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69th ASME Turbomachinery Technical Conference and Exposition (ASME Turbo Expo) (GT)

作者： Eder, Alexander J. Merk, Moritz Hollweck, Thomas Fischer, Andre Lahiri, Claus Silva, Camilo F. Polifke, Wolfgang Tech Univ Munich TUM Sch Engn & Design Dept Engn Phys & Computat Garching Germany Rolls Royce Deutschland Combust & Turbine Dept Blankenfelde Mahlow Germany

ISBN: (纸本)9780791887943

Flame dynamics in the form of a flame transfer matrix (FTM) is not directly measurable in a test rig, but must be deduced from transfer matrix measurements of the combustion system. The burner-flame transfer matrix (BFTM) approach for estimating the FTM is based on local pressure signals from two microphone arrays located upstream and downstream of the combustor. It combines acoustic transfer matrix measurements in non-reacting and reacting conditions, where the latter implicitly includes the flame dynamics. A simple matrix operation then yields the FTM. However, this approach assumes that there is loss-free wave propagation at a constant speed of sound with no change in cross-sectional area between the microphone locations and the burner/flame. The present work demonstrates the limitations of these assumptions when applied to a test rig with complex features, such as effusion cooling, bypass annulus, and downstream end contraction. To remedy the shortcomings of the BFTM approach, this work proposes a novel method to infer the FTM for complex combustors by combining reactive transfer matrix measurements of the entire combustor with an accurate low-order thermoacoustic network model (LOM) of the test rig. This generalized method reduces to the BFTM approach as a special case. In this work, the Rolls-Royce Scaled Acoustic Rig for Low Emission Technology (SCARLET) operated under realistic engine conditions (T-in approximate to 825 K, p(in) approximate to 25 bar, kerosene) is used to analyze the capabilities of the proposed model-based inference method and the limitations of the BFTM approach. In a first step, a LOM based on the geometry and operating point of SCARLET is formulated using a generic FTM. This generic model is used to visualize the limitations of the BFTM approach in terms of various physical and geometrical parameters. Finally, experimental measurement data is used to deduce the FTM of SCARLET using the proposed approach.

关键词： Thermoacoustics flame transfer matrix multi-microphone method acoustic network modeling aero-engine model-based inference

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Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under model-based inference

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REMOTE SENSING 2023年第14期15卷 3508页

作者： Mukhopadhyay, Ritwika Naesset, Erik Gobakken, Terje Mienna, Ida Marielle Bielza, Jaime Candelas Austrheim, Gunnar Persson, Henrik Jan orka, Hans Ole Roald, Bjorn-Eirik Bollandsas, Ole Martin Swedish Univ Agr Sci Dept Forest Resource Management S-90183 Umea Sweden Norwegian Univ Life Sci Fac Environm Sci & Nat Resource Management N-1433 As Norway Univ Oslo Nat Hist Museum Geoecol Res Grp Ecol Res Grp N-0318 Oslo Norway Norwegian Univ Sci & Technol Dept Nat Hist N-7491 Trondheim Norway

Due to climate change, treelines are moving to higher elevations and latitudes. The estimation of biomass of trees and shrubs advancing into alpine areas is necessary for carbon reporting. Remotely sensed (RS) data have previously been utilised extensively for the estimation of forest variables such as tree height, volume, basal area, and aboveground biomass (AGB) in various forest types. model-based inference is found to be efficient for the estimation of forest attributes using auxiliary RS data, and this study focused on testing model-based estimations of AGB in the treeline ecotone using an area-based approach. Shrubs (Salix spp., Betula nana) and trees (Betula pubescens ssp. czerepanovii, Sorbus aucuparia, Populus tremula, Pinus sylvestris, Picea abies) with heights up to about five meters constituted the AGB components. The study was carried out in a treeline ecotone in Hol, southern Norway, using field plots and point cloud data obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP). The field data were acquired for two different strata: tall and short vegetation. Two separate models for predicting the AGB were constructed for each stratum based on metrics calculated from ALS and DAP point clouds, respectively. From the stratified predictions, mean AGB was estimated for the entire study area. Despite the prediction models showing a weak fit, as indicated by their R-2-values, the 95% CIs were relatively narrow, indicating adequate precision of the AGB estimates. No significant difference was found between the mean AGB estimates for the ALS and DAP models for either of the strata. Our results imply that RS data from ALS and DAP can be used for the estimation of AGB in treeline ecotones.

