Abstract: The present study was designed to test the influence
of confirmed expectations, perceived usefulness and perceived
competence on e-learning satisfaction among university teachers. A
questionnaire was completed by 125 university teachers from 12
different universities in Norway. We found that 51% of the variance
in university teachers- satisfaction with e-learning could be explained
by the three proposed antecedents. Perceived usefulness seems to be
the most important predictor of teachers- satisfaction with e-learning.
Abstract: In this paper we present a novel approach for face image coding. The proposed method makes a use of the features of video encoders like motion prediction. At first encoder selects appropriate prototype from the database and warps it according to features of encoding face. Warped prototype is placed as first I frame. Encoding face is placed as second frame as P frame type. Information about features positions, color change, selected prototype and data flow of P frame will be sent to decoder. The condition is both encoder and decoder own the same database of prototypes. We have run experiment with H.264 video encoder and obtained results were compared to results achieved by JPEG and JPEG2000. Obtained results show that our approach is able to achieve 3 times lower bitrate and two times higher PSNR in comparison with JPEG. According to comparison with JPEG2000 the bitrate was very similar, but subjective quality achieved by proposed method is better.
Abstract: This paper presents the development of a Bayesian
belief network classifier for prediction of graft status and survival
period in renal transplantation using the patient profile information
prior to the transplantation. The objective was to explore feasibility
of developing a decision making tool for identifying the most suitable
recipient among the candidate pool members. The dataset was
compiled from the University of Toledo Medical Center Hospital
patients as reported to the United Network Organ Sharing, and had
1228 patient records for the period covering 1987 through 2009. The
Bayes net classifiers were developed using the Weka machine
learning software workbench. Two separate classifiers were induced
from the data set, one to predict the status of the graft as either failed
or living, and a second classifier to predict the graft survival period.
The classifier for graft status prediction performed very well with a
prediction accuracy of 97.8% and true positive values of 0.967 and
0.988 for the living and failed classes, respectively. The second
classifier to predict the graft survival period yielded a prediction
accuracy of 68.2% and a true positive rate of 0.85 for the class
representing those instances with kidneys failing during the first year
following transplantation. Simulation results indicated that it is
feasible to develop a successful Bayesian belief network classifier for
prediction of graft status, but not the graft survival period, using the
information in UNOS database.
Abstract: Reliable secure multicast communication in mobile
adhoc networks is challenging due to its inherent characteristics of
infrastructure-less architecture with lack of central authority, high
packet loss rates and limited resources such as bandwidth, time and
power. Many emerging commercial and military applications require
secure multicast communication in adhoc environments. Hence key
management is the fundamental challenge in achieving reliable
secure communication using multicast key distribution for mobile
adhoc networks. Thus in designing a reliable multicast key
distribution scheme, reliability and congestion control over
throughput are essential components. This paper proposes and
evaluates the performance of an enhanced optimized multicast cluster
tree algorithm with destination sequenced distance vector routing
protocol to provide reliable multicast key distribution. Simulation
results in NS2 accurately predict the performance of proposed
scheme in terms of key delivery ratio and packet loss rate under
varying network conditions. This proposed scheme achieves
reliability, while exhibiting low packet loss rate with high key
delivery ratio compared with the existing scheme.
Abstract: A simple model for studying convectional lifting
processes in the tropics is described in this paper with some tests of
the model in dry air. The model consists of the density equation, the
wind equation, the vertical velocity equation, and the temperature
equation. The model domain is two-dimensional with length 100 km
and height 17.5 km. Plan for experiments to investigate the effects of
the heating surface, the deep convection approximation and the
treatment of velocities at the boundaries are discussed. Equations for
the simplified treatment of moisture in the atmosphere in future
numerical experiments are also given.
Abstract: This study deals with the experimental investigation
and theoretical modeling of Semi crystalline polymeric materials with
a rubbery amorphous phase (HDPE) subjected to a uniaxial cyclic
tests with various maximum strain levels, even at large deformation.
