Abstract: The evaluation and measurement of human body
dimensions are achieved by physical anthropometry. This research
was conducted in view of the importance of anthropometric indices
of the face in forensic medicine, surgery, and medical imaging. The
main goal of this research is to optimization of facial feature point by
establishing a mathematical relationship among facial features and
used optimize feature points for age classification. Since selected
facial feature points are located to the area of mouth, nose, eyes and
eyebrow on facial images, all desire facial feature points are extracted
accurately. According this proposes method; sixteen Euclidean
distances are calculated from the eighteen selected facial feature
points vertically as well as horizontally. The mathematical
relationships among horizontal and vertical distances are established.
Moreover, it is also discovered that distances of the facial feature
follows a constant ratio due to age progression. The distances
between the specified features points increase with respect the age
progression of a human from his or her childhood but the ratio of the
distances does not change (d = 1 .618 ) . Finally, according to the
proposed mathematical relationship four independent feature
distances related to eight feature points are selected from sixteen
distances and eighteen feature point-s respectively. These four feature
distances are used for classification of age using Support Vector
Machine (SVM)-Sequential Minimal Optimization (SMO) algorithm
and shown around 96 % accuracy. Experiment result shows the
proposed system is effective and accurate for age classification.
Abstract: Identity verification of authentic persons by their multiview faces is a real valued problem in machine vision. Multiview faces are having difficulties due to non-linear representation in the feature space. This paper illustrates the usability of the generalization of LDA in the form of canonical covariate for face recognition to multiview faces. In the proposed work, the Gabor filter bank is used to extract facial features that characterized by spatial frequency, spatial locality and orientation. Gabor face representation captures substantial amount of variations of the face instances that often occurs due to illumination, pose and facial expression changes. Convolution of Gabor filter bank to face images of rotated profile views produce Gabor faces with high dimensional features vectors. Canonical covariate is then used to Gabor faces to reduce the high dimensional feature spaces into low dimensional subspaces. Finally, support vector machines are trained with canonical sub-spaces that contain reduced set of features and perform recognition task. The proposed system is evaluated with UMIST face database. The experiment results demonstrate the efficiency and robustness of the proposed system with high recognition rates.
Abstract: Because of increasing demands for security in today-s
society and also due to paying much more attention to machine
vision, biometric researches, pattern recognition and data retrieval in
color images, face detection has got more application. In this article
we present a scientific approach for modeling human skin color, and
also offer an algorithm that tries to detect faces within color images
by combination of skin features and determined threshold in the
model. Proposed model is based on statistical data in different color
spaces. Offered algorithm, using some specified color threshold, first,
divides image pixels into two groups: skin pixel group and non-skin
pixel group and then based on some geometric features of face
decides which area belongs to face.
Two main results that we received from this research are as follow:
first, proposed model can be applied easily on different databases and
color spaces to establish proper threshold. Second, our algorithm can
adapt itself with runtime condition and its results demonstrate
desirable progress in comparison with similar cases.
Abstract: End milling process is one of the common metal
cutting operations used for machining parts in manufacturing
industry. It is usually performed at the final stage in manufacturing a
product and surface roughness of the produced job plays an
important role. In general, the surface roughness affects wear
resistance, ductility, tensile, fatigue strength, etc., for machined parts
and cannot be neglected in design. In the present work an
experimental investigation of end milling of aluminium alloy with
carbide tool is carried out and the effect of different cutting
parameters on the response are studied with three-dimensional
surface plots. An artificial neural network (ANN) is used to establish
the relationship between the surface roughness and the input cutting
parameters (i.e., spindle speed, feed, and depth of cut). The Matlab
ANN toolbox works on feed forward back propagation algorithm is
used for modeling purpose. 3-12-1 network structure having
minimum average prediction error found as best network architecture
for predicting surface roughness value. The network predicts surface
roughness for unseen data and found that the result/prediction is
better. For desired surface finish of the component to be produced
there are many different combination of cutting parameters are
available. The optimum cutting parameter for obtaining desired
surface finish, to maximize tool life is predicted. The methodology is
demonstrated, number of problems are solved and algorithm is coded
in Matlab®.
