108,679 research outputs found
Decentralized fault-tolerant control of inland navigation networks: a challenge
Inland waterways are large-scale networks used principally for navigation. Even if the transport planning is an important issue, the water resource management is a crucial point. Indeed, navigation is
not possible when there is too little or too much water inside the waterways. Hence, the water resource management of waterways has to be particularly efficient in a context of climate change and increase of water demand. This management has to be done by considering different time and space scales and still requires the development of new methodologies and tools in the topics of the Control and Informatics communities. This work addresses the problem of waterways management in terms of modeling, control, diagnosis and fault-tolerant control by focusing in the inland waterways of the north of France. A review of proposed tools and the ongoing research topics are provided in this paper.Peer ReviewedPostprint (published version
Predictive modeling of die filling of the pharmaceutical granules using the flexible neural tree
In this work, a computational intelligence (CI) technique named flexible neural tree (FNT) was developed to predict die filling performance of pharmaceutical granules and to identify significant die filling process variables. FNT resembles feedforward neural network, which creates a tree-like structure by using genetic programming. To improve accuracy, FNT parameters were optimized by using differential evolution algorithm. The performance of the FNT-based CI model was evaluated and compared with other CI techniques: multilayer perceptron, Gaussian process regression, and reduced error pruning tree. The accuracy of the CI model was evaluated experimentally using die filling as a case study. The die filling experiments were performed using a model shoe system and three different grades of microcrystalline cellulose (MCC) powders (MCC PH 101, MCC PH 102, and MCC DG). The feed powders were roll-compacted and milled into granules. The granules were then sieved into samples of various size classes. The mass of granules deposited into the die at different shoe speeds was measured. From these experiments, a dataset consisting true density, mean diameter (d50), granule size, and shoe speed as the inputs and the deposited mass as the output was generated. Cross-validation (CV) methods such as 10FCV and 5x2FCV were applied to develop and to validate the predictive models. It was found that the FNT-based CI model (for both CV methods) performed much better than other CI models. Additionally, it was observed that process variables such as the granule size and the shoe speed had a higher impact on the predictability than that of the powder property such as d50. Furthermore, validation of model prediction with experimental data showed that the die filling behavior of coarse granules could be better predicted than that of fine granules
A Bayesian - Deep Learning model for estimating Covid-19 evolution in Spain
This work proposes a semi-parametric approach to estimate Covid-19
(SARS-CoV-2) evolution in Spain. Considering the sequences of 14 days
cumulative incidence of all Spanish regions, it combines modern Deep Learning
(DL) techniques for analyzing sequences with the usual Bayesian Poisson-Gamma
model for counts. DL model provides a suitable description of observed
sequences but no reliable uncertainty quantification around it can be obtained.
To overcome this we use the prediction from DL as an expert elicitation of the
expected number of counts along with their uncertainty and thus obtaining the
posterior predictive distribution of counts in an orthodox Bayesian analysis
using the well known Poisson-Gamma model. The overall resulting model allows us
to either predict the future evolution of the sequences on all regions, as well
as, estimating the consequences of eventual scenarios.Comment: Related to: https://github.com/scabras/covid19-bayes-d
The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations
In recent years scholars have built maps of science by connecting the
academic fields that cite each other, are cited together, or that cite a
similar literature. But since scholars cannot always publish in the fields they
cite, or that cite them, these science maps are only rough proxies for the
potential of a scholar, organization, or country, to enter a new academic
field. Here we use a large dataset of scholarly publications disambiguated at
the individual level to create a map of science-or research space-where links
connect pairs of fields based on the probability that an individual has
published in both of them. We find that the research space is a significantly
more accurate predictor of the fields that individuals and organizations will
enter in the future than citation based science maps. At the country level,
however, the research space and citations based science maps are equally
accurate. These findings show that data on career trajectories-the set of
