2,352 research outputs found
Extraction of sesquiterpenic lactones with CO2 supercritical fluid and quantification of the extract by FTIR spectroscopy
Monitoring spatial sustainable development: Semi-automated analysis of satellite and aerial images for energy transition and sustainability indicators
Solar panels are installed by a large and growing number of households due to
the convenience of having cheap and renewable energy to power house appliances.
In contrast to other energy sources solar installations are distributed very
decentralized and spread over hundred-thousands of locations. On a global level
more than 25% of solar photovoltaic (PV) installations were decentralized. The
effect of the quick energy transition from a carbon based economy to a green
economy is though still very difficult to quantify. As a matter of fact the
quick adoption of solar panels by households is difficult to track, with local
registries that miss a large number of the newly built solar panels. This makes
the task of assessing the impact of renewable energies an impossible task.
Although models of the output of a region exist, they are often black box
estimations. This project's aim is twofold: First automate the process to
extract the location of solar panels from aerial or satellite images and
second, produce a map of solar panels along with statistics on the number of
solar panels. Further, this project takes place in a wider framework which
investigates how official statistics can benefit from new digital data sources.
At project completion, a method for detecting solar panels from aerial images
via machine learning will be developed and the methodology initially developed
for BE, DE and NL will be standardized for application to other EU countries.
In practice, machine learning techniques are used to identify solar panels in
satellite and aerial images for the province of Limburg (NL), Flanders (BE) and
North Rhine-Westphalia (DE).Comment: This document provides the reader with an overview of the various
datasets which will be used throughout the project. The collection of
satellite and aerial images as well as auxiliary information such as the
location of buildings and roofs which is required to train, test and validate
the machine learning algorithm that is being develope
Dosage en ligne d'une molécule d'origine naturelle dans le CO2 supercritique à l'aide d'une cellule à haute pression équipée de fibres optiques couplant un spectrophotomère IRTF à un extracteur à fluide supercritique
Peer reviewe
SMALL SAMPLE SIZE CAPABILITY INDEX FOR ASSESSING VALIDITY OF ANALYTICAL METHODS
peer reviewedaudience: researcher, professional, studentAnalytical method’s capability evaluation can be a useful methodology to assess the fitness of purpose of these methods for their future routine application. However, care on how to compute the capability indices has to be made. Indeed, the commonly used formulas to compute capability indices such as Cpk, will highly overestimate the true capability of the methods. Especially during methods validation or transfer, there are only few experiments performed and, using in these situations the commonly applied capability indices to declare a method as valid or as transferable to a receiving laboratory will conduct to inadequate decisions.
In this work, an improved capability index, namely Cpk-tol and the corresponding estimator of proportion of non conforming results (tolCpk−π) is proposed. Through Monte-Carlo simulations, they have been shown to greatly increase the estimation of analytical methods capability in particular in low sample size situations as encountered during methods validation or transfer. Additionally, the usefulness of this capability index is illustrated through several case studies
Determination of fenofibrate, ciprofibrate and bezafibrate in mixtures by FTIR spectroscopy
Peer reviewe
Quantitative in-line monitoring of pharmaceutical pellets active content using near infrared spectroscopy
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