The SWEET-HOME Project: Audio Technology in Smart Homes to improve Well-being and Reliance

International audienceThe SWEET-HOME project aims at providing audio-based interaction technology that lets the user have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. This paper presents an overview of the project focusing on the implemented techniques for speech and sound recognition as context-aware decision making with uncertainty. A user experiment in a smart home demonstrates the interest of this audio-based technology

Evaluation of a Real-Time Voice Order Recognition System from Multiple Audio Channels in a Home

International audienceThe SWEET-HOME project aims at providing audio-based interaction technology that lets the user have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. This paper presents an overview of the project focusing on the implemented techniques for speech and sound recognition as context-aware decision making with uncertainty. A user experiment in a smart home demonstrates the interest of this audio-based technology

Methodology for integrated multicriteria decision-making with uncertainty (MIMDU) for robust analysis. Case study about agricultural efficiency in Colombia

Selecting the best among different alternatives may require asking for experts’ opinions to weight key criteria and assess the alternatives. Their opinions can be: 1) hesitant, and 2) difficult to quantify on a numerical scale. The Methodology for Integrated Multicriteria Decision-making with Uncertainty (MIMDU) allows performing robust multicriteria analysis considering both factors of uncertainty. Fuzzy rating scales are integrated into the Compromise Ranking Method to finally rank alternatives based on a comparison of a crisp ranking (without uncertainty) and a fuzzy-based ranking (with uncertainty). The soundness of MIMDU is shown with an example case which highlights its capacity of precisely modelling uncertain opinions and assist decision-making. Finally, MIMDU is used to select the most sustainable technology to improve agriculture efficiency in rural underprivileged areas by means of a real small-scale farm in Colombia.Objectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.1 - Per a 2030, garantir l’accés universal a serveis d’energia assequibles, confiables i modernsObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energiaObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.3 - Per a 2030, duplicar la taxa mundial de millora de l’eficiència energèticaPostprint (author's final draft

Perspective Chapter: A Novel Method for Integrated Multicriteria Decision-Making with Uncertainty: A Case Study on Sustainable Agriculture in Colombia

Multicriteria decision-making usually requires a set of experts to evaluate the importance of selected criteria and the adequacy of feasible alternatives according to the criteria. Uncertainty can arise in these evaluations, since experts can be hesitant about their responses due to the difficulty of quantifying human language or lack of required knowledge. The Methodology for Integrated Multicriteria Decision-making with Uncertainty (MIMDU) tackles both factors of uncertainty by using non-predefined fuzzy numbers that are continuously adapted taking into account the level of confidence of the experts’ opinions. The methodology also offers useful and complementary information to lead to a robust decision-making. This chapter proposes a novel methodology and provides a sample use case to demonstrate its capability to model uncertainty during decision-making process. In particular, a sensitivity analysis is included, which demonstrates (i) how uncertainty is incorporated into alternatives evaluation, and (ii) that the integrated multicriteria decision-making with uncertainty can be more reliable for decision-makers. The methodology is applied to the robust selection of the most sustainable technology to improve agriculture efficiency in rural areas by means of a case study of a low-cost biogas digester in a small-scale farm in Colombia

Intelligent Control of a Sensor-Actuator System via Kernelized Least-Squares Policy Iteration

In this paper a new framework, called Compressive Kernelized Reinforcement Learning (CKRL), for computing near-optimal policies in sequential decision making with uncertainty is proposed via incorporating the non-adaptive data-independent Random Projections and nonparametric Kernelized Least-squares Policy Iteration (KLSPI). Random Projections are a fast, non-adaptive dimensionality reduction framework in which high-dimensionality data is projected onto a random lower-dimension subspace via spherically random rotation and coordination sampling. KLSPI introduce kernel trick into the LSPI framework for Reinforcement Learning, often achieving faster convergence and providing automatic feature selection via various kernel sparsification approaches. In this approach, policies are computed in a low-dimensional subspace generated by projecting the high-dimensional features onto a set of random basis. We first show how Random Projections constitute an efficient sparsification technique and how our method often converges faster than regular LSPI, while at lower computational costs. Theoretical foundation underlying this approach is a fast approximation of Singular Value Decomposition (SVD). Finally, simulation results are exhibited on benchmark MDP domains, which confirm gains both in computation time and in performance in large feature spaces

