Abstract: This paper proposes an effective algorithm approach to hybrid control systems combining fuzzy logic and conventional control techniques of controlling the speed of induction motor assumed to operate in high-performance drives environment. The introducing of fuzzy logic in the control systems helps to achieve good dynamical response, disturbance rejection and low sensibility to parameter variations and external influences. Some fundamentals of the fuzzy logic control are preliminary illustrated. The developed control algorithm is robust, efficient and simple. It also assures precise trajectory tracking with the prescribed dynamics. Experimental results have shown excellent tracking performance of the proposed control system, and have convincingly demonstrated the validity and the usefulness of the hybrid fuzzy controller in high-performance drives with parameter and load uncertainties. Satisfactory performance was observed for most reference tracks.
Abstract: This paper proposes a hybrid method for eyes localization
in facial images. The novelty is in combining techniques
that utilise colour, edge and illumination cues to improve accuracy.
The method is based on the observation that eye regions have dark
colour, high density of edges and low illumination as compared
to other parts of face. The first step in the method is to extract
connected regions from facial images using colour, edge density and
illumination cues separately. Some of the regions are then removed
by applying rules that are based on the general geometry and shape
of eyes. The remaining connected regions obtained through these
three cues are then combined in a systematic way to enhance the
identification of the candidate regions for the eyes. The geometry
and shape based rules are then applied again to further remove the
false eye regions. The proposed method was tested using images from
the PICS facial images database. The proposed method has 93.7%
and 87% accuracies for initial blobs extraction and final eye detection
respectively.
Abstract: The paper investigates the potential of support vector
machines and Gaussian process based regression approaches to
model the oxygen–transfer capacity from experimental data of
multiple plunging jets oxygenation systems. The results suggest the
utility of both the modeling techniques in the prediction of the
overall volumetric oxygen transfer coefficient (KLa) from operational
parameters of multiple plunging jets oxygenation system. The
correlation coefficient root mean square error and coefficient of
determination values of 0.971, 0.002 and 0.945 respectively were
achieved by support vector machine in comparison to values of
0.960, 0.002 and 0.920 respectively achieved by Gaussian process
regression. Further, the performances of both these regression
approaches in predicting the overall volumetric oxygen transfer
coefficient was compared with the empirical relationship for multiple
plunging jets. A comparison of results suggests that support vector
machines approach works well in comparison to both empirical
relationship and Gaussian process approaches, and could successfully
be employed in modeling oxygen-transfer.
Abstract: The paper gives the pilot results of the project that is
oriented on the use of data mining techniques and knowledge
discoveries from production systems through them. They have been
used in the management of these systems. The simulation models of
manufacturing systems have been developed to obtain the necessary
data about production. The authors have developed the way of
storing data obtained from the simulation models in the data
warehouse. Data mining model has been created by using specific
methods and selected techniques for defined problems of production
system management. The new knowledge has been applied to
production management system. Gained knowledge has been tested
on simulation models of the production system. An important benefit
of the project has been proposal of the new methodology. This
methodology is focused on data mining from the databases that store
operational data about the production process.
Abstract: Matrix metalloproteinase-3 (MMP3) is key member
of the MMP family, and is known to be present in coronary
atherosclerotic. Several studies have demonstrated that MMP-3
5A/6A polymorphism modify each transcriptional activity in allele
specific manner. We hypothesized that this polymorphism may play
a role as risk factor for development of coronary stenosis. The aim of
our study was to estimate MMP-3 (5A/6A) gene polymorphism on
interindividual variability in risk for coronary stenosis in an Iranian
population.DNA was extracted from white blood cells and genotypes
were obtained from coronary stenosis cases (n=95) and controls
(n=100) by PCR (polymerase chain reaction) and restriction
fragment length polymorphism techniques. Significant differences
between cases and controls were observed for MMP3 genotype
frequencies (X2=199.305, p< 0.001); the 6A allele was less
frequently seen in the control group, compared to the disease group
(85.79 vs. 78%, 6A/6A+5A/6A vs. 5A/5A, P≤0.001). These data
imply the involvement of -1612 5A/6A polymorphism in coronary
stenosis, and suggest that probably the 6A/6A MMP-3 genotype is a
genetic susceptibility factor for coronary stenosis.
