Robust parameter estimation of density functions under fuzzy interval observations

International audienceThis study deals with the derivation of a probabilistic parametric model from interval data using the maximum likelihood principle. In contrast with classical techniques such as the EM algorithm, that define a precise likelihood function by computing the probability of observations viewed as a collection of non-elementary events, our approach presupposes that each imprecise observation underlies a precise one, and that the uncertainty that pervades its observation is epistemic, rather than representing noise. We define an interval-valued likelihood function and apply robust optimisation methods to find a safe plausible estimate of the statistical parameters. The approach is extended to fuzzy data by optimizing the average of lower likelikoods over a collection of data sets obtained from cuts of the fuzzy intervals, as a trade off between optimistic and pessimistic interpretations of fuzzy data. The principles of this method are compared with those of other existing approaches to handle incompleteness of observations, especially the EM technique

Credible Autocoding of Convex Optimization Algorithms

The efficiency of modern optimization methods, coupled with increasing computational resources, has led to the possibility of real-time optimization algorithms acting in safety critical roles. There is a considerable body of mathematical proofs on on-line optimization programs which can be leveraged to assist in the development and verification of their implementation. In this paper, we demonstrate how theoretical proofs of real-time optimization algorithms can be used to describe functional properties at the level of the code, thereby making it accessible for the formal methods community. The running example used in this paper is a generic semi-definite programming (SDP) solver. Semi-definite programs can encode a wide variety of optimization problems and can be solved in polynomial time at a given accuracy. We describe a top-to-down approach that transforms a high-level analysis of the algorithm into useful code annotations. We formulate some general remarks about how such a task can be incorporated into a convex programming autocoder. We then take a first step towards the automatic verification of the optimization program by identifying key issues to be adressed in future work

Tractable Predictive Control Strategies For Heating Systems In Buildings

International audienceModel Predictive control is an advanced control tech-nique that has been used to optimize thermal comfort in buildings. Nowadays, the new buildings are char-acterized by an important inertia as well as low power heating systems. Since the thermal losses are very low, taking into account the intermittent occupancies in the control strategy is questionable. More precisely, in this paper, two model predictive controllers are devel-oped to reduce energy consumption while preserving the thermal comfort. These strategies keep using the local controllers and they are adapted for being imple-mented in embedded systems. The simulation results show lower energy consumptions and higher comfort levels in comparison with non-predictive strategies

Seeking stability of supply chain management decisions under uncertain criteria

The leading theme of MOSIM’12 is "Performance, Interoperability and Safety for sustainable development"International audienceThis paper tackles the question of the anticipation of the supply chain partner's decisional behaviour under uncertain criteria. In other words, we propose a model to support sequential decisions under uncertainty where the decision maker has to make hypothesis about the decision criteria. For example, Hurwicz criterion weights extreme optimism and pessimism positions and a classic criticism of this criterion consisting in the difficulty of the weight assessment and the involving decision instability. To achieve this, we present a method based on fuzzy representation of weight vision. Finally, the model allows sequential decision of a Decision Tree to be compute thanks a pignistic probabilities treatment of the fuzzy representation of the decision maker optimism-pessimism index. This approach is illustrated through an industrial case study

Decision support with ill-known criteria in the collaborative supply chain context

International audienceIn the field of Supply Chain Risk Management, the attitude of managers toward risk affect the tactical decision-making process in collaborative supply chains under an uncertain environment, concerning especially capacity levels, lot-sizing rules, purchasing strategies, production scheduling,…, etc. The issue can be formulated as a sequential decision problem under uncertainty where the customer decisions affect the decisions made by the supplier. In this paper we deal with two kinds of uncertainties. The first one is the uncertainty on the indicators of performance (which are not comparable) used by the decision maker to choose a solution (for example: service quality or inventory cost). Hence, we propose an approach based on subjective probability to evaluate the probability that a decision is optimal for the first actor and the probability that it is optimal for both. From these two evaluations, we propose a ranking function to help the first actor to take into account the second one when selecting a decision. The second kind of uncertainty pertains to the demand. A classical criterion under total uncertainty is Hurwicz criterion where a weight expresses a degree of pessimism. Nevertheless, the degree of pessimism is itself ill-known. Thus, it becomes difficult to take into account the behavior of the actors. Hence, we propose an approach based on possibility theory and the so-called pignistic transform, which computes a subjective probability distribution over the criteria. Then, we apply the method used for uncertain criterion. This approach is illustrated through an example and an industrial case study

Proposition of a benchmark for evaluation of cores mapping onto NoC architectures

Proposition of a MC-CDMA Radiocommunication benchmark for evaluation of cores mapping onto NoC architectures. Illustration with CEA-LETI FAUST NoC in the context of 4-more European project

Ozone Effects on Botrytis cinerea Conidia using a Bubble Column: Germination Inactivation and Membrane Phospholipids Oxidation

Ozone treatments were applied on conidia aqueous suspensions in order to determine theminimal applied ozone dose to limit conidia germination and to observe the mechanismsinvolved in the spores inactivation. Conidia germination was significantly reduced, bubbling forat least 0.5 min as a gas with a minimal ozone concentration of 1 g.m−3. The applied ozone dosesinduce the membrane phospholipids oxidation, determined by the malondialdehyde quantifica-tion. Membrane phospholipids oxidation and inactivation rate are correlated. So, lipid peroxida-tion and consequently the alteration of the membrane integrity are involved in the antifungalaction of ozone