A combined methodology of H∞ fuzzy tracking control and virtual reference model for a PMSM

The aim of this paper is to present a new fuzzy tracking strategy for a permanent magnet synchronous machine (PMSM) by using Takagi-Sugeno models (T-S). A feedback-based fuzzy control with h-infinity tracking performance and a concept of virtual reference model are combined to develop a fuzzy tracking controller capable to track a reference signal and ensure a minimum effect of disturbance on the PMSM system. First, a T-S fuzzy model is used to represent the PMSM nonlinear system with disturbance. Next, an integral fuzzy tracking control based on the concept of virtual desired variables (VDVs) is formulated to simplify the design of the virtual reference model and the control law. Finally, based on this concept, a two-stage design procedure is developed: i) determine the VDVs from the nonlinear system output equation and generalized kinematics constraints ii) calculate the feedback controller gains by solving a set of linear matrix inequalities (LMIs). Simulation results are provided to demonstrate the validity and the effectiveness of the proposed method

Integration of 2D Textural and 3D Geometric Features for Robust Facial Expression Recognition

Recognition of facial expressions is critical for successful social interactions and relationships. Facial expressions transmit emotional information, which is critical for human-machine interaction; therefore, significant research in computer vision has been conducted, with promising findings in using facial expression detection in both academia and industry. 3D pictures acquired enormous popularity owing to their ability to overcome some of the constraints inherent in 2D imagery, such as lighting and variation. We present a method for recognizing facial expressions in this article by combining features extracted from 2D textured pictures and 3D geometric data using the Local Binary Pattern (LBP) and the 3D Voxel Histogram of Oriented Gradients (3DVHOG), respectively. We performed various pre-processing operations using the MDPA-FACE3D and Bosphorus datasets, then we carried out classification process to classify images into seven universal emotions, namely anger, disgust, fear, happiness, sadness, neutral, and surprise. Using Support Vector Machine classifier, we achieved the accuracy of 88.5 % and 92.9 % on the MDPA-FACE3D and the Bosphorus datasets, respectively

Characterization and chemosystematics of Algerian thuriferous juniper (Juniperus thurifera L.)

Leaf essential oils of Juniperus thurifera L. collected at six locates from Aures Mountains in Algeria, were analyzed by gas chromatography (GC) and gas chromatography-mass spectrometry (GC/MS) for the first time. The main components identified were: sabinene (5.2–19.78%), terpinene-4-ol (5.43–9.37%), elemol (0.69–7.61%), δ-cadinene (3.26–6.11%). The results were submitted to principal component and cluster analysis which allowed two groups of essential oils to be distinguished: cluster I (Tkout 1, Tibhirine, Tizi nerrsas and El-Mahmel) containing a high percentage of sabinene, linalyle acetate, linalool, γ-terpinene, myrcene, and bulnesol, and cluster II (Tkout 2 and Chelia) characterized by a high content of valencene, γ-eudesmol, epi-α-cadinol, epi-α-muurolol, α-cadinol, and 4-epi-abietal. The chemovariation observed appears to be determined by the environment. Chemical composition of leaf essential oils of Algerian thuriferous juniper is similar to that of oils of J. thurifera from Moroccan populations, and different from that of essential oils obtained from European populations. Therefore, we propose to name the Algerian taxon: Juniperus thurifera subsp. africana var. aurasiaca Syst Nov

Chemical Composition and Antibacterial Activity of Berries Essential Oil of Algerian Juniperus thurifera (Var. aurasiaca)

Background: Over the past decade, most antibiotic research programs have focused on finding new compounds with antimicrobial activity. This study aims to investigate the chemical composition and antibacterial activity of the essential oil (EO) extracted from ripe berries of Algerian Juniperus thurifera var. aurasiaca. Methods: The chemical composition of J. thurifera EO extracted by hydrodistillation was analyzed by using the GC-MS technique. Antibacterial activity of EO alone and in combination with three conventional antibiotics was assessed by using disc diffusion method against four bacterial strains. Results: Thirty-five components were identified, representing ~87 % of the oil. The main components were m-mentha-6,8-diene (15.43 %), β-pinene (10.59 %), elemol (8.31 %) and terpinene-4-ol (7.44 %). The essential oil showed strong antibacterial activity against S. aureus and E. coli, but no activity against P. aeruginosa and B. subtilis. Synergistic effects were observed because of the combined application of EO with gentamicin against all strains tested, and with amoxicillin against B. subtilis. Furthermore, the combination of EO/cefazolin demonstrated an additive effect against B. subtilis. In contrast, the combination of EO with amoxicillin and céfazoline revealed antagonistic effects against S. aureus, E. coli, and P. aeruginosa. Conclusion: This is the first report on the chemical composition and antibacterial activity of Algerian juniper berries’ essential oil. The results indicate that the studied EO may be a promising source of antibacterial compounds that could be useful for pharmaceutical applications especially in combination with conventional antibiotics

Distribution Maps of the Different Levels of Elemental Concentrations Accumulated by the Lichen in the Northeast of Algeria

An evaluation of environmental pollution in the region of Bordj Bou Arreridj (BBA), Algeria according to metallic trace elements has been carried out, to determine the levels of the 10 elements accumulated in lichens and the different sources found in the region. A total of 192 samples of Xanthoria parietina lichen were collected over an area of 3920.42 km². Sampling sites include urban sites, rural sites, green parks, sites near high traffic streets and industrial enterprises. The lichen samples were analyzed by FAAS for the ten elements and their concentrations were mapped. Concentrations of Pb, Cd, Sb and Zn were higher at urban sites and increased with proximity to highways and industrial areas. These results suggest that the composition of lichen elements is strongly affected by road traffic. While the sources of the elements Co, Ni, Fe, Mn and Cr probably come from dust from quarrying and contaminated soil deposits in particular, to the north and west of the region. This mapping of metal pollution can establish the first biological monitoring network in the study area. Keywords: Biomonitoring of lichens, Metallic elements, Pollution sources, Distribution maps, BBA. DOI: 10.7176/JEES/12-2-03 Publication date: February 28th 202

