Abstract: A modified Genetic Algorithm (GA) based optimal selection of parameters for Automatic Generation Control (AGC) of multi-area electric energy systems is proposed in this paper. Simulations on multi-area reheat thermal system with and without consideration of nonlinearity like governor dead band followed by 1% step load perturbation is performed to exemplify the optimum parameter search. In this proposed method, a modified Genetic Algorithm is proposed where one point crossover with modification is employed. Positional dependency in respect of crossing site helps to maintain diversity of search point as well as exploitation of already known optimum value. This makes a trade-off between exploration and exploitation of search space to find global optimum in less number of generations. The proposed GA along with decomposition technique as developed has been used to obtain the optimum megawatt frequency control of multi-area electric energy systems. Time-domain simulations are conducted with trapezoidal integration along with decomposition technique. The superiority of the proposed method over existing one is verified from simulations and comparisons.
Abstract: TiO2 nanoparticles were synthesized by hydrothermal
method at 180°C from TiOSO4 aqueous solution with1m/l
concentration. The obtained products were coated with silica by
means of a seeded polymerization technique for a coating time of
1440 minutes to obtain well defined TiO2@SiO2 core-shell structure.
The uncoated and coated nanoparticles were characterized by using
X-Ray diffraction technique (XRD), Fourier Transform Infrared
Spectroscopy (FT-IR) to study their physico-chemical properties.
Evidence from XRD and FTIR results show that SiO2 is
homogenously coated on the surface of titania particles. FTIR spectra
show that there exists an interaction between TiO2 and SiO2 and
results in the formation of Ti-O-Si chemical bonds at the interface of
TiO2 particles and SiO2 coating layer. The non linear optical limiting
properties of TiO2 and TiO2@SiO2 nanoparticles dispersed in
ethylene glycol were studied at 532nm using 5ns Nd:YAG laser
pulses. Three-photon absorption is responsible for optical limiting
characteristics in these nanoparticles and it is seen that the optical
nonlinearity is enhanced in core-shell structures when compared with
single counterparts. This effective three-photon type absorption at
this wavelength, is of potential application in fabricating optical
limiting devices.
Abstract: A simple analytical model has been developed to
optimize biasing conditions for obtaining maximum linearity among
lattice-matched, pseudomorphic and metamorphic HEMT types as
well as enhancement and depletion HEMT modes. A nonlinear
current-voltage model has been simulated based on extracted data to
study and select the most appropriate type and mode of HEMT in
terms of a given gate-source biasing voltage within the device so as
to employ the circuit for the highest possible output current or
voltage linear swing. Simulation results can be used as a basis for the
selection of optimum gate-source biasing voltage for a given type
and mode of HEMT with regard to a circuit design. The
consequences can also be a criterion for choosing the optimum type
or mode of HEMT for a predetermined biasing condition.
Abstract: An HPLC-UV analytical method was developed to
determine ethylenediaminetetraacetic acid (EDTA) in dairy
wastewater and surface water. The optimizing separation was achieved
by reversed–phase ion-pair liquid chromatography on a C18 column
using methanol as mobile phase solvent, tetrabutylammonium bromide
as the ion-pair reagent in pH 3.3 formate buffer solution at a flow rate
of 0.9 mL min-1 with a UV detector at 265 nm. No interference of Ca,
Mg or NO3
- was detected. Method performance was evaluated in terms
of linearity, repeatability and reproducibility. The method detection
limit was 5 μg L-1. The contents of EDTA in dairy effluents were 72 ~
261 μg L-1 at a large dairy site. A change of EDTA concentration was
observed downstream of the dairy effluent discharge, but this was well
under the predicted no effect concentration for aquatic ecosystem.
Abstract: Cluster analysis divides data into groups that are
meaningful, useful, or both. Analysis of biological data is creating a
new generation of epidemiologic, prognostic, diagnostic and
treatment modalities. Clustering of protein sequences is one of the
current research topics in the field of computer science. Linear
relation is valuable in rule discovery for a given data, such as if value
X goes up 1, value Y will go down 3", etc. The classical linear
regression models the linear relation of two sequences perfectly.
However, if we need to cluster a large repository of protein sequences
into groups where sequences have strong linear relationship with
each other, it is prohibitively expensive to compare sequences one by
one. In this paper, we propose a new technique named General
Regression Model Technique Clustering Algorithm (GRMTCA) to
benignly handle the problem of linear sequences clustering. GRMT
gives a measure, GR*, to tell the degree of linearity of multiple
sequences without having to compare each pair of them.
