Abstract: Collaborative planning, forecasting and
replenishment (CPFR) coordinates the various supply chain
management activities including production and purchase planning,
demand forecasting and inventory replenishment between supply
chain trading partners. This study proposes a systematic way of
analyzing CPFR supporting factors using fuzzy cognitive map
(FCM) approach. FCMs have proven particularly useful for solving
problems in which a number of decision variables and
uncontrollable variables are causally interrelated. Hence the FCMs
of CPFR are created to show the relationships between the factors
that influence on effective implementation of CPFR in the supply
chain.
Abstract: Identification of cancer genes that might anticipate
the clinical behaviors from different types of cancer disease is
challenging due to the huge number of genes and small number of
patients samples. The new method is being proposed based on
supervised learning of classification like support vector machines
(SVMs).A new solution is described by the introduction of the
Maximized Margin (MM) in the subset criterion, which permits to
get near the least generalization error rate. In class prediction
problem, gene selection is essential to improve the accuracy and to
identify genes for cancer disease. The performance of the new
method was evaluated with real-world data experiment. It can give
the better accuracy for classification.
Abstract: Total weighted tardiness is a measure of customer
satisfaction. Minimizing it represents satisfying the general
requirement of on-time delivery. In this research, we consider an ant
colony optimization (ACO) algorithm to solve the problem of
scheduling unrelated parallel machines to minimize total weighted
tardiness. The problem is NP-hard in the strong sense. Computational
results show that the proposed ACO algorithm is giving promising
results compared to other existing algorithms.
Abstract: In this paper we consider a nonlinear feedback control called augmented automatic choosing control (AACC) for nonlinear systems with constrained input. Constant terms which arise from section wise linearization of a given nonlinear system are treated as coefficients of a stable zero dynamics.Parameters included in the control are suboptimally selectedby extremizing a combination of Hamiltonian and Lyapunov functions with the aid of the genetic algorithm. This approach is applied to a field excitation control problem of power system to demonstrate the splendidness of the AACC. Simulation results show that the new controller can improve performance remarkably well.
Abstract: Web-based systems have become increasingly
important due to the fact that the Internet and the World Wide Web
have become ubiquitous, surpassing all other technological
developments in our history. The Internet and especially companies
websites has rapidly evolved in their scope and extent of use, from
being a little more than fixed advertising material, i.e. a "web
presences", which had no particular influence for the company's
business, to being one of the most essential parts of the company's
core business.
Traditional software engineering approaches with process models
such as, for example, CMM and Waterfall models, do not work very
well since web system development differs from traditional
development. The development differs in several ways, for example,
there is a large gap between traditional software engineering designs
and concepts and the low-level implementation model, many of the
web based system development activities are business oriented (for
example web application are sales-oriented, web application and
intranets are content-oriented) and not engineering-oriented.
This paper aims to introduce Increment Iterative extreme
Programming (IIXP) methodology for developing web based
systems. In difference to the other existence methodologies, this
methodology is combination of different traditional and modern
software engineering and web engineering principles.
Abstract: In this paper two different Antilock braking system (ABS) are simulated and compared. One is the ordinary hydraulic ABS system which we call it ABS and the other is Electromagnetic Antilock braking system which is called (EMABS) the basis of performance of an EMABS is based upon Electromagnetic force. In this system there is no need to use servo hydraulic booster which are used in ABS system. In EMABS to generate the desired force we have use a magnetic relay which works with an input voltage through an air gap (g). The generated force will be amplified by the relay arm, and is applied to the brake shoes and thus the braking torque is generated. The braking torque is proportional to the applied electrical voltage E. to adjust the braking torque it is only necessary to regulate the electrical voltage E which is very faster and has a much smaller time constant T than the ABS system. The simulations of these two different ABS systems are done with MATLAB/SIMULINK software and the superiority of the EMABS has been shown.
Abstract: A novel application of neural network approach to
fault classification and fault location of Medium voltage cables is
demonstrated in this paper. Different faults on a protected cable
should be classified and located correctly. This paper presents the use
of neural networks as a pattern classifier algorithm to perform these
tasks. The proposed scheme is insensitive to variation of different
parameters such as fault type, fault resistance, and fault inception
angle. Studies show that the proposed technique is able to offer high
accuracy in both of the fault classification and fault location tasks.
Abstract: Prediction of sinusoidal signals with time-varying
frequencies has been an important research topic in power electronics
systems. To solve this problem, we propose a new fuzzy
predictive filtering scheme, which is based on a Finite Impulse
Response (FIR) filter bank. Fuzzy logic is introduced here to provide
appropriate interpolation of individual filter outputs. Therefore,
instead of regular 'hard' switching, our method has the advantageous
'soft' switching among different filters. Simulation
comparisons between the fuzzy predictive filtering and conventional
filter bank-based approach are made to demonstrate that the
new scheme can achieve an enhanced prediction performance for
slowly changing sinusoidal input signals.
