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Safety-impact on flight-critical systems such as flight or engine control systems is a major concern for aircraft equipment designers in civil and military fields. Current avionic equipments related to safety-critical systems are able to detect trivial faults such as loss of power, short circuits, open circuits or threshold overflow. The occurrence of these faults in actuator control loops, if detected, triggers a fail-safe mode. So, although system availability is reduced, the required safety level can still be ensured. This paper emphasizes a design methodology of nonlinear model-based FDI1 algorithms applied to a Hybrid Stepper Motor (HSM). The proposed design methodology combines a nonlinear dynamic inversion and residual generation using standard continuous Kalman Filter. The proposed fault detection method is based on residual mean-checking analysis, where the parameters are tuned with Kriging method.

Products appearances are made of design choices influencing the way products are perceived. Products semantics is a methods used to understand and anticipate this phenomena. Nowadays, consumers consider sport products not only as “sport” but also as “health” products. Designers may then develop them as “sport-health” products. However, perception of “sport-health” products may vary according to the need to fit sport or health context of use. We present in this paper our experimental approach to understand the influence of sport and health contexts on “sport-health” semantics.

Nowadays, the high competitiveness of a company depends on its ability to deal quickly and cost-effectively with the market disruptions. For this reason, companies should have an agile and flexible supply chains to solve problems related to the market fluctuation. In fact, supply chain reconfigurability is the ability to modify their capacity and functionality at the lowest cost. The objective of this article is to assess reconfigurability based on its characteristics (modularity, scalability, integrability, convertibility, diagnosability and customization) using Fuzzy logic. For this purpose, a quantitative evaluation of reconfigurability is proposed. In order to validate our assessment model, a case study is applied to evaluate the degree of reconfigurability after supply chain reconfiguration.

Smart environments are currently overpowering traditional Human-Computer Interaction (HCI) approaches for people's everyday life applications. Proxemic interaction is an emerging area for improving HCI experiences in such environments, causing the so-called proxemic environments to emerge. Proxemic interaction describes how the five dimensions (i.e., Distance, Identity, Location, Movement, and Orientation - DILMO) can be used to define interactions among people and digital devices. Current studies in this area are focused on developing proxemic applications with specific functionalities, based on a toolkit that allows developers to obtain DILMO information from sensors. However, there exists a notable lack of general approaches able to support the whole implementation process, starting from the modelling of proxemic environments to represent general proxemics behaviours, and finalizing with the development of specific applications. In order to facilitate the integration of proxemic capabilities in HCI, we propose an approach for modelling proxemic environments based on a graphical Domain-Specific Language (DSL). The DSL allows non-specialist designers to express proxemic interactions for modelling proxemic environments and supports the development process. In order to show its suitability and functionality, we have implemented a prototype to design proxemic behaviours in different proxemic environments.

International audience; Electric grid, as we nowadays know it, is undergoing a significant transformation. What we are now witnessing is an undoubted change of trend towards a decentralized and decarbonized electric grid, where the electric generation based on local resources will take on special relevance. In this context, the encouragement of collective selfconsumption becomes one of the key issues when it comes to taking steps forward to this end. One of the aspects that will contribute to this aim is the development of a consumptionforecasting tool. Hence, a load-forecasting model based on NARX Neural Network is proposed in the following paper. The prediction of the next day (24h) load profile of an individual building is carried out aiming an optimal management of the flexible loads so to achieve the maximum self-consumption. To ensure a consistent behavior of the NARX Neural Network model, identification and removing of outliers, together with data normalization and fixing common time interval has been carried out. The first results of the research are promising, being obtained a 17,6% MAPE in NARX and 25,19% with LSTM model, both evaluated during a regular week on winter in adverse conditions .

Aiming to take full advantage of Electric Vehicles' (EVs) batteries despite unpredictability in their arrival and departure time, in this paper, a two-level hierarchical model predictive controller coupled with an innovative charging-discharging scheduler for EVs in building microgrids is proposed. Besides providing a complete framework for its design, this paper also analyses different scenarios of EVs' batteries exploitation that would be beneficial for both building microgrids and EVs' owners. The simulation results conducted in MATLAB Simulink demonstrated that the proposed hierarchical controller could assure that most EVs are completely charged before their departure time while enabling building microgrids to increase their annual photovoltaic self-consumption rate by about 3 percent point compared to the uncontrolled strategy.

Current development of renewable energy systems (RES) is characterised by an increasing participation of citizens in the upstream decision-making process. These citizens can be future users of the RES but also members of a Renewable Energy Community that develop RES. They can be at the same time Renewable Energy producer, investor and consumer. Moreover, several type of businesses and terms are used to cope with social innovations within the energy sector: local renewable projects, sustainable energy communities or community of renewable energy production. So, actors' engagement opens new solutions for designers who are induced to share alternatives before making decisions. They usually impose constraints since the early phases of the design process. This approach implies for designers to consider new criteria related to citizens motivations and barriers. This paper presents a study to define the main factors that drive people to contribute in social innovation schemes for clean-energy transition. After a state of the art, a survey about 6 main factors and 18 criteria is presented. The analysis based on the responses from 34 participants (i.e. experts) reveals 2 most important factors of motivation and 2 principal barrier sources.

