H ∞ filtering for singular bilinear systems with application to a single-link flexible-joint robot

In this paper, we consider the H _∞ filters design for singular bilinear systems. The approach is based on the parameterized solution of a set of constrained Sylvester equations. The exponential convergence and l ₂ gain attenuation problems are solved by using the bounded real lemma, which leads to linear matrix inequalities (LMI) formulation. Finally, a detailed design procedure is given for the estimation of the states of a flexible joint robot, which demonstrates the effectiveness of the proposed method.     

Constructed wetland as a low cost and sustainable solution for wastewater treatment adapted to rural settlements: the Chorfech wastewater treatment pilot plant

The paper presents the detailed design and some preliminary results obtained from a study regarding a wastewater treatment pilot plant (WWTPP), serving as a multistage constructed wetland (CW) located at the rural settlement of 'Chorfech 24' (Tunisia). The WWTPP implemented at Chorfech 24 is mainly designed as a demonstration of sustainable water management solutions (low-cost wastewater treatment), in order to prove the efficiency of these solutions working under real Tunisian conditions and ultimately allow the further spreading of the demonstrated techniques. The pilot activity also aims to help gain experience with the implemented techniques and to improve them when necessary to be recommended for wide application in rural settlements in Tunisia and similar situations worldwide. The selected WWTPP at Chorfech 24 (rural settlement of 50 houses counting 350 inhabitants) consists of one Imhoff tank for pre-treatment, and three stages in series: as first stage a horizontal subsurface flow CW system, as second stage a subsurface vertical flow CW system, and a third horizontal flow CW. The sludge of the Imhoff tank is treated in a sludge composting bed. The performances of the different components as well as the whole treatment system were presented based on 3 months monitoring. The results shown in this paper are related to carbon, nitrogen and phosphorus removal as well as to reduction of micro-organisms. The mean overall removal rates of the Chorfech WWTPP during the monitored period have been, respectively, equal to 97% for total suspended solids and biochemical oxygen demand (BOD5), 95% for chemical oxygen demand, 71% for total nitrogen and 82% for P-PO4. The removal of E. coli by the whole system is 2.5 log units.     

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.     

A quantitative model for software engineering trends

Many decision-makers in industry, government and academia routinely make decisions whose outcome depends on the evolution of software technology trends. Even though the stakes of these decisions are usually very high, decision makers routinely depend on expert opinions and qualitative assessments to model the evolution of software technology; both of these sources of decision-making information are subjective, are based on opinions rather than facts, and are prone to error. In this paper, we report on our ongoing work to build quantitative models of the evolution of software technology trends. In particular, we discuss how we took specific evolutionary models and merged them into a single (general-purpose) model. The original specific models are derived empirically using statistical methods on trend data we had collected over several years, and have been validated individually; in this paper we further validate the generic (general-purpose) model.     

Solid–Liquid Phase Behaviors of Binary Mixtures of Various Partial Acylglycerols by Differential Scanning Calorimetry

Monoacylglycerol (MAG), diacylglycerol (DAG), and triacylglycerol (TAG) are impurities in biodiesel and a major cause of precipitation. Understanding the behavior of such acylglycerols is essential for predicting biodiesel cold flow properties (CFPs). The previous study on MAG/MAG binary mixtures shows that they tend to solidify by forming molecular compounds. In contrast, TAG/TAG mixtures, which have been studied extensively, are commonly eutectic or monotectic systems, in which each component solidifies separately. The present study focuses on binary mixtures of DAG/DAG and different acylglycerol pairs (MAG/DAG, TAG/MAG, and DAG/TAG), and determination of their solid–liquid phase behavior by differential scanning calorimetry. These mixtures are found to behave as eutectic or monotectic systems with no sign of compound formation. As DAG and TAG have lower contents than MAG in biodiesel and they are unlikely to form molecular compounds with MAG, it is suggested that DAG and TAG have little effect on the biodiesel CFPs. Practical Applications: Biodiesel has attracted much interest because its blending with conventional fossil diesel has become more standard with biofuel mandates. From an energy perspective, the solid–liquid phase behavior of acylglycerols will contribute to building prediction models for biodiesel CFPs.     