Evaluación del proceso de toma de decisiones en el contexto del arbitraje deportivo : propiedades psicométricas de la adaptación espanola del cuestionario DMQ II en árbitros de balonmano

En este trabajo presentamos un estudio preliminar para adaptar el cuestionario Decision Making Questionaire II (DMQ-II) a la población española de árbitros deportivos, con el propósito de contar con un instrumento auxiliar que facilite el estudio de las diferencias individuales en la toma de decisiones. La muestra estaba compuesta por 131 árbitros de balonmano pertenecientes a las categorías de división de honor, primera división y categoría territorial. La estructura factorial de los datos, así como la consistencia interna del instrumento, confirman la existencia de distintos patrones de conducta ante situaciones de decisión en el contexto del arbitraje en el deporte del balonmano relacionados con: estrés en la toma de decisiones; decisión rápida con incertidumbre; y determinación y compromiso en la toma de decisiones.The present pilot study was carried out with a sample of 131 handball umpires of Spain, with the aim of adapting the Decision Making Questionnaire II (DMQ-II questionaire) and developing an instrument that may help to study individual differences in decision making. Three factors were extracted:stress management and decision making; quick decision making with uncertainty; and determination and commitment to decision making. The factorial structure of the data, as well as the values of internal consistency of the scales, confirm the existence of different patterns of behaviour

Design of ships and offshore structures: a probabilistic approach for multi-year ice and iceberg impact loads for decision-making with uncertainty

