Navigation Scheme with Priority-Based Scheduling of Mobile Agents: Application to AGV-Based Flexible Manufacturing System

This paper proposes a new navigation approach for mobile agents in AGV (Autonomous Guided Vehicles)-based flexible manufacturing system. The navigation scheme combines a scheduled motion planner and a priority-based negotiation. The scheduled motion planner ensures the product transportation while choosing the appropriate resource among several. The priority policy is designed using a negotiation process to solve conflicts when agents navigate close to each other or towards the same resource. Some simulations are provided in order to show the pertinence of the proposed scheme as well as its feasibility when the number of mobile agents increases. They highlight the cooperation scheme, the appropriate selection of the resource during the navigation as well as the flexibility of the proposed approach.     

The bond of near-surface mounted reinforcement to low-strength concrete

This paper focuses on the bond between near-surface mounted (NSM) reinforcement and low-strength concrete. In order to investigate this, eight beams made of low-strength concrete were made. The compressive strength of this concrete varied from 14.22?MPa to 16.83?MPa. These beams were then tensioned under monotonic loading until failure. The test setups differed in terms of their groove size and the type of reinforcement (a rod and plate of carbon fiber reinforced polymer, prestressing steel). Based on the achieved results and analysis, it was found that the NSM method can be applied to low-strength concrete. Furthermore, the application of a NSM reinforcement rod and plate, made of the carbon fiber reinforced polymer, and prestressing steel showed a satisfactory bond strength when compared to low-strength concrete. However, the carbon plates performed better in terms of failure load and rate use than the rods made of carbon and the prestressing steel. Moreover, the results showed that the increase of groove size for the near-surface mounted reinforcement made of prestressing steel did not have an effect on the failure mode. In addition, a significant increase of the failure load was observed for the prestressing steel. Finally, the effect of concrete strength was analyzed and compared with the results found in literature.     

How fuzzy logic can improve PEM fuel cell MPPT performances?

This paper addresses the development of new variable step size fuzzy based MPPT controller. In this study, the fuzzy logic approach is firstly used to auto-scale the variable step size of the Incremental Conductance (IC) MPPT controller. Secondly, the proposed variable step size fuzzy based MPPT controller is used to track the output power of the PEM fuel cell system composed of 7 kW fuel cell supplying a 50Ω resistive load via a DC-DC boost converter controlled using the proposed MPPT. The proposed variable step size fuzzy-based MPPT controller is compared to the conventional fixed step size IC, the variable step size IC and the fuzzy scaled variable step size IC MPPTs using the implemented Matlab/Simulink PEM Fuel Cell power system model. Comparative simulation results between the four studied MPPTs show better performances for the proposed fuzzy based variable step size MPPT in term of: response time reduction between 3.6% and 82.35%; overshoot reduction between 34.55% and 100%; and ripple reduction between 70.93% and 100%, improving as consequence the fuel cell lifetime affected by high current ripple.     

Near-field scanning optical microscopy using polymethylmethacrylate optical fiber probes

We report the first use of polymethylmethacrylate (PMMA) optical fiber-made probes for scanning near-field optical microscopy (SNOM). The sharp tips were prepared by chemical etching of the fibers in ethyl acetate, and the probes were prepared by proper gluing of sharpened fibers onto the tuning fork in the conditions of the double resonance (working frequency of a tuning fork coincides with the resonance frequency of dithering of the free-standing part of the fiber) reported earlier for the case of glass fibers. Quality factors of the probes in the range 2000–6000 were obtained, which enables the realization of an excellent topographical resolution including state-of-art imaging of single DNA molecules. Near-field optical performance of the microscope is illustrated by the Photon Scanning Tunneling Microscope images of fluorescent beads with a diameter of 100 nm. The preparation of these plastic fiber probes proved to be easy, needs no hazardous material and/or procedures, and typical lifetime of a probe essentially exceeds that characteristic for the glass fiber probe.     

Effect of chemical treatment on newspaper fibers reinforced polymer poly(vinyl chloride) composites

Natural fibers used as reinforcement in composite materials present specific mechanical properties, which are comparable to glass fibers. In addition, they have the advantage of being renewable and recyclable. However, their main drawback is their inherent susceptibility to moisture expansion, which has the effect of inducing a decrease in mechanical properties, and of debonding in the composite. In this study, lignocellulosic fibers from newspapers were modified with acetic anhydride, NaOH, and KMnO₄ in order to enhance the interfacial adhesion between poly(viny lchloride) (PVC) matrix and the newspaper fibers. Composites samples were prepared with different treated fibers at the same loading (20 wt%). X‐ray and scanning electron microscopy (SEM) were used to characterize the fiber's surfaces. The mechanical, morphological, and thermal properties of PVC/newspaper composites were also studied. Moreover, the maximum improvement in the mechanical properties (tensile strength) was obtained for the permanganate treated PVC/newspaper composites. J. VINYL ADDIT. TECHNOL., 22:173–181, 2016. © 2014 Society of Plastics Engineers     

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.     

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.