Université de technologie de belfort-montbéliard

The global energy transition towards a carbon neutral society requires a profound transformation of electricity generation and consumption, as well as of electric power systems. Hydrogen has an important potential to accelerate the process of scaling up clean and renewable energy, however its integration in power systems remains little studied. This paper reviews the current progress and outlook of hydrogen technologies and their application in power systems for hydrogen production, re-electrification and storage. The characteristics of electrolysers and fuel cells are demonstrated with experimental data and the deployments of hydrogen for energy storage, power-to-gas, co- and tri-generation and transportation are investigated using examples from worldwide projects. The current techno-economic status of these technologies and applications is presented, in which cost, efficiency and durability are identified as the main critical aspects. This is also confirmed by the results of a statistical analysis of the literature. Finally, conclusions show that continuous efforts on performance improvements, scale ramp-up, technical prospects and political support are required to enable a cost-competitive hydrogen economy.

A discrete-time analysis of the orthogonal frequency division multiplex/offset QAM (OFDM/OQAM) multicarrier modulation technique, leading to a modulated transmultiplexer, is presented. The conditions of discrete orthogonality are established with respect to the polyphase components of the OFDM/OQAM prototype filter, which is assumed to be symmetrical and with arbitrary length. Fast implementation schemes of the OFDM/OQAM modulator and demodulator are provided, which are based on the inverse fast Fourier transform. Non-orthogonal prototypes create intersymbol and interchannel interferences (ISI and ICI) that, in the case of a distortion-free transmission, are expressed by a closed-form expression. A large set of design examples is presented for OFDM/OQAM systems with the number of subcarriers going from four up to 2048, which also allows a comparison between different approaches to get well-localized prototypes.

Abstract In the framework of fully cooperative multi-agent systems, independent (non-communicative) agents that learn by reinforcement must overcome several difficulties to manage to coordinate. This paper identifies several challenges responsible for the non-coordination of independent agents: Pareto-selection, non-stationarity, stochasticity, alter-exploration and shadowed equilibria. A selection of multi-agent domains is classified according to those challenges: matrix games, Boutilier's coordination game, predators pursuit domains and a special multi-state game. Moreover, the performance of a range of algorithms for independent reinforcement learners is evaluated empirically. Those algorithms are Q-learning variants: decentralized Q-learning, distributed Q-learning, hysteretic Q-learning, recursive frequency maximum Q-value and win-or-learn fast policy hill climbing. An overview of the learning algorithms’ strengths and weaknesses against each challenge concludes the paper and can serve as a basis for choosing the appropriate algorithm for a new domain. Furthermore, the distilled challenges may assist in the design of new learning algorithms that overcome these problems and achieve higher performance in multi-agent applications.

The prediction of cardiac disease helps practitioners make more accurate decisions regarding patients' health. Therefore, the use of machine learning (ML) is a solution to reduce and understand the symptoms related to heart disease. The aim of this work is the proposal of a dimensionality reduction method and finding features of heart disease by applying a feature selection technique. The information used for this analysis was obtained from the UCI Machine Learning Repository called Heart Disease. The dataset contains 74 features and a label that we validated by six ML classifiers. Chi-square and principal component analysis (CHI-PCA) with random forests (RF) had the highest accuracy, with 98.7% for Cleveland, 99.0% for Hungarian, and 99.4% for Cleveland-Hungarian (CH) datasets. From the analysis, ChiSqSelector derived features of anatomical and physiological relevance, such as cholesterol, highest heart rate, chest pain, features related to ST depression, and heart vessels. The experimental results proved that the combination of chi-square with PCA obtains greater performance in most classifiers. The usage of PCA directly from the raw data computed lower results and would require greater dimensionality to improve the results.

A new fabrication method to produce homogeneously fluorescent nanodiamonds with high yields is described. The powder obtained by high energy ball milling of fluorescent high pressure, high temperature diamond microcrystals was converted in a pure concentrated aqueous colloidal dispersion of highly crystalline ultrasmall nanoparticles with a mean size less than or equal to 10 nm. The whole fabrication yield of colloidal quasi-spherical nanodiamonds was several orders of magnitude higher than those previously reported starting from microdiamonds. The results open up avenues for the industrial cost-effective production of fluorescent nanodiamonds with well-controlled properties.

