International audienceThis paper deals with an image processing methodology based on a sky-imaging system developed at the PROMES-CNRS laboratory. It is a part of a project which aims at improving solar plant control procedures using Direct Normal Irradiance (DNI) forecasts under various sky conditions at short term horizon (5-30 minutes) and high spatial resolution (~1 km²). The work presented in this paper is about the improvement of the cloud cover estimation, which is the main step in DNI forecasting. First, an overview of the existing sky-imaging systems and the current cloud detection algorithms is presented. Next, the experimental setup is introduced. Then, the methodology used to estimate the cloud cover is detailed. Finally, the paper ends with some results and discussion

International audienceThe present work is part of a global development of reliable real time control and supervision tools applied to wastewater pollution removal processes. In this processes, oxygen is a key substrate in animal cell metabolism and its consumption is thus a parameter of great interest for the monitoring. In this paper, are presented and discussed the results of the Dissolved Oxygen (DO) control in a SBR pilot plant based on a predefined 8 hours step-feed cycle. As first approach, the application of classical methods (on/off and PID) was considered. Due to the non linear character of the process, the PID parameter adjusting was very difficult and the obtained results showed a beating phenomenon around the setpoint. This phenomenon was more and less amplified according to the step of the cycle and the water pollution level. The second approach to achieve more stable DO control was based on a fuzzy logic strategy, taking into account the step and the difference between the measured DO and the setpoint. In this case, control action performances were highly improved. It's also shown that, using the fuzzy controller, the pH profile made it possible to clearly detect the ammonia valley during the aerobic phases. Thus, fuzzy logic proved to be a robust and effective DO control tool, easy to integrate in a global monitoring system for cost managing

the actual European energy context highlights the building sector as one of the largest sectors of energy consumption. Consequently, the "Energy Performance of Buildings Directive", adopted in 2002 and focusing on energy use in buildings, requires all the EU members to enhance their building regulations and to introduce energy certification schemes, with the aim of both reducing energy consumption and improving energy efficiency. That is why carrying out an energy performance diagnosis is mandatory, notably when buying or selling properties. Indeed, invisible defaults, like, for example, non-emerging cracks or delaminations, could have a detrimental effect on insulating qualities. Esimaing in-situ thermo-physical properties allowing locating these defaults, the present work focuses on proposing new and efficient approaches based on the use of both artificial intelligence tools (artificial neural networks and neuro-fuzzy systems) and inverse methods for characterizing building materials i.e. for estimating their thermal diffusivity using thermograms obtained thanks to a non-destructive photothermal method.