Abstract: Fuzzy C-means Clustering algorithm (FCM) is a
method that is frequently used in pattern recognition. It has the
advantage of giving good modeling results in many cases, although,
it is not capable of specifying the number of clusters by itself. In
FCM algorithm most researchers fix weighting exponent (m) to a
conventional value of 2 which might not be the appropriate for all
applications. Consequently, the main objective of this paper is to use
the subtractive clustering algorithm to provide the optimal number of
clusters needed by FCM algorithm by optimizing the parameters of
the subtractive clustering algorithm by an iterative search approach
and then to find an optimal weighting exponent (m) for the FCM
algorithm. In order to get an optimal number of clusters, the iterative
search approach is used to find the optimal single-output Sugenotype
Fuzzy Inference System (FIS) model by optimizing the
parameters of the subtractive clustering algorithm that give minimum
least square error between the actual data and the Sugeno fuzzy
model. Once the number of clusters is optimized, then two
approaches are proposed to optimize the weighting exponent (m) in
the FCM algorithm, namely, the iterative search approach and the
genetic algorithms. The above mentioned approach is tested on the
generated data from the original function and optimal fuzzy models
are obtained with minimum error between the real data and the
obtained fuzzy models.
Abstract: Flow through micro and mini channels requires relatively
high driving pressure due to the large fluid pressure drop
through these channels. Consequently the forces acting on the walls of
the channel due to the fluid pressure are also large. Due to these forces
there are displacement fields set up in the solid substrate containing
the channels. If the movement of the substrate is constrained at some
points, then stress fields are established in the substrate. On the other
hand, if the deformation of the channel shape is sufficiently large
then its effect on the fluid flow is important to be calculated. Such
coupled fluid-solid systems form a class of problems known as fluidstructure
interactions. In the present work a co-located finite volume
discretization procedure on unstructured meshes is described for
solving fluid-structure interaction type of problems. A linear elastic
solid is assumed for which the effect of the channel deformation
on the flow is neglected. Thus the governing equations for the
fluid and the solid are decoupled and are solved separately. The
procedure is validated by solving two benchmark problems, one from
fluid mechanics and another from solid mechanics. A fluid-structure
interaction problem of flow through a U-shaped channel embedded
in a plate is solved.
Abstract: A property is persistent if for any many-sorted term rewriting system , has the property if and only if term rewriting system , which results from by omitting its sort information, has the property. Zantema showed that termination is persistent for term rewriting systems without collapsing or duplicating rules. In this paper, we show that the Zantema's result can be extended to term rewriting systems on ordered sorts, i.e., termination is persistent for term rewriting systems on ordered sorts without collapsing, decreasing or duplicating rules. Furthermore we give the example as application of this result. Also we obtain that completeness is persistent for this class of term rewriting systems.
Abstract: The advances in location-based data collection
technologies such as GPS, RFID etc. and the rapid reduction of their
costs provide us with a huge and continuously increasing amount of
data about movement of vehicles, people and goods in an urban area.
This explosive growth of geospatially-referenced data has far
outpaced the planner-s ability to utilize and transform the data into
insightful information thus creating an adverse impact on the return
on the investment made to collect and manage this data. Addressing
this pressing need, we designed and developed DIVAD, a dynamic
and interactive visual analytics dashboard to allow city planners to
explore and analyze city-s transportation data to gain valuable
insights about city-s traffic flow and transportation requirements. We
demonstrate the potential of DIVAD through the use of interactive
choropleth and hexagon binning maps to explore and analyze large
taxi-transportation data of Singapore for different geographic and
time zones.
Abstract: Titanium alloys like Ti-6Al-2Sn-4Zr-6Mo (Ti-
6246) are widely used in aerospace applications. Component
manufacturing, however, is difficult and expensive as their
machinability is extremely poor. A thorough understanding of the
chip formation process is needed to improve related metal cutting
operations.In the current study, orthogonal cutting experiments have
been performed and theresulting chips were analyzed by optical
microscopy and scanning electron microscopy.Chips from aTi-
6246ingot were produced at different cutting speeds and cutting
depths. During the experiments, depending of the cutting conditions,
continuous or segmented chips were formed. Narrow, highly
deformed and grain oriented zones, the so-called shear zone,
separated individual segments. Different material properties have
been measured in the shear zones and the segments.
Abstract: Tumor cells have an invasive and metastatic phenotype
that is the main cause of death for cancer patients. Tumor
establishment and penetration consists of a series of complex
processes involving multiple changes in gene expression. In this study,
intraperitoneal administration of a high concentration of ascorbic acid
inhibited tumor establishment and decreased tumor mass in BALB/C
mice implanted with S-180 sarcoma cancer cells. To identify proteins
involved in the ascorbic acid-mediated inhibition of tumor
progression, changes in the tumor proteome associated with ascorbic
acid treatment of BALB/C mice implanted with S-180 were
investigated using two-dimensional gel electrophoresis and mass
spectrometry. Twenty protein spots were identified whose expression
was different between control and ascorbic acid treatment groups.