International audience; Electricity-distribution network operators face several operational constraints in the provision of safe and reliable power given that investments for network area reinforcement must be commensurate with improvements in network reliability. This paper provides an integrated approach for assessing the impact of different operational constraints on distribution-network reliability by incorporating component lifetime models, time-varying component failure rates, as well as the monetary cost of customer interruptions in an all-inclusive probabilistic methodology that applies a time-sequential Monte Carlo simulation. A test distribution network based on the Roy Billinton test system was modelled to investigate the system performance when overloading limits are exceeded as well as when preventive maintenance is performed. Standard reliability indices measuring the frequency and duration of interruptions and the energy not supplied were complemented with a novel monetary reliability index. The comprehensive assessment includes not only average indices but also their probability distributions to adequately describe the risk of customer interruptions. Results demonstrate the effectiveness of this holistic approach, as the impacts of operational decisions are assessed from both reliability and monetary perspectives. This informs network planning decisions through optimum investments and consideration of customer outage costs.

International audience; Human emotion recognition from visual expressions is an important research area in computer vision and machine learning owing to its significant scientific and commercial potential. Since visual expressions can be captured from different modalities (e.g., face expressions, body posture, hands pose), multi-modal methods are becoming popular for analyzing human reactions. In contexts in which human emotion detection is performed to associate emotions to certain events or objects to support decision making or for further analysis, it is useful to keep this information in semantic repositories, which offers a wide range of possibilities for implementing smart applications. We propose a multi-modal method for human emotion recognition and an ontology-based approach to store the classification results in EMONTO, an extensible ontology to model emotions. The multi-modal method analyzes facial expressions, body gestures, and features from the body and the environment to determine an emotional state; this processes each modality with a specialized deep learning model and applying a fusion method. Our fusion method, called EmbraceNet+, consists of a branched architecture that integrates the EmbraceNet fusion method with other ones. We experimentally evaluate our multi-modal method on an adaptation of the EMOTIC dataset. Results show that our method outperforms the single-modal methods.

International audience; The paper has a focus on energy consumption habits, trends and intervention strategies in residential buildings, mainly through the serious game approach with a combination of direct consumer feedback through smart metering. More than 150 homes in France and Spain have been involved in the research experiment and the consumption habits of approximately 50 homes were deeply analyzed. The applied methods, processes, results and findings of the monitoring data analysis are presented in the paper with two aims. First, consumption profiles and trends were determined for apartment homes with regard to heating, domestic hot water and electric consumption. Second, the impact of a serious game experiment was assessed comparing energy consumption, indoor air temperature and users' habits (based on questionnaires) before and after launching the experiment.

International audience; Design space exploration (DSE) describes the systematic activity of discovery and evaluation of the elements in a design space in order to identify optimal solutions by reducing the design space to an area of performance. Designers sample thousands of design points iteratively, explore the design space, gain knowledge about the problem and make design decision. The literature tells us that DSE results in a decision of quality called informed decision, which is supported by information visualization. The representation of design points is seen as primordial to gain an understanding of the problem and make an informed decision. In our work, we have sought to identify what type of graph is best suited to the discovery phase, and enables designers to make an informed decision. We designed a web platform with four design problems, and carried out an experiment with 42 participants. We found a graph that was better suited to making a decision of quality and to gaining greater understanding: the scatter plot matrix.

While the 7 million-head French sheep industry is contracting, the average herd size is rising, providing challenging issues to maintain and improve its productivity. Precision Livestock Farming has allowed the sheeps and herds to be equipped with sensors and has a result the amount of data to be processed by farmers has surged. We argue that in order for the farmers to take appropriate actions there is a need for the development of a decision support system that take into account not only real-time data but also expert knowledge. In this work, we first highlight the specific challenges presented by the field of precision sheep farming to the development of a Cyber-Physical and Human System. We then introduce a methodology to implement a decision support in this context.

International audience; The aim of the work presented in this paper is the energy management in residential microgrids. The study focuses on Multi Agent System approach based on sensors and actuators network. The system operation requires a large amount of data both for its architectural description and for its real time control. Dealing with this, the fundamental step in the energy management is the organization of data in order to construct the suitable relational database model. The second step is to analyze the system architecture and identify the agents to use for the system. When data are structured and Multi Agent System is modelled, the energy management algorithms are developed and validated taking into account the data availability and the agents properties.

International audience; During the last decade, the smart grid (SG) concept has started to become a reality, mainly thanks to the technical progress achieved in telecommunications, informatics and power electronics, among other domains, leading to an evolution of the traditional electrical grid into an intelligent one. Nowadays, the SG can be seen as a system of smart systems that include cyber and physical parts from different technologies that interact with each other. In this context, intelligent buildings (IBs) constitute a paradigm in which such smart systems are able to guarantee the comfort of residents while ensuring an appropriate tradeoff of energy production and consumption by means of an energy management system (EMS). These interconnected EMSs remain the objective of potential cyber-attacks, which is a major concern. Therefore, this paper conducts a survey, from a multidisciplinary point of view, of some of the main security and privacy issues related to IBs as part of the SG, including an overview of EMS, smart meters, and the main communication networks employed to connect IBs to the overall SG. Future research directions towards a security enhancement from both technical and human perspectives are also provided.

International audience; Model order reduction (MOR) has demonstrated its robustness and wide applicability in simulating large-scale mathematical models in the engineering research domain. In this paper, MOR techniques are applied to quantify relevant reliability metrics of power distribution systems and the impact associated with the integration of different smart grid technologies. To the best of the authors' knowledge, this is the first application of MOR techniques of balanced truncation to derive reliability models of electricity networks, which exhibit a reduced number of equivalent components and thus simplify the complexity for network analysis. The extensive case studies presented, based on both radial and meshed systems, demonstrate that the proposed technique allows for a faster reliability assessment through Monte Carlo simulation while preserving high accuracy. The proposed methodology can also be applied to systems endowed with photovoltaic and energy storage technologies, emphasising that this approach represents a promising starting point for reliability analysis of more complex systems, which are normally characterised by a large penetration of these distributed energy resources.