Modification of Silica Nanoparticles with Cysteine or Methionine Amino Acids for the Removal of Uranium (VI) from Aqueous Solution

Silicon - Silica nanoparticles (SiO2-NPs), modified silica nanoparticles with cysteine (SiO2-Cys) or methionine (SiO2-Meth) were used for sorption of uranium (VI) ion from aqueous solution. Silica...     

Surface Activities, Foam Properties, HLB, and Krafft Point of Some n-Alkanesulfonates (C14–C18) with Different Isomeric Distributions

A homologue series of sodium secondary n-alkanesulfonates (C₁₄, C₁₆ and C₁₈) were obtained by photosulfochlorination process with two different reaction conditions. Different length chains with different isomeric distributions of n-alkanesulfonates are expected to present variations in physicochemical properties. In this investigation, the relationships between their isomeric distribution and their chain length and micellar behaviors were thoroughly explored. Their CMC at different temperatures were determined using specific conductivity and surface tension measurements. Through surface tension isotherms, the surface activities (??_CMC) were obtained. The surface absorption amounts (??_max) and the molecular areas (A _min) were calculated using Gibb??s equation. As expected, these surfactants exhibit good surface properties. It was shown that the CMC values increase with increasing the percentage of secondary isomers, with a surface tension decrease. It was also shown that the CMC values decrease with increasing chain length. The HLB values were calculated for each surfactant and the results obtained suggest that they are O/W emulsifiers. The foam properties of synthesized surfactants were evaluated and compared to those obtained for commercial samples. It was shown that the foamability is influenced both by the length of the hydrophobic moiety and the percentage of secondary isomers. It can be easily concluded that the C₁₄ sulfonates show the best foaming properties independently of their isomeric distribution. The Krafft point values obtained indicate that the micellization and the surfactant solubility mainly depend on the proportion of secondary isomers and the length of hydrophobic moiety.     

Fuel cells static and dynamic characterizations as tools for the estimation of their ageing time

This paper deals with a pattern-recognition-based diagnosis approach, which aim is to estimate the Fuel Cell (FC) operating time, and consequently its remaining duration life. With the method proposed, both static and dynamic information extracted from the stack (i.e. polarization curve records and Electrochemical Impedance Spectroscopy (EIS) measurements) can be used. The complete diagnosis method consists of several steps. First, features are extracted from EIS measurements and polarization curves independently. This enables us to simplify the extracted information without losing relevant information, and to remove noise. For the polarization curves, an empiric model is exploited to ensure the feature extraction. For the impedance spectra, both expert knowledge and parametric modeling are used to extract features. In particular, a latent regression model is used to split automatically the imaginary part of the spectra into several segments that are approximated by polynomials. The next step of the method consists in selecting the most relevant features from the whole set of extracted features. This helps us to estimate the operating time, while adjusting the complexity of the model. The final step of the approach is a linear regression that uses the selected subset of features to estimate the FC operating time. The performances of the proposed approach are evaluated on a dataset made up of EIS measurements and polarization curves extracted from two FC lifetime tests. A mean error of about 2 h over a global operating duration of 1000 h can be obtained. Moreover, the portability of the method is shown by considering another FC ageing test conducted on a different FC stack type.     

Estimation of fuel cell operating time for predictive maintenance strategies

Durability is one of the limiting factors for spreading and commercialization of fuel cell technology. That is why research to extend fuel cell durability is being conducted world wide. A pattern-recognition approach aiming to estimate fuel cell operating time based on electrochemical impedance spectroscopy measurements is presented here. It is based on extracting the features from the impedance spectra. For that purpose, two approaches have been investigated. In the first one, particular points of the spectrum are empirically extracted as features. In the second approach, a parametric modeling is performed to extract features from both the real and the imaginary parts of the impedance spectrum. In particular, a latent regression model is used to automatically split the spectrum into several segments that are approximated by polynomials. The number of segments is adjusted taking into account the a priori knowledge about the physical behavior of the fuel cell components. Then, a linear regression model using different subsets of extracted features is employed for an estimate of the fuel cell operating time. The effectiveness of the proposed approach is evaluated on an experimental dataset. Allowing the estimation of the fuel cell operating time, and consequently its remaining duration life, these results could lead to interesting perspectives for predictive fuel cells maintenance policy.