Robustness of pole assignment in a specified region for perturbed systems

This paper deals with the pole location of a perturbed matrix A + E. Two perturbation classes are discussed: one with structured perturbations |E| ≤ U (a given non-negative matrix) and one where only the spectral norm ||E|| is given. Sufficient conditions are derived for the eigenvalues of A + E to be located within a circle C(α, r). The case of a diagonalizable matrix A is also considered and a sufficient condition for robust stability of a class of perturbed discrete systems is derived. Numerical examples are presented for illustration.     

Implicit co-simulation method for constraint coupling with improved stability behavior

This paper deals with a novel co-simulation approach for coupling mechanical subsystems in time domain. The submodels are assumed to be coupled by algebraic constraint equations. In contrast to well-known coupling techniques from the literature, the here presented index-1 approach uses a special technique for approximating the coupling variables so that the constraint equations together with the hidden constraints on velocity and acceleration level can be enforced simultaneously at the communication time points. The method discussed here uses second- and third-order approximation polynomials. Because of the high approximation order, the numerical errors are very small, and a good convergence behavior is achieved. A stability analysis is carried out, and it is shown that—despite the fact that higher-order approximation polynomials are applied—also a good numerical stability behavior is observed. Different numerical examples are presented, which illustrate the practical application of the approach.     

Block-matching algorithm based on harmony search optimization for motion estimation

Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, Block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing a certain matching metric that is produced for the current frame over a determined search window from the previous frame. Unfortunately, the evaluation of such matching measurement is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be viewed as an optimization problem whose goal is to find the best-matching block within a search space. The simplest available BM method is the Full Search Algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of all the elements of the search space. Recently, several fast BM algorithms have been proposed to reduce the search positions by calculating only a fixed subset of motion vectors despite lowering its accuracy. On the other hand, the Harmony Search (HS) algorithm is a population-based optimization method that is inspired by the music improvisation process in which a musician searches for harmony and continues to polish the pitches to obtain a better harmony. In this paper, a new BM algorithm that combines HS with a fitness approximation model is proposed. The approach uses motion vectors belonging to the search window as potential solutions. A fitness function evaluates the matching quality of each motion vector candidate. In order to save computational time, the approach incorporates a fitness calculation strategy to decide which motion vectors can be only estimated or actually evaluated. Guided by the values of such fitness calculation strategy, the set of motion vectors is evolved through HS operators until the best possible motion vector is identified. The proposed method has been compared to other BM algorithms in terms of velocity and coding quality. Experimental results demonstrate that the proposed algorithm exhibits the best balance between coding efficiency and computational complexity.     

An improved feature transformation method using mutual information

The feature transformation is a very important step in pattern recognition systems. A feature transformation matrix can be obtained using different criteria such as discrimination between classes or feature independence or mutual information between features and classes. The obtained matrix can also be used for feature reduction. In this paper, we propose a new method for finding a feature transformation-based on Mutual Information (MI). For this purpose, we suppose that the Probability Density Function (PDF) of features in classes is Gaussian, and then we use the gradient ascent to maximize the mutual information between features and classes. Experimental results show that the proposed MI projection consistently outperforms other methods for a variety of cases. In the UCI Glass database we improve the classification accuracy up to 7.95 %. Besides, the improvement of phoneme recognition rate is 3.55 % on TIMIT.     

