Abstract: Feature selection and attribute reduction are crucial
problems, and widely used techniques in the field of machine
learning, data mining and pattern recognition to overcome the
well-known phenomenon of the Curse of Dimensionality. This paper
presents a feature selection method that efficiently carries out attribute
reduction, thereby selecting the most informative features of a dataset.
It consists of two components: 1) a measure for feature subset
evaluation, and 2) a search strategy. For the evaluation measure,
we have employed the fuzzy-rough dependency degree (FRFDD)
of the lower approximation-based fuzzy-rough feature selection
(L-FRFS) due to its effectiveness in feature selection. As for the
search strategy, a modified version of a binary shuffled frog leaping
algorithm is proposed (B-SFLA). The proposed feature selection
method is obtained by hybridizing the B-SFLA with the FRDD. Nine
classifiers have been employed to compare the proposed approach
with several existing methods over twenty two datasets, including
nine high dimensional and large ones, from the UCI repository.
The experimental results demonstrate that the B-SFLA approach
significantly outperforms other metaheuristic methods in terms of the
number of selected features and the classification accuracy.
Abstract: The water-based bioconvection of a nanofluid
containing motile gyrotactic micro-organisms over nonlinear
inclined stretching sheet has been investigated. The governing
nonlinear boundary layer equations of the model are reduced to a
system of ordinary differential equations via Oberbeck-Boussinesq
approximation and similarity transformations. Further, the modified
set of equations with associated boundary conditions are solved using
Finite Element Method. The impact of various pertinent parameters
on the velocity, temperature, nanoparticles concentration, density of
motile micro-organisms profiles are obtained and analyzed in details.
The results show that with the increase in angle of inclination δ,
velocity decreases while temperature, nanoparticles concentration,
a density of motile micro-organisms increases. Additionally, the
skin friction coefficient, Nusselt number, Sherwood number, density
number are computed for various thermophysical parameters. It
is noticed that increasing Brownian motion and thermophoresis
parameter leads to an increase in temperature of fluid which results
in a reduction in Nusselt number. On the contrary, Sherwood number
rises with an increase in Brownian motion and thermophoresis
parameter. The findings have been validated by comparing the
results of special cases with existing studies.
Abstract: The Oscillatory electroosmotic flow (OEOF) in power
law fluids through a microchannel is studied numerically. A
time-dependent external electric field (AC) is suddenly imposed
at the ends of the microchannel which induces the fluid motion.
The continuity and momentum equations in the x and y direction
for the flow field were simplified in the limit of the lubrication
approximation theory (LAT), and then solved using a numerical
scheme. The solution of the electric potential is based on the
Debye-H¨uckel approximation which suggest that the surface potential
is small,say, smaller than 0.025V and for a symmetric (z : z)
electrolyte. Our results suggest that the velocity profiles across
the channel-width are controlled by the following dimensionless
parameters: the angular Reynolds number, Reω, the electrokinetic
parameter, ¯κ, defined as the ratio of the characteristic length scale
to the Debye length, the parameter λ which represents the ratio
of the Helmholtz-Smoluchowski velocity to the characteristic length
scale and the flow behavior index, n. Also, the results reveal that
the velocity profiles become more and more non-uniform across the
channel-width as the Reω and ¯κ are increased, so oscillatory OEOF
can be really useful in micro-fluidic devices such as micro-mixers.
Abstract: In this work, we introduce the qualitative and
quantitative concept of the strong stability method in the risk process
modeling two lines of business of the same insurance company or
an insurance and re-insurance companies that divide between them
both claims and premiums with a certain proportion. The approach
proposed is based on the identification of the ruin probability
associate to the model considered, with a stationary distribution of a
Markov random process called a reversed process. Our objective, after clarifying the condition and the perturbation
domain of parameters, is to obtain the stability inequality of the ruin
probability which is applied to estimate the approximation error of a
model with disturbance parameters by the considered model. In the
stability bound obtained, all constants are explicitly written.
Abstract: In this paper, a mixed integer linear programming (MILP) model is presented to solve the flexible job shop scheduling problem (FJSP). This problem is one of the hardest combinatorial problems. The objective considered is the minimization of the makespan. The computational results of the proposed MILP model were compared with those of the best known mathematical model in the literature in terms of the computational time. The results show that our model has better performance with respect to all the considered performance measures including relative percentage deviation (RPD) value, number of constraints, and total number of variables. By this improved mathematical model, larger FJS problems can be optimally solved in reasonable time, and therefore, the model would be a better tool for the performance evaluation of the approximation algorithms developed for the problem.
