Abstract: In this paper the development of a heat exchanger as a
pilot plant for educational purpose is discussed and the use of neural
network for controlling the process is being presented. The aim of the
study is to highlight the need of a specific Pseudo Random Binary
Sequence (PRBS) to excite a process under control. As the neural
network is a data driven technique, the method for data generation
plays an important role. In light of this a careful experimentation
procedure for data generation was crucial task. Heat exchange is a
complex process, which has a capacity and a time lag as process
elements. The proposed system is a typical pipe-in- pipe type heat
exchanger. The complexity of the system demands careful selection,
proper installation and commissioning. The temperature, flow, and
pressure sensors play a vital role in the control performance. The
final control element used is a pneumatically operated control valve.
While carrying out the experimentation on heat exchanger a welldrafted
procedure is followed giving utmost attention towards safety
of the system. The results obtained are encouraging and revealing
the fact that if the process details are known completely as far as
process parameters are concerned and utilities are well stabilized then
feedback systems are suitable, whereas neural network control
paradigm is useful for the processes with nonlinearity and less
knowledge about process. The implementation of NN control
reinforces the concepts of process control and NN control paradigm.
The result also underlined the importance of excitation signal
typically for that process. Data acquisition, processing, and
presentation in a typical format are the most important parameters
while validating the results.
Abstract: Laser Profiler (LP) data from aerial laser surveys have
been increasingly used as topographical inputs to numerical
simulations of flooding and inundation in river basins. LP data has
great potential for reproducing topography, but its effective usage has
not yet been fully established. In this study, flooding and inundation
are simulated numerically using LP data for the Jobaru River basin of
Japan’s Saga Plain. The analysis shows that the topography is
reproduced satisfactorily in the computational domain with urban and
agricultural areas requiring different grid sizes. A 2-D numerical
simulation shows that flood flow behavior changes as grid size is
varied.
Abstract: Understanding proteins functions is a major goal in
the post-genomic era. Proteins usually work in context of other
proteins and rarely function alone. Therefore, it is highly relevant to
study the interaction partners of a protein in order to understand its
function. Machine learning techniques have been widely applied to
predict protein-protein interactions. Kernel functions play an
important role for a successful machine learning technique. Choosing
the appropriate kernel function can lead to a better accuracy in a
binary classifier such as the support vector machines. In this paper,
we describe a Bayesian kernel for the support vector machine to
predict protein-protein interactions. The use of Bayesian kernel can
improve the classifier performance by incorporating the probability
characteristic of the available experimental protein-protein
interactions data that were compiled from different sources. In
addition, the probabilistic output from the Bayesian kernel can assist
biologists to conduct more research on the highly predicted
interactions. The results show that the accuracy of the classifier has
been improved using the Bayesian kernel compared to the standard
SVM kernels. These results imply that protein-protein interaction can
be predicted using Bayesian kernel with better accuracy compared to
the standard SVM kernels.
Abstract: Using a texture database, a statistical estimation of
spring-back was conducted in this study on the basis of statistical
analysis. Both spring-back in bending deformation and experimental
data related to the crystal orientation show significant dispersion.
Therefore, a probabilistic statistical approach was established for the
proper quantification of these values. Correlation was examined
among the parameters F(x) of spring-back, F(x) of the buildup fraction
to three orientations after 92° bending, and F(x) at an as-received part
on the basis of the three-parameter Weibull distribution. Consequent
spring-back estimation using a texture database yielded excellent
estimates compared with experimental values.
Abstract: One of the most common practices for strengthening
the reinforced concrete structures is the application of FRP (Fiber
Reinforce Plastic) sheets to increase the flexural and shear strengths
of the member. The elastic modulus of FRP is considerably higher
than that of concrete. This will result in debonding between the FRP
sheets and concrete surface. With conventional surface preparation of
concrete, the ultimate capacity of the FRP sheets can hardly be
achieved. New methods for preparation of the bonding surface have
shown improvements in reducing the premature debonding of FRP
sheets from concrete surface. The present experimental study focuses
on the application of grooving method to postpone debonding of the
FRP sheets attached to the side faces of concrete beams for shear
strengthening. Comparison has also been made with conventional
surface preparation method. This study clearly shows the efficiency
of grooving method compared to surface preparation method, in
preventing the debonding phenomenon and in increasing the load
carrying capacity of FRP.
Abstract: An enhanced particle swarm optimization algorithm
(PSO) is presented in this work to solve the non-convex OPF
problem that has both discrete and continuous optimization variables.
The objective functions considered are the conventional quadratic
function and the augmented quadratic function. The latter model
presents non-differentiable and non-convex regions that challenge
most gradient-based optimization algorithms. The optimization
variables to be optimized are the generator real power outputs and
voltage magnitudes, discrete transformer tap settings, and discrete
reactive power injections due to capacitor banks. The set of equality
constraints taken into account are the power flow equations while the
inequality ones are the limits of the real and reactive power of the
generators, voltage magnitude at each bus, transformer tap settings,
and capacitor banks reactive power injections. The proposed
algorithm combines PSO with Newton-Raphson algorithm to
minimize the fuel cost function. The IEEE 30-bus system with six
generating units is used to test the proposed algorithm. Several cases
were investigated to test and validate the consistency of detecting
optimal or near optimal solution for each objective. Results are
compared to solutions obtained using sequential quadratic
programming and Genetic Algorithms.