For social robots, knowledge regarding human emotional states is an essential part of adapting their behavior or associating emotions to other entities. Robots gather the information from which emotion detection is processed via different media, such as text, speech, images, or videos. The multimedia content is then properly processed to recognize emotions/sentiments, for example, by analyzing faces and postures in images/videos based on machine learning techniques or by converting speech into text to perform emotion detection with natural language processing (NLP) techniques. Keeping this information in semantic repositories offers a wide range of possibilities for implementing smart applications. We propose a framework to allow social robots to detect emotions and to store this information in a semantic repository, based on EMONTO (an EMotion ONTOlogy), and in the first figure or table caption. Please define if appropriate. an ontology to represent emotions. As a proof-of-concept, we develop a first version of this framework focused on emotion detection in text, which can be obtained directly as text or by converting speech to text. We tested the implementation with a case study of tour-guide robots for museums that rely on a speech-to-text converter based on the Google Application Programming Interface (API) and a Python library, a neural network to label the emotions in texts based on NLP transformers, and EMONTO integrated with an ontology for museums, thus, it is possible to register the emotions that artworks produce in visitors. We evaluate the classification model, obtaining equivalent results compared with a state-of-the-art transformer-based model and with a clear roadmap for improvement.

A life cycle assessment (LCA) was conducted on an innovative concrete 3D printing system, offering the following main advantages: (1) additive and subtractive capabilities, allowing for the automated post-processing of printed parts, including operations such as surface polishing, grooving and drilling and (2) the use of a cable robot, which is less expensive, lighter, more transportable, more energy-efficient and more easily reconfigurable than alternatives such as gantry-type systems. The production of a 4-m height structural pillar was assessed, comparing it to production with traditional methods, namely, using a mould. The study included the entire supply chain of the 3D printing equipment, operation and end-of-life, based on real data from the design and operation of a demonstration plant installed in Spain. Data for traditional construction was based on literature and expert judgement. The 3D production process included printing the pillar perimeter in four pieces with 3D printing concrete, transporting to the construction site and reinforcing and casting with conventional concrete. Traditional production involved reinforcing and casting with the mould on-site. The results show that when only one pillar needs to be produced, 3D printing has a lower environmental impact in all the environmental indicators assessed when compared to using a mould that is discarded after a single use. As an example, GHG emissions are lower by 38%. It was also found that the contribution of 3D printing to the environmental impact of producing a pillar is almost negligible, representing less than 1% of the pillar’s total GHG emissions. However, when the same pillar needs to be produced in higher numbers, the results show that 3D printing and conventional production have a similar environmental impact, given that the mould used in conventional production can be reused, becoming a comparatively efficient option.

International audience; In recent years, advanced manufacturing processes have been developed to increase the speed of production in order to reduce production costs. At the scale of thermoplastic composites, the translation is the combination of advanced manufacturing processes. The focus in this study is more specifically on the coupling of automated lay-up (AFP) and stamp forming processes. To date, a consolidation process, such as press-consolidation of thermoplastic composites, obtained blanks. Several trials have begun using an automated fiber placement consolidation to reduce manufacturing time and use unidirectional material. However, the combination of AFP and stamp forming is useful if it optimizes this process without the blank's full consolidation, which by resulting reduces the manufacturing time. This study estimates blank characteristics through thermal history imposed by a more rapid manufacturing process. A set of blanks with varying process parameters is produced to investigate the influence at the microscopic scale. The interface behaviour is observed with optical microscope and image processing. A statistical study applied to the process is carried out in order to relate the material observations to the input parameters. The results of this study are used for the study of the next process of the combination: the stamp forming.

6 pages; International audience; In the last decade, HCI research has proposed promising technologies for shape-changing interfaces. The usefulness and the user experience of shape-change are, however, still to be explored and understood. This paper extends the understanding of the potential utility and usability of axisymmetric shape-change. First, we present 16 potential use cases for a cylindrical shape-changing display. Second, we present a two-month comparative field study in the workplace. Six participants had to shift energy consumption by using energy storage. To do so, they were notified about local energy forecasts. Compared with flat-screen animations, early results show that cylindrical shape-change animations keep a better attractiveness over time.

International audience; Due to decarbonisation and decentralisation of energy sectors, the rise of distributed generation (DG) will modify generation and demand patterns at grid supply points (GSPs), where the interface between distribution and transmission systems takes place. With the increasing penetration of such devices, methodologies able to evaluate its contribution to both local distribution network and transmission system become crucial. This paper proposes an analytical method to evaluate the DG locational incremental contribution (LIC) to the interface with the transmission grid. Accordingly, the original model of the UK engineering recommendation P2 is enhanced by studying the impacts from high-voltage distribution networks (i.e. ≤ 132 kV) under normal and contingent conditions. This approach enables a more accurate network security assessment, especially when considering contingencies such as distribution system faults. To illustrate the proposed method, the original P2 model is compared against different enhanced approaches on three basic distribution networks, followed by a case study and a sensitivity analysis on a revised IEEE 14-bus GSP system. The proposed LIC method produces results that assess a wider range of conditions including DG penetration, concentration, and system reliability. Furthermore, it provides an increased DG visibility for transmission planning and operation.

