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.

Solves prognostic information selection and decision-level information fusion issues Presents integrated evaluation methodologies for complex aerospace system health conditions and software system reliability assessment Proposes a framework to perform fault diagnostics with a distributed intelligent agent system and a data mining approach for multistate systems Explains prognostic methods that combine both the qualitative system running state prognostics and the quantitative remaining useful life prediction

This volume is the proceedings of the International Conference on Advances in Engineering Structures,Mechanics& Construction,convenedat the UniversityofWaterloo on May14–17,2006. TheconferencewasheldtocelebratefortyyearsofrelatedresearchachievementattheUniversity of Waterloo. The Solid Mechanics Division (SMD) of the university was founded in 1966 as a research centre of excellence. During the next three decades, SMD hosted many scienti?c visitors from around the world and held more than a dozen international conferences, lecture series and symposia on a diverse range of topics in mechanics and structural engineering. Upon the retirement of many of its founding members in the 1990s, SMD was renamed the Structures, Mechanics & Construction Division (SMCD) to re?ect the changing research interests of its newer members. The conference also celebrated the academic career of Professor Donald E. Grierson as a dist- guished researcher and educator. Professor Grierson has been a faculty member of the Department of Civil Engineering and SMD/SMCD of the University of Waterloo since 1968. He was an early contributor to the Solid Mechanics Division in the 1960s, and was pivotal in the reformation of the Structures, Mechanics & Construction Division in the mid of 1990s. After a productive career of thirty-eight years in research and teaching, Professor Grierson retired from the university at the end of 2005.

For each user role, the authors discuss its privacy concerns and the strategies that it can adopt to solve the privacy problems.The book also proposes a simple game model to analyze the interactions among data provider, data collector and data miner.

This book constitutes the refereed post-conference proceedings of the 18th EAI International Conference on Tools for Design, Implementation and Verification of Emerging Information Technologies, TridentCom 2023, which was held in Nanjing, China, during November 11-13, 2023.The 9 full papers were selected from 30 submissions and deal the emerging technologies of big data, cyber-physical systems and computer communications. The papers are grouped in thematical sessions on blockchain and its applications; emerging applications; AI and its security.

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 book also proposes a simple game model to analyze the interactions among data provider, data collector and data miner. By solving the equilibria of the proposed game, readers can get useful guidance on how to deal with the trade-off between privacy and data utility. Moreover, to elaborate the analysis on data collector’s strategies, the authors propose a contract model and a multi-armed bandit model respectively. 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.

This book is a compilation of selected papers from the 8th International Multidisciplinary Conference on Optofluidics (IMCO 2018) held in Shanghai on August 5-8, 2018, as well as papers from the IMCO 2019 held in Hong Kong on June 14-17, 2019.