Abstract: New physical insights into the nonlinear Lorenz
equations related to flow resistance is discussed in this work. The
chaotic dynamics related to Lorenz equations has been studied in
many papers, which is due to the sensitivity of Lorenz equations to
initial conditions and parameter uncertainties. However, the physical
implication arising from Lorenz equations about convectional motion
attracts little attention in the relevant literature. Therefore, as a first
step to understand the related fluid mechanics of convectional motion,
this paper derives the Lorenz equations again with different forced
conditions in the model. Simulation work of the modified Lorenz
equations without the viscosity or buoyancy force is discussed. The
time-domain simulation results may imply that the states of the
Lorenz equations are related to certain flow speed and flow resistance.
The flow speed of the underlying fluid system increases as the flow
resistance reduces. This observation would be helpful to analyze the
coupling effects of different fluid parameters in a convectional model
in future work.
Abstract: Over the past few years, the online multimedia
collection has grown at a fast pace. Several companies showed
interest to study the different ways to organise the amount of audio
information without the need of human intervention to generate
metadata. In the past few years, many applications have emerged on
the market which are capable of identifying a piece of music in a
short time. Different audio effects and degradation make it much
harder to identify the unknown piece. In this paper, an audio
fingerprinting system which makes use of a non-parametric based
algorithm is presented. Parametric analysis is also performed using
Gaussian Mixture Models (GMMs). The feature extraction methods
employed are the Mel Spectrum Coefficients and the MPEG-7 basic
descriptors. Bin numbers replaced the extracted feature coefficients
during the non-parametric modelling. The results show that nonparametric
analysis offer potential results as the ones mentioned in
the literature.
Abstract: Standard processes, similar and limited production
lines, the production of high direct costs will be more accurate than
the use of parts of the traditional cost systems in the literature.
However, direct costs, overhead expenses, in turn, decrease the
burden of increasingly sophisticated production facilities, a situation
that led the researchers to look for the cost of traditional systems of
alternative techniques. Variety cost management approaches for
example Total quality management (TQM), just-in-time (JIT),
benchmarking, kaizen costing, targeting cost, life cycle costs (LLC),
activity-based costing (ABC) value engineering have been
introduced. Management and cost applications have changed over the
past decade and will continue to change. Modern cost systems can
provide relevant and accurate cost information. These methods
provide the decisions about customer, product and process
improvement. The aim of study is to describe and explain the
adoption and application of costing systems in SME. This purpose
reports on a survey conducted during 2014 small and medium sized
enterprises (SME) in Ankara. The survey results were evaluated
using SPSS18 package program.
Abstract: The Cone Penetration Test (CPT) is a common in-situ
test which generally investigates a much greater volume of soil more
quickly than possible from sampling and laboratory tests. Therefore,
it has the potential to realize both cost savings and assessment of soil
properties rapidly and continuously. The principle objective of this
paper is to demonstrate the feasibility and efficiency of using
artificial neural networks (ANNs) to predict the soil angle of internal
friction (Φ) and the soil modulus of elasticity (E) from CPT results
considering the uncertainties and non-linearities of the soil. In
addition, ANNs are used to study the influence of different
parameters and recommend which parameters should be included as
input parameters to improve the prediction. Neural networks discover
relationships in the input data sets through the iterative presentation
of the data and intrinsic mapping characteristics of neural topologies.
General Regression Neural Network (GRNN) is one of the powerful
neural network architectures which is utilized in this study. A large
amount of field and experimental data including CPT results, plate
load tests, direct shear box, grain size distribution and calculated data
of overburden pressure was obtained from a large project in the
United Arab Emirates. This data was used for the training and the
validation of the neural network. A comparison was made between
the obtained results from the ANN's approach, and some common
traditional correlations that predict Φ and E from CPT results with
respect to the actual results of the collected data. The results show
that the ANN is a very powerful tool. Very good agreement was
obtained between estimated results from ANN and actual measured
results with comparison to other correlations available in the
literature. The study recommends some easily available parameters
that should be included in the estimation of the soil properties to
improve the prediction models. It is shown that the use of friction
ration in the estimation of Φ and the use of fines content in the
estimation of E considerable improve the prediction models.