Abstract: The problem of N cracks interaction in an isotropic
elastic solid is decomposed into a subproblem of a homogeneous solid
without crack and N subproblems with each having a single crack
subjected to unknown tractions on the two crack faces. The unknown
tractions, namely pseudo tractions on each crack are expanded into
polynomials with unknown coefficients, which have to be determined
by the consistency condition, i.e. by the equivalence of the original
multiple cracks interaction problem and the superposition of the N+1
subproblems. In this paper, Kachanov-s approach of average tractions
is extended into the method of moments to approximately impose the
consistence condition. Hence Kachanov-s method can be viewed as
the zero-order method of moments. Numerical results of the stress
intensity factors are presented for interactions of two collinear cracks,
three collinear cracks, two parallel cracks, and three parallel cracks.
As the order of moment increases, the accuracy of the method of
moments improves.
Abstract: This paper proposes the use of metrics in design space exploration that highlight where in the structure of the model and at what point in the behaviour, prevention is needed against transient faults. Previous approaches to tackle transient faults focused on recovery after detection. Almost no research has been directed towards preventive measures. But in real-time systems, hard deadlines are performance requirements that absolutely must be met and a missed deadline constitutes an erroneous action and a possible system failure. This paper proposes the use of metrics to assess the system design to flag where transient faults may have significant impact. These tools then allow the design to be changed to minimize that impact, and they also flag where particular design techniques – such as coding of communications or memories – need to be applied in later stages of design.
Abstract: This paper presents an optimal and unsupervised satellite image segmentation approach based on Pearson system and k-Means Clustering Algorithm Initialization. Such method could be considered as original by the fact that it utilised K-Means clustering algorithm for an optimal initialisation of image class number on one hand and it exploited Pearson system for an optimal statistical distributions- affectation of each considered class on the other hand. Satellite image exploitation requires the use of different approaches, especially those founded on the unsupervised statistical segmentation principle. Such approaches necessitate definition of several parameters like image class number, class variables- estimation and generalised mixture distributions. Use of statistical images- attributes assured convincing and promoting results under the condition of having an optimal initialisation step with appropriated statistical distributions- affectation. Pearson system associated with a k-means clustering algorithm and Stochastic Expectation-Maximization 'SEM' algorithm could be adapted to such problem. For each image-s class, Pearson system attributes one distribution type according to different parameters and especially the Skewness 'β1' and the kurtosis 'β2'. The different adapted algorithms, K-Means clustering algorithm, SEM algorithm and Pearson system algorithm, are then applied to satellite image segmentation problem. Efficiency of those combined algorithms was firstly validated with the Mean Quadratic Error 'MQE' evaluation, and secondly with visual inspection along several comparisons of these unsupervised images- segmentation.
Abstract: This paper summarizes and compares approaches to
solving the knapsack problem and its known application in capital
budgeting. The first approach uses deterministic methods and can be
applied to small-size tasks with a single constraint. We can also
apply commercial software systems such as the GAMS modelling
system. However, because of NP-completeness of the problem, more
complex problem instances must be solved by means of heuristic
techniques to achieve an approximation of the exact solution in a
reasonable amount of time. We show the problem representation and
parameter settings for a genetic algorithm framework.
Abstract: An effective approach for extracting document images from a noisy background is introduced. The entire scheme is divided into three sub- stechniques – the initial preprocessing operations for noise cluster tightening, introduction of a new thresholding method by maximizing the ratio of stan- dard deviations of the combined effect on the image to the sum of weighted classes and finally the image restoration phase by image binarization utiliz- ing the proposed optimum threshold level. The proposed method is found to be efficient compared to the existing schemes in terms of computational complexity as well as speed with better noise rejection.
Abstract: In the present study, computational fluid dynamics
(CFD) simulation has been executed to investigate the transition
boundaries of different flow patterns for moderately viscous oil-water
(viscosity ratio 107, density ratio 0.89 and interfacial tension of 0.032
N/m.) two-phase flow through a horizontal pipeline with internal
diameter and length of 0.025 m and 7.16 m respectively. Volume of
Fluid (VOF) approach including effect of surface tension has been
employed to predict the flow pattern. Geometry and meshing of the
present problem has been drawn using GAMBIT and ANSYS
FLUENT has been used for simulation. A total of 47037 quadrilateral
elements are chosen for the geometry of horizontal pipeline. The
computation has been performed by assuming unsteady flow,
immiscible liquid pair, constant liquid properties, co-axial flow and a
T-junction as entry section. The simulation correctly predicts the
transition boundaries of wavy stratified to stratified mixed flow.
