Robust HI and dysarthric speaker recognition – perceptual features and models

Multimedia Tools and Applications - This paper explores the necessity of having hearing impaired (HI) and dysarthric speakers be part of the person authentication system and it is considered to be...     

Freight car models and their computer-aided dynamic analysis

Computer models of freight cars with three-piece and Y25 bogies are considered and compared. Efficient numeric methods for simulation of vehicle models in the presence of frictional contacts are discussed. Some simulation results on derailment safety analysis and influence of track gauge value on freight-car dynamics are presented. Methods of stress loading and durability analysis based on computer simulation and some results are presented. A tribodynamic model of railway vehicle-track interaction and results of computation of wheel and rail profile wear are discussed.     

Adaptive estimation and stochastic control for uncertain models†

Ordinarily, in sequential state estimation problems, it is assumed that the parameters describing the system are completely known including the statistics of the noise terms. In many physical processes, however, the parameters, and even the mathematical model, may not be completely known. Hence, another class of problems may be considered in which the system model is in doubt. This type of problem is commonly referred to as ‘ Estimation-Under-Uncertainty ’. In this paper general formulations and solutions of state estimation and control problems are presented for a large class of linear stochastic systems with uncertain models, A general structure with continuous dynamical equations and discrete observations is adopted, since many physical situations are best modelled in this way.

The problem of estimation-under-uncertainty is formulated using the assumption that the model is one of a set of finite number of candidate models. Recursive estimation and identification algorithms are presented. A feedback control strategy is also developed for uncertain systems using the results derived for estimation-under-uncertainty,     

Estimation and validation of LAI using physical and semi‐empirical BRDF models

This letter deals with estimation of LAI for a wheat crop using physical and semi‐empirical BRDF models and IRS‐1D LISS‐III sensor data. NDVI was computed for both the models with LAI as a free parameter. The model‐computed NDVI was compared with corresponding atmospherically corrected LISS‐III NDVI. The estimation of LAI was carried out on the basis of a look‐up table approach and minimum root mean squared deviation between model computed and observed NDVI. The estimated LAI was validated against field measurements carried out during the months of February and March 2003, at the Central State Farm, Rajasthan, India. It was found that LAI was underestimated in both physical and semi‐empirical models. Results show that inclusion of multiple scattering in physical models may not always lead to a more accurate estimation of LAI and that it may be possible to estimate LAI at early stages of crop growth using semi‐empirical models. The coefficient of determination (R ²) between model estimated and measured LAI was 0.57 (standard error of estimate (SE) 0.156) and 0.63 (SE 0.187) for semi‐empirical and physical models, respectively, in the single scattering approximation, for February data. The corresponding values for March data were 0.57 (SE 0.206) and 0.51 (SE 0.216), respectively.     

The concurrency models of Ada and occam—a practical comparison

A comparison of the concurrency models of Ada and occam is presented. This comparison is performed in terms of an Ada-to-occam compiler. A subset of the Ada programming language may be compiled allowing a study of how programs and algorithms expressed using the Ada concurrency model can be mapped to the occam concurrency model. The resultant occam code may then be executed on a transputer. This paper describes the Ada and occam concurrency models, highlights the major differences between them, discusses the problems encountered in trying to map concurrent Ada programs to equivalent occam programs as a result of these differences and explains how these problems were overcome in the present compiler.     

Predicting user’s preferences using neural networks and psychology models

In this paper we describe a new model suitable for optimization problems with explicitly unknown optimization functions using user’s preferences. The model addresses an ability to learn not known optimization functions thus perform also a learning of user’s preferences. The model consists of neural networks using fuzzy membership functions and interactive evolutionary algorithms in the process of learning. Fuzzy membership functions of basic human values and their priorities were prepared by utilizing Schwartz’s model of basic human values (achievement, benevolence, conformity, hedonism, power, security, self-direction, stimulation, tradition and universalism). The quality of the model was tested on “the most attractive font face problem” and it was evaluated using the following criteria: a speed of optimal parameters computation, a precision of achieved results, Wilcoxon signed rank test and a similarity of letter images. The results qualify the developed model as very usable in user’s preference modeling.     

