Abstract: A clustering based technique has been developed and implemented for Short Term Load Forecasting, in this article. Formulation has been done using Mean Absolute Percentage Error (MAPE) as an objective function. Data Matrix and cluster size are optimization variables. Model designed, uses two temperature variables. This is compared with six input Radial Basis Function Neural Network (RBFNN) and Fuzzy Inference Neural Network (FINN) for the data of the same system, for same time period. The fuzzy inference system has the network structure and the training procedure of a neural network which initially creates a rule base from existing historical load data. It is observed that the proposed clustering based model is giving better forecasting accuracy as compared to the other two methods. Test results also indicate that the RBFNN can forecast future loads with accuracy comparable to that of proposed method, where as the training time required in the case of FINN is much less.
Abstract: We present on the method of inverse coherence matrix for the estimation of network connectivity from multivariate time series of a complex system. In a model system of coupled chaotic oscillators, it is shown that the inverse coherence matrix defined as the inverse of cross coherence matrix is proportional to the network connectivity. Therefore the inverse coherence matrix could be used for the distinction between the directly connected links from indirectly connected links in a complex network. We compare the result of network estimation using the method of the inverse coherence matrix with the results obtained from the coherence matrix and the partial coherence matrix.
Abstract: To satisfy the need of outfield tests of star sensors, a
method is put forward to construct the reference attitude benchmark.
Firstly, its basic principle is introduced; Then, all the separate
conversion matrixes are deduced, which include: the conversion
matrix responsible for the transformation from the Earth Centered
Inertial frame i to the Earth-centered Earth-fixed frame w according to
the time of an atomic clock, the conversion matrix from frame w to the
geographic frame t, and the matrix from frame t to the platform frame
p, so the attitude matrix of the benchmark platform relative to the
frame i can be obtained using all the three matrixes as the
multiplicative factors; Next, the attitude matrix of the star sensor
relative to frame i is got when the mounting matrix from frame p to the
star sensor frame s is calibrated, and the reference attitude angles for
star sensor outfield tests can be calculated from the transformation
from frame i to frame s; Finally, the computer program is finished to
solve the reference attitudes, and the error curves are drawn about the
three axis attitude angles whose absolute maximum error is just 0.25ÔÇ│.
The analysis on each loop and the final simulating results manifest that
the method by precise timing to acquire the absolute reference attitude
is feasible for star sensor outfield tests.
Abstract: State-dependent Riccati equation based controllers are
becoming increasingly popular because of having attractive
properties like optimality, stability and robustness. This paper focuses
on the design of a roll autopilot for a fin stabilized and canard
controlled 122mm artillery rocket using state-dependent Riccati
equation technique. Initial spin is imparted to rocket during launch
and it quickly decays due to straight tail fins. After the spin phase, the
roll orientation of rocket is brought to zero with the canard deflection
commands generated by the roll autopilot. Roll autopilot has been
developed by considering uncoupled roll, pitch and yaw channels.
The canard actuator is modeled as a second-order nonlinear system.
Elements of the state weighing matrix for Riccati equation have been
chosen to be state dependent to exploit the design flexibility offered
by the Riccati equation technique. Simulation results under varying
conditions of flight demonstrate the wide operating range of the
proposed autopilot.
Abstract: A 7-step method (with 25 sub-steps) to assess risk of
air pollutants is introduced. These steps are: pre-considerations,
sampling, statistical analysis, exposure matrix and likelihood, doseresponse
matrix and likelihood, total risk evaluation, and discussion
of findings. All mentioned words and expressions are wellunderstood;
however, almost all steps have been modified, improved,
and coupled in such a way that a comprehensive method has been
prepared. Accordingly, the SADRA (Statistical Analysis-Driven Risk
Assessment) emphasizes extensive and ongoing application of
analytical statistics in traditional risk assessment models. A Sulfur
Dioxide case study validates the claim and provides a good
illustration for this method.
Abstract: This research proposes a methodology for patent-citation-based technology input-output analysis by applying the patent information to input-output analysis developed for the dependencies among different industries. For this analysis, a technology relationship matrix and its components, as well as input and technology inducement coefficients, are constructed using patent information. Then, a technology inducement coefficient is calculated by normalizing the degree of citation from certain IPCs to the different IPCs (International patent classification) or to the same IPCs. Finally, we construct a Dependency Structure Matrix (DSM) based on the technology inducement coefficient to suggest a useful application for this methodology.
