Bilevel stochastic linear programming problems with quantile criterion

We propose a setting for a bilevel stochastic linear programming problem with quantile criterion. We study continuity properties of the criterial function and prove the existence theorem for a solution. We propose a deterministic equivalent of the problem for the case of a scalar random parameter. We show an equivalent problem in the form of a two-stage stochastic programming problem with equilibrium constraints and quantile criterion. For the case of a discrete distribution of random parameters, the problem reduces to a mixed linear programming problem. We show results of numerical experiments.     

Phrase-based correction model for improving handwriting recognition accuracies

We propose a method for increasing word recognition accuracies by correcting the output of a handwriting recognition system. We treat the handwriting recognizer as a black box, such that there is no access to its internals. This enables us to keep our algorithm general and independent of any particular system. We use a novel method for correcting the output based on a “phrase-based” system in contrast to traditional source-channel models. We report the accuracies of two in-house handwritten word recognizers before and after the correction. We achieve highly encouraging results for a large synthetically generated dataset. We also report results for a commercially available OCR on real data.     

The Control Problem for Stage-by-Stage Changing Linear Systems of Loaded Differential Equations

We consider the control problem for stage-by-stage changing linear differential equations and the optimal control problem with a quality criterion defined for the entire time interval. We formulate necessary and sufficient conditions for complete controllability and the existence of a program control and motion. We construct an explicit form of the control action for the control problem and propose a method for solving the optimal control problem. We give a solution of the control problem for a specific loaded system.     

On stochastic linear programming problems with the quantile criterion

We study qualitative properties of a stochastic linear programming problem with quantile criterion for a wide class of distributions. We show convexity conditions for the criterion function with respect to the strategy, and continuity conditions with respect to the strategy and reliability level. We give sufficient conditions for the existence of a solution. We present a new algorithm for finding a guaranteeing solution of the problem, i.e., an admissible solution for which the quantile criterion function's value turns out to be close to optimal.     

ON THE THEORY OF BAGS

We introduce the bag structure as a set-like object in which repeated elements are significant. We discuss operations on bags such as intersection, union and addition. We introduce the operation of selecting elements from a bag based upon their membership in a set. We show the usefulness of the bag structure in relational data bases. We provide a definition for fuzzy bags. In these fuzzy bags the count of the number of elements itself becomes a crisp bags. We investigate a calculus for fuzzy bags.     

Quick-VDR: out-of-core view-dependent rendering of gigantic models

We present a novel approach for interactive view-dependent rendering of massive models. Our algorithm combines view-dependent simplification, occlusion culling, and out-of-core rendering. We represent the model as a clustered hierarchy of progressive meshes (CHPM). We use the cluster hierarchy for coarse-grained selective refinement and progressive meshes for fine-grained local refinement. We present an out-of-core algorithm for computation of a CHPM that includes cluster decomposition, hierarchy generation, and simplification. We introduce novel cluster dependencies in the preprocess to generate crack-free, drastic simplifications at runtime. The clusters are used for LOD selection, occlusion culling, and out-of-core rendering. We add a frame of latency to the rendering pipeline to fetch newly visible clusters from the disk and avoid stalls. The CHPM reduces the refinement cost of view-dependent rendering by more than an order of magnitude as compared to a vertex hierarchy. We have implemented our algorithm on a desktop PC. We can render massive CAD, isosurface, and scanned models, consisting of tens or a few hundred million triangles at 15-35 frames per second with little loss in image quality.     

Adaptive controller for a multi-mode object

We consider an adaptive controller for a single-mode object and study the possibility of using it for a multi-mode object. We propose an enhanced adaptive control algorithm for a multi-mode object. We study the influence of the quantization effect in DA and AD converters on the results of adaptive control. We study the influence of test signal amplitudes on identification results and show the findings of experimental studies for an adaptive controller with improved algorithms.     

Approximating sets on a plane with optimal sets of circles

We study optimal networks on a plane. We generalize the Chebyshev center of a set on the case of several points. We propose numerical and analytic methods for finding a placement of a fixed number of points that minimizes the Hausdorff deviation of a given set from these points. We develop and experiment with software for computing a network of two or three points for the case of flat figures. We show examples of modeling optimal coverings of polyhedra by sets of one, two, or three circles. Based on these networks, we propose an approximation of flat, in general nonconvex, sets by collections of circles.     

Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach

We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control.     

A Boussinesq model for pressure and flow velocity waves in arterial segments

We derive a nonlinear model for the pressure and flow velocity wave propagation in an arterial segment. We then study the transmission and reflection of pulses at bifurcation. We observe a linear dependence of the transmitted speeds to the incoming speeds, and similarly for the reflected speeds. We propose a method for validating the numerical results obtained from this model against real data.     

