Abstract: This paper and its companion (Part 2) deal with
modeling and optimization of two NP-hard problems in production
planning of flexible manufacturing system (FMS), part type selection
problem and loading problem. The part type selection problem and
the loading problem are strongly related and heavily influence the
system-s efficiency and productivity. The complexity of the problems
is harder when flexibilities of operations such as the possibility of
operation processed on alternative machines with alternative tools are
considered. These problems have been modeled and solved
simultaneously by using real coded genetic algorithms (RCGA)
which uses an array of real numbers as chromosome representation.
These real numbers can be converted into part type sequence and
machines that are used to process the part types. This first part of the
papers focuses on the modeling of the problems and discussing how
the novel chromosome representation can be applied to solve the
problems. The second part will discuss the effectiveness of the
RCGA to solve various test bed problems.
Abstract: In this paper a numerical algorithm is described for solving the boundary value problem associated with axisymmetric, inviscid, incompressible, rotational (and irrotational) flow in order to obtain duct wall shapes from prescribed wall velocity distributions. The governing equations are formulated in terms of the stream function ψ (x,y)and the function φ (x,y)as independent variables where for irrotational flow φ (x,y)can be recognized as the velocity potential function, for rotational flow φ (x,y)ceases being the velocity potential function but does remain orthogonal to the stream lines. A numerical method based on the finite difference scheme on a uniform mesh is employed. The technique described is capable of tackling the so-called inverse problem where the velocity wall distributions are prescribed from which the duct wall shape is calculated, as well as the direct problem where the velocity distribution on the duct walls are calculated from prescribed duct geometries. The two different cases as outlined in this paper are in fact boundary value problems with Neumann and Dirichlet boundary conditions respectively. Even though both approaches are discussed, only numerical results for the case of the Dirichlet boundary conditions are given. A downstream condition is prescribed such that cylindrical flow, that is flow which is independent of the axial coordinate, exists.
Abstract: Granular computing deals with representation of information in the form of some aggregates and related methods for transformation and analysis for problem solving. A granulation scheme based on clustering and Rough Set Theory is presented with focus on structured conceptualization of information has been presented in this paper. Experiments for the proposed method on four labeled data exhibit good result with reference to classification problem. The proposed granulation technique is semi-supervised imbibing global as well as local information granulation. To represent the results of the attribute oriented granulation a tree structure is proposed in this paper.
Abstract: Proteins or genes that have similar sequences are likely to perform the same function. One of the most widely used techniques for sequence comparison is sequence alignment. Sequence alignment allows mismatches and insertion/deletion, which represents biological mutations. Sequence alignment is usually performed only on two sequences. Multiple sequence alignment, is a natural extension of two-sequence alignment. In multiple sequence alignment, the emphasis is to find optimal alignment for a group of sequences. Several applicable techniques were observed in this research, from traditional method such as dynamic programming to the extend of widely used stochastic optimization method such as Genetic Algorithms (GAs) and Simulated Annealing. A framework with combination of Genetic Algorithm and Simulated Annealing is presented to solve Multiple Sequence Alignment problem. The Genetic Algorithm phase will try to find new region of solution while Simulated Annealing can be considered as an alignment improver for any near optimal solution produced by GAs.
Abstract: This paper presents a comparative study of Ant Colony and Genetic Algorithms for VLSI circuit bi-partitioning. Ant colony optimization is an optimization method based on behaviour of social insects [27] whereas Genetic algorithm is an evolutionary optimization technique based on Darwinian Theory of natural evolution and its concept of survival of the fittest [19]. Both the methods are stochastic in nature and have been successfully applied to solve many Non Polynomial hard problems. Results obtained show that Genetic algorithms out perform Ant Colony optimization technique when tested on the VLSI circuit bi-partitioning problem.
Abstract: For the past one decade, biclustering has become popular data mining technique not only in the field of biological data analysis but also in other applications like text mining, market data analysis with high-dimensional two-way datasets. Biclustering clusters both rows and columns of a dataset simultaneously, as opposed to traditional clustering which clusters either rows or columns of a dataset. It retrieves subgroups of objects that are similar in one subgroup of variables and different in the remaining variables. Firefly Algorithm (FA) is a recently-proposed metaheuristic inspired by the collective behavior of fireflies. This paper provides a preliminary assessment of discrete version of FA (DFA) while coping with the task of mining coherent and large volume bicluster from web usage dataset. The experiments were conducted on two web usage datasets from public dataset repository whereby the performance of FA was compared with that exhibited by other population-based metaheuristic called binary Particle Swarm Optimization (PSO). The results achieved demonstrate the usefulness of DFA while tackling the biclustering problem.
