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Respecting the requirements of lightweight, small size, low power consumption and especially low cost, commercial off-the-shelf (COTS) analog-to-digital converters (ADCs) are gradually being used to replace specifically designed aerospace -grade ones in space-borne miniature interferometric fiber-optic gyroscopes (IFOGs). However, the total ionizing dose (TID) effect of $\gamma $ -ray radiation in the space environment may cause the damage of COTS ADCs, and lead to performance degradation and even the failure. In order to measure the safety of COTS ADCs in IFOGs under the TID effect, the radiation-induced degradation model and the corresponding reliability model are proposed in this work. First of all, the relationship between the degradation of COTS ADCs and the IFOG’s angular random walk (ARW) is deduced. Then, a radiation accelerated test is designed and performed, with a real-time, dynamic data acquisition method based on the first-input-first-output (FIFO) memories to determine the degradation of COTS ADCs. Finally, a degradation path model is established based on the degradation data, and the reliability of COTS ADCs used for space-borne IFOGs is further obtained by using the Monte Carlo method. An engineering application is introduced to show the effectiveness of the proposed model, and the results show that the reliability of the experimental COTS ADCs makes them usable for the space-borne miniature IFOGs in low-earth orbit (LEO) and geostationary earth orbit (GEO) space missions.

In the oil and gas industry, empirical models are used to estimate the drift of pipes. These models encode pipe geometrical features, measured along the pipe length. If the estimated drift ...

Extracting features for early failure detection in rotating machinery of nuclear power plants (NPPs) is difficult because in the early stages of failure the impact on the vibration signals is weak. To improve early fault detection in rotating machinery, a fault feature extraction method based on the combination of parameter-adaptive Variational Mode Decomposition (VMD) and Teager energy operator (TEO) is proposed in this paper. Firstly, we introduce the maximum weighted kurtosis index (WKI) as the objective function, and the Artificial Bee Colony (ABC) is used to optimize the VMD parameters. Then, the optimized VMD is used to decompose the vibration signal into multiple intrinsic mode functions (IMFs). Finally, TEO is used to demodulate the sensitive mode with the maximum WKI and identify the fault frequencies. Simulation and experiment show that the early fault features in vibration signals can be effectively extracted by the proposed method, and the comparisons with other three methods highlight the advantages of the proposed method.

The electrical power grid is one of the most critical infrastructures (CIs) in many developed and developing countries and must be planned, operated, maintained, and managed to ensure reliable, secure, and resilient service supply. Being coupled to the transport network, water distribution, Internet, food, etc., all are highly mutually dependent, including through information and communication technologies (the so-called cyber-based systems). We examine the threats to the electrical CIs of natural and man-made disasters that cause loss of power systems for several weeks over vast urban or statewide areas, affect many millions, and result in intersystems cascading failures. Key future questions include: how high and reliable to build flooding defenses; how to enhance backup power systems for all CI fail-safe interconnections; and establishing increased investment. What next must and can only be better systems, improved reliability, more effective emergency management, through decision-making guided by quantified risk and resilience of coupled CI. What we have learned from past real events discussed in this chapter has national as well as systems engineering implications.

The digitalization of nuclear power plants, with the rapid growth of information technology, opens the door to the development of new methods of condition-based maintenance. In this work, a semi-supervised method for characterizing the level of degradation of nuclear power plant components using measurements collected during plant operational transients is proposed. It is based on the fusion of selected features extracted from the monitored signals. Feature selection is formulated as a multi-objective optimization problem. The objectives are the maximization of the feature monotonicity and trendability, and the maximization of a novel measure of correlation between the feature values and the results of non-destructive tests performed to assess the component degradation. The features of the Pareto optimal set are normalized and the component degradation level is defined as the median of the obtained values. The developed method is applied to real data collected from steam generators of pressurized water reactors. It is shown able to identify degradation level with errors comparable to those obtained by ad-hoc non-destructive tests.

