Abstract: In this paper we present an approach for 3D face
recognition based on extracting principal components of range
images by utilizing modified PCA methods namely 2DPCA and
bidirectional 2DPCA also known as (2D) 2 PCA.A preprocessing
stage was implemented on the images to smooth them using median
and Gaussian filtering. In the normalization stage we locate the nose
tip to lay it at the center of images then crop each image to a standard
size of 100*100. In the face recognition stage we extract the principal
component of each image using both 2DPCA and (2D) 2 PCA.
Finally, we use Euclidean distance to measure the minimum distance
between a given test image to the training images in the database. We
also compare the result of using both methods. The best result
achieved by experiments on a public face database shows that 83.3
percent is the rate of face recognition for a random facial expression.
Abstract: In this paper, we consider a new particle filter inspired
by biological evolution. In the standard particle filter, a resampling
scheme is used to decrease the degeneracy phenomenon and improve
estimation performance. Unfortunately, however, it could cause the
undesired the particle deprivation problem, as well. In order to
overcome this problem of the particle filter, we propose a novel
filtering method called the genetic filter. In the proposed filter, we
embed the genetic algorithm into the particle filter and overcome the
problems of the standard particle filter. The validity of the proposed
method is demonstrated by computer simulation.
Abstract: Smoke from domestic wood burning has been
identified as a major contributor to air pollution, motivating detailed
emission measurements under controlled conditions. A series of
experiments was performed to characterise the emissions from wood
combustion in a fireplace and in a woodstove of two common species
of trees grown in Spain: Pyrenean oak (Quercus pyrenaica) and
black poplar (Populus nigra). Volatile organic compounds (VOCs) in
the exhaust emissions were collected in Tedlar bags, re-sampled in
sorbent tubes and analysed by thermal desorption-gas
chromatography-flame ionisation detection. Pyrenean oak presented
substantially higher emissions in the woodstove than in the fireplace,
for the majority of compounds. The opposite was observed for
poplar. Among the 45 identified species, benzene and benzenerelated
compounds represent the most abundant group, followed by
oxygenated VOCs and aliphatics. Emission factors obtained in this
study are generally of the same order than those reported for
residential experiments in the USA.
Abstract: The contribution deals with analysis of identity style
at adolescents (N=463) at the age from 16 to 19 (the average age is
17,7 years). We used the Identity Style Inventory by Berzonsky,
distinguishing three basic, measured identity styles: informational,
normative, diffuse-avoidant identity style and also commitment. The
informational identity style influencing on personal adaptability,
coping strategies, quality of life and the normative identity style, it
means the style in which an individual takes on models of authorities
at self-defining were found to have the highest representation in the
studied group of adolescents by higher scores at girls in comparison
with boys. The normative identity style positively correlates with the
informational identity style. The diffuse-avoidant identity style was
found to be positively associated with maladaptive decisional
strategies, neuroticism and depressive reactions. There is the style,
in which the individual shifts aside defining his personality. In our
research sample the lowest score represents it and negatively
correlates with commitment, it means with coping strategies, thrust in
oneself and the surrounding world. The age of adolescents did not
significantly differentiate representation of identity style. We were
finding the model, in which informational and normative identity
style had positive relationship and the informational and diffuseavoidant
style had negative relationship, which were determinated
with commitment. In the same time the commitment is influenced
with other outside factors.
Abstract: In this paper, we aim to investigate a new stability analysis for discrete-time switched linear systems based on the comparison, the overvaluing principle, the application of Borne-Gentina criterion and the Kotelyanski conditions. This stability conditions issued from vector norms correspond to a vector Lyapunov function. In fact, the switched system to be controlled will be represented in the Companion form. A comparison system relative to a regular vector norm is used in order to get the simple arrow form of the state matrix that yields to a suitable use of Borne-Gentina criterion for the establishment of sufficient conditions for global asymptotic stability. This proposed approach could be a constructive solution to the state and static output feedback stabilization problems.
Abstract: Square pipes (pipes with square cross sections) are
being used for various industrial objectives, such as machine
structure components and housing/building elements. The utilization
of them is extending rapidly and widely. Hence, the out-put of those
pipes is increasing and new application fields are continually
developing.