Abstract: The goal of image segmentation is to cluster pixels
into salient image regions. Segmentation could be used for object
recognition, occlusion boundary estimation within motion or stereo
systems, image compression, image editing, or image database lookup.
In this paper, we present a color image segmentation using
support vector machine (SVM) pixel classification. Firstly, the pixel
level color and texture features of the image are extracted and they
are used as input to the SVM classifier. These features are extracted
using the homogeneity model and Gabor Filter. With the extracted
pixel level features, the SVM Classifier is trained by using FCM
(Fuzzy C-Means).The image segmentation takes the advantage of
both the pixel level information of the image and also the ability of
the SVM Classifier. The Experiments show that the proposed method
has a very good segmentation result and a better efficiency, increases
the quality of the image segmentation compared with the other
segmentation methods proposed in the literature.
Abstract: This paper presents an evolutionary algorithm for
solving multi-objective optimization problems-based artificial neural
network (ANN). The multi-objective evolutionary algorithm used in
this study is genetic algorithm while ANN used is radial basis
function network (RBFN). The proposed algorithm named memetic
elitist Pareto non-dominated sorting genetic algorithm-based RBFN
(MEPGAN). The proposed algorithm is implemented on medical
diseases problems. The experimental results indicate that the
proposed algorithm is viable, and provides an effective means to
design multi-objective RBFNs with good generalization capability
and compact network structure. This study shows that MEPGAN
generates RBFNs coming with an appropriate balance between
accuracy and simplicity, comparing to the other algorithms found in
literature.
Abstract: The thermal conductivity of a fluid can be
significantly enhanced by dispersing nano-sized particles in it, and
the resultant fluid is termed as "nanofluid". A theoretical model for
estimating the thermal conductivity of a nanofluid has been proposed
here. It is based on the mechanism that evenly dispersed
nanoparticles within a nanofluid undergo Brownian motion in course
of which the nanoparticles repeatedly collide with the heat source.
During each collision a rapid heat transfer occurs owing to the solidsolid
contact. Molecular dynamics (MD) simulation of the collision
of nanoparticles with the heat source has shown that there is a pulselike
pick up of heat by the nanoparticles within 20-100 ps, the extent
of which depends not only on thermal conductivity of the
nanoparticles, but also on the elastic and other physical properties of
the nanoparticle. After the collision the nanoparticles undergo
Brownian motion in the base fluid and release the excess heat to the
surrounding base fluid within 2-10 ms. The Brownian motion and
associated temperature variation of the nanoparticles have been
modeled by stochastic analysis. Repeated occurrence of these events
by the suspended nanoparticles significantly contributes to the
characteristic thermal conductivity of the nanofluids, which has been
estimated by the present model for a ethylene glycol based nanofluid
containing Cu-nanoparticles of size ranging from 8 to 20 nm, with
Gaussian size distribution. The prediction of the present model has
shown a reasonable agreement with the experimental data available
in literature.
Abstract: The main purpose of this research is to
comprehensively explore and identify the problems of attestation of
the public servants and to propose solutions for these issues through
deeply analyzing laws and the legal theoretical literature. For the
detailed analysis of the above-mentioned problems we will use some
research methods, the implementation of which has a goal to ensure
the objectivity and clarity of scientific research and its results.
Abstract: In today’s highly competitive, dynamic and
technology driven business circumstances, marketers are under
steady pressure to deliver the best. Organizations are continuously
improving and upgrading themselves to meet customer expectations
and demands. Technology has not only changed the way in which
business is done in modern times but has also transformed the way to
reach out to target audience. Marketers have identified most recent
media options to communicate and convince potential customers.
Numerous scholars have studied the research domain of advertising
and have tried to recognize different measures of advertisement
effectiveness in context of various media. The objective of this paper
is to critically review accessible literature on advertisement
effectiveness in context of varied advertising media, recognize major
gaps in the literature and identify future research prospects on the
basis of critical analysis of literature.
Abstract: In this paper, we propose a new packing strategy to
find a free resource for run-time mapping of application tasks to
NoC-based Heterogeneous MPSoC. The proposed strategy minimizes
the task mapping time in addition to placing the communicating tasks
close to each other. To evaluate our approach, a comparative study is
carried out for a platform containing single task supported PEs.
