A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction

Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data.     

Application du recuit simulé à la synthèse d’antennes en réseau constituées d’éléments annulaires imprimés

This publication consists of an analysis and a synthesis by the amplitude law of linear and planar ring printed antennas array. The analysis of the structure is based on the modified cavity model which allows taking into account the fringing fields effects. The synthesis problem is solved by the simulated annealing method based on a random process research controlled by temperature parameter. The feeding network amplitude permitting to produce an optimal radiation pattern is then given. Various synthesis results for linear and planar arrays are presented.     

Level of natural and artificial radioactivity in Algeria

A national environmental sampling program was carried out during 1993 to determine natural and artificial radionuclides contents in the (0-15 cm) upper layer of the soil. The main objective was to establish a radioactive reference level in the whole territory, since 131I, 134Cs and 137Cs were detected in most of the analysed samples collected right after the Chernobyl accident (May 1986). Soil samples were analysed by direct counting by gamma-ray spectrometry. In addition, terrestrial gamma-ray dose rates in air have been measured out of doors throughout Algeria. In each of the 48 administrative divisions of the country selected sites were chosen to collect soil samples and measure gamma-ray dose rates. The gamma-emitting radionuclides resulting from the radioactive decay of 238U and 232Th, 40K and 137Cs were detected in most of the analysed samples. Radioactivity concentrations in Bq kg-1 dry mass in soil samples of 226Ra, 214Pb, 214Bi, 212Pb, 228Ac, 40K and 137Cs range between (5-176), (2-107), (3-65), (2-97), (3-144), (36-1405) and (0.3-41) respectively. In addition, six selected soil samples were analysed to determine plutonium isotopes contents. Radioactivity concentrations in Bq kg-1 dry mass of 238Pu and 239 + 240Pu vary between (0.012-0.013) and (0.24-0.61) respectively. The dose rates in air measured over the whole country were found to range between 20 and 133 nGy h-1. Presence of 137Cs has been clearly observed. An approach has been made to determine its origin, considering the global fallout, the Chernobyl accident and the French nuclear bomb tests in the 60s as the main potential sources. It is concluded that Algeria has indeed been affected by the Chernobyl accident.     

A new application of Hydroxyapatite extracted from dromedary bone: Adsorptive removal of Congo red from aqueous solution

The aim of this paper is to evaluate the efficiency of the natural hydroxyapatite powder for anionic dye (Congo red) elimination from water and optimization of adsorption process parameters that are contact time (0-360 min), solution pH (4-9), initial dye concentration (400-2400 mg/l), and adsorbent dosage (0.4-20 g/l). Natural hydroxyapatite with high surface area was prepared from animal agriculture waste dromedary bone. X-ray diffraction, transmission electron microscopy, transform infrared spectroscopy, and elemental analysis characterizations of the powder prepared show a hexagonal structure, the irregular form of hydroxyapatite and presence of other elements as trace element. The results of adsorption tests demonstrated the high efficiency of natural hydroxyapatite for removal of Congo red from water solution whose maximum experimental capacity adsorption of Congo red by natural hydroxyapatite is 900 mg/g at pH 4.5, equilibrium time 240 min and adsorbent dosage 2 g/l. According to the correlation coefficient value, the pseudo-second order kinetic model identified the sorption mechanism of pollutant used on natural hydroxyapatite. The Freundlich isotherm is the best representative theoretical model of the dye molecules’ adsorption on the natural hydroxyapatite support.     

Graphic approach for the determination of the existence of sequences guaranteeing observability of switched linear systems

This paper deals with the design of sequences allowing the observability of the state of a switched linear system. The proposed method, based on a graph-theoretic approach, needs only the knowledge of the system’s structure. The necessary and sufficient conditions for the existence of at least one switching sequence guaranteeing the observability of a switched structured system are provided. Such conditions are particularly intuitive and they can be implemented with classic algorithms of the graph theory.     

