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Bayesian Methods for Structural Dynamics and Civil Engineering
Bayesian methods are a powerful tool in many areas of science and engineering, especially statistical physics, medical sciences, electrical engineering, and information sciences. They are also ideal for civil engineering applications, given the numerous types of modeling and parametric uncertainty in civil engineering problems. For example, earthquake ground motion cannot be predetermined at the structural design stage. Complete wind pressure profiles are difficult to measure under operating conditions. Material properties can be difficult to determine to a very precise level – especially concrete, rock, and soil. For air quality prediction, it is difficult to measure the hourly/daily poll...
Bayesian Real-Time System Identification
This book introduces some recent developments in Bayesian real-time system identification. It contains two different perspectives on data processing for system identification, namely centralized and distributed. A centralized Bayesian identification framework is presented to address challenging problems of real-time parameter estimation, which covers outlier detection, system, and noise parameters tracking. Besides, real-time Bayesian model class selection is introduced to tackle model misspecification problem. On the other hand, a distributed Bayesian identification framework is presented to handle asynchronous data and multiple outlier corrupted data. This book provides sufficient background to follow Bayesian methods for solving real-time system identification problems in civil and other engineering disciplines. The illustrative examples allow the readers to quickly understand the algorithms and associated applications. This book is intended for graduate students and researchers in civil and mechanical engineering. Practitioners can also find useful reference guide for solving engineering problems.
Air Quality Monitoring and Advanced Bayesian Modeling
Air Quality Monitoring and Advanced Bayesian Modeling introduces recent developments in urban air quality monitoring and forecasting. The book presents concepts, theories, and case studies related to monitoring methods of criteria air pollutants, advanced methods for real-time characterization of chemical composition of PM and VOCs, and emerging strategies for air quality monitoring. The book illustrates concepts and theories through case studies about the development of common statistical air quality forecasting models. Readers will also learn advanced topics such as the Bayesian model class selection, adaptive forecasting model development with Kalman filter, and the Bayesian model averaging of multiple adaptive forecasting models. - Covers fundamental to advanced applications of urban air quality monitoring and forecasting - Includes detailed descriptions and applications of the instruments necessary for the most successful monitoring techniques - Presents case studies throughout to provide real-world context to the research presented in the book
Practice of Bayesian Probability Theory in Geotechnical Engineering
This book introduces systematically the application of Bayesian probabilistic approach in soil mechanics and geotechnical engineering. Four typical problems are analyzed by using Bayesian probabilistic approach, i.e., to model the effect of initial void ratio on the soil–water characteristic curve (SWCC) of unsaturated soil, to select the optimal model for the prediction of the creep behavior of soft soil under one-dimensional straining, to identify model parameters of soils and to select constitutive model of soils considering critical state concept. This book selects the simple and easy-to-understand Bayesian probabilistic algorithm, so that readers can master the Bayesian method to anal...
Computational Methods in Engineering & Science
Here are the printed proceedings of EPMESC X, held on August 21-23, 2006 in Sanya, Hainan Island of China. It includes 14 full papers of plenary and semi-plenary lectures and approximately 166 one-page summaries. The accompanying CD-ROM includes all 180 full papers presented at the conference.
Deep Learning for Biomedical Data Analysis
This book is the first overview on Deep Learning (DL) for biomedical data analysis. It surveys the most recent techniques and approaches in this field, with both a broad coverage and enough depth to be of practical use to working professionals. This book offers enough fundamental and technical information on these techniques, approaches and the related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of DL for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine fundamental theory of Artificial Intelligence (...
Structural Health Monitoring Based on Data Science Techniques
The modern structural health monitoring (SHM) paradigm of transforming in situ, real-time data acquisition into actionable decisions regarding structural performance, health state, maintenance, or life cycle assessment has been accelerated by the rapid growth of “big data” availability and advanced data science. Such data availability coupled with a wide variety of machine learning and data analytics techniques have led to rapid advancement of how SHM is executed, enabling increased transformation from research to practice. This book intends to present a representative collection of such data science advancements used for SHM applications, providing an important contribution for civil engineers, researchers, and practitioners around the world.
Computational Stochastic Mechanics
Proceedings of the June, 1998 conference. Seventy contributions discuss Monte Carlo and signal processing methods, random vibrations, safety and reliability, control/optimization and modeling of nonlinearity, earthquake engineering, random processes and fields, damage/fatigue materials, applied prob
Deep Learning for Autonomous Vehicle Control
The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.
Material Engineering And Mechanical Engineering - Proceedings Of Material Engineering And Mechanical Engineering (Meme2015)
The aim of proceeding of International Conference on Material Engineering and Mechanical Engineering [MEME2015] is to provide a platform for researchers, engineers, and academicians, as well as industrial professionals, to present their research results and applications developed for Material Engineering and Mechanical Engineering. It provides an opportunities for the delegates to exchange new ideas and application experiences, to enhance business or research relations and to find global partners for future collaboration. The object is to strengthen national academic exchanges and cooperation in the field, promote the rapid development of machinery, materials science and engineering application, effectively improve China's machinery, materials science and engineering applications in the field of academic status and international influence.