Abstract: This paper focuses on the data-driven generation
of fuzzy IF...THEN rules. The resulted fuzzy rule base can be
applied to build a classifier, a model used for prediction, or
it can be applied to form a decision support system. Among
the wide range of possible approaches, the decision tree and
the association rule based algorithms are overviewed, and two
new approaches are presented based on the a priori fuzzy
clustering based partitioning of the continuous input variables.
An application study is also presented, where the developed
methods are tested on the well known Wisconsin Breast Cancer
classification problem.
Abstract: In order to apply partially filled steel grid composite deck as the horizontal supporting structure of various kinds of infrastructures, the variation of its flexural strength according to design parameters such as cross and longitudinal bars constituting the steel grid and the type of shear connection is evaluated and compared experimentally. The result shows that the design sensitivity of the deck to the spacing of the cross bars is insignificant in the case of structure with low risk of punching failure or without load distribution problem. By means of shear connection composed by transverse rebar and longitudinal bar without additional shear stud bolts, the complete interaction between steel grid and concrete slab is able to be achieved and the composite deck can develop its bending resistance capacity.
Abstract: This paper presents investigation effects of a sharp edged gust on aeroelastic behavior and time-domain response of a typical section model using Jones approximate aerodynamics for pure plunging motion. Flutter analysis has been done by using p and p-k methods developed for presented finite-state aerodynamic model for a typical section model (airfoil). Introduction of gust analysis as a linear set of ordinary differential equations in a simplified procedure has been carried out by using transformation into an eigenvalue problem.
Abstract: This paper presents a perturbation based search method
to solve the unconstrained binary quadratic programming problem.
The proposed algorithm was tested with some of the standard test
problems and the results are reported for 10 instances of 50, 100, 250,
& 500 variable problems. A comparison of the performance of the
proposed algorithm with other heuristics and optimization software is
made. Based on the results, it was found that the proposed algorithm
is computationally inexpensive and the solutions obtained match the
best known solutions for smaller sized problems. For larger instances,
the algorithm is capable of finding a solution within 0.11% of the
best known solution. Apart from being used as a stand-alone method,
this algorithm could also be incorporated with other heuristics to find
better solutions.
Abstract: In this paper a deterministic polynomial-time
algorithm is presented for the Clique problem. The case is considered
as the problem of omitting the minimum number of vertices from the
input graph so that none of the zeroes on the graph-s adjacency
matrix (except the main diagonal entries) would remain on the
adjacency matrix of the resulting subgraph. The existence of a
deterministic polynomial-time algorithm for the Clique problem, as
an NP-complete problem will prove the equality of P and NP
complexity classes.
Abstract: Predicting protein-protein interactions represent a key step in understanding proteins functions. This is due to the fact that proteins usually work in context of other proteins and rarely function alone. Machine learning techniques have been applied to predict protein-protein interactions. However, most of these techniques address this problem as a binary classification problem. Although it is easy to get a dataset of interacting proteins as positive examples, there are no experimentally confirmed non-interacting proteins to be considered as negative examples. Therefore, in this paper we solve this problem as a one-class classification problem using one-class support vector machines (SVM). Using only positive examples (interacting protein pairs) in training phase, the one-class SVM achieves accuracy of about 80%. These results imply that protein-protein interaction can be predicted using one-class classifier with comparable accuracy to the binary classifiers that use artificially constructed negative examples.
Abstract: In this paper, we develop quartic nonpolynomial
spline method for the numerical solution of third order two point
boundary value problems. It is shown that the new method gives
approximations, which are better than those produced by other spline
methods. Convergence analysis of the method is discussed through
standard procedures. Two numerical examples are given to illustrate
the applicability and efficiency of the novel method.
Abstract: This paper proposes a method which reduces power consumption in single-error correcting, double error-detecting checker circuits that perform memory error correction code. Power is minimized with little or no impact on area and delay, using the degrees of freedom in selecting the parity check matrix of the error correcting codes. The genetic algorithm is employed to solve the non linear power optimization problem. The method is applied to two commonly used SEC-DED codes: standard Hamming and odd column weight Hsiao codes. Experiments were performed to show the performance of the proposed method.
