This paper outlines a simple approach for incorporating extraneous predictions into structural models. The method allows the forecaster to combine predictions derived from any source in a way that is consistent with the underlying structure of the model. The method is flexible enough that predictions can be up-weighted or down-weighted on a case-by-case basis. We illustrate the approach using a small quarterly structural and real-time data for the United States.
Organized as an introduction followed by several self-contained chapters, this tutorial takes the reader from use cases to complete architectures for real-time embedded systems using SysML, UML, and MARTE and shows how to apply the COMET/RTE design method to real-world problems. --
The aim of this handbook is to summarize the recent rapidly developed real-time computing technologies, from theories to applications. This handbook benefits the readers as a full and quick technical reference with a high-level historic review of technology, detailed technical descriptions and the latest practical applications. In general, the handbook is divided into three main parts (subjected to be modified): theory, design, and application covering different but not limited to the following topics: - Real-time operating systems - Real-time scheduling - Timing analysis - Programming languages and run-time systems - Middleware systems - Design and analysis tools - Real-time aspects of wireless sensor networks - Energy aware real-time methods
Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the third volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on: Introduction of Uncertainty Quantification Uncertainty Quantification in Dynamics Model Form Uncertainty and Selection incl. Round Robin Challenge Sensor and Information Fusion Virtual Sensing, Certification, and Real-Time Monitoring Surrogate Modeling
Adopt a diagrammatic approach to creating robust real-time embedded systems Key FeaturesExplore the impact of real-time systems on software designUnderstand the role of diagramming in the software development processLearn why software performance is a key element in real-time systemsBook Description From air traffic control systems to network multimedia systems, real-time systems are everywhere. The correctness of the real-time system depends on the physical instant and the logical results of the computations. This book provides an elaborate introduction to software engineering for real-time systems, including a range of activities and methods required to produce a great real-time system. The book kicks off by describing real-time systems, their applications, and their impact on software design. You will learn the concepts of software and program design, as well as the different types of programming, software errors, and software life cycles, and how a multitasking structure benefits a system design. Moving ahead, you will learn why diagrams and diagramming plays a critical role in the software development process. You will practice documenting code-related work using Unified Modeling Language (UML), and analyze and test source code in both host and target systems to understand why performance is a key design-driver in applications. Next, you will develop a design strategy to overcome critical and fault-tolerant systems, and learn the importance of documentation in system design. By the end of this book, you will have sound knowledge and skills for developing real-time embedded systems. What you will learnDifferentiate between correct, reliable, and safe softwareDiscover modern design methodologies for designing a real-time systemUse interrupts to implement concurrency in the systemTest, integrate, and debug the codeDemonstrate test issues for OOP constructsOvercome software faults with hardware-based techniquesWho this book is for If you are interested in developing a real-time embedded system, this is the ideal book for you. With a basic understanding of programming, microprocessor systems, and elementary digital logic, you will achieve the maximum with this book. Knowledge of assembly language would be an added advantage.
Intelligent computing refers greatly to artificial intelligence with the aim at making computer to act as a human. This newly developed area of real-time intelligent computing integrates the aspect of dynamic environments with the human intelligence. This book presents a comprehensive practical and easy to read account which describes current state-of-the art in designing and implementing real-time intelligent computing to robotics, alert systems, IoT, remote access control, multi-agent systems, networking, mobile smart systems, crowd sourcing, broadband systems, cloud computing, streaming data and many other applications areas. The solutions discussed in this book will encourage the researchers and IT professional to put the methods into their practice.
This book is a collection of articles that present the most recent cutting edge results on specification and estimation of economic models written by a number of the world’s foremost leaders in the fields of theoretical and methodological econometrics. Recent advances in asymptotic approximation theory, including the use of higher order asymptotics for things like estimator bias correction, and the use of various expansion and other theoretical tools for the development of bootstrap techniques designed for implementation when carrying out inference are at the forefront of theoretical development in the field of econometrics. One important feature of these advances in the theory of econometrics is that they are being seamlessly and almost immediately incorporated into the “empirical toolbox” that applied practitioners use when actually constructing models using data, for the purposes of both prediction and policy analysis and the more theoretically targeted chapters in the book will discuss these developments. Turning now to empirical methodology, chapters on prediction methodology will focus on macroeconomic and financial applications, such as the construction of diffusion index models for forecasting with very large numbers of variables, and the construction of data samples that result in optimal predictive accuracy tests when comparing alternative prediction models. Chapters carefully outline how applied practitioners can correctly implement the latest theoretical refinements in model specification in order to “build” the best models using large-scale and traditional datasets, making the book of interest to a broad readership of economists from theoretical econometricians to applied economic practitioners.
Growth models are among the core methods for analyzing how and when people change. Discussing both structural equation and multilevel modeling approaches, this book leads readers step by step through applying each model to longitudinal data to answer particular research questions. It demonstrates cutting-edge ways to describe linear and nonlinear change patterns, examine within-person and between-person differences in change, study change in latent variables, identify leading and lagging indicators of change, evaluate co-occurring patterns of change across multiple variables, and more. User-friendly features include real data examples, code (for Mplus or NLMIXED in SAS, and OpenMx or nlme in R), discussion of the output, and interpretation of each model's results. User-Friendly Features *Real, worked-through longitudinal data examples serving as illustrations in each chapter. *Script boxes that provide code for fitting the models to example data and facilitate application to the reader's own data. *"Important Considerations" sections offering caveats, warnings, and recommendations for the use of specific models. *Companion website supplying datasets and syntax for the book's examples, along with additional code in SAS/R for linear mixed-effects modeling.
