Abstract: In this paper, we introduced a gradient-based inverse
solver to obtain the missing boundary conditions based on the
readings of internal thermocouples. The results show that the method
is very sensitive to measurement errors, and becomes unstable when
small time steps are used. The artificial neural networks are shown to
be capable of capturing the whole thermal history on the run-out
table, but are not very effective in restoring the detailed behavior of
the boundary conditions. Also, they behave poorly in nonlinear cases
and where the boundary condition profile is different.
GA and PSO are more effective in finding a detailed
representation of the time-varying boundary conditions, as well as in
nonlinear cases. However, their convergence takes longer. A
variation of the basic PSO, called CRPSO, showed the best
performance among the three versions. Also, PSO proved to be
effective in handling noisy data, especially when its performance
parameters were tuned. An increase in the self-confidence parameter
was also found to be effective, as it increased the global search
capabilities of the algorithm. RPSO was the most effective variation
in dealing with noise, closely followed by CRPSO. The latter
variation is recommended for inverse heat conduction problems, as it
combines the efficiency and effectiveness required by these
problems.
Abstract: A model was constructed to predict the amount of
solar radiation that will make contact with the surface of the earth in
a given location an hour into the future. This project was supported
by the Southern Company to determine at what specific times during
a given day of the year solar panels could be relied upon to produce
energy in sufficient quantities. Due to their ability as universal
function approximators, an artificial neural network was used to
estimate the nonlinear pattern of solar radiation, which utilized
measurements of weather conditions collected at the Griffin, Georgia
weather station as inputs. A number of network configurations and
training strategies were utilized, though a multilayer perceptron with
a variety of hidden nodes trained with the resilient propagation
algorithm consistently yielded the most accurate predictions. In
addition, a modeled direct normal irradiance field and adjacent
weather station data were used to bolster prediction accuracy. In later
trials, the solar radiation field was preprocessed with a discrete
wavelet transform with the aim of removing noise from the
measurements. The current model provides predictions of solar
radiation with a mean square error of 0.0042, though ongoing efforts
are being made to further improve the model’s accuracy.
Abstract: Nature is the immense gifted source for solving
complex problems. It always helps to find the optimal solution to
solve the problem. Mobile Ad Hoc NETwork (MANET) is a wide
research area of networks which has set of independent nodes. The
characteristics involved in MANET’s are Dynamic, does not depend
on any fixed infrastructure or centralized networks, High mobility.
The Bio-Inspired algorithms are mimics the nature for solving
optimization problems opening a new era in MANET. The typical
Swarm Intelligence (SI) algorithms are Ant Colony Optimization
(ACO), Artificial Bee Colony (ABC), Particle Swarm Optimization
(PSO), Modified Termite Algorithm, Bat Algorithm (BA), Wolf
Search Algorithm (WSA) and so on. This work mainly concentrated
on nature of MANET and behavior of nodes. Also it analyses various
performance metrics such as throughput, QoS and End-to-End delay
etc.
Abstract: Data mining idea is mounting rapidly in admiration
and also in their popularity. The foremost aspire of data mining
method is to extract data from a huge data set into several forms that
could be comprehended for additional use. The data mining is a
technology that contains with rich potential resources which could be
supportive for industries and businesses that pay attention to collect
the necessary information of the data to discover their customer’s
performances. For extracting data there are several methods are
available such as Classification, Clustering, Association,
Discovering, and Visualization… etc., which has its individual and
diverse algorithms towards the effort to fit an appropriate model to
the data. STATISTICA mostly deals with excessive groups of data
that imposes vast rigorous computational constraints. These results
trials challenge cause the emergence of powerful STATISTICA Data
Mining technologies. In this survey an overview of the STATISTICA
software is illustrated along with their significant features.