Social media and other platforms on Internet are commonly used to communicate and generate information. In many cases, this information is not validated, which makes it difficult to use and analyze. Although there exist studies focused on information validation, most of them are limited to specific scenarios. Thus, a more general and flexible architecture is needed, that can be adapted to user/developer requirements and be independent of the social media platform. We propose a framework to automatically and in real-time perform credibility analysis of posts on social media, based on three levels of credibility: Text, User, and Social. The general architecture of our framework is composed of a front-end, a light client proposed as a web plug-in for any browser; a back-end that implements the logic of the credibility model; and a third-party services module. We develop a first version of the proposed system, called T-CREo (Twitter CREdibility analysis framework) and evaluate its performance and scalability. In summary, the main contributions of this work are: the general framework design; a credibility model adaptable to various social networks, integrated into the framework; and T-CREo as a proof of concept that demonstrates the framework applicability and allows evaluating its performance for unstructured information sources; results show that T-CREo qualifies as a highly scalable real-time service. The future work includes the improvement of T-CREo implementation, to provide a robust architecture for the development of third-party applications, as well as the extension of the credibility model for considering bots detection, semantic analysis and multimedia analysis. This research was supported by the FONDO NACIONAL DE DESARROLLO CIENTIFICO, TECNOLOGICO Y DE INNOVACION TECNOLOGICA ~ FONDECYT as an executing entity of CONSEJO NACIONAL DE CIENCIA, TECNOLOGIA E INNOVACION TECNOLOGICA - CONCYTEC under grant agreement No. 01-2019-FONDECYT-BM-INC.INV in the project RUTAS: Robots for Urban Tourism centers, Autonomous and Semantic-based.

In this paper a hierarchical energy management strategy design methodology for total cost of ownership management at fleet level is proposed. The decisions are taken from the fleet level point of view, to optimize the whole fleet based on the hierarchical decision maker and management, composed of three levels. The outer part is the offline route-to-bus data exploitation and decision maker, aiming to establish the dynamic programming optimization design. The next level is the offline optimization bus-to-route. Based on the previous decision, the neuro-fuzzy learns from the global optimal solutions. Finally, the trained fuzzy-logic strategy is used to manage the online operation. This fleet is re-organized and the online operation energy management strategy is updated throughout the bus lifetime. These decisions are made based on the evaluated battery lifetime of the fleet, with the aim to meet the planned total cost of ownership requirements. The total cost of ownership for the bus-to-route energy management strategy, has been improved a 7.65 % at fleet level against a charge-sustaining charge-depleting strategy. In the route-to-bus fleet re-organization and update, up to 5.51 % total cost of ownership improvement at fleet level has been obtained against not applying the fleet management.

Until now, construction is an area where most activities are artisanal. Concrete is linked to the masonry trade where few machines are used. Moreover, this material is mainly used for building construction, but it can be used for other applications like urban furniture for example where a mold is necessary to pour and maintain the concrete until it is dry. This method can be expensive for unique part because of the implementation time. Moreover, the part geometry is limited by the mold shape. For a few years, the additive manufacturing is used with the concrete material, mainly to build house walls but without finishing step to have smooth or assembling surfaces. So, it is composed by the layers of concrete and it is difficult to assembly two parts for example because of the shape but also dimensional accuracy. For these reasons, in the frame of the HINDCON project, a low-cost subtractive task has been introduced, to perform post-printing machining with respect of geometrical constraints.

International audience; As stated in the literature, Low Power Listening (LPL) duty cycle is one of the most common energy conservation solution for WSN. By using channel check mechanism, the purpose of LPL solutions is to reduce the energy consumption of the listening phase. In this paper, we propose to study the performances and limitations of this kind of solutions. Therefore, we deploy a ContikiMAC LPL on both real and simulated WSN platform to demonstrate the impact of LPL on the energy consumptions of the node radio and microcontroller but also on the application Quality of Service. Based on the obtained results, shortcomings of LPL solutions are highlighted and potential improvements are discussed such as the use of multi-parameter dynamic duty cycle.

This paper develops a novel two-stage coordinated volt-pressure optimization (VPO) for integrated energy systems (IES) networked with energy hubs considering renewable energy sources. The promising power-to-gas (P2G) facilities are used for improving the interdependency of the IES. The proposed VPO contains the traditional volt-VAR optimization functionality to mitigate the voltage deviation while ensuring a satisfying gas quality due to the hydrogen mixture. In addition to the conventional voltage regulating devices, i.e., on-load tap changers and capacitor banks, P2G converter and gas storage are used to address the voltage fluctuation problem caused by renewable penetration. Moreover, an effective two-stage distributionally robust optimization (DRO) based on Wasserstein metric is utilized to capture the renewable uncertainty with tractable robust counterpart reformulations. The Wasserstein-metric based ambiguity set enables to provide additional flexibility hedging against renewable uncertainty. Extensive case studies are conducted in a modified IEEE 33-bus system connected with a 20-node gas system. The proposed VPO provides a voltage-regulated economic operation scheme with gas quality ensured that contributes to high-quality but low-cost multi-energy supply to customers. British Council (Grant Number: 515761951); National Natural Science Foundation of China (Grant Number: 72042018 and 71621002).