Multivariate Statistical Process Control Using Enhanced Bottleneck Neural Network

Monitoring process upsets and malfunctions as early as possible and then finding and removing the factors causing the respective events is of great importance for safe operation and improved productivity. Conventional process monitoring using principal component analysis (PCA) often supposes that process data follow a Gaussian distribution. However, this kind of constraint cannot be satisfied in practice because many industrial processes frequently span multiple operating states. To overcome this difficulty, PCA can be combined with nonparametric control charts for which there is no assumption need on the distribution. However, this approach still uses a constant confidence limit where a relatively high rate of false alarms are generated. Although nonlinear PCA (NLPCA) using autoassociative bottle-neck neural networks plays an important role in the monitoring of industrial processes, it is difficult to design correct monitoring statistics and confidence limits that check new performance. In this work, a new monitoring strategy using an enhanced bottleneck neural network (EBNN) with an adaptive confidence limit for non Gaussian data is proposed. The basic idea behind it is to extract internally homogeneous segments from the historical normal data sets by filling a Gaussian mixture model (GMM). Based on the assumption that process data follow a Gaussian distribution within an operating mode, a local confidence limit can be established. The EBNN is used to reconstruct input data and estimate probabilities of belonging to the various local operating regimes, as modelled by GMM. An abnormal event for an input measurement vector is detected if the squared prediction error (SPE) is too large, or above a certain threshold which is made adaptive. Moreover, the sensor validity index (SVI) is employed successfully to identify the detected faulty variable. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms, and is hence expected to better monitor many practical processes

Fault-tolerant power extraction strategy for photovoltaic energy systems

Photovoltaic (PV) arrays are subject to various types of environmental disturbances and component-related faults that affect their normal operation and result in a considerable energy loss. The nonlinear current-voltage (I-V) characteristic curve of the PV array prevents the detection and isolation of the faults and also makes the tracking of the maximum power operating point (MPP) more difficult. Fault detection and identification (FDI) techniques methods have been proposed to detect the presence of faults and isolate them. Many maximum power point tracking (MPPT) methods have been proposed to find the best operating point in the presence of disturbed environmental conditions. However, existing FDI methods do not consider the tracking of the MPP in faulted operating conditions, and available MPP tracking methods do not consider the occurrence of faults in the PV system. The objective of this study is to propose a fault-tolerant control (FTC) strategy to detect the presence of abnormal operating conditions and reconfigure the MPPT procedure to search for the new suboptimal operating point. The FDI method is based on monitoring the PV panel generated power for the presence of abrupt changes; the MPPT reconfiguration is based on a combination between Incremental Conductance (IncCond) Algorithm and an Improved Current-based Particle Swarm Optimization (ICPSO) tracking technique. Simulation and experimental results show an excellent performance of the proposed FTC method in the presence of various types of faults

Ability of metal trace elements accumulation by Lichens, Xanthoria parietina and Ramalina farinacea, in Megres area (Setif, Algeria)

The accumulating ability of the atmospheric Metal Trace Elements (MTE) of two lichenic species thalli; Xanthoria parietina and Ramalina farinacea were evaluated in the region of Megres. The recorded concentrations of MTE (Fe, Cu, Mn, Cd, and Pb) were determined by atomic absorption spectrophotometry (AASF). The ability to accumulate MTE in X. parietina thalli is considerably greater than that of the fruticulous lichen R. farinacea in all stations studied. The general pattern of the elements accumulated in the thalli of the two species in decreasing order of their concentrations was Fe> Mn> Pb> Cu> Cd. The Fe values are very high in X. parietina thalli with an average of 35237.5 ± 3394.2 mg/kg dry wt. In contrast, the Pb concentrations are high, especially in the southern station of the Megres region. The results showed that X. parietina is a hyper-accumulating species of MTE, compared to R. farinacea. This work highlights the ecological importance of this species as a stable and resistant pioneer in this fragile region

Online quality measurement of face localization obtained by neural networks trained with Zernike moments feature vectors

International audienceQuality measurement of face localization using neural networks is presented in this communication. First, neural network was trained with Zernike moments feature parameters vectors. Coordinate vectors of pixels surrounding faces in images were used as target vectors on the supervised training procedure. Thus, trained neural network provides on its output layer a coordinate's vector (?,θ) representing pixels surrounding the face contained in treated image. In second stage, another neural network, trained using TSL color space of images, is used to give a measure quantifying the quality of the localization obtained in the first stage. Experiments of the proposed method were carried out on the XM2VTS database

Fuzzy Evidential Approximate Reasoning Scheme for fault diagnosis of complex processes

International audienceSupervision of nonlinear and complex processes is of great importance to industries as a means of achieving improved productivity and stable product quality. The advanced model-based condition monitoring methodologies, can contribute significantly to the achievement of these objectives. In this paper, a fuzzy evidential model based fault detection and diagnosis method is presented. The multi-model based symptom generation procedure is used to detect changes of the current process behavior. The diagnosis task is accomplished by an evidential approximate reasoning scheme to handle different kinds of uncertainty that are inherently present in many real word processes. The validity of the method is illustrated on the well-known benchmark of three tanks and different faults can be detected and isolated continuously, over all ranges of operation