Abstract: Diabetes Mellitus is a chronic metabolic disorder, where the improper management of the blood glucose level in the diabetic patients will lead to the risk of heart attack, kidney disease and renal failure. This paper attempts to enhance the diagnostic accuracy of the advancing blood glucose levels of the diabetic patients, by combining principal component analysis and wavelet neural network. The proposed system makes separate blood glucose prediction in the morning, afternoon, evening and night intervals, using dataset from one patient covering a period of 77 days. Comparisons of the diagnostic accuracy with other neural network models, which use the same dataset are made. The comparison results showed overall improved accuracy, which indicates the effectiveness of this proposed system.
Abstract: The object of this work is the probabilistic performance evaluation of safety instrumented systems (SIS), i.e. the average probability of dangerous failure on demand (PFDavg) and the average frequency of failure (PFH), taking into account the uncertainties related to the different parameters that come into play: failure rate (λ), common cause failure proportion (β), diagnostic coverage (DC)... This leads to an accurate and safe assessment of the safety integrity level (SIL) inherent to the safety function performed by such systems. This aim is in keeping with the requirement of the IEC 61508 standard with respect to handling uncertainty. To do this, we propose an approach that combines (1) Monte Carlo simulation and (2) fuzzy sets. Indeed, the first method is appropriate where representative statistical data are available (using pdf of the relating parameters), while the latter applies in the case characterized by vague and subjective information (using membership function). The proposed approach is fully supported with a suitable computer code.
Abstract: This paper focuses on the data-driven generation
of fuzzy IF...THEN rules. The resulted fuzzy rule base can be
applied to build a classifier, a model used for prediction, or
it can be applied to form a decision support system. Among
the wide range of possible approaches, the decision tree and
the association rule based algorithms are overviewed, and two
new approaches are presented based on the a priori fuzzy
clustering based partitioning of the continuous input variables.
An application study is also presented, where the developed
methods are tested on the well known Wisconsin Breast Cancer
classification problem.
Abstract: Facial expression analysis plays a significant role for
human computer interaction. Automatic analysis of human facial
expression is still a challenging problem with many applications. In
this paper, we propose neuro-fuzzy based automatic facial expression
recognition system to recognize the human facial expressions like
happy, fear, sad, angry, disgust and surprise. Initially facial image is
segmented into three regions from which the uniform Local Binary
Pattern (LBP) texture features distributions are extracted and
represented as a histogram descriptor. The facial expressions are
recognized using Multiple Adaptive Neuro Fuzzy Inference System
(MANFIS). The proposed system designed and tested with JAFFE
face database. The proposed model reports 94.29% of classification
accuracy.
Abstract: This work deals with aspects of support vector learning for large-scale data mining tasks. Based on a decomposition algorithm that can be run in serial and parallel mode we introduce a data transformation that allows for the usage of an expensive generalized kernel without additional costs. In order to speed up the decomposition algorithm we analyze the problem of working set selection for large data sets and analyze the influence of the working set sizes onto the scalability of the parallel decomposition scheme. Our modifications and settings lead to improvement of support vector learning performance and thus allow using extensive parameter search methods to optimize classification accuracy.
Abstract: With the hardware technology advancing, the cost of
storing is decreasing. Thus there is an urgent need for new techniques
and tools that can intelligently and automatically assist us in
transferring this data into useful knowledge. Different techniques of
data mining are developed which are helpful for handling these large
size databases [7]. Data mining is also finding its role in the field of
biotechnology. Pedigree means the associated ancestry of a crop
variety. Genetic diversity is the variation in the genetic composition
of individuals within or among species. Genetic diversity depends
upon the pedigree information of the varieties. Parents at lower
hierarchic levels have more weightage for predicting genetic
diversity as compared to the upper hierarchic levels. The weightage
decreases as the level increases. For crossbreeding, the two varieties
should be more and more genetically diverse so as to incorporate the
useful characters of the two varieties in the newly developed variety.
This paper discusses the searching and analyzing of different possible
pairs of varieties selected on the basis of morphological characters,
Climatic conditions and Nutrients so as to obtain the most optimal
pair that can produce the required crossbreed variety. An algorithm
was developed to determine the genetic diversity between the
selected wheat varieties. Cluster analysis technique is used for
retrieving the results.