Syllable based text to speech synthesis system using auto associative neural network prosody prediction

This paper presents the design and development of an Auto Associative Neural Network (AANN) based unrestricted prosodic information synthesizer. Unrestricted Text To Speech System (TTS) is capable of synthesize different domain speech with improved quality. This paper deals with a corpus-driven text-to speech system based on the concatenative synthesis approach. Concatenative speech synthesis involves the concatenation of the basic units to synthesize an intelligent, natural sounding speech. A corpus-based method (unit selection) uses a large inventory to select the units and concatenate. The prosody prediction is done with the help of five layer auto associative neural network which helps us to improve the quality of speech synthesis. Here syllables are used as basic unit of speech synthesis database. The database consisting of the units along with their annotated information is called annotated speech corpus. A clustering technique is used in annotated speech corpus that provides way to select the appropriate unit for concatenation, based on the lowest total join cost of the speech unit. Discontinuities present at the unit boundaries are lowered by using the mel-LPC smoothing technique. The experiment has been made for the Dravidian language Tamil and the results reveal to demonstrate the improved intelligibility and naturalness of the proposed method. The proposed system is applicable to all the languages if the syllabification rules has been changed.     

Electrochemical determination of glutamic pyruvic transaminase using a microfluidic chip

The activity of glutamic pyruvic transaminase (GPT) is an important clinical evidence for some acute diseases such as acute hepatopathy and myocardial infarction. Thus, there is a demand for rapid determination of GPT in small formats at point-of-need. Herein, we describe a novel method of electrochemical determination of GPT with microfluidic technique. GPT activity was indirectly determined via the electrochemical (EC) detection of nicotinamide adenine dinucleotide (NADH) produced from the GPT transdeamination reaction. A type of microfluidic chip was developed, in which a passive mixer comprising 100 sub-ribs and a three-electrode strip for EC were integrated. To verify the response to NADH, a series of NADH concentrations varying from 19 µM to 5 mM were calibrated with cyclic voltammetry within the microfluidic chip. And a linear relationship with R ² 0.9982 between the peak current and the concentration of NADH was obtained. Then, the GPT activity was determined using the chips containing and not containing a ribs-type mixer. And a linear relationship which contained two sections between the GPT activity and the peak current was obtained. The chip with a ribs-type mixer exhibited the sensitivity of 0.0341 μA U^?1 L in the range of 10–50 U L^?1 and 0.0236 μA U^?1 L in the range of 50–250 U L^?1. And the detection limit of the chip with a ribs-type mixer was 9.25 U L^?1. The complete detection process of GPT activity within the microfluidic chip was realized, and the time-consuming problem was remarkably improved too.     

Development of a Variational Multiscale Large-Eddy Simulation Code ‘MISTRAL’ Using Double-Scale Finite Elements

A variational multiscale large-eddy simulation (VMS-LES) code, named MISTRAL, has been developed based upon the finite element method (FEM) for accurate and practical computation of geometrically complicated turbulent flow problems. The numerical strategy of the FEM-based VMS-LES is explained, especially focusing on the double-scale approximation for velocity and pressure in the incompressible Navier-Stokes equations, a pressure stabilization technique and a multiscale turbulence modeling. A unique technique is also employed in the time integration to realize an efficient inversion of the multiscale mass matrix and to form the multiscale pressure Poisson equation used in the approximate projection method for divergence-free constraint of velocity. As a numerical demonstration, a 2D driven cavity flow problem has been solved with the MISTRAL code in a wide range of Reynolds number (Re=1000 to 50000). The results are compared with reference data to quantitatively estimate the accuracy (magnitude of errors in terms of L ² norms) of the proposed VMS-LES method.     

Revelation on demand

Private data sometimes must be made public. A corporation may keep its customer sales data secret, but reveals totals by sector for marketing reasons. A hospital keeps individual patient data secret, but might reveal outcome information about the treatment of particular illnesses over time to support epidemiological studies. In these and many other situations, aggregate data or partial data is revealed, but other data remains private. Moreover, the aggregate data may depend not only on private data but on public data as well, e.g. commodity prices, general health statistics. Our GhostDB platform allows queries that combine private and public data, produce aggregates to data warehouses for OLAP purposes, and reveal exactly what is desired, neither more nor less. We call this functionality “revelation on demand”.     

