1,173 research outputs found
Risk Management Plan (RMP) Estimated Climate Risks
No full textThis dataset contains a list of 3,856 Risk Management Plan facilities that are located in areas of the US thought to be at risk of climate-related natural disasters -- that is, inland flooding, coastal flooding, wildfires, and storm surge due to hurricanes.
What is the Risk Management Plan?
The Risk Management Plan (RMP) -- also known as the Chemical Disaster Rule -- was created as part of Section 112(r) of the 1990 Clean Air Act. The Rule requires certain chemical, manufacturing and industrial facilities to create and submit a plan to the US Environmental Protection Agency for how they plan to prevent chemical and industrial disasters including those caused by natural disasters. For more information see: https://www.epa.gov/rmp/risk-management-plan-rmp-rule-overview
Methods
Detailed methods for the analysis that was carried out to create this file can be found in the appendix section of the Preventing Double Disaster Policy Brief
Data Sources
Inland Flooding- FEMA, National Flood Hazard Layer
Storm Surge- NOAA, Probabilistic Storm Surge Areas
Future Coastal Flooding- Union of Concerned Scientists, Modeled Coastal Flooding - 2040 High Climate Scenario
RMP- Homeland Infrastructure Foundation-Level Data (HIFLD), EPA Emergency Response (ER) Risk Management Plan (RMP) Facilities
NAICS Codes- US EPA Enforcement and Compliance History Online (ECHO), ECHO Exporter
NAICS Titles/Definitions- US Census Bureau, 2-6 Digit 2017 Code File
Wildfires Risk Areas
Wildland Fire Decision Support System, Historic Wildfire Perimeters (2012-2018)
USDA Forest Service, Probabilistic Wildfire Risk
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Estimação de tempos de chegada de ônibus urbano utilizando filtros de Kalman
No full textDissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia de Automação e Sistemas.A fim de diminuir congestionamentos, poluição do ar, consumo de combustível, entre outros, tem-se buscado constantemente o desenvolvimento e modernização de sistemas de transporte público, pois sistemas mais eficientes, confortáveis e convenientes atraem um maior número de pessoas. A aplicação de linhas de ônibus em ambientes urbanos, tem sido um dos modos de transporte público mais utilizados. Para operação eficiente destas linhas, é importante conhecer a posição do veículo em tempo real, possibilitando o controle dos instantes de partida e a implantação de sistemas de informação sobre chegadas futuras, melhorando a percepção de qualidade do serviço prestado. A predição dos tempos de chegada do ônibus depende de uma série de fatores (por exemplo, atrasos em interseções sinalizadas, número de passageiros em pontos de parada, etc.). Estes fatores aumentam significativamente o nível das incertezas associadas ao processo e à medição. Este trabalho apresenta um algoritmo para predição dos tempos de chegada de ônibus urbano em pontos de parada, utilizando a abordagem de filtro de Kalman com análise de dados históricos. Fatores que aumentam o nível das incertezas associadas ao processo e à medição são considerados como propriedades estocásticas das perturbações do processo. A geração de dados de medição é realizada através de dois cenários distintos desenvolvidos e simulados no software de simulações microscópicas Aimsun 6.1. O teste de ajustamento de Kolmogorov-Smirnov é aplicado para análise das distribuições estatísticas destes dados. Os parâmetros necessários para configuração do filtro de Kalman são obtidos a partir de dados históricos através de dois métodos de análise estatística propostos: o método de análise longitudinal e o método de análise transversal. O filtro de Kalman é utilizado para estimação de dois estados do veículo, sua posição e sua velocidade. Por fim, é proposto um algoritmo que utiliza as estimações oriundas do filtro de Kalman para realizar a predição dos tempos de chegada do ônibus em pontos de parada.In order to reduce traffic congestions, air pollution, fuel consumption, and others, it has been constantly sought the development and modernization of public transportation systems, because more efficient, comfortable, and convenient systems attract more people. The application of bus lines in urban environments has been one of the public transportation modes most used. However, for efficient operation of bus lines, its important to know the vehicle position in real-time, enabling the control of departure times and implantation of information systems about future arrivals. The prediction of bus arrival time depends on a number of factors (e.g., delays at signalized intersections, number of passengers at bus stops, etc.). These