Abstract: The present paper was concerned primarily with the
analysis, simulation of the air flow and thermal patterns in a lecture
room. The paper is devoted to numerically investigate the influence
of location and number of ventilation and air conditioning supply and
extracts openings on air flow properties in a lecture room. The work
focuses on air flow patterns, thermal behaviour in lecture room where
large number of students. The effectiveness of an air flow system is
commonly assessed by the successful removal of sensible and latent
loads from occupants with additional of attaining air pollutant at a
prescribed level to attain the human thermal comfort conditions and
to improve the indoor air quality; this is the main target during the
present paper. The study is carried out using computational fluid
dynamics (CFD) simulation techniques as embedded in the
commercially available CFD code (FLUENT 6.2). The CFD
modelling techniques solved the continuity, momentum and energy
conservation equations in addition to standard k – ε model equations
for turbulence closure.
Throughout the investigations, numerical validation is carried out by
way of comparisons of numerical and experimental results. Good
agreement is found among both predictions.
Abstract: Addition of milli or micro sized particles to the heat
transfer fluid is one of the many techniques employed for improving
heat transfer rate. Though this looks simple, this method has
practical problems such as high pressure loss, clogging and erosion
of the material of construction. These problems can be overcome by
using nanofluids, which is a dispersion of nanosized particles in a
base fluid. Nanoparticles increase the thermal conductivity of the
base fluid manifold which in turn increases the heat transfer rate.
Nanoparticles also increase the viscosity of the basefluid resulting in
higher pressure drop for the nanofluid compared to the base fluid. So
it is imperative that the Reynolds number (Re) and the volume
fraction have to be optimum for better thermal hydraulic
effectiveness. In this work, the heat transfer enhancement using
aluminium oxide nanofluid using low and high volume fraction
nanofluids in turbulent pipe flow with constant wall temperature has
been studied by computational fluid dynamic modeling of the
nanofluid flow adopting the single phase approach. Nanofluid, up till
a volume fraction of 1% is found to be an effective heat transfer
enhancement technique. The Nusselt number (Nu) and friction factor
predictions for the low volume fractions (i.e. 0.02%, 0.1 and 0.5%)
agree very well with the experimental values of Sundar and Sharma
(2010). While, predictions for the high volume fraction nanofluids
(i.e. 1%, 4% and 6%) are found to have reasonable agreement with
both experimental and numerical results available in the literature.
So the computationally inexpensive single phase approach can be
used for heat transfer and pressure drop prediction of new nanofluids.
Abstract: CScheme, a concurrent programming paradigm based
on scheme concept enables concurrency schemes to be constructed
from smaller synchronization units through a GUI based composer
and latter be reused on other concurrency problems of a similar
nature. This paradigm is particularly important in the multi-core
environment prevalent nowadays. In this paper, we demonstrate
techniques to separate concurrency from functional code using the
CScheme paradigm. Then we illustrate how the CScheme
methodology can be used to solve some of the traditional
concurrency problems – critical section problem, and readers-writers
problem - using synchronization schemes such as Single Threaded
Execution Scheme, and Readers Writers Scheme.
Abstract: Intellectual capital measurement is a central aspect of knowledge management. The measurement and the evaluation of intangible assets play a key role in allowing an effective management of these assets as sources of competitiveness. For these reasons, managers and practitioners need conceptual and analytical tools taking into account the unique characteristics and economic significance of Intellectual Capital. Following this lead, we propose an efficiency and productivity analysis of Intellectual Capital, as a determinant factor of the company competitive advantage. The analysis is carried out by means of Data Envelopment Analysis (DEA) and Malmquist Productivity Index (MPI). These techniques identify Bests Practice companies that have accomplished competitive advantage implementing successful strategies of Intellectual Capital management, and offer to inefficient companies development paths by means of benchmarking. The proposed methodology is employed on the Biotechnology industry in the period 2007-2010.