An accurate algorithm for lithium polymer battery state-of-charge (SOC) estimation is proposed based on adaptive unscented Kalman filters (AUKF) and least-square support vector machines (LSSVM). A novel approach using the moving window method is applied, with AUKF and LSSVM to accurately establish the battery model with limited initial training samples. The effectiveness of the moving window modeling method is validated by both simulations and lithium polymer battery experimental results. The measurement equation of the proposed AUKF method is established by the LSSVM battery model and AUKF has the advantage of adaptively adjusting noise covariance during the estimation process. In addition, the developed LSSVM model is continuously updated online with new samples during the battery operation, in order to minimize the influence of the changes in battery internal characteristics on modeling accuracy and estimation results after a period of operation. Finally, a comparison of accuracy and performance between the AUKF and UKF is made. Simulation and experiment results indicate that the proposed algorithm is capable of predicting lithium battery SOC with a limited number of initial training samples.

The performance of data-driven prognostics approaches is closely dependent on the form and trend of extracted features. Indeed, features that clearly reflect the machine degradation should lead to accurate prognostics, which is the global objective of this paper. This paper contributes a new approach for feature extraction/selection: The extraction is based on trigonometric functions and cumulative transformation, and the selection is performed by evaluating feature fitness using monotonicity and trendability characteristics. The proposition is applied to the time-frequency analysis of nonstationary signals using a discrete wavelet transform. The main idea is to map raw vibration data into monotonic features with early trends, which can be easily predicted. To show that, selected features are used to build a model with a data-driven approach, namely, the summation wavelet-extreme learning machine, that enables good balance between model accuracy and complexity. For validation and generalization purposes, the vibration data from two real applications of prognostics and health management challenges are used: (1) cutting tools from a computer numerical control machine (2010); and (2) bearings from the platform PRONOSTIA (2012). The performance of the proposed approach is thoroughly compared with the classical approach by performing feature fitness analysis, cutting-tool wear “estimation”, and bearings' “long-term prediction” tasks, which validates the proposition.

In this paper, a model-based robust control is proposed for the polymer electrolyte membrane fuel cell air-feed system, based on the second-order sliding mode algorithm. The control objective is to maximize the fuel cell net power and avoid the oxygen starvation by regulating the oxygen excess ratio to its desired value during fast load variations. The oxygen excess ratio is estimated via an extended state observer (ESO) from the measurements of the compressor flow rate, the load current, and the supply manifold pressure. A hardware-in-loop test bench, which consists of a commercial twin screw air compressor and a real-time fuel cell emulation system, is used to validate the performance of the proposed ESO-based controller. The experimental results show that the controller is robust and has a good transient performance in the presence of load variations and parametric uncertainties.

A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or "in development", hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.

Intelligent transport systems are the rising technology in the near future to build cooperative vehicular networks in which a variety of different ITS applications are expected to communicate with a variety of different units. Therefore, the demand for highly customized communication channel for each or sets of similar ITS applications is increased. This article explores the capabilities of available wireless communication technologies in order to produce a win-win situation while selecting suitable carrier( s) for a single application or a profile of similar applications. Communication requirements for future ITS applications are described to select the best available communication interface for the target application(s).

Since the early stages of thermal spray, it has been recognized that the powder composition, size distribution, shape, mass density, mechanical resistance, components distribution for composite particles play a key role in coating microstructure and thermo mechanical properties. The principal characteristics of particles are strongly linked to the manufacturing process. Coatings also depend on the process used to spray particles and spray parameters. Many papers have been devoted to the relationships existing between coating properties and structures at different scales and manufacturing processes. In many conventional spray conditions resulting in micrometric structures, among the different parameters, good powder flow ability, and dense particles are important features. Thermal plasma treatment, especially by RF plasma, of particles, prepared by different manufacturing processes, allows achieving such properties and it is now developed at an industrial scale. Advantages and drawbacks of this process will be discussed. Another point, which will be approached, is the self-propagating high-temperature synthesis, depending very strongly upon the starting composite particle manufacturing. However, as everybody knows, “small is beautiful” and nano- or finely structured coatings are now extensively studied with spraying of: (i) very complex alloys containing multiple elements which exhibit a glass forming capability when cooled-down, their under-cooling temperature being below the glass transition temperature; (ii) conventional micrometer-sized particles (in the 30-90 μm range) made of agglomerated nanometer-sized particles; (iii) sub-micrometer- or nanometer-sized particles via a suspension in which also, instead of particles, stable sol of nanometer-sized particles can be introduced; and (iv) spray solutions of final material precursor. These different processes using plasma, HVOF or sometimes flame and also cold-gas spray will be discussed together with the production of nanometer-sized particles via the chemical reaction method or by a special type of milling: the cryogenic milling process often referred to as “cryomilling.”