Abstract: Static Var Compensator (SVC) is a shunt type FACTS
device which is used in power system primarily for the purpose of
voltage and reactive power control. In this paper, a fuzzy logic based
supplementary controller for Static Var Compensator (SVC) is
developed which is used for damping the rotor angle oscillations and
to improve the transient stability of the power system. Generator
speed and the electrical power are chosen as input signals for the
Fuzzy Logic Controller (FLC). The effectiveness and feasibility of
the proposed control is demonstrated with Single Machine Infinite
Bus (SMIB) system and multimachine system (WSCC System)
which show improvement over the use of a fixed parameter
controller.
Abstract: The problem of ranking (rank regression) has become popular in the machine learning community. This theory relates to problems, in which one has to predict (guess) the order between objects on the basis of vectors describing their observed features. In many ranking algorithms a convex loss function is used instead of the 0-1 loss. It makes these procedures computationally efficient. Hence, convex risk minimizers and their statistical properties are investigated in this paper. Fast rates of convergence are obtained under conditions, that look similarly to the ones from the classification theory. Methods used in this paper come from the theory of U-processes as well as empirical processes.
Abstract: In this work a new offline signature recognition system
based on Radon Transform, Fractal Dimension (FD) and Support Vector Machine (SVM) is presented. In the first step, projections of
original signatures along four specified directions have been performed using radon transform. Then, FDs of four obtained
vectors are calculated to construct a feature vector for each
signature. These vectors are then fed into SVM classifier for recognition of signatures. In order to evaluate the effectiveness of
the system several experiments are carried out. Offline signature
database from signature verification competition (SVC) 2004 is used
during all of the tests. Experimental result indicates that the proposed method achieved high accuracy rate in signature recognition.
Abstract: The numerous qualities of squirrel cage induction
machines enhance their use in industry. However, various faults can
occur, such as stator short-circuits and rotor failures.
In this paper, we use a technique based on the spectral analysis of
stator current in order to detect the fault in the machine: broken rotor
bars. Thus, the number effect of the breaks has been highlighted. The
effect is highlighted by considering the machine controlled by the
Direct Torque Control (DTC). The key to fault detection is the
development of a simplified dynamic model of a squirrel cage
induction motor taking account the broken bars fault and the stator
current spectrum analysis (FFT).
Abstract: This paper explores steady-state characteristics of
grid-connected doubly fed induction motor (DFIM) in case of unity
power factor operation. Based on the synchronized mathematical
model, analytic determination of the control laws is presented and
illustrated by various figures to understand the effect of the applied
rotor voltage on the speed and the active power. On other hand,
unlike previous works where the stator resistance was neglected, in
this work, stator resistance is included such that the equations can be
applied to small wind turbine generators which are becoming more
popular. Finally the work is crowned by integration of the studied
induction generator in a wind system where an open loop control is
proposed confers a remarkable simplicity of implementation
compared to the known methods.
Abstract: Using spatial models as a shared common basis of
information about the environment for different kinds of contextaware
systems has been a heavily researched topic in the last years.
Thereby the research focused on how to create, to update, and to
merge spatial models so as to enable highly dynamic, consistent and
coherent spatial models at large scale. In this paper however, we
want to concentrate on how context-aware applications could use this
information so as to adapt their behavior according to the situation
they are in. The main idea is to provide the spatial model
infrastructure with a situation recognition component based on
generic situation templates. A situation template is – as part of a
much larger situation template library – an abstract, machinereadable
description of a certain basic situation type, which could be
used by different applications to evaluate their situation. In this
paper, different theoretical and practical issues – technical, ethical
and philosophical ones – are discussed important for understanding
and developing situation dependent systems based on situation
templates. A basic system design is presented which allows for the
reasoning with uncertain data using an improved version of a
learning algorithm for the automatic adaption of situation templates.
Finally, for supporting the development of adaptive applications, we
present a new situation-aware adaptation concept based on
workflows.
