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An efficient model-free setting for longitudinal and lateral vehicle control. Validation through the interconnected pro-SiVIC/RTMaps prototyping platform
No full textInternational audienceIn this paper, the problem of tracking desired longitudinal and lateral motions for a vehicle is addressed. Let us point out that a "good" modeling is often quite difficult or even impossible to obtain. It is due for example to parametric uncertainties, for the vehicle mass, inertia or for the interaction forces between the wheels and the road pavement. To overcome this type of difficulties, we consider a model-free control approach leading to "intelligent" controllers. The longitudinal and the lateral motions, on one hand, and the driving/braking torques and the steering wheel angle, on the other hand, are respectively the output and the input variables. An important part of this work is dedicated to present simulation results with actual data. Actual data, used in Matlab as reference trajectories, have been previously recorded with an instrumented Peugeot 406 experimental car. The simulation results show the efficiency of our approach. Some comparisons with a nonlinear flatness-based control in one hand, and with a classical PID control in another hand confirm this analysis. Other virtual data have been generated through the interconnected platform SiVIC/RTMaps, which is a virtual simulation platform for prototyping and validation of advanced driving assistance systems
Curvilinear structure analysis by ranking the orientation responses of path operators
No full textInternational audienceThe analysis of thin curvilinear objects in 3D images is a complex and challenging task. In this article, we introduce a new, non-linear operator, called RORPO (Ranking Orientation Responses of Path Operators). Inspired by the multidirectional paradigm currently used in linear filtering for thin structure analysis, RORPO is built upon the notion of path operator from mathematical morphology. This operator, unlike most operators commonly used for 3D curvilinear structure analysis, is discrete, non-linear and non-local. From this new operator, two main curvilinear structure characteristics can be estimated: an intensity feature, that can be assimilated to a quantitative measure of curvilinearity; and a directional feature, providing a quantitative measure of the structure’s orientation. We provide a full description of the structural and algorithmic details for computing these two features from RORPO, and we discuss computational issues. We experimentally assess RORPO by comparison with three of the most popular curvilinear structure analysis filters, namely Frangi Vesselness, Optimally Oriented Flux, and Hybrid Diffusion with Continuous Switch. In particular, we show that our method provides up to 8% more true positive and 50% less false positives than the next best method, on synthetic and real 3D images
Progressive Semisupervised Learning of Multiple Classifiers
No full textInternational audienceSemisupervised learning methods are often adopted to handle datasets with very small number of labeled samples. However, conventional semisupervised ensemble learning approaches have two limitations: 1) most of them cannot obtain satisfactory results on high dimensional datasets with limited labels and 2) they usually do not consider how to use an optimization process to enlarge the training set. In this paper, we propose the progressive semisupervised ensemble learning approach (PSEMISEL) to address the above limitations and handle datasets with very small number of labeled samples. When compared with traditional semisupervised ensemble learning approaches, PSEMISEL is characterized by two properties: 1) it adopts the random subspace technique to investigate the structure of the dataset in the subspaces and 2) a progressive training set generation process and a self evolutionary sample selection process are proposed to enlarge the training set. We also use a set of nonparametric tests to compare different semisupervised ensemble learning methods over multiple datasets. The experimental results on 18 real-world datasets from the University of California, Irvine machine learning repository show that PSEMISEL works well on most of the real-world datasets, and outperforms other state-of-the-art approaches on 10 out of 18 datasets
Characterizing Approximate-Matching Dependencies in Formal Concept Analysis with Pattern Structures
No full textInternational audienceFunctional dependencies (FDs) provide valuable knowledge on the relations between the attributes of a data table. A functional dependency holds when the values of an attribute can be determined by another. It is shown that FDs can be expressed in terms of partitions of tuples that are in agreement w.r.t. the values taken by some subsets of attributes. To extend the use of FDs, several generalizations are proposed. In this work, we study approximate-matching dependencies that generalize FDs by relaxing the constraints on the attributes, i.e. agreement is based on a similarity relation rather than on equality. Such dependencies are attracting attention in the database field since they allow to uncrisp the basic notion of FDs, and can be applied in many different fields, e.g. data quality, data mining, behavior analysis, data cleaning or data partition... Here we show that these dependencies can be formalized in the framework of Formal Concept Analysis (FCA). Such a formalization was previously introduced for basic FDs, but needs to be adapted and extended for approximate-matching dependencies. Our new result states that, starting from the conceptual structure of a pattern structure and generalizing the notion of relation between tuples, approximate-matching dependencies can be characterized as implications in a pattern concept lattice. We finally show how to adapt basic FCA algorithms to construct a pattern concept lattice that entails these dependencies after a slight and tractable transformation of the original data