factors increase significantly the level of uncertainties associated to process and measurement. This work presents an algorithm for prediction of bus arrival times at bus stops using Kalman filter with historical data analysis approach. Factors that increase the level of uncertainties associated to process and measurement are considerate stochastic properties of process disturbance. The generating of measurement data is performed by two different scenarios developed and simulated on the microscopic simulation software Aimsun 6.1. The Kolmogorov-Smirnov goodness of fit test is applied for analysis of the statistical distributions of these data. The parameters required for configuration of the Kalman filter are obtained from historical data through two proposed methods of statistical analysis: the method of longitudinal analysis, and the method of transversal analysis. The Kalman filter is used for estimation of two vehicle states, its position and its velocity. Finally, an algorithm is proposed that uses the estimates given by the Kalman filter to perform the prediction of bus arrival times at bus stops
State estimation in chemometrics : the Kalman filter and beyond /
No full textThis unique text blends together state estimation and chemometrics for the application of advanced data-processing techniques. It further applies system theory in order to develop a modular framework to be implemented on computer for the development of simple intelligent analyzers. Short reviews on the history of state estimation and chemometrics are given, together with examples of the applications described, including classical estimation, state estimation, non-linear estimation, the multi-component, calibration and titration systems and the Kalman filter. The contents are very systematic and build the ideas up logically to appeal to specialist post-graduates working in this area, together with professionals in other areas of chemistry and engineering. Blends together state estimation and chemometrics for the application of advanced data-processing techniquesProvides short reviews on the history of state estimation and chemometrics, together with examples of the applications described.Includes bibliographical references and index.Print version record.This unique text blends together state estimation and chemometrics for the application of advanced data-processing techniques. It further applies system theory in order to develop a modular framework to be implemented on computer for the development of simple intelligent analyzers. Short reviews on the history of state estimation and chemometrics are given, together with examples of the applications described, including classical estimation, state estimation, non-linear estimation, the multi-component, calibration and titration systems and the Kalman filter. The contents are very systematic and build the ideas up logically to appeal to specialist post-graduates working in this area, together with professionals in other areas of chemistry and engineering. Blends together state estimation and chemometrics for the application of advanced data-processing techniquesProvides short reviews on the history of state estimation and chemometrics, together with examples of the applications described.Cover; ABOUT OUR AUTHOR; STATE ESTIMATION IN CHEMOMETRICS: The Kalman Filter and Beyond; Copyright; Contents; 1 Introduction; 1.1 History; 1.2 Chemometrics; 1.3 System view; 2 Classical estimation; 2.1 Linear model; 2.2 Least squares; 2.3 Curve fitting; 2.4 Recursive approach; 2.5 Examples; 3 State estimation; 3.1 State space model; 3.2 Intermezzo; 3.3 Prediction; 3.4 Filtering; 3.5 Kalman filter; 3.6 Smoothing; 3.7 Examples; 4 Statistics; 4.1 Verification; 4.2 Evaluation; 4.3 Selection; 4.4 Normality; 4.5 Example; 5 Non-linear estimation; 5.1 Non-linear state space model.5.2 Extended Kalman filter5. 3 Iterated extended Kalman filter; 5.4 Iterated linear filter-smoother; 5.5 Non-linear smoothing; 5.6 Examples; 6 The multicomponent system; 6.1 Multicomponent analysis; 6.2 Stochastic drift; 6.3 Examples; 7 The calibration system; 7.1 Linear calibration; 7.2 Non-linear calibration; 7.3 Examples; 8 The titration system; 8.1 Discrete titration; 8.2 Continuous titration; 8.3 Non-linear model; 8.4 Examples; 9 Miscellaneous; 9.1 Multiple modeling; 9.2 Principal components; 9.3 Examples; Appendix; Matrix fundamentals; Bibliography; Index.Elsevie
A low dimensional Kalman filter for systems with lagged observables