Abstract: One of the important applications of gas turbines is
their utilization for heat recovery steam generator in combine-cycle technology. Exhaust flow and energy are two key parameters for
determining heat recovery steam generator performance which are mainly determined by the main gas turbine components performance
data. For this reason a method was developed for determining the
exhaust energy in the new edition of ASME PTC22. The result of this investigation shows that the method of standard has considerable
error. Therefore in this paper a new method is presented for modifying of the performance calculation. The modified method is
based on exhaust gas constituent analysis and combustion calculations. The case study presented here by two kind of General
Electric gas turbine design data for validation of methodologies. The
result shows that the modified method is more precise than the ASME PTC22 method. The exhaust flow calculation deviation from
design data is 1.5-2 % by ASME PTC22 method so that the deviation regarding with modified method is 0.3-0.5%. Based on precision of
analyzer instruments, the method can be suitable alternative for gas
turbine standard performance test. In advance two methods are
proposed based on known and unknown fuel in modified method procedure. The result of this paper shows that the difference between
the two methods is below than %0.02. In according to reasonable esult of the second procedure (unknown fuel composition), the
method can be applied to performance evaluation of gas turbine, so that the measuring cost and data gathering should be reduced.
Abstract: During the last decade Panicum virgatum, known as
Switchgrass, has been broadly studied because of its remarkable
attributes as a substitute pasture and as a functional biofuel source.
The objective of this investigation was to establish soil suitability for
Switchgrass in the State of Mississippi. A linear weighted additive
model was developed to forecast soil suitability. Multicriteria
analysis and Sensitivity analysis were utilized to adjust and optimize
the model. The model was fit using seven years of field data
associated with soils characteristics collected from Natural Resources
Conservation System - United States Department of Agriculture
(NRCS-USDA). The best model was selected by correlating
calculated biomass yield with each model's soils-based output for
Switchgrass suitability. Coefficient of determination (r2) was the
decisive factor used to establish the 'best' soil suitability model.
Coefficients associated with the 'best' model were implemented
within a Geographic Information System (GIS) to create a map of
relative soil suitability for Switchgrass in Mississippi. A Geodatabase
associated with soil parameters was built and is available for future
Geographic Information System use.
Abstract: Starting from the basic pillars of the supportability
analysis this paper queries its characteristics in LCI (Life Cycle
Integration) environment. The research methodology contents a
review of modern logistics engineering literature with the objective to
collect and synthesize the knowledge relating to standards of
supportability design in e-logistics environment. The results show
that LCI framework has properties which are in fully compatibility
with the requirement of simultaneous logistics support and productservice
bundle design. The proposed approach is a contribution to the
more comprehensive and efficient supportability design process.
Also, contributions are reflected through a greater consistency of
collected data, automated creation of reports suitable for different
analysis, as well as the possibility of their customization according
with customer needs. In addition to this, convenience of this approach
is its practical use in real time. In a broader sense, LCI allows
integration of enterprises on a worldwide basis facilitating electronic
business.
Abstract: With the prevalence of computer and development of information technology, Geographic Information Systems (GIS) have long used for a variety of applications in electrical engineering. GIS are designed to support the analysis, management, manipulation and mapping of spatial data. This paper presents several usages of GIS in power utilities such as automated route selection for the construction of new power lines which uses a dynamic programming model for route optimization, load forecasting and optimizing planning of substation-s location and capacity with comprehensive algorithm which involves an accurate small-area electric load forecasting procedure and simulates the different cost functions of substations.
Abstract: This paper presents the automated methods employed
for extracting craniofacial landmarks in white light images as part of
a registration framework designed to support three neurosurgical
procedures. The intraoperative space is characterised by white light
stereo imaging while the preoperative plan is performed on CT scans.
