An evidential fusion approach for activity recognition in ambient intelligence environments

With the growing emergence of ambient intelligence, ubiquitous computing, sensor networks and wireless networking technologies, “ubiquitous networked robotics” is becoming an active research domain of intelligent autonomous systems. It targets new innovative applications in which robotic systems will become part of these networks of artifacts to provide novel capabilities and various assistive services anywhere and anytime, such as healthcare and monitoring services for elderly in Ambient Assisted Living (AAL) environments. Situation recognition, in general, and activity recognition, in particular, provide an added value on the contextual information that can help the ubiquitous networked robot to autonomously provide the best service that meet the needs of the elderly. Dempster–Shafer theory of evidence and its derivatives are an efficient tool to handle uncertainty and incompleteness in smart homes and ubiquitous computing environments. However, their combination rules yield counter-intuitive results in high conflicting activities. In this paper, we propose a new approach to support conflict resolution in activity recognition in AAL environments. This approach is based on a new mapping for conflict evidential fusion to increase the efficiency and accuracy of activity recognition. It gives intuitive interpretation for combining multiple sources in all conflicting situations. The proposed approach, evaluated on a real world smart home dataset, achieves 78% of accuracy in activity recognition. The obtained results outperform those obtained with the existing combination rules.     

Adaptive GA-based reconfiguration of photovoltaic array combating partial shading conditions

This paper concerns the study and simulation of a PV array self-organizing configuration. It introduces a new method to reconfigure the PV array using a genetic algorithm in order to maximize the output power as well as reducing the number of switching. The proposed method involves the simulation of a PV array composed of 16 panels 4 strings with 4 panels in series and associated parallel, as well as an algorithm that controls the improvement of the overall performance under different shading conditions. The obtained results using MATLAB/Simulink simulation show improvement rating varying between 106.49 and 171.03%, which is huge compared to a static configuration operating below the total available power. Another important point is the number of iterations needed to find the optimal configuration (between 6 and 132 for a population of 50 configurations tested at each generation); this means that in the worst case (132 iterations), the proposed algorithm performed 132 × 50 = 6600 configurations instead of 16¹⁶ = 1.84 × 10¹⁹ necessary in case of exhaustive search to test all possible configurations. This last point is very important in the implementation of the proposed system in auto-tuning of the system in real-time condition. Besides using genetic algorithm to track the optimal configuration, our main contribution consists of improving the output power while reducing the number of switching by keeping PV modules, if possible, in same position (0 switching) or on the same line/column (1 switching) in few iteration needing only two sensors one for the voltage and another for the current of the PV array.

     

An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem

Flexible job-shop scheduling problem (FJSP) is very important in many research fields such as production management and combinatorial optimization. The FJSP problems cover two difficulties namely machine assignment problem and operation sequencing problem. In this paper, we apply particle swarm optimization (PSO) algorithm to solve this FJSP problem aiming to minimize the maximum completion time criterion. Various benchmark data taken from literature, varying from Partial FJSP and Total FJSP, are tested. Experimental results proved that the developed PSO is enough effective and efficient to solve the FJSP. Our other objective in this paper, is to study the distribution of the PSO-solving method for future implementation on embedded systems that can make decisions in real time according to the state of resources and any unplanned or unforeseen events. For this aim, two multi-agent based approaches are proposed and compared using different benchmark instances.     

Three-dimensional face recognition using shapes of facial curves

We study shapes of facial surfaces for the purpose of face recognition. The main idea is to 1) represent surfaces by unions of level curves, called facial curves, of the depth function and 2) compare shapes of surfaces implicitly using shapes of facial curves. The latter is performed using a differential geometric approach that computes geodesic lengths between closed curves on a shape manifold. These ideas are demonstrated using a nearest-neighbor classifier on two 3D face databases: Florida State University and Notre Dame, highlighting a good recognition performance     

Crystallization kinetics study of dynamically vulcanized PA6/NBR/HNTs nanocomposites by nonisothermal differential scanning calorimetry

Investigation of crystallization behavior and kinetics of thermoplastic elastomer nanocomposites was the subject of limited works because of complexities associated with semiexperimental modeling of such phenomenon in a system containing components having completely different behavior in the molten state. Nonisothermal crystallization kinetics of dynamically vulcanized PA6/NBR/HNTs thermoplastic elastomer nanocomposites was mathematically modeled applying well‐known Avrami, Ozawa, and Mo theoretical models to the differential scanning calorimetry data gathered at various cooling rates. It was found that HNTs contribute as nucleating agents to the crystallization kinetics and cause acceleration of crystallization. Activation energy of the crystallization was calculated by correlating the crystallization peak temperature with the cooling rate using Kissinger model. It was found that Mo equation could properly describe nonisothermal crystallization kinetics of the PA6/NBR/HNTs thermoplastic elastomer nanocomposites. This was recognized from the obtained parameters of Mo equation in terms of HNTs loading level, which suggested a higher rate for dynamic crystallization. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46488.     

