Abstract: This paper proposes an efficient method for the design
of two channel quadrature mirror filter (QMF) bank. To achieve
minimum value of reconstruction error near to perfect reconstruction,
a linear optimization process has been proposed. Prototype low pass
filter has been designed using Kaiser window function. The modified
algorithm has been developed to optimize the reconstruction error
using linear objective function through iteration method. The result
obtained, show that the performance of the proposed algorithm is
better than that of the already exists methods.
Abstract: This paper describes a one-dimensional numerical model for natural gas production from the dissociation of methane hydrate in hydrate-capped gas reservoir under depressurization and thermal stimulation. Some of the hydrate reservoirs discovered are overlying a free-gas layer, known as hydrate-capped gas reservoirs. These reservoirs are thought to be easiest and probably the first type of hydrate reservoirs to be produced. The mathematical equations that can be described this type of reservoir include mass balance, heat balance and kinetics of hydrate decomposition. These non-linear partial differential equations are solved using finite-difference fully implicit scheme. In the model, the effect of convection and conduction heat transfer, variation change of formation porosity, the effect of using different equations of state such as PR and ER and steam or hot water injection are considered. In addition distributions of pressure, temperature, saturation of gas, hydrate and water in the reservoir are evaluated. It is shown that the gas production rate is a sensitive function of well pressure.
Abstract: This paper aims to establish a delayed dynamical relationship between payoffs of players in a zero-sum game. By introducing Markovian chain and time delay in the network model, a delayed game network model with sector bounds and slope bounds restriction nonlinear function is first proposed. As a result, a direct dynamical relationship between payoffs of players in a zero-sum game can be illustrated through a delayed singular system. Combined with Finsler-s Lemma and Lyapunov stable theory, a sufficient condition guaranteeing the unique existence and stability of zero-sum game-s Nash equilibrium is derived. One numerical example is presented to illustrate the validity of the main result.
Abstract: Probabilistic characteristics of seismic responses of the
Partially Restrained connection rotation (PRCR) and panel zone
deformation (PZD) installed in older steel moment frames were
investigated in accordance with statistical inference in
decision-making process. The 4, 6 and 8 story older steel moment
frames with clip angle and T-stub connections were designed and
analyzed using 2%/50yrs ground motions in four cities of the
Mid-America earthquake region. The probability density function and
cumulative distribution function of PRCR and PZD were determined
by the goodness-of-fit tests based on probabilistic parameters
measured from the results of the nonlinear time-history analyses. The
obtained probabilistic parameters and distributions can be used to find
out what performance level mainly PR connections and panel zones
satisfy and how many PR connections and panel zones experience a
serious damage under the Mid-America ground motions.
Abstract: In high powered dense wavelength division
multiplexed (WDM) systems with low chromatic dispersion,
four-wave mixing (FWM) can prove to be a major source of noise.
The MultiCanonical Monte Carlo Method (MCMC) and the Split
Step Fourier Method (SSFM) are combined to accurately evaluate the
probability density function of the decision variable of a receiver,
limited by FWM. The combination of the two methods leads to more
accurate results, and offers the possibility of adding other optical
noises such as the Amplified Spontaneous Emission (ASE) noise.
Abstract: Base on extenics theory, the statistical physical and mechanical properties from laboratory experiments are used to evaluate the bearing capacity of lateritic soil foundation. The properties include water content, bulk density, liquid limit, cohesion, and so on. The matter-element and the dependent function are defined. Then the synthesis dependent degree and the final grade index are calculated. The results show that predicted outcomes can be matched with the in-situ test data, and a evaluate grade associate with bearing capacity can be deduced. The results provide guidance to assess and determine the bearing capacity grade of lateritic soil foundation.
Abstract: Mathematical models of dynamics employing exterior calculus are mathematical representations of the same unifying principle; namely, the description of a dynamic system with a characteristic differential one-form on an odd-dimensional differentiable manifold leads, by analysis with exterior calculus, to a set of differential equations and a characteristic tangent vector (vortex vector) which define transformations of the system. Using this principle, a mathematical model for economic growth is constructed by proposing a characteristic differential one-form for economic growth dynamics (analogous to the action in Hamiltonian dynamics), then generating a pair of characteristic differential equations and solving these equations for the rate of economic growth as a function of labor and capital. By contracting the characteristic differential one-form with the vortex vector, the Lagrangian for economic growth dynamics is obtained.
