Abstract: An experimental and simulation flight test has been carried out to evaluate the longitudinal gliding characteristics of a lifting body with blunted half-cone geometry. The novelty here is the lifting body's pitch control mechanism, which consists of a pair of leading-edge rotating cylinders. Flight simulation uses aerodynamic data from computational fluid dynamics supported by wind-tunnel test. Flight test consists of releasing an aluminum lifting body model from a moving vehicle at the appropriate wind speed while measuring the lifting body's variation of altitude against time of flight. Results show that leading-edge rotating cylinder is able to give small amounts of improvement to the longitudinal stability and pitch control to the lifting body.
Abstract: In this paper a unified approach via block-pulse functions (BPFs) or shifted Legendre polynomials (SLPs) is presented to solve the linear-quadratic-Gaussian (LQG) control problem. Also a recursive algorithm is proposed to solve the above problem via BPFs. By using the elegant operational properties of orthogonal functions (BPFs or SLPs) these computationally attractive algorithms are developed. To demonstrate the validity of the proposed approaches a numerical example is included.
Abstract: High Voltage (HV) transmission lines are widely
spread around residential places. They take all forms of shapes:
concrete, steel, and timber poles. Earth grid always form part of the
HV transmission structure, whereat soil resistivity value is one of the
main inputs when it comes to determining the earth grid
requirements. In this paper, the soil structure and its implication on
the electrode resistance of HV transmission poles will be explored. In
Addition, this paper will present simulation for various soil structures
using IEEE and Australian standards to verify the computation with
CDEGS software. Furthermore, the split factor behavior under
different soil resistivity structure will be presented using CDEGS
simulations.
Abstract: The problem of ranking (rank regression) has become popular in the machine learning community. This theory relates to problems, in which one has to predict (guess) the order between objects on the basis of vectors describing their observed features. In many ranking algorithms a convex loss function is used instead of the 0-1 loss. It makes these procedures computationally efficient. Hence, convex risk minimizers and their statistical properties are investigated in this paper. Fast rates of convergence are obtained under conditions, that look similarly to the ones from the classification theory. Methods used in this paper come from the theory of U-processes as well as empirical processes.
Abstract: This paper presents a integer frequency offset (IFO)
estimation scheme for the 3GPP long term evolution (LTE) downlink
system. Firstly, the conventional joint detection method for IFO and
sector cell index (CID) information is introduced. Secondly, an IFO
estimation without explicit sector CID information is proposed, which
can operate jointly with the proposed IFO estimation and reduce
the time delay in comparison with the conventional joint method.
Also, the proposed method is computationally efficient and has almost
similar performance in comparison with the conventional method over
the Pedestrian and Vehicular channel models.
Abstract: In this paper, a new learning approach for network
intrusion detection using naïve Bayesian classifier and ID3 algorithm
is presented, which identifies effective attributes from the training
dataset, calculates the conditional probabilities for the best attribute
values, and then correctly classifies all the examples of training and
testing dataset. Most of the current intrusion detection datasets are
dynamic, complex and contain large number of attributes. Some of
the attributes may be redundant or contribute little for detection
making. It has been successfully tested that significant attribute
selection is important to design a real world intrusion detection
systems (IDS). The purpose of this study is to identify effective
attributes from the training dataset to build a classifier for network
intrusion detection using data mining algorithms. The experimental
results on KDD99 benchmark intrusion detection dataset demonstrate
that this new approach achieves high classification rates and reduce
false positives using limited computational resources.
Abstract: In a previous work, we presented the numerical
solution of the two dimensional second order telegraph partial
differential equation discretized by the centred and rotated five-point
finite difference discretizations, namely the explicit group (EG) and
explicit decoupled group (EDG) iterative methods, respectively. In
this paper, we utilize a domain decomposition algorithm on these
group schemes to divide the tasks involved in solving the same
equation. The objective of this study is to describe the development
of the parallel group iterative schemes under OpenMP programming
environment as a way to reduce the computational costs of the
solution processes using multicore technologies. A detailed
performance analysis of the parallel implementations of points and
group iterative schemes will be reported and discussed.
Abstract: The identification and elimination of bad
measurements is one of the basic functions of a robust state estimator
as bad data have the effect of corrupting the results of state
estimation according to the popular weighted least squares method.
However this is a difficult problem to handle especially when dealing
with multiple errors from the interactive conforming type. In this
paper, a self adaptive genetic based algorithm is proposed. The
algorithm utilizes the results of the classical linearized normal
residuals approach to tune the genetic operators thus instead of
making a randomized search throughout the whole search space it is
more likely to be a directed search thus the optimum solution is
obtained at very early stages(maximum of 5 generations). The
algorithm utilizes the accumulating databases of already computed
cases to reduce the computational burden to minimum. Tests are
conducted with reference to the standard IEEE test systems. Test
results are very promising.