Design optimization of the quantization and a pipelined 2D-DCT for real-time applications

The Discrete Cosine Transform (DCT) is one of the most widely used techniques for image compression. Several algorithms are proposed to implement the DCT-2D. The scaled SDCT algorithm is an optimization of the DCT-1D, which consists in gathering all the multiplications at the end. In this paper, in addition to the hardware implementation on an FPGA, an extended optimization has been performed by merging the multiplications in the quantization block without having an impact on the image quality. A simplified quantization has been performed also to keep higher the performances of the all chain. Tests using MATLAB environment have shown that our proposed approach produces images with nearly the same quality of the ones obtained using the JPEG standard. FPGA-based implementations of this proposed approach is presented and compared to other state of the art techniques. The target is an an Altera Cyclone II FPGA using the Quartus synthesis tool. Results show that our approach outperforms the other ones in terms of processing-speed, used resources and power consumption. A comparison has been done between this architecture and a distributed arithmetic based architecture.     

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

Magnetic resonance imaging (MRI) is increasingly used in the diagnosis of Alzheimer's disease (AD) in order to identify abnormalities in the brain. Indeed, cortical atrophy, a powerful biomarker for AD, can be detected using structural MRI (sMRI), but it cannot detect impairment in the integrity of the white matter (WM) preceding cortical atrophy. The early detection of these changes is made possible by the novel MRI modality known as diffusion tensor imaging (DTI). In this study, we integrate DTI and sMRI as complementary imaging modalities for the early detection of AD in order to create an effective computer-assisted diagnosis tool. The fused Bag-of-Features (BoF) with Speeded-Up Robust Features (SURF) and modified AlexNet convolutional neural network (CNN) are utilized to extract local and deep features. This is applied to DTI scalar metrics (fractional anisotropy and diffusivity metric) and segmented gray matter images from T1-weighted MRI images. Then, the classification of local unimodal and deep multimodal features is first performed using support vector machine (SVM) classifiers. Then, the majority voting technique is adopted to predict the final decision from the ensemble SVMs. The study is directed toward the classification of AD versus mild cognitive impairment (MCI) versus cognitively normal (CN) subjects. Our proposed method achieved an accuracy of 98.42% and demonstrated the robustness of multimodality imaging fusion.     

Efficient data aggregation with in-network integrity control for WSN

Energy is a scarce resource in Wireless Sensor Networks (WSN). Some studies show that more than 70% of energy is consumed in data transmission in WSN. Since most of the time, the sensed information is redundant due to geographically collocated sensors, most of this energy can be saved through data aggregation. Furthermore, data aggregation improves bandwidth usage and reduces collisions due to interference. Unfortunately, while aggregation eliminates redundancy, it makes data integrity verification more complicated since the received data is unique.     

Effects of aluminium nitride silane-treatment on the mechanical and thermal properties of polybenzoxazine matrix

A new kind of polymer composite, produced from the typical polybenzoxazine and 0–30 wt-% native and silane-treated aluminium nitride (T-AlN), was investigated. The mechanical tests revealed a significant increase in the microhardness and flexural properties upon adding the T-AlN particles compared to that obtained from the untreated ones. By adding 0–30 wt-% T-AlN, the tensile moduli were accurately reproduced by the Halpin-Tsai and Nielsen models. At 30 wt-% T-AlN, dynamic mechanical analysis showed a significant increase in the storage moduli and the glass transition temperature (T_g), reaching 3.2?GPa and 217°C, respectively. The thermal stability of these materials was significantly improved upon the addition of the T-AlN fillers. These improvements are attributed to the high thermal and mechanical properties of the fillers and their good dispersion and adhesion in and to the matrix as revealed by a morphological analysis.     

Portfolio Selection Using Multivariate Semiparametric Estimators and a Copula PCA-Based Approach

Computational Economics - This paper investigates the implications for portfolio theory of using multivariate semiparametric estimators and a copula-based approach, especially when the number of...