Carbon Permits

The Encyclopedia of Global Warming and Climate Change helps readers learn about the astonishingly intricate processes that make ours the only planet known to be habitable. These three volumes include more than 750 articles that explore major topics related to global warming and climate change—ranging geographically from the North Pole to the South Pole, and thematically from social effects to scientific causes

American Electric Power

The Encyclopedia of Global Warming and Climate Change helps readers learn about the astonishingly intricate processes that make ours the only planet known to be habitable. These three volumes include more than 750 articles that explore major topics related to global warming and climate change—ranging geographically from the North Pole to the South Pole, and thematically from social effects to scientific causes

Attribution of Global Warming

The Encyclopedia of Global Warming and Climate Change helps readers learn about the astonishingly intricate processes that make ours the only planet known to be habitable. These three volumes include more than 750 articles that explore major topics related to global warming and climate change—ranging geographically from the North Pole to the South Pole, and thematically from social effects to scientific causes

Bayesian Estimation Via Sequential Monte Carlo Sampling-Constrained Dynamic Systems

Nonlinear and non-Gaussian processes with constraints are commonly encountered in dynamic estimation problems. Methods for solving such problems either ignore the constraints or rely on crude approximations of the model or probability distributions. Such approximations may reduce the accuracy of the estimates since they often fail to capture the variety of probability distributions encountered in constrained linear and nonlinear dynamic systems. This article describes a practical approach that overcomes these shortcomings via a novel extension of sequential Monte Carlo (SMC) sampling or particle filtering. Inequality constraints are imposed by accept/reject steps in the algorithm. The proposed approach provides samples representing the posterior distribution at each time point, and is shown to satisfy the same theoretical properties as unconstrained SMC. Illustrative examples show that results of the proposed approach are at least as accurate as moving horizon estimation, but computationally more efficient and in addition, the approach indicates the uncertainty associated with these estimates

Techno-Ecological Synergy: A Framework for Sustainable Engineering

Even though the importance of ecosystems in sustaining all human activities is well-known, methods for sustainable engineering fail to fully account for this role of nature. Most methods account for the demand for ecosystem services, but almost none account for the supply. Incomplete accounting of the very foundation of human well-being can result in perverse outcomes from decisions meant to enhance sustainability and lost opportunities for benefiting from the ability of nature to satisfy human needs in an economically and environmentally superior manner. This paper develops a framework for understanding and designing synergies between technological and ecological systems to encourage greater harmony between human activities and nature. This framework considers technological systems ranging from individual processes to supply chains and life cycles, along with corresponding ecological systems at multiple spatial scales ranging from local to global. The demand for specific ecosystem services is determined from information about emissions and resource use, while the supply is obtained from information about the capacity of relevant ecosystems. Metrics calculate the sustainability of individual ecosystem services at multiple spatial scales and help define necessary but not sufficient conditions for local and global sustainability. Efforts to reduce ecological overshoot encourage enhancement of life cycle efficiency, development of industrial symbiosis, innovative designs and policies, and ecological restoration, thus combining the best features of many existing methods. Opportunities for theoretical and applied research to make this framework practical are also discussed

Multiscale Analysis and Modeling Using Wavelets

Measured data from most processes are inherently multiscale in nature due to contributions from events occurring at different locations and with different localization in time and frequency. Consequently, data analysis and modeling methods that represent the measured variables at multiple scales are better suited for extracting information from measured data than methods that represent the variables at a single scale. This paper presents an overview of multiscale data analysis and empirical modeling methods based on wavelet analysis. These methods exploit the ability of wavelets to extract events at different scales, compress deterministic features in a small number of relatively large coefficients, and approximately decorrelate a variety of stochastic processes. Multiscale data analysis methods for off-line and on-line removal of Gaussian stationary noise eliminate coefficients smaller than a threshold. These methods are extended to removing non-Gaussian errors by combining them with ..

A multiscale, Bayesian and error-in-variables approach for linear dynamic data rectification

A multiscale approach to data rectification is proposed for data containing features with different time and frequency localization. Noisy data are decomposed into contributions at multiple scales and a Bayesian optimization problem is solved to rectify the wavelet coefficients at each scale. A linear dynamic model is used to constrain the optimization problem, which facilitates an error-in-variables (EIV) formulation and reconciles all measured variables. Time-scale recursive algorithms are obtained by propagating the prior with temporal and scale models. The multiscale Kalman filter is a special case of the proposed Bayesian EIV approach. 2000 Elsevier Science Ltd. All rights reserved. Keywords: Rectification; Bayesian; Wavelets; Error-in-variables; Kalman filter www.elsevier.com/locate/compchemeng 1. Introduction Most physical systems exhibit inherent multiscale features, where the physical phenomena and the associated measurements may contain contributions at multiple scales in ..