关键词： aboveground biomass airborne laser scanning image matching model-based inference treeline vegetation uncertainty estimation

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Older adults fail to form stable task representations during model-based reversal inference

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NEUROBIOLOGY OF AGING 2019年 74卷 90-100页

作者： Haemmerer, Dorothea Schwartenbeck, Philipp Gallagher, Maria FitzGerald, Thomas Henry Benedict Duezel, Emrah Dolan, Raymond Joseph UCL Inst Cognit Neurosci 17 Queen Sq London WC1N 3AZ England Otto von Guericke Univ Inst Cognit Neurol & Dementia Res Magdeburg Germany German Ctr Neurodegenerat Dis Magdeburg Germany Univ Salzburg Ctr Cognit Neurosci Salzburg Austria Paracelsus Med Univ Salzburg Neurosci Inst Christian Doppler Klin Salzburg Austria UCL Wellcome Trust Ctr Neuroimaging London England Univ Oxford Nuffield Dept Clin Neurosci Oxford Ctr Funct MRI Brain Oxford England Royal Holloway Univ London Dept Psychol Egham Surrey England Univ East Anglia Sch Psychol Norwich Norfolk England Max Planck UCL Ctr Computat Psychiat & Ageing Res London England

Older adults struggle in dealing with changeable and uncertain environments across several cognitive domains. This has been attributed to difficulties in forming adequate task representations that help navigate uncertain environments. Here, we investigate how, in older adults, inadequate task representations impact on model-based reversal learning. We combined computational modeling and pupillometry during a novel model-based reversal learning task, which allowed us to isolate the relevance of task representations at feedback evaluation. We find that older adults overestimate the changeability of task states and consequently are less able to converge on unequivocal task representations through learning. Pupillometric measures and behavioral data show that these unreliable task representations in older adults manifest as a reduced ability to focus on feedback that is relevant for updating task representations, and as a reduced metacognitive awareness in the accuracy of their actions. Instead, the data suggested older adults' choice behavior was more consistent with a guidance by uninformative feedback properties such as outcome valence. Our study highlights that an inability to form adequate task representations may be a crucial factor underlying older adults' impaired model-based inference. (C) 2018 The Authors. Published by Elsevier Inc.

关键词： Decision-making Task representations Pupillometry model-based inference Aging Feedback evaluation

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Effects of temporally external auxiliary data on model-based inference

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REMOTE SENSING OF ENVIRONMENT 2017年 198卷 150-159页

作者： Hou, Zhengyang Xu, Qing McRoberts, Ronald E. Greenberg, Jonathan A. Liu, Jinxiu Heiskanen, Janne Pitkanen, Sari Packalen, Petteri Univ Eastern Finland Fac Sci & Forestry Sch Forest Sci POB 111 FI-80101 Joensuu Finland Univ Nevada Nat Resources & Environm Sci Reno NV 89667 USA Univ Illinois Dept Geog & Geog Informat Sci Urbana IL 61801 USA US Forest Serv Northern Res Stn St Paul MN USA Univ Helsinki Dept Geosci & Geog POB 64 FI-00014 Helsinki Finland

One of the benefits of model-based inference relative to design-based inference is that probability samples are not required which means that models can be constructed using data external to the area of interest. Although "external" usually means spatially or geographically external, it could also be used in the temporal sense that the model is constructed using data whose dates are temporally external to the dates of the data to which the model is applied. This study focuses on assessing the effects of such temporally external application data on model-based inference using remotely sensed auxiliary information. The study area was in Burkina Faso, and the variable of interest was firewood volume (m(3)/ha). A sample of 160 field plots was selected from the population and measured, and auxiliary datasets from Landsat 8 were acquired. models were fit using weighted least squares;the population mean, mu, was estimated;and the variance of the population mean,Var((mu) over cap), was estimated using both an analytical variance estimator, <(<(V)over cap>)over bar>(mu) over cap (an), and an empirical bootstrap estimator, V (mu) over cap (boot). The estimates, (mu) over cap and (Var) over cap(mu) over cap, were compared for models constructed using calibration and application data of the same date and models constructed using calibration and application data whose dates differed. The primary results were twofold. First, for cases for which the dates of the model calibration and application data were the same, (mu) over cap, <(<(V)over cap>)over bar>(mu) over cap (an), V (mu) over cap (boot) and (Bias) over cap(mu) over cap were similar across datasets. These results suggest that the particular date of the dataset from which the calibration and application data are obtained may be mostly arbitrary assuming the relation between the dependent and independent variables does not change over time. Second, for a model for which the calibration and application data were obtained fro