The registration aims at aligning these two modalities to provide a
calibrated environment to enable image-guided solutions. The
neurosurgical procedures can then be carried out by mapping the
entry and target points from CT space onto the patient-s space. The
registration basis adopted consists of natural landmarks (eye corner
and ear tragus). A 5mm accuracy is deemed sufficient for these three
procedures and the validity of the selected registration basis in
achieving this accuracy has been assessed by simulation studies. The
registration protocol is briefly described, followed by a presentation
of the automated techniques developed for the extraction of the
craniofacial features and results obtained from tests on the AR and
FERET databases. Since the three targeted neurosurgical procedures
are routinely used for head injury management, the effect of
bruised/swollen faces on the automated algorithms is assessed. A
user-interactive method is proposed to deal with such unpredictable
circumstances.
Abstract: This article combines two techniques: data
envelopment analysis (DEA) and Factor analysis (FA) to data
reduction in decision making units (DMU). Data envelopment
analysis (DEA), a popular linear programming technique is useful to
rate comparatively operational efficiency of decision making units
(DMU) based on their deterministic (not necessarily stochastic)
input–output data and factor analysis techniques, have been proposed
as data reduction and classification technique, which can be applied
in data envelopment analysis (DEA) technique for reduction input –
output data. Numerical results reveal that the new approach shows a
good consistency in ranking with DEA.
Abstract: With the proliferation of Weblogs (blogs) use in
educational contexts, gaining a better understanding of why
students are willing to utilize blog systems has become an
important topic for practitioners and academics. While perceived
enjoyment has been found to have a significant influence on
behavioral intentions to use blogs or hedonic systems, few studies
have investigated the antecedents of perceived enjoyment in the
acceptance of blogging. The main purpose of the present study is to
explore the individual difference antecedents of perceived
enjoyment and examine how they influence behavioral intention to
blog through the mediation of perceived enjoyment. Based on the
previous literature, the Big Five personality traits (i.e.,
extraversion, agreeableness, conscientiousness, neuroticism, and
openness to experience), as well as computer self-efficacy and
personal innovation in information technology (PIIT), are
hypothesized as potential antecedents of perceived enjoyment in
the acceptance of blogging. Data collected from 358 respondents in
Taiwan are tested against the research model using the structural
equation modeling approach. The results indicate that extraversion,
agreeableness, conscientiousness, and PIIT have a significant
influence on perceived enjoyment, which in turn significantly
influences the behavioral intention to blog. These findings lead to
several important implications for future research.
Abstract: Calculations of energy efficiency of several AACbased
building envelopes under different climatic conditions are
presented. As thermal insulating materials, expanded polystyrene and
hydrophobic and hydrophilic mineral wools are assumed. The
computations are accomplished using computer code HEMOT
developed at Department of Materials Engineering, Faculty of Civil
Engineering at the Czech Technical University in Prague. The
climatic data of Athens, Kazan, Oslo, Prague and Reykjavík are
obtained using METEONORM software.
Abstract: The relationship between different types of Molybdenum disulfide greases under extreme pressure loading and different speed situations have been studied using Design of Experiment (DOE) under 1200rpm steady state rotational speed and cyclic frequencies between 2400 and 1200rpm using a Plint machine software to set up the different rotational speed situations.
Research described here is aimed at providing good friction and wear performance while optimizing cyclic frequencies and MoS2 concentration due to the recent concern about grease behavior in extreme pressure applications. Extreme load of 785 Newton was used in conjunction with different cyclic frequencies (2400rpm -3.75min, 1200rpm -7.5min, 2400rpm -3.75min, 1200rpm -7.5min), to examine lithium based grease with and without MoS2 for equal number of revolutions, and a total run of 36000 revolutions; then compared to 1200rpm steady speed for the same total number of revolutions. 4 Ball wear tester was utilized to run large number of experiments randomly selected by the DOE software. The grease was combined with fine grade MoS2 or technical grade then heated to 750C and the wear scar width was collected at the end of each test. DOE model validation results verify that the data were very significant and can be applied to a wide range of extreme pressure applications. Based on simulation results and Scanning Electron images (SEM), it has been found that wear was largely dependent on the cyclic frequency condition. It is believed that technical grade MoS2 greases under faster cyclic speeds perform better and provides antiwear film that can resist extreme pressure loadings. Figures showed reduced wear scars width and improved frictional values.
