International Professional University of Technology in Nagoya Repository
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Extracting Decision Rules from Qualitative Data via Sugeno Utility Functionals
No full textInternational audienceSugeno integrals are qualitative aggregation functions. They are used in multiple criteria decision making and decision under uncertainty, for computing global evaluations of items, based on local evaluations. The combination of a Sugeno integral with unary order preserving functions on each criterion is called a Sugeno utility functionals (SUF). A noteworthy property of SUF is that they represent multi-threshold decision rules, while Sugeno integrals represent single-threshold ones. However, not all sets of multi-threshold rules can be represented by a single SUF. In this paper, we consider functions defined as the minimum or the maximum of several SUF. These max-SUF and min-SUF can represent all functions that can be described by a set of multi-threshold rules, i.e., all order-preserving functions on finite scales. We study their potential advantages as a compact representation of a big set of rules, as well as an intermediary step for extracting rules from empirical datasets
Non-Decoupled Locomotion and Manipulation Planning for Low-Dimensional Systems
No full textInternational audienceWe demonstrate the possibility of solving planning problems by inter-leaving locomotion and manipulation in a non-decoupled way. We choose three low-dimensional minimalistic robotic systems and use them to illustrate our paradigm: a basic one-legged locomotor, a two-link manipulator with a manipulated object, and a simultaneous locomotion-and-manipulation system. Using existing motion planning and control methods initially designed for either locomotion or manipulation tasks, we see how they apply to both our locomotion-only and manipulation-only systems through parallel derivations, and extend them to the simultaneous locomotion-and-manipulation system. Motion planning is solved for these three systems using two different methods : (i) a geometric path-planning-based one, and (ii) a kinematic control-theoretic-based one. Motion control is then derived by dynamically realizing the geometric paths or kinematic trajectories under the Couloumb friction model using torques as control inputs. All three methods apply successfully to all three systems, showing that the non-decoupled planning is possible
Stability analysis of a general class of singularly perturbed linear hybrid systems
No full textInternational audienceWe introduce and analyze a general class of singularly perturbed linear hybrid systems with both switches and impulses, in which the slow or fast nature of the variables can be mode-dependent. This means that, at switching instants, some of the slow variables can become fast and vice-versa. Firstly, we show that using a mode-dependent variable reordering we can rewrite this class of systems in a form in which the variables preserve their slow or fast nature over time. Secondly, we establish, through singular perturbation techniques, an upper bound on the minimum dwell-time ensuring the overall system's stability. Remarkably, this bound is the sum of two terms. The first term, which can be equal to zero, only depends on the matrices of the reduced order linear hybrid system describing the slow dynamics and corresponds to an upper bound on the minimum dwell time ensuring the stability of that system. The order of magnitude of the second term is determined by that of the parameter defining the ratio between the two timescales of the singularly perturbed system. We show that the proposed framework can also take into account the change of dimension of the state vector at switching instants. Numerical illustrations complete our study
Inverse regression approach to robust nonlinear high-to-low dimensional mapping
No full textInternational audienceThe goal of this paper is to address the issue of nonlinear regression with outliers, possibly in high dimension, without specifying the form of the link function and under a parametric approach. Nonlinearity is handled via an underlying mixture of affine regressions. Each regression is encoded in a joint multivariate Student distribution on the responses and covariates. This joint modeling allows the use of an inverse regression strategy to handle the high dimensionality of the data, while the heavy tail of the Student distribution limits the contamination by outlying data. The possibility to add a number of latent variables similar to factors to the model further reduces its sensitivity to noise or model misspecification. The mixture model setting has the advantage of providing a natural inference procedure using an EM algorithm. The tractability and flexibility of the algorithm are illustrated in simulations and real high-dimensional data with good performance that compares favorably with other existing methods
Wideband reflector-backed folded bowtie antenna for Ground Penetrating Radar
No full textInternational audienceA loaded ultra-wideband unidirectional folded triangular bowtie antenna is proposed for ground penetrating radar (GPR) applications covering the [0.5 GHz - 3 GHz] frequency range. The behavior of the proposed structure has been modelled and optimized using full-wave numerical simulations and measurements, in far-field and near-field settings in both time and frequency domains in free space and sandy medium. In particular, an impedance bandwidth of 100% with SWR ≤ 2, a broadside gain between 5-12 dBi, for total antenna size of 1.4 λm x λm, λm free space wavelength at the center frequency, are achieved as confirmed by laboratory-controlled measurements in free space and in GPR settings
Model‐order nonlinear subspace reduction of electric machines by means of POD and DEI methods for copper losses calculation