Abstract: We consider linear regression models where both input data (the values of independent variables) and output data (the observations of the dependent variable) are interval-censored. We introduce a possibilistic generalization of the least squares estimator, so called OLS-set for the interval model. This set captures the impact of the loss of information on the OLS estimator caused by interval censoring and provides a tool for quantification of this effect. We study complexity-theoretic properties of the OLS-set. We also deal with restricted versions of the general interval linear regression model, in particular the crisp input – interval output model. We give an argument that natural descriptions of the OLS-set in the crisp input – interval output cannot be computed in polynomial time. Then we derive easily computable approximations for the OLS-set which can be used instead of the exact description. We illustrate the approach by an example.
Abstract: Artificial Immune System is adopted as a Heuristic
Algorithm to solve the combinatorial problems for decades.
Nevertheless, many of these applications took advantage of the benefit
for applications but seldom proposed approaches for enhancing the
efficiency. In this paper, we continue the previous research to develop
a Self-evolving Artificial Immune System II via coordinating the T
and B cell in Immune System and built a block-based artificial
chromosome for speeding up the computation time and better
performance for different complexities of problems. Through the
design of Plasma cell and clonal selection which are relative the
function of the Immune Response. The Immune Response will help
the AIS have the global and local searching ability and preventing
trapped in local optima. From the experimental result, the significant
performance validates the SEAIS II is effective when solving the
permutation flows-hop problems.
Abstract: Cosmic showers, during the transit through space, produce
sub - products as a result of interactions with the intergalactic
or interstellar medium which after entering earth generate secondary
particles called Extensive Air Shower (EAS). Detection and analysis
of High Energy Particle Showers involve a plethora of theoretical and
experimental works with a host of constraints resulting in inaccuracies
in measurements. Therefore, there exist a necessity to develop a
readily available system based on soft-computational approaches
which can be used for EAS analysis. This is due to the fact that soft
computational tools such as Artificial Neural Network (ANN)s can be
trained as classifiers to adapt and learn the surrounding variations. But
single classifiers fail to reach optimality of decision making in many
situations for which Multiple Classifier System (MCS) are preferred
to enhance the ability of the system to make decisions adjusting
to finer variations. This work describes the formation of an MCS
using Multi Layer Perceptron (MLP), Recurrent Neural Network
(RNN) and Probabilistic Neural Network (PNN) with data inputs
from correlation mapping Self Organizing Map (SOM) blocks and
the output optimized by another SOM. The results show that the setup
can be adopted for real time practical applications for prediction
of primary energy and location of EAS from density values captured
using detectors in a circular grid.
Abstract: In Image processing the Image compression can improve
the performance of the digital systems by reducing the cost and
time in image storage and transmission without significant reduction
of the Image quality. This paper describes hardware architecture of
low complexity Discrete Cosine Transform (DCT) architecture for
image compression[6]. In this DCT architecture, common computations
are identified and shared to remove redundant computations
in DCT matrix operation. Vector processing is a method used for
implementation of DCT. This reduction in computational complexity
of 2D DCT reduces power consumption. The 2D DCT is performed
on 8x8 matrix using two 1-Dimensional Discrete cosine transform
blocks and a transposition memory [7]. Inverse discrete cosine
transform (IDCT) is performed to obtain the image matrix and
reconstruct the original image. The proposed image compression
algorithm is comprehended using MATLAB code. The VLSI design
of the architecture is implemented Using Verilog HDL. The proposed
hardware architecture for image compression employing DCT was
synthesized using RTL complier and it was mapped using 180nm
standard cells. . The Simulation is done using Modelsim. The
simulation results from MATLAB and Verilog HDL are compared.
Detailed analysis for power and area was done using RTL compiler
from CADENCE. Power consumption of DCT core is reduced to
1.027mW with minimum area[1].
Abstract: We consider a network of two M/M/1 parallel queues having the same poisonnian arrival stream with rate λ. Upon his arrival to the system a customer heads to the shortest queue and stays until being served. If the two queues have the same length, an arriving customer chooses one of the two queues with the same probability. Each duration of service in the two queues is an exponential random variable with rate μ and no jockeying is permitted between the two queues. A new numerical method, based on linear programming and convex optimization, is performed for the computation of the steady state solution of the system.
Abstract: A new approach based on the consideration that electroencephalogram (EEG) signals are chaotic signals was presented for automated diagnosis of electroencephalographic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. This paper presented the usage of statistics over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents of the EEG signals were used as inputs of the MLPNN trained with Levenberg- Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.