Abstract: The success of an e-learning system is highly
dependent on the quality of its educational content and how effective,
complete, and simple the design tool can be for teachers. Educational
modeling languages (EMLs) are proposed as design languages
intended to teachers for modeling diverse teaching-learning
experiences, independently of the pedagogical approach and in
different contexts. However, most existing EMLs are criticized for
being too abstract and too complex to be understood and manipulated
by teachers. In this paper, we present a visual EML that simplifies the
process of designing learning scenarios for teachers with no
programming background. Based on the conceptual framework of the
activity theory, our resulting visual EML focuses on using Domainspecific
modeling techniques to provide a pedagogical level of
abstraction in the design process.
Abstract: This paper introduces a framework based on the collaboration of multi agent and hyper-heuristics to find a solution of the real single machine production problem. There are many techniques used to solve this problem. Each of it has its own advantages and disadvantages. By the collaboration of multi agent system and hyper-heuristics, we can get more optimal solution. The hyper-heuristics approach operates on a search space of heuristics rather than directly on a search space of solutions. The proposed framework consists of some agents, i.e. problem agent, trainer agent, algorithm agent (GPHH, GAHH, and SAHH), optimizer agent, and solver agent. Some low level heuristics used in this paper are MRT, SPT, LPT, EDD, LDD, and MON
Abstract: One important problem in today organizations is the
existence of non-integrated information systems, inconsistency and
lack of suitable correlations between legacy and modern systems.
One main solution is to transfer the local databases into a global one.
In this regards we need to extract the data structures from the legacy
systems and integrate them with the new technology systems. In
legacy systems, huge amounts of a data are stored in legacy
databases. They require particular attention since they need more
efforts to be normalized, reformatted and moved to the modern
database environments. Designing the new integrated (global)
database architecture and applying the reverse engineering requires
data normalization. This paper proposes the use of database reverse
engineering in order to integrate legacy and modern databases in
organizations. The suggested approach consists of methods and
techniques for generating data transformation rules needed for the
data structure normalization.
Abstract: Graph based image segmentation techniques are
considered to be one of the most efficient segmentation techniques
which are mainly used as time & space efficient methods for real
time applications. How ever, there is need to focus on improving the
quality of segmented images obtained from the earlier graph based
methods. This paper proposes an improvement to the graph based
image segmentation methods already described in the literature. We
contribute to the existing method by proposing the use of a weighted
Euclidean distance to calculate the edge weight which is the key
element in building the graph. We also propose a slight modification
of the segmentation method already described in the literature, which
results in selection of more prominent edges in the graph. The
experimental results show the improvement in the segmentation
quality as compared to the methods that already exist, with a slight
compromise in efficiency.
Abstract: The current paper conceptualizes the technique of
release consistency indispensable with the concept of
synchronization that is user-defined. Programming model concreted
with object and class is illustrated and demonstrated. The essence of
the paper is phases, events and parallel computing execution .The
technique by which the values are visible on shared variables is
implemented. The second part of the paper consist of user defined
high level synchronization primitives implementation and system
architecture with memory protocols. There is a proposition of
techniques which are core in deciding the validating and invalidating
a stall page .
Abstract: There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson-s fuzzy min-max neural networks where the input variables for learning and classification are just numerical. The proposed method extends the input to categorical variables by introducing new fuzzy sets, a new operation and a new architecture. The procedure is tested and compared with others using opinion poll data.
Abstract: The healthcare environment is generally perceived as
being information rich yet knowledge poor. However, there is a lack
of effective analysis tools to discover hidden relationships and trends
in data. In fact, valuable knowledge can be discovered from
application of data mining techniques in healthcare system. In this
study, a proficient methodology for the extraction of significant
patterns from the Coronary Heart Disease warehouses for heart
attack prediction, which unfortunately continues to be a leading cause
of mortality in the whole world, has been presented. For this purpose,
we propose to enumerate dynamically the optimal subsets of the
reduced features of high interest by using rough sets technique
associated to dynamic programming. Therefore, we propose to
validate the classification using Random Forest (RF) decision tree to
identify the risky heart disease cases. This work is based on a large
amount of data collected from several clinical institutions based on
the medical profile of patient. Moreover, the experts- knowledge in
this field has been taken into consideration in order to define the
disease, its risk factors, and to establish significant knowledge
relationships among the medical factors. A computer-aided system is
developed for this purpose based on a population of 525 adults. The
performance of the proposed model is analyzed and evaluated based
on set of benchmark techniques applied in this classification problem.