No full textThis note describes how the Kalman filter can be modified to allow for the vector of observables to be a function of lagged variables without increasing the dimension of the state vector in the filter. This is useful in applications where it is desirable to keep the dimension of the state vector low. The modified filter and accompanying code (which nests the standard filter) can be used to compute (i) the steady state Kalman filter (ii) the log likelihood of a parameterized state space model conditional on a history of observables (iii) a smoothed estimate of latent state variables and (iv) a draw from the distribution of latent states conditional on a history of observables.Kalman filter, lagged observables, Kalman smoother, simulation smoother
Tensor network Kalman filter for LTI systems
No full textAn extension of the Tensor Network (TN) Kalman filter [2], [3] for large scale LTI systems is presented in this paper. The TN Kalman filter can handle exponentially large state vectors without constructing them explicitly. In order to have efficient algebraic operations, a low TN rank is required. We exploit the possibility to approximate the covariance matrix as a TN with a low TN rank. This reduces the computational complexity for general SISO and MIMO LTI systems with TN rank greater than one significantly while obtaining an accurate estimation. Improvements of this method in terms of computational complexity compared to the conventional Kalman filter are demonstrated in numerical simulations for large scale systems.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Raf Van de PlasTeam Jan-Willem van Wingerde
Henri Temianka Correspondence; (kalman)
No full textThis collection contains material pertaining to the life, career, and activities of Henri Temianka, violin virtuoso, conductor, music teacher, and author. Materials include correspondence, concert programs and flyers, music scores, photographs, and books.https://digitalcommons.chapman.edu/temianka_correspondence/3818/thumbnail.jp
Kalman filter data assimilation: Targeting observations and parameter estimation
No full textabstract: This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.Copyright 2014 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. along with the following message: The following article appeared in 24, 2 (2014) and may be found at http://dx.doi.org/10.1063/1.487191
Henri Temianka Correspondence; (kalman)
No full textThis collection contains material pertaining to the life, career, and activities of Henri Temianka, violin virtuoso, conductor, music teacher, and author. Materials include correspondence, concert programs and flyers, music scores, photographs, and books.https://digitalcommons.chapman.edu/temianka_correspondence/3816/thumbnail.jp
A hybrid EKF and switching PSO algorithm for joint state and parameter estimation of lateral flow immunoassay models
No full textThis is the post-print version of the Article. The official published can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.This work was supported in part by the International Science and Technology Cooperation Project of China under Grant
2009DFA32050, Natural Science Foundation of China under Grants 61104041, International Science and Technology Cooperation Project of Fujian Province of China under Grant
2009I0016
Degrees of Kalman varieties of tensors
No full textFunding Information: The idea of this project was conceived during Shahidi's postdoc at Università di Firenze. We thank Giorgio Ottaviani for very useful discussions and encouragement. We thank Jan Draisma for explaining to us a way of deriving the equations in Example 41 . The first author thanks Dr. Alireza Firoozfar and Dr. Mohsen Afsharchi for their support. The second author is partially supported by the Academy of Finland Grant 323416 . During most of the preparation of the manuscript, the third author was a postdoc at Universität Bern, supported by Vici Grant 639.033.514 of Jan Draisma from the Netherlands Organisation for Scientific Research . We thank two anonymous referees for their useful comments and questions that also helped to improve the presentation. Publisher Copyright: © 2022 The Author(s)Kalman varieties of tensors are algebraic varieties consisting of tensors whose singular vector k-tuples lay on prescribed subvarieties. They were first studied by Ottaviani and Sturmfels in the context of matrices. We extend recent results of Ottaviani and the first author to the partially symmetric setting. We describe a generating function whose coefficients are the degrees of these varieties and we analyze its asymptotics, providing analytic results à la Zeilberger and Pantone. We emphasize the special role of isotropic vectors in the spectral theory of tensors and describe the totally isotropic Kalman variety as a dual variety.Peer reviewe
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