Abstract: In this paper we present a quick technique to measure the similarity between binary images. The technique is based on a probabilistic mapping approach and is fast because only a minute percentage of the image pixels need to be compared to measure the similarity, and not the whole image. We exploit the power of the Probabilistic Matching Model for Binary Images (PMMBI) to arrive at an estimate of the similarity. We show that the estimate is a good approximation of the actual value, and the quality of the estimate can be improved further with increased image mappings. Furthermore, the technique is image size invariant; the similarity between big images can be measured as fast as that for small images. Examples of trials conducted on real images are presented.
Abstract: This work compares the results of multidimensional
function approximation using two algorithms: the classical Particle
Swarm Optimization (PSO) and the Quantum Particle Swarm
Optimization (QPSO). These algorithms were both tested on three
functions - The Rosenbrock, the Rastrigin, and the sphere functions
- with different characteristics by increasing their number of
dimensions. As a result, this study shows that the higher the function
space, i.e. the larger the function dimension, the more evident the
advantages of using the QPSO method compared to the PSO method
in terms of performance and number of necessary iterations to reach
the stop criterion.
Abstract: The earliest theories of sloshing waves and solitary waves based on potential theory idealisations and irrotational flow have been extended to be applicable to more realistic domains. To this end, the computational fluid dynamics (CFD) methods are widely used. Three-dimensional CFD methods such as Navier-Stokes solvers with volume of fluid treatment of the free surface and Navier-Stokes solvers with mappings of the free surface inherently impose high computational expense; therefore, considerable effort has gone into developing depth-averaged approaches. Examples of such approaches include Green–Naghdi (GN) equations. In Cartesian system, GN velocity profile depends on horizontal directions, x-direction and y-direction. The effect of vertical direction (z-direction) is also taken into consideration by applying weighting function in approximation. GN theory considers the effect of vertical acceleration and the consequent non-hydrostatic pressure. Moreover, in GN theory, the flow is rotational. The present study illustrates the application of GN equations to propagation of sloshing waves and solitary waves. For this purpose, GN equations solver is verified for the benchmark tests of Gaussian hump sloshing and solitary wave propagation in shallow basins. Analysis of the free surface sloshing of even harmonic components of an initial Gaussian hump demonstrates that the GN model gives predictions in satisfactory agreement with the linear analytical solutions. Discrepancies between the GN predictions and the linear analytical solutions arise from the effect of wave nonlinearities arising from the wave amplitude itself and wave-wave interactions. Numerically predicted solitary wave propagation indicates that the GN model produces simulations in good agreement with the analytical solution of the linearised wave theory. Comparison between the GN model numerical prediction and the result from perturbation analysis confirms that nonlinear interaction between solitary wave and a solid wall is satisfactorilly modelled. Moreover, solitary wave propagation at an angle to the x-axis and the interaction of solitary waves with each other are conducted to validate the developed model.
Abstract: This work reports about an approach for an automatic adaptation of concrete formulations based on genetic algorithms (GA) to optimize a wide range of different fit-functions. In order to achieve the goal, a method was developed which provides a numerical description of a fibre reinforced concrete (FRC) mixture regarding the production technology and the property spectrum of the concrete. In a first step, the FRC mixture with seven fixed components was characterized by varying amounts of the components. For that purpose, ten concrete mixtures were prepared and tested. The testing procedure comprised flow spread, compressive and bending tensile strength. The analysis and approximation of the determined data was carried out by GAs. The aim was to obtain a closed mathematical expression which best describes the given seven-point cloud of FRC by applying a Gene Expression Programming with Free Coefficients (GEP-FC) strategy. The seven-parametric FRC-mixtures model which is generated according to this method correlated well with the measured data. The developed procedure can be used for concrete mixtures finding closed mathematical expressions, which are based on the measured data.
Abstract: In this paper, we propose a blind and robust audio watermarking scheme based on spread spectrum in Discrete Wavelet Transform (DWT) domain. Watermarks are embedded in the low-frequency coefficients, which is less audible. The key idea is dividing the audio signal into small frames, and magnitude of the 6th level of DWT approximation coefficients is modifying based upon the Direct Sequence Spread Spectrum (DSSS) technique. Also, the psychoacoustic model for enhancing in imperceptibility, as well as Savitsky-Golay filter for increasing accuracy in extraction, is used. The experimental results illustrate high robustness against most common attacks, i.e. Gaussian noise addition, Low pass filter, Resampling, Requantizing, MP3 compression, without significant perceptual distortion (ODG is higher than -1). The proposed scheme has about 83 bps data payload.
