Copula-based stochastic modelling of evapotranspiration time series conditioned on rainfall as design tool in water resources management

In the last few decades, the frequency and intensity of water-related disasters, also called climate-related disasters, e.g. floods, storms, heat waves and droughts, has gone up considerably at both global and regional scales, causing significant damage to many societies and ecosystems. Understanding the behavior and frequency of these disasters is extremely important, not only for reducing their damages but also for the management of water resources. These disasters can often be characterized by multiple dependent variables and therefore require a flexible multivariate approach for studying such phenomena. In this study, we focus on copulas, which are multivariate functions that describe the dependence structure between stochastic variables, independently of their marginal behaviors. The study aimed at different potential applications of copulas in hydrology, such as a multivariate frequency analysis and a copula-based approach for assessing a rainfall model. And further, a stochastic copula-based evapotranspiration generator was developed. As an application, the potential impacts of climate change on river discharge was investigated partly based the latter generator

An assessment of the ability of Bartlett–Lewis type of rainfall models to reproduce drought statistics

Of all natural disasters, the economic and environmental consequences of droughts are among the highest because of their longevity and widespread spatial extent. Because of their extreme behaviour, studying droughts generally requires long time series of historical climate data. Rainfall is a very important variable for calculating drought statistics, for quantifying historical droughts or for assessing the impact on other hydrological (e. g. water stage in rivers) or agricultural (e. g. irrigation requirements) variables. Unfortunately, time series of historical observations are often too short for such assessments. To circumvent this, one may rely on the synthetic rainfall time series from stochastic point process rainfall models, such as Bartlett-Lewis models. The present study investigates whether drought statistics are preserved when simulating rainfall with Bartlett-Lewis models. Therefore, a 105 yr 10 min rainfall time series obtained at Uccle, Belgium is used as a test case. First, drought events were identified on the basis of the Effective Drought Index (EDI), and each event was characterized by two variables, i.e. drought duration (D) and drought severity (S). As both parameters are interdependent, a multivariate distribution function, which makes use of a copula, was fitted. Based on the copula, four types of drought return periods are calculated for observed as well as simulated droughts and are used to evaluate the ability of the rainfall models to simulate drought events with the appropriate characteristics. Overall, all Bartlett-Lewis model types studied fail to preserve extreme drought statistics, which is attributed to the model structure and to the model stationarity caused by maintaining the same parameter set during the whole simulation period

Parameter Estimation and Predictive Speed Control of Chopper-Fed Brushed DC Motors

This paper presents an effective speed control method for brushed DC motors fed by a DC chopper using the concept of Finite Control Set-Model Predictive Control (FCS-MPC). As this control algorithm requires the parameters of the controlled object, the estimation of motor parameters is first performed by using two types of data. The first data includes the output speed response corresponding to the step input voltage to obtain the transfer function in the no-load regime. The second data consists of the motor speed and armature current when a load torque is applied to the motor shaft. The discrete-time equation of the motor armature circuit is used to obtain the future values of the armature circuit current and the motor speed. A cost function is defined based on the difference between the reference and predicted motor speed. The optimal switching states of the DC chopper are selected corresponding to the maximum value of the cost function. The performance of the proposed speed control algorithm is validated on an experimental system. The simulation and experimental results obtained show that the MPC controller can outperform the conventional proportional-integral (PI) controller

Utilisation de Polynômes de Chebychev pour l'Identification à Temps Continu

National audienceDans ce papier, une présentation des propriétés des polynômes de Tchebychev et une formulation dérivée utilisant ces polynômes sont e ffectuées. La méthode d'identifi cation présentée consiste à appliquer une transformation linéaire sur le système d'équations qui régit le processus. Pour cela, l'opérateur mathématique utilisé s'appuie sur une décomposition du signal dans une base formée de polynômes orthogonaux. Nous montrons que cette projection se com- porte comme un filtre passe-bande, de telle sorte qu'une seule opération est nécessaire pour le pré-traitement des données avant le processus d'identi cation, à savoir la di- mension de la base des polynômes Tchebychev. L'identi fication expérimentale en boucle fermée d'un robot à 2 axes avec cette méthode est effectuée dans une dernière partie. Les résultats obtenus sont comparés à une technique utilisée classiquement en robotique et montrent des résultats identiques