Experiments show that our strategy provides better results when
compared to latest dynamic mapping strategies reported in the
literature.
Abstract: This paper presents general results on the Java source
code snippet detection problem. We propose the tool which uses
graph and subgraph isomorphism detection. A number of solutions
for all of these tasks have been proposed in the literature. However,
although that all these solutions are really fast, they compare just the
constant static trees. Our solution offers to enter an input sample
dynamically with the Scripthon language while preserving an
acceptable speed. We used several optimizations to achieve very low
number of comparisons during the matching algorithm.
Abstract: The generalized wave equation models various
problems in sciences and engineering. In this paper, a new three-time
level implicit approach based on cubic trigonometric B-spline for the
approximate solution of wave equation is developed. The usual finite
difference approach is used to discretize the time derivative while
cubic trigonometric B-spline is applied as an interpolating function in
the space dimension. Von Neumann stability analysis is used to
analyze the proposed method. Two problems are discussed to exhibit
the feasibility and capability of the method. The absolute errors and
maximum error are computed to assess the performance of the
proposed method. The results were found to be in good agreement
with known solutions and with existing schemes in literature.
Abstract: This paper presents a new meta-heuristic bio-inspired
optimization algorithm which is called Cuttlefish Algorithm (CFA).
The algorithm mimics the mechanism of color changing behavior of
the cuttlefish to solve numerical global optimization problems. The
colors and patterns of the cuttlefish are produced by reflected light
from three different layers of cells. The proposed algorithm considers
mainly two processes: reflection and visibility. Reflection process
simulates light reflection mechanism used by these layers, while
visibility process simulates visibility of matching patterns of the
cuttlefish. To show the effectiveness of the algorithm, it is tested with
some other popular bio-inspired optimization algorithms such as
Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and
Bees Algorithm (BA) that have been previously proposed in the
literature. Simulations and obtained results indicate that the proposed
CFA is superior when compared with these algorithms.
Abstract: Tailor Welded Blanks (TWB) are utilized in automotive industries widely because of their advantage of weight and cost reduction and maintaining required strength and structural integrity. TWB consist of two or more sheet having dissimilar or similar material and thickness; welded together to form a single sheet before forming it to desired shape. Forming of the tailor welded blank is affected by ratio of thickness of blanks, ratio of their strength, etc. mainly due to in-homogeneity of material. In the present work the relative effect of these parameters on weld line movement is studied during deep drawing of TWB using FE simulation using HYPERWORKS. The simulation is validated with results from the literature. Simulations were than performed based on Taguchi orthogonal array followed by the ANOVA analysis to determine the significance of these parameters on forming of TWB.
Abstract: This paper presents a complete dynamic modeling
of a membrane distillation process. The model contains two
consistent dynamic models. A 2D advection-diffusion equation
for modeling the whole process and a modified heat equation
for modeling the membrane itself. The complete model describes
the temperature diffusion phenomenon across the feed, membrane,
permeate containers and boundary layers of the membrane. It gives
an online and complete temperature profile for each point in the
domain. It explains heat conduction and convection mechanisms that
take place inside the process in terms of mathematical parameters, and
justify process behavior during transient and steady state phases. The
process is monitored for any sudden change in the performance at any
instance of time. In addition, it assists maintaining production rates
as desired, and gives recommendations during membrane fabrication
stages. System performance and parameters can be optimized
and controlled using this complete dynamic model. Evolution of
membrane boundary temperature with time, vapor mass transfer along
the process, and temperature difference between membrane boundary
layers are depicted and included. Simulations were performed over
the complete model with real membrane specifications. The plots
show consistency between 2D advection-diffusion model and the
expected behavior of the systems as well as literature. Evolution
of heat inside the membrane starting from transient response till
reaching steady state response for fixed and varying times is
illustrated.
Abstract: HF Communication system is one of the attractive fields among many researchers since it can be reached long-distance areas with low-cost. This long-distance communication can be achieved by exploiting the ionosphere as a transmission medium for the HF radio wave. However, due to the dynamic nature of ionosphere, the channel characteristic of HF communication has to be investigated in order to gives better performances. Many techniques to characterize HF channel are available in the literature. However, none of those techniques describe the HF channel characteristic in low-latitude regions, especially equatorial areas. Since the ionosphere around equatorial region has an ESF phenomenon, it becomes an important investigation to characterize the wideband HF Channel in low-latitude region. On the other sides, the appearance of software-defined radio attracts the interest of many researchers. Accordingly, in this paper a SDR-based channel measurement system is proposed to be used for characterizing the HF channel in low-latitude region.
