Jay Lee – författare

Engineering Asset Management 2010 represents state-of-the art trends and developments in the emerging field of engineering asset management as presented at the Fifth World Congress on Engineering Asset Management (WCEAM).

The proceedings of the WCEAM 2010 is an excellent reference for practitioners, researchers and students in the multidisciplinary field of asset management, covering topics such as:

This book introduces readers to spatiotemporal analytics that are extended from spatial statistics. Spatiotemporal analytics help analysts to quantitatively recognize and evaluate the spatial patterns and their temporal trends of a set of geographic events or objects. Spatiotemporal analyses are very important in geography, environmental sciences, economy, and many other domains. Spatiotemporal Analytics explains in very simple terms the concepts of spatiotemporal data and statistics, theories, and methods used. Each chapter introduces a case study as an example application for an in-depth learning process. The software used and the codes provided enable readers not only to learn statistics but also to use them effectively in their projects.

• Provides a comprehensive understanding of spatiotemporal analytics to readers with minimum knowledge in statistics.

• Written in simple, understandable language with step-by-step instructions.

• Includes numerous examples for all theories and methods explained in the book covering a wide range of applications from different disciplines.

• Each application includes a software code needed to follow the instructions.

• Each chapter also has a set of prepared PowerPoint slides to help spatiotemporal analytics instructors explain the content.

Undergraduate and graduate students who use Geographic Information Systems or study Geographical Information Science will find this book useful. The subject matter is also pertinent to an array of disciplines such as agriculture, anthropology, archaeology, architecture, biology, business administration and management, civic engineering, criminal justice, epidemiology, geography, geology, marketing, political science, and public health.

This book introduces readers to spatiotemporal analytics that are extended from spatial statistics. Spatiotemporal analytics help analysts to quantitatively recognize and evaluate the spatial patterns and their temporal trends of a set of geographic events or objects. Spatiotemporal analyses are very important in geography, environmental sciences, economy, and many other domains. Spatiotemporal Analytics explains in very simple terms the concepts of spatiotemporal data and statistics, theories, and methods used. Each chapter introduces a case study as an example application for an in-depth learning process. The software used and the codes provided enable readers not only to learn statistics but also to use them effectively in their projects.

• Provides a comprehensive understanding of spatiotemporal analytics to readers with minimum knowledge in statistics.

• Written in simple, understandable language with step-by-step instructions.

• Includes numerous examples for all theories and methods explained in the book covering a wide range of applications from different disciplines.

• Each application includes a software code needed to follow the instructions.

• Each chapter also has a set of prepared PowerPoint slides to help spatiotemporal analytics instructors explain the content.

Undergraduate and graduate students who use Geographic Information Systems or study Geographical Information Science will find this book useful. The subject matter is also pertinent to an array of disciplines such as agriculture, anthropology, archaeology, architecture, biology, business administration and management, civic engineering, criminal justice, epidemiology, geography, geology, marketing, political science, and public health.

This text represents state-of-the-art trends and developments in the emerging field of engineering asset management as presented at the Sixth World Congress on Engineering Asset Management (WCEAM) held in Cincinnati, OH, USA from October 3-5, 2011 The Proceedings of the WCEAM 2011 is an excellent reference for practitioners, researchers and students in the multidisciplinary field of asset management, covering topics such as: Asset condition monitoring and intelligent maintenance; Asset data warehousing, data mining and fusion; Asset performance and level-of-service models; Design and lifecycle integrity of physical assets; Deterioration and preservation models for assets; Education and training in asset management; Engineering standards in asset management; Fault diagnosis and prognostics; Financial analysis methods for physical assets; Human dimensions in integrated asset management; Information quality management; Information systems and knowledge management; Intelligent maintenance; Intelligent sensors and devices; Maintenance strategies in asset management; Optimization decisions in asset management; Prognostics & Health Management; Risk management in asset management; Strategic asset management; and Sustainability in asset management.

This book introduces Industrial AI in multiple dimensions. Industrial AI is a systematic discipline which focuses on developing, validating and deploying various machine learning algorithms for industrial applications with sustainable performance. Combined with the state-of-the-art sensing, communication and big data analytics platforms, a systematic Industrial AI methodology will allow integration of physical systems with computational models. The concept of Industrial AI is in infancy stage and may encompass the collective use of technologies such as Internet of Things, Cyber-Physical Systems and Big Data Analytics under the Industry 4.0 initiative where embedded computing devices, smart objects and the physical environment interact with each other to reach intended goals. A broad range of Industries including automotive, aerospace, healthcare, semiconductors, energy, transportation, mining, construction, and industrial automation could harness the power of Industrial AI to gain insights into the invisible relationship of the operation conditions and further use that insight to optimize their uptime, productivity and efficiency of their operations. In terms of predictive maintenance, Industrial AI can detect incipient changes in the system and predict the remains useful life and further to optimize maintenance tasks to avoid disruption to operations.