A Robotic Platform for Endovascular Aneurysm Repair

International audienceAn EndoVascular Aneurysm Repair (EVAR) isa procedure used to fix an aneurysm of the aorta. In thisprocedure, a guide is inserted by the femoral artery. This guidegoes through to the height of the aneurysm and then a catheterfollows the guide. Next, a stent graft is deployed in order torepair the aortic aneurysm. The objectives of our work is todevelop a low-cost robotic system and implement a programthat helps the trajectory planning during an endovascularoperation. More precisely, this program can predict if the aortawill break or not depending on the guide used. Such a roboticplatform could serve as a teaching instrument by creating anenvironment for young surgeons in which they will be able topractice their skills to perform an EVAR. This paper describesthe different components of this platform and provides someexperimental results

A coupled stochastic rainfall-evapotranspiration model for hydrological impact analysis

A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such an exercise, discharge is often considered, as floods originating from extremely high discharges often cause damage. Investigating the impact of extreme discharges generally requires long time series of precipitation and evapotranspiration to be used to force a rainfall-runoff model. However, such kinds of data may not be available and one should resort to stochastically generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events, is not well studied. In this paper, stochastically generated rainfall and corresponding evapotranspiration time series, generated by means of vine copulas, are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall-evapotranspiration model has great potential for hydrological impact analysis

A Machine Learning-based Approach to Vietnamese Handwritten Medical Record Recognition

Handwritten text recognition has been an active research topic within computer vision division. Existing deep-learning solutions are practical; however, recognizing Vietnamese handwriting has shown to be a challenge with the presence of extra six distinctive tonal symbols and extra vowels. Vietnam is a developing country with a population of approximately 100 million, but has only focused on digitalization transforms in recent years, and so Vietnam has a significant number of physical documents, that need to be digitized. This digitalization transform is urgent when considering the public health sector, in which medical records are mostly still in hand-written form and still are growing rapidly in number. Digitization would not only help current public health management but also allow preparation and management in future public health emergencies. Enabling the digitalization of old physical records will allow efficient and precise care, especially in emergency units. We proposed a solution to Vietnamese text recognition that is combined into an end-to-end document-digitalization system. We do so by performing segmentation to word-level and then leveraging an artificial neural network consisting of both convolutional neural network (CNN) and a long short-term memory recurrent neural network (LSTM) to propagate the sequence information. From the experiment with the records written by 12 doctors, we have obtained encouraging results of 6.47% and 19.14% of CER and WER respectively

SICOMAT : a system for SImulation and COntrol analysis of MAchine Tools

International audienceThis paper presents a software package for the simulation and the control analysis of machine tool axes. This package which is called SICOMAT (SImulation and COntrol analysis of MAchine Tools), provides a large variety of toolboxes to analyze the behavior and the control of the machine. The software takes into account several elements such as the flexibility of bodies, the interaction between several axes, the effect of numerical control and the availability to reduce models

SICOMAT : a system for SImulation and COntrol analysis of MAchine Tools

International audienceThis paper presents a software package for the simulation and the control analysis of machine tool axes. This package which is called SICOMAT (SImulation and COntrol analysis of MAchine Tools), provides a large variety of toolboxes to analyze the behavior and the control of the machine. The software takes into account several elements such as the exibility of bodies, the interaction between several axes, the effect of numerical control and the availability to reduce models

Simulation of an instrumental childbirth for the training of the forceps extraction: control algorithm and evaluation

International audienceThis paper presents the control algorithm implanted on the childbirth simulator BirthSIMin order to provide training to novice obstetricians. The forceps extraction is an obstetric manipulation learned by experience. However, nowadays the training is mainly provided during real childbirths. This kind of training could lead to dramatic consequences due to the lack of experience of some operators. This paper explains the approach which has been used to simulate the dynamic process of a childbirth on the BirthSIM simulator. We especially focus on one procedure which reproduces a difficult instrumental delivery. The recorded tractive force to extract the fetus corresponds to the literature results which confirms the realism of the simulator. The novice results emphasize the need of a childbirth simulator in order to gain initial experience without any risks