Abstract: In this study we present our developed formative
assessment tool for students' assignments. The tool enables lecturers
to define assignments for the course and assign each problem in each
assignment a list of criteria and weights by which the students' work
is evaluated. During assessment, the lecturers feed the scores for each
criterion with justifications. When the scores of the current
assignment are completely fed in, the tool automatically generates
reports for both students and lecturers. The students receive a report
by email including detailed description of their assessed work, their
relative score and their progress across the criteria along the course
timeline. This information is presented via charts generated
automatically by the tool based on the scores fed in. The lecturers
receive a report that includes summative (e.g., averages, standard
deviations) and detailed (e.g., histogram) data of the current
assignment. This information enables the lecturers to follow the class
achievements and adjust the learning process accordingly. The tool
was examined on two pilot groups of college students that study a
course in (1) Object-Oriented Programming (2) Plane Geometry.
Results reveal that most of the students were satisfied with the
assessment process and the reports produced by the tool. The
lecturers who used the tool were also satisfied with the reports and
their contribution to the learning process.
Abstract: Proper management of residues originated from
industrial activities is considered as one of the serious challenges
faced by industrial societies due to their potential hazards to the
environment. Common disposal methods for industrial solid wastes
(ISWs) encompass various combinations of solely management
options, i.e. recycling, incineration, composting, and sanitary
landfilling. Indeed, the procedure used to evaluate and nominate the
best practical methods should be based on environmental, technical,
economical, and social assessments. In this paper an environmentaltechnical
assessment model is developed using analytical network
process (ANP) to facilitate the decision making practice for ISWs
generated at Gilan province, Iran. Using the results of performed
surveys on industrial units located at Gilan, the various groups of
solid wastes in the research area were characterized, and four
different ISW management scenarios were studied. The evaluation
process was conducted using the above-mentioned model in the
Super Decisions software (version 2.0.8) environment. The results
indicates that the best ISW management scenario for Gilan province
is consist of recycling the metal industries residues, composting the
putrescible portion of ISWs, combustion of paper, wood, fabric and
polymeric wastes as well as energy extraction in the incineration
plant, and finally landfilling the rest of the waste stream in addition
with rejected materials from recycling and compost production plants
and ashes from the incineration unit.
Abstract: The aim of this paper to characterize a larger set of
wavelet functions for implementation in a still image compression
system using SPIHT algorithm. This paper discusses important
features of wavelet functions and filters used in sub band coding to
convert image into wavelet coefficients in MATLAB. Image quality
is measured objectively using peak signal to noise ratio (PSNR) and
its variation with bit rate (bpp). The effect of different parameters is
studied on different wavelet functions. Our results provide a good
reference for application designers of wavelet based coder.
Abstract: The Block Sorting problem is to sort a given
permutation moving blocks. A block is defined as a substring
of the given permutation, which is also a substring of the
identity permutation. Block Sorting has been proved to be
NP-Hard. Until now two different 2-Approximation algorithms
have been presented for block sorting. These are the best known
algorithms for Block Sorting till date. In this work we present
a different characterization of Block Sorting in terms of a
transposition cycle graph. Then we suggest a heuristic,
which we show to exhibit a 2-approximation performance
guarantee for most permutations.
Abstract: A lateral trench-gate power metal-oxide-semiconductor on 4H-SiC is proposed. The device consists of two separate trenches in which two gates are placed on both sides of P-body region resulting two parallel channels. Enhanced current conduction and reduced-surface-field effect in the structure provide substantial improvement in the device performance. Using two dimensional simulations, the performance of proposed device is evaluated and compare of with that of the conventional device for same cell pitch. It is demonstrated that the proposed structure provides two times higher output current, 11% decrease in threshold voltage, 70% improvement in transconductance, 70% reduction in specific ON-resistance, 52% increase in breakdown voltage, and nearly eight time improvement in figure-of-merit over the conventional device.
Abstract: The paper presents the potential of fuzzy logic (FL-I)
and neural network techniques (ANN-I) for predicting the
compressive strength, for SCC mixtures. Six input parameters that is
contents of cement, sand, coarse aggregate, fly ash, superplasticizer
percentage and water-to-binder ratio and an output parameter i.e. 28-
day compressive strength for ANN-I and FL-I are used for modeling.
The fuzzy logic model showed better performance than neural
network model.
Abstract: Due to growing environmental concerns of the cement
industry, alternative cement technologies have become an area of
increasing interest. It is now believed that new binders are
indispensable for enhanced environmental and durability
performance. Self-compacting Geopolymer concrete is an innovative
method and improved way of concreting operation that does not
require vibration for placing it and is produced by complete
elimination of ordinary Portland cement.