Abstract: Hydrothermally synthesized high silica borosilicates
with the MFI structure was subjected to several characterization
techniques. The effect of boron on the structure and acidity of
HZSM-5 catalyst were studied by XRD, SEM, N2 adsorption, solid
state NMR, NH3-TPD. It was confirmed that boron had entered the
framework in the boron samples. The results also revealed that strong
acidity was weakened and weak acidity was strengthened by the
boron added zeolite framework compared with parent catalyst. The
catalytic performance was carried out in a fixed bed at 460°C for
methanol to propylene (MTP) reaction. The results of MTP reaction
showed a great increment of the propylene selectivity and excellent
stability for the B-HZSM-5. The catalyst exhibited about 81%
selectivity to C2
= - C4
= olefins with 40% selectivity of propylene as
major component at near 100% methanol conversion, and the stable
performance in the studied period was 100h.
Abstract: Experimental data from an atmospheric air/water terrain slugging case has been made available by the Shell Amsterdam research center, and has been subject to numerical simulation and comparison with a one-dimensional two-phase slug tracking simulator under development at the Norwegian University of Science and Technology. The code is based on tracking of liquid slugs in pipelines by use of a Lagrangian grid formulation implemented in Cµ by use of object oriented techniques. An existing hybrid spatial discretization scheme is tested, in which the stratified regions are modelled by the two-fluid model. The slug regions are treated incompressible, thus requiring a single momentum balance over the whole slug. Upon comparison with the experimental data, the period of the simulated severe slugging cycle is observed to be sensitive to slug generation in the horizontal parts of the system. Two different slug initiation methods have been tested with the slug tracking code, and grid dependency has been investigated.
Abstract: The review performed on the condition of energy
consumption & rate in Iran, shows that unfortunately the subject of
optimization and conservation of energy in active industries of
country lacks a practical & effective method and in most factories,
the energy consumption and rate is more than in similar industries of
industrial countries. The increasing demand of electrical energy and
the overheads which it imposes on the organization, forces
companies to search for suitable approaches to optimize energy
consumption and demand management. Application of value
engineering techniques is among these approaches. Value
engineering is considered a powerful tool for improving profitability.
These tools are used for reduction of expenses, increasing profits,
quality improvement, increasing market share, performing works in
shorter durations, more efficient utilization of sources & etc.
In this article, we shall review the subject of value engineering and
its capabilities for creating effective transformations in industrial
organizations, in order to reduce energy costs & the results have
been investigated and described during a case study in Mazandaran
wood and paper industries, the biggest consumer of energy in north
of Iran, for the purpose of presenting the effects of performed tasks
in optimization of energy consumption by utilizing value engineering
techniques in one case study.
Abstract: In this paper, the implementation of a rule-based
intuitive reasoner is presented. The implementation included two
parts: the rule induction module and the intuitive reasoner. A large
weather database was acquired as the data source. Twelve weather
variables from those data were chosen as the “target variables"
whose values were predicted by the intuitive reasoner. A “complex"
situation was simulated by making only subsets of the data available
to the rule induction module. As a result, the rules induced were
based on incomplete information with variable levels of certainty.
The certainty level was modeled by a metric called "Strength of
Belief", which was assigned to each rule or datum as ancillary
information about the confidence in its accuracy. Two techniques
were employed to induce rules from the data subsets: decision tree
and multi-polynomial regression, respectively for the discrete and the
continuous type of target variables. The intuitive reasoner was tested
for its ability to use the induced rules to predict the classes of the
discrete target variables and the values of the continuous target
variables. The intuitive reasoner implemented two types of
reasoning: fast and broad where, by analogy to human thought, the
former corresponds to fast decision making and the latter to deeper
contemplation. . For reference, a weather data analysis approach
which had been applied on similar tasks was adopted to analyze the
complete database and create predictive models for the same 12
target variables. The values predicted by the intuitive reasoner and
the reference approach were compared with actual data. The intuitive
reasoner reached near-100% accuracy for two continuous target
variables. For the discrete target variables, the intuitive reasoner
predicted at least 70% as accurately as the reference reasoner. Since
the intuitive reasoner operated on rules derived from only about 10%
of the total data, it demonstrated the potential advantages in dealing
with sparse data sets as compared with conventional methods.