International audience; The accurate prediction of wind energy production is crucial for an affordable and reliable power supply to consumers. Prediction models are used as decision-aid tools for electric grid operators to dynamically balance the energy production provided by a pool of diverse sources in the energy mix. However, different sources of uncertainty affect the predictions, providing the decision-makers with non-accurate and possibly misleading information for grid operation. In this regard, this work aims to quantify the possible sources of uncertainty that affect the predictions of wind energy production provided by an ensemble of Artificial Neural Network (ANN) models. The proposed Bootstrap (BS) technique for uncertainty quantification relies on estimating Prediction Intervals (PIs) for a predefined confidence level. The capability of the proposed BS technique is verified, considering a 34 MW wind plant located in Italy. The obtained results show that the BS technique provides a more satisfactory quantification of the uncertainty of wind energy predictions than that of a technique adopted by the wind plant owner and the Mean-Variance Estimation (MVE) technique of literature. The PIs obtained by the BS technique are also analyzed in terms of different weather conditions experienced by the wind plant and time horizons of prediction.

International audience; Animal origin food products, including fish and seafood, meat and poultry, milk and dairy foods, and other related products play significant roles in human nutrition. However, fraud in this food sector frequently occurs, leading to negative economic impacts on consumers and potential risks to public health and the environment. Therefore, the development of analytical techniques that can rapidly detect fraud and verify the authenticity of such products is of paramount importance. Traditionally, a wide variety of targeted approaches, such as chemical, chromatographic, molecular, and protein-based techniques, among others, have been frequently used to identify animal species, production methods, provenance, and processing of food products. Although these conventional methods are accurate and reliable, they are destructive, time-consuming, and can only be employed at the laboratory scale. On the contrary, alternative methods based mainly on spectroscopy have emerged in recent years as invaluable tools to overcome most of the limitations associated with traditional measurements. The number of scientific studies reporting on various authenticity issues investigated by vibrational spectroscopy, nuclear magnetic resonance, and fluorescence spectroscopy has increased substantially over the past few years, indicating the tremendous potential of these techniques in the fight against food fraud. It is the aim of the present manuscript to review the state-of-the-art research advances since 2015 regarding the use of analytical methods applied to detect fraud in food products of animal origin, with particular attention paid to spectroscopic measurements coupled with chemometric analysis. The opportunities and challenges surrounding the use of spectroscopic techniques and possible future directions will also be discussed.

Reliable and accurate wind speed forecasting (WSF) is fundamental for efficient exploitation of wind power. In particular, high accuracy short-term WSF (ST-WSF) has a significant impact on the efficiency of wind power generation systems. Due to the non-stationarity and stochasticity of the wind speed (WS), a single model is often not sufficient in practice for the accurate estimation of the WS. Hybrid models are being proposed to overcome the limitations of single models and increase the WS forecasting performance. In this paper, a new hybrid WSF model is developed based on long short-term memory (LSTM) network and decomposition methods with grey wolf optimizer (GWO). In the pre-processing stage, the missing data is filled by the weighted moving average (WMA) method, the WS time series (WSTS) data are smoothed by WMA filtering and the smoothed data are used as model input after Z -score normalization. The forecasting model is formed by the combination of a single model, a decomposition method and an advanced optimization algorithm. Successively, the hybrid WSF model is developed by combining the LSTM and decomposition methods, and optimizing the intrinsic mode function (IMF) estimated outputs with a grey wolf optimizer (GWO). The developed non-linear hybrid model is utilized on the data collected from five wind farms in the Marmara region, Turkey. The obtained experimental results indicate that the proposed combined model can capture non-linear characteristics of WSTS, achieving better forecasting performance than single forecasting models, in terms of accuracy.