Abstract: This paper presents the development and application of an adaptive neuro fuzzy inference system (ANFIS) based intelligent hybrid neuro fuzzy controller for automatic generation control (AGC) of two-area interconnected thermal power system with reheat non linearity. The dynamic response of the system has been studied for 1% step load perturbation in area-1. The performance of the proposed neuro fuzzy controller is compared against conventional proportional-integral (PI) controller, state feedback linear quadratic regulator (LQR) controller and fuzzy gain scheduled proportionalintegral (FGSPI) controller. Comparative analysis demonstrates that the proposed intelligent neuro fuzzy controller is the most effective of all in improving the transients of frequency and tie-line power deviations against small step load disturbances. Simulations have been performed using Matlab®.
Abstract: Reliability assessment and risk analysis of rotating
machine rotors in various overload and malfunction situations
present challenge to engineers and operators. In this paper a new
analytical method for evaluation of rotor under large deformation is
addressed. Model is presented in general form to include also
composite rotors. Presented simulation procedure is based on
variational work method and has capability to account for geometric
nonlinearity, large displacement, nonlinear support effect and rotor
contacting other machine components. New shape functions are
presented which capable to predict accurate nonlinear profile of
rotor. The closed form solutions for various operating and
malfunction situations are expressed. Analytical simulation results
are discussed
Abstract: S-boxes (Substitution boxes) are keystones of modern
symmetric cryptosystems (block ciphers, as well as stream ciphers).
S-boxes bring nonlinearity to cryptosystems and strengthen their
cryptographic security. They are used for confusion in data security
An S-box satisfies the strict avalanche criterion (SAC), if and only if
for any single input bit of the S-box, the inversion of it changes each
output bit with probability one half. If a function (cryptographic
transformation) is complete, then each output bit depends on all of
the input bits. Thus, if it were possible to find the simplest Boolean
expression for each output bit in terms of the input bits, each of these
expressions would have to contain all of the input bits if the function
is complete. From some important properties of S-box, the most
interesting property SAC (Strict Avalanche Criterion) is presented
and to analyze this property three analysis methods are proposed.
Abstract: Pipeline ADCs are becoming popular at high speeds
and with high resolution. This paper discusses the options of number
of bits/stage conversion techniques in pipelined ADCs and their
effect on Area, Speed, Power Dissipation and Linearity. The basic
building blocks like op-amp, Sample and Hold Circuit, sub converter,
DAC, Residue Amplifier used in every stage is assumed to be
identical. The sub converters use flash architectures. The design is
implemented using 0.18
Abstract: The main objective developed in this paper is to find a
graphic technique for modeling, simulation and diagnosis of the
industrial systems. This importance is much apparent when it is about
a complex system such as the nuclear reactor with pressurized water
of several form with various several non-linearity and time scales. In
this case the analytical approach is heavy and does not give a fast
idea on the evolution of the system. The tool Bond Graph enabled us
to transform the analytical model into graphic model and the
software of simulation SYMBOLS 2000 specific to the Bond Graphs
made it possible to validate and have the results given by the
technical specifications. We introduce the analysis of the problem
involved in the faults localization and identification in the complex
industrial processes. We propose a method of fault detection applied
to the diagnosis and to determine the gravity of a detected fault. We
show the possibilities of application of the new diagnosis approaches
to the complex system control. The industrial systems became
increasingly complex with the faults diagnosis procedures in the
physical systems prove to become very complex as soon as the
systems considered are not elementary any more. Indeed, in front of
this complexity, we chose to make recourse to Fault Detection and
Isolation method (FDI) by the analysis of the problem of its control
and to conceive a reliable system of diagnosis making it possible to
apprehend the complex dynamic systems spatially distributed applied
to the standard pressurized water nuclear reactor.
Abstract: In this paper, the performance of two adaptive
observers applied to interconnected systems is studied. The
nonlinearity of systems can be written in a fractional form. The first
adaptive observer is an adaptive sliding mode observer for a Lipchitz
nonlinear system and the second one is an adaptive sliding mode
observer having a filtered error as a sliding surface. After comparing
their performances throughout the inverted pendulum mounted on a
car system, it was shown that the second one is more robust to
estimate the state.
Abstract: This paper analyses the non linear properties
exhibited by a drill string system under various un balanced mass
conditions. The drill string is affected by continuous friction in the
form of drill bit and well bore hole interactions. This paper proves
the origin of limit cycling and increase of non linearity with increase
in speed of the drilling in the presence of friction. The spectrum of
the frequency response is also studied to detect the presence of
vibration abnormalities arising during the drilling process.
Abstract: This paper presents a procedure for estimating VAR
using Sequential Discounting VAR (SDVAR) algorithm for online
model learning to detect fraudulent acts using the telecommunications
call detailed records (CDR). The volatility of the VAR is observed
allowing for non-linearity, outliers and change points based on the
works of [1]. This paper extends their procedure from univariate
to multivariate time series. A simulation and a case study for
detecting telecommunications fraud using CDR illustrate the use of
the algorithm in the bivariate setting.
