A local stability analysis for a class of adaptive systems

An analysis of adaptive systems is presented where a localL_{infty}-stability is ensured under a persistent excitation condition.     

Optimizing decomposition of software architecture for local recovery

The increasing size and complexity of software systems has led to an amplified number of potential failures and as such makes it harder to ensure software reliability. Since it is usually hard to prevent all the failures, fault tolerance techniques have become more important. An essential element of fault tolerance is the recovery from failures. Local recovery is an effective approach whereby only the erroneous parts of the system are recovered while the other parts remain available. For achieving local recovery, the architecture needs to be decomposed into separate units that can be recovered in isolation. Usually, there are many different alternative ways to decompose the system into recoverable units. It appears that each of these decomposition alternatives performs differently with respect to availability and performance metrics. We propose a systematic approach dedicated to optimizing the decomposition of software architecture for local recovery. The approach provides systematic guidelines to depict the design space of the possible decomposition alternatives, to reduce the design space with respect to domain and stakeholder constraints and to balance the feasible alternatives with respect to availability and performance. The approach is supported by an integrated set of tools and illustrated for the open-source MPlayer software.     

The complex local mean decomposition

The local mean decomposition (LMD) has been recently developed for the analysis of time series which have nonlinearity and nonstationarity. The smoothed local mean of the LMD surpasses the cubic spline method used by the empirical mode decomposition (EMD) to extract amplitude and frequency modulated components. To process complex-valued data, we propose complex LMD, a natural and generic extension to the complex domain of the original LMD algorithm. It is shown that complex LMD extracts the frequency modulated rotation and envelope components. Simulations on both artificial and real-world complex-valued signals support the analysis.     

A class of iterative methods for computing polar decomposition

In this article, there is offered a parametric class of iterative methods for computing the polar decomposition of a matrix. Each iteration of this class needs only one scalar-by-matrix and three matrix-by-matrix multiplications. It is no use computing inversion, so no numerical problems can be created because of ill-conditioning. Some available methods can be included in this class by choosing a suitable value for the parameter. There are obtained conditions under which this class is always quadratically convergent. The numerical comparison performed among six quadratically convergent methods for computing polar decomposition, and a special method of this class, chosen based on a specific value for the parameter, shows that the number of iterations of the special method is considerably near that of a cubically convergent Halley's method. Ten n×n matrices with n=5, 10, 20, 50, 100 were chosen to make this comparison.     

A hybrid approach for classification of rare class data

Learning of rare class data is a challenging problem in field of classification process. A rare class or imbalanced class learning is the common problem faced by many real-world applications, because of this many researcher work focused on this issue. Rare class data always generate wrong results because of overwhelming accuracy of minority class by majority class. There are lots of methods been proposed to handle imbalanced class or rare class or skew class problem. This paper proposes a hybrid method, i. e. classification- and clustering-based method, solving rare class problem. This proposed hybrid method uses k-means, ensemble and divide and merge methods. This method tries to improve detection rate of every class. For experimental work, the proposed method is tested on real datasets. The experimental results show that proposed method works well as compared with other algorithms.     

Annihilating ideals for an algebraic local cohomology class

Hypersurface isolated singularities are considered in the context of algebraic analysis. A method for computing relative ?ech cohomology representations of algebraic local cohomology classes supported at the isolated singular point is described. An effective method to test membership and a method to compute the normal form for the Jacobi ideal are presented. The main purpose of this paper is to provide an effective algorithm for computing annihilating ideals, in the ring of partial differential operators, of the algebraic local cohomology class that generates the dual vector space to the local Milnor algebra.     

Pattern decomposition method for hyper-multi-spectral data analysis

The ‘pattern decomposition method’ (PDM) is a new analysis method originally developed for Landsat Thematic Mapper (TM) satellite data. Applying the PDM to the radiospectrometer data of ground objects, 121 dimensional data in the wavelength region 350–2500?nm were successfully reduced into three-dimensional data. The nearly continuous spectral reflectance of land cover objects could be decomposed by three standard spectral patterns with an accuracy of 4.17% per freedom. We introduced a concept of supplementary spectral patterns for the study of specific ground objects. As an example, availability of a supplementary spectral pattern that can rectify standard spectral pattern of vivid vegetation for spectra of withered vegetation was studied. The new Revised Vegetation Index based on Pattern Decomposition (RVIPD) for hyper-multi-spectra is proposed as a simple function of the pattern decomposition coefficients including the supplementary vegetation pattern. It was confirmed that RVIPD is linear to the area cover ratio and also to the vegetation quantum efficiency.     

