352 research outputs found
Development and Evaluation of Plant Growth Models: Methodology and Implementation in the PYGMALION platform
International audienceMathematical models of plant growth are generally characterized by a large number of interacting processes, a large number of model parameters and costly experimental data acquisition. Such complexities make model parameterization a difficult process. Moreover, there is a large variety of models that coexist in the literature with generally an absence of benchmarking between the different approaches and insufficient model evaluation. In this context, this paper aims at enhancing good modelling practices in the plant growth modeling community and at increasing model design efficiency. It gives an overview of the different steps in modelling and specify them in the case of plant growth models specifically regarding their above mentioned characteristics. Different methods allowing to perform these steps are implemented in a dedicated platform PYGMALION (Plant Growth Model Analysis, Identification and Optimization). Some of these methods are original. The C++ platform proposes a framework in which stochastic or deterministic discrete dynamic models can be implemented, and several efficient methods for sensitivity analysis, uncertainty analysis, parameter estimation, model selection or data assimilation can be used for model design, evaluation or application. Finally, a new model, the LNAS model for sugar beet growth, is presented and serves to illustrate how the different methods in PYGMALION can be used for its parameterization, its evaluation and its application to yield prediction. The model is evaluated from real data and is shown to have interesting predictive capacities when coupled with data assimilation techniques
Towards an EDSL to enhance good modelling practice for non-linear stochastic discrete dynamical models Application to plant growth models
International audienceA computational formalism is presented that structures a C++ library which aims at the modelling, simulation and statistical analysis of stochastic non-linear discrete dynamical system models. Applications concern the development and analysis of general plant growth models
Assimilation of remote sensing into crop growth models: Current status and perspectives
Timely monitoring of crop lands is important in order to make agricultural activities more sustainable, as well as
ensuring food security. The use of Earth Observation (EO) data allows crop monitoring at a range of spatial
scales, but can be hampered by limitations in the data. Crop growth modelling, on the other hand, can be used to
simulate the physiological processes that result in crop development. Data assimilation (DA) provides a way of
blending the monitoring properties of EO data with the predictive and explanatory abilities of crop growth
models. In this paper, we first provide a critique of both the advantages and disadvantages of both EO data and
crop growth models. We use this to introduce a solid and robust framework for DA, where different DA methods
are shown to be derived from taking different assumptions in solving for the a posteriori probability density
function (pdf) using Bayes’ rule. This treatment allows us to provide some recommendation on the choice of DA
method for particular applications. We comment on current computational challenges in scaling DA applications
to large spatial scales. Future areas of research are sketched, with an emphasis on DA as an enabler for blending
different observations, as well as facilitating different approaches to crop growth models. We have illustrated
this review with a large number of examples from the literature
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Roadmap on signal processing for next generation measurement systems
Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System
Flood Forecasting Using Machine Learning Methods
This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate
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Gaussian Process Modeling for Upsampling Algorithms With Applications in Computer Vision and Computational Fluid Dynamics
Across a variety of fields, interpolation algorithms have been used to upsample lowresolution or coarse data fields. In this work, novel Gaussian Process based methodsare employed to solve a variety of upsampling problems. Specifically threeapplications are explored: coarse data prolongation in Adaptive Mesh Refinement(AMR) in the field of Computational Fluid Dynamics, accurate document imageupsampling to enhance Optical Character Recognition (OCR) accuracy, and fastand accurate Single Image Super Resolution (SISR). For AMR, a new, efficient,and “3rd order accurate” algorithm called GP-AMR is presented. Next, a novel,non-zero mean, windowed GP model is generated to upsample low resolution documentimages to generate a higher OCR accuracy, when compared to the industrystandard. Finally, a hybrid GP convolutional neural network algorithm is used togenerate a computationally efficient and high quality SISR model
Stochastic particle advection velocimetry (SPAV): theory, simulations, and proof-of-concept experiments
Particle tracking velocimetry (PTV) is widely used to measure time-resolved,
three-dimensional velocity and pressure fields in fluid dynamics research.
Inaccurate localization and tracking of particles is a key source of error in
PTV, especially for single camera defocusing, plenoptic imaging, and digital
in-line holography (DIH) sensors. To address this issue, we developed
stochastic particle advection velocimetry (SPAV): a statistical data loss that
improves the accuracy of PTV. SPAV is based on an explicit particle advection
model that predicts particle positions over time as a function of the estimated
velocity field. The model can account for non-ideal effects like drag on
inertial particles. A statistical data loss that compares the tracked and
advected particle positions, accounting for arbitrary localization and tracking
uncertainties, is derived and approximated. We implement our approach using a
physics-informed neural network, which simultaneously minimizes the SPAV data
loss, a Navier-Stokes physics loss, and a wall boundary loss, where
appropriate. Results are reported for simulated and experimental DIH-PTV
measurements of laminar and turbulent flows. Our statistical approach
significantly improves the accuracy of PTV reconstructions compared to a
conventional data loss, resulting in an average reduction of error close to
50%. Furthermore, our framework can be readily adapted to work with other data
assimilation techniques like state observer, Kalman filter, and
adjoint-variational methods
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
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