Abstract: It is known that an analog Hopfield neural network
with time delay can generate the outputs which are similar to the
human electroencephalogram. To gain deeper insights into the
mechanisms of rhythm generation by the Hopfield neural networks
and to study the effects of noise on their activities, we investigated
the behaviors of the networks with symmetric and asymmetric
interneuron connections. The neural network under the study consists
of 10 identical neurons. For symmetric (fully connected) networks all
interneuron connections aij = +1; the interneuron connections for
asymmetric networks form an upper triangular matrix with non-zero
entries aij = +1. The behavior of the network is described by 10
differential equations, which are solved numerically. The results of
simulations demonstrate some remarkable properties of a Hopfield
neural network, such as linear growth of outputs, dependence of
synchronization properties on the connection type, huge
amplification of oscillation by the external uniform noise, and the
capability of the neural network to transform one type of noise to
another.
Abstract: Considering toxicity of heavy metals and their
accumulation in domestic wastes, immobilization of lead and
cadmium is envisaged inside glass-ceramics. We particularly
focused this work on calcium-rich phases embedded in a
glassy matrix.
Glass-ceramics were synthesized from glasses doped with
12 wt% and 16 wt% of PbO or CdO. They were observed and
analyzed by Electron MicroProbe Analysis (EMPA) and
Analytical Scanning Electron Microscopy (ASEM). Structural
characterization of the samples was performed by powder XRay
Diffraction.
Diopside crystals of CaMgSi2O6 composition are shown to
incorporate significant amounts of cadmium (up to 9 wt% of
CdO). Two new crystalline phases are observed with very
high Cd or Pb contents: about 40 wt% CdO for the cadmiumrich
phase and near 60 wt% PbO for the lead-rich phase. We
present complete chemical and structural characterization of
these phases. They represent a promising way for the
immobilization of toxic elements like Cd or Pb since glass
ceramics are known to propose a “double barrier" protection
(metal-rich crystals embedded in a glass matrix) against metal
release in the environment.
Abstract: In this paper, we investigate two parallel alternating methods for solving the system of linear equations Ax = b and give convergence theorems for the parallel alternating methods when the coefficient matrix is a nonsingular H-matrix. Furthermore, we give one example to show our results.
Abstract: The harmonic Arnoldi method can be used to find interior eigenpairs of large matrices. However, it has been shown that this method may converge erratically and even may fail to do so. In this paper, we present a new method for computing interior eigenpairs of large nonsymmetric matrices, which is called weighted harmonic Arnoldi method. The implementation of the method has been tested by numerical examples, the results show that the method converges fast and works with high accuracy.
Abstract: In this paper, based on linear matrix inequality (LMI), by using Lyapunov functional theory, the exponential stability criterion is obtained for a class of uncertain Takagi-Sugeno fuzzy Hopfield neural networks (TSFHNNs) with time delays. Here we choose a generalized Lyapunov functional and introduce a parameterized model transformation with free weighting matrices to it, these techniques lead to generalized and less conservative stability condition that guarantee the wide stability region. Finally, an example is given to illustrate our results by using MATLAB LMI toolbox.
Abstract: We have previously introduced an ultrasonic imaging
approach that combines harmonic-sensitive pulse sequences with a
post-beamforming quadratic kernel derived from a second-order
Volterra filter (SOVF). This approach is designed to produce images
with high sensitivity to nonlinear oscillations from microbubble
ultrasound contrast agents (UCA) while maintaining high levels of
noise rejection. In this paper, a two-step algorithm for computing the
coefficients of the quadratic kernel leading to reduction of tissue
component introduced by motion, maximizing the noise rejection and
increases the specificity while optimizing the sensitivity to the UCA
is presented. In the first step, quadratic kernels from individual
singular modes of the PI data matrix are compared in terms of their
ability of maximize the contrast to tissue ratio (CTR). In the second
step, quadratic kernels resulting in the highest CTR values are
convolved. The imaging results indicate that a signal processing
approach to this clinical challenge is feasible.
Abstract: The proposed system identifies the species of the wood
using the textural features present in its barks. Each species of a wood
has its own unique patterns in its bark, which enabled the proposed
system to identify it accurately. Automatic wood recognition system
has not yet been well established mainly due to lack of research in this
area and the difficulty in obtaining the wood database. In our work, a
wood recognition system has been designed based on pre-processing
techniques, feature extraction and by correlating the features of those
wood species for their classification. Texture classification is a problem
that has been studied and tested using different methods due to its
valuable usage in various pattern recognition problems, such as wood
recognition, rock classification. The most popular technique used
for the textural classification is Gray-level Co-occurrence Matrices
(GLCM). The features from the enhanced images are thus extracted
using the GLCM is correlated, which determines the classification
between the various wood species. The result thus obtained shows a
high rate of recognition accuracy proving that the techniques used in
suitable to be implemented for commercial purposes.
Abstract: In this paper, we present parallel alternating two-stage methods for solving linear system Ax = b, where A is a monotone matrix or an H-matrix. And we give some convergence results of these methods for nonsingular linear system.
Abstract: The electromagnetic imaging of inhomogeneous
dielectric cylinders buried in a slab medium by transverse electric
(TE) wave illumination is investigated. Dielectric cylinders of
unknown permittivities are buried in second space and scattered a
group of unrelated waves incident from first space where the scattered
field is recorded. By proper arrangement of the various unrelated
incident fields, the difficulties of ill-posedness and nonlinearity are
circumvented, and the permittivity distribution can be reconstructed
through simple matrix operations. The algorithm is based on the
moment method and the unrelated illumination method. Numerical
results are given to demonstrate the capability of the inverse
algorithm. Good reconstruction is obtained even in the presence of
additive Gaussian random noise in measured data. In addition, the
effect of noise on the reconstruction result is also investigated.
Abstract: This paper presents a methodology for operational and
economic characteristics based evaluation and selection of a power
plant using Graph theoretic approach. A universal evaluation index
on the basis of Operational and economics characteristics of a plant is
proposed which evaluates and ranks the various types of power plants.
The index thus obtained from the pool of operational characteristics
of the power plant attributes Digraph. The Digraph is developed
considering Operational and economics attributes of the power plants
and their relative importance for their smooth operation, installation
and commissioning and prioritizing their selection. The sensitivity
analysis of the attributes towards the objective has also been carried
out in order to study the impact of attributes over the desired outcome
i.e. the universal operational-economics index of the power plant.
Abstract: A composite made of plasma functionalized multiwall
carbon nanotubes (MWNTs) coated with SnO2 was synthesized by
sonochemical precipitation method. Thick layer of this
nanocomposite material was used as ethanol sensor at low
temperatures. The composite sensitivity for ethanol has increased by
a factor of 2 at room temperature and by a factor of 13 at 250°C in
comparison to that of pure SnO2. SEM image of nanocomposite
material showed MWNTs were embedded in SnO2 matrix and also a
higher surface area was observed in the presence of functionalized
MWNTs. Greatly improved sensitivity of the composite material to
ethanol can be attributed to new gas accessing passes through
MWNTs and higher specific surface area.
Abstract: In recent years, the use of vector variance as a
measure of multivariate variability has received much attention in
wide range of statistics. This paper deals with a more economic
measure of multivariate variability, defined as vector variance minus
all duplication elements. For high dimensional data, this will increase
the computational efficiency almost 50 % compared to the original
vector variance. Its sampling distribution will be investigated to make
its applications possible.
Abstract: An optimal solution for a large number of constraint
satisfaction problems can be found using the technique of
substitution and elimination of variables analogous to the technique
that is used to solve systems of equations. A decision function
f(A)=max(A2) is used to determine which variables to eliminate. The
algorithm can be expressed in six lines and is remarkable in both its
simplicity and its ability to find an optimal solution. However it is
inefficient in that it needs to square the updated A matrix after each
variable elimination. To overcome this inefficiency the algorithm is
analyzed and it is shown that the A matrix only needs to be squared
once at the first step of the algorithm and then incrementally updated
for subsequent steps, resulting in significant improvement and an
algorithm complexity of O(n3).
Abstract: In this paper, we propose a new algorithm for joint time-delay and direction-of-arrival (DOA) estimation, here called two-dimensional code acquisition, in an asynchronous directsequence code-division multiple-access (DS-CDMA) array system. This algorithm depends on eigenvector-eigenvalue decomposition of sample correlation matrix, and requires to know desired user-s training sequence. The performance of the algorithm is analyzed both analytically and numerically in uncorrelated and coherent multipath environment. Numerical examples show that the algorithm is robust with unknown number of coherent signals.