Biogeochemical and Isotopic Gradients in a BTEX/PAH Contaminant Plume: Model-Based Interpretation of a High-Resolution Field Data Set

2009 ◽  
Vol 43 (21) ◽  
pp. 8206-8212 ◽  
Author(s):  
Henning Prommer ◽  
Bettina Anneser ◽  
Massimo Rolle ◽  
Florian Einsiedl ◽  
Christian Griebler
Keyword(s):  
High Resolution ◽  
Field Data ◽  
Contaminant Plume ◽  
Data Set ◽  
Plume Model ◽  
Model Based

scholarly journals Benchmarking Range-Based and Image-Based Techniques for Digitizing a Glazed Earthenware Frieze

2015 ◽  
Vol II-5/W3 ◽  
pp. 315-322 ◽  
Author(s):  
G. Tucci ◽  
V. Bonora ◽  
A. Conti ◽  
L. Fiorini
Keyword(s):  
High Resolution ◽  
Data Acquisition ◽  
Field Data ◽  
Reference Data ◽  
Effective Technique ◽  
Commercial Software ◽  
Data Set ◽  
Scan Data ◽  
High Resolution Models

3D high resolution models can be produced both using range-based and image-based techniques. In this work, we evaluate the performance of the Photoscan commercial software in a challenging project: the digitization of an earthenware frieze of about 36 m in length. In order to choose the most effective technique and to define the best workflow for on-the-field data acquisition and the subsequent data elaboration, some tests were performed on a portion of the frieze. We discuss the results and compare the models resulting from different workflows with a reference data set taken from the scan data.

Algorithms for automated montage synthesis of images from laser-scanning confocal microscopes

1995 ◽  
Vol 53 ◽  
pp. 650-651
Author(s):  
D. E. Becker
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Laser Scanning ◽  
Cell Counting ◽  
Wide Area ◽  
Data Set ◽  
Computational Synthesis ◽  
Automated Cell Counting ◽  
Partial View ◽  
The Individual

An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational approach overcomes mechanical limitations, such as hysterisis and backlash, of microscope stages. It also automates a procedure that is currently done manually. One application is the high-resolution visualization and/or quantitation of large batches of specimens that are much wider than the field of view of the microscope.The automated montage synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the images of interest. In many cases, image analysis performed on each data set can provide useful landmarks. Even when no such “natural” landmarks are available, image processing can often provide useful landmarks.

scholarly journals MODES: model-based optimization on distributed embedded systems

2021 ◽  
Author(s):  
Junjie Shi ◽  
Jiang Bian ◽  
Jakob Richter ◽  
Kuan-Hsun Chen ◽  
Jörg Rahnenführer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Embedded Systems ◽  
Learning Model ◽  
Black Box ◽  
Distributed Embedded Systems ◽  
Data Set ◽  
Individual Model ◽  
Model Based ◽  
Machine Learning Model ◽  
Distributed Machine Learning

AbstractThe predictive performance of a machine learning model highly depends on the corresponding hyper-parameter setting. Hence, hyper-parameter tuning is often indispensable. Normally such tuning requires the dedicated machine learning model to be trained and evaluated on centralized data to obtain a performance estimate. However, in a distributed machine learning scenario, it is not always possible to collect all the data from all nodes due to privacy concerns or storage limitations. Moreover, if data has to be transferred through low bandwidth connections it reduces the time available for tuning. Model-Based Optimization (MBO) is one state-of-the-art method for tuning hyper-parameters but the application on distributed machine learning models or federated learning lacks research. This work proposes a framework $$\textit{MODES}$$ MODES that allows to deploy MBO on resource-constrained distributed embedded systems. Each node trains an individual model based on its local data. The goal is to optimize the combined prediction accuracy. The presented framework offers two optimization modes: (1) $$\textit{MODES}$$ MODES -B considers the whole ensemble as a single black box and optimizes the hyper-parameters of each individual model jointly, and (2) $$\textit{MODES}$$ MODES -I considers all models as clones of the same black box which allows it to efficiently parallelize the optimization in a distributed setting. We evaluate $$\textit{MODES}$$ MODES by conducting experiments on the optimization for the hyper-parameters of a random forest and a multi-layer perceptron. The experimental results demonstrate that, with an improvement in terms of mean accuracy ($$\textit{MODES}$$ MODES -B), run-time efficiency ($$\textit{MODES}$$ MODES -I), and statistical stability for both modes, $$\textit{MODES}$$ MODES outperforms the baseline, i.e., carry out tuning with MBO on each node individually with its local sub-data set.

Adaptive sampling of potential-field data: A direct approach to compressive inversion

Geophysics ◽  
2014 ◽  
Vol 79 (1) ◽  
pp. IM1-IM9 ◽  
Author(s):  
Nathan Leon Foks ◽  
Richard Krahenbuhl ◽  
Yaoguo Li
Keyword(s):  
Potential Field ◽  
Adaptive Sampling ◽  
Field Data ◽  
Computational Cost ◽  
Large Field ◽  
Magnetic Data ◽  
Data Sampling ◽  
Data Types ◽  
Data Set ◽  
Potential Field Data

Compressive inversion uses computational algorithms that decrease the time and storage needs of a traditional inverse problem. Most compression approaches focus on the model domain, and very few, other than traditional downsampling focus on the data domain for potential-field applications. To further the compression in the data domain, a direct and practical approach to the adaptive downsampling of potential-field data for large inversion problems has been developed. The approach is formulated to significantly reduce the quantity of data in relatively smooth or quiet regions of the data set, while preserving the signal anomalies that contain the relevant target information. Two major benefits arise from this form of compressive inversion. First, because the approach compresses the problem in the data domain, it can be applied immediately without the addition of, or modification to, existing inversion software. Second, as most industry software use some form of model or sensitivity compression, the addition of this adaptive data sampling creates a complete compressive inversion methodology whereby the reduction of computational cost is achieved simultaneously in the model and data domains. We applied the method to a synthetic magnetic data set and two large field magnetic data sets; however, the method is also applicable to other data types. Our results showed that the relevant model information is maintained after inversion despite using 1%–5% of the data.

scholarly journals Robustness of model-based high-resolution prediction of forest biomass against different field plot designs

2015 ◽  
Vol 10 (1) ◽  
Author(s):  
Virpi Junttila ◽  
Basanta Gautam ◽  
Bhaskar Singh Karky ◽  
Almasi Maguya ◽  
Katri Tegel ◽  
...  
Keyword(s):  
High Resolution ◽  
Forest Biomass ◽  
Field Plot ◽  
Model Based

A high-resolution model of the inner organs based on the visible muman data set

2002 ◽  
Vol 5 (3) ◽  
pp. 212-212 ◽  
Author(s):  
U. Tiede ◽  
A. Pommert ◽  
B. Pflesser ◽  
E. Richter ◽  
M. Riemer ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Set ◽  
Inner Organs ◽  
Resolution Model ◽  
High Resolution Model
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