Abstract: We study the temperature dependence of the interaction energies (IEs) of X (=Ru, Rh) impurities in Pd, due to the Fermi-Dirac (FD) distribution and the thermal vibration effect by the Debye-Grüneisen model. The n-body (n=2~4) IEs among X impurities in Pd, being used to calculate the internal energies in the free energies of the Pd-rich PdX alloys, are determined uniquely and successively from the lower-order to higher-order, by the full-potential Korringa-Kohn-Rostoker Green’s function method (FPKKR), combined with the generalized gradient approximation in the density functional theory. We found that the temperature dependence of IEs due to the FD distribution, being usually neglected, is very important to reproduce the X-concentration dependence of the observed solvus temperatures of the Pd-rich PdX (X=Ru, Rh) alloys.
Abstract: Acoustic sensors are extensively used in recent days not only for sensing and condition monitoring applications but also for small scale energy harvesting applications to power wireless sensor networks (WSN) due to their inherent advantages. The natural frequency of the structure plays a major role in energy harvesting applications since the sensor key element has to operate at resonant frequency. In this paper, circular diaphragm based MEMS acoustic sensor is modelled by Lumped Element Model (LEM) and the natural frequency is compared with the simulated model using Finite Element Method (FEM) tool COMSOL Multiphysics. The sensor has the circular diaphragm of 3000 µm radius and thickness of 30 µm to withstand the high SPL (Sound Pressure Level) and also to withstand the various fabrication steps. A Piezoelectric ZnO layer of thickness of 1 µm sandwiched between two aluminium electrodes of thickness 0.5 µm and is coated on the diaphragm. Further, a channel with radius 3000 µm radius and length 270 µm is connected at the bottom of the diaphragm. The natural frequency of the structure by LEM method is approximately 16.6 kHz which is closely matching with that of simulated structure with suitable approximations.
Abstract: In this paper, a dynamic and power efficient 8-bit and 100-MSPS Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) is presented. The circuit uses a non-differential capacitive Digital-to-Analog (DAC) architecture segmented by 2. The prototype is produced in a commercial 65-nm 1P7M CMOS technology with 1.2-V supply voltage. The size of the core ADC is 208.6 x 103.6 µm2. The post-layout noise simulation results feature a SNR of 46.9 dB at Nyquist frequency, which means an effective number of bit (ENOB) of 7.5-b. The total power consumption of this SAR ADC is only 1.55 mW at 100-MSPS. It achieves then a figure of merit of 85.6 fJ/step.
Abstract: Transportation Problem (TP) is based on supply and demand of commodities transported from one source to the different destinations. Usual methods for finding solution of TPs are North-West Corner Rule, Least Cost Method Vogel’s Approximation Method etc. The transportation costs tend to vary at each time. We can use fuzzy numbers which would give solution according to this situation. In this study the Best Candidate Method (BCM) is applied. For ranking Centroid Ranking Technique (CRT) and Robust Ranking Technique have been adopted to transform the fuzzy TP and the above methods are applied to EDWARDS Vacuum Company, Crawley, in West Sussex in the United Kingdom. A Comparative study is also given. We see that the transportation cost can be minimized by the application of CRT under BCM.
Abstract: In the last years, the crashworthiness of an automotive body structure can be improved, since the beginning of the design stage, thanks to the development of specific optimization tools. It is well known how the finite element codes can help the designer to investigate the crashing performance of structures under dynamic impact. Therefore, by coupling nonlinear mathematical programming procedure and statistical techniques with FE simulations, it is possible to optimize the design with reduced number of analytical evaluations. In engineering applications, many optimization methods which are based on statistical techniques and utilize estimated models, called meta-models, are quickly spreading. A meta-model is an approximation of a detailed simulation model based on a dataset of input, identified by the design of experiments (DOE); the number of simulations needed to build it depends on the number of variables. Among the various types of meta-modeling techniques, Kriging method seems to be excellent in accuracy, robustness and efficiency compared to other ones when applied to crashworthiness optimization. Therefore the application of such meta-model was used in this work, in order to improve the structural optimization of a bumper for a racing car in composite material subjected to frontal impact. The specific energy absorption represents the objective function to maximize and the geometrical parameters subjected to some design constraints are the design variables. LS-DYNA codes were interfaced with LS-OPT tool in order to find the optimized solution, through the use of a domain reduction strategy. With the use of the Kriging meta-model the crashworthiness characteristic of the composite bumper was improved.
