slide sets support undergraduate and graduate classrooms.Case studies from research and industrial R&D cover autism, drug design, and other science, engineering, and healthcare applications—trade-offs explained.Transformers demystified: attention, scaling, BERT-like designs, and practical guidance for building chatbots without blind hype.Whether you are preparing for Modern AI or refreshing your skills, you will learn to choose methods wisely, validate honestly, and recognize failure modes. You leave with code-ready intuition for classical models, deep networks, and transformers—plus perspectives on advanced GPU workflows and emerging quantum-enabled learning.

Im Krokodilbecken des Frankfurter Zoos kommt ein Nachtwächter zu Tode. Auf den ersten Blick gibt es keinen Zusammenhang mit dem Fall des ermordeten Zuhälters, in dem Hauptkommissar Klaus Sebald ermittelt. Dann stößt er auf eine uralte ägyptische Sage und ein brisantes Forschungsgeheimnis. Doch als Sebald erkennt, wie er den Fall lösen kann, ist es bereits zu spät, denn er wird selbst zum Gejagten.

Aus dem Mainstream der wissenschaftlichen Debatte über Medien, Journalismus und Öffentlichkeit sind seit Jahrzehnten wichtigeBegriffe praktisch verschwunden: "Herrschaft", "Propaganda" und auch "Ideologie".Dieses Buch übt Ideologiekritik an den Kommunikationsverhältnissen in westlich-kapitalistischen Demokratien. Seine Autorinnen und Autoren sind Forscher aus dem Netzwerk Kritische Kommunikationswissenschaft und beleuchten Ideologien in der massenmedialen Berichterstattung, der Medienpolitik, der Medienindustrie und der Medienwissenschaft.