Bayesian Prediction And Adaptive Sampling Algorithms For Mobile Sensor Networks PDF Download
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Author | : Yunfei Xu |
Publisher | : Springer |
Total Pages | : 124 |
Release | : 2015-10-27 |
Genre | : Technology & Engineering |
ISBN | : 3319219219 |
Download Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks Book in PDF, ePub and Kindle
This brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved. In particular, spatio-temporal Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of observations, measurement noise, and prior distributions for obtaining the predictive distribution of a scalar environmental field of interest. New techniques are introduced to avoid computationally prohibitive Markov chain Monte Carlo methods for resource-constrained mobile sensors. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks starts with a simple spatio-temporal model and increases the level of model flexibility and uncertainty step by step, simultaneously solving increasingly complicated problems and coping with increasing complexity, until it ends with fully Bayesian approaches that take into account a broad spectrum of uncertainties in observations, model parameters, and constraints in mobile sensor networks. The book is timely, being very useful for many researchers in control, robotics, computer science and statistics trying to tackle a variety of tasks such as environmental monitoring and adaptive sampling, surveillance, exploration, and plume tracking which are of increasing currency. Problems are solved creatively by seamless combination of theories and concepts from Bayesian statistics, mobile sensor networks, optimal experiment design, and distributed computation.
Author | : Han-Lim Choi |
Publisher | : |
Total Pages | : 219 |
Release | : 2009 |
Genre | : |
ISBN | : |
Download Adaptive Sampling and Forecasting with Mobile Sensor Networks Book in PDF, ePub and Kindle
(cont.) This work proposes the smoother form of the mutual information inspired by the conditional independence relations, and demonstrates its advantages over a simple extension of the state-of-the-art: (a) it does not require integration of differential equations for long time intervals, (b) it allows for the calculation of accumulated information on-the-fly, and (c) it provides a legitimate information potential field combined with spatial interpolation techniques. The primary benefits of the presented methods are confirmed with numerical experiments using the Lorenz-2003 idealistic chaos model.
Author | : Marcel van Oijen |
Publisher | : Springer Nature |
Total Pages | : 261 |
Release | : |
Genre | : |
ISBN | : 3031660854 |
Download Bayesian Compendium Book in PDF, ePub and Kindle
Author | : Shuo Huang |
Publisher | : |
Total Pages | : 220 |
Release | : 2012 |
Genre | : |
ISBN | : |
Download Adaptive Sampling with Mobile Sensor Networks Book in PDF, ePub and Kindle
Author | : Frederica Darema |
Publisher | : Springer Nature |
Total Pages | : 937 |
Release | : 2023-10-16 |
Genre | : Computers |
ISBN | : 3031279867 |
Download Handbook of Dynamic Data Driven Applications Systems Book in PDF, ePub and Kindle
This Second Volume in the series Handbook of Dynamic Data Driven Applications Systems (DDDAS) expands the scope of the methods and the application areas presented in the first Volume and aims to provide additional and extended content of the increasing set of science and engineering advances for new capabilities enabled through DDDAS. The methods and examples of breakthroughs presented in the book series capture the DDDAS paradigm and its scientific and technological impact and benefits. The DDDAS paradigm and the ensuing DDDAS-based frameworks for systems’ analysis and design have been shown to engender new and advanced capabilities for understanding, analysis, and management of engineered, natural, and societal systems (“applications systems”), and for the commensurate wide set of scientific and engineering fields and applications, as well as foundational areas. The DDDAS book series aims to be a reference source of many of the important research and development efforts conducted under the rubric of DDDAS, and to also inspire the broader communities of researchers and developers about the potential in their respective areas of interest, of the application and the exploitation of the DDDAS paradigm and the ensuing frameworks, through the examples and case studies presented, either within their own field or other fields of study. As in the first volume, the chapters in this book reflect research work conducted over the years starting in the 1990’s to the present. Here, the theory and application content are considered for: Foundational Methods Materials Systems Structural Systems Energy Systems Environmental Systems: Domain Assessment & Adverse Conditions/Wildfires Surveillance Systems Space Awareness Systems Healthcare Systems Decision Support Systems Cyber Security Systems Design of Computer Systems The readers of this book series will benefit from DDDAS theory advances such as object estimation, information fusion, and sensor management. The increased interest in Artificial Intelligence (AI), Machine Learning and Neural Networks (NN) provides opportunities for DDDAS-based methods to show the key role DDDAS plays in enabling AI capabilities; address challenges that ML-alone does not, and also show how ML in combination with DDDAS-based methods can deliver the advanced capabilities sought; likewise, infusion of DDDAS-like approaches in NN-methods strengthens such methods. Moreover, the “DDDAS-based Digital Twin” or “Dynamic Digital Twin”, goes beyond the traditional DT notion where the model and the physical system are viewed side-by-side in a static way, to a paradigm where the model dynamically interacts with the physical system through its instrumentation, (per the DDDAS feed-back control loop between model and instrumentation).