Abstract: Omni directional mobile robots have been popularly
employed in several applications especially in soccer player robots
considered in Robocup competitions. However, Omni directional
navigation system, Omni-vision system and solenoid kicking
mechanism in such mobile robots have not ever been combined. This
situation brings the idea of a robot with no head direction into
existence, a comprehensive Omni directional mobile robot. Such a
robot can respond more quickly and it would be capable for more
sophisticated behaviors with multi-sensor data fusion algorithm for
global localization base on the data fusion. This paper has tried to
focus on the research improvements in the mechanical, electrical and
software design of the robots of team ADRO Iran. The main
improvements are the world model, the new strategy framework,
mechanical structure, Omni-vision sensor for object detection, robot
path planning, active ball handling mechanism and the new kicker
design, , and other subjects related to mobile robot
Abstract: A cognitive collaborative reinforcement learning
algorithm (CCRL) that incorporates an advisor into the learning
process is developed to improve supervised learning. An autonomous
learner is enabled with a self awareness cognitive skill to decide
when to solicit instructions from the advisor. The learner can also
assess the value of advice, and accept or reject it. The method is
evaluated for robotic motion planning using simulation. Tests are
conducted for advisors with skill levels from expert to novice. The
CCRL algorithm and a combined method integrating its logic with
Clouse-s Introspection Approach, outperformed a base-line fully
autonomous learner, and demonstrated robust performance when
dealing with various advisor skill levels, learning to accept advice
received from an expert, while rejecting that of less skilled
collaborators. Although the CCRL algorithm is based on RL, it fits
other machine learning methods, since advisor-s actions are only
added to the outer layer.
Abstract: During recent years, attention in 'Green Computing'
has moved research into energy-saving techniques for home
computers to enterprise systems' Client and Server machines. Saving
energy or reduction of carbon footprints is one of the aspects of
Green Computing. The research in the direction of Green Computing
is more than just saving energy and reducing carbon foot prints. This
study provides a brief account of Green Computing. The emphasis of
this study is on current trends in Green Computing; challenges in the
field of Green Computing and the future trends of Green Computing.
Abstract: This paper presents the development of low cost Nano membrane fabrication system. The system is specially designed for anodic aluminum oxide membrane. This system is capable to perform the processes such as anodization and electro-polishing. The designed machine was successfully tested for 'mild anodization' (MA) for 48 hours and 'hard anodization' (HA) for 3 hours at constant 0oC. The system is digitally controlled and guided for temperature maintenance during anodization and electro-polishing. The total cost of the developed machine is 20 times less than the multi-cooling systems available in the market which are generally used for this purpose.
Abstract: In theoretical computer science, the Turing machine has played a number of important roles in understanding and exploiting basic concepts and mechanisms in computing and information processing [20]. It is a simple mathematical model of computers [9]. After that, M.Blum and C.Hewitt first proposed two-dimensional automata as a computational model of two-dimensional pattern processing, and investigated their pattern recognition abilities in 1967 [7]. Since then, a lot of researchers in this field have been investigating many properties about automata on a two- or three-dimensional tape. On the other hand, the question of whether processing fourdimensional digital patterns is much more difficult than two- or threedimensional ones is of great interest from the theoretical and practical standpoints. Thus, the study of four-dimensional automata as a computasional model of four-dimensional pattern processing has been meaningful [8]-[19],[21]. This paper introduces a cooperating system of four-dimensional finite automata as one model of four-dimensional automata. A cooperating system of four-dimensional finite automata consists of a finite number of four-dimensional finite automata and a four-dimensional input tape where these finite automata work independently (in parallel). Those finite automata whose input heads scan the same cell of the input tape can communicate with each other, that is, every finite automaton is allowed to know the internal states of other finite automata on the same cell it is scanning at the moment. In this paper, we mainly investigate some accepting powers of a cooperating system of eight- or seven-way four-dimensional finite automata. The seven-way four-dimensional finite automaton is an eight-way four-dimensional finite automaton whose input head can move east, west, south, north, up, down, or in the fu-ture, but not in the past on a four-dimensional input tape.
