Numerical Methods for Hyperbolic Functional Differential Problems on the Haar Pyramid

The paper deals with the local Cauchy problem for nonlinear functional differential systems. We investigate a general class of difference methods for this problem. We construct interpolating operators on the Haar pyramid and we give an error estimate for approximate solutions. We adopt nonlinear estimates of the Perron type for given functions with respect to the functional variable. The proof of the stability is based on functional difference inequalities.     

Validating the ISO/IEC 15504 measure of software requirementsanalysis process capability

ISO/IEC 15504 is an emerging international standard on software process assessment. It defines a number of software engineering processes and a scale for measuring their capability. One of the defined processes is software requirements analysis (SRA). A basic premise of the measurement scale is that higher process capability is associated with better project performance (i.e., predictive validity). The paper describes an empirical study that evaluates the predictive validity of SRA process capability. Assessments using ISO/IEC 15504 were conducted on 56 projects world-wide over a period of two years. Performance measures on each project were also collected using questionnaires, such as the ability to meet budget commitments and staff productivity. The results provide strong evidence of predictive validity for the SRA process capability measure used in ISO/IEC 15504, but only for organizations with more than 50 IT staff. Specifically, a strong relationship was found between the implementation of requirements analysis practices as defined in ISO/IEC 15504 and the productivity of software projects. For smaller organizations, evidence of predictive validity was rather weak. This can be interpreted in a number of different ways: that the measure of capability is not suitable for small organizations or that the SRA process capability has less effect on project performance for small organizations     

Texture mixing and texture movie synthesis using statisticallearning

We present an algorithm based on statistical learning for synthesizing static and time-varying textures matching the appearance of an input texture. Our algorithm is general and automatic and it works well on various types of textures, including 1D sound textures, 2D texture images, and 3D texture movies. The same method is also used to generate 2D texture mixtures that simultaneously capture the appearance of a number of different input textures. In our approach, input textures are treated as sample signals generated by a stochastic process. We first construct a tree representing a hierarchical multiscale transform of the signal using wavelets. From this tree, new random trees are generated by learning and sampling the conditional probabilities of the paths in the original tree. Transformation of these random trees back into signals results in new random textures. In the case of 2D texture synthesis, our algorithm produces results that are generally as good as or better than those produced by previously described methods in this field. For texture mixtures, our results are better and more general than those produced by earlier methods. For texture movies, we present the first algorithm that is able to automatically generate movie clips of dynamic phenomena such as waterfalls, fire flames, a school of jellyfish, a crowd of people, etc. Our results indicate that the proposed technique is effective and robust     

Locality-preserved maximum information projection.

Dimensionality reduction is usually involved in the domains of artificial intelligence and machine learning. Linear projection of features is of particular interest for dimensionality reduction since it is simple to calculate and analytically analyze. In this paper, we propose an essentially linear projection technique, called locality-preserved maximum information projection (LPMIP), to identify the underlying manifold structure of a data set. LPMIP considers both the within-locality and the between-locality in the processing of manifold learning. Equivalently, the goal of LPMIP is to preserve the local structure while maximize the out-of-locality (global) information of the samples simultaneously. Different from principal component analysis (PCA) that aims to preserve the global information and locality-preserving projections (LPPs) that is in favor of preserving the local structure of the data set, LPMIP seeks a tradeoff between the global and local structures, which is adjusted by a parameter alpha, so as to find a subspace that detects the intrinsic manifold structure for classification tasks. Computationally, by constructing the adjacency matrix, LPMIP is formulated as an eigenvalue problem. LPMIP yields orthogonal basis functions, and completely avoids the singularity problem as it exists in LPP. Further, we develop an efficient and stable LPMIP/QR algorithm for implementing LPMIP, especially, on high-dimensional data set. Theoretical analysis shows that conventional linear projection methods such as (weighted) PCA, maximum margin criterion (MMC), linear discriminant analysis (LDA), and LPP could be derived from the LPMIP framework by setting different graph models and constraints. Extensive experiments on face, digit, and facial expression recognition show the effectiveness of the proposed LPMIP method.     

Iterative Fuzzy Clustering Algorithm With Supervision to Construct Probabilistic Fuzzy Rule Base From Numerical Data

To deal with data patterns with linguistic ambiguity and with probabilistic uncertainty in a single framework, we construct an interpretable probabilistic fuzzy rule-based system that requires less human intervention and less prior knowledge than other state of the art methods. Specifically, we present a new iterative fuzzy clustering algorithm that incorporates a supervisory scheme into an unsupervised fuzzy clustering process. The learning process starts in a fully unsupervised manner using fuzzy c-means (FCM) clustering algorithm and a cluster validity criterion, and then gradually constructs meaningful fuzzy partitions over the input space. The corresponding fuzzy rules with probabilities are obtained through an iterative learning process of selecting clusters with supervisory guidance based on the notions of cluster-pureness and class-separability. The proposed algorithm is tested first with synthetic data sets and benchmark data sets from the UCI Repository of Machine Learning Database and then, with real facial expression data and TV viewing data.     

