Abstract: In this paper a stochastic scenario-based model predictive control applied to molten salt storage systems in concentrated solar tower power plant is presented. The main goal of this study is to build up a tool to analyze current and expected future resources for evaluating the weekly power to be advertised on electricity secondary market. This tool will allow plant operator to maximize profits while hedging the impact on the system of stochastic variables such as resources or sunlight shortage.
Solving the problem first requires a mixed logic dynamic modeling of the plant. The two stochastic variables, respectively the sunlight incoming energy and electricity demands from secondary market, are modeled by least square regression. Robustness is achieved by drawing a certain number of random variables realizations and applying the most restrictive one to the system. This scenario approach control technique provides the plant operator a confidence interval containing a given percentage of possible stochastic variable realizations in such a way that robust control is always achieved within its bounds. The results obtained from many trajectory simulations show the existence of a ‘’reliable’’ interval, which experimentally confirms the algorithm robustness.
Abstract: This paper addresses the problem of the partial state
feedback stabilization of a class of nonlinear systems. In order to
stabilization this class systems, the especial place of this paper is
to reverse designing the state feedback control law from the method
of judging system stability with the center manifold theory. First of
all, the center manifold theory is applied to discuss the stabilization
sufficient condition and design the stabilizing state control laws for a
class of nonlinear. Secondly, the problem of partial stabilization for a
class of plane nonlinear system is discuss using the lyapunov second
method and the center manifold theory. Thirdly, we investigate specially
the problem of the stabilization for a class of homogenous plane
nonlinear systems, a class of nonlinear with dual-zero eigenvalues and
a class of nonlinear with zero-center using the method of lyapunov
function with homogenous derivative, specifically. At the end of this
paper, some examples and simulation results are given show that the
approach of this paper to this class of nonlinear system is effective
and convenient.
Abstract: This paper presents application artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), to estimate the real power transfer between generators and loads. Since these AI techniques adopt supervised learning, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of both AI methods compared to that of the MNE method. The mean squared error of the estimate of ANN and ANFIS power transfer allocation methods are 1.19E-05 and 2.97E-05, respectively. Furthermore, when compared to MNE method, ANN and ANFIS methods computes generator contribution to loads within 20.99 and 39.37msec respectively whereas the MNE method took 360msec for the calculation of same real power transfer allocation.
Abstract: Predicting short term wind speed is essential in order
to prevent systems in-action from the effects of strong winds. It also
helps in using wind energy as an alternative source of energy, mainly
for Electrical power generation. Wind speed prediction has
applications in Military and civilian fields for air traffic control,
rocket launch, ship navigation etc. The wind speed in near future
depends on the values of other meteorological variables, such as
atmospheric pressure, moisture content, humidity, rainfall etc. The
values of these parameters are obtained from a nearest weather
station and are used to train various forms of neural networks. The
trained model of neural networks is validated using a similar set of
data. The model is then used to predict the wind speed, using the
same meteorological information. This paper reports an Artificial
Neural Network model for short term wind speed prediction, which
uses back propagation algorithm.
Abstract: Spatial trends are one of the valuable patterns in geo
databases. They play an important role in data analysis and
knowledge discovery from spatial data. A spatial trend is a regular
change of one or more non spatial attributes when spatially moving
away from a start object. Spatial trend detection is a graph search
problem therefore heuristic methods can be good solution. Artificial
immune system (AIS) is a special method for searching and
optimizing. AIS is a novel evolutionary paradigm inspired by the
biological immune system. The models based on immune system
principles, such as the clonal selection theory, the immune network
model or the negative selection algorithm, have been finding
increasing applications in fields of science and engineering.
In this paper, we develop a novel immunological algorithm based
on clonal selection algorithm (CSA) for spatial trend detection. We
are created neighborhood graph and neighborhood path, then select
spatial trends that their affinity is high for antibody. In an
evolutionary process with artificial immune algorithm, affinity of
low trends is increased with mutation until stop condition is satisfied.