Abstract: The key role in phenomenological modelling of cyclic
plasticity is good understanding of stress-strain behaviour of given
material. There are many models describing behaviour of materials
using numerous parameters and constants. Combination of individual
parameters in those material models significantly determines whether
observed and predicted results are in compliance. Parameter
identification techniques such as random gradient, genetic algorithm
and sensitivity analysis are used for identification of parameters using
numerical modelling and simulation. In this paper genetic algorithm
and sensitivity analysis are used to study effect of 4 parameters of
modified AbdelKarim-Ohno cyclic plasticity model. Results
predicted by Finite Element (FE) simulation are compared with
experimental data from biaxial ratcheting test with semi-elliptical
loading path.
Abstract: In this paper, we discuss the performance of applying
hybrid spiral dynamic bacterial chemotaxis (HSDBC) optimisation
algorithm on an intelligent controller for a differential drive robot. A
unicycle class of differential drive robot is utilised to serve as a basis
application to evaluate the performance of the HSDBC algorithm. A
hybrid fuzzy logic controller is developed and implemented for the
unicycle robot to follow a predefined trajectory. Trajectories of
various frictional profiles and levels were simulated to evaluate the
performance of the robot at different operating conditions. Controller
gains and scaling factors were optimised using HSDBC and the
performance is evaluated in comparison to previously adopted
optimisation algorithms. The HSDBC has proven its feasibility in
achieving a faster convergence toward the optimal gains and resulted
in a superior performance.
Abstract: The present work describes the implementation of the
Enhanced Collaborative Optimization (ECO) multilevel architecture
with a gradient-based optimization algorithm with the aim of
performing a multidisciplinary design optimization of a generic
unmanned aerial vehicle with morphing technologies. The concepts
of weighting coefficient and dynamic compatibility parameter are
presented for the ECO architecture. A routine that calculates the
aircraft performance for the user defined mission profile and vehicle’s
performance requirements has been implemented using low fidelity
models for the aerodynamics, stability, propulsion, weight, balance
and flight performance. A benchmarking case study for evaluating
the advantage of using a variable span wing within the optimization
methodology developed is presented.
Abstract: The Markov decision process (MDP) based
methodology is implemented in order to establish the optimal
schedule which minimizes the cost. Formulation of MDP problem
is presented using the information about the current state of pipe,
improvement cost, failure cost and pipe deterioration model. The
objective function and detailed algorithm of dynamic programming
(DP) are modified due to the difficulty of implementing the
conventional DP approaches. The optimal schedule derived from
suggested model is compared to several policies via Monte
Carlo simulation. Validity of the solution and improvement in
computational time are proved.
Abstract: Customer churn prediction is one of the most useful
areas of study in customer analytics. Due to the enormous amount
of data available for such predictions, machine learning and data
mining have been heavily used in this domain. There exist many
machine learning algorithms directly applicable for the problem of
customer churn prediction, and here, we attempt to experiment on
a novel approach by using a cognitive learning based technique in
an attempt to improve the results obtained by using a combination
of supervised learning methods, with cognitive unsupervised learning
methods.
Abstract: This work deals with parameter identification of
permanent magnet motors, a class of ac motor which is particularly
important in industrial automation due to characteristics like
applications high performance, are very attractive for applications
with limited space and reducing the need to eliminate because they
have reduced size and volume and can operate in a wide speed range,
without independent ventilation. By using experimental data and
genetic algorithm we have been able to extract values for both the
motor inductance and the electromechanical coupling constant, which
are then compared to measured and/or expected values.
Abstract: Robotic surgery is used to enhance minimally invasive
surgical procedure. It provides greater degree of freedom for surgical
tools but lacks of haptic feedback system to provide sense of touch to
the surgeon. Surgical robots work on master-slave operation, where
user is a master and robotic arms are the slaves. Current, surgical
robots provide precise control of the surgical tools, but heavily rely
on visual feedback, which sometimes cause damage to the inner
organs. The goal of this research was to design and develop a realtime
Simulink based robotic system to study force feedback
mechanism during instrument-object interaction. Setup includes three
VelmexXSlide assembly (XYZ Stage) for three dimensional
movement, an end effector assembly for forceps, electronic circuit for
four strain gages, two Novint Falcon 3D gaming controllers,
microcontroller board with linear actuators, MATLAB and Simulink
toolboxes. Strain gages were calibrated using Imada Digital Force
Gauge device and tested with a hard-core wire to measure
instrument-object interaction in the range of 0-35N. Designed
Simulink model successfully acquires 3D coordinates from two
Novint Falcon controllers and transfer coordinates to the XYZ stage
and forceps. Simulink model also reads strain gages signal through
10-bit analog to digital converter resolution of a microcontroller
assembly in real time, converts voltage into force and feedback the
output signals to the Novint Falcon controller for force feedback
mechanism. Experimental setup allows user to change forward
kinematics algorithms to achieve the best-desired movement of the
XYZ stage and forceps. This project combines haptic technology
with surgical robot to provide sense of touch to the user controlling
forceps through machine-computer interface.