Random sprouts as internet models,and Pólya processes

The sprout, a tree growing in real time is introduced. The sprout is a generalization and an embedding in time of the standard recursive tree. The sprout is proposed as a model for the growth of the Internet. The tree size is analyzed via an associated two-color stochastic process (the sprout process), which is a special case of the Pólya process. Owing to its potential as a modeling tool, the more general Pólya process is analyzed on average. In addition to the usage of the Pólya process in evaluating sprouts, we also give a heuristic interpretation of the result for Pólya urns, which might be a first step toward understanding several nonclassic urn models, as those with nonconstant row sum and those with multiple eigenvalues.Received: 23 September 2002, Revised: 6 March 2004, Published online: 14 October 2004     

The renormalization group,the ɛ-expansion and derivation of turbulence models

We reformulate the renormalization group (RNG) and the-expansion for derivation of turbulence models. The procedure is developed for the Navier-Stokes equations and the transport equations for the kinetic energyK and energy dissipation rate . The derivation draws on the works of Yakhot and Orszag (1986) and Smith and Reynolds (1992), and all results are found at low order in the underlying perturbation expansion in powers of. The sum of the source terms in the -equation is known to beO(1) due to the balance at leading order ofO(R _T ^1/2 ) terms. Smith and Reynolds (1992) showed the cancellation of some of theO(R _T ^1/2 ) terms generated by the RNG procedure. Here we show that including the random-force contribution to -production results in the cancellation ofall theO(R _T ^1/2 ) terms. We find that two of theO(1) terms in the RNG equation for the mean dissipation rate have the same form as those in the widely used model-equation. The values of the coefficients of the familiar terms are close to those used in practice. An extra production term is predicted which is small for slow distortions, but important for rapid distortions. Hence, it may be a term that should be added to the model equation. We believe that the present derivation places the model equation on a more solid theoretical basis.     

On the combination of logical and probabilistic models for?information analysis

Formal logical tools are able to provide some amount of reasoning support for information analysis, but are unable to represent uncertainty. Bayesian network tools represent probabilistic and causal information, but in the worst case scale as poorly as some formal logical systems and require specialized expertise to use effectively. We describe a framework for systems that incorporate the advantages of both Bayesian and logical systems. We define a formalism for the conversion of automatically generated natural deduction proof trees into Bayesian networks. We then demonstrate that the merging of such networks with domain-specific causal models forms a consistent Bayesian network with correct values for the formulas derived in the proof. In particular, we show that hard evidential updates in which the premises of a proof are found to be true force the conclusions of the proof to be true with probability one, regardless of any dependencies and prior probability values assumed for the causal model. We provide several examples that demonstrate the generality of the natural deduction system by using inference schemes not supportable directly in Horn clause logic. We compare our approach to other ones, including some that use non-standard logics.     

Disruption management in the airline industry—Concepts,models and methods

This paper provides a thorough review of the current state-of-the-art within airline disruption management of resources, including aircraft, crew, passenger and integrated recovery. An overview of model formulations of the aircraft and crew scheduling problems is presented in order to emphasize similarities between solution approaches applied to the planning and recovery problems. A brief overview of research within schedule robustness in airline scheduling is included in the review, since this proactive measure is a natural complement to disruption management.     

Multiple models,multiplicative noise and linear quadratic control—algorithmic aspects

Numerical solution of certain linear quadratic (LQ) control problems for robust design is addressed. An iterative method is suggested and analysed for solving a minimax multiple model LQ control problem. A convergent iterative method is studied for finding the constant feedback gains of a given linear controller so as to solve a spectral radius functional minimization problem for multiple plants. Furthermore, a convergent iterative method is proposed for solving the maximum entropy parametric LQ control problem with multiplicative noise.     

A shadow detection and extraction algorithm using digital elevation models and x‐band weather radar measurements

The aviation industry has been investigating the potential of synthetic and enhanced vision systems (SVS and EVS) to increase the situational awareness of pilots who are operating in low‐visibility weather conditions. Synthetic vision displays provide a real‐time depiction of a terrain model from the pilot's perspective. To ensure the integrity of this terrain depiction, consistency checking using remote sensing of the terrain environment has been suggested. This requires the detection and extraction of terrain features from both the model and the sensor measurements. Further, the features must be represented in the same reference domain.

Terrain shadowing occurs when areas are not in the line‐of‐sight of the observer. It is these shadowed regions and their morphological characteristics that are identified as the feature domain in which consistency can be assessed between two sources of terrain information. This paper describes an algorithm to extract shadow features from digital elevation models during flight to enable direct comparison with x‐band radar modus operandi. Results are presented using flight‐test data acquired from two platforms with different radar equipment.

The proposed algorithm not only has application to the consistency‐checking problem, but also to terrain navigation, image fusion, and digital elevation model accuracy assessments.     

A new model for slab and beam structures––comparison with other models

In this paper an optimized model for the analysis of plates reinforced with beams is presented as compared with other models used by various researchers. The adopted model contrary to the models used previously takes into account the resulting inplane forces and deformations of the plate as well as the axial forces and deformations of the beams, due to combined response of the system. According to this model the stiffening beams of the structure are isolated from the plate by sections parallel to the lower outer surface of the plate. The forces at the interface, which produce lateral deflection and inplane deformation to the plate and lateral deflection and axial deformation to the beam, are established using continuity conditions at the interface. The adopted model describes better the actual response of the plate-beams system and permits the evaluation of the shear forces at the interface, the knowledge of which is very important in the design of composite or prefabricated ribbed plates. Four additional models neglecting the shear forces at the interfaces are presented and used for comparison reasons, while a three-dimensional elasticity model is also employed for the verification of the accuracy of the results of the examined models. The findings from this investigation, using the adopted model, which approximates better the actual response of the plate-beams system, necessitate the consideration of the inplane forces and deformations.     

