Plate-forme de simulation pour l'aide à la décision : application à la régulation des systèmes de transport urbain

Transport systems are becoming more complex and must incorporate not only technological components, but also sociological and political ones. In particular, they should be easy to adapt in order to incorporate the goals set by decision makers, such as the integration of sustainable development settings. The main objective of our research is to propose software architecture of a simulation tool to help a decision maker, responsible for the regulation of an urban transportation system to analyze and evaluate the impacts of regulatory strategies. We propose a system to assist in the definition of an urban transportation policy. Our system is based on an agent-based simulation integrating spatial and temporal information to evaluate regulatory scenarios. In terms of system architecture, we adopted a “system of systems” approach, mainly structured in layers, in order to model the main elements of the system. The validation of our simulation tool could be done from a case study of significant size and complexity because we have travel surveys, census, and traffic measurements. We made a prototype for the movement of people in the city of La Rochelle from statistical data of INSEE and the BD TOPO 2 of IGN using the GAMA platform.La complexité des systèmes de transport rend difficile la maîtrise de leur conception dans la mesure où ils intègrent des composantes technologiques, mais aussi sociologiques et politiques. Il est alors opportun de proposer un système destiné à aider à la définition d’une politique de transport urbain. L’objectif principal de notre recherche est de proposer l’architecture logicielle d’un outil de simulation visant à aider un décideur, chargé de la régulation d’un système de transport urbain, pour son travail d’analyse et d’évaluation des impacts des stratégies de régulation. Notre système est basé sur un simulateur à base d’agents intégrant des informations géographiques et temporelles pour évaluer des scénarii de régulation. En termes d’architecture du système, nous avons adopté une approche «système de systèmes», principalement structurée par couches, afin de modéliser les principaux éléments du système. La validation de notre outil de simulation a pu être effectuée à partir d’une étude de cas de taille et de complexité significative puisque nous disposons des enquêtes de déplacement, de recensement, et des mesures de trafic. Nous avons réalisé un prototype pour les déplacements des usagers dans la ville de La Rochelle à partir des données statistiques de l’INSEE et de la BD TOPO 2 de l’IGN en utilisant la plate-forme de simulation GAMA

A lightweight secure image super resolution using network coding

Images play an important part in our daily life. They convey our personal stories and maintain meaningful objects, events, emotions etc. People, therefore, mostly use images as visual information for their communication with each other. Data size and privacy are, however, two of important aspects whilst transmitting data through network like internet, i.e. the time prolongs when the amount of data are increased and the risk of exposing private data when being captured and accessed by irrelevant people. In this paper, we introduce a unified framework, namely Deep-NC, to address these problems seamlessly. Our method contains three important components: the first component, adopted from Random Linear Network Coding (RLNC), to protect the sharing of private image from the eavesdropper; the second component to remove noise causing to image data due to transmission over wireless media; and the third component, utilising Image Super-Resolution (ISR) with Deep Learning (DL), to recover high-resolution images from low-resolution ones due to image sizes reduced. This is a general framework in which each component can be enhanced by sophisticated methods. Simulation results show that an outperformance of up to 32 dB, in terms of Peak Signal-to-Noise Ratio (PSNR), can be obtained when the eavesdropper does not have any knowledge of parameters and the reference image used in the mixing schemes. Various impacts of the method are deeply evaluated to show its effectiveness in securing transmitted images. Furthermore, the original image is shown to be able to downscale to a much lower resolution for saving significantly the transmission bandwidth with negligible performance loss

Enzyme-Assisted Aqueous Extraction Of Cashew Nut (Anacardium occidentale L.) Oil

Enzyme-assisted aqueous extraction method was applied to extract oil from cashew nut (Anacardium occidentale L.). The commercial enzyme (Viscozyme cassava C) was tested for effectiveness in releasing oil during the aqueous extraction. The effect of several parameters such as material/water ratio, enzyme concentration and duration for enzyme incubation on the oil yield was investigated. The conditions for maximum oil release were found with the material/water ratio of 1:9, enzyme concentration of 1% (v/w E/S), and in 3 h of enzyme incubation at 50oC with constant shaking. The maximum oil yield obtained at those conditions (38.88 % raw material) was significantly (p <0.05) higher than that of the control (without enzyme) (35.92 %), and it represented 86.28 % recovery of the total oil in seed. No hexane and other organic solvents were needed for this process. The cashew nut oil by enzyme-assisted aqueous extraction was relatively stable. Both peroxide value and free fatty acid value were lower than those in the oil obtained by Soxhlet method. Total un-saturated fatty acid in the cashew nut oil was about 84.43 %, in which the most abundant was oleic acid (65.0 %), followed by linoleic acid (18.53%). Cashew nut oil is a good dietary source of un-saturated fatty acids