Abstract: In this paper, the application of neural networks to study the design of short-term load forecasting (STLF) Systems for Illam state located in west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STLF systems was used. Our study based on MLP was trained and tested using three years (2004-2006) data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STLF systems.
Abstract: Particle Swarm Optimization (PSO) with elite PSO
parameters has been developed for power flow analysis under
practical constrained situations. Multiple solutions of the power flow
problem are useful in voltage stability assessment of power system.
A method of determination of multiple power flow solutions is
presented using a hybrid of Particle Swarm Optimization (PSO) and
local search technique. The unique and innovative learning factors of
the PSO algorithm are formulated depending upon the node power
mismatch values to be highly adaptive with the power flow problems.
The local search is applied on the pbest solution obtained by the PSO
algorithm in each iteration. The proposed algorithm performs reliably
and provides multiple solutions when applied on standard and illconditioned
systems. The test results show that the performances of
the proposed algorithm under critical conditions are better than the
conventional methods.
Abstract: An optimal power flow (OPF) based on particle swarm
optimization (PSO) was developed with more realistic generator
security constraint using the capability curve instead of only Pmin/Pmax
and Qmin/Qmax. Neural network (NN) was used in designing digital
capability curve and the security check algorithm. The algorithm is
very simple and flexible especially for representing non linear
generation operation limit near steady state stability limit and under
excitation operation area. In effort to avoid local optimal power flow
solution, the particle swarm optimization was implemented with
enough widespread initial population. The objective function used in
the optimization process is electric production cost which is
dominated by fuel cost. The proposed method was implemented at
Java Bali 500 kV power systems contain of 7 generators and 20
buses. The simulation result shows that the combination of generator
power output resulted from the proposed method was more economic
compared with the result using conventional constraint but operated
at more marginal operating point.
Abstract: This paper features the modeling and design of a Fast
Output Sampling (FOS) Feedback control technique for the Active
Vibration Control (AVC) of a smart flexible aluminium cantilever
beam for a Single Input Single Output (SISO) case. Controllers are
designed for the beam by bonding patches of piezoelectric layer as
sensor / actuator to the master structure at different locations along
the length of the beam by retaining the first 2 dominant vibratory
modes. The entire structure is modeled in state space form using the
concept of piezoelectric theory, Euler-Bernoulli beam theory, Finite
Element Method (FEM) and the state space techniques by dividing
the structure into 3, 4, 5 finite elements, thus giving rise to three
types of systems, viz., system 1 (beam divided into 3 finite
elements), system 2 (4 finite elements), system 3 (5 finite elements).
The effect of placing the sensor / actuator at various locations along
the length of the beam for all the 3 types of systems considered is
observed and the conclusions are drawn for the best performance and
for the smallest magnitude of the control input required to control the
vibrations of the beam. Simulations are performed in MATLAB. The
open loop responses, closed loop responses and the tip displacements
with and without the controller are obtained and the performance of
the proposed smart system is evaluated for vibration control.
Abstract: Recently, many existing partially blind signature scheme based on a single hard problem such as factoring, discrete logarithm, residuosity or elliptic curve discrete logarithm problems. However sooner or later these systems will become broken and vulnerable, if the factoring or discrete logarithms problems are cracked. This paper proposes a secured partially blind signature scheme based on factoring (FAC) problem and elliptic curve discrete logarithms (ECDL) problem. As the proposed scheme is focused on factoring and ECDLP hard problems, it has a solid structure and will totally leave the intruder bemused because it is very unlikely to solve the two hard problems simultaneously. In order to assess the security level of the proposed scheme a performance analysis has been conducted. Results have proved that the proposed scheme effectively deals with the partial blindness, randomization, unlinkability and unforgeability properties. Apart from this we have also investigated the computation cost of the proposed scheme. The new proposed scheme is robust and it is difficult for the malevolent attacks to break our scheme.