fields that individuals have previously published in-provide more accurate
predictors of future research output for more focalized units-such as
individuals or organizations-than citation based science maps
Model-based sensor supervision inland navigation networks: Cuinchy-Fontinettes case study
In recent years, inland navigation networks benefit from the innovation of the instrumentation and
SCADA systems. These data acquisition and control systems lead to the improvement of the manage-
ment of these networks. Moreover, they allow the implementation of more accurate automatic control
to guarantee the navigation requirements. However, sensors and actuators are subject to faults due to
the strong effects of the environment, aging, etc. Thus, before implementing automatic control strate-
gies that rely on the fault-free mode, it is necessary to design a fault diagnosis scheme. This fault
diagnosis scheme has to detect and isolate possible faults in the system to guarantee fault-free data and
the efficiency of the automatic control algorithms. Moreover, the proposed supervision scheme could
predict future incipient faults that are necessary to perform predictive maintenance of the equipment. In
this paper, a general architecture of sensor fault detection and isolation using model-based approaches
will be proposed for inland navigation networks. The proposed approach will be particularized for the
Cuinchy-Fontinettes reach located in the north of France. The preliminary results show the effectiveness
of the proposed fault diagnosis methodologies using a realistic simulator and fault scenarios.In recent years, inland navigation networks bene¿t from the innovation of the instrumentation and SCADA systems. These data acquisition and control systems lead to the improvement of the management of these networks. Moreover, they allow the implementation of more accurate automatic control to guarantee the navigation requirements. However, sensors and actuators are subject to faults due to the strong effects of the environment, aging, etc. Thus, before implementing automatic control strategies that rely on the fault-free mode, it is necessary to design a fault diagnosis scheme. This fault diagnosis scheme has to detect and isolate possible faults in the system to guarantee fault-free data and the efficiency of the automatic control algorithms. Moreover, the proposed supervision scheme could predict future incipient faults that are necessary to perform predictive maintenance of the equipment. In this paper, a general architecture of sensor fault detection and isolation using model-based approaches will be proposed for inland navigation networks. The proposed approach will be particularized for the Cuinchy-Fontinettes reach located in the north of France. The preliminary results show the effectiveness of the proposed fault diagnosis methodologies using a realistic simulator and fault scenarios.Peer ReviewedPostprint (author's final draft
Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks
Blockage of some ion channels and in particular, the hERG cardiac potassium
channel delays cardiac repolarization and can induce arrhythmia. In some cases
it leads to a potentially life-threatening arrhythmia known as Torsade de
Pointes (TdP). Therefore recognizing drugs with TdP risk is essential.
Candidate drugs that are determined not to cause cardiac ion channel blockage
are more likely to pass successfully through clinical phases II and III trials
(and preclinical work) and not be withdrawn even later from the marketplace due
to cardiotoxic effects. The objective of the present study is to develop an SAR
model that can be used as an early screen for torsadogenic (causing TdP
arrhythmias) potential in drug candidates. The method is performed using
descriptors comprised of atomic NMR chemical shifts and corresponding
interatomic distances which are combined into a 3D abstract space matrix. The
method is called 3D-SDAR (3 dimensional spectral data-activity relationship)
and can be interrogated to identify molecular features responsible for the
activity, which can in turn yield simplified hERG toxicophores. A dataset of 55
hERG potassium channel inhibitors collected from Kramer et al. consisting of 32
drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDAR
model.An ANN model with multilayer perceptron was used to define collinearities
among the independent 3D-SDAR features. A composite model from 200 random
iterations with 25% of the molecules in each case yielded the following figures
of merit: training, 99.2 %; internal test sets, 66.7%; external (blind
validation) test set, 68.4%. In the external test set, 70.3% of positive TdP
drugs were correctly predicted. Moreover, toxicophores were generated from TdP
drugs. A 3D-SDAR was successfully used to build a predictive model for
drug-induced torsadogenic and non-torsadogenic drugs.Comment: Accepted for publication in BMC Bioinformatics (Springer) July 201
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