Sensor stability is defined as the ability to maintain a relatively stable and repeatable signal over a sufficient period. Long-term stability for gas sensors is an essential capability for carrying out long-term data collection of human exhaled breath, environmental monitoring and other gas detection in the modern electronic information age. This article reviews the research advances on the stability of metal oxide semiconductor gas sensors in the past five years. The impact of structure, environment, toxicity and sensor array on the sensor stability are discussed. Then, the improvement schemes of existing materials and structure design are summarized. The achievements of structure doping, humidity, anti-poisoning and photoactivation are overviewed. Finally, the great significance of elucidating the sensing mechanism and carrying out the life acceleration test for future research and development is pointed out.

Pansharpening aims to fuse a multispectral (MS) image with an associated panchromatic (PAN) image, producing a composite image with the spectral resolution of the former and the spatial resolution of the latter. Traditional pansharpening methods can be ascribed to a unified detail injection context, which views the injected MS details as the integration of PAN details and bandwise injection gains. In this paper, we design a new detail injection based convolutional neural network (DiCNN) framework for pansharpening with the MS details being directly formulated in end-to-end manners, where the first detail injection based CNN (DiCNN1) mines MS details through the PAN image and the MS image, and the second one (DiCNN2) utilizes only the PAN image. The main advantage of the proposed DiCNNs is that they provide explicit physical interpretations and can achieve fast convergence while achieving high pansharpening quality. Furthermore, the effectiveness of the proposed approaches is also analyzed from a relatively theoretical point of view. Our methods are evaluated via experiments on real MS image datasets, achieving excellent performance when compared to other state-of-the-art methods.

The authors present a new system for variable speed using a double-fed induction machine. With a special operating mode, the apparent power of the inverter can reach only 20% of the maximum mechanical power, compared to a classical solution which needs 120%. Control laws are also studied with and without a sensor and they are particularly robust. The approach is validated by simulations and experimental results.

The self-discharge of an electrochemical capacitor, also referred to as a supercapacitor, is an important factor in determining the duration of maintaining stored energy, especially in low-duty-cycle applications. The study of self-discharge is conducted as follows: first, the self-discharge is characterized by measuring the decline of open-circuit voltage of the electrochemical capacitor. Second, the mechanisms of self-discharge, leakage current, and diffusion of ions at the electrode-electrolyte interfaces are modeled by an electrical equivalent circuit. The equivalent circuit elements are experimentally determined according to the self-discharge time behavior. In addition, the dependence of the self-discharge parameters on both temperature and initial voltage across the electrochemical capacitor is described in detail.

BACKGROUND: Letrozole is safe and effective in postmenopausal women with estrogen receptor-positive early breast cancer, but long-term aromatase inhibitor use may cause bone loss and increase fracture risk. This study evaluated an immediate or delayed strategy of bone protection therapy with zoledronic acid. METHODS: A total of 1065 patients who were receiving adjuvant letrozole were randomized to immediate-start or delayed-start zoledronic acid (4 mg intravenously biannually for 5 years). The delayed group received zoledronic acid if lumbar spine or total hip T-score decreased below -2.0 or when a nontraumatic fracture occurred. The primary endpoint was change in lumbar spine bone mineral density (BMD) at Month 12. Secondary endpoints included changes in total hip BMD, serum bone turnover markers, and safety at Month 12. RESULTS: Lumbar spine BMD increased from baseline in the immediate arm, while it decreased from baseline in delayed-arm patients. At Month 12, the differences between the groups in lumbar spine and total hip BMD were 5.7% (P < .0001; 95% confidence intervals [CI], 5.2% to 6.1%), and 3.6% (P < .0001; 95% CI, 3.3 to 4.0%), respectively. Both regimens were well tolerated with few serious adverse events. Bone pain was higher in the immediate group, as expected, because some patients experienced acute-phase reactions after zoledronic acid infusion. CONCLUSIONS: At 12 months, immediate zoledronic acid therapy prevented bone loss in postmenopausal women who were receiving adjuvant letrozole.