Differential evolution and quantum-inquired differential evolution for evolving Takagi–Sugeno fuzzy models

The differential evolution (DE) is a global optimization algorithm to solve numerical optimization problems. Recently the quantum-inquired differential evolution (QDE) has been proposed for binary optimization. This paper proposes DE/QDE to learn the Takagi–Sugeno (T–S) fuzzy model. DE/QDE can simultaneously optimize the structure and the parameters of the model. Moreover a new encoding scheme is given to allow DE/QDE to be easily performed. The two benchmark problems are used to validate the performance of DE/QDE. Compared to some existing methods, DE/QDE shows the competitive performance in terms of accuracy.     

Coupling of mesoscale and microscale models—an approach to simulate scale interaction

Atmospheric flow and pollutant dispersion over built-up areas are affected by phenomena occurring at different scales. Hence, scale interactions should also be considered in the mathematical modelling of atmospheric flow and pollutant dispersion. In this paper a method is presented to couple prognostic mesoscale and microscale flow models. Results from mesoscale simulations are used to generate the initial state and boundary conditions for microscale simulations. The method comprises a three-dimensional interpolation scheme and a vertical adjustment of the interpolated quantities in the surface layer based on similarity theory. The method is applied to couple the microscale model MIMO with the mesoscale model MEMO.The coupled system MEMO–MIMO is applied to simulate the local scale flow for an industrial area in southwestern Germany. Model results are presented and compared with available measurements.     

Large language models as an “operating” system for software and systems modeling

Software and Systems Modeling -     

Thermodynamic optimization of the Gd–Pb system using random solution and associate models

The Gd–Pb system was critically modeled by means of the CALPHAD technique on the basis of experimental data in the literature. Given the asymmetric shape of the liquidus in the Gd–Pb phase diagram, the associate model for the liquid phase was tested and compared with the substitutional solution model. The results of the optimization show that a better agreement with the available experimental data is obtained by means of the associate model than the substitutional solution model. The solution phases (liquid, bcc, fcc, hcp) were treated with the Redlich–Kister equation. The intermetallic compounds Gd₅Pb₃, αGd₅Pb₄, βGd₅Pb₄, Gd₁₁Pb₁₀, Gd₆Pb₇, GdPb₂, GdPb₃ were treated as stoichiometric compounds. Two sets of self-consistent thermodynamic parameters of the Gd–Pb system were obtained.     

Using surrogate models and response surfaces in structural optimization – with application to crashworthiness design and sheet metal forming

The aim of this paper is to determine if the Space Mapping technique using surrogate models together with response surfaces is useful in the optimization of crashworthiness and sheet metal forming. In addition, the efficiency of optimization using Space Mapping will be compared to traditional structural optimization using the Response Surface Methodology (RSM). Five examples are used to study the algorithm: one optimization of an analytic function and four structural optimization problems. All examples are constrained optimization problems. In all examples, the algorithm converged to an improved design with all constraints fulfilled, even when a conventional RSM optimization failed to converge. For the crashworthiness design problems, the total computing time for convergence was reduced by 53% using Space Mapping compared to conventional RSM. For the sheet metal forming problems the total computing time was reduced by 63%. The conclusions are that optimization using Space Mapping and surrogate models can be used for optimization in crashworthiness design and sheet metal forming applications with a significant reduction in computing time.     

SWATShare – A web platform for collaborative research and education through online sharing,simulation and visualization of SWAT models

Hydrologic models for a particular watershed or a region are created for addressing a specific research or management problem, and most of the models do not get reused after the project is completed. Similarly, multiple models may exist for a particular geographic location from different researchers or organizations. To avoid the duplication of efforts, and enable model reuse and enhancement through collaborative efforts, a prototype cyberinfrastructure, called SWATShare, is developed for sharing, execution and visualization of Soil and Water Assessment Tool (SWAT). The objective of this paper is to present the software architecture, functional capabilities and implementation of SWATShare as a collaborative environment for hydrology research and education using the models published and shared in the system. Besides the capability of publishing, sharing, discovery and downloading of SWAT models, some of the functions in SWATShare such as model calibration are supported by providing access to high performance computing resources including the XSEDE and cloud. Additionally, SWATShare can create dynamic spatial and temporal plots of model outputs at different scales. SWATShare can also be used as an educational tool within a classroom setting for comparing the hydrologic processes under different geographic and climatic settings. The utility of SWATShare for collaborative research and education is demonstrated by using three case studies. Even though this paper focuses on the SWAT model, the system’s architecture can be replicated for other models as well.