Due to various demands in recent time, the products have to
satisfy difficult specifications with high accuracy in dimensions. The
reshaping process design of pipes with square cross sections;
however, is performed by trial and error and based on expert-s
experience.
In this paper, a computer-aided simulation is developed based on
the 2-D elastic-plastic method with consideration of the shear
deformation to analyze the reshaping process. Effect of various
parameters such as diameter of the circular pipe and mechanical
properties of metal on product dimension and quality can be
evaluated by using this simulation. Moreover, design of reshaping
process include determination of shrinkage of cross section,
necessary number of stands, radius of rolls and height of pipe at each
stand, are investigated. Further, it is shown that there are good
agreements between the results of the design method and the
experimental results.
Abstract: Pabdeh shaly formation (Paleocene-Oligomiocene)
has been expanded in Fars, Khozestan and Lorestan. The lower
lithostratigraphic limit of this formation in Shiraz area is
distinguished from Gurpi formation by purple shale. Its upper limit is
gradational and conformable with Asmari formation. In order to
study sequence stratigraphy and microfacies of Pabdeh formation in
Shiraz area, one stratigraphic section have been chosen (Zanjiran
section). Petrographic studies resulted in the identification of 9
pelagic and calciturbidite microfacies. The calciturbidite microfacies
have been formed when the sea level was high, the rate of carbonate
deposition was high and it slumped into the deep marine. Sequence
stratigraphy studies show that Pabdeh formation in the studied zone
consists of two depositional sequences (DS) that the lower contact is
erosional (purple shale - type one, SBI or type two, SB2) and the
upper contact is correlative conformity (type two, SB2).
Abstract: The avian phytohaemagglutinin skin test is being
proved as an in vivo system for the evaluation an avian in vivo T cell
mitogenicity. The test system was one week old Gallus domesticus
broiler Chickens. Five replicates were done for each of the whole,
1:10 dilutions of each of 0.05 IU tuberculin, tetanus immunoglobulin
and DPT vaccine as test materials. The evaluation parameters were
the skin indurations and lymphoblast percentages in bone marrow
lymphocytes.
Tuberculin indurations were 2.06 and 1.26mm for 0.05 IU
respectively while lymphoblast percent were 0.234 and 0.1
accordingly.
The skin indurations of 135mg/ml and 1.35mg/ml tetanus
immunoglobulin were 4.86 and 3.96mm while lymphoblast
percentages were 0.3 and 0.14 respectively.
The whole DPT and 1:10 concentration were with 4.5 and 3.2mm
while their lymphoblast percentages were 0.28 and 0.12 accordingly.
Thus the mitogenicity of the test materials was of dependant type.
Abstract: Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. The GA has been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly non-linear, mixed integer optimization problems that are typical of complex engineering systems. PSO technique is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. In this paper both PSO and GA optimization are employed for finding stable reduced order models of single-input- single-output large-scale linear systems. Both the techniques guarantee stability of reduced order model if the original high order model is stable. PSO method is based on the minimization of the Integral Squared Error (ISE) between the transient responses of original higher order model and the reduced order model pertaining to a unit step input. Both the methods are illustrated through numerical example from literature and the results are compared with recently published conventional model reduction technique.
Abstract: This is an application research presenting the
improvement of production quality using the six sigma solutions and
the analyses of benefit-cost ratio. The case of interest is the
production of tile-concrete. Such production has faced with the
problem of high nonconforming products from an inappropriate
surface coating and had low process capability based on the strength
property of tile. Surface coating and tile strength are the most critical
to quality of this product. The improvements followed five stages of
six sigma solutions. After the improvement, the production yield was
improved to 80% as target required and the defective products from
coating process was remarkably reduced from 29.40% to 4.09%. The
process capability based on the strength quality was increased from
0.87 to 1.08 as customer oriented. The improvement was able to save
the materials loss for 3.24 millions baht or 0.11 million dollars. The
benefits from the improvement were analyzed from (1) the reduction
of the numbers of non conforming tile using its factory price for
surface coating improvement and (2) the materials saved from the
increment of process capability. The benefit-cost ratio of overall
improvement was high as 7.03. It was non valuable investment in
define, measure, analyses and the initial of improve stages after that
it kept increasing. This was due to there were no benefits in define,
measure, and analyze stages of six sigma since these three stages
mainly determine the cause of problem and its effects rather than
improve the process. The benefit-cost ratio starts existing in the
improve stage and go on. Within each stage, the individual benefitcost
ratio was much higher than the accumulative one as there was an
accumulation of cost since the first stage of six sigma. The
consideration of the benefit-cost ratio during the improvement
project helps make decisions for cost saving of similar activities
during the improvement and for new project. In conclusion, the
determination of benefit-cost ratio behavior through out six sigma
implementation period provides the useful data for managing quality
improvement for the optimal effectiveness. This is the additional
outcome from the regular proceeding of six sigma.
