Analytical modeling of bending and vibration of thick advanced composite plates using a four-variable quasi 3D HSDT

Engineering with Computers - This work presents an efficient and original high-order shear and normal deformation theory for the static and free vibration analysis of functionally graded plates....     

Design,optimization and simulation of a low-voltage shunt capacitive RF-MEMS switch

This paper presents the design, optimization and simulation of a radio frequency (RF) micro-electromechanical system (MEMS) switch. The capacitive RF-MEMS switch is electrostatically actuated. The structure contains a coplanar waveguide, a big suspended membrane, four folded beams to support the membrane and four straight beams to provide the bias voltage. The switch is designed in standard 0.35 µm complementary metal oxide semiconductor process and has a very low pull-in voltage of 3.04 V. Taguchi method and weighted principal component analysis is employed to optimize the geometric parameters of the beams, in order to obtain a low spring constant, low pull-in voltage, and a robust design. The optimized parameters were obtained as w = 2.5 µm, L1 = 30 µm, L2 = 30 µm and L3 = 65 µm. The mechanical and electrical behaviours of the RF-MEMS switch were simulated by the finite element modeling in software of COMSOL Multiphysics 4.3^® and IntelliSuite v8.7^®. RF performance of the switch was obtained by simulation results, which are insertion loss of ?5.65 dB and isolation of ?24.38 dB at 40 GHz.     

Smooth estimates of multiple quantiles in dynamically varying data streams

Pattern Analysis and Applications - In this paper, we investigate the problem of estimating multiple quantiles when samples are received online (data stream). We assume a dynamical system, i.e.,...     

Robust Fault Tolerant Control Design for Nonlinear Systems not Satisfying Matching and Minimum Phase Conditions

International Journal of Control, Automation and Systems - This paper proposes an adaptive sliding mode fault tolerant control design for lipschitz nonlinear system subject to simultaneous actuator...     

Experimental Investigation of Passive Fault Tolerant Control for Induction Machine Using Sliding Mode Approach

This paper presents a passive fault tolerant control approach dedicated to stator inter‐turn short‐circuit fault of an induction machine. This control is based on sliding mode strategy and is implemented for validation on real‐time data acquisition and control platform. The proposed work highlights the integral sliding mode controller benefits during healthy and faulty operations. It can make the induction machine outputs to track their desired reference signals in finite‐time and allows to obtain a better dynamic response performances even in presence of fault. Moreover to avoid the use of a flux sensor, a second order sliding mode observer is used to estimate the rotor flux. Since the used observer converges in finite time, the closed‐loop stability of the proposed system (controller+observer) is proved using the Lyapunov stability theorem. Experimental results are conducted for squirrel cage induction machine to highlight the efficiency and applicability of the proposed fault tolerant control.     

Fuzzy reasoning framework to improve semantic video interpretation

A video retrieval system user hopes to find relevant information when the proposed queries are ambiguous. The retrieval process based on detecting concepts remains ineffective in such a situation. Potential relationships between concepts have been shown as a valuable knowledge resource that can enhance the retrieval effectiveness, even for ambiguous queries. Recent researches in multimedia retrieval have focused on ontology modeling as a common framework to manage knowledge. Handling these ontologies has to cope with issues related to generic knowledge management and processing scalability. Considering these issues, we suggest a context-based fuzzy ontology framework for video content analysis and indexing. In this paper, we focused on the way in which we modeled our fuzzy ontology: First, we populate automatically the generated ontology by gathering various available video annotation datasets. Then, the ontology content was used to infer enhanced video semantic interpretation. Finally, considering user feedback, the content of the ontology was improved. Experimental results showed that our approach achieves the goal of scalability while at the same time allowing better video content semantic interpretation.     

Study of Methane Decomposition on Fe/MgO-Based Catalyst Modified by Ni,Co, and Mn Additives

Catalytic decomposition of methane (CDM) generates clean hydrogen and carbon nanomaterials. In this study, methane decomposition to hydrogen and carbon was investigated over Ni-, Co-, or Mn-doped Fe/MgO catalysts. The doping effect of different metals, varying from 3 to 10?wt%, was investigated. The catalytic performance of the obtained materials (noted 15%Fe+x%metal/MgO) revealed that the doping effect of Ni, Co, and Mn significantly improved the activity of Fe/MgO. Among the Ni-doped catalyst series, the 15%Fe+3%Ni/MgO catalyst performed better than the rest of the Ni catalysts. The 6%Co-containing catalyst remained the best in terms of activity in the Co-doped catalyst series and the 15%Fe+6%Mn/MgO solid showed better methane conversion for the Mn-doped series. Overall, 3%Ni-containing catalyst displayed the best catalytic performance among all Ni-, Co-, and Mn-doped catalysts. XRD, N₂ sorption, and H₂ temperature-programmed reduction (TPR), Laser–Raman spectroscopy, thermogravimetric analysis (TGA) under air, and temperature-programmed oxidation (TPO) were used for catalyst characterization. The results revealed that all the doped catalysts exhibited better metallic active site distribution than 15%Fe/MgO and proved that metal doping played a crucial role in catalytic performance.     

Open writer identification from offline handwritten signatures by jointing the one-class symbolic data analysis classifier and feature-dissimilarities

Usually, a large number of reference signatures are required for building the writing style model from offline handwritten signatures (OHSs). Moreover, the existing writer identification systems from OHSs are generally closed systems that require a retraining process when a new writer is added. This paper proposes an open writer identification system from OHSs, based on a new scheme of the one-class symbolic data analysis (OC-SDA) classifier, using few reference signatures. For generating more data, intra-class feature-dissimilarities, generated from curvelet transform, are introduced for building the symbolic representation model (SRM) associated with each writer. Feature-dissimilarities allow capturing more efficiently the intra-personnel variability produced naturally by a writer and, thus, increase the inter-personnel variability. Instead of using the mean and the standard deviation for building the OC-SDA model, intra-class feature-dissimilarities generated for each writer are modeled through a new weighted membership function, inspired from the real probability distribution of training intra-class feature-dissimilarities. The comparative analysis against the state-of-the-art works shows that the proposed OC-SDA classifier outperforms the existing classifiers on three public signature datasets GPDS-300, CEDAR-55 and MCYT-75, using only five reference signatures, achieving 98.31%, 98.06% and 99.89%, respectively, even when a combination of multiple classifiers is performed or even using learned features. Moreover, the evaluation of the proposed writer identification system in front of skilled forgeries shows its ability to detect also possible forged signatures in addition to the genuine ones.