Reasoning with truth values on compacted fuzzy chained rules

In this paper, we consider the problem of executing a fuzzy knowledge base (FKB) with rule chaining. The inference process used as starting point is the one based on forward reasoning functions which, obtained from the compositional rule of inference, permits performing the execution of rules in the truth space. This way the process is totally independent from the universes of discourse in which the different variables are defined, allowing a homogeneous treatment for all the variables in the FKB. The execution of the rules is interpreted as the "propagation" of linguistic truth values of the linguistic truth variable that reflect the linguistic degree of fulfillment of each of the propositions in the rules. This execution process is analyzed in two fields of application: control systems, where it is customary to assume t-norm operators as implication functions, and the aggregation process is implemented through the maximum operator and expert systems applications, where other implication functions may be needed and t-norm operators are generally used as aggregation operators. For both of these situations, we present a compaction mechanism which allows a noticeable part of the operations to be performed a priori, thus achieving an important computation time saving.     

Tunable Diode Laser Absorption Spectroscopy of Carbon Monoxide Around 2.35 microm

Novel GaInSbAs/GaSb multiple-quantum-well lasers operating near room temperature have been successfully used for tunable diode laser absorption spectroscopy in the vicinity of 2.35 mum. Continuous current tuning over a more than 150-GHz frequency range has been realized. Direct absorption measurements have been carried out on the R, R, R, and R lines of carbon monoxide. Traces of carbon monoxide at the level of 0.3 part in 10(6) in volume at 100 Torr could be detected by the low-frequency wavelength-modulation technique and an astigmatic multipass cell. These results show a potential use of these diode lasers in portable low-cost trace-pollutant sensors.     

Indexing and querying segmented web pages: the BlockWeb Model

We present in this paper a model for indexing and querying web pages, based on the hierarchical decomposition of pages into blocks. Splitting up a page into blocks has several advantages in terms of page design, indexing and querying such as (i) blocks of a page most similar to a query may be returned instead of the page as a whole (ii) the importance of a block can be taken into account, as well as (iii) the permeability of the blocks to neighbor blocks: a block b is said to be permeable to a block b?? in the same page if b?? content (text, image, etc.) can be (partially) inherited by b upon indexing. An engine implementing this model is described including: the transformation of web pages into blocks hierarchies, the definition of a dedicated language to express indexing rules and the storage of indexed blocks into an XML repository. The model is assessed on a dataset of electronic news, and a dataset drawn from web pages of the ImagEval campaign where it improves by 16% the mean average precision of the baseline.     

Simulation of rotating stall in a whole stage of an axial compressor

Aerodynamic instabilities that naturally occur in compression systems, such as surge and rotating stall, largely reduce the life duration and performance of system components. The prediction of the compressor operating range is thus a key parameter for the design of gas turbines. This paper investigates the ability of an unsteady flow solver to simulate the rotating stall phenomenon in the full annulus of an axial compressor stage. A comparison with experimental data indicates that the simulation correctly estimates the stability limit. However the rotating stall flow patterns are different. While measurements show only one full span rotating stall cell (40 Hz), the simulation shows first a part span stall with 10 cells (790 Hz) that evolves then towards a full span stall with three cells (170 Hz). A spectral analysis based on numerical results underlines the role of rotor-stator interactions in the development of rotating stall. The effects of downstream volumes and inlet distortions are also discussed, showing the necessity to consider the whole geometry to correctly predict the rotating stall frequency.     

Learning a generic 3D face model from 2D image databases using incremental Structure-from-Motion

Over the last decade 3D face models have been extensively used in many applications such as face recognition, facial animation and facial expression analysis. 3D Morphable Models (MMs) have become a popular tool to build and fit 3D face models to images. Critical to the success of MMs is the ability to build a generic 3D face model. Major limitations in the MMs building process are: (1) collecting 3D data usually involves the use of expensive laser scans and complex capture setups, (2) the number of available 3D databases is limited, and typically there is a lack of expression variability and (3) finding correspondences and registering the 3D model is a labor intensive and error prone process.     

A tabu search with an oscillation strategy for the discriminant analysis problem

This article proposes a tabu search approach to solve a mathematical programming formulation of the linear classification problem, which consists of determining an hyperplane that separates two groups of points as well as possible in ?^m. The tabu search approach proposed is based on a non-standard formulation using linear system infeasibility. The search space is the set of bases defined on the matrix that describes the linear system. The moves are performed by pivoting on a specified row and column. On real machine learning databases, our approach compares favorably with implementations based on parametric programming and irreducible infeasible constraint sets. Additional computational results for randomly generated instances confirm that our method provides a suitable alternative to the mixed integer programming formulation that is solved by a commercial code when the number of attributes m increases.     

Connecting the 3D DGS Calques3D with the CAS Maple

Many (2D) Dynamic Geometry Systems (DGSs) are able to export numeric coordinates and equations with numeric coefficients to Computer Algebra Systems (CASs). Moreover, different approaches and systems that link (2D) DGSs with CASs, so that symbolic coordinates and equations with symbolic coefficients can be exported from the DGS to the CAS, already exist. Although the 3D DGS Calques3D can export numeric coordinates and equations with numeric coefficients to Maple and Mathematica, it cannot export symbolic coordinates and equations with symbolic coefficients. A connection between the 3D DGS Calques3D and the CAS Maple, that can handle symbolic coordinates and equations with symbolic coefficients, is presented here. Its main interest is to provide a convenient time-saving way to explore problems and directly obtain both algebraic and numeric data when dealing with a 3D extension of “ruler and compass geometry”. This link has not only educational purposes but mathematical ones, like mechanical theorem proving in geometry, geometric discovery (hypotheses completion), geometric loci finding… As far as we know, there is no comparable “symbolic” link in the 3D case, except the prototype 3D-LD (restricted to determining algebraic surfaces as geometric loci).     

A comparison of several neural networks to predict the execution times in injection molding production for automotive industry

In the industrial environment, specifically in the automotive industry, an accurate prediction of execution times for each production task is very useful in order to plan the work and to optimize the human, technical and material resources. In this paper, we applied several regression neural networks to predict the execution times of the tasks in the production of parts for plastic injection molds. These molds are used to make a variety of car components in automotive industry. The prediction is based on the geometric features of the mold parts to be made. The accuracy of the predicted times is high enough to be used as a tool for the design stage of the mold parts, e.g. guiding the design process in order to get the lowest production time.     

A branch-and-cut algorithm for the minimum labeling Hamiltonian cycle problem and two variants

This paper proposes a mathematical model, valid inequalities and polyhedral results for the minimum labeling Hamiltonian cycle problem. This problem is defined on an unweighted graph in which each edge has a label. The aim is to determine a Hamiltonian cycle with the least number of labels. We also define two variants of this problem by assigning weights to the edges and by considering the tour length either as an objective or as a constraint. A branch-and-cut algorithm for the three problems is developed, and computational results are reported on randomly generated instances and on modified instances from TSPLIB.