Abstract: Grid computing is a form of distributed computing
that involves coordinating and sharing computational power, data
storage and network resources across dynamic and geographically
dispersed organizations. Scheduling onto the Grid is NP-complete,
so there is no best scheduling algorithm for all grid computing
systems. An alternative is to select an appropriate scheduling
algorithm to use in a given grid environment because of the
characteristics of the tasks, machines and network connectivity. Job
and resource scheduling is one of the key research area in grid
computing. The goal of scheduling is to achieve highest possible
system throughput and to match the application need with the
available computing resources. Motivation of the survey is to
encourage the amateur researcher in the field of grid computing, so
that they can understand easily the concept of scheduling and can
contribute in developing more efficient scheduling algorithm. This
will benefit interested researchers to carry out further work in this
thrust area of research.
Abstract: An image texture analysis and target recognition approach of using an improved image texture feature coding method (TFCM) and Support Vector Machine (SVM) for target detection is presented. With our proposed target detection framework, targets of interest can be detected accurately. Cascade-Sliding-Window technique was also developed for automated target localization. Application to mammogram showed that over 88% of normal mammograms and 80% of abnormal mammograms can be correctly identified. The approach was also successfully applied to Synthetic Aperture Radar (SAR) and Ground Penetrating Radar (GPR) images for target detection.
Abstract: The paper presents the case study of hazard
identification and sensitivity of potential resource of emergency
water supply as part of the application of methodology classifying
the resources of drinking water for emergency supply of population.
The case study has been carried out on a selected resource of
emergency water supply in one region of the Czech Republic. The
hazard identification and sensitivity of potential resource of
emergency water supply is based on a unique procedure and
developed general registers of selected types of hazards and
sensitivities. The registers have been developed with the help of the
“Fault Tree Analysis” method in combination with the “What if
method”. The identified hazards for the assessed resource include
hailstorms and torrential rains, drought, soil erosion, accidents of
farm machinery, and agricultural production. The developed registers
of hazards and vulnerabilities and a semi-quantitative assessment of
hazards for individual parts of hydrological structure and
technological elements of presented drilled wells are the basis for a
semi-quantitative risk assessment of potential resource of emergency
supply of population and the subsequent classification of such
resource within the system of crisis planning.
Abstract: Genetic algorithms (GAs) have been widely used for
global optimization problems. The GA performance depends highly
on the choice of the search space for each parameter to be optimized.
Often, this choice is a problem-based experience. The search space
being a set of potential solutions may contain the global optimum
and/or other local optimums. A bad choice of this search space
results in poor solutions. In this paper, our approach consists in
extending the search space boundaries during the GA optimization,
only when it is required. This leads to more diversification of GA
population by new solutions that were not available with fixed search
space boundaries. So, these dynamic search spaces can improve the
GA optimization performances. The proposed approach is applied to
power system stabilizer optimization for multimachine power system
(16-generator and 68-bus). The obtained results are evaluated and
compared with those obtained by ordinary GAs. Eigenvalue analysis
and nonlinear system simulation results show the effectiveness of the
proposed approach to damp out the electromechanical oscillation and
enhance the global system stability.
Abstract: Industrial surveys shows that manufacturing
companies define the qualities of thermal removing process based on
the dimension and physical appearance of the cutting material
surface. Therefore, the roughness of the surface area of the material
cut by the plasma arc cutting process and the rate of the removed
material by the manual plasma arc cutting machine was importantly
considered. Plasma arc cutter Selco Genesis 90 was used to cut
Standard AISI 1017 Steel of 200 mm x100 mm x 6 mm manually
based on the selected parameters setting. The material removal rate
(MRR) was measured by determining the weight of the specimens
before and after the cutting process. The surface roughness (SR)
analysis was conducted using Mitutoyo CS-3100 to determine the
average roughness value (Ra). Taguchi method was utilized to
achieve optimum condition for both outputs studied. The
microstructure analysis in the region of the cutting surface is
performed using SEM. The results reveal that the SR values are
inversely proportional to the MRR values. The quality of the surface
roughness depends on the dross peak that occurred after the cutting
process.