Each cycle is loaded in tension up to certain maximum strain and
then unloaded down to zero stress with N number of cycles. This
work is focuses on the measure of the volume strain due to the
phenomena of damage during this kind of tests. On the basis of
thermodynamics of relaxation processes, a constitutive model for
large strain deformation has been developed, taking into account the
damage effect, to predict the complex elasto-viscoelastic-viscoplastic
behavior of material. A direct comparison between the model
predictions and the experimental data show that the model accurately
captures the material response. The model is also capable of
predicting the influence damage causing volume variation.
Abstract: Gas Metal Arc Welding (GMAW) processes is an
important joining process widely used in metal fabrication
industries. This paper addresses modeling and optimization of this
technique using a set of experimental data and regression analysis.
The set of experimental data has been used to assess the influence
of GMAW process parameters in weld bead geometry. The
process variables considered here include voltage (V); wire feed
rate (F); torch Angle (A); welding speed (S) and nozzle-to-plate
distance (D). The process output characteristics include weld bead
height, width and penetration. The Taguchi method and regression
modeling are used in order to establish the relationships between
input and output parameters. The adequacy of the model is
evaluated using analysis of variance (ANOVA) technique. In the
next stage, the proposed model is embedded into a Simulated
Annealing (SA) algorithm to optimize the GMAW process
parameters. The objective is to determine a suitable set of process
parameters that can produce desired bead geometry, considering
the ranges of the process parameters. Computational results prove
the effectiveness of the proposed model and optimization
procedure.
Abstract: Full search block matching algorithm is widely used for hardware implementation of motion estimators in video compression algorithms. In this paper we are proposing a new architecture, which consists of a 2D parallel processing unit and a 1D unit both working in parallel. The proposed architecture reduces both data access power and computational power which are the main causes of power consumption in integer motion estimation. It also completes the operations with nearly the same number of clock cycles as compared to a 2D systolic array architecture. In this work sum of absolute difference (SAD)-the most repeated operation in block matching, is calculated in two steps. The first step is to calculate the SAD for alternate rows by a 2D parallel unit. If the SAD calculated by the parallel unit is less than the stored minimum SAD, the SAD of the remaining rows is calculated by the 1D unit. Early termination, which stops avoidable computations has been achieved with the help of alternate rows method proposed in this paper and by finding a low initial SAD value based on motion vector prediction. Data reuse has been applied to the reference blocks in the same search area which significantly reduced the memory access.
Abstract: Large metal and concrete structures suffer by various kinds of deterioration, and accurate prediction of the remaining life is important. This paper informs about two methods for its assessment. One method, suitable for steel bridges and other constructions exposed to fatigue, monitors the loads and damage accumulation using information systems for the operation and the finite element model of the construction. In addition to the operation load, the dead weight of the construction and thermal stresses can be included into the model. The second method is suitable for concrete bridges and other structures, which suffer by carbonatation and other degradation processes, driven by diffusion. The diffusion constant, important for the prediction of future development, can be determined from the depth-profile of pH, obtained by pH measurement at various depths. Comparison with measurements on real objects illustrates the suitability of both methods.
Abstract: Globalization and therefore increasing tight competition among companies, have resulted to increase the importance of making well-timed decision. Devising and employing effective strategies, that are flexible and adaptive to changing market, stand a greater chance of being effective in the long-term. In other side, a clear focus on managing the entire product lifecycle has emerged as critical areas for investment. Therefore, applying wellorganized tools to employ past experience in new case, helps to make proper and managerial decisions. Case based reasoning (CBR) is based on a means of solving a new problem by using or adapting solutions to old problems. In this paper, an adapted CBR model with k-nearest neighbor (K-NN) is employed to provide suggestions for better decision making which are adopted for a given product in the middle of life phase. The set of solutions are weighted by CBR in the principle of group decision making. Wrapper approach of genetic algorithm is employed to generate optimal feature subsets. The dataset of the department store, including various products which are collected among two years, have been used. K-fold approach is used to evaluate the classification accuracy rate. Empirical results are compared with classical case based reasoning algorithm which has no special process for feature selection, CBR-PCA algorithm based on filter approach feature selection, and Artificial Neural Network. The results indicate that the predictive performance of the model, compare with two CBR algorithms, in specific case is more effective.
