Vector transport for shape-from-shading

In this paper we describe a new shape-from-shading method. We show how the parallel transport of surface normals can be used to impose curvature consistency and also to iteratively update surface normal directions so as to improve the brightness error. We commence by showing how to make local estimates of the Hessian matrix from surface normal information. With the local Hessian matrix to hand, we develop an “EM-like” algorithm for updating the surface normal directions. At each image location, parallel transport is applied to the neighbouring surface normals to generate a sample of local surface orientation predictions. From this sample, a local weighted estimate of the image brightness is made. The transported surface normal which gives the brightness prediction which is closest to this value is selected as the revised estimate of surface orientation. The revised surface normals obtained in this way may in turn be used to re-estimate the Hessian matrix, and the process iterated until stability is reached. We experiment with the method on a variety of real world and synthetic data. Here we explore the properties of the fields of surface normals and the height data delivered by the method.     

On convexified robust control synthesis

In this paper, we study a convexified robust control problem and its relation to gain-scheduling control. It reveals that the robust control synthesis condition becomes convex under a special plant structure. Moreover, for this class of robust control problems, the gain-scheduling control approach from scaled small-gain theorem will not provide any performance improvement over convexified robust controllers. Based on this observation, a convexified robust control synthesis framework with stringent performance and computational efficacy is proposed.     

Steelmaking technology for a sustainable society

For the reduction of CO₂ emission, two major developments are being conducted in COURSE50 (“ CO₂ Ultimate Reduction in the Steelmaking Process by Innovative Technologies for Cool Earth 50”). The one is separation of CO₂ gas from BFG (Blast Furnace Gas) and recharge of the rest of BFG including H₂ and CO into blast furnace. Hydrogen iron ore reduction technology is also going to be developed. The other one is amplification of H₂ gas from CH₄, for example, in COG (Coke Oven Gas). The produced hydrogen gas will be supplied to the society or the reformed COG will be charged to blast furnace. In addition to these drastic challenging technology developments, a variety of measures for CO₂ reduction is under taken. In the frame of Asia-Pacific Partnership on Clean Development and Climate, the best available technology for energy savings are discussed to be transferred within seven member nations, which has the effect of 1.27 million ton reduction of CO₂ emission a year. By supplying energy saving steel products to society such as high strength steels for automobiles and ships, which realizes the fuel consumption reduction, high performance electrical steels for motors and transformers, which realize electricity loss reduction, and by recycling waste city plastics and tires in steel processes for hydrogen gas generation, chemical raw material conversion and iron ore reduction, etc., it is expected that equivalent 10% reduction of CO₂ gas emission in steel production is counted. In steelmaking process the reduction of refining slags contributes materials use efficiency and less emission of unuseful byproducts. The control and utilization of nonmetallic inclusions, such as deoxidation products, are one of the key technology for obtaining product performance, which is required in the above-mentioned steel products. In order to optimize steelmaking process for these purposes, computational thermodynamics is applied. Optimization of demanganization, and control of chemical composition of nonmetallic inclusions by the use of computational thermodynamics are mentioned.     

On coefficient inequalities of functions associated with conic domains

In this paper, the concepts of Janowski functions and the conic regions are combined to define a new domain which represents the conic type regions. Different views of this modified conic domain for specific values are shown graphically for better understanding of the behavior of this domain. The class of such functions which map the open unit disk E onto this modified conic domain is defined. Also the classes of k-uniformly Janowski convex and k-Janowski starlike functions are defined and their coefficient inequalities are formulated. The coefficient bound for a certain class of analytic functions, proved by Owa et al. (2006) in [16], has also been improved.     

A simple and efficient method for variable ranking according to their usefulness for learning

The selection of a subset of input variables is often based on the previous construction of a ranking to order the variables according to a given criterion of relevancy. The objective is then to linearize the search, estimating the quality of subsets containing the topmost ranked variables. An algorithm devised to rank input variables according to their usefulness in the context of a learning task is presented. This algorithm is the result of a combination of simple and classical techniques, like correlation and orthogonalization, which allow the construction of a fast algorithm that also deals explicitly with redundancy. Additionally, the proposed ranker is endowed with a simple polynomial expansion of the input variables to cope with nonlinear problems. The comparison with some state-of-the-art rankers showed that this combination of simple components is able to yield high-quality rankings of input variables. The experimental validation is made on a wide range of artificial data sets and the quality of the rankings is assessed using a ROC-inspired setting, to avoid biased estimations due to any particular learning algorithm.     

Multivariate time series modeling and classification via hierarchical VAR mixtures

A novel class of models for multivariate time series is presented. We consider hierarchical mixture-of-expert (HME) models in which the experts, or building blocks of the model, are vector autoregressions (VAR). It is assumed that the VAR-HME model partitions the covariate space, specifically including time as a covariate, into overlapping regions called overlays. In each overlay a given number of VAR experts compete with each other so that the most suitable one for the overlay is favored by a large weight. The weights have a particular parametric form that allows the modeler to include relevant covariates. Estimation of the model parameters is achieved via the EM (expectation-maximization) algorithm. A new algorithm to select the optimal number of overlays, the number of VAR models and the model orders of the VARs that define a particular VAR-HME model configuration, is also developed. The algorithm uses the Bayesian information criterion (BIC) as an optimality criterion. Issues of model checking and inference of latent structure in multiple time series are investigated. The new methodology is illustrated by analyzing a synthetic data set and a 7-channel electroencephalogram data set.     