Abstract: The detection of moving objects from a video image
sequences is very important for object tracking, activity recognition,
and behavior understanding in video surveillance.
The most used approach for moving objects detection / tracking is
background subtraction algorithms. Many approaches have been
suggested for background subtraction. But, these are illumination
change sensitive and the solutions proposed to bypass this problem
are time consuming.
In this paper, we propose a robust yet computationally efficient
background subtraction approach and, mainly, focus on the ability to
detect moving objects on dynamic scenes, for possible applications in
complex and restricted access areas monitoring, where moving and
motionless persons must be reliably detected. It consists of three
main phases, establishing illumination changes invariance,
background/foreground modeling and morphological analysis for
noise removing.
We handle illumination changes using Contrast Limited Histogram
Equalization (CLAHE), which limits the intensity of each pixel to
user determined maximum. Thus, it mitigates the degradation due to
scene illumination changes and improves the visibility of the video
signal. Initially, the background and foreground images are extracted
from the video sequence. Then, the background and foreground
images are separately enhanced by applying CLAHE.
In order to form multi-modal backgrounds we model each channel
of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture
Model (GMM). Finally, we post process the resulting binary
foreground mask using morphological erosion and dilation
transformations to remove possible noise.
For experimental test, we used a standard dataset to challenge the
efficiency and accuracy of the proposed method on a diverse set of
dynamic scenes.
Abstract: Power systems are operating under stressed condition
due to continuous increase in demand of load. This can lead to
voltage instability problem when face additional load increase or
contingency. In order to avoid voltage instability suitable size of
reactive power compensation at optimal location in the system is
required which improves the load margin. This work aims at
obtaining optimal size as well as location of compensation in the 39-
bus New England system with the help of Bacteria Foraging and
Genetic algorithms. To reduce the computational time the work
identifies weak candidate buses in the system, and then picks only
two of them to take part in the optimization. The objective function is
based on a recently proposed voltage stability index which takes into
account the weighted average sensitivity index is a simpler and faster
approach than the conventional CPF algorithm. BFOA has been
found to give better results compared to GA.
Abstract: Numerical studies were conducted using Lattice
Boltzmann Method (LBM) to study the natural convection in a square
cavity in the presence of roughness. An algorithm based on a single
relaxation time Bhatnagar-Gross-Krook (BGK) model of Lattice
Boltzmann Method (LBM) was developed. Roughness was
introduced on both the hot and cold walls in the form of sinusoidal
roughness elements. The study was conducted for a Newtonian fluid
of Prandtl number (Pr) 1.0. The range of Ra number was explored
from 10^3 to 10^6 in a laminar region. Thermal and hydrodynamic
behavior of fluid was analyzed using a differentially heated square
cavity with roughness elements present on both the hot and cold wall.
Neumann boundary conditions were introduced on horizontal walls
with vertical walls as isothermal. The roughness elements were at the
same boundary condition as corresponding walls. Computational
algorithm was validated against previous benchmark studies
performed with different numerical methods, and a good agreement
was found to exist. Results indicate that the maximum reduction in
the average heat transfer was 16.66 percent at Ra number 10^5.