Differential quadrature domain decomposition method for a class of parabolic equations

In this paper the differential quadrature method (DQM) and the domain decomposition method (DDM) are combined to form the differential quadrature domain decomposition method (DQDDM), in which the boundary reduction technique (BRM) is adopted. The DQDDM is applied to a class of parabolic equations, which have discontinuity in the coefficients of the equation, or weak discontinuity in the initial value condition. Two numerical examples belonging to this class are computed. It is found that the application of this method to the above mentioned problems is seen to lead to accurate results with relatively small computational effort.     

Evaluation of local dependencies of images wavelet decomposition

An approach to identifying local area structure that is used in the spatial interaction models adapted to the image characteristic properties using mutual information criterion is reviewed in this article. Experimental results that demonstrate the value of using the represented method are shown. The text was submitted by authors in English. Arcady Lvovich Zhiznyakov. Candidate of engineering science. Docent of the Department of Information Systems at the Murom Institute of Vladimir State University. The field of science interests is digital image processing and analysis. He is the author of more than 150 science publications. Vasilii Evgenevich Gai. A postgraduate student of the Department of Information Systems at the Murom Institute of Vladimir State University. The field of science interests is digital image processing and analysis. He is the author of about 40 science publications. Sultan Sadykovich Sadykov. Doctor of engineering science, professor. Professor of the Department of Information Systems at the Murom Institute of Vladimir State University. The field of science interests is digital image processing and analysis. He is the author of more than 200 science publications.     

A fine-grained Random Forests using class decomposition: an application to medical diagnosis

Class decomposition describes the process of segmenting each class into a number of homogeneous subclasses. This can be naturally achieved through clustering. Utilising class decomposition can provide a number of benefits to supervised learning, especially ensembles. It can be a computationally efficient way to provide a linearly separable data set without the need for feature engineering required by techniques like support vector machines and deep learning. For ensembles, the decomposition is a natural way to increase diversity, a key factor for the success of ensemble classifiers. In this paper, we propose to adopt class decomposition to the state-of-the-art ensemble learning Random Forests. Medical data for patient diagnosis may greatly benefit from this technique, as the same disease can have a diverse of symptoms. We have experimentally validated our proposed method on a number of data sets that are mainly related to the medical domain. Results reported in this paper show clearly that our method has significantly improved the accuracy of Random Forests.     

Integrating local action elements for action analysis

In this paper, we propose a framework for human action analysis from video footage. A video action sequence in our perspective is a dynamic structure of sparse local spatial–temporal patches termed action elements, so the problems of action analysis in video are carried out here based on the set of local characteristics as well as global shape of a prescribed action. We first detect a set of action elements that are the most compact entities of an action, then we extend the idea of Implicit Shape Model to space time, in order to properly integrate the spatial and temporal properties of these action elements. In particular, we consider two different recipes to construct action elements: one is to use a Sparse Bayesian Feature Classifier to choose action elements from all detected Spatial Temporal Interest Points, and is termed discriminative action elements. The other one detects affine invariant local features from the holistic Motion History Images, and picks up action elements according to their compactness scores, and is called generative action elements. Action elements detected from either way are then used to construct a voting space based on their local feature representations as well as their global configuration constraints. Our approach is evaluated in the two main contexts of current human action analysis challenges, action retrieval and action classification. Comprehensive experimental results show that our proposed framework marginally outperforms all existing state-of-the-arts techniques on a range of different datasets.     

The analysis of decomposition methods for support vector machines

The support vector machine (SVM) is a promising technique for pattern recognition. It requires the solution of a large dense quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, very few methods can handle the memory problem and an important one is the "decomposition method." However, there is no convergence proof so far. We connect this method to projected gradient methods and provide theoretical proofs for a version of decomposition methods. An extension to bound-constrained formulation of SVM is also provided. We then show that this convergence proof is valid for general decomposition methods if their working set selection meets a simple requirement.     