Abstract: The objective of this paper is to study the electrical
resistivity complexity between field and laboratory measurement, in
order to improve the effectiveness of data interpretation for
geophysical ground resistivity survey. The geological outcrop in
Penang, Malaysia with an obvious layering contact was chosen as the
study site. Two dimensional geoelectrical resistivity imaging were
used in this study to maps the resistivity distribution of subsurface,
whereas few subsurface sample were obtained for laboratory
advance. In this study, resistivity of samples in original conditions is
measured in laboratory by using time domain low-voltage technique,
particularly for granite core sample and soil resistivity measuring set
for soil sample. The experimentation results from both schemes are
studied, analyzed, calibrated and verified, including basis and
correlation, degree of tolerance and characteristics of substance.
Consequently, the significant different between both schemes is
explained comprehensively within this paper.
Abstract: In this paper, transversal vibration of buried pipelines
during loading induced by underground explosions is analyzed. The
pipeline is modeled as an infinite beam on an elastic foundation, so
that soil-structure interaction is considered by means of transverse
linear springs along the pipeline. The pipeline behavior is assumed to
be ideal elasto-plastic which an ultimate strain value limits the plastic
behavior. The blast loading is considered as a point load, considering
the affected length at some point of the pipeline, in which the
magnitude decreases exponentially with time. A closed-form solution
for the quasi-static problem is carried out for both elastic and elasticperfect
plastic behaviors of pipe materials. At the end, a comparative
study on steel and polyethylene pipes with different sizes buried in
various soil conditions, affected by a predefined underground
explosion is conducted, in which effect of each parameter is
discussed.
Abstract: Histogram equalization is often used in image enhancement, but it can be also used in auto exposure. However, conventional histogram equalization does not work well when many pixels are concentrated in a narrow luminance range.This paper proposes an auto exposure method based on 2-way histogram equalization. Two cumulative distribution functions are used, where one is from dark to bright and the other is from bright to dark. In this paper, the proposed auto exposure method is also designed and implemented for image signal processors with full-HD images.
Abstract: The issue of unintentional islanding in PV grid
interconnection still remains as a challenge in grid-connected
photovoltaic (PV) systems. This paper discusses the overview of
popularly used anti-islanding detection methods, practically applied
in PV grid-connected systems. Anti-islanding methods generally can
be classified into four major groups, which include passive methods,
active methods, hybrid methods and communication base methods.
Active methods have been the preferred detection technique over the
years due to very small non-detected zone (NDZ) in small scale
distribution generation. Passive method is comparatively simpler
than active method in terms of circuitry and operations. However, it
suffers from large NDZ that significantly reduces its performance.
Communication base methods inherit the advantages of active and
passive methods with reduced drawbacks. Hybrid method which
evolved from the combination of both active and passive methods
has been proven to achieve accurate anti-islanding detection by many
researchers. For each of the studied anti-islanding methods, the
operation analysis is described while the advantages and
disadvantages are compared and discussed. It is difficult to pinpoint a
generic method for a specific application, because most of the
methods discussed are governed by the nature of application and
system dependent elements. This study concludes that the setup and
operation cost is the vital factor for anti-islanding method selection in
order to achieve minimal compromising between cost and system
quality.