Other transition boundaries are yet to be simulated. Simulated data
has been validated with our own experimental results.
Abstract: Attack graph is an integral part of modeling the
overview of network security. System administrators use attack graphs to determine how vulnerable their systems are and to determine
what security measures to deploy to defend their systems. Previous methods on AGG(attack graphs generation) are aiming at
the whole network, which makes the process of AGG complex and
non-scalable. In this paper, we propose a new approach which is
simple and scalable to AGG by decomposing the whole network into atomic domains. Each atomic domain represents a host with a specific privilege. Then the process for AGG is achieved by communications
among all the atomic domains. Our approach simplifies the process
of design for the whole network, and can gives the attack graphs including each attack path for each host, and when the network changes we just carry on the operations of corresponding atomic
domains which makes the process of AGG scalable.
Abstract: The migration of a deformable drop in simple shear
flow at finite Reynolds numbers is investigated numerically by
solving the full Navier-Stokes equations using a finite
difference/front tracking method. The objectives of this study are to
examine the effectiveness of the present approach to predict the
migration of a drop in a shear flow and to investigate the behavior of
the drop migration with different drop sizes and non-unity viscosity
ratios. It is shown that the drop deformation depends strongly on the
capillary number, so that; the proper non-dimensional number for the
interfacial tension is the capillary number. The rate of migration
increased with increasing the drop radius. In other words, the
required time for drop migration to the centreline decreases. As the
viscosity ratio increases, the drop rotates more slowly and the
lubrication force becomes stronger. The increased lubrication force
makes it easier for the drop to migrate to the centre of the channel.
The migration velocity of the drop vanishes as the drop reaches the
centreline under viscosity ratio of one and non-unity viscosity ratios.
To validate the present calculations, some typical results are
compared with available experimental and theoretical data.
Abstract: In this paper, self-starting block hybrid method of
order (5,5,5,5)T is proposed for the solution of the special second
order ordinary differential equations with associated initial or
boundary conditions. The continuous hybrid formulations enable us
to differentiate and evaluate at some grids and off – grid points to
obtain four discrete schemes, which were used in block form for
parallel or sequential solutions of the problems. The computational
burden and computer time wastage involved in the usual reduction of
second order problem into system of first order equations are avoided
by this approach. Furthermore, a stability analysis and efficiency of
the block method are tested on stiff ordinary differential equations,
and the results obtained compared favorably with the exact solution.
Abstract: Urban problems are problems of organized complexity. Thus, many models and scientific methods to resolve urban problems are failed. This study is concerned with proposing of a fuzzy system driven approach for classification and solving urban problems. The proposed study investigated mainly the selection of the inputs and outputs of urban systems for classification of urban problems. In this research, five categories of urban problems, respect to fuzzy system approach had been recognized: control, polytely, optimizing, open and decision making problems. Grounded Theory techniques were then applied to analyze the data and develop new solving method for each category. The findings indicate that the fuzzy system methods are powerful processes and analytic tools for helping planners to resolve urban complex problems. These tools can be successful where as others have failed because both incorporate or address uncertainty and risk; complexity and systems interacting with other systems.
Abstract: In this paper we discuss the development of an Augmented Reality (AR) - based scientific visualization system prototype that supports identification, localisation, and 3D visualisation of oil leakages sensors datasets. Sensors generates significant amount of multivariate datasets during normal and leak situations. Therefore we have developed a data model to effectively manage such data and enhance the computational support needed for the effective data explorations. A challenge of this approach is to reduce the data inefficiency powered by the disparate, repeated, inconsistent and missing attributes of most available sensors datasets. To handle this challenge, this paper aim to develop an AR-based scientific visualization interface which automatically identifies, localise and visualizes all necessary data relevant to a particularly selected region of interest (ROI) along the virtual pipeline network. Necessary system architectural supports needed as well as the interface requirements for such visualizations are also discussed in this paper.
Abstract: Behavior of dams against the seismic loads has been
studied by many researchers. Most of them proposed new numerical
methods to investigate the dam safety. In this paper, to study the
effect of nonlinear parameters of concrete in gravity dams, a twodimensional
approach was used including the finite element method,
staggered method and smeared crack approach. Effective parameters
in the models are physical properties of concrete such as modulus of
elasticity, tensile strength and specific fracture energy. Two different
models were used in foundation (mass-less and massed) in order to
determine the seismic response of concrete gravity dams. Results
show that when the nonlinear analysis includes the dam- foundation
interaction, the foundation-s mass, flexibility and radiation damping
are important in gravity dam-s response.