Abstract: The gases generated in oil filled transformers can be
used for qualitative determination of incipient faults. The Dissolved
Gas Analysis has been widely used by utilities throughout the world
as the primarily diagnostic tool for transformer maintenance. In this
paper, various Artificial Intelligence Techniques that have been used
by the researchers in the past have been reviewed, some conclusions
have been drawn and a sequential hybrid system has been proposed.
The synergy of ANN and FIS can be a good solution for reliable
results for predicting faults because one should not rely on a single
technology when dealing with real–life applications.
Abstract: The construction of a civil structure inside a urban
area inevitably modifies the outdoor microclimate at the building
site. Wind speed, wind direction, air pollution, driving rain, radiation
and daylight are some of the main physical aspects that are subjected
to the major changes. The quantitative amount of these modifications
depends on the shape, size and orientation of the building and on its
interaction with the surrounding environment.The flow field over a
flat roof model building has been numerically investigated in order to
determine two-dimensional CFD guidelines for the calculation of the
turbulent flow over a structure immersed in an atmospheric boundary
layer. To this purpose, a complete validation campaign has been
performed through a systematic comparison of numerical simulations
with wind tunnel experimental data.Several turbulence models and
spatial node distributions have been tested for five different vertical
positions, respectively from the upstream leading edge to the
downstream bottom edge of the analyzed model. Flow field
characteristics in the neighborhood of the building model have been
numerically investigated, allowing a quantification of the capabilities
of the CFD code to predict the flow separation and the extension of
the recirculation regions.The proposed calculations have allowed the
development of a preliminary procedure to be used as a guidance in
selecting the appropriate grid configuration and corresponding
turbulence model for the prediction of the flow field over a twodimensional
roof architecture dominated by flow separation.
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: 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: 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.
Abstract: Prediction of fault-prone modules provides one way to
support software quality engineering. Clustering is used to determine
the intrinsic grouping in a set of unlabeled data. Among various
clustering techniques available in literature K-Means clustering
approach is most widely being used. This paper introduces K-Means
based Clustering approach for software finding the fault proneness of
the Object-Oriented systems. The contribution of this paper is that it
has used Metric values of JEdit open source software for generation
of the rules for the categorization of software modules in the
categories of Faulty and non faulty modules and thereafter
empirically validation is performed. The results are measured in
terms of accuracy of prediction, probability of Detection and
Probability of False Alarms.
Abstract: Safety, river environment, and sediment utilization are the elements of the target of sediment management. As a change in an element by sediment management, may affect the other two elements, and the priority among three elements depends on stakeholders. It is necessary to develop a method to evaluate the effect of sediment management on each element and an integrated evaluation method for socio-economic effect. In this study, taking Mount Merapi basin as an investigation field, the method for an active volcanic basin was developed. An integrated evaluation method for sediment management was discussed from a socio-economic point on safety, environment, and sediment utilization and a case study of sediment management was evaluated by means of this method. To evaluate the effect of sediment management, some parameters on safety, utilization, and environment have been introduced. From a utilization point of view, job opportunity, additional income of local people, and tax income to local government were used to evaluate the effectiveness of sediment management. The risk degree of river infrastructure was used to describe the effect of sediment management on a safety aspect. To evaluate the effects of sediment management on environment, the mean diameter of grain size distribution of riverbed surface was used. On the coordinate system designating these elements, the direction of change in basin condition by sediment management can be predicted, so that the most preferable sediment management can be decided. The results indicate that the cases of sediment management tend to give the negative impacts on sediment utilization. However, these sediment managements will give positive impacts on safety and environment condition. Evaluation result from a social-economic point of view shows that the case study of sediment management reduces job opportunity and additional income for inhabitants as well as tax income for government. Therefore, it is necessary to make another policy for creating job opportunity for inhabitants to support these sediment managements.