Author | : Jose Eugenio Valenzuela-Del Rio |
Publisher | : |
Total Pages | : |
Release | : 2013 |
Genre | : Adaptive sampling (Statistics) |
ISBN | : |
Download Bayesian Adaptive Sampling for Discrete Design Alternatives in Conceptual Design Book in PDF, ePub and Kindle
The number of technology alternatives has lately grown to satisfy the increasingly demanding goals in modern engineering. These technology alternatives are handled in the design process as either concepts or categorical design inputs. Additionally, designers desire to bring into early design more and more accurate, but also computationally burdensome, simulation tools to obtain better performing initial designs that are more valuable in subsequent design stages. It constrains the computational budget to optimize the design space. These two factors unveil the need of a conceptual design methodology to use more efficiently sophisticated tools for engineering problems with several concept solutions and categorical design choices. Enhanced initial designs and discrete alternative selection are pursued. Advances in computational speed and the development of Bayesian adaptive sampling techniques have enabled the industry to move from the use of look-up tables and simplified models to complex physics-based tools in conceptual design. These techniques focus computational resources on promising design areas. Nevertheless, the vast majority of the work has been done on problems with continuous spaces, whereas concepts and categories are treated independently. However, observations show that engineering objectives experience similar topographical trends across many engineering alternatives. In order to address these challenges, two meta-models are developed. The first one borrows the Hamming distance and function space norms from machine learning and functional analysis, respectively. These distances allow defining categorical metrics that are used to build an unique probabilistic surrogate whose domain includes, not only continuous and integer variables, but also categorical ones. The second meta-model is based on a multi-fidelity approach that enhances a concept prediction with previous concept observations. These methodologies leverage similar trends seen from observations and make a better use of sample points increasing the quality of the output in the discrete alternative selection and initial designs for a given analysis budget. An extension of stochastic mixed-integer optimization techniques to include the categorical dimension is developed by adding appropriate generation, mutation, and crossover operators. The resulted stochastic algorithm is employed to adaptively sample mixed-integer-categorical design spaces. The proposed surrogates are compared against traditional independent methods for a set of canonical problems and a physics-based rotor-craft model on a screened design space. Next, adaptive sampling algorithms on the developed surrogates are applied to the same problems. These tests provide evidence of the merit of the proposed methodologies. Finally, a multi-objective rotor-craft design application is performed in a large domain space. This thesis provides several novel academic contributions. The first contribution is the development of new efficient surrogates for systems with categorical design choices. Secondly, an adaptive sampling algorithm is proposed for systems with mixed-integer-categorical design spaces. Finally, previously sampled concepts can be brought to construct efficient surrogates of novel concepts. With engineering judgment, design community could apply these contributions to discrete alternative selection and initial design assessment when similar topographical trends are observed across different categories and/or concepts. Also, it could be crucial to overcome the current cost of carrying a set of concepts and wider design spaces in the categorical dimension forward into preliminary design.
Author | : |
Publisher | : |
Total Pages | : |
Release | : 2012 |
Genre | : |
ISBN | : |
Download Adaptive Sampling for Multiscale Environmental Sensor Networks Book in PDF, ePub and Kindle
Author | : Krit, Salahddine |
Publisher | : IGI Global |
Total Pages | : 279 |
Release | : 2020-06-26 |
Genre | : Computers |
ISBN | : 1799843823 |
Download Sensor Network Methodologies for Smart Applications Book in PDF, ePub and Kindle
Technologies in today’s society are rapidly developing at a pace that is challenging to stay up to date with. As an increasing number of global regions are implementing smart methods and strategies for sustainable development, they are continually searching for modern advancements within computer science, sensor networks, software engineering, and smart technologies. A compilation of research is needed that displays current applications of computing methodologies in the progression of global cities and how smart technologies are being utilized. Sensor Network Methodologies for Smart Applications is a collection of innovative research on the methods of intelligent systems and technologies and their various applications within sustainable development practices. While highlighting topics including machine learning, network security, and optimization algorithms, this book is ideally designed for researchers, scientists, developers, programmers, engineers, educators, policymakers, geographers, planners, and students seeking current research on smart technologies and sensor networks.
Author | : Xingming Sun |
Publisher | : Springer |
Total Pages | : 743 |
Release | : 2018-10-31 |
Genre | : Computers |
ISBN | : 3030000060 |
Download Cloud Computing and Security Book in PDF, ePub and Kindle
This six volume set LNCS 11063 – 11068 constitutes the thoroughly refereed conference proceedings of the 4th International Conference on Cloud Computing and Security, ICCCS 2018, held in Haikou, China, in June 2018. The 386 full papers of these six volumes were carefully reviewed and selected from 1743 submissions. The papers cover ideas and achievements in the theory and practice of all areas of inventive systems which includes control, artificial intelligence, automation systems, computing systems, electrical and informative systems. The six volumes are arranged according to the subject areas as follows: cloud computing, cloud security, encryption, information hiding, IoT security, multimedia forensics.
Author | : Bo Cai (PhD.) |
Publisher | : |
Total Pages | : 424 |
Release | : 2003 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
Download Adaptive Sampling Schemes and Bayesian Semiparametric Survival Analysis Book in PDF, ePub and Kindle