Characterization of tin dioxide film for chemical vapors sensor

Recently, oxide semiconductor material used as transducer has been the central topic of many studies for gas sensor. In this paper we investigated the characteristic of a thick film of tin dioxide (SnO₂) film for chemical vapor sensor. It has been prepared by screen-printing technology and deposited on alumina substrate provided with two gold electrodes. The morphology, the molecular composition and the electrical properties of this material have been characterized respectively by Atomic Force Spectroscopy (AFM), Fourier Transformed Infrared Spectroscopy (FTIR) and Impedance Spectroscopy (IS). The electrical properties showed a resistive behaviour of this material less than 300 °C which is the operating temperature of the sensor. The developed sensor can identify the nature of the detected gas, oxidizing or reducing.     

Role of organic nitro compounds in selective reduction of NOx with ethanol over different supported silver catalysts

The adsorption of organic nitro compounds such as nitromethane and nitroethane on different supported silver catalysts (Ag/Al₂O₃, Ag/TiO₂, Ag/SiO₂) has been studied using infrared spectroscopy. The adsorbed NCO species formation was strongly influenced by the catalyst support and therefore clearly detected on Ag/Al₂O₃ and Ag/TiO₂ catalysts by thermal decomposition of nitromethane and nitroethane at temperatures higher than 150°C. With the Ag/SiO₂ catalyst, very little NCO formation was observed at 350°C. On the other hand, the catalyst support was found to affect the N₂ formation in the selective reduction of NO_x on supported silver catalysts. On the basis of these findings, the role of adsorbed nitromethane, nitroethane and isocyanate species in the selective reduction of NO_x is discussed with respect to the catalyst support effect and the catalytic activity. This revised version was published online in July 2006 with corrections to the Cover Date.     

Inhibition of mild steel corrosion in hydrochloric acid solution by triazole derivatives: Part I. Polarization and EIS studies

A comparative study of 5-amino-1,2,4-triazole (5-ATA), 5-amino-3-mercapto-1,2,4-triazole (5-AMT), 5-amino-3-methylthio-1,2,4-triazole (5-AMeTT) and 1-amino-3-methylthio-1,2,4-triazole (1-AMeTT) as inhibitors for mild steel corrosion in 0.1 M HCl solution at 20 °C was carried out. Potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) techniques were applied to study the metal corrosion behaviour in the absence and presence of different concentrations of these inhibitors under the influence of various experimental conditions. Measurements of open circuit potential (OCP) as a function of time till reaching the steady-state potentials (E_st) were also established. The studies have shown that 5-AMT was the most efficient inhibitor reaching values of inhibition efficiency (IE%) up to 96% at a concentration of 10⁻³ M. Polarization curves showed that the four studied compounds act as mixed inhibitors. The potential of zero charge (PZC) of mild steel was determined in 0.1 M HCl in the absence and presence of the studied inhibitors. The effect of chemical structure of the four tested inhibitors was discussed. Results obtained from OCP versus time, polarization and impedance measurements are in good agreement.     

Transition metal carbides and nitrides as oxygen reduction reaction catalyst or catalyst support in proton exchange membrane fuel cells (PEMFCs)

Fuel cells are potentially efficient, silent, and environmentally friendly tools for electrical power generation. One of the obstacles facing the development and the commercialization of fuel cells is the dependence on the precious metal catalyst, i.e., Platinum (Pt) and Pt - alloy, especially at the cathode where high catalyst loading used to compensate the sluggish oxygen reduction reaction (ORR). Pt is not only an expensive and rare element but also has insufficient durability. The development of an efficient non-precious catalyst, i.e., electrochemically active, chemically and mechanically stable, and electrically conductive, is one of the basic requirements for the commercialization of fuel cells. The bonding to carbon and nitrogen to form metal carbides and nitrides modify the nature of the d-band of the parent metal, thus improve its catalytic properties relative to the parent metals to be similar to those of group VIII noble metals. In this article, we summarize the progress in the development of the transition metal carbides (TMCs) and transition metals nitrides (TMNs) relative to their application as catalysts for the ORR in fuel cells. The preparation of TMCs and TMNs via different routes which significantly affects its activity is discussed. The ORR catalytic activity of the TMCs and TMNs as a non-precious catalyst or catalyst support in fuel cells is discussed and compared to that of the Pt-based catalyst in this review article. Moreover, the recent progress in the preparation of the nano-sized (which is a critical factor for increasing the activity at low temperature) TMCs and TMNs are discussed.