Measurements of microbubble generation process in microchannel using ultra high-speed micro-PTV system

Ultra high-speed micron-resolution particle tracking velocimetry (UH-μPTV) technique has been developed to advance the novel method to generate microbubbles using a T-shaped microchannel. The method can produce microbubbles with 10-μm order diameter by applying the gas pressure of several tens of kilopascal and injecting the deionized water with the speed of a few meters per second. The conventional μPTV was restricted to the velocity measurement of the order of millimeter per second due to a few kilohertz frame rate CMOS camera. On the other hand, the present UH-μPTV technique achieves to measure the liquid velocity of the order of meter per second by combining the bright-field microscopy and the ultra high-speed camera with 1 MHz frame rate. For improving the spatial resolution, the phase sampling method has been introduced and results in 10 velocity vectors in 20 μm × 20 μm area. The validation of the velocity measurement using UH-μPTV has been conducted through the comparison with the theoretical solution, and it has been shown that the proposed technique can capture the velocity vector field higher than 1 m/s. Furthermore, from the 1-μs time-series imaging, the microbubble generation process has been classified into two stages: the intruding stage and the growing stage. It has been shown that the bubble diameter becomes smaller by increasing the liquid velocity with reducing the period of the growing stage. In addition, from the velocity-vector maps, the normal components of velocities to the gas–liquid interface in the intruding stage are compared with those in the growing stage, and it has been observed that the velocity amplitudes in the growing stage are much larger than those in the intruding stage. This fact suggests that the high-speed liquid flow normal to the gas–liquid interface plays an important role in microbubble generation process.     

Robuste multikriterielle Dienstkomposition in Informationssystemen

Service compositions are used to implement business processes in a variety of application domains. A quality of service (QoS)-aware selection of the service to be composed involves multiple, usually conflicting and possibly uncertain QoS attributes. A multi-criteria solution approach is desired to generate a set of alternative service selections. In addition, the uncertainty of QoS-attributes is neglected in existing solution approaches. Hence, the need for service reconfigurations is imposed to avoid the violation of QoS restrictions. The researched problem is NP-hard. This article presents a heuristic multi-criteria service selection approach that is designed to determine a Pareto frontier of alternative service selections in a reasonable amount of time. Taking into account the uncertainty of response times, the obtained service selections are robust with respect to the constrained execution time. The proposed solution approach is based on the Non-dominated Sorting Genetic Algorithm (NSGA)-II extended by heuristics that exploit problem specific characteristics of the QoS-aware service selection. The applicability of the solution approach is demonstrated by a simulation study.     

Anomalous phenomena in pressure drops of water flows through micro-orifices

Pressure drops were measured for high-velocity water flows through micro-orifices of various diameters. The observed pressure drop values agreed well with the values predicted by the Navier–Stokes equation for 400 and 100 μm diameter orifices, but were lower than the predicted values for orifices less than 50 μm in diameter. In particular, the measured pressure drop value was almost two orders of magnitude lower than the predicted value for the 10 and 5 μm diameter orifices. Several factors that may cause a reduction in pressure drop were considered, including orifice shape and deformation of the orifice foil, but none proved to be significant enough to cause such a large reduction. Elastic stress in orifice flow appeared to be the most plausible cause of the pressure drop reduction. The elastic stress, which was estimated by the jet thrust method, was found to be dependent on the mean velocity passing through the micro-orifices, which strongly supported the elasticity of water flows.     

DSMC investigation of rarefied gas flow through diverging micro- and nanochannels