关键词： Natural resource inventory model-based inference Sampling Uncertainty Bootstrapping Covariance matrix estimator

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model-based inference for small area estimation with sampling weights

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SPATIAL STATISTICS 2016年第PartB期18卷 455-473页

作者： Vandendijck, Y. Faes, C. Kirby, R. S. Lawson, A. Hens, N. Hasselt Univ Interuniv Inst Biostat & Stat Bioinformat Diepenbeek Belgium Univ S Florida Coll Publ Hlth Dept Community & Family Hlth Tampa FL USA Univ South Carolina Dept Publ Hlth Charleston SC USA Univ Antwerp Ctr Hlth Econ Res & Modeling Infect Dis Vaccine & Infect Dis Inst Antwerp Belgium

Obtaining reliable estimates about health outcomes for areas or domains where only few to no samples are available is the goal of small area estimation (SAE). Often, we rely on health surveys to obtain information about health outcomes. Such surveys are often characterised by a complex design, stratification, and unequal sampling weights as common features. Hierarchical Bayesian models are well recognised in SAE as a spatial smoothing method, but often ignore the sampling weights that reflect the complex sampling design. In this paper, we focus on data obtained from a health survey where the sampling weights of the sampled individuals are the only information available about the design. We develop a predictive model-based approach to estimate the prevalence of a binary outcome for both the sampled and non-sampled individuals, using hierarchical Bayesian models that take into account the sampling weights. A simulation study is carried out to compare the performance of our proposed method with other established methods. The results indicate that our proposed method achieves great reductions in mean squared error when compared with standard approaches. It performs equally well or better when compared with more elaborate methods when there is a relationship between the responses and the sampling weights. The proposed method is applied to estimate asthma prevalence across districts. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Integrated nested Laplace approximations model-based inference Small area estimation Spatial smoothing Survey weighting

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Forest aboveground biomass mapping and estimation across multiple spatial scales using model-based inference

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REMOTE SENSING OF ENVIRONMENT 2016年第0期184卷 350-360页

作者： Chen, Qi McRoberts, Ronald E. Wang, Changwei Radtke, Philip J. Univ Hawaii Manoa Dept Geog 422 Saunders Hall2424 Maile Way Honolulu HI USA US Forest Serv Northern Res Stn St Paul MN USA South China Agr Univ Coll Nat Resources & Environm Guangzhou Guangdong Peoples R China Virginia Tech Dept Forest Resources & Environm Conservat Blacksburg VA USA

Remotely sensed data have been widely used in recent years for mapping and estimating biomass. However, the characterization of the uncertainty of mapped or estimated biomass in previous studies was either based on ad-hoc approaches (e.g., using model fitting statistics such root mean square errors derived from purposive samples) or mostly limited to the analysis of mean biomass for the whole study area. This study proposed a novel uncertainty analysis method that can characterize biomass uncertainty across multiple spatial scales and multiple spatial resolutions. The uncertainty analysis method built on model-based inference and can propagate errors from trees to field plots, individual pixels, and small areas or large regions that consist of multiple pixels (up to all pixels within a study area). We developed and tested this method over northern Minnesota forest areas of approximately 69,508 km(2) via a unique combination of several datasets for biomass mapping and estimation: wall-to-wall airborne lidar data, national forest inventory (NFI) plots, and destructive measurements of tree aboveground biomass (AGB). We found that the pixel-level AGB prediction error is dominated by lidar-based AGB model residual errors when the spatial resolution is near 380 m or finer and by model parameter estimate errors when the spatial resolution is coarser. We also found that the relative error of AGB predicted from lidar can be reduced to approximately 11% (or mean 5.1 Mg/ha;max 43.6 Mg/ha) at one-hectare scale (or at 100 m spatial resolution) over our study area. Because our uncertainty analysis method uses model-based inference and does not require probability samples of field plots, our methodology has potential applications worldwide, especially over tropics and developing countries where NFI systems are not well-established. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Biomass Uncertainty Lidar Inventory plots Destructive tree AGB measurements model-based inference