Abstract: This paper proposes a method, combining color and
layout features, for identifying documents captured from lowresolution
handheld devices. On one hand, the document image color
density surface is estimated and represented with an equivalent
ellipse and on the other hand, the document shallow layout structure
is computed and hierarchically represented. The combined color and
layout features are arranged in a symbolic file, which is unique for
each document and is called the document-s visual signature. Our
identification method first uses the color information in the
signatures in order to focus the search space on documents having a
similar color distribution, and finally selects the document having the
most similar layout structure in the remaining search space. Finally,
our experiment considers slide documents, which are often captured
using handheld devices.
Abstract: This paper presents a simplified version of Data Envelopment Analysis (DEA) - a conventional approach to evaluating the performance and ranking of competitive objects characterized by two groups of factors acting in opposite directions: inputs and outputs. DEA with a Perfect Object (DEA PO) augments the group of actual objects with a virtual Perfect Object - the one having greatest outputs and smallest inputs. It allows for obtaining an explicit analytical solution and making a step to an absolute efficiency. This paper develops this approach further and introduces a DEA model with Partially Perfect Objects. DEA PPO consecutively eliminates the smallest relative inputs or greatest relative outputs, and applies DEA PO to the reduced collections of indicators. The partial efficiency scores are combined to get the weighted efficiency score. The computational scheme remains simple, like that of DEA PO, but the advantage of the DEA PPO is taking into account all of the inputs and outputs for each actual object. Firm evaluation is considered as an example.
Abstract: A conventional binding method for low power in a
high-level synthesis mainly focuses on finding an optimal binding for
an assumed input data, and obtains only one binding table. In this
paper, we show that a binding method which uses multiple binding
tables gets better solution compared with the conventional methods
which use a single binding table, and propose a dynamic bus binding
scheme for low power using multiple binding tables. The proposed
method finds multiple binding tables for the proper partitions of an
input data, and switches binding tables dynamically to produce the
minimum total switching activity. Experimental result shows that the
proposed method obtains a binding solution having 12.6-28.9%
smaller total switching activity compared with the conventional
methods.
Abstract: The objective of this paper is to estimate realistic
principal extrusion process parameters by means of artificial neural
network. Conventionally, finite element analysis is used to derive
process parameters. However, the finite element analysis of the
extrusion model does not consider the manufacturing process
constraints in its modeling. Therefore, the process parameters
obtained through such an analysis remains highly theoretical.
Alternatively, process development in industrial extrusion is to a
great extent based on trial and error and often involves full-size
experiments, which are both expensive and time-consuming. The
artificial neural network-based estimation of the extrusion process
parameters prior to plant execution helps to make the actual extrusion
operation more efficient because more realistic parameters may be
obtained. And so, it bridges the gap between simulation and real
manufacturing execution system. In this work, a suitable neural
network is designed which is trained using an appropriate learning
algorithm. The network so trained is used to predict the
manufacturing process parameters.
Abstract: Electrospinning is a broadly used technology to obtain
polymeric nanofibers ranging from several micrometers down to
several hundred nanometers for a wide range of applications. It offers
unique capabilities to produce nanofibers with controllable porous
structure. With smaller pores and higher surface area than regular
fibers, electrospun fibers have been successfully applied in various
fields, such as, nanocatalysis, tissue engineering scaffolds, protective
clothing, filtration, biomedical, pharmaceutical, optical electronics,
healthcare, biotechnology, defense and security, and environmental
engineering. In this study, polyurethane nanofibers were obtained
under different electrospinning parameters. Fiber morphology and
diameter distribution were investigated in order to understand them
as a function of process parameters.
Abstract: Statistical learning theory was developed by Vapnik. It
is a learning theory based on Vapnik-Chervonenkis dimension. It also
has been used in learning models as good analytical tools. In general, a
learning theory has had several problems. Some of them are local
optima and over-fitting problems. As well, statistical learning theory
has same problems because the kernel type, kernel parameters, and
regularization constant C are determined subjectively by the art of
researchers. So, we propose an evolutionary statistical learning theory
to settle the problems of original statistical learning theory.
Combining evolutionary computing into statistical learning theory,
our theory is constructed. We verify improved performances of an
evolutionary statistical learning theory using data sets from KDD cup.