Decentralized Iterative Learning Control to Large-Scale Industrial Processes for Nonrepetitive Trajectory Tracking

In the procedure of steady-state hierarchical optimization with feedback for a large-scale industrial process, it is usual that a sequence of step set-point changes is carried out and used by the decision-making units while searching the eventual optimum. In this case, the real process experiences a form of disturbances around its operating set-point. In order to improve the dynamic performance of transient responses for such a large-scale system driven by the set-point changes, an open-loop proportional integral derivative-type iterative learning control (ILC) strategy is explored in this paper by considering the different magnitudes of the controller's step set-point change sequence. Utilizing the Hausdorff-Young inequality of convolution integral, the convergence of the algorithm is derived in the sense of Lebesgue-P norm. Furthermore, the extended higher order ILC rule is developed, and the convergence is analyzed. Simulation results illustrate that the proposed ILC strategies can remarkably improve the dynamic performance such as decreasing the overshoot, accelerating the transient response, shortening the settling time, etc.     

Wear and biological activity of highly crosslinked polyethylene in the hip under low serum protein concentrations

Crosslinked ultra-high molecular weight polyethylene (UHMWPE) has been developed and introduced into clinical practice in order to reduce wear in the hip. Zero wear of highly crosslinked UHMWPE in vitro has been reported by some groups using lubricants with high concentrations of serum proteins in hip simulators. In contrast, some clinical studies have reported finite wear rates. The aim of this study was to compare the wear rates, wear surfaces, and wear debris produced by UHMWPE with different levels of crosslinking in a hip joint simulator, with lower, more physiologically relevant concentrations of protein in the lubricant. The UHMWPEs were tested in the Leeds ProSim hip joint simulator against cobalt-chromium (CoCr) femoral heads. The wear particles were isolated and imaged using a field emission gun scanning electron microscope (FEGSEM) at high resolution. The highly crosslinked UHMWPEs had significantly lower wear volumes than the non-crosslinked UHMWPEs. No significant difference was found in the percentage number and percentage volume of the particles in different size ranges from any of the materials. They had similar values of specific biological activity. The functional biological activity (FBA), which takes into account the wear volume and specific biological activity, showed that the highly crosslinked UHMWPEs had lower FBAs due to their lower wear volume.     

A method to quantify hand-transmitted vibration exposure based on the biodynamic stress concept

This study generally hypothesized that the vibration-induced biodynamic stress and number of its cycles in a substructure of the hand-arm system play an important role in the development of vibration-induced disorders in the substructure. As the first step to test this hypothesis, the specific aims of this study were to develop a practical method to quantify the biodynamic stress-cycle measure, to compare it with ISO-weighted and unweighted accelerations, and to assess its potential for applications. A mechanical-equivalent model of the system was established using reported experimental data. The model was used to estimate the average stresses in the fingers and palm. The frequency weightings of the stresses in these substructures were derived using the proposed stress-cycle measure. This study found the frequency dependence of the average stress distributed in the fingers is different from that in the palm. Therefore, this study predicted that the frequency dependencies of finger disorders could also be different from those of the disorders in the palm, wrist, and arms. If vibration-induced white finger (VWF) is correlated better with unweighted acceleration than with ISO-weighted acceleration, the biodynamic stress distributed in the fingers is likely to play a more important role in the development of VWF than is th e biodynamic stressdistributed in the other substructures of the hand-arm system. The results of this study also suggest that the ISO weighting underestimates the high-frequency effect on the finger disorder development but it may provide a reasonable risk assessment of the disorders in the wrist and arm.     

Problems of boundary observability of elastic vibrations described by the system of telegrapher equations

Problems are solved for the observability of elastic vibrations described by various boundary problems for the system of telegrapher equations. The initial conditions that generate observable vibrations are found in the explicit form. The necessary time period of an observation is defined. The solutions of observation problems are given in the case of the classical and generalized solutions of the class L ₂ of appropriate boundary problems.     

Curve/surface representation and evolution using vector level sets with application to the shape-based segmentation problem

In this paper, we revisit the implicit front representation and evolution using the vector level set function (VLSF) proposed in (H. E. Abd El Munim, et al., Oct. 2005). Unlike conventional scalar level sets, this function is designed to have a vector form. The distance from any point to the nearest point on the front has components (projections) in the coordinate directions included in the vector function. This kind of representation is used to evolve closed planar curves and 3D surfaces as well. Maintaining the VLSF property as the distance projections through evolution will be considered together with a detailed derivation of the vector partial differential equation (PDE) for such evolution. A shape-based segmentation framework will be demonstrated as an application of the given implicit representation. The proposed level set function system will be used to represent shapes to give a dissimilarity measure in a variational object registration process. This kind of formulation permits us to better control the process of shape registration, which is an important part in the shape-based segmentation framework. The method depends on a set of training shapes used to build a parametric shape model. The color is taken into consideration besides the shape prior information. The shape model is fitted to the image volume by registration through an energy minimization problem. The approach overcomes the conventional methods problems like point correspondences and weighing coefficients tuning of the evolution (PDEs). It is also suitable for multidimensional data and computationally efficient. Results in 2D and 3D of real and synthetic data will demonstrate the efficiency of the framework