Abstract: This study aimed at assessing whether and to what extent moral judgment and behaviour were: 1. situation-dependent; 2. selectively dependent on cognitive and affective components; 3. influenced by gender and age; 4. reciprocally congruent. In order to achieve these aims, four different types of moral dilemmas were construed and five types of thinking were presented for each of them – representing five possible ways to evaluate the situation. The judgment criteria included selfishness, altruism, sense of justice, and the conflict between selfishness and the two moral issues. The participants were 250 unpaid volunteers (50% male; 50% female) belonging to two age-groups: young people and adults. The study entailed a 2 (gender) x 2 (age-group) x 5 (type of thinking) x 4 (situation) mixed design: the first two variables were betweensubjects, the others were within-subjects. Results have shown that: 1. moral judgment and behaviour are at least partially affected by the type of situations and by interpersonal variables such as gender and age; 2. moral reasoning depends in a similar manner on cognitive and affective factors; 3. there is not a gender polarity between the ethic of justice and the ethic of cure/ altruism; 4. moral reasoning and behavior are perceived as reciprocally congruent even though their congruence decreases with a more objective assessment. Such results were discussed in the light of contrasting theories on morality.
Abstract: In this paper, the effects of the restoring force device on the response of a space frame structure resting on sliding type of bearing with a restoring force device is studied. The NS component of the El - Centro earthquake and harmonic ground acceleration is considered for earthquake excitation. The structure is modeled by considering six-degrees of freedom (three translations and three rotations) at each node. The sliding support is modeled as a fictitious spring with two horizontal degrees of freedom. The response quantities considered for the study are the top floor acceleration, base shear, bending moment and base displacement. It is concluded from the study that the displacement of the structure reduces by the use of the restoring force device. Also, the peak values of acceleration, bending moment and base shear also decreases. The simulation results show the effectiveness of the developed and proposed method.
Abstract: This paper reports a new pattern recognition approach for face recognition. The biological model of light receptors - cones and rods in human eyes and the way they are associated with pattern vision in human vision forms the basis of this approach. The functional model is simulated using CWD and WPD. The paper also discusses the experiments performed for face recognition using the features extracted from images in the AT & T face database. Artificial Neural Network and k- Nearest Neighbour classifier algorithms are employed for the recognition purpose. A feature vector is formed for each of the face images in the database and recognition accuracies are computed and compared using the classifiers. Simulation results show that the proposed method outperforms traditional way of feature extraction methods prevailing for pattern recognition in terms of recognition accuracy for face images with pose and illumination variations.
Abstract: Ground-source heat pumps achieve higher efficiencies
than conventional air-source heat pumps because they exchange heat
with the ground that is cooler in summer and hotter in winter than the
air environment. Earth heat exchangers are essential parts of the
ground-source heat pumps and the accurate prediction of their
performance is of fundamental importance. This paper presents the
development and validation of a numerical model through an
incompressible fluid flow, for the simulation of energy and
temperature changes in and around a U-tube borehole heat
exchanger. The FlexPDE software is used to solve the resulting
simultaneous equations that model the heat exchanger. The validated
model (through a comparison with experimental data) is then used to
extract conclusions on how various parameters like the U-tube
diameter, the variation of the ground thermal conductivity and
specific heat and the borehole filling material affect the temperature
of the fluid.
Abstract: Wireless sensor networks have been used in wide
areas of application and become an attractive area for researchers in
recent years. Because of the limited energy storage capability of
sensor nodes, Energy consumption is one of the most challenging
aspects of these networks and different strategies and protocols deals
with this area. This paper presents general methods for designing low
power wireless sensor network. Different sources of energy
consumptions in these networks are discussed here and techniques for
alleviating the consumption of energy are presented.
Abstract: Phytases are acid phosphatase enzymes, which
efficiently cleave phosphate moieties from phytic acid, thereby
generating myo-inositol and inorganic phosphate. Thirty four
isolates of endophytic fungi to produce of phytases were isolated
from leaf, stem and root fragments of soybean. Screening of 34
isolates of endophytic fungi identified the phytases produced by
Rhizoctonia sp. and Fusarium verticillioides . The phytase
production were the best induced by phytic acid and rice bran
compared the others inducer in submerged fermentation medium
used. The phytase produced by both Rhizoctonia sp. and F.
verticillioides have pH optimum at 4.0 and 5.0 respectively. The
characterization of phytase from Fusarium verticillioides showed that
temperature optimum was 500C and stability until 600C, the pH
optimum 5.0 and pH stability was 2.5 – 6.0, and substrate specificity
were rice bran>soybean meal>corn> coconut cake, respectively.
Abstract: Artificial Immune System is applied as a Heuristic
Algorithm for decades. Nevertheless, many of these applications
took advantage of the benefit of this algorithm but seldom proposed
approaches for enhancing the efficiency. In this paper, a
Self-evolving Artificial Immune System is proposed via developing
the T and B cell in Immune System and built a self-evolving
mechanism for the complexities of different problems. In this
research, it focuses on enhancing the efficiency of Clonal selection
which is responsible for producing Affinities to resist the invading of
Antigens. T and B cell are the main mechanisms for Clonal
Selection to produce different combinations of Antibodies.