The widespread diffusion of smart and mobile devices continuously connected to the Internet has facilitated the users’ contact and interaction with other people, devices, and with their physical surroundings. Proxemic interaction, derived from proxemics theory, focuses on how Human-Computer Interaction (HCI) works with smart devices, using the five proxemic dimensions: Distance, Identity, Location, Movement, and orientation (DILMO). The current tools for developing proxemic applications require fixed devices that make it difficult to build proxemic mobile apps. In this work, we propose a framework to manage all components in a proxemic environment (i.e., interaction objects and DILMO dimensions that govern the HCI). We demonstrate and evaluate the effectiveness and suitability of our framework, through the development of two proxemic mobile applications, as proof-of-concept.

This paper proposes several contributions to the development of an agricultural robot performing spraying task autonomously, in the framework of vineyard or orchard. Since an important point of such an application relies on the treatment of the vegetation while reducing the dissemination of hazardous chemical products, a control approach related to the detection of plants using Lidar is proposed for both controlling the robot motion and achieving spraying automation. A dedicated spraying device, allowing to (de)activate independently several nozzles has been designed and on-boarded on a mobile robot, following autonomously a detected structure (typically a row of trees). The synchronization of the sprayer and the robot with respect to the vegetation is ensured through an anticipative approach allowing to apply spraying on the vegetation pending on the robot motion.

International audience; This paper evaluates deterministic and probabilistic approaches for assessing hosting capacity (HC) of distribution networks for wind-based distributed generation (DG). The presented methodology considers variations of demands and DG power outputs, as well as dynamic thermal ratings (DTR) of network components. Deterministic approaches are based on a limited number of scenarios with minimum and maximum demands and DTR limits, while probabilistic approaches use simultaneous hourly values of all input parameters. The presented methodology has three stages. First, locational HC (LHC) of individual buses is calculated assuming connection of a single DG unit in the considered network. Afterwards, the LHC results are used to calculate network HC (NHC), assuming that DG units are connected at all network buses. Finally, busto-bus LHC-sensitivity factors are used to determine LHC and NHC for any number of DG units connected at arbitrary network buses.

In this paper, a new power division approach is proposed for a Four-Wires Three-Level Neutral Point Clamped (4W-3L-NPC) inverter used as the unique interface for a Hybrid Energy Storage System (HESS) supplied by a renewable energy system in the context of a Microgrid. The objective of this work is to define the limits of the investigated topology and prove its capacity to manage the power division and power exchange capabilities of a HESS. The proposed power division index is expressed using the DC mean power and AC active power, giving a clearer and simpler insight of the limits which are facilitating the implementation in the final controller. To prove its capability, the investigated approach has been tested in the context of a Microgrid with and without Renewable Energy Sources injection on the DC bus.

Power-to-gas (P2G) can convert excessive renewable energy into hydrogen via electrolysis, which can then be transported by natural gas systems to bypass constrained electricity systems. However, the injection of hydrogen could impact gas security since gas composition fundamentally changes, adversely effecting the combustion, safety and lifespan of appliances. This paper develops a new gas security management scheme for hydrogen injection into natural gas systems produced from excessive wind power. It introduces four gas security indices for the integrated electricity and gas system (IEGS) measuring gas security, considering the coordinated operation of tightly coupled infrastructures. To maintain gas security under an acceptable range, the gas mixture of nitrogen and liquid petroleum gas with hydrogen is adopted to address the gas security violation caused by hydrogen injection. A distributionally robust optimization (DRO) modelled by Kullback-Leibler (KL) divergence-based ambiguity set is applied to flexibly control the robustness to capture wind uncertainty. The KL divergence-based ambiguity set defines uncertainties within a measured space which limits the shape of probability distributions. Case studies illustrate that wind power is maximally utilized and gas mixture is effectively managed, thus improving gas security and performance of IEGS. This work can bring many benefits: i) ensured gas security under hydrogen injection ii) low system operation cost and iii) high renewable energy penetration. It can be easily extended to manage injections of other green gases into IEGS.

International audience; Building microgrids have emerged as an advantageous alternative for tackling environmental issues while enhancing the electricity distribution system. However, uncertainties in power generation, electricity prices and power consumption, along with stringent requirements concerning power quality restrain the wider development of building microgrids. This is due to the complexity of designing a reliable and robust energy management system. Within this context, hierarchical control has proved suitable for handling different requirements simultaneously so that it can satisfactorily adapt to building environments. In this paper, a comprehensive literature review of the main hierarchical control algorithms for building microgrids is discussed and compared, emphasising their most important strengths and weaknesses. Accordingly, a detailed explanation of the primary, secondary and tertiary levels is presented, highlighting the role of each control layer in adapting building microgrids to current and future electrical grid structures. Finally, some insights for forthcoming building prosumers are outlined, identifying certain barriers when dealing with building microgrid communities.

The large number of Wireless Sensor Networks (WSN) simulators available nowadays, differ in their design, goals, and characteristics. Users who have to decide which simulator is the most appropriate for their particular requirements, are today lost, faced with a panoply of disparate and diverse simulators. Hence, it is obvious the need for establishing guidelines that support users in the tasks of selecting a simulator to suit their preferences and needs. In previous works, we proposed a generic and novel approach to evaluate networks simulators, considering a methodological process and a set of qualitative and quantitative criteria. In particularly, for WSN simulators, the criteria include relevant aspects for this kind of networks, such as energy consumption modelling and scalability capacity. The aims of this work are: (i) describe deeply the criteria related to WSN aspects; (ii) extend and update the state of the art of WSN simulators elaborated in our previous works to identify the most used and cited in scientific articles; and (iii) demonstrate the suitability of our novel methodological approach by evaluating and comparing the three most cited simulators, specially in terms of energy modelling and scalability capacities. Results show that our proposed approach provides researchers with an evaluation tool that can be used to describe and compare WSN simulators in order to select the most appropriate one for a given scenario, 20 Pages