Abstract: In this paper we present a linear autozeroing ultra lowvoltage amplifier. The autozeroing performed by all ULV circuits is important to reduce the impact of noise and especially avoid power supply noise in mixed signal low-voltage CMOS circuits. The simulated data presented is relevant for a 90nm TSMC CMOS process.
Abstract: Support vector regression (SVR) has been regarded
as a state-of-the-art method for approximation and regression. The
importance of kernel function, which is so-called admissible support
vector kernel (SV kernel) in SVR, has motivated many studies
on its composition. The Gaussian kernel (RBF) is regarded as a
“best" choice of SV kernel used by non-expert in SVR, whereas
there is no evidence, except for its superior performance on some
practical applications, to prove the statement. Its well-known that
reproducing kernel (R.K) is also a SV kernel which possesses many
important properties, e.g. positive definiteness, reproducing property
and composing complex R.K by simpler ones. However, there are a
limited number of R.Ks with explicit forms and consequently few
quantitative comparison studies in practice. In this paper, two R.Ks,
i.e. SV kernels, composed by the sum and product of a translation
invariant kernel in a Sobolev space are proposed. An exploratory
study on the performance of SVR based general R.K is presented
through a systematic comparison to that of RBF using multiple
criteria and synthetic problems. The results show that the R.K is
an equivalent or even better SV kernel than RBF for the problems
with more input variables (more than 5, especially more than 10) and
higher nonlinearity.
Abstract: Much time series data is generally from continuous dynamic system. Firstly, this paper studies the detection of the nonlinearity of time series from continuous dynamics systems by applying the Phase-randomized surrogate algorithm. Then, the Delay Vector Variance (DVV) method is introduced into nonlinearity test. The results show that under the different sampling conditions, the opposite detection of nonlinearity is obtained via using traditional test statistics methods, which include the third-order autocovariance and the asymmetry due to time reversal. Whereas the DVV method can perform well on determining nonlinear of Lorenz signal. It indicates that the proposed method can describe the continuous dynamics signal effectively.
Abstract: One of the most important requirements for the
operation and planning activities of an electrical utility is the
prediction of load for the next hour to several days out, known as
short term load forecasting. This paper presents the development of
an artificial neural network based short-term load forecasting model.
The model can forecast daily load profiles with a load time of one
day for next 24 hours. In this method can divide days of year with
using average temperature. Groups make according linearity rate of
curve. Ultimate forecast for each group obtain with considering
weekday and weekend. This paper investigates effects of temperature
and humidity on consuming curve. For forecasting load curve of
holidays at first forecast pick and valley and then the neural network
forecast is re-shaped with the new data. The ANN-based load models
are trained using hourly historical. Load data and daily historical
max/min temperature and humidity data. The results of testing the
system on data from Yazd utility are reported.
Abstract: In this paper, Neuro-Fuzzy based Fuzzy Subtractive
Clustering Method (FSCM) and Self Tuning Fuzzy PD-like
Controller (STFPDC) were used to solve non-linearity and trajectory
problems of pitch AND yaw angles of Twin Rotor MIMO system
(TRMS). The control objective is to make the beams of TRMS reach
a desired position quickly and accurately. The proposed method
could achieve control objectives with simpler controller. To simplify
the complexity of STFPDC, ANFIS based FSCM was used to
simplify the controller and improve the response. The proposed
controllers could achieve satisfactory objectives under different input
signals. Simulation results under MATLAB/Simulink® proved the
improvement of response and superiority of simplified STFPDC on
Fuzzy Logic Controller (FLC).
Abstract: The paper presented a transient population dynamics of phase singularities in 2D Beeler-Reuter model. Two stochastic modelings are examined: (i) the Master equation approach with the transition rate (i.e., λ(n, t) = λ(t)n and μ(n, t) = μ(t)n) and (ii) the nonlinear Langevin equation approach with a multiplicative noise. The exact general solution of the Master equation with arbitrary time-dependent transition rate is given. Then, the exact solution of the mean field equation for the nonlinear Langevin equation is also given. It is demonstrated that transient population dynamics is successfully identified by the generalized Logistic equation with fractional higher order nonlinear term. It is also demonstrated the necessity of introducing time-dependent transition rate in the master equation approach to incorporate the effect of nonlinearity.
Abstract: We provide a maximum norm analysis of a finite
element Schwarz alternating method for a nonlinear elliptic boundary
value problem of the form -Δu = f(u), on two overlapping sub
domains with non matching grids. We consider a domain which is
the union of two overlapping sub domains where each sub domain
has its own independently generated grid. The two meshes being
mutually independent on the overlap region, a triangle belonging to
one triangulation does not necessarily belong to the other one. Under
a Lipschitz assumption on the nonlinearity, we establish, on each sub
domain, an optimal L∞ error estimate between the discrete Schwarz
sequence and the exact solution of the boundary value problem.