An analysis of decomposition for subjective estimation in decisionanalysis

In decision analysis, it is frequently necessary to obtain reasonably precise estimates of unknown quantities, including both probabilities and utilities. Decomposition is often used for this purpose, under the assumption that it is preferable to direct elicitation. In this context, decomposition involves estimating the conditional means of the quantity of interest for a finite number of conditioning events, and weighting the means by the estimated probabilities of these events. We propose a model for how the precision of estimates obtained using decomposition depends on the choice of conditioning events. A novel feature of this model is that it captures how the choice of conditioning events influences both the conditional means and the conditional variances simultaneously. This makes it possible to characterize not only when decomposition is preferable to direct elicitation, but also when highly informative decompositions are preferred to less informative ones. Intuitive explanations of these results are provided. In addition, when the estimated probabilities of the conditioning events are reasonably precise, our results are consistent with conventional wisdom in decision analysis, and bear strong similarity to results on the construction of strata in stratified sampling     

Singular value decomposition for dynamic system design sensitivity analysis

A computer-based method for automatic generation and efficient numerical solution of mixed differential-algebraic equations for dynamic and design sensitivity analysis of dynamic systems is developed. The equations are written in terms of a maximal set of Cartesian coordinates to facilitate general formulation of kinematic and design constraints and forcing functions. Singular value decomposition of the system Jacobian matrix generates a set of composite generalized coordinates that are best suited to represent the system. The coordinates naturally partition into optimal independent and dependent sets, and integration of only the independent coordinates generates all of the system information. An adjoint variable method is used to compute design sensitivities of dynamic performance measures of the system. A general-purpose computer program incorporating these capabilities has been developed. A numerical example is presented to illustrate accuracy and properties of the method.     

Multi-resolution analysis with frontal decomposition

Use of the compactlyB-spline wavelet of Chui and Wang (1991); Chui (1992) is hindered by loss of accuracy on decomposition, through truncation of weight sequences which are countably infinite. Adaptations to finite intervals often encounter problems at boundaries. For multiresolution analysis on a finite interval employing the linearB-wavelet the present research provides a frontal approach to decomposition which avoids truncation of weight sequences, experiences no problems at boundaries, and which is exhibits a factor of three increase in computational efficiency. The boundary wavelets which complete the linearB-wavelet basis on a finite interval are constructed.     

AdaDT: An adaptive decision tree for addressing local class imbalance based on multiple split criteria

Applied Intelligence - As it is well known, decision tree is a kind of data-driven classification model, and its primary core is the split criterion. Although a great deal of split criteria have...     

Task decomposition and module combination based on class relations: a modular neural network for pattern classification.

We propose a method for decomposing pattern classification problems based on the class relations among training data. By using this method, we can divide a K-class classification problem into a series of ((2)(K)) two-class problems. These two-class problems are to discriminate class C(i) from class C(j) for i=1, ..., K and j=i+1, while the existence of the training data belonging to the other K-2 classes is ignored. If the two-class problem of discriminating class C(i) from class C(j) is still hard to be learned, we can further break down it into a set of two-class subproblems as small as we expect. Since each of the two-class problems can be treated as a completely separate classification problem with the proposed learning framework, all of the two-class problems can be learned in parallel. We also propose two module combination principles which give practical guidelines in integrating individual trained network modules. After learning of each of the two-class problems with a network module, we can easily integrate all of the trained modules into a min-max modular (M(3)) network according to the module combination principles and obtain a solution to the original problem. Consequently, a large-scale and complex K-class classification problem can be solved effortlessly and efficiently by learning a series of smaller and simpler two-class problems in parallel.     

A class of stochastic extremal problems with rare events

Translated from Kibernetika, No. 1, pp. 80–83, 98, January–February, 1988.     

Overlapping decomposition: a system-theoretic method for modeling and analysis of complex manufacturing systems

A system-theoretic method, referred to as overlapping decomposition, is presented to evaluate the performance of a complex manufacturing system with assembly, parallel, rework, feedforward, and scrap operations. The idea of the method is to decompose the complex system into a set of serial production lines, with the first or last machines of each serial line overlapped with another line, and to modify the parameters of overlapping machines to accommodate the effects of machines and buffers in other lines. Iterative procedures are introduced to estimate the system production rate. The convergence of the procedures and the uniqueness of the solutions are proved analytically and the accuracy of the estimates is evaluated numerically. Note to practitioners - Many large volume manufacturing systems consist of complex operations, for instance, assembly, disassembly, rework loop, parallel lines, feedforward lines, scrap, etc. Development of a performance evaluation method to provide fast and accurate analysis of system throughput is important for design and continuous improvements. This paper introduces a system-theoretic method, referred to as, overlapping decomposition, to analyze the performance of such complex manufacturing systems. The complex system is decomposed into overlapped serial production lines and modifications are introduced to accommodate the coupling effects among all these lines. From the theoretical point of view, this paper presents the proofs of the convergence of recursive procedures and the uniqueness of solution. From the application point of view, the method has obtained good results in solving practical problems on the factory floor.