Asymptotic behaviour of the inductance coefficient for thin conductors

We study the asymptotic behaviour of the inductance coefficient for a thin toroidal inductor whose thickness depends on a small parameter \eps>0. We give an explicit form of the singular part of the corresponding potential u\ue which allows to construct the limit potential $u$ (as \eps\to 0) and an approximation of the inductance coefficient L\ue. We establish some estimates of the deviation u\ue-u and of the error of approximation of the inductance. We show that L\ue behaves asymptotically as \ln\eps, when \eps\to 0

A flexible modular master programme in technology developped whithin a Tempus Project

In today’s competitive industry and in view of recent economic turbulences new frontiers of challenges emerge that require new educational paradigms accompanied by new tools and methodologies applicable to all aspects of engineering areas including the functional and organizational aspects. In accordance with the objectives stipulated by the Council of European Union work programme on the future of education and training, a Tempus project (2010-2013) has been mounted to develop a novel model for modular programmes to be used in education of technology specialities at master level. The model is implemented in manufacturing technology and management area and has general applicability for technology education in several fields. The main feature of this project consists in flexibility, adaptability, dynamic interactivity while consolidating theoretical and practical skills. MasTech is the name of a flexible modular master two-year programme in technology being developed according to the Bologna process that is to be adapted to the particular conditions of the universities in Algeria, Morocco and Tunisia. Three European Universities (Sweden, Germany, France) are involved in the project. This paper introduces MasTech and describes the different steps that have been followed to develop the master programme taking into account both academic and industrial needs and priorities. Results are expressed in terms of a professional master programme that has been submitted for accreditation.TEMPUS - MASTECH -2010 - 3369 / 001 - 00

Kinetic decomposition for periodic homogenization problems

We develop an analytical tool which is adept for detecting shapes of oscillatory functions, is useful in decomposing homogenization problems into limit-problems for kinetic equations, and provides an efficient framework for the validation of multi-scale asymptotic expansions. We apply it first to a hyperbolic homogenization problem and transform it to a hyperbolic limit problem for a kinetic equation. We establish conditions determining an effective equation and counterexamples for the case that such conditions fail. Second, when the kinetic decomposition is applied to the problem of enhanced diffusion, it leads to a diffusive limit problem for a kinetic equation that in turn yields the effective equation of enhanced diffusion

A Smart Grid Voltage Sag Detector using an EEMD-Based Approach

International audienceSmart grids have become a focal point in renewable energy source researches. Sustainability and viability of distributed grids are highly dependent on the reduction of the operational and maintenance costs. The most efficient way of reducing these costs would be to continuously monitor the condition of these systems. This allows for early detection of the power quality degeneration, and facilitating a proactive response, prevent a fault ride-through the renewable energy conversion system, minimizing downtime, and maximizing productivity. This paper provides then the assessment of an advanced signal processing technique (demodulation tool) using the instantaneous power for voltage sags detection in smart grids

Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter

International audienc

Modeling Retention Indices of a Series Components Food and Pollutants of the Environment: Methods; OLS, LAD

The gas chromatographic retention indices for 89 pyrazines of test and 25 of validation on O V-101 and Carbowax -20M are successfuty modeled with the ald of a computer and the Software system Structural descriptors are calculated and multiple linear regression analysis are used to generate model equations relating structural features to observed retention characteristics then was treated with two methods The detection of influential observations for the standard least squares regression model is a problem which has been extensively studied LAD regression diagnostics offers alternative dicapproaches whose main feature is the robustness Here a nonparametric method for detecting influential observations is presented and compared with other classical diagnostics methods Comparisons are between models generated for the two stationary was carried out with two methods and descriptors that may encode differences in solute interactions with stationary phases of differing polarity are discussed and validated results in the state approached by the tests statistics Test of Anderson-Darling shapiro-wilk Agostino Jarque-Bera and the confidence interval thanks to the concept of robustness to check if the distribution of the errors is really approximat

Joint segmentation of multivariate time series with hidden process regression for human activity recognition

The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as health monitoring, well being, security, etc. There is therefore a growing need to build accurate models which can take into account the variability of the human activities over time (dynamic models) rather than static ones which can have some limitations in such a dynamic context. In this paper, the problem of activity recognition is analyzed through the segmentation of the multidimensional time series of the acceleration data measured in the 3-d space using body-worn accelerometers. The proposed model for automatic temporal segmentation is a specific statistical latent process model which assumes that the observed acceleration sequence is governed by sequence of hidden (unobserved) activities. More specifically, the proposed approach is based on a specific multiple regression model incorporating a hidden discrete logistic process which governs the switching from one activity to another over time. The model is learned in an unsupervised context by maximizing the observed-data log-likelihood via a dedicated expectation-maximization (EM) algorithm. We applied it on a real-world automatic human activity recognition problem and its performance was assessed by performing comparisons with alternative approaches, including well-known supervised static classifiers and the standard hidden Markov model (HMM). The obtained results are very encouraging and show that the proposed approach is quite competitive even it works in an entirely unsupervised way and does not requires a feature extraction preprocessing step

Asymptotic behaviour of the inductance coefficient for thin conductors

We study the asymptotic behaviour of the inductance coefficient for a thin toroidal inductor whose thickness depends on a small parameter \eps>0. We give an explicit form of the singular part of the corresponding potential u\ue which allows to construct the limit potential $u$ (as \eps\to 0) and an approximation of the inductance coefficient L\ue. We establish some estimates of the deviation u\ue-u and of the error of approximation of the inductance. We show that L\ue behaves asymptotically as \ln\eps, when \eps\to 0

Modeling Retention Indices of a Series Components Food and Pollutants of the Environment: Methods; OLS, LAD

The gas chromatographic retention indices for 89 pyrazines of test and 25 of validation on O V-101 and Carbowax -20M are successfuty modeled with the ald of a computer and the Software system Structural descriptors are calculated and multiple linear regression analysis are used to generate model equations relating structural features to observed retention characteristics then was treated with two methods The detection of influential observations for the standard least squares regression model is a problem which has been extensively studied LAD regression diagnostics offers alternative dicapproaches whose main feature is the robustness Here a nonparametric method for detecting influential observations is presented and compared with other classical diagnostics methods Comparisons are between models generated for the two stationary was carried out with two methods and descriptors that may encode differences in solute interactions with stationary phases of differing polarity are discussed and validated results in the state approached by the tests statistics Test of Anderson-Darling shapiro-wilk Agostino Jarque-Bera and the confidence interval thanks to the concept of robustness to check if the distribution of the errors is really approximat