Abstract: This paper illustrates an application of granular computing approach, namely rough set theory in data mining. The paper outlines the formalism of granular computing and elucidates the mathematical underpinning of rough set theory, which has been widely used by the data mining and the machine learning community. A real-world application is illustrated, and the classification performance is compared with other contending machine learning algorithms. The predictive performance of the rough set rule induction model shows comparative success with respect to other contending algorithms.
Abstract: Some properties of approximation sets are studied in multi-granulation optimist model in rough set theory using maximal compatible classes. The relationships between or among lower and upper approximations in single and multiple granulation are compared and discussed. Through designing Boolean functions and discernibility matrices in incomplete information systems, the lower and upper approximation sets and reduction in multi-granulation environments can be found. By using examples, the correctness of computation approach is consolidated. The related conclusions obtained are suitable for further investigating in multiple granulation RSM.
Abstract: We present and analyze reliable numerical techniques
for simulating complex flow and transport phenomena related to
natural gas transportation in pipelines. Such kind of problems
are of high interest in the field of petroleum and environmental
engineering. Modeling and understanding natural gas flow and
transformation processes during transportation is important for the
sake of physical realism and the design and operation of pipeline
systems. In our approach a two fluid flow model based on a system
of coupled hyperbolic conservation laws is considered for describing
natural gas flow undergoing hydratization. The accurate numerical
approximation of two-phase gas flow remains subject of strong
interest in the scientific community. Such hyperbolic problems are
characterized by solutions with steep gradients or discontinuities, and
their approximation by standard finite element techniques typically
gives rise to spurious oscillations and numerical artefacts. Recently,
stabilized and discontinuous Galerkin finite element techniques
have attracted researchers’ interest. They are highly adapted to the
hyperbolic nature of our two-phase flow model. In the presentation
a streamline upwind Petrov-Galerkin approach and a discontinuous
Galerkin finite element method for the numerical approximation of
our flow model of two coupled systems of Euler equations are
presented. Then the efficiency and reliability of stabilized continuous
and discontinous finite element methods for the approximation is
carefully analyzed and the potential of the either classes of numerical
schemes is investigated. In particular, standard benchmark problems
of two-phase flow like the shock tube problem are used for the
comparative numerical study.
Abstract: In accordance with the industry 4.0 concept, manufacturing process steps as well as the materials themselves are going to be more and more digitalized within the next years. The “digital twin” representing the simulated and measured dataset of the (semi-finished) product can be used to control and optimize the individual processing steps and help to reduce costs and expenditure of time in product development, manufacturing, and recycling. In the present work, two material characterization methods based on Lamb waves were evaluated and compared. For demonstration purpose, both methods were shown at a standard industrial product - copper ribbons, often used in photovoltaic modules as well as in high-current microelectronic devices. By numerical approximation of the Rayleigh-Lamb dispersion model on measured phase velocities second order elastic constants (Young’s modulus, Poisson’s ratio) were determined. Furthermore, the effective third order elastic constants were evaluated by applying elastic, “non-destructive”, mechanical stress on the samples. In this way, small microstructural variations due to mechanical preconditioning could be detected for the first time. Both methods were compared with respect to precision and inline application capabilities. Microstructure of the samples was systematically varied by mechanical loading and annealing. Changes in the elastic ultrasound transport properties were correlated with results from microstructural analysis and mechanical testing. In summary, monitoring the elastic material properties of plate-like structures using Lamb waves is valuable for inline and non-destructive material characterization and manufacturing process control. Second order elastic constants analysis is robust over wide environmental and sample conditions, whereas the effective third order elastic constants highly increase the sensitivity with respect to small microstructural changes. Both Lamb wave based characterization methods are fitting perfectly into the industry 4.0 concept.
Abstract: In this article, we will try to find an efficient boundary
approximation for the bi-objective location problem with coherent
coverage for two levels of hierarchy (CCLP). We present the
mathematical formulation of the model used. Supported efficient
solutions and unsupported efficient solutions are obtained by solving
the bi-objective combinatorial problem through the weights method
using a Lagrangean heuristic. Subsequently, the results are validated
through the DEA analysis with the GEM index (Global efficiency
measurement).