Abstract: A hybrid learning automata-genetic algorithm (HLGA) is proposed to solve QoS routing optimization problem of next generation networks. The algorithm complements the advantages of the learning Automato Algorithm(LA) and Genetic Algorithm(GA). It firstly uses the good global search capability of LA to generate initial population needed by GA, then it uses GA to improve the Quality of Service(QoS) and acquiring the optimization tree through new algorithms for crossover and mutation operators which are an NP-Complete problem. In the proposed algorithm, the connectivity matrix of edges is used for genotype representation. Some novel heuristics are also proposed for mutation, crossover, and creation of random individuals. We evaluate the performance and efficiency of the proposed HLGA-based algorithm in comparison with other existing heuristic and GA-based algorithms by the result of simulation. Simulation results demonstrate that this paper proposed algorithm not only has the fast calculating speed and high accuracy but also can improve the efficiency in Next Generation Networks QoS routing. The proposed algorithm has overcome all of the previous algorithms in the literature.
Abstract: Frequency domain independent component analysis has
a scaling indeterminacy and a permutation problem. The scaling
indeterminacy can be solved by use of a decomposed spectrum. For
the permutation problem, we have proposed the rules in terms of gain
ratio and phase difference derived from the decomposed spectra and
the source-s coarse directions.
The present paper experimentally clarifies that the gain ratio and
the phase difference work effectively in a real environment but their
performance depends on frequency bands, a microphone-space and
a source-microphone distance. From these facts it is seen that it is
difficult to attain a perfect solution for the permutation problem in a
real environment only by either the gain ratio or the phase difference.
For the perfect solution, this paper gives a solution to the problems
in a real environment. The proposed method is simple, the amount of
calculation is small. And the method has high correction performance
without depending on the frequency bands and distances from source
signals to microphones. Furthermore, it can be applied under the real
environment. From several experiments in a real room, it clarifies
that the proposed method has been verified.
Abstract: Cardiac pulse-related artifacts in the EEG recorded
simultaneously with fMRI are complex and highly variable. Their
effective removal is an unsolved problem. Our aim is to develop an
adaptive removal algorithm based on the matching pursuit (MP)
technique and to compare it to established methods using a visual
evoked potential (VEP). We recorded the VEP inside the static
magnetic field of an MR scanner (with artifacts) as well as in an
electrically shielded room (artifact free). The MP-based artifact
removal outperformed average artifact subtraction (AAS) and
optimal basis set removal (OBS) in terms of restoring the EEG field
map topography of the VEP. Subsequently, a dipole model was fitted
to the VEP under each condition using a realistic boundary element
head model. The source location of the VEP recorded inside the MR
scanner was closest to that of the artifact free VEP after cleaning
with the MP-based algorithm as well as with AAS. While none of the
tested algorithms offered complete removal, MP showed promising
results due to its ability to adapt to variations of latency, frequency
and amplitude of individual artifact occurrences while still utilizing a
common template.
Abstract: Security risk models have been successful in estimating the likelihood of attack for simple security threats. However, modeling complex system and their security risk is even a challenge. Many methods have been proposed to face this problem. Often difficult to manipulate, and not enough all-embracing they are not as famous as they should with administrators and deciders. We propose in this paper a new tool to model big systems on purpose. The software, takes into account attack threats and security strength.
Abstract: The protection of parallel transmission lines has been a challenging task due to mutual coupling between the adjacent circuits of the line. This paper presents a novel scheme for detection and classification of faults on parallel transmission lines. The proposed approach uses combination of wavelet transform and neural network, to solve the problem. While wavelet transform is a powerful mathematical tool which can be employed as a fast and very effective means of analyzing power system transient signals, artificial neural network has a ability to classify non-linear relationship between measured signals by identifying different patterns of the associated signals. The proposed algorithm consists of time-frequency analysis of fault generated transients using wavelet transform, followed by pattern recognition using artificial neural network to identify the type of the fault. MATLAB/Simulink is used to generate fault signals and verify the correctness of the algorithm. The adaptive discrimination scheme is tested by simulating different types of fault and varying fault resistance, fault location and fault inception time, on a given power system model. The simulation results show that the proposed scheme for fault diagnosis is able to classify all the faults on the parallel transmission line rapidly and correctly.
Abstract: In this paper, we investigate the solution of a two dimensional parabolic free boundary problem. The free boundary of this problem is modelled as a nonlinear integral equation (IE). For this integral equation, we propose an asymptotic solution as time is near to maturity and develop an integral iterative method. The computational results reveal that our asymptotic solution is very close to the numerical solution as time is near to maturity.