This paper documents the assessment of the compressive strength
and workability characteristics of low-calcium fly ash based selfcompacting
geopolymer concrete. The essential workability
properties of the freshly prepared Self-compacting Geopolymer
concrete such as filling ability, passing ability and segregation
resistance were evaluated by using Slump flow, V-funnel, L-box and
J-ring test methods. The fundamental requirements of high
flowability and segregation resistance as specified by guidelines on
Self Compacting Concrete by EFNARC were satisfied. In addition,
compressive strength was determined and the test results are included
here. This paper also reports the effect of extra water, curing time and
curing temperature on the compressive strength of self-compacting
geopolymer concrete. The test results show that extra water in the
concrete mix plays a significant role. Also, longer curing time and
curing the concrete specimens at higher temperatures will result in
higher compressive strength.
Abstract: this paper presents a multi-context recurrent network for time series analysis. While simple recurrent network (SRN) are very popular among recurrent neural networks, they still have some shortcomings in terms of learning speed and accuracy that need to be addressed. To solve these problems, we proposed a multi-context recurrent network (MCRN) with three different learning algorithms. The performance of this network is evaluated on some real-world application such as handwriting recognition and energy load forecasting. We study the performance of this network and we compared it to a very well established SRN. The experimental results showed that MCRN is very efficient and very well suited to time series analysis and its applications.
Abstract: Mobile learning (m-learning) is a new method in teaching and learning process which combines technology of mobile device with learning materials. It can enhance student's engagement in learning activities and facilitate them to access the learning materials at anytime and anywhere. In Kolej Poly-Tech Mara (KPTM), this method is seen as an important effort in teaching practice and to improve student learning performance. The aim of this paper is to discuss the development of m-learning application called Mobile EEF Learning System (MEEFLS) to be implemented for Electric and Electronic Fundamentals course using Flash, XML (Extensible Markup Language) and J2ME (Java 2 micro edition). System Development Life Cycle (SDLC) was used as an application development approach. It has three modules in this application such as notes or course material, exercises and video. MEELFS development is seen as a tool or a pilot test for m-learning in KPTM.
Abstract: This study investigates the possibility providing gully
erosion map by the supervised classification of satellite images
(ETM+) in two mountainous and plain land types. These land types
were the part of Varamin plain, Tehran province, and Roodbar subbasin,
Guilan province, as plain and mountain land types,
respectively. The position of 652 and 124 ground control points were
recorded by GPS respectively in mountain and plain land types. Soil
gully erosion, land uses or plant covers were investigated in these
points. Regarding ground control points and auxiliary points, training
points of gully erosion and other surface features were introduced to
software (Ilwis 3.3 Academic). The supervised classified map of
gully erosion was prepared by maximum likelihood method and then,
overall accuracy of this map was computed. Results showed that the
possibility supervised classification of gully erosion isn-t possible,
although it need more studies for results generalization to other
mountainous regions. Also, with increasing land uses and other
surface features in plain physiography, it decreases the classification
of accuracy.
Abstract: This research paper evaluates and compares the
performance of equal cost adaptive multi-path routing algorithms
taking the transport protocols TCP (Transmission Control Protocol)
and UDP (User Datagram Protocol) using network simulator ns2 and
concludes which one is better.
Abstract: A Watson-Crick automaton is recently introduced as a
computational model of DNA computing framework. It works on
tapes consisting of double stranded sequences of symbols. Symbols
placed on the corresponding cells of the double-stranded sequences are
related by a complimentary relation. In this paper, we investigate a
variation of Watson-Crick automata in which both heads read the tape
in reverse directions. They are called reverse Watson-Crick finite
automata (RWKFA). We show that all of following four classes, i.e.,
simple, 1-limited, all-final, all-final and simple, are equal to
non-restricted version of RWKFA.
Abstract: Machine Translation (MT 3) of English text to its Urdu equivalent is a difficult challenge. Lot of attempts has been made, but a few limited solutions are provided till now. We present a direct approach, using an expert system to translate English text into its equivalent Urdu, using The Unicode Standard, Version 4.0 (ISBN 0-321-18578-1) Range: 0600–06FF. The expert system works with a knowledge base that contains grammatical patterns of English and Urdu, as well as a tense and gender-aware dictionary of Urdu words (with their English equivalents).
Abstract: Date palm (Phoenix dactylifera L.) seeds are waste streams which are considered a major problem to the food industry. They contain potentially useful protein (10-15% of the whole date-s weight). Global production, industrialisation and utilisation of dates are increasing steadily. The worldwide production of date palm fruit has increased from 1.8 million tons in 1961 to 6.9 million tons in 2005, thus from the global production of dates are almost 800.000 tonnes of date palm seeds are not currently used [1]. The current study was carried out to convert the date palm seeds into useful protein powder. Compositional analysis showed that the seeds were rich in protein and fat 5.64 and 8.14% respectively. We used several laboratory scale methods to extract proteins from seed to produce a high protein powder. These methods included simple acid or alkali extraction, with or without ultrafiltration and phenol trichloroacetic acid with acetone precipitation (Ph/TCA method). The highest protein content powder (68%) was obtained by Ph/TCA method with yield of material (44%) whereas; the use of just alkali extraction gave the lowest protein content of 8%, and a yield of 32%.