Abstract: Data of wave height and wind speed were collected
from three existing oil fields in South China Sea – offshore
Peninsular Malaysia, Sarawak and Sabah regions. Extreme values
and other significant data were employed for analysis. The data were
recorded from 1999 until 2008. The results show that offshore
structures are susceptible to unacceptable motions initiated by wind
and waves with worst structural impacts caused by extreme wave
heights. To protect offshore structures from damage, there is a need
to quantify descriptive statistics and determine spectra envelope of
wind speed and wave height, and to ascertain the frequency content
of each spectrum for offshore structures in the South China Sea
shallow waters using measured time series. The results indicate that
the process is nonstationary; it is converted to stationary process by
first differencing the time series. For descriptive statistical analysis,
both wind speed and wave height have significant influence on the
offshore structure during the northeast monsoon with high mean wind
speed of 13.5195 knots ( = 6.3566 knots) and the high mean wave
height of 2.3597 m ( = 0.8690 m). Through observation of the
spectra, there is no clear dominant peak and the peaks fluctuate
randomly. Each wind speed spectrum and wave height spectrum has
its individual identifiable pattern. The wind speed spectrum tends to
grow gradually at the lower frequency range and increasing till it
doubles at the higher frequency range with the mean peak frequency
range of 0.4104 Hz to 0.4721 Hz, while the wave height tends to
grow drastically at the low frequency range, which then fluctuates
and decreases slightly at the high frequency range with the mean
peak frequency range of 0.2911 Hz to 0.3425 Hz.
Abstract: This paper deals with rheological behavior of tomato
paste from the view point of time independent properties inclusive of
processing variables such as sample temperature which influence on
rheological properties as well as breaking temperature and
concentration which beside the rheological properties, influence on
the quality of final product. With this aim 10 tomato paste samples at
various concentrations (17-25%) and breaking temperatures (65-
85 C o ) have been produced. The experimental results showed tomato
paste behaves as a non-Newtonian semi-fluid which follows power
law model that consistency coefficient (K) is supposed function of
breaking temperature, concentration and sample temperature with
consideration to superimpose function.
Abstract: In this paper, linear multistep technique using power
series as the basis function is used to develop the block methods
which are suitable for generating direct solution of the special second
order ordinary differential equations of the form y′′ = f(x,y), a < = x < = b with associated initial or boundary conditions. The continuaous hybrid formulations enable us to differentiate and evaluate at some
grids and off – grid points to obtain two different three discrete
schemes, each of order (4,4,4)T, 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 linear and non-linear ordinary
differential equations whose solutions are oscillatory or nearly
periodic in nature, and the results obtained compared favourably with
the exact solution.
Abstract: An effort estimation model is needed for softwareintensive
projects that consist of hardware, embedded software or
some combination of the two, as well as high level software
solutions. This paper first focuses on functional decomposition
techniques to measure functional complexity of a computer system
and investigates its impact on system development effort. Later, it
examines effects of technical difficulty and design team capability
factors in order to construct the best effort estimation model. With
using traditional regression analysis technique, the study develops a
system development effort estimation model which takes functional
complexity, technical difficulty and design team capability factors as
input parameters. Finally, the assumptions of the model are tested.
Abstract: To establish optical communication between any two
satellites, the transmitter satellite must track the beacon of the
receiver satellite and point the information optical beam in its
direction. Optical tracking and pointing systems for free space suffer
during tracking from high-amplitude vibration because of
background radiation from interstellar objects such as the Sun, Moon,
Earth, and stars in the tracking field of view or the mechanical
impact from satellite internal and external sources. The vibrations of
beam pointing increase the bit error rate and jam communication
between the two satellites. One way to overcome this problem is the
use of very small transmitter beam divergence angles of too narrow
divergence angle is that the transmitter beam may sometimes miss
the receiver satellite, due to pointing vibrations. In this paper we
propose the use of genetic algorithm to optimize the BER as function
of transmitter optics aperture.
Abstract: Data mining (DM) is the process of finding and extracting frequent patterns that can describe the data, or predict unknown or future values. These goals are achieved by using various learning algorithms. Each algorithm may produce a mining result completely different from the others. Some algorithms may find millions of patterns. It is thus the difficult job for data analysts to select appropriate models and interpret the discovered knowledge. In this paper, we describe a framework of an intelligent and complete data mining system called SUT-Miner. Our system is comprised of a full complement of major DM algorithms, pre-DM and post-DM functionalities. It is the post-DM packages that ease the DM deployment for business intelligence applications.
