The large amount of features recorded from GPS and inertial sensors (external load) and well-being questionnaires (internal load) can be used together in a multi-dimensional non-linear machine learning based model for a better prediction of non-contact injuries. In this study we put forward the main hypothesis that the use of such models would be able to inform better about injury risks by considering the evolution of both internal and external loads over two horizons (one week and one month). Predictive models were trained with data collected by both GPS and subjective questionnaires and injury data from 40 elite male soccer players over one season. Various classification machine-learning algorithms that performed best on external and internal loads features were compared using standard performance metrics such as accuracy, precision, recall and the area under the receiver operator characteristic curve. In particular, tree-based algorithms based on non-linear models with an important interpretation aspect were privileged as they can help to understand internal and external load features impact on injury risk. For 1-week injury prediction, internal load features data were more accurate than external load features while for 1-month injury prediction, the best performances of classifiers were reached by combining internal and external load features.

Designing the way a complex system should evolve to better match customers' requirements provides an interesting class of applications for muticriteria techniques. The models required to support the improvement design of a complex system must include both preference models and system behavioral models. A MAUT model captures decisions related to design preferences, whereas a fuzzy representation is proposed to model relationships between system parameters and the fulfillment of system assessment criteria. The way in which these models are jointly used throughout our entire design procedure highlights that both models must be used in tandem to address managerial and implementation issues involved in an improvement project. The iterative improvement process is supported by a mathematical model, in addition to a software tool that allows our approach to be tested in an industrial case study. The search for adequate parameters regarding the improvement design is supported by a branch and bound algorithm to compute the most relevant actions to be performed. The findings confirm the efficiency of the algorithm.

Besides the ecological issues, recycling of plastics involves economical matters that encourage industrial firms to invest in the field. Part of them have focused on the waste sorting phase by designing optical device able to discriminate on line among plastics categories. For achieving ecological and economical objectives, sorting errors must be minimized to avoid serious recycling problems and significant quality degradation of the final recycled product. Even with the most recent acquisition technologies based on spectra imaging, plastic recognition remains a tough task due to the presence of imprecision and uncertainty, e.g., variability in the measurement due to atmospheric disturbances, ageing of plastics, dark or black coloured materials etc. The enhancement of the recent sorting techniques based on classification algorithms leads to rather good performance results, however for such applications, the remaining errors have serious consequences. In this article, we propose an imprecise classification algorithm to minimize sorting errors of standard classifiers when dealing with incomplete data by both integrating the processing of classification's doubt and hesitation in the decision process and improving the classification performances. To this aim, we propose a relabelling procedure that allows to better represent the imprecision of the learning data and we introduce the belief functions framework to represent the posterior probability provided by a classifier. Finally, the performances of our approach compared to existing imprecise classifiers is illustrated on the sorting problem of four plastics categories from mid-wavelength infra-red spectra acquired in an industrial context.

The design of mechatronic systems involves several technical and scientific disciplines. It is often difficult to anticipate, at the outset, the consequences of design decisions on the ultimate effectiveness of such complex systems, in which case the evaluation process is required to support the designers each time engineering choices must be made or justified. Since designers may belong to different technical and scientific cultures however, their understanding of both the design stakes and the evaluation process is too often biased. Moreover, design choices take place in an uncertain context and according to multiple criteria, some of which may be contradictory. In order to track the consequences of design decisions, we are proposing a conceptual data model to perform evaluations within the MBSE framework of Systems Engineering. We then proceed by relying on the relationships demonstrated by such a model to identify the potential impacts of design choices on future product performance. Since data available during the conceptual phase of the design are typically uncertain or imprecise, an original research protocol is extended to a qualitative impact analysis for the purpose of highlighting the most promising alternative system design solutions (ASDS). An example in the mechatronics field serves to illustrate our proposals.

Designing the way a complex system should evolve to better match the customers' requirements provides an interesting class of applications for muticriteria techniques. The required models to support the improvement design of a complex system must include both preference models and system behavioral models. A MAUT model captures the decisions related to customers' preferences whereas a fuzzy representation is proposed to model the relationships between systems parameters and performances to capture operational constraints. This latter part of the improvement design is supported by a branch and bound algorithm to efficiently compute the most relevant actions to be performed.

With the increase in waste streams, industrial sorting has become a major issue. The main challenge is to minimise sorting errors to avoid serious recycling problems and significant quality degradation of the final recycled product. Making use of near infrared (NIR) technology, some industrialists have already designed sorting machines able to discriminate between several types of plastics with good reliability. However, these devices are not suited to dark plastics, which are very common in WEEE (Waste Electronic and Electrical Equipment). In order to overcome this obstacle, mid-wavelength infrared (MIR) technology can be used instead of NIR. Nevertheless, the new spectral range is poorer in terms of wavelength for some plastics of interest (2712 − 5274nm), which makes the sorting task harder in an industrial context where spectrum identification is subject to imprecision and uncertainty. This article shows the benefit of combining this promising optical technology with a cautious machine learning procedure to optimise recycling. When the information provided by the device regarding a plastic fragment to be sorted is insufficient to discriminate between candidate materials, the pro-posed procedure, taking advantage of the belief functions theory, blows the fragment into a container dedicated to more than one specific material. This cautious sorting enables the containers dedicated to the specific ma-terials to contain less impurities, which leads to higher-quality secondary raw materials. The proposed sorting procedure is illustrated and compared with a more conventional approach using real industrial data.

International audienceWithin an emergency unit, the head manager is required to make difficult decisions based on experts' assessments of many criteria, including personal injuries, environmental impacts, economic and media consequences. Uncertainty in this collective assessment is related to multiplicity of experts' points of view and imprecise assessments. We are proposing a decision support system derived from a situation awareness model, generalized herein to the case of multiple actors. It is able of representing, merging and aggregating expert assessments. Imprecise criteria assessments are first represented by intervals and then merged in the form of a possibility distribution that keeps track of all information provided, i.e. without any loss of information. Next, a Choquet integral-based aggregation is carried out in order to consider the relative importance of criteria and interactions between criteria in the overall assessment of the foreseeable alternatives to get out of the crisis. Lastly, a determination of the contributions of each criterion assessment uncertainty to the overall assessment uncertainty provides useful information to the head manager in controlling the decision deliberation by reducing the inconsistent points in the experts' assessments. The proposals are applied to the emergency issues resulting from a traffic accident occurring at a grade crossing

Belief functions are quite generic models when it comes to represent uncertain data, as it extends a wide range of uncertainty models (possiblity and probability distributions, among others). Usually, belief functions are defined over finite spaces, however many real word problems require to deal with beliefs over a continuous space while maintaining computational efficiency. This paper discusses the case of focal sets on the unit simplex, and proposes efficient inference tools to manipulate them. Such sets can be used to represent unknown proportions that one may face in various fields like soil contamination managing, plastic sorting or image reconstruction. In this paper, we illustrate their use on an industrial problem of plastic sorting, where the proportion of material impurities must not go over a limit while minimizing the rejection of sorted materials, whose nature is uncertain.