International audience; Fatigue crack propagation affects the operational reliability of engine turbo-fan blades. In this article, we integrate a Kriging regression model and a distributed collaborative response surface method (DCRSM) for the reliability assessment of turbo-fan blades, considering the relevant uncertainty. Following a series of deterministic analyses, such as steady-state aerodynamic analysis, harmonic response analysis and Campbell diagram, and based on the assumption that vibration stress is mainly from aerodynamic load, the fatigue strength is calculated for turbo-fan blades under coupling aerodynamic forces, according to a modified Goodman curve of titanium-alloy. Giving consideration to the uncertainty of the resonance frequencies and material properties, the fatigue strength of the turbo-fan blade is evaluated, including probabilistic analysis and sensitivity analysis. In the case study analyzed, the conclusions are that the fatigue strength reliability reaches 96.808% with confidence level of 0.95 for the turbo-fan blade under the coupling aerodynamic forces, and the first three-order resonant frequencies are found to have important influence on the fatigue performance of turbo-fan blades.

International audience; In this paper, we develop a new quantitative method to assess the Strength of Knowledge (SoK) of a risk assessment. A hierarchical framework is first developed to conceptually represent the SoK in terms of three attributes (assumptions, data, phenomenological understanding), which are further broken down in sub-attributes and “leaf” attributes to facilitate their assessment in practice. The hierarchical framework, is, then, quantified in a top-down, bottom-up fashion for assessing the SoK. In the top-down phase, a reduced-order risk model is constructed to limit the complexity and number of basic elements considered in the SoK assessment. In the bottom-up phase, the SoK of each basic element in the reduced-order risk model is assessed based on predefined scoring guidelines and, then, aggregated using a weighted average of “leaf” attributes, where the weights are determined based on the Analytical Hierarchical Process (AHP). The strength of knowledge of the basic events is in turn, aggregated using a weighted average to obtain the SoK for the whole risk assessment model. The developed methods are applied to a real-world case study, where the SoK of the Probabilistic Risk Assessment (PRA) models of a Nuclear Power Plants (NPP) is assessed for two hazards groups, i.e., external flooding and internal events.

The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. On the other hand, UAVs can operate as flying mobile terminals within a cellular network. Such cellular-connected UAVs can enable several applications ranging from real-time video streaming to item delivery. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as three-dimensional deployment, performance analysis, channel modeling, and energy efficiency are explored along with representative results. Then, open problems and potential research directions pertaining to UAV communications are introduced. Finally, various analytical frameworks and mathematical tools such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems., Comment: Accepted in IEEE Communications Surveys and Tutorials, 2019

For nuclear power plants (NPPs) to have long lifetimes, ageing is a major issue. Currently, ageing management for NPP systems is based on correlations built from generic experimental data. However, each system has its own characteristics, operational history, and environment. To account for this, it is possible to resort to prognostics that predicts the future state and time to failure (TTF) of the target system by updating the generic correlation with specific information of the target system. In this paper, we present an application of particle filtering for the prediction of degradation in steam generator tubes. With a case study, we also show how the prediction results vary depending on the uncertainty of the measurement data. Keywords: Prognostics, Particle filtering, Model-based method, Steam generator tube rupture, Nuclear power plant

To efficiently and precisely evaluate the low-cycle fatigue (LCF) damage of turbine blades regarding dynamic cyclic loads, a dynamic substructure (DS)-based distributed collaborative extremum moving least squares (DCEM) (called as DS-DCEM) surrogate model is developed by absorbing moving least squares (MLS) with extremum response concept into the substructure-based distributed collaborative strategy in this paper. The probabilistic analysis procedure is given and the corresponding mathematical model is derived. Following that, the DS-DCEM is integrated with confidence level-based strain-life functions to introduce the numerical procedure for the dynamic LCF damage assessment of turbine blades with respect to the uncertainties in working loads, geometric sizes and material properties. From the dynamic probabilistic-based LCF damage assessment of the turbine blade, it is indicated that the confidence levels have important impacts on the damage reliability. As the confidence levels increase from 0.50, 0.90 to 0.95, the reliability values reduce from 0.99625, 0.90285 to 0.80304, respectively. In addition, the four fatigue parameters play a leading role on the LCF life prediction. The comparison of methods shows that DS-DCEM holds high numerical accuracy and computational efficiency for the dynamic LCF damage assessment of turbine blades. This paper develops a promising approach for the dynamic fatigue failure assessment of complex structures.

