Lei Xu – författare

ISHM is an innovative combination of technologies and methods that offers solutions to the reliability problems caused by increased complexities in design, manufacture, use conditions, and maintenance. Its key strength is in the successful integration of reliability (quantitative estimation of successful operation or failure), "diagnosibility" (ability to determine the fault source), and maintainability (how to maintain the performance of a system in operation). It draws on engineering issues such as advanced sensor monitoring, redundancy management, probabilistic reliability theory, artificial intelligence for diagnostics and prognostics, and formal validation methods, but also "quasi-technical" techniques and disciplines such as quality assurance, systems architecture and engineering, knowledge capture, information fusion, testability and maintainability, and human factors.

This groundbreaking book defines and explains this new discipline, providing frameworks and methodologies for implementation and further research. Each chapter includes experiments, numerical examples, simulations and case studies. It is the ideal guide to this crucial topic for professionals or researchers in aerospace systems, systems engineering, production engineering, and reliability engineering.

With the growing popularity of “big data”, the potential value of personal data has attracted more and more attention. Applications built on personal data can create tremendous social and economic benefits. Meanwhile, they bring serious threats to individual privacy. The extensive collection, analysis and transaction of personal data make it difficult for an individual to keep the privacy safe. People now show more concerns about privacy than ever before. How to make a balance between the exploitation of personal information and the protection of individual privacy has become an urgent issue.

In this book, the authors use methodologies from economics, especially game theory, to investigate solutions to the balance issue. They investigate the strategies of stakeholders involved in the use of personal data, and try to find the equilibrium. 

The book proposes a user-role based methodology to investigate the privacy issues in data mining, identifying four different types of users, i.e. four user roles, involved in data mining applications. For each user role, the authors discuss its privacy concerns and the strategies that it can adopt to solve the privacy problems.

The authors discuss how the owners of data (e.g. an individual or a data miner) deal with the trade-off between privacy and utility in data mining. Specifically, they study users’ strategies in collaborative filtering based recommendation system and distributed classification system. They built game models to formulate the interactions among data owners, and propose learning algorithms to find the equilibria.

This book constitutes the refereed proceedings of the joint International Conference on Artificial Neural Networks and International Conference on Neural Information Processing, ICANN/ICONIP 2003, held in Istanbul, Turkey, in June 2003.

The 138 revised full papers were carefully reviewed and selected from 346 submissions. The papers are organized in topical sections on learning algorithms, support vector machine and kernel methods, statistical data analysis, pattern recognition, vision, speech recognition, robotics and control, signal processing, time-series prediction, intelligent systems, neural network hardware, cognitive science, computational neuroscience, context aware systems, complex-valued neural networks, emotion recognition, and applications in bioinformatics.