Testing Planning Domains (without Model Checkers)

We address the problem of verifying planning domains as used in model-based planning, for example in space missions. We propose a methodology for testing flight rules of planning domains which is self-contained, in the sense that flight rules are verified using a planner and no external tools are required. We review and analyse coverage conditions for requirements-based testing, and we reason in detail on "Unique First Cause" (UFC) coverage for test suites. We characterise flight rules using patterns, encoded using LTL, and we provide UFC coverage for them. We then present a translation of LTL formulae into planning goals, and illustrate our approach on a case study.     

Eigenspaces of the discrete Walsh transform

We refine the notion of a discrete Walsh function and generalize the notion of a discrete Walsh transform, for which we propose a method for generating a corresponding W-matrix. We propose spectral decompositions of the discrete Walsh transform operators in arbitrary enumerations, as well as methods for finding bases of eigenspaces, one of them using a new direct product of matrices. We propose a notation for the fast discrete Walsh transform algorithm in the Paley enumeration. We construct Parseval frames for eigenspaces of the discrete Walsh transform in the Paley enumeration and demonstrate methods for applying them in error detection and correction.     

Adaptive estimation of variable weights in a linear adder

We consider the adaptive estimation problem for variable weights in a linear adder from known values of a scalar function. We further develop an adaptive identification algorithm for static systems. We obtain a nonlinear adaptive estimation algorithm based on correcting prior values of variable weights. In special cases, we establish conditions for the existence of a solution in the estimation problem. We demonstrate the efficiency of the proposed nonlinear adaptive estimation algorithm for variable weights in a linear adder with an example.     

A framework for the design and implementation of FFT permutationalgorithms

We propose an algebraic framework for the design and implementation of a large class of data-sorting procedures, including all index-digit permutations used in FFTs. We discuss both old and new algorithms in terms of this framework. We show that the algebraic formulation of the new algorithms can be easily encoded using a functional programming language, and that the resulting code introduces no inefficiencies. We present performance results for implementations of three new algorithms for mixed-radix digit-reversal on a Cray C-90 and on a Sun Sparc 5     

In praise of algebra

We survey the well-known algebraic laws of sequential programming, and extend them with some less familiar laws for concurrent programming. We give an algebraic definition of the Hoare triple, and algebraic proofs of all the relevant laws for concurrent separation logic. We give the provable concurrency laws for Milner transitions, for the Back/Morgan refinement calculus, and for Dijkstra’s weakest preconditions. We end with a section in praise of algebra, of which Carroll Morgan is such a master.     

Quantitative PCR with internal controls.

We examine the use of internal controls for estimating the expected initial copy number of the target in a polymerase chain reaction (PCR). We base our investigation on an extended branching-process model. In terms of that model, we delineate the necessary assumptions for this methodology to yield approximately unbiased answers, and we provide means for testing some of those assumptions. We show how to design a series of PCRs to attain optimal precision of the estimate. We provide an algorithm for conducting the statistical analysis of the data, including a formula for a confidence interval for the unknown expected initial copy number.     

Untying of Clutter-Family Supports and Its Role in the Monotone-Structure Reliability Theory

We suggest a transformation of supports of a clutter family. It is a natural generalization of the untying transformation of a one-clutter support previously introduced by the author. We show that the new transformation does not decrease the reliability of the corresponding clutter sum. We give new bounds for the clutter-sum reliability. We demonstrate that the untying transformation of clutter-family supports and the factor transformation introduced by McDiarmid provide a combinatorial basis for the monotone-structure reliability theory.     

Localization of multiple faults with group control on a discrete device

We consider the set of control points in a discrete device where testing is organized in groups with a signature analyzer. We solve the localization problem for a fault of any multiplicity located in one or different groups. For this purpose, in addition we construct various partitions of the set of control points where we organize similar group testing. We show an algorithm for constructing additional partitions. We solve the minimization problem for the testing hardware needed to implement it.     

Generalized triangular norm and conorm aggregation operators on ordinal spaces

We describe the basic features of the t-norm operator and then introduce a family of t-norm operators that are defined on an ordinal space. We then do the same for the t-conorms. We note the strong limitation that the requirement of associativity places on the t-norm and t-conorm operators. We particularly note how it limits our ability to model different types of reinforcement. We then define a generalization of the t-conorm aggregation operator, which relaxes the requirement of associativity, we denote these operators as GENOR operators. We show that these operators have the same functionality as the t-conorm. We provide some examples of GENOR operators which allow us to control the reinforcement process. We define a related extension for the t-norm, the GENAND operator and provide some examples.