Abstract: The implicit block methods based on the backward
differentiation formulae (BDF) for the solution of stiff initial value
problems (IVPs) using variable step size is derived. We construct a
variable step size block methods which will store all the coefficients
of the method with a simplified strategy in controlling the step size
with the intention of optimizing the performance in terms of
precision and computation time. The strategy involves constant,
halving or increasing the step size by 1.9 times the previous step size.
Decision of changing the step size is determined by the local
truncation error (LTE). Numerical results are provided to support the
enhancement of method applied.
Abstract: The effects of dynamic subgrid scale (SGS) models are
investigated in variational multiscale (VMS) LES simulations of bluff
body flows. The spatial discretization is based on a mixed finite
element/finite volume formulation on unstructured grids. In the VMS
approach used in this work, the separation between the largest and the
smallest resolved scales is obtained through a variational projection
operator and a finite volume cell agglomeration. The dynamic version
of Smagorinsky and WALE SGS models are used to account for
the effects of the unresolved scales. In the VMS approach, these
effects are only modeled in the smallest resolved scales. The dynamic
VMS-LES approach is applied to the simulation of the flow around a
circular cylinder at Reynolds numbers 3900 and 20000 and to the flow
around a square cylinder at Reynolds numbers 22000 and 175000. It
is observed as in previous studies that the dynamic SGS procedure
has a smaller impact on the results within the VMS approach than in
LES. But improvements are demonstrated for important feature like
recirculating part of the flow. The global prediction is improved for
a small computational extra cost.
Abstract: This paper makes an attempt to solve the problem of
searching and retrieving of similar MRI photos via Internet services
using morphological features which are sourced via the original
image. This study is aiming to be considered as an additional tool of
searching and retrieve methods. Until now the main way of the
searching mechanism is based on the syntactic way using keywords.
The technique it proposes aims to serve the new requirements of
libraries. One of these is the development of computational tools for
the control and preservation of the intellectual property of digital
objects, and especially of digital images. For this purpose, this paper
proposes the use of a serial number extracted by using a previously
tested semantic properties method. This method, with its center being
the multi-layers of a set of arithmetic points, assures the following
two properties: the uniqueness of the final extracted number and the
semantic dependence of this number on the image used as the
method-s input. The major advantage of this method is that it can
control the authentication of a published image or its partial
modification to a reliable degree. Also, it acquires the better of the
known Hash functions that the digital signature schemes use and
produces alphanumeric strings for cases of authentication checking,
and the degree of similarity between an unknown image and an
original image.
Abstract: This paper presents a novel approach for optimal
reconfiguration of radial distribution systems. Optimal
reconfiguration involves the selection of the best set of branches to
be opened, one each from each loop, such that the resulting radial
distribution system gets the desired performance. In this paper an
algorithm is proposed based on simple heuristic rules and identified
an effective switch status configuration of distribution system for the
minimum loss reduction. This proposed algorithm consists of two
parts; one is to determine the best switching combinations in all loops
with minimum computational effort and the other is simple optimum
power loss calculation of the best switching combination found in
part one by load flows. To demonstrate the validity of the proposed
algorithm, computer simulations are carried out on 33-bus system.
The results show that the performance of the proposed method is
better than that of the other methods.
Abstract: In theoretical computer science, the Turing machine has played a number of important roles in understanding and exploiting basic concepts and mechanisms in computing and information processing [20]. It is a simple mathematical model of computers [9]. After that, M.Blum and C.Hewitt first proposed two-dimensional automata as a computational model of two-dimensional pattern processing, and investigated their pattern recognition abilities in 1967 [7]. Since then, a lot of researchers in this field have been investigating many properties about automata on a two- or three-dimensional tape. On the other hand, the question of whether processing fourdimensional digital patterns is much more difficult than two- or threedimensional ones is of great interest from the theoretical and practical standpoints. Thus, the study of four-dimensional automata as a computasional model of four-dimensional pattern processing has been meaningful [8]-[19],[21]. This paper introduces a cooperating system of four-dimensional finite automata as one model of four-dimensional automata. A cooperating system of four-dimensional finite automata consists of a finite number of four-dimensional finite automata and a four-dimensional input tape where these finite automata work independently (in parallel). Those finite automata whose input heads scan the same cell of the input tape can communicate with each other, that is, every finite automaton is allowed to know the internal states of other finite automata on the same cell it is scanning at the moment. In this paper, we mainly investigate some accepting powers of a cooperating system of eight- or seven-way four-dimensional finite automata. The seven-way four-dimensional finite automaton is an eight-way four-dimensional finite automaton whose input head can move east, west, south, north, up, down, or in the fu-ture, but not in the past on a four-dimensional input tape.