Abstract: In this paper we study the use of a new code called
Random Diagonal (RD) code for Spectral Amplitude Coding (SAC)
optical Code Division Multiple Access (CDMA) networks, using
Fiber Bragg-Grating (FBG), FBG consists of a fiber segment whose
index of reflection varies periodically along its length. RD code is
constructed using code level and data level, one of the important
properties of this code is that the cross correlation at data level is
always zero, which means that Phase intensity Induced Phase (PIIN)
is reduced. We find that the performance of the RD code will be
better than Modified Frequency Hopping (MFH) and Hadamard code
It has been observed through experimental and theoretical simulation
that BER for RD code perform significantly better than other codes.
Proof –of-principle simulations of encoding with 3 channels, and 10
Gbps data transmission have been successfully demonstrated together
with FBG decoding scheme for canceling the code level from SAC-signal.
Abstract: A systems approach model for prostate cancer in prostate duct, as a sub-system of the organism is developed. It is accomplished in two steps. First this research work starts with a nonlinear system of coupled Fokker-Plank equations which models continuous process of the system like motion of cells. Then extended to PDEs that include discontinuous processes like cell mutations, proliferation and deaths. The discontinuous processes is modeled by using intensity poisson processes. The model incorporates the features of the prostate duct. The system of PDEs spatial coordinate is along the proximal distal axis. Its parameters depend on features of the prostate duct. The movement of cells is biased towards distal region and mutations of prostate cancer cells is localized in the proximal region. Numerical solutions of the full system of equations are provided, and are exhibit traveling wave fronts phenomena. This motivates the use of the standard transformation to derive a canonically related system of ODEs for traveling wave solutions. The results obtained show persistence of prostate cancer by showing that the non-negative cone for the traveling wave system is time invariant. The traveling waves have a unique global attractor is proved also. Biologically, the global attractor verifies that evolution of prostate cancer stem cells exhibit the avascular tumor growth. These numerical solutions show that altering prostate stem cell movement or mutation of prostate cancer cells lead to avascular tumor. Conclusion with comments on clinical implications of the model is discussed.
Abstract: Average current analysis checking the impact of
current flow is very important to guarantee the reliability of
semiconductor systems. As semiconductor process technologies
improve, the coupling capacitance often become bigger than self
capacitances. In this paper, we propose an analytic technique for
analyzing average current on interconnects in multi-conductor
structures. The proposed technique has shown to yield the acceptable
errors compared to HSPICE results while providing computational
efficiency.
Abstract: The POD-assisted projective integration method based on the equation-free framework is presented in this paper. The method is essentially based on the slow manifold governing of given system. We have applied two variants which are the “on-line" and “off-line" methods for solving the one-dimensional viscous Bergers- equation. For the on-line method, we have computed the slow manifold by extracting the POD modes and used them on-the-fly along the projective integration process without assuming knowledge of the underlying slow manifold. In contrast, the underlying slow manifold must be computed prior to the projective integration process for the off-line method. The projective step is performed by the forward Euler method. Numerical experiments show that for the case of nonperiodic system, the on-line method is more efficient than the off-line method. Besides, the online approach is more realistic when apply the POD-assisted projective integration method to solve any systems. The critical value of the projective time step which directly limits the efficiency of both methods is also shown.
Abstract: In this study, active tendons with Proportional Integral
Derivation type controllers were applied to a SDOF and a MDOF
building model. Physical models of buildings were constituted with
virtual springs, dampers and rigid masses. After that, equations of
motion of all degrees of freedoms were obtained. Matlab Simulink
was utilized to obtain the block diagrams for these equations of
motion. Parameters for controller actions were found by using a trial
method. After earthquake acceleration data were applied to the
systems, building characteristics such as displacements, velocities,
accelerations and transfer functions were analyzed for all degrees of
freedoms. Comparisons on displacement vs. time, velocity vs. time,
acceleration vs. time and transfer function (Db) vs. frequency (Hz)
were made for uncontrolled and controlled buildings. The results
show that the method seems feasible.
