5,599 research outputs found
A Novel Euler's Elastica based Segmentation Approach for Noisy Images via using the Progressive Hedging Algorithm
Euler's Elastica based unsupervised segmentation models have strong
capability of completing the missing boundaries for existing objects in a clean
image, but they are not working well for noisy images. This paper aims to
establish a Euler's Elastica based approach that properly deals with random
noises to improve the segmentation performance for noisy images. We solve the
corresponding optimization problem via using the progressive hedging algorithm
(PHA) with a step length suggested by the alternating direction method of
multipliers (ADMM). Technically, all the simplified convex versions of the
subproblems derived from the major framework of PHA can be obtained by using
the curvature weighted approach and the convex relaxation method. Then an
alternating optimization strategy is applied with the merits of using some
powerful accelerating techniques including the fast Fourier transform (FFT) and
generalized soft threshold formulas. Extensive experiments have been conducted
on both synthetic and real images, which validated some significant gains of
the proposed segmentation models and demonstrated the advantages of the
developed algorithm
Chinese industrial air pollution emissions based on the continuous emission monitoring systems network
As the world's largest industrial producer, China has generated large amount of industrial atmospheric pollution, particularly for particulate matter (PM), SO2 and NOx emissions. A nationwide, time-varying, and up-to-date air pollutant emission inventory by industrial sources has great significance to understanding industrial emission characteristics. Here, we present a nationwide database of industrial emissions named Chinese Industrial Emissions Database (CIED), using the real smokestack concentrations from China's continuous emission monitoring systems (CEMS) network during 2015-2018 to enhance the estimation accuracy. This hourly, source-level CEMS data enables us to directly estimate industrial emission factors and absolute emissions, avoiding the use of many assumptions and indirect parameters that are common in existing research. The uncertainty analysis of CIED database shows that the uncertainty ranges are quite small, within ±7.2% for emission factors and ±4.0% for emissions, indicating the reliability of our estimates. This dataset provides specific information on smokestack concentrations, emissions factors, activity data and absolute emissions for China's industrial emission sources, which can offer insights into associated scientific studies and future policymaking
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