Abstract: In this paper, we used data mining to extract
biomedical knowledge. In general, complex biomedical data
collected in studies of populations are treated by statistical methods,
although they are robust, they are not sufficient in themselves to
harness the potential wealth of data. For that you used in step two
learning algorithms: the Decision Trees and Support Vector Machine
(SVM). These supervised classification methods are used to make the
diagnosis of thyroid disease. In this context, we propose to promote
the study and use of symbolic data mining techniques.
Abstract: Electroencephalogram (EEG) is a noninvasive
technique that registers signals originating from the firing of neurons
in the brain. The Emotiv EEG Neuroheadset is a consumer product
comprised of 14 EEG channels and was used to record the reactions
of the neurons within the brain to two forms of stimuli in 10
participants. These stimuli consisted of auditory and visual formats
that provided directions of ‘right’ or ‘left.’ Participants were
instructed to raise their right or left arm in accordance with the
instruction given. A scenario in OpenViBE was generated to both
stimulate the participants while recording their data. In OpenViBE,
the Graz Motor BCI Stimulator algorithm was configured to govern
the duration and number of visual stimuli. Utilizing EEGLAB under
the cross platform MATLAB®, the electrodes most stimulated during
the study were defined. Data outputs from EEGLAB were analyzed
using IBM SPSS Statistics® Version 20. This aided in determining
the electrodes to use in the development of a brain-machine interface
(BMI) using real-time EEG signals from the Emotiv EEG
Neuroheadset. Signal processing and feature extraction were
accomplished via the Simulink® signal processing toolbox. An
Arduino™ Duemilanove microcontroller was used to link the Emotiv
EEG Neuroheadset and the right and left Mecha TE™ Hands.
Abstract: This paper describes the problem of building secure
computational services for encrypted information in the Cloud
Computing without decrypting the encrypted data; therefore, it meets
the yearning of computational encryption algorithmic aspiration
model that could enhance the security of big data for privacy,
confidentiality, availability of the users. The cryptographic model
applied for the computational process of the encrypted data is the
Fully Homomorphic Encryption Scheme. We contribute a theoretical
presentations in a high-level computational processes that are based
on number theory and algebra that can easily be integrated and
leveraged in the Cloud computing with detail theoretic mathematical
concepts to the fully homomorphic encryption models. This
contribution enhances the full implementation of big data analytics
based cryptographic security algorithm.
Abstract: Over the past few years, the online multimedia
collection has grown at a fast pace. Several companies showed
interest to study the different ways to organise the amount of audio
information without the need of human intervention to generate
metadata. In the past few years, many applications have emerged on
the market which are capable of identifying a piece of music in a
short time. Different audio effects and degradation make it much
harder to identify the unknown piece. In this paper, an audio
fingerprinting system which makes use of a non-parametric based
algorithm is presented. Parametric analysis is also performed using
Gaussian Mixture Models (GMMs). The feature extraction methods
employed are the Mel Spectrum Coefficients and the MPEG-7 basic
descriptors. Bin numbers replaced the extracted feature coefficients
during the non-parametric modelling. The results show that nonparametric
analysis offer potential results as the ones mentioned in
the literature.
Abstract: In this paper, performances of shuffled frog leaping
algorithm was investigated on the stealth laser dicing process. Effect
of problem on the performance of the algorithm was based on the
tolerance of meandering data. From the customer specification it
could be less than five microns with the target of zero microns.
Currently, the meandering levels are unsatisfactory when compared
to the customer specification. Firstly, the two-level factorial design
was applied to preliminarily study the statistically significant effects
of five process variables. In this study one influential process variable
is integer. From the experimental results, the new operating condition
from the algorithm was superior when compared to the current
manufacturing condition.
Abstract: Content Based Image Retrieval (CBIR) coupled with
Case Based Reasoning (CBR) is a paradigm that is becoming
increasingly popular in the diagnosis and therapy planning of medical
ailments utilizing the digital content of medical images. This paper
presents a survey of some of the promising approaches used in the
detection of abnormalities in retina images as well in
mammographic screening and detection of regions of interest
in MRI scans of the brain. We also describe our proposed
algorithm to detect hard exudates in fundus images of the
retina of Diabetic Retinopathy patients.