Conventional Li-ion battery ageing models, such as electrochemical, semi-empirical and empirical models, require a significant amount of time and experimental resources to provide accurate predictions under realistic operating conditions. At the same time, there is significant interest from industry in the introduction of new data collection telemetry technology. This implies the forthcoming availability of a significant amount of real-world battery operation data. In this context, the development of ageing models able to learn from in-field battery operation data is an interesting solution to mitigate the need for exhaustive laboratory testing. In a series of two papers, a data-driven ageing model is developed for Li-ion batteries under the Gaussian Process framework. A special emphasis is placed on illustrating the ability of the Gaussian Process model to learn from new data observations, providing more accurate and confident predictions, and extending the operating window of the model. This first paper focusses on the systematic modelling and experimental verification of cell degradation through calendar ageing. A specific covariance function is composed, tailored for use in a battery ageing application. Over an extensive dataset involving 32 cells tested during more than three years, different training possibilities are contemplated in order to quantify the minimal number of laboratory tests required for the design of an accurate ageing model. A model trained with only 18 tested cells achieves an overall mean-absolute-error of 0.53% in the capacity curves prediction, after being validated under a broad window of both dynamic and static temperature and SOC storage conditions. This investigation work was financially supported by ELKARTEK (CICe2018 -Desarrollo de actividades de investigacion fundamental estrategica en almacenamiento de energia electroquimica y termica para sistemas de almacenamiento hibridos, KK-2018/00098) and EMAITEK Strategic Programs of the Basque Government. In addition, the research was undertaken as a part of ELEVATE project (EP/M009394/1) funded by the Engineering and Physical Sciences Research Council (EPSRC) and partnership with the WMG High Value Manufacturing (HVM) Catapult. Authors would like to thank the FP7 European project Batteries 2020 consortium (grant agreement No. 608936) for the valuable battery ageing data provided during the course of the project.

International audience; Over the past number of years, competence management has become a key issue for companies. Similarly, to the importance of knowledge management, competence management considers an individual’s performance, based on his or her knowledge. Thus, knowledge management becomes an important aspect for companies to manage resources, through the ability of characterizing key competences and evaluating how they are improved through past experiences and roles, thus selecting project team members according to the respective existing skills. This paper focuses on the coordination of design activities in order to propose a tool dedicated to operation-level project managers, to help prepare their technical teams based on the required skills. The goal is to enhance the effectiveness of the teams, as well as their performance in the short-term, as well as the longterm, while coordinating with the human resources department. This work is based on the results of the Aides et assisTances pour la conception, la conduite et leur coupLage par les connAissanceS – Help and support for design, coordination and their coupling by knowledge (ATLAS, 2008 - 2011). This project studies the coupling between systems design and management. We propose an initial tool to manage skills in a design project.

This paper aims to present a novel control strategy for modular multilevel converters (MMC) based on differential flatness theory, in which instantaneous active and reactive power values are considered as the flat outputs. To this purpose, a mathematical model of the MMC taking into account dynamics of the ac-side current and the dc-side voltage of the converter is derived in a d-q reference frame. Using this model, the flat outputs-based dynamic model of MMC is obtained to reach the initial value of the proposed controller inputs. In order to mitigate the negative effects of the input disturbance, model errors, and system uncertainties on the operating performance of the MMC, the integral-proportional terms of the flat output errors are added to the initial inputs. This can be achieved through defining a control Lyapunov function which can ensure the stability of the MMC under various operating points. Moreover, the small-signal linearization method is applied to the proposed flat output-based model to separately evaluate the variation effects of controller inputs on flat outputs. The proficiency of the proposed method is researched via MATLAB simulation. Simulation results highlight the capability of the proposed controller in both steady-state and transient conditions in maintaining MMC currents and voltages, through managing active and reactive power.

A multi-loop control strategy based on a six-order dynamic model of the modular multilevel converter (MMC) is presented in this paper for the high-voltage direct current (HVDC) applications. For the initial analysis of the operation of MMC, a capability curve based on active and reactive power of the MMC is achieved through a part of the six order dynamic equations. According to the MMC’s control aims, the first loop known as the outer loop is designed based on passivity control theory to force the MMC state variables to follow their reference values. As the second loop with the use of sliding mode control, the central loop should provide appropriate performance for the MMC under variations of the MMC’s parameters. Another main part of the proposed controller is defined for the third inner loop to accomplish the accurate generation of reference values. Also, for a deeper analysis of the MMC’s dc link voltage stability, two phase diagrams of the dc-link voltage are assessed. Matlab/Simulink environment is used to thoroughly validate the ability of the proposed control technique for control of the MMC in HVDC application under both load and MMC’s parameters changes.

New applications for robots include tasks that were performed by specialized machines such as polish, milling, placement of fibers, among others. The implementation using robots in these new applications has the inaccuracies problems due to the flexibilities within the robot arm or the tooling when external forces are applied. The industrial robot controller does not take into account the flexibilities and the external forces. This paper is an introduction to the work done to improve the precision in the robotic fiber placement, taking into account the deformation in the compaction roller.

This paper proposes a digital control scheme to control operation of a proton exchange membrane fuel cell module of 1.2 kW and a supercapacitor through a DC/DC hybrid converter. A FC is proposed as a primary source of energy along with a SC as an auxiliary source of energy. A control scheme is proposed for control of the proposed system. An experimental setup of the proposed system is implemented in the laboratory, and several scenarios have been defined for tests to verify that the proposed system achieves an excellent output voltage regulation and SC voltage control, under disturbances from FC voltage, load voltage and other perturbations described in results analysis.