Abstract: Recognizing human action from videos is an active
field of research in computer vision and pattern recognition. Human
activity recognition has many potential applications such as video
surveillance, human machine interaction, sport videos retrieval and
robot navigation. Actually, local descriptors and bag of visuals words
models achieve state-of-the-art performance for human action
recognition. The main challenge in features description is how to
represent efficiently the local motion information. Most of the
previous works focus on the extension of 2D local descriptors on 3D
ones to describe local information around every interest point. In this
paper, we propose a new spatio-temporal descriptor based on a spacetime
description of moving points. Our description is focused on an
Accordion representation of video which is well-suited to recognize
human action from 2D local descriptors without the need to 3D
extensions. We use the bag of words approach to represent videos.
We quantify 2D local descriptor describing both temporal and spatial
features with a good compromise between computational complexity
and action recognition rates. We have reached impressive results on
publicly available action data set
Abstract: Tehran, one of the heavily-populated capitals, is
severely suffering from increasing air pollution. To show a
documented trend of such pollutants during last years, plane tree
species (Platanus orientalis) were suited to be studied as indicators,
for the species have been planted throughout the city many years
ago. Two areas (Saadatabad and Narmak districts) allotting different
contents of crowed and highly-traffic routs but the same ecological
characteristics were selected. Twelve sample individuals were cored
twice perpendicularly in each area. Tree-rings of each core were
measured by a binocular microscope and separated annually for the
last 25 years. Two heavy metals including Cd and Pb accompanied
by a mineral element (Ca) were analyzed using Hatch method. Treerings
analysis of the two areas showed different groups in term of
physiologically ability as the growths were plunged during the last
10 years in Saadatabad district and showed a slight decrease in the
same period for another studying area. In direct contrast to
decreasing growth trend in Saadatabad, all three mentioned elements
increased sharply during last 25 years in the same area. When it came
to Narmak district, the trend was completely different with
Saadatabad. There were some fluctuations in absorbing trace
elements like tree-rings widths were, yet calcium showed an upward
trend all the last 25 years. The results of the study proved the
possibility of using tree species of each region to monitor its air
pollution trends of the past, hence to depict a pollution assessment of
a populated city for last years and then to make appropriate decisions
for the future as it is well-known what the trend is. On the other
hand, risen values of calcium (as the stress-indicator element)
accompanied by increased trace elements suggests non-sustainable
state of the trees.
Abstract: This paper proposes a neural network weights and
topology optimization using genetic evolution and the
backpropagation training algorithm. The proposed crossover and
mutation operators aims to adapt the networks architectures and
weights during the evolution process. Through a specific inheritance
procedure, the weights are transmitted from the parents to their
offsprings, which allows re-exploitation of the already trained
networks and hence the acceleration of the global convergence of the
algorithm. In the preprocessing phase, a new feature extraction
method is proposed based on Legendre moments with the Maximum
entropy principle MEP as a selection criterion. This allows a global
search space reduction in the design of the networks. The proposed
method has been applied and tested on the well known MNIST
database of handwritten digits.
Abstract: Mobile ad hoc network is a collection of mobile
nodes communicating through wireless channels without any
existing network infrastructure or centralized administration.
Because of the limited transmission range of wireless network
interfaces, multiple "hops" may be needed to exchange data
across the network. Consequently, many routing algorithms
have come into existence to satisfy the needs of
communications in such networks. Researchers have
conducted many simulations comparing the performance of
these routing protocols under various conditions and
constraints. One question that arises is whether speed of nodes
affects the relative performance of routing protocols being
studied. This paper addresses the question by simulating two
routing protocols AODV and DSDV. Protocols were
simulated using the ns-2 and were compared in terms of
packet delivery fraction, normalized routing load and average
delay, while varying number of nodes, and speed.
Abstract: This paper describes topic of computer simulation with regard to the ground movement above an underground mine. Simulation made with software package ADINA for nonlinear elastic-plastic analysis with finite elements method. The one of representative profiles from Mine 'Stara Jama' in Zenica has been investigated. A collection and selection of both geo-mechanical data and geometric parameters of the mine was necessary for performing these simulations. Results of estimation have been compared with measured values (vertical displacement of surface), and then simulation performed with assumed dynamic and dimensions of excavation, over a period of time. Results are presented with bitmaps and charts.