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Baseline estimates of soil organic carbon by proximal sensing: Comparing design-based, model-assisted and model-based inference

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GEODERMA 2016年 265卷 152-163页

作者： Rossel, R. A. Viscarra Brus, D. J. Lobsey, C. Shi, Z. McLachlan, G. CSIRO Land & Water Bruce E Butler Lab POB 1666 Canberra ACT 2601 Australia Alterra Univ Wageningen & Res Ctr Environm Sci Grp Wageningen Netherlands Zhejiang Univ Coll Environm & Resource Sci Inst Appl Remote Sensing & Informat Technol 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China CSIRO Agr Flagship Bruce E Butler Lab POB 1666 Canberra ACT 2601 Australia

For baselining and to assess changes in soil organic carbon (C) we need efficient soil sampling designs and methods for measuring C stocks. Conventional analytical methods are time-consuming, expensive and impractical, particularly for measuring at depth. Here we demonstrate the use of proximal soil sensors for estimating the total soil organic C stocks and their accuracies in the 0-10 cm, 0-30 cm and 0-100 cm layers, and for mapping the stocks in each of the three depth layers across 2837 ha of grazing land. Sampling locations were selected by probability sampling, which allowed design-based, model-assisted and model-based estimation of the total organic C stock in the study area. We show that spectroscopic and gamma attenuation sensors can produce accurate measures of soil organic C and bulk density at the sampling locations, in this case every 5 cm to a depth of 1 m. Interpolated data from a mobile multisensor platform were used as covariates in Cubist to map soil organic C. The Cubist map was subsequently used as a covariate in the model-assisted and model-based estimation of the total organic C stock. The design-based, model-assisted and model-based estimates of the total organic C stocks in the study area were similar. However, the variances of the model-assisted and model-based estimates were smaller compared to those of the design-based method. The model-based method produced the smallest variances for all three depth layers. Maps helped to assess variability in the C stock of the study area. The contribution of the spectroscopic model prediction error to our uncertainty about the total soil organic C stocks was relatively small. We found that in soil under unimproved pastures, remnant vegetation and forests there is good rationale for measuring soil organic C beyond the commonly recommended depth of 0-30 cm. Crown Copyright (C) 2015 Published by Elsevier B.V.

关键词： Proximal soil sensing Soil organic carbon stocks Visible-near Infrared spectroscopy Design-based sampling Regression estimator model-based inference

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A comparison of design-based and model-based approaches for finite population spatial sampling and inference

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METHODS IN ECOLOGY AND EVOLUTION 2022年第9期13卷 2018-2029页

作者： Dumelle, Michael Higham, Matt Ver Hoef, Jay M. Olsen, Anthony R. Madsen, Lisa US EPA Corvallis OR 97333 USA St Lawrence Univ Dept Math Comp Sci & Stat Canton NY 13617 USA NOAA Marine Mammal Lab Alaska Fisheries Sci Ctr Seattle WA USA Oregon State Univ Dept Stat Corvallis OR 97331 USA

The design-based and model-based approaches to frequentist statistical inference rest on fundamentally different foundations. In the design-based approach, inference relies on random sampling. In the model-based approach, inference relies on distributional assumptions. We compare the approaches in a finite population spatial context. We provide relevant background for the design-based and model-based approaches and then study their performance using simulated data and real data. The real data are from the United States Environmental Protection Agency's 2012 National Lakes Assessment. A variety of sample sizes, location layouts, dependence structures, and response types are considered. The population mean is the parameter of interest, and performance is measured using statistics like bias, squared error and interval coverage. When studying the simulated and real data, we found that regardless of the strength of spatial dependence in the data, the generalized random tessellation stratified (GRTS) algorithm, which explicitly incorporates spatial locations into sampling, tends to outperform the simple random sampling (SRS) algorithm, which does not explicitly incorporate spatial locations into sampling. We also found that model-based inference tends to outperform design-based inference, even for skewed data where the model-based distributional assumptions are violated. The performance gap between design-based inference and model-based inference is small when GRTS samples are used but large when SRS samples are used, suggesting that the sampling choice (whether to use GRTS or SRS) is most important when performing design-based inference. There are many benefits and drawbacks to the design-based and model-based approaches for finite population spatial sampling and inference that practitioners must consider when choosing between them. We provide relevant background contextualizing each approach and study their properties in a variety of scenarios, making recommendations fo