Uncertain jumping systems with strong and weak functional delays

This paper provides complete results on the stability behavior of a class of uncertain dynamical systems with jumping parameters and functional time-delays. The jumping parameters are modeled as a continuous-time, discrete-state Markov process. The parametric uncertainties are norm-bounded appearing in all system matrices and the delay factor depends on the mode of operation. Notions of weak and strong stochastic stability for the jumping system are developed depending on the available information using a prescribed -performance. Memoryless and delayed-state feedback are considered to guarantee the closed-loop stability. All the results are cast into linear matrix inequalities format. A numerical example is given to illustrate the developed results.     

On a new NBUE property in multivariate sense: An application

Since multivariate lifetime data frequently occur in applications, various properties of multivariate distributions have been previously considered to model and describe the main concepts of aging commonly considered in the univariate setting. The generalization of univariate aging notions to the multivariate case involves, among other factors, appropriate definitions of multivariate quantiles and related notions, which are able to correctly describe the intrinsic characteristics of the concepts of aging that should be generalized, and which provide useful tools in the applications. A new multivariate version of the well-known New Better than Used in Expectation univariate aging notion is provided, by means of the concepts of the upper corrected orthant and multivariate excess-wealth function. Some of its properties are described, with particular attention paid to those that can be useful in the analysis of real data sets. Finally, through an example it is illustrated how the new multivariate aging notion influences the final results in the analysis of data on tumor growth from the Comprehensive Cohort Study performed by the German Breast Cancer Study Group.     

On the formulation and solution of robust performance problems

A new approach to robust performance problems is proposed in this paper. The approach involves the optimisation of the so-called performance weights subject to a constraint, formulated in terms of the structured singular value, which ensures the existence of a stabilising feedback compensator that achieves robust performance with respect to the optimised performance weights and an uncertain plant set. Optimising over the performance weights in this way gives rise to an algorithm for systematically trading-off desired performance against specified plant uncertainty and performance limitations due to plant dynamics. The algorithm also yields a robust controller. The designer is only required to specify the uncertain plant set and an optimisation directionality, which is used to reflect desired closed-loop performance over frequency in terms of a corresponding cost function. Design of this directionality appears to be simpler than designing the performance weights directly.     

An inexact hybrid projection-proximal point algorithm for solving generalized mixed variational inequalities

In this paper, we investigate an inexact hybrid projection-proximal method for solving a class of generalized mixed variational inequalities in Hilbert spaces. We construct a general inexact hybrid projection-proximal point algorithm, in which an inexact relaxed proximal point step is followed by a suitable orthogonal projection onto a hyperplane. Under some suitable conditions concerned with the pseudomonotone set-valued mapping T, the nonsmooth convex function f and the step size λ_k, we prove the convergence of the inexact hybrid projection-proximal point algorithm for solving generalized mixed variational inequalities in Hilbert spaces.     

A reactive GRASP and path relinking for a combined production–distribution problem

An NP-hard production–distribution problem for one product over a multi-period horizon is investigated. The aim is to minimize total cost taking production setups, inventory levels and distribution into account. An integer linear model is proposed as a compact problem specification but it cannot be solved to optimality for large instances. Instead of using a classical two-phase approach (production planning and then route construction for each day), metaheuristics that simultaneously tackle production and routing decisions are developed: a GRASP (greedy randomized adaptive search procedure) and two improved versions using either a reactive mechanism or a path-relinking process. These algorithms are evaluated on 90 randomly generated instances with 50, 100 and 200 customers and 20 periods. The results confirm the interest of integrating production and distribution decisions, compared to classical two-phase methods. Moreover, reaction and path-relinking give better results than the GRASP alone.     

The solitary solution of the Liouville equation produced by the Exp-function method does not hold for all initial conditions

In this paper we argue that the solitary solution of the Liouville equation produced by the Exp-function method does not satisfy the original differential equation for all initial conditions. Moreover, the region where the solution is correct is located entirely on a curve in the parameter plane of initial conditions. We derive the explicit equation for this curve and argue that classical Exp-function type methods cannot identify such constraints related to initial conditions.     

A goodness-of-fit test for Archimedean copula models in the presence of right censoring

A goodness-of-fit testing procedure for Archimedean copula (AC) models is developed based on right-censored data. The proposed approach extends an existing method, which is suitable for the Clayton model, to general AC models. Asymptotic properties of the proposed test statistics under the true model assumption are derived. Simulation analysis shows that the proposed test has reasonable performance. Finally, two real data examples are analyzed for illustrative purposes.