No full textInternational audienceThe simulation of electric machines in order to calculate the copper losses is about a time-dependent electromagnetic problem. When the finite element method associated with a time stepping scheme is used to solve the problem, the solution is strongly linked to initial conditions, among which the most important is the solution at the initial time. Because it is practically chosen as an arbitrary solution, several time-consuming electrical excitation periods must be simulated therefore to reach finally the steady-state conditions. The copper losses can be calculated now without any transient components that can affect the credibility of the copper losses amount. This article suggests a model-order reduction method that benefits from the complete model finite element solution of the first transient electrical period, to calculate the reduced model solution in the subsequent periods using the proper orthogonal decomposition approach combined with the discrete empirical interpolation method. Nevertheless, in case of relatively high frequency excitation, the full reduction of the problem leads to significant imprecision in the amount of copper losses. To improve the accuracy, therefore, a nonlinear subspace model-order reduction is adopted. It ensures concurrently higher precision and a reduced computational time
The Matrix Reproved: Verification Pearl
No full textInternational audienceIn this paper we describe a complete solution for the first challenge of the VerifyThis 2016 competition held at the 18th ETAPS Forum. We present the proof of two variants for the multiplication of matrices: a naive version using three nested loops and Strassen's algorithm. The proofs are conducted using the Why3 platform for deductive program verification and automated theorem provers to discharge proof obligations. In order to specify and prove the two multiplication algorithms, we develop a new Why3 theory of matrices. In order to prove the matrix identities on which Strassen's algorithm is based, we apply the proof by reflection methodology, which we implement using ghost state.To our knowledge, this is the first time such a methodology is used under an auto-active setting
Exploratory Knowledge Discovery over Web of Data
No full textInternational audienceWith an increased interest in machine processable data and with the progress of semantic technologies, many datasets are now published in the form of RDF triples for constituting the so-called Web of Data. Data can be queried using SPARQL but there are still needs for integrating, classifying and exploring the data for data analysis and knowledge discovery purposes. This research work proposes a new approach based on Formal Concept Analysis and Pattern Structures for building a pattern concept lattice from a set of RDF triples. This lattice can be used for data exploration and in particular visualized thanks to an adapted tool. The specific pattern structure introduced for RDF data allows to make a bridge with other studies on the use of structured attribute sets when building concept lattices. Our approach is experimentally validated on the classification of RDF data showing the efficiency of the underlying algorithms
Conceptual and Methodological Issues in Evaluating Multidimensional Visualizations for Decision Support
No full textInternational audienceWe explore how to rigorously evaluate multidimensional visualizations for their ability to support decision making. We first define multi-attribute choice tasks, a type of decision task commonly performed with such visualizations. We then identify which of the existing multidimensional visualizations are compatible with such tasks, and set out to evaluate three elementary visualizations: parallel coordinates, scatterplot matrices and tabular visualizations. Our method consists in first giving participants low-level analytic tasks, in order to ensure that they properly understood the visualizations and their interactions. Participants are then given multi-attribute choice tasks consisting of choosing holiday packages. We assess decision support through multiple objective and subjective metrics, including a decision accuracy metric based on the consistency between the choice made and self-reported preferences for attributes. We found the three visualizations to be comparable on most metrics, with a slight advantage for tabular visualizations. In particular, tabular visualizations allow participants to reach decisions faster. Thus, although decision time is typically not central in assessing decision support, it can be used as a tie-breaker when visualizations achieve similar decision accuracy. Our results also suggest that indirect methods for assessing choice confidence may allow to better distinguish between visualizations than direct ones. We finally discuss the limitations of our methods and directions for future work, such as the need for more sensitive metrics of decision support
Multi-layer based multi-path routing algorithm for maximizing spectrum availability
No full textInternational audienceLast 2 decades have witnessed the spectrum resources scarcity which is caused by wireless networks’ ubiquitous applications. To utilize the rare spectrum resources more efficiently, Cognitive Radio (CR) technology has been developed as a promising scheme. However, in CR networks, a novel NP-Hard disjoint multi-path routing problem has been encountered due to the Primary Users’ (PUs’) random movements. To settle this problem, we present a Spectrum History Matrix mechanism to define long-term spectrum sensing information on time-spectrum level such that spectrum availability and communication efficiency can be quantized in CR networks. To lessen the possibility for an active PU to interrupt all paths simultaneously, a sub-optimal Multi-layer based Multi-path Routing Algorithm (MMRA) is provided to determine how to route multiple paths which are not under the same PUs’ interference ranges. Through theoretical and simulation analyses, MMRA can not only settle the disjoint multi-path routing problem in polynomial time complexity, but also maximize communication efficiency