Stability and robustness analysis for switched systems with time-varying delays
No full textInternational audienceA new technique is presented for the stability and robustness analysis of nonlinear switched time-varying systems with uncertainties and time-varying delays. The delays are allowed to be discontinuous (but are required to be piecewise continuous) and arbitrarily long with known upper bounds. The technique uses an adaptation of Halanay's inequality and a trajectory based technique, and is used for designing switched controllers to stabilize linear time-varying systems with time-varying delays
Business Model Design: Lessons Learned from Tesla Motors
No full textInternational audienceElectric vehicle (EV) industry is still in the introduction stage in product life cycle, and dominant design remains unclear. EV companies, both incumbent from the car industry and new comers, have long taken numerous endeavors to promote EV in the niche market by providing innovative products and business models. While most carmakers still take 'business as usual' approach for developing their EV production and offers, Tesla Motors, an EV entrepreneurial firm, stands out by providing disruptive innovation solutions. We review the business model approach in the literature, then classify the innovation dimensions in the EV ecosystem. We study Tesla Motors in terms of: (i) innovation related to the vehicle, (ii) innovation related to the battery (iii) innovation concerning the recharging system, and (iv) innovation toward the EV ecosystem. Lessons for incumbent carmakers for their EV business model design: Tesla Motors 1) holds a product strategy entering from high-end market and moving to mass market, with a high level of innovation adaptation and learning by doing; 2) pays considerable attention to reduce range anxiety by high performance supercharger station network and high capacity battery; 3) shows a very high level of integration of information technology into many aspects of the EV business model, such as advanced in-car services and digital distribute channel; 4) shows a new value configuration which involving in high level of vertical integration towards battery and recharging network. All these lessons of this chapter would be worth the attention of the carmakers if the disruptive choices of Tesla succeed in challenging the dominant design
Usage-driven problem design for radical innovation in healthcare
No full textInternational audienceWhilst the diffusion and evaluation of healthcare innovations receive a lot of attention, the early design stages are less studied and potential innovators lack methods to identify where new innovations are necessary and to propose concepts relevant to users. To change this, we propose a structured methodology, Radical Innovation Design ® (RID), which supports designers who want to work on the unstated needs of potential end-users in order to create superior value. In this article, the first part of RID is introduced with its two sub-processes: Problem Design and Knowledge Design. In this first period, RID guides innovators to systematically explore users' problems and evaluate which ones are most pressing in terms of innovation, taking into account existing solutions. The result is an ambition perimeter, composed of a set of value buckets, i.e. important usage situations where major problems are experienced and the current solutions provide little or no relief. The methodology then moves on to Solution Design and Business Design (which are not detailed in this paper) to address the value buckets identified. With its emphasis on problem exploration, RID differs from methods based on early prototyping. The RID methodology has been validated in various industrial sectors, and is well-adapted for healthcare innovation. To exemplify the methodology, we present a case study in dental imagery performed by ten students in 8 weeks. This example demonstrates how RID favors efficiency in Problem Design and allows designers to explore unaddressed and sometimes undeclared user needs
Z-segmentation of a transmit array head coil improves RF ramp pulse design at 7T
No full textInternational audienceIn Time-Of-Flight sequences, ramp pulses such as TONE's are frequently used to compensate for thru-slab blood flow saturation in cerebral MRA. At Ultra High Field, fast-kz spokes in parallel transmission allow to mitigate B1 + heterogeneities in the slab selection process. Here we extend their use for TONE pulses and show improvement of the flip angle ramp fidelity with a homemade z-segmented head array in comparison to a purely azimuthally-distributed commercial coil
Modeling added spatial variability due to soil improvement: Coupling FEM with binary random fields for seismic risk analysis
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Convex Optimization approach to signals with fast varying instantaneous frequency
No full textInternational audienceMotivated by the limitation of analyzing oscillatory signals composed of multiple components with fast-varying instantaneous frequency, we approach the time-frequency analysis problem by optimization. Based on the proposed adaptive harmonic model, the time-frequency representation of a signal is obtained by directly minimizing a functional, which involves few properties an "ideal time-frequency representation" should satisfy, for example, the signal reconstruction and concentrative time frequency representation. FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) is applied to achieve an efficient numerical approximation of the functional. We coin the algorithm as {\it Time-frequency bY COnvex OptimizatioN} (Tycoon). The numerical results confirm the potential of the Tycoon algorithm