Therefore, the development of T and B cell will influence the
efficiency of Clonal Selection for searching better solution.
Furthermore, for better cooperation of the two cells, a co-evolutional
strategy is applied to coordinate for more effective productions of
Antibodies. This work finally adopts Flow-shop scheduling
instances in OR-library to validate the proposed algorithm.
Abstract: In this work, we consider a deterministic model for
the transmission of leptospirosis which is currently spreading in the
Thai population. The SIR model which incorporates the features of
this disease is applied to the epidemiological data in Thailand. It is
seen that the numerical solutions of the SIR equations are in good
agreement with real empirical data. Further improvements are
discussed.
Abstract: With the necessity of increased processing capacity with less energy consumption; power aware multiprocessor system has gained more attention in the recent future. One of the additional challenges that is to be solved in a multi-processor system when compared to uni-processor system is job allocation. This paper presents a novel task dependent job allocation algorithm: Energy centric- Allocation (Ec-A) and Rate Monotonic (RM) scheduling to minimize energy consumption in a multiprocessor system. A simulation analysis is carried out to verify the performance increase with reduction in energy consumption and required number of processors in the system.
Abstract: In the age of global communications, heterogeneous
networks are seen to be the best choice of strategy to ensure continuous and uninterruptible services. This will allow mobile
terminal to stay in connection even they are migrating into different segment coverage through the handoff process. With the increase of
teletraffic demands in mobile cellular system, hierarchical cellular systems have been adopted extensively for more efficient channel
utilization and better QoS (Quality of Service). This paper presents a
bidirectional call overflow scheme between two layers of microcells and macrocells, where handoffs are decided by the velocity of mobile
making the call. To ensure that handoff calls are given higher priorities, it is assumed that guard channels are assigned in both
macrocells and microcells. A hysteresis value introduced in mobile velocity is used to allow mobile roam in the same cell if its velocity
changes back within the set threshold values. By doing this the number of handoffs is reduced thereby reducing the processing overhead and enhancing the quality of service to the end user.
Abstract: A predictive clustering hybrid regression (pCHR)
approach was developed and evaluated using dataset from H2-
producing sucrose-based bioreactor operated for 15 months. The aim
was to model and predict the H2-production rate using information
available about envirome and metabolome of the bioprocess. Selforganizing
maps (SOM) and Sammon map were used to visualize the
dataset and to identify main metabolic patterns and clusters in
bioprocess data. Three metabolic clusters: acetate coupled with other
metabolites, butyrate only, and transition phases were detected. The
developed pCHR model combines principles of k-means clustering,
kNN classification and regression techniques. The model performed
well in modeling and predicting the H2-production rate with mean
square error values of 0.0014 and 0.0032, respectively.
Abstract: This paper made an attempt to investigate the problem associated with enhancement of emulsions of light crude oil-water recovery in an oil field of Algerian Sahara. Measurements were taken through experiments using RheoStress (RS600). Factors such as shear rate, temperature and light oil concentration on the viscosity behavior were considered. Experimental measurements were performed in terms of shear stress–shear rate, yield stress and flow index on mixture of light crude oil–water. The rheological behavior of emulsion showed Non-Newtonian shear thinning behavior (Herschel-Bulkley). The experiments done in the laboratory showed the stability of some water in light crude oil emulsions form during consolidate oil recovery process. To break the emulsion using additives may involve higher cost and could be very expensive. Therefore, further research should be directed to find solution of these problems that have been encountered.
Abstract: The cables in a nuclear power plant are designed to be
used for about 40 years in safe operation environment. However, the
heat and radiation in the nuclear power plant causes the rapid
performance deterioration of cables in nuclear vessels and heat
exchangers, which requires cable lifetime estimation. The most
accurate method of estimating the cable lifetime is to evaluate the
cables in a laboratory. However, removing cables while the plant is
operating is not allowed because of its safety and cost. In this paper, a
robot system to estimate the cable lifetime in nuclear power plants is
developed and tested. The developed robot system can calculate a
modulus value to estimate the cable lifetime even when the nuclear
power plant is in operation.
Abstract: Network security attacks are the violation of
information security policy that received much attention to the
computational intelligence society in the last decades. Data mining
has become a very useful technique for detecting network intrusions
by extracting useful knowledge from large number of network data
or logs. Naïve Bayesian classifier is one of the most popular data
mining algorithm for classification, which provides an optimal way
to predict the class of an unknown example. It has been tested that
one set of probability derived from data is not good enough to have
good classification rate. In this paper, we proposed a new learning
algorithm for mining network logs to detect network intrusions
through naïve Bayesian classifier, which first clusters the network
logs into several groups based on similarity of logs, and then
calculates the prior and conditional probabilities for each group of
logs. For classifying a new log, the algorithm checks in which cluster
the log belongs and then use that cluster-s probability set to classify
the new log. We tested the performance of our proposed algorithm by
employing KDD99 benchmark network intrusion detection dataset,
and the experimental results proved that it improves detection rates
as well as reduces false positives for different types of network
intrusions.