Abstract: The main purpose of this work is to experimentally investigate the effect of pipe orientation on two phase flow phenomenon. Flow pattern, void fraction and two phase pressure drop is measured in a polycarbonate pipe with an inside diameter of 12.7mm for inclination angles ranging from -20o to +20o using air-water fluid combination. The experimental data covers all flow patterns and the entire range of void fraction typically observed in two phase flow. The effect of pipe orientation on void fraction and two phase pressure drop is justified with reference to the change in flow structure and two phase flow behavior. In addition to this, the top performing void fraction and two phase pressure drop correlations available in the literature are presented and their performance is assessed against the experimental data in the present study and that available in the literature.
Abstract: Materials with ultrafine-grained structure and unique physical and mechanical properties can be obtained by methods of severe plastic deformation, which include processes of asymmetric rolling (AR). Asymmetric rolling is a very effective way to create ultrafine-grained structures of metals and alloys. Since the asymmetric rolling is a continuous process, it has great potential for industrial production of ultrafine-grained structure sheets. Basic principles of asymmetric rolling are described in detail in scientific literature. In this work finite element modeling of asymmetric rolling and metal forming processes in multiroll gauge was performed. Parameters of the processes which allow achieving significant values of shear strain were defined. The results of the study will be useful for the research of the evolution of ultra-fine metal structure in asymmetric rolling.
Abstract: The strength of reinforced concrete depends on the member dimensions and material properties. The properties of concrete and steel materials are not constant but random variables. The variability of concrete strength is due to batching errors, variations in mixing, cement quality uncertainties, differences in the degree of compaction and disparity in curing. Similarly, the variability of steel strength is attributed to the manufacturing process, rolling conditions, characteristics of base material, uncertainties in chemical composition, and the microstructure-property relationships. To account for such uncertainties, codes of practice for reinforced concrete design impose resistance factors to ensure structural reliability over the useful life of the structure. In this investigation, the effects of reductions in concrete and reinforcing steel strengths from the nominal values, beyond those accounted for in the structural design codes, on the structural reliability are assessed. The considered limit states are flexure, shear and axial compression based on the ACI 318-11 structural concrete building code. Structural safety is measured in terms of a reliability index. Probabilistic resistance and load models are compiled from the available literature. The study showed that there is a wide variation in the reliability index for reinforced concrete members designed for flexure, shear or axial compression, especially when the live-to-dead load ratio is low. Furthermore, variations in concrete strength have minor effect on the reliability of beams in flexure, moderate effect on the reliability of beams in shear, and sever effect on the reliability of columns in axial compression. On the other hand, changes in steel yield strength have great effect on the reliability of beams in flexure, moderate effect on the reliability of beams in shear, and mild effect on the reliability of columns in axial compression. Based on the outcome, it can be concluded that the reliability of beams is sensitive to changes in the yield strength of the steel reinforcement, whereas the reliability of columns is sensitive to variations in the concrete strength. Since the embedded target reliability in structural design codes results in lower structural safety in beams than in columns, large reductions in material strengths compromise the structural safety of beams much more than they affect columns.
Abstract: Sudoku is a logic-based combinatorial puzzle game which is popular among people of different ages. Due to this popularity, computer softwares are being developed to generate and solve Sudoku puzzles with different levels of difficulty. Several methods and algorithms have been proposed and used in different softwares to efficiently solve Sudoku puzzles. Various search methods such as stochastic local search have been applied to this problem. Genetic Algorithm (GA) is one of the algorithms which have been applied to this problem in different forms and in several works in the literature. In these works, chromosomes with little or no information were considered and obtained results were not promising. In this paper, we propose a new way of applying GA to this problem which uses more-informed chromosomes than other works in the literature. We optimize the parameters of our GA using puzzles with different levels of difficulty. Then we use the optimized values of the parameters to solve various puzzles and compare our results to another GA-based method for solving Sudoku puzzles.