关键词： design-based inference finite population block kriging (FPBK) generalized random tessellation stratified (GRTS) algorithm local neighbourhood variance estimator model-based inference restricted maximum likelihood (REML) estimation spatial covariance spatially balanced sampling

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A Tale of Two Datasets: Representativeness and Generalisability of inference for Samples of Networks

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2023年第544期118卷 2213-2224页

作者： Krivitsky, Pavel N. Coletti, Pietro Hens, Niel Univ New South Wales Sch Math & Stat Dept Stat Sydney Australia Univ New South Wales Sch Math & Stat UNSW Data Sci Hub Sydney Australia Hasselt Univ I BioStat Data Sci Inst Hasselt Belgium Univ Antwerp Ctr Hlth Econ Res & Modelling Infect Dis Vaccine & Infect Dis Inst Antwerp Belgium Univ New South Wales Sch Math & Stat Sydney Australia

The last two decades have seen considerable progress in foundational aspects of statistical network analysis, but the path from theory to application is not straightforward. Two large, heterogeneous samples of small networks of within-household contacts in Belgium were collected using two different but complementary sampling designs: one smaller but with all contacts in each household observed, the other larger and more representative but recording contacts of only one person per household. We wish to combine their strengths to learn the social forces that shape household contact formation and facilitate simulation for prediction of disease spread, while generalising to the population of households in the *** accomplish this, we describe a flexible framework for specifying multi-network models in the exponential family class and identify the requirements for inference and prediction under this framework to be consistent, identifiable, and generalisable, even when data are incomplete;explore how these requirements may be violated in practice;and develop a suite of quantitative and graphical diagnostics for detecting violations and suggesting improvements to candidate models. We report on the effects of network size, geography, and household roles on household contact patterns (activity, heterogeneity in activity, and triadic closure). Supplementary materials for this article are available online.

关键词： ERGM Exponential-family random graph model Missing data model-based inference Network size Regression diagnostics

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Mapping large-scale pantropical forest canopy height by integrating GEDI lidar and TanDEM-X InSAR data

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REMOTE SENSING OF ENVIRONMENT 2025年 318卷

作者： Qi, Wenlu Armston, John Choi, Changhyun Stovall, Atticus Saarela, Svetlana Pardini, Matteo Fatoyinbo, Lola Papathanassiou, Konstantinos Pascual, Adrian Dubayah, Ralph Univ Maryland Coll Pk College Pk MD 20742 USA German Aerosp Ctr Cologne Germany Norwegian Univ Life Sci As Norway NASA Goddard Space Flight Ctr Greenbelt MD USA

NASA's Global Ecosystem Dynamic Investigation (GEDI) mission provides billions of lidar samples of canopy structure over the Earth's temperate and pantropical forests. Using the GEDI sample data alone, gridded height and biomass products have been created at a spatial resolution of 1 km or coarser. However, this resolution may be too coarse for some applications. In this study, we present a new method of mapping high spatial resolution forest height across large areas using fusion of data acquired by GEDI and TanDEM-X (TDX) Interferometric Synthetic Aperture Radar (InSAR). Our method utilizes GEDI waveforms to provide vertical profiles of scatterers needed to invert a physically-based InSAR model to solve for canopy height. We then use 2-year GEDI canopy height and adaptive wavenumber (kZ)-based calibration models to reduce errors in the inverted canopy height caused by the limited penetration capability of the X-band signal in dense tropical forests and the impact of terrain. We apply this novel method over large areas including Gabon, Mexico, French Guiana and most of the Amazon basin, and generate continuous forest height products at 25 m and 100 m. After validating against airborne lidar data, we find that our canopy height products have a bias of 0.31 m and 0.46 m, and a root mean square error (RMSE) of 8.48 m (30.02 %) and 6.91 m (24.08 %) at 25 m and 100 m respectively, for all sites combined. Compared to existing data products that integrate GEDI with passive optical data using machine learning approaches, our method reduces bias, has a lower RMSE, and does not saturate for tall canopy heights up to 56 m. A key feature of this study is that our canopy height product is complemented with an uncertainty of prediction map which provides information on the predictor's uncertainty around the actual value -an advancement over the standard error maps used in earlier studies, which provide uncertainty around the expectation of the predicted value. This integration a

关键词： Forest height Fusion Lidar GEDI InSAR TanDEM-X model-based inference

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