From the pioneering works of McPherson in 1973 who identified nanometre-sized features in thermal spray conventional alumina coatings (using sprayed particles in the tens of micrometres size range) to the most recent and most advanced work aimed at manufacturing nanostructured coatings from nanometre-sized feedstock particles, the thermal spray community has been involved with nanometre-sized features and feedstock for more than 30 years. Both the development of feedstock (especially through cryo-milling, and processes able to manufacture coatings structured at the sub-micrometre or nanometre sizes, such as micrometre-sized agglomerates made of nanometre-sized particles for feedstock) and the emergence of thermal spray processes such as suspension and liquid precursor thermal spray techniques have been driven by the need to manufacture coatings with enhanced properties. These techniques result in two different types of coatings: on the one hand, those with a so-called bimodal structure having nanometre-sized zones embedded within micrometre ones, for which the spray process is similar to that of conventional coatings and on the other hand, sub-micrometre or nanostructured coatings achieved by suspension or solution spraying. Compared with suspension spraying, solution precursor spraying uses molecularly mixed precursors as liquids, avoiding a separate processing route for the preparation of powders and enabling the synthesis of a wide range of oxide powders and coatings. Such coatings are intended for use in various applications ranging from improved thermal barrier layers and wear-resistant surfaces to thin solid electrolytes for solid oxide fuel cell systems, among other numerous applications. Meanwhile these processes are more complex to operate since they are more sensitive to parameter variations compared with conventional thermal spray processes. Progress in this area has resulted from the unique combination of modelling activities, the evolution of diagnostic tools and strategies, and experimental advances that have enabled the development of a wide range of coating structures exhibiting in numerous cases unique properties. Several examples are detailed. In this paper the following aspects are presented successively (i) the two spray techniques used for manufacturing such coatings: thermal plasma and HVOF, (ii) sensors developed for in-flight diagnostics of micrometre-sized particles and the interaction of a liquid and hot gas flow, (iii) three spray processes: conventional spraying using micrometre-sized agglomerates of nanometre-sized particles, suspension spraying and solution spraying and (iv) the emerging issues resulting from the specific structures of these materials, particularly the characterization of these coatings and (v) the potential industrial applications. Further advances require the scientific and industrial communities to undertake new research and development activities to address, understand and control the complex mechanisms occurring, in particular, thermal flow—liquid drops or stream interactions when considering suspension and liquid precursor thermal spray techniques. Work is still needed to develop new measurement devices to diagnose in-flight droplets or particles below 2 µm average diameter and to validate that the assumptions made for liquid–hot gas interactions. Efforts are also required to further develop some of the characterization protocols suitable to address the specificities of such nanostructured coatings, as some existing ‘conventional’ protocols usually implemented on thermal spray coatings are not suitable anymore, in particular to address the void network architectures from which numerous coatings properties are derived.

This paper presents the analytical, numerical, and experimental results obtained with a double excited synchronous machine (DESM) prototype designed and constructed for an electric vehicle traction system. To obtain a wide speed range, the flux-weakening technique is implemented. Analytical design, 2-D finite element method (FEM) analysis, thermal analysis, and prototype construction of the DESM are discussed, and the performances are assessed with experimental data.

Falls are a major health problem for the frail community dwelling old people. For more than two decades, falls have been extensively investigated by medical institutions to mitigate their impact (e.g., lack of independence and fear of falling) and minimize their consequences (e.g., cost of hospitalization and so on). However, the problem of elderly falling does not only concern health-professionals but has also drawn the interest of the scientific community. In fact, falls have been the object of many research studies and the purpose of many commercial products from academia and industry. These studies have tackled the problem using fall detection approaches exhausting a variety of sensing methods. Lately, researcher has shifted their efforts to fall prevention where falls might be spotted before they even happen. Despite their restriction to clinical studies, early fall prediction systems have started to emerge. At the same time, current reviews in this field lack a common ground classification. In this context, the main contribution of this paper is to give a comprehensive overview on elderly falls and to propose a generic classification of fall-related systems based on their sensor deployment. An extensive research scheme from fall detection to fall prevention systems have also been conducted based on this common ground classification. Data processing techniques in both fall detection and fall prevention tracks are also highlighted. The objective of this paper is to deliver medical technologists in the field of public health a good position regarding fall-related systems.

This paper presents a maximum power point tracking (MPPT) technique for high-performance wind generators with induction machines based on the growing neural gas (GNG) network. In this paper, a GNG network has been trained offline to learn the turbine-characteristic surface torque versus wind and machine speeds and has been implemented online to obtain the wind tangential speed on the basis of the estimated torque and measured machine speed (surface function inversion). The machine reference speed is then computed on the basis of the optimal tip speed ratio. For the experimental application, a back-to-back configuration with two voltage source converters has been considered, one on the machine side and the other on the grid side. The field-oriented control of the machine has been further integrated with an intelligent sensorless technique; in particular, the so-called total least squares (TLS) EXIN full-order observer has been adopted. Finally, a comparison with a classic perturb-and-observe MPPT has been made on a real wind-speed profile.