Abstract: Bidding is a very important business function to find
latent contractors of construction projects. Moreover, bid markup is
one of the most important decisions for a bidder to gain a reasonable
profit. Since the bidding system is a complex adaptive system, bidding
agent need a learning process to get more valuable knowledge for a bid,
especially from past public bidding information. In this paper, we
proposed an iterative agent leaning model for bidders to make markup
decisions. A classifier for public bidding information named PIBS is
developed to make full use of history data for classifying new bidding
information. The simulation and experimental study is performed to
show the validity of the proposed classifier. Some factors that affect
the validity of PIBS are also analyzed at the end of this work.
Abstract: Understanding proteins functions is a major goal in
the post-genomic era. Proteins usually work in context of other
proteins and rarely function alone. Therefore, it is highly relevant to
study the interaction partners of a protein in order to understand its
function. Machine learning techniques have been widely applied to
predict protein-protein interactions. Kernel functions play an
important role for a successful machine learning technique. Choosing
the appropriate kernel function can lead to a better accuracy in a
binary classifier such as the support vector machines. In this paper,
we describe a Bayesian kernel for the support vector machine to
predict protein-protein interactions. The use of Bayesian kernel can
improve the classifier performance by incorporating the probability
characteristic of the available experimental protein-protein
interactions data that were compiled from different sources. In
addition, the probabilistic output from the Bayesian kernel can assist
biologists to conduct more research on the highly predicted
interactions. The results show that the accuracy of the classifier has
been improved using the Bayesian kernel compared to the standard
SVM kernels. These results imply that protein-protein interaction can
be predicted using Bayesian kernel with better accuracy compared to
the standard SVM kernels.
Abstract: Regulatory relationships of 686 intronic miRNA and 784 intergenic miRNAs with mRNAs of 51 intronic miRNA coding genes were established. Interaction features of studied miRNAs with 5'UTR, CDS and 3'UTR of mRNA of each gene were revealed. Functional regions of mRNA were shown to be significantly heterogenous according to the number of binding sites of miRNA and to the location density of these sites.
Abstract: This paper reviews various approaches that have been
used for the modeling and simulation of large-scale engineering
systems and determines their appropriateness in the development of a
RICS modeling and simulation tool. Bond graphs, linear graphs,
block diagrams, differential and difference equations, modeling
languages, cellular automata and agents are reviewed. This tool
should be based on linear graph representation and supports symbolic
programming, functional programming, the development of noncausal
models and the incorporation of decentralized approaches.
Abstract: System identification is the process of creating
models of dynamic process from input- output signals. The aim of
system identification can be identified as “ to find a model with
adjustable parameters and then to adjust them so that the predicted
output matches the measured output". This paper presents a method
of modeling and simulating with system identification to achieve the
maximum fitness for transformation function. First by using
optimized KLM equivalent circuit for PVDF piezoelectric transducer
and assuming different inputs including: sinuside, step and sum of
sinusides, get the outputs, then by using system identification
toolbox in MATLAB, we estimate the transformation function from
inputs and outputs resulted in last program. Then compare the fitness
of transformation function resulted from using ARX,OE(Output-
Error) and BJ(Box-Jenkins) models in system identification toolbox
and primary transformation function form KLM equivalent circuit.
Abstract: ZnO heteronanostructured nanowires arrays have
been fabricated by low temperature solution method. Various
heterostructures were synthesized including CdS/ZnO,
CdSe/CdS/ZnO nanowires and Co3O4/ZnO, ZnO/SiC
nanowires. These multifunctional heterostructure nanowires
showed important applications in photocatalysts, sensors,
wettability control and solar energy conversion.
Abstract: An enhanced particle swarm optimization algorithm
(PSO) is presented in this work to solve the non-convex OPF
problem that has both discrete and continuous optimization variables.
The objective functions considered are the conventional quadratic
function and the augmented quadratic function. The latter model
presents non-differentiable and non-convex regions that challenge
most gradient-based optimization algorithms. The optimization
variables to be optimized are the generator real power outputs and
voltage magnitudes, discrete transformer tap settings, and discrete
reactive power injections due to capacitor banks. The set of equality
constraints taken into account are the power flow equations while the
inequality ones are the limits of the real and reactive power of the
generators, voltage magnitude at each bus, transformer tap settings,
and capacitor banks reactive power injections. The proposed
algorithm combines PSO with Newton-Raphson algorithm to
minimize the fuel cost function. The IEEE 30-bus system with six
generating units is used to test the proposed algorithm. Several cases
were investigated to test and validate the consistency of detecting
optimal or near optimal solution for each objective. Results are
compared to solutions obtained using sequential quadratic
programming and Genetic Algorithms.