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: There are four challenges of sustainable development
and in corporate level sustainability management-s role is to answer
for ecological sustainability challenge, social sustainability challenge,
economic sustainability challenges to environment and social
management and integration challenge of corporate sustainable
challenges by the help of different concepts, methods, instruments,
which are in the toolbox of sustainability management. These
instruments, concepts have different relevance in these challenges,
and according to different literatures environmental management is
outside of social and integration challenge. Main aim of this paper is
to represent the answer for the question that: is it true that social and
integration point of view is outside of the concept environmental
accounting? Using literature review and primer research at the end of
the paper the answer will be confirmed.
Abstract: Nowadays, several techniques such as; Fuzzy
Inference System (FIS) and Neural Network (NN) are employed for
developing of the predictive models to estimate parameters of water
quality. The main objective of this study is to compare between the
predictive ability of the Adaptive Neuro-Fuzzy Inference System
(ANFIS) model and Artificial Neural Network (ANN) model to
estimate the Biochemical Oxygen Demand (BOD) on data from 11
sampling sites of Saen Saep canal in Bangkok, Thailand. The data is
obtained from the Department of Drainage and Sewerage, Bangkok
Metropolitan Administration, during 2004-2011. The five parameters
of water quality namely Dissolved Oxygen (DO), Chemical Oxygen
Demand (COD), Ammonia Nitrogen (NH3N), Nitrate Nitrogen
(NO3N), and Total Coliform bacteria (T-coliform) are used as the
input of the models. These water quality indices affect the
biochemical oxygen demand. The experimental results indicate that
the ANN model provides a higher correlation coefficient (R=0.73)
and a lower root mean square error (RMSE=4.53) than the
corresponding ANFIS model.
Abstract: Bidding is a very important business function to find
latent contractors of construction projects. Moreover, bid markup is
one of the most important decisions for a bidder to gain a reasonable
profit. Since the bidding system is a complex adaptive system, bidding
agent need a learning process to get more valuable knowledge for a bid,
especially from past public bidding information. In this paper, we
proposed an iterative agent leaning model for bidders to make markup
decisions. A classifier for public bidding information named PIBS is
developed to make full use of history data for classifying new bidding
information. The simulation and experimental study is performed to
show the validity of the proposed classifier. Some factors that affect
the validity of PIBS are also analyzed at the end of this work.
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: In this paper, we observe that developed countries are generally equipped with innovation capabilities and produce major chunk of the world-s knowledge and technology. The contribution of developing countries, on the other hand, is insignificant, and most of them far behind the global technological front. More specifically, we empirically observe that the developing world neither contributes substantially to the world-s scientific publications nor to the R&D activities. They also have lesser “absorptive capacity" and “technological capability", and their “innovation systems" are plagued with many problems. Finally, we argue that these countries can break the shackles and improve their innovation capabilities by pursuing genuine innovation policies on long-term basis with honesty and commitment.
Abstract: This research is part of a broad program aimed at
advancing the science and technology involved in the rescue and
rehabilitation of oiled wildlife. One aspect of this research involves
the use of oil-sequestering magnetic particles for the removal of
contaminants from plumage – so-called “magnetic cleansing". This
treatment offers a number of advantages over conventional
detergent-based methods including portability - which offers the
possibility of providing a “quick clean" to the animal upon first
encounter in the field. This could be particularly advantageous
when the contaminant is toxic and/or corrosive and/or where there
is a delay in transporting the victim to a treatment centre. The
method could also be useful as part of a stabilization protocol when
large numbers of affected animals are awaiting treatment. This
presentation describes the design, development and testing of a
prototype field kit for providing a “quick clean" to contaminated
wildlife in the field.
Abstract: The cyberspace is an instrument through which
internet users could get new experiences. It could contribute to foster
one-s own growth, widening cognitive, creative and communicative
abilities and promoting relationships. In the cyberspace, in fact, it is
possible to create virtual learning communities where internet users
improve their interpersonal sphere, knowledge and skills. The main
element of e-learning is the establishment of online relationships, that
are often collaborative.
Abstract: Dense slurry flow through centrifugal pump casing
has been modeled using the Eulerian-Eulerian approach with
Eulerian multiphase model in FLUENT 6.1®. First order upwinding
is considered for the discretization of momentum, k and ε terms.
SIMPLE algorithm has been applied for dealing with pressurevelocity
coupling. A mixture property based k-ε turbulence model
has been used for modeling turbulence. Results are validated first
against mesh independence and experiments for a particular set of
operational and geometric conditions. Parametric analysis is then
performed to determine the effect on important physical quantities
viz. solid velocities, solid concentration and solid stresses near the
wall with various operational geometric conditions of the pump.
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: State-based testing is frequently used in software testing. Test data generation is one of the key issues in software testing. A properly generated test suite may not only locate the errors in a software system, but also help in reducing the high cost associated with software testing. It is often desired that test data in the form of test sequences within a test suite can be automatically generated to achieve required test coverage. This paper proposes an Ant Colony Optimization approach to test data generation for the state-based software testing.
Abstract: Regulatory relationships of 686 intronic miRNA and 784 intergenic miRNAs with mRNAs of 51 intronic miRNA coding genes were established. Interaction features of studied miRNAs with 5'UTR, CDS and 3'UTR of mRNA of each gene were revealed. Functional regions of mRNA were shown to be significantly heterogenous according to the number of binding sites of miRNA and to the location density of these sites.
Abstract: Graphene-metal contact resistance limits the performance of graphene-based electrical devices. In this work, we have fabricated both graphene field-effect transistors (GFET) and transfer length measurement (TLM) test devices with titanium contacts. The purpose of this work is to compare the contact resistances that can be numerically extracted from the GFETs and measured from the TLM