Direct simulation Monte Carlo (DSMC) method with simplified Bernoulli trials (SBT) collision scheme has been used to study the rarefied pressure-driven nitrogen flow through diverging micro- and nanochannels. The fluid behaviours flowing between two plates with different divergence angles ranging between 0° and 17° are described at different pressure ratios (1.5 ≤ Π ≤ 2.5) and Knudsen numbers (0.03 ≤ Kn ≤ 12.7). The primary flow field properties, including pressure, velocity, and temperature, are presented for divergent micro- and nanochannels and are compared with those of a micro- and nanochannel with a uniform cross section. The variations of the flow field properties in divergent micro- and nanochannels which are influenced by the area change, the channel pressure ratio, and the rarefication are discussed. The results show no flow separation in divergent micro- and nanochannels for all the range of simulation parameters studied in the present work. It has been found that a divergent channel can carry higher amounts of mass in comparison with an equivalent straight channel geometry. A correlation between the mass flow rate through micro- and nanochannels, the divergence angle, the pressure ratio, and the Knudsen number has been suggested. The present numerical findings prove the occurrence of Knudsen minimum phenomenon in micro- and nanochannels with non-uniform cross sections.     

Querying Probabilistic Business Processes for Sub-Flows

A Business Process (BP for short) consists of a set of activities which, combined in a flow, achieve some business goal. A given BP may have a large, possibly infinite, number of possible execution flows (EX-flows for short), each having some probability to occur at run time. This paper studies query evaluation over such probabilistic BPs. We focus on two important classes of queries, namely boolean queries that compute the probability that a random EX-flow of a BP satisfies a given property, and projection queries focusing on portions of EX-flows that are of interest to the user. For the latter queries the answer consists of the top-k instances of these portions that are most likely to occur at run-time. We study the complexity of query evaluation for both kinds of queries, showing in particular that projection queries may be harder to evaluate than boolean queries. We present a picture of which combinations of BP classes and query features lead to PTIME algorithms and which to NP-hard or infeasible problems.     

A nonsmooth nonlinear conjugate gradient method for interactive contact force problems

Interactive rigid body simulation is important for robot simulation and virtual design. A vital part of the simulation is the computation of contact forces. This paper addresses the contact force problem, as used in interactive simulation. The contact force problem can be formulated in the form of a nonlinear complementarity problem (NCP), which can be solved using an iterative splitting method, such as the projected Gauss–Seidel (PGS) method. We present a novel method for solving the NCP problem by applying a Fletcher–Reeves type nonlinear nonsmooth conjugate gradient (NNCG) type method. We analyze and present experimental convergence behavior and properties of the new method. Our results show that the NNCG method has at least the same convergence rate as PGS, and in many cases better.     

Hybrid particle–grid fluid animation with enhanced details

Simulating large-scale fluid while retaining and rendering details still remains to be a difficult task in spite of rapid advancements of computer graphics during the last two decades. Grid-based methods can be easily extended to handle large-scale fluid, yet they are unable to preserve sub-grid surface details like spray and foam without multi-level grid refinement. On the other hand, the particle-based methods model details naturally, but at the expense of increasing particle densities. This paper proposes a hybrid particle–grid coupling method to simulate fluid with finer details. The interaction between particles and fluid grids occurs in the vicinity of “coupling band” where multiple particle level sets are introduced simultaneously. First, fluids free of interaction could be modeled by grids and SPH particles independently after initialization. A coupling band inside and near the interface is then identified where the grids interact with the particles. Second, the grids inside and far away from the interface are adaptively sampled for large-scale simulation. Third, the SPH particles outside the coupling band are enhanced by diffuse particles which render little computational cost to simulate spray, foam, and bubbles. A distance function is continuously updated to adaptively coarsen or refine the grids near the coupling band and provides the coupling weights for the two-way coupling between grids and particles. One characteristic of our hybrid approach is that the two-way coupling between these particles of spray and foam and the grids of fluid volume can retain details with little extra computational cost. Our rendering results realistically exhibit fluids with enhanced details like spray, foam, and bubbles. We make comprehensive comparisons with existing works to demonstrate the effectiveness of our new method.     