Abstract: Due to the coexistence of different Radio Access
Technologies (RATs), Next Generation Wireless Networks (NGWN)
are predicted to be heterogeneous in nature. The coexistence of
different RATs requires a need for Common Radio Resource
Management (CRRM) to support the provision of Quality of Service
(QoS) and the efficient utilization of radio resources. RAT selection
algorithms are part of the CRRM algorithms. Simply, their role is to
verify if an incoming call will be suitable to fit into a heterogeneous
wireless network, and to decide which of the available RATs is most
suitable to fit the need of the incoming call and admit it.
Guaranteeing the requirements of QoS for all accepted calls and at
the same time being able to provide the most efficient utilization of
the available radio resources is the goal of RAT selection algorithm.
The normal call admission control algorithms are designed for
homogeneous wireless networks and they do not provide a solution
to fit a heterogeneous wireless network which represents the NGWN.
Therefore, there is a need to develop RAT selection algorithm for
heterogeneous wireless network. In this paper, we propose an
approach for RAT selection which includes receiving different
criteria, assessing and making decisions, then selecting the most
suitable RAT for incoming calls. A comprehensive survey of
different RAT selection algorithms for a heterogeneous wireless
network is studied.
Abstract: The selection for plantation of a particular type of
mustard plant depending on its productivity (pod yield) at the stage
of maturity. The growth of mustard plant dependent on some
parameters of that plant, these are shoot length, number of leaves,
number of roots and roots length etc. As the plant is growing, some
leaves may be fall down and some new leaves may come, so it can
not gives the idea to develop the relationship with the seeds weight at
mature stage of that plant. It is not possible to find the number of
roots and root length of mustard plant at growing stage that will be
harmful of this plant as roots goes deeper to deeper inside the land.
Only the value of shoot length which increases in course of time can
be measured at different time instances. Weather parameters are
maximum and minimum humidity, rain fall, maximum and minimum
temperature may effect the growth of the plant. The parameters of
pollution, water, soil, distance and crop management may be
dominant factors of growth of plant and its productivity. Considering
all parameters, the growth of the plant is very uncertain, fuzzy
environment can be considered for the prediction of shoot length at
maturity of the plant. Fuzzification plays a greater role for
fuzzification of data, which is based on certain membership
functions. Here an effort has been made to fuzzify the original data
based on gaussian function, triangular function, s-function,
Trapezoidal and L –function. After that all fuzzified data are
defuzzified to get normal form. Finally the error analysis
(calculation of forecasting error and average error) indicates the
membership function appropriate for fuzzification of data and use to
predict the shoot length at maturity. The result is also verified using
residual (Absolute Residual, Maximum of Absolute Residual, Mean
Absolute Residual, Mean of Mean Absolute Residual, Median of
Absolute Residual and Standard Deviation) analysis.
Abstract: The purpose of this research was to study the factors
that influenced the success of mobile phone entrepreneurs at Central
Plaza. The sample group included 187 entrepreneurs at Central Plaza.
A questionnaire was utilized as a tool to collect data. Statistics used
in this research included frequency, percentage, mean, and standard
deviation. Independent- sample t- test, one way ANOVA, and
multiple regression analysis. Data were analyzed by using Statistical
Package for the Social Sciences.The findings disclosed that the
majority of respondents were male between 25-40 years old, and held
an undergraduate degree. The average income of respondents was
between 15,001-25,000 baht. The majority of respondents had less
than 5 years of working experience.
In terms of personality, the findings revealed that expression and
agreement were ranked at the highest level. Whereas, emotion
stability, consciousness, open to new experience were ranked at high.
From the hypotheses testing, the findings revealed that different
genders had different success in their mobile phone business with
different income from the last 6 months. However, difference in age,
income, level of education, and experience affected the success in
terms of income, number of customers, and overall success of
business. Moreover, the factors of personalities included expression,
agreement, emotion stability, consciousness, open to new experience,
and competitive strategy. From the findings, these factors were able
to predict mobile phone business success at 66.9 percent.