International audience; A MicroGrid (MG) is a distributed power system consisting of a number of heterogeneous components having direct/indirect impacts on each other. In order to provide an appropriate collaboration (from several perspectives) between components, we propose a "Digital Ecosystem Cooperative Framework" called DECF. In this paper, we present the clustering algorithm of DECF designed to build Alliances by gathering all the DE heterogeneous components having similar needs and preferences. Conducted simulations showed that the proposed algorithms yield significant results. \textcopyright 2017 The Author(s).

ISBN: 978-1-925953-09-1; International audience; Nowadays, there exists a large number of available network simulators, that differ in their design, goals, and characteristics. Users who have to decide which simulator is the most appropriate for their particular requirements, are today lost, faced with a panoply of disparate and diverse simulators. Hence, it is obvious the need for establishing guidelines that support users in the tasks of selecting and customizing a simulator to suit their preferences and needs. In previous works, we proposed a generic and novel methodological approach to evaluate network simulators, considering a set of qualitative and quantitative criteria. However, it lacks criteria related to Wireless Sensor Networks (WSN). Thus, the aim of this work is three fold: (i) extend the previous proposed methodology to include the evaluation of WSN simulators, such as energy consumption modelling and scalability; (ii) elaborate a study of the state of the art of WSN simulators, with the intention of identifying the most used and cited in scientific articles; and (iii) demonstrate the suitability of our novel methodology by evaluating and comparing three of the most cited simulators. Our novel methodology provides researchers with an evaluation tool that can be used to describe and compare WSN simulators in order to select the most appropriate one for a given scenario.

International audience; In this paper, an energy management system (EMS) of a grid-connected hybrid system, based on MPC methodology is presented. This approach is a control system based on the storage system model, which predicts the future output behavior of the storage system handling the power balance between the electricity supplied by the hybrid plant and the demand. This EMS aims to minimize electricity consumption from the grid by producing and consuming as much as possible renewable energy. To comply this, the hybrid power plant combines photovoltaic panels (PV), Li-Ion batteries and a chain of hydrogen (fuel cell, electrolyzer and hydrogen tank). The proposed control strategy has been implemented and tested using the Matlab/Simulink environment.

Product success depends on its capacity to meet users’ expectations. Human Centred Design approach helps to reach this success by focussing on users’ needs in the design process. These needs are as well functional as hedonic. Designing products requires then to design hedonic properties affecting users’ perception. For sport products, people wants to improve their performances while maintaining their health. Sport products are then considered not only “sporty” but also “healthy”. Thus, integrating both health and sport expectations into the design process are necessary.Last decades, Affective Engineering was developed to integrate perception into the design process. Applying this approach for sport products may allow defining and mixing sport and health perceptual characteristics all along the design process. However, defining these characterisitics into requirements implies to translate them into semantic terms. If we observe semantic descriptors for sport products and for health products, they seem opposite. In this paper, we aim defining a semantic space representative and respectful of both domains, sport and health, while they oppose.

Early phases of product development are critical for next phases and impact the product definition. During bid process, suppliers generate offers for a customer that must both meet customer's requirements and be realizable in terms of technical aspects, costs and due date. Our aim is to propose a methodology for implementing a generic bid model, composed of context parameters, customer's requirements, the product i.e. technical solution, its delivery process, and associated risks. Key Performance Indicators allow to evaluate different solutions. The bid model is exploited with two different approaches. First, we use Constraint Satisfaction Problems to formalize expert knowledge and identify variables/constraints and relations. Second, we use case database to reuse past experiences. This model and the methodology are applied with a company developing harbour cranes. An initialisation phase allows to define existing bid process. Then, the generic model is adapted through a specialisation phase, using specific knowledge from company's experts. Finally, the specific model is implemented and tested in an implementation phase. Future work will be focused on a software tool development.

We present CairnFORM, a shape-changing cylindrical display that physicalizes forecasts of renewable energy availability. CairnFORM aims at creating and encouraging new socially-shared practices by displaying energy data in collective and public spaces, such as public places and workplaces. It is 360°-readable, and as a dynamic physical ring chart, it can change its cylindrical symmetry with quiet motion. We conducted two user studies. The first study clearly revealed the attractiveness of CairnFORM in a public place and its usability for a range task and for a compare task. Consequently, this makes CairnFORM useful to analyze renewable energy availability. The second study revealed that a non-constant motion speed is the better visualization stimulus at a workplace.Daniel, M., Rivière G. and Couture N. CairnFORM: a Shape-Changing Ring Chart Notifying Renewable Energy Availability in Peripheral Locations. Proceedings of the 13th ACM International Conference on Tangible, Embedded and Embodied Interaction (Tempe, Arizona, USA, March 17-20, 2019). ACM Press (2019).

International audience; This paper presents an integrated approach for assessing the impact that distributed energy resources (DERs), including intermittent photovoltaic (PV) generation, might have on the reliability performance of power networks. A test distribution system, based on a typical urban MV and LV networks in the UK, is modelled and used to investigate potential benefits of the local renewable generation, demand-manageable loads and coordinated energy storage. The conventional Monte Carlo method is modified to include time-variation of electricity demand profiles and failure rates of network components. Additionally, a theoretical interruption model is employed to assess more accurately the moment in time when interruptions to electricity customers are likely to occur. Accordingly, the impact of the spatio-temporal variation of DERs on reliability performance is quantified in terms of the effect of network outages. The potential benefits from smart grid functionalities are assessed through both system-and customer-oriented reliability indices, with special attention to energy not supplied to customers, as well as frequency and duration of supply interruptions. The paper also discusses deployment of an intelligent energy management system to control local energy generation-storage-demand resources that can resolve uncertainties in renewable-based generation and ensure highly reliable and continuous supply to all connected customers.