Ice is a complex material that exhibits different failure properties depending on the loading rate, temperature and salinity. Under fast loading rates such as a ship ramming a multi-year (MY) ice, it fails as a brittle fracturing material. Fracture and spalling processes nonsimultaneously reshape the contact zone resulting in concentrated forces on localized contact areas. These localized High Pressure Zones (HPZs) are highly variable in time and space. The relationship between local and global processes is that the sum of n HPZs forces transferred into the structure at any point in time is the total global force transmitted to the structure. As with other fracturing materials, an inherent scale effect exists. Global pressures result from the sum of n HPZ forces averaged over the nominal contact area (e.g. the imprint of a ship’s bow into the ice without correction for spalling effects). The maximum global force will generally occur at the end of a ram at the maximum nominal contact area. Due to the random occurrence of natural flaws in the ice, pressures will vary as fractures occur, continually changing the contact face. A global scale effect exists such that pressures on larger contact areas, including zones of low and zero pressure, average out to be smaller. Unlike global pressures, maximum local pressures may occur on any panel and at any point through the ram duration. Modeling exposure is important as design pressures will increase for increasing number of interaction events as well as increased penetration or duration. The scale effect for local pressures within the nominal contact area is more demanding than for global pressures such that pressures on smaller areas are considerably higher. While this is expected, given confinement can suppress damage and limit fracturing events, a force limit exists where microstructural damage occurs, softening the ice and causing HPZs to fail. Local pressures on varying panel areas were studied based on spatial HPZ density and HPZ force. Building on earlier HPZ analysis using Louis S. St. Laurent data, in this thesis HPZ density and forces were derived from analysis of four Polar Sea data sets. The occurrence and intensity of HPZs on panel areas were simulated using a Poisson Process and an exponential distribution for HPZ force. The influence of modeling HPZ cutoff force on HPZ density, HPZ force distribution as well as local pressure parameters were studied and appropriate combinations recommended. Building on the Polar Sea HPZ analysis, a new model was developed for this thesis that considers HPZ occurrence in time through a ramming event, modeling HPZ rate. This was further enhanced by correlating HPZ rate with ship speed. Such a model allows the designer to determine baseline ‘parent’ local pressure design parameters based on vessel size and expected operational speed. The faster a ship operates through an ice regime, the greater the HPZ rate. Larger and faster ships will penetrate further, having longer interaction durations and hence a greater number of HPZs forming (unless, for example, the ship passes through a ridge). For design, we are interested in the maximum local pressure on a single panel area through the ram duration. Rates too will vary along the vessel being greater on the bow and least from mid-body to stern. For fixed structures designed for iceberg impacts, rate and duration based on iceberg size and drift can be used to model exposure in time. For floaters, modeling HPZ formation in time provides a means to estimate dynamic global forces and mooring loads illustrating benefit of compliance effects. Modeling of HPZ occurrence over a panel area is also very attractive for structure response analysis. The random placement of n HPZs over a structural panel gives a better basis to model stress localization, which is very important for limit states design. A preliminary review of the IACS Polar Class rules was carried out in this thesis. Global impact forces are estimated using a kinetic energy collision model. Consideration for modeling ice crushing strength assumes a pressure-area relationship that is proportional to A⁻⁰·¹ which is not consistent with experimental results demonstrating a scale effect proportional to A⁻⁰·⁴. The resultant design formulation models excessive semi-local pressures increasing with increasing semi-local contact area. While the intent is to model increasing pressures locally with increasing vessel displacement and subsequent penetration and contact area, justification for this trend suggests that there is no reason for traditional pressure area scale effects to exist and that with confinement, fracturing processes will be limited. But fracturing processes exist at all scales. The occurrence and behavior of HPZs either lead to very large stress localization that enhances fracture events or they undergo microstructure damage that softens the ice at the structure interface. While the design trend in the Polar Class rules may be okay, the background ice mechanics can be improved. An alternative collision model is developed in this thesis with an ice strength model based on data and an exposure algorithm to model pressures increasing locally with larger displacement vessels. In the mid 1990s as part of the Arctic Shipping Pollution Prevention Regulations (ASPPR) proposal reviews, a probabilistic time-step ship ram model was developed to estimate impact forces. Consistent with the ASPPR work, exposure based on annual number of collisions was mapped to each Polar Class (e.g. PC1, PC2, PC3 can expect on the order of 10000, 1000, 100 rams per year respectively). Using the MV Arctic as a test case and exercising extremal analysis, impact forces were estimated for each Polar Class. Characteristic 10⁻² global forces were compared with Polar Class rule estimates. Probabilistic local pressures were also compared with rule based estimates. Assuming impacts with MY ice, preliminary results show that plating design pressures may be reasonable, with recommendation for adjustment to the Polar Class 1 coefficients to reduce conservatism, and possible increases for lower classes. Analysis should be extended to other vessels and operating conditions. A probabilistic methodology for design of ships based on the principles of safety and consequences is important and necessary both for design and safety validation. Such approaches can consider the class of the vessel on the basis of expected number of annual interactions with extreme ice features. An example illustration of a design based on an arctic shipping route, ice conditions, design strategy, risk mitigation via detection and avoidance and resultant local pressures on the hull for structural design

A resource allocation model for deep uncertainty (RAM-DU) with application to the Deepwater Horizon oil spill

Deep uncertainty usually refers to problems with epistemic uncertainty in which the analyst or decision maker has very little information about the system, data are severely lacking, and different mathematical models to describe the system may be possible. Since little information is available to forecast the future, selecting probability distributions to represent this uncertainty is very challenging. Traditional methods of decision making with uncertainty may not be appropriate for deep uncertainty problems. This paper introduces a novel approach to allocate resources within complex and very uncertain situations. The resource allocation model for deep uncertainty (RAM-DU) incorporates different types of uncertainty (e.g., parameter, structural, model uncertainty) and can consider every possible model, different probability distributions, and possible futures. Instead of identifying a single optimal alternative as in most resource allocation models, RAM-DU recommends an interval of allocation amounts. The RAM-DU solution generates an interval for one or multiple decision variables so that the decision maker can allocate any amount within that interval and still ensure that the objective function is within a predefined level of optimality for all the different parameters, models, and futures under consideration. RAM-DU is applied to allocating resources to prepare for and respond to a Deepwater Horizon-type oil spill. The application identifies allocation intervals for how much should be spent to prepare for this type of oil spill and how much should be spent to help industries recover from the spill