Optimal vorticity accuracy in an efficient velocity–vorticity method for the 2D Navier–Stokes equations

We study a velocity–vorticity scheme for the 2D incompressible Navier–Stokes equations, which is based on a formulation that couples the rotation form of the momentum equation with the vorticity equation, and a temporal discretization that stably decouples the system at each time step and allows for simultaneous solving of the vorticity equation and velocity–pressure system (thus if special care is taken in its implementation, the method can have no extra cost compared to common velocity–pressure schemes). This scheme was recently shown to be unconditionally long-time \(H^1\) stable for both velocity and vorticity, which is a property not shared by any common velocity–pressure method. Herein, we analyze the scheme’s convergence, and prove that it yields unconditional optimal accuracy for both velocity and vorticity, thus making it advantageous over common velocity–pressure schemes if the vorticity variable is of interest. Numerical experiments are given that illustrate the theory and demonstrate the scheme’s usefulness on some benchmark problems.     

Role of interface shape on the laminar flow through an array of superhydrophobic pillars

In this study, measurements of the pressure drop and the velocity vector fields through a regular array of superhydrophobic pillars were systematically taken to investigate the role of air–water interface shape on laminar drag reduction. A polydimethylsiloxane microfluidic channel was created with a regular array of apple-core-shaped and circular pillars bridging across the entire channel. Due to the shape and hydrophobicity of the apple-core-shaped pillars, air was trapped on the side of the pillars after filling the microchannel with water. The measurements were taken at a capillary number of Ca = 6.6 × 10^?5. The shape of the air–water interface trapped within the superhydrophobic apple-core-shaped pillars was systematically modified from concave to convex by changing the static pressure within the microchannel. The pressure drop through the microchannel containing the superhydrophobic apple-core-shaped pillars was found to be sensitive to the shape of the air–water interface. For static pressures which resulted in the apple-core-shaped superhydrophobic pillars having a circular cross section, D/D ₀ = 1, a drag reduction of 7% was measured as a result of slip along the air–water interface. At large static pressures, the interface was driven into the apple-core-shaped pillars, resulting in decrease in the effective size of the pillars and an increase in the effective spacing between pillars. When combined with a slip velocity measured to be 10% of the average velocity between the pillars, the result was a pressure drop reduction of 18% compared to the circular pillars at a non-dimensional interface diameter of D/D ₀ = 0.8. At low static pressures, the pressure drop increased significantly as the expanded air–water interface constricted flow through the array of pillars even as large interfacial slip velocity was maintained. At D/D ₀ = 1.1, for example, the pressure drop increased by 17% compared to the circular pillar. This drag increase was the result of an increased form drag due to a decrease in porosity and permeability of the pillar array and a decrease in the skin friction drag due to the presence of the air–water interface. For D/D ₀ = 1.1, the slip velocity was measured to be 45% of the average streamwise velocity between the pillars. When compared to no-slip pillars of similar shape, the drag reduction was found to increase from 6 to 9% with increasing convex curvature of the air–water interface.     

Similarity measuring strategy of image patterns based on fuzzy entropy and energy variations in intelligent robot’s manipulative task

A similarity measuring strategy of image patterns based on fuzzy entropy and energy variations, using an intelligent robot’s part macro-assembly (part-bringing) as an example, is presented. A part macro-assembly, locating various shaped assembly holes (targets) in a workspace corresponding to shapes of parts and then bringing a part to a corresponding target for the purpose of part mating despite existing obstalces, is introduced. This is accomplished by cooperating a neural network system with a fuzzy optimal control. Fuzzy entropy and energy functions, which are useful measures of variability and information in terms of uncertainty, are introduced to measure its overall performance of task execution related to the part-bringing task. An interrelation among learning, fuzzy entropy, and energy variations used as a measuring tool for a degree of similarity of image patterns is described. Through variations of fuzzy entropy and energy, a degree of similarity between input and desired output image patterns of neural network can be measured. The proposed technique is not only a useful tool to measure a degree of similarity between image patterns, but applicable to a wide range of robotic tasks including motion planning, manufacturing, maneuvering around workspace, and part mating with various shaped parts and targets.