As the size of the Smart Grid (SG) grows, the economic significance of power generation, consumption and storage scheduling becomes more and more apparent. A proper scheduling for electricity generation, consumption and storage will also ensure the reliability of the SG and extend the operational lives of its constituent units. Besides, it can achieve economical and ecological benefits for the SG. In this work, we propose a multi-objective cooperative scheduling consisting of two main modules: (1) the Preference-based Compromise Builder and (2) the Multi-objective Scheduler. The Preference-based Compromise Builder aims at generating the best balance or what we call ‘the compromise’ between the preferences or associations of the sellers and the buyers that must exchange power simultaneously. Once done, the Multi-objective Scheduler aims at proposing a power schedule for the associations, in order to achieve three-dimensional benefits: economical by reducing the electricity costs, ecological by minimizing the toxic gas emissions, and operational by reducing the peak load of the SG and its components, and by increasing their comfort. Conducted experiments showed that the proposed algorithms provide convincing results. Keywords: Multi-objective optimization, Smart Grid, Scheduling

International audience; In Design by Shopping, designers explore the design space to gain an insight into trades, and feasible and impractical solutions, as well as to learn about alternatives before optimization and selection. The design space consists of multidimensional sets of data and, in order to select the best design from among numerous alternatives, designers may use several different graphs. In this study, we test to find the most appropriate graph to indicate the best solution corresponding to a set of objectives represented by a design scenario (1). A further constraint is that this must be done in the shortest possible time (2). Three graph types are tested in three different design scenarios where one car has to be chosen from a total of 40. A response quality index is proposed which computes the quality of a designer’s choice for any given scenario. In total, 90 tests with 30 participants were performed. The parallel coordinates plot proved to be the best graph for selection in Design by Shopping.

International audience; This article presents a collaborative system, called SCORE, useful for a multidisciplinary team designing a new nuclear power plant (NPP). It was developed during the first phase of the I 2 S-LWR project (Integral Inherently Safe Light Water Reactor). SCORE enables the generation of design cards (DCs). A DC includes four main spaces (Boy, 2005): (1) a rationalization space where the various components of the system being designed (SBD) are described in terms of design rationale, integration and requirements; this space includes declarative and procedural descriptions and statements; (2) an activity space where the current version of the SBD is displayed; it includes static and dynamic features; this space enables SBD manipulation; (3) a structure space where the various components and their interrelations are formally and declaratively described as systems of systems; (4) a function space where the various functions of the SBD are described in terms of procedural knowledge and dynamic processes involved; this space includes qualitative and quantitative physical and cognitive models. The rationalization space is informed using an adapted version of the QOC method (Questions, Options, Criteria), which was tested within the I 2 S-LWR design team. The activity space contains 3D models developed using AutoDesk Inventor, and transferred into the Unity game engine web player in order to facilitate integration within the DC spaces and enable intuitive manipulation of objects in the activity space. Two additional spaces were added: an instant messaging capability that allows design team members (DTMs) to exchange with one another on a DC; and a structured evaluation space. DCs are cooperatively created and refined by DTMs, and synthesized during periodic design meetings, the frequency of which may vary. Incrementally combining abstract explanations of designed elements and integration with their explicit visual representation improves mutual understanding among DTMs, and consolidates design decisions. This human-centered design (HCD) approach also improves individual and collective familiarization with the complexity of mixing several expert contributions in NPP design. In this case, HCD is not focused on end-users, but rather on the designers themselves.

In this paper, a non-adaptive and a novel adaptive energy management strategy (EMS) are proposed for a series hybrid electric bus with a dual energy storage system (ESS) combining batteries and supercapacitors. The scenario considers the hybrid ( genset +dual ESS) and full-electric (only dual ESS) operation during the daily journey. The main goal of this paper is to highlight the improvements that can be achieved either with an enhanced adaptive EMS (fuzzy-based) compared with the non-adaptive (rule-based) approach as well as with the co-optimization of ESS sizing and operation targets in economic, fuel saving, and efficiency terms. To do so, a multi-objective optimization methodology is proposed (considering economic and ageing models) to minimize the operation cost (cycling cost) of the dual ESS and its replacements along the vehicle lifetime as well as the fuel consumption, while fulfilling the performance of the bus in its daily journey. The simulation results showed that the adaptive EMS can reduce both the operation cost of the vehicle and the average fuel consumption (up to 15% and 19%, respectively) compared with the optimal solution obtained with the non-adaptive EMS.

International audience; In this paper, an energy management system (EMS) based on model predictive control (MPC) and quadratic programming (QP) is presented. The control strategy manages the power performance of an island grid-connected hybrid plant combining a wind energy conversion system (WECS) and a Li-ION battery energy storage system (BESS). The hybrid plant should inject power into the grid continuously, subject to a commitment band previously fixed. For this, the proposed EMS handles the battery's charge/discharge cycles and state-of-charge (SoC) efficiently, to maintain the generation engagements while considering the BESS lifespan. The energy management system is tested via Matlab/Simulink software by using real wind data.