International audience; From the lectures presented at the 2nd International Workshop on Obesity and Environmental Contaminants, which was held in Uppsala, Sweden, on 8-9 October 2015, it became evident that the findings from numerous animal and epidemiological studies are consistent with the hypothesis that environmental contaminants could contribute to the global obesity epidemic. To increase awareness of this important issue among scientists, regulatory agencies, politicians, chemical industry management, and the general public, the authors summarize compelling scientific evidence that supports the hypothesis and discuss actions that could restrict the possible harmful effects of environmental contaminants on obesity.

International audience; An important feature of the intelligent environments is that they monitor user activities and help them in making decisions based on their progress in activities. Many such environments have been designed previously for healthcare management. However, more than often users are reluctant to consider the feedback from the environment alone. So we consider expert's recommendations and social network of the user as additional entities that form part of the intelligent environment. However, unlike traditional approaches, they influence the user indirectly through various persuasion techniques. The main idea is to change the behavior of the user for improving healthcare management. In this article we identify salient features of our framework for healthcare self-management in intelligent environments that combines ubiquitous and social computing as persuasion media. The framework enables social interactions between the patients, doctors, and other users in their online social community through a web portal as well as through their smart phones. Both user's behavior and preferences are taken into account to help them in adopting healthy behavior. This is done by using different persuasion strategies created on the basis of the user's behavior model. As a case study we consider diabetes self-management in this article.

International audience; In this article we propose our framework for healthcare self-management that combines ubiquitous and social computing as persuasion media. The framework enables social interactions between the patients, doctors, and other users in their online social community through a web portal as well as through their smartphones. To help users in adopting healthy behavior, they are monitored for various activities and persuaded using different persuasion strategies that are adaptive and are according to user's behavior. Persuasion strategies are applied using persuasion profile of a user. A behavior model of each user is created that is based on Fogg's behavior model but also encompasses user preferences, health profile and social profile.

International audience; Vertically aligned multi-walled carbon nanotubes have been grown on Ni-coated silicon substrates, by using either direct current diode or triode plasma-enhanced chemical vapor deposition at low temperature (around 620 °C). Acetylene gas has been used as the carbon source while ammonia and hydrogen have been used for etching. However densely packed (∼ 109 cm− 2) CNTs were obtained when the pressure was ∼ 100 Pa. The alignment of nanotubes is a necessary, but not a sufficient condition in order to get an efficient electron emission: the growth of nanotubes should be controlled along regular arrays, in order to minimize the electrostatic interactions between them. So a three dimensional numerical simulation has been developed to calculate the local electric field in the vicinity of the tips for a finite square array of nanotubes and thus to calculate the maximum of the electron emission current density as a function of the spacing between nanotubes. Finally the triode plasma- enhanced process combined with pre-patterned catalyst films (using different lithography techniques) has been chosen in order to grow regular arrays of aligned CNTs with different pitches in the micrometer range. The comparison between the experimental and the simulation data permits to define the most efficient CNT-based electron field emitters

International audience; In bottom-contact organic field-effect transistors (OFETs), the functionalization of source/drain electrodes leads to a tailored surface chemistry for film growth and controlled interface energetics for charge injection. This report describes a comprehensive investigation into separating and correlating the energetic and morphological effects of a self-assembled monolayers (SAMs) treatment on Au, Ag, and Cu electrodes. Fluorinated 5,11-bis(triethylsilylethynyl) anthradithiophene (diF-TES-ADT) and pentafluorobenzenethiol (PFBT) are employed as a soluble small-molecule semiconductor and a SAM material, respectively. Upon SAM modification, the Cu electrode devices benefit from a particularly dramatic performance improvement, closely approaching the performance of OFETs with PFBT-Au and PFBT-Ag. Ultraviolet photoemission spectroscopy, polarized optical microscopy, grazing-incidence wide-angle X-ray scattering elucidate the metal work function change and templated crystal growth with high crystallinity resulting from SAMs. The transmission-line method separates the channel and contact properties from the measured OFET current–voltage data, which conclusively describes the impact of the SAMs on charge injection and transport behavior.