Abstract: The number of framework conceived for e-learning
constantly increase, unfortunately the creators of learning materials
and educational institutions engaged in e-formation adopt a
“proprietor" approach, where the developed products (courses,
activities, exercises, etc.) can be exploited only in the framework
where they were conceived, their uses in the other learning
environments requires a greedy adaptation in terms of time and
effort. Each one proposes courses whose organization, contents,
modes of interaction and presentations are unique for all learners,
unfortunately the latter are heterogeneous and are not interested by
the same information, but only by services or documents adapted to
their needs. Currently the new tendency for the framework
conceived for e-learning, is the interoperability of learning materials,
several standards exist (DCMI (Dublin Core Metadata Initiative)[2],
LOM (Learning Objects Meta data)[1], SCORM (Shareable Content
Object Reference Model)[6][7][8], ARIADNE (Alliance of Remote
Instructional Authoring and Distribution Networks for Europe)[9],
CANCORE (Canadian Core Learning Resource Metadata
Application Profiles)[3]), they converge all to the idea of learning
objects. They are also interested in the adaptation of the learning
materials according to the learners- profile. This article proposes an
approach for the composition of courses adapted to the various
profiles (knowledge, preferences, objectives) of learners, based on
two ontologies (domain to teach and educational) and the learning
objects.
Abstract: Many computational techniques were applied to
solution of heat conduction problem. Those techniques were the
finite difference (FD), finite element (FE) and recently meshless
methods. FE is commonly used in solution of equation of heat
conduction problem based on the summation of stiffness matrix of
elements and the solution of the final system of equations. Because
of summation process of finite element, convergence rate was
decreased. Hence in the present paper Cellular Automata (CA)
approach is presented for the solution of heat conduction problem.
Each cell considered as a fixed point in a regular grid lead to the
solution of a system of equations is substituted by discrete systems of
equations with small dimensions. Results show that CA can be used
for solution of heat conduction problem.
Abstract: The present models and simulation algorithms of intracellular stochastic kinetics are usually based on the premise that diffusion is so fast that the concentrations of all the involved species are homogeneous in space. However, recents experimental measurements of intracellular diffusion constants indicate that the assumption of a homogeneous well-stirred cytosol is not necessarily valid even for small prokaryotic cells. In this work a mathematical treatment of diffusion that can be incorporated in a stochastic algorithm simulating the dynamics of a reaction-diffusion system is presented. The movement of a molecule A from a region i to a region j of the space is represented as a first order reaction Ai k- ! Aj , where the rate constant k depends on the diffusion coefficient. The diffusion coefficients are modeled as function of the local concentration of the solutes, their intrinsic viscosities, their frictional coefficients and the temperature of the system. The stochastic time evolution of the system is given by the occurrence of diffusion events and chemical reaction events. At each time step an event (reaction or diffusion) is selected from a probability distribution of waiting times determined by the intrinsic reaction kinetics and diffusion dynamics. To demonstrate the method the simulation results of the reaction-diffusion system of chaperoneassisted protein folding in cytoplasm are shown.
Abstract: In this paper, we generalize several techniques in
developing Fault Tolerant Software. We introduce property
“Correctness" in evaluating N-version Systems and compare it to
some commonly used properties such as reliability or availability.
We also find out the relation between this property and the number of
versions of system. Our experiments to verify the correctness and the
applicability of the relation are also presented.
Abstract: This paper proposes a fast code acquisition scheme for
optical code division multiple access (O-CDMA) systems. Unlike the
conventional scheme, the proposed scheme employs multiple thresholds
providing a shorter mean acquisition time (MAT) performance.
The simulation results show that the MAT of the proposed scheme
is shorter than that of the conventional scheme.