Forecasting of renewable energy balance on Medium Term.

International audienceThe general purpose of the paper is to explore the way of performing renewable energy balance predictions prognostics so that energy market actors can act consequently. Different aspects of forecasting are discussed to point out pragmatic challenges of this approach. An illustration, with real monitored data, based on a neuro-fuzzy predictor is given. The architecture of the artificial intelligence technique used for forecasting is modified in order to obtain accurate estimations for medium term

Medium term load forecasting using ANFIS predictor.

International audienceNowadays, there are huge ranges of energy market participants. Commercial success of this area actor depends on the ability to submit competitive predictions relative to energy balance trends Thus, it seems convenient to "anticipate" this parameter evolution in time in order to act consequently and resort to protective actions. In this context, this paper proposes a tool for energy balance prediction based on ANFIS (Adaptive Neuro Fuzzy Inference System). This neuro- fuzzy predictor is modified in order to obtain an accurate forecasting for medium term. The solutions are illustrated on a real application and take into account the known "future”: the programmed actions

Framework for a distributed and hybrid prognostic system.

International audienceThe purpose of this paper is to define a framework for the implantation of a distributed, adaptable and open prognostic system able to take into account, on one hand, the dynamic of the monitored equipment and, on the other hand, the evolution of performance criteria. In this way, the prognostic process is (re)defined : at the component level and at the global level of the system (whole equipment). In the distributed model proposed, the interest of neural networks as "prognostic tools" is pointed out. The work is in coherence with actual industrial maintenance developments like "e-maintenance systems" or "web-services applications"

Review of prognostic problem in condition-based maintenance.

International audienceprognostic is nowadays recognized as a key feature in maintenance strategies as it should allow avoiding inopportune maintenance spending. Real prognostic systems are however scarce in industry. That can be explained from different aspects, on of them being the difficulty of choosing an efficient technology ; many approaches to support the prognostic process exist, whose applicability is highly dependent on industrial constraints. Thus, the general purpose of the paper is to explore the way of performing failure prognostics so that manager can act consequently. Diffent aspects of prognostic are discussed. The prognostic process is (re)defined and an overview of prognostic metrics is given. Following that, the "prognostic approaches" are described. The whole aims at giving an overview of the prognostic area, both from the academic and industrial points of views

A fuzzy approach for discrete event systems recovery.

International audienceA fuzzy approach for modelling and analysing the recovery activities in discrete event systems is presented. Those essential components of the management of discrete event systems require special reasoning and methods to manage uncertain knowledge. For those purposes, we introduce a tool derived from the fuzzy Petri nets. This tool, inspired from the fault tree, generalizes the defects analysis by a temporal fuzzy approach. The recovery, modelled by a dedicated tool, preserves the fuzzy temporal aspect due to a real time information exchange mechanism provied by the monitoring system

Estimation of the Technical State of Automotive Disc Brakes Using Fuzzy Logic

According to existing studies the phenomena that occur in the exploitationof the braking system are very complex and an analytical mathematical modelingof braking process it is difficult to be developed [1]. Since these phenomena are alsocharacterized by some uncertainties, a fuzzy logic approach has been employed in thisresearch for the estimation of technical state of the disc brakes. Their technical statewas expressed through the thickness variation, which was used as the output linguisticvariable. The vibrations and temperature of the disc brakes were used as the inputlinguistic variables. The fuzzy decision system for the estimation of technical stateof the disc brakes has been implemented with the Fuzzy Logic Toolbox of theMatlab software, which can be employed to determine if the thickness of the discbrakes becomes smaller than the limit value prescribed by the manufacturer

CT evaluation of HU bone density of the vertebrae in dogs with spine compression

Bone mineral density (BMD) is defined as the mineral concentration in bone. BMD is directly related to bone strength and is a useful predictor of osteoporotic fracture; it is therefore used to diagnose and monitor osteoporosis in humans. The purpose of this study was to evaluate if there are changes in the adjacent vertebral body (cranial and caudal) consistency in case of disk protrusion or IVDD. The result show changes of the HU of the vertebral body of the vertebrae situated cranial and caudal of the protrusion site, but there is no statistical correlation between the disk protrusion or IVDD and those changes

Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

Background: Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine. Methods: Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naive Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types. Results: Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 +/- 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 +/- 0.03) or SERS data (AUC = 0.84 +/- 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 +/- 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 +/- 0.04) or SERS (AUC = 0.92 +/- 0.05) individually, although SERS alone performed better in terms of classification accuracy. Conclusion: miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged