Abstract: An effort to develop a unit commitment approach
capable of handling large power systems consisting of both thermal
and hydro generating units offers a large profitable return. In order to
be feasible, the method to be developed must be flexible, efficient
and reliable. In this paper, various proposed methods have been
described along with their strengths and weaknesses. As all of these
methods have some sort of weaknesses, a comprehensive algorithm
that combines the strengths of different methods and overcomes each
other-s weaknesses would be a suitable approach for solving
industry-grade unit commitment problem.
Abstract: In Data mining, Fuzzy clustering algorithms have
demonstrated advantage over crisp clustering algorithms in dealing
with the challenges posed by large collections of vague and uncertain
natural data. This paper reviews concept of fuzzy logic and fuzzy
clustering. The classical fuzzy c-means algorithm is presented and its
limitations are highlighted. Based on the study of the fuzzy c-means
algorithm and its extensions, we propose a modification to the cmeans
algorithm to overcome the limitations of it in calculating the
new cluster centers and in finding the membership values with
natural data. The efficiency of the new modified method is
demonstrated on real data collected for Bhutan-s Gross National
Happiness (GNH) program.
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: The Minimum Weighted Vertex Cover (MWVC) problem is a classic graph optimization NP - complete problem. Given an undirected graph G = (V, E) and weighting function defined on the vertex set, the minimum weighted vertex cover problem is to find a vertex set S V whose total weight is minimum subject to every edge of G has at least one end point in S. In this paper an effective algorithm, called Support Ratio Algorithm (SRA), is designed to find the minimum weighted vertex cover of a graph. Computational experiments are designed and conducted to study the performance of our proposed algorithm. Extensive simulation results show that the SRA can yield better solutions than other existing algorithms found in the literature for solving the minimum vertex cover problem.
Abstract: In this paper, by introducing twice continuously differentiable mappings, we develop an interior path following following method, which enables us to give a constructive proof of the general Brouwer fixed point theorem and thus to solve fixed point problems in a class of non-convex sets. Under suitable conditions, a smooth path can be proven to exist. This can lead to an implementable globally convergent algorithm. Several numerical examples are given to illustrate the results of this paper.
Abstract: This paper emphasizes on the application of genetic algorithm (GA) to optimize the parameters of the TMD for achieving the best results in the reduction of the building response under earthquake excitations. The Integral of the Time multiplied Absolute value of the Error (ITAE) based on relative displacement of all floors in the building is taken as a performance index of the optimization criterion. The problem of robustly TMD controller design is formatted as an optimization problem based on the ITAE performance index to be solved using GA that has a story ability to find the most optimistic results. An 11–story realistic building, located in the city of Rasht, Iran is considered as a test system to demonstrate effectiveness of the proposed GA based TMD (GATMD) controller without specifying which mode should be controlled. The results of the proposed GATMD controller are compared with the uncontrolled structure through timedomain simulation and some performance indices. The results analysis reveals that the designed GA based TMD controller has an excellent capability in reduction of the seismically excited example building and the ITAE performance, that is so for remains as unknown, can be introduced a new criteria - method for structural dynamic design.
Abstract: Effective estimation of just noticeable distortion (JND) for images is helpful to increase the efficiency of a compression algorithm in which both the statistical redundancy and the perceptual redundancy should be accurately removed. In this paper, we design a DCT-based model for estimating JND profiles of color images. Based on a mathematical model of measuring the base detection threshold for each DCT coefficient in the color component of color images, the luminance masking adjustment, the contrast masking adjustment, and the cross masking adjustment are utilized for luminance component, and the variance-based masking adjustment based on the coefficient variation in the block is proposed for chrominance components. In order to verify the proposed model, the JND estimator is incorporated into the conventional JPEG coder to improve the compression performance. A subjective and fair viewing test is designed to evaluate the visual quality of the coding image under the specified viewing condition. The simulation results show that the JPEG coder integrated with the proposed DCT-based JND model gives better coding bit rates at visually lossless quality for a variety of color images.
Abstract: In this paper we propose a Particle Swarm heuristic
optimized Multi-Antenna (MA) system. Efficient MA systems
detection is performed using a robust stochastic evolutionary
computation algorithm based on movement and intelligence of
swarms. This iterative particle swarm optimized (PSO) detector
significantly reduces the computational complexity of conventional
Maximum Likelihood (ML) detection technique. The simulation
results achieved with this proposed MA-PSO detection algorithm
show near optimal performance when compared with ML-MA
receiver. The performance of proposed detector is convincingly
better for higher order modulation schemes and large number of
antennas where conventional ML detector becomes non-practical.
Abstract: This paper investigates how the use of machine learning techniques can significantly predict the three major dimensions of learner-s emotions (pleasure, arousal and dominance) from brainwaves. This study has adopted an experimentation in which participants were exposed to a set of pictures from the International Affective Picture System (IAPS) while their electrical brain activity was recorded with an electroencephalogram (EEG). The pictures were already rated in a previous study via the affective rating system Self-Assessment Manikin (SAM) to assess the three dimensions of pleasure, arousal, and dominance. For each picture, we took the mean of these values for all subjects used in this previous study and associated them to the recorded brainwaves of the participants in our study. Correlation and regression analyses confirmed the hypothesis that brainwave measures could significantly predict emotional dimensions. This can be very useful in the case of impassive, taciturn or disabled learners. Standard classification techniques were used to assess the reliability of the automatic detection of learners- three major dimensions from the brainwaves. We discuss the results and the pertinence of such a method to assess learner-s emotions and integrate it into a brainwavesensing Intelligent Tutoring System.
Abstract: The overall objective of this paper is to retrieve soil
surfaces parameters namely, roughness and soil moisture related to
the dielectric constant by inverting the radar backscattered signal
from natural soil surfaces.
Because the classical description of roughness using statistical
parameters like the correlation length doesn't lead to satisfactory
results to predict radar backscattering, we used a multi-scale
roughness description using the wavelet transform and the Mallat
algorithm. In this description, the surface is considered as a
superposition of a finite number of one-dimensional Gaussian
processes each having a spatial scale. A second step in this study
consisted in adapting a direct model simulating radar backscattering
namely the small perturbation model to this multi-scale surface
description. We investigated the impact of this description on radar
backscattering through a sensitivity analysis of backscattering
coefficient to the multi-scale roughness parameters.
To perform the inversion of the small perturbation multi-scale
scattering model (MLS SPM) we used a multi-layer neural network
architecture trained by backpropagation learning rule. The inversion
leads to satisfactory results with a relative uncertainty of 8%.
Abstract: In order to provide accurate heart rate variability
indices of sympathetic and parasympathetic activity, the low
frequency and high frequency components of an RR heart rate signal
must be adequately separated. This is not always possible by just
applying spectral analysis, as power from the high and low frequency
components often leak into their adjacent bands. Furthermore,
without the respiratory spectra it is not obvious that the low
frequency component is not another respiratory component, which
can appear in the lower band. This paper describes an adaptive filter,
which aids the separation of the low frequency sympathetic and high
frequency parasympathetic components from an ECG R-R interval
signal, enabling the attainment of more accurate heart rate variability
measures. The algorithm is applied to simulated signals and heart rate
and respiratory signals acquired from an ambulatory monitor
incorporating single lead ECG and inductive plethysmography
sensors embedded in a garment. The results show an improvement
over standard heart rate variability spectral measurements.
Abstract: In data mining, the association rules are used to find
for the associations between the different items of the transactions
database. As the data collected and stored, rules of value can be found
through association rules, which can be applied to help managers
execute marketing strategies and establish sound market frameworks.
This paper aims to use Fuzzy Frequent Pattern growth (FFP-growth)
to derive from fuzzy association rules. At first, we apply fuzzy
partition methods and decide a membership function of quantitative
value for each transaction item. Next, we implement FFP-growth
to deal with the process of data mining. In addition, in order to
understand the impact of Apriori algorithm and FFP-growth algorithm
on the execution time and the number of generated association
rules, the experiment will be performed by using different sizes of
databases and thresholds. Lastly, the experiment results show FFPgrowth
algorithm is more efficient than other existing methods.
Abstract: In this paper a new cost function for blind equalization
is proposed. The proposed cost function, referred to as the modified
maximum normalized cumulant criterion (MMNC), is an extension
of the previously proposed maximum normalized cumulant criterion
(MNC). While the MNC requires a separate phase recovery system
after blind equalization, the MMNC performs joint blind equalization
and phase recovery. To achieve this, the proposed algorithm
maximizes a cost function that considers both amplitude and phase of
the equalizer output. The simulation results show that the proposed
algorithm has an improved channel equalization effect than the MNC
algorithm and simultaneously can correct the phase error that the
MNC algorithm is unable to do. The simulation results also show that
the MMNC algorithm has lower complexity than the MNC algorithm.
Moreover, the MMNC algorithm outperforms the MNC algorithm
particularly when the symbols block size is small.
Abstract: In this paper, we propose APO, a new packet scheduling
scheme with Quality of Service (QoS) support for hybrid of
real and non-real time services in HSDPA networks. The APO
scheduling algorithm is based on the effective channel anticipation
model. In contrast to the traditional schemes, the proposed method is
implemented based on a cyclic non-work-conserving discipline.
Simulation results indicated that proposed scheme has good
capability to maximize the channel usage efficiency in compared to
another exist scheduling methods. Simulation results demonstrate the
effectiveness of the proposed algorithm.
Abstract: This paper presents a hybrid algorithm for solving a timetabling problem, which is commonly encountered in many universities. The problem combines both teacher assignment and course scheduling problems simultaneously, and is presented as a mathematical programming model. However, this problem becomes intractable and it is unlikely that a proven optimal solution can be obtained by an integer programming approach, especially for large problem instances. A hybrid algorithm that combines an integer programming approach, a greedy heuristic and a modified simulated annealing algorithm collaboratively is proposed to solve the problem. Several randomly generated data sets of sizes comparable to that of an institution in Indonesia are solved using the proposed algorithm. Computational results indicate that the algorithm can overcome difficulties of large problem sizes encountered in previous related works.
Abstract: This work presents a recursive identification algorithm. This algorithm relates to the identification of closed loop system with Variable Structure Controller. The approach suggested includes two stages. In the first stage a genetic algorithm is used to obtain the parameters of switching function which gives a control signal rich in commutations (i.e. a control signal whose spectral characteristics are closest possible to those of a white noise signal). The second stage consists in the identification of the system parameters by the instrumental variable method and using the optimal switching function parameters obtained with the genetic algorithm. In order to test the validity of this algorithm a simulation example is presented.
Abstract: We constructed a method of noise reduction for
JPEG-compressed image based on Bayesian inference using the
maximizer of the posterior marginal (MPM) estimate. In this method,
we tried the MPM estimate using two kinds of likelihood, both of
which enhance grayscale images converted into the JPEG-compressed
image through the lossy JPEG image compression. One is the
deterministic model of the likelihood and the other is the probabilistic
one expressed by the Gaussian distribution. Then, using the Monte
Carlo simulation for grayscale images, such as the 256-grayscale
standard image “Lena" with 256 × 256 pixels, we examined the
performance of the MPM estimate based on the performance measure
using the mean square error. We clarified that the MPM estimate via
the Gaussian probabilistic model of the likelihood is effective for
reducing noises, such as the blocking artifacts and the mosquito noise,
if we set parameters appropriately. On the other hand, we found that
the MPM estimate via the deterministic model of the likelihood is not
effective for noise reduction due to the low acceptance ratio of the
Metropolis algorithm.
Abstract: Most integrated inertial navigation systems (INS) and
global positioning systems (GPS) have been implemented using the
Kalman filtering technique with its drawbacks related to the need for
predefined INS error model and observability of at least four
satellites. Most recently, a method using a hybrid-adaptive network
based fuzzy inference system (ANFIS) has been proposed which is
trained during the availability of GPS signal to map the error
between the GPS and the INS. Then it will be used to predict the
error of the INS position components during GPS signal blockage.
This paper introduces a genetic optimization algorithm that is used to
update the ANFIS parameters with respect to the INS/GPS error
function used as the objective function to be minimized. The results
demonstrate the advantages of the genetically optimized ANFIS for
INS/GPS integration in comparison with conventional ANFIS
specially in the cases of satellites- outages. Coping with this problem
plays an important role in assessment of the fusion approach in land
navigation.
Abstract: Automated discovery of Rule is, due to its applicability, one of the most fundamental and important method in KDD. It has been an active research area in the recent past. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and easy to understand systems is Hierarchical Production rule (HPRs) system. A HPR, a standard production rule augmented with generality and specificity information, is of the following form: Decision If < condition> Generality Specificity . HPRs systems are capable of handling taxonomical structures inherent in the knowledge about the real world. This paper focuses on the issue of mining Quantified rules with crisp hierarchical structure using Genetic Programming (GP) approach to knowledge discovery. The post-processing scheme presented in this work uses Quantified production rules as initial individuals of GP and discovers hierarchical structure. In proposed approach rules are quantified by using Dempster Shafer theory. Suitable genetic operators are proposed for the suggested encoding. Based on the Subsumption Matrix(SM), an appropriate fitness function is suggested. Finally, Quantified Hierarchical Production Rules (HPRs) are generated from the discovered hierarchy, using Dempster Shafer theory. Experimental results are presented to demonstrate the performance of the proposed algorithm.
Abstract: Like any sentient organism, a smart environment
relies first and foremost on sensory data captured from the real
world. The sensory data come from sensor nodes of different
modalities deployed on different locations forming a Wireless Sensor
Network (WSN). Embedding smart sensors in humans has been a
research challenge due to the limitations imposed by these sensors
from computational capabilities to limited power. In this paper, we
first propose a practical WSN application that will enable blind
people to see what their neighboring partners can see. The challenge
is that the actual mapping between the input images to brain pattern
is too complex and not well understood. We also study the
connectivity problem in 3D/2D wireless sensor networks and propose
distributed efficient algorithms to accomplish the required
connectivity of the system. We provide a new connectivity algorithm
CDCA to connect disconnected parts of a network using cooperative
diversity. Through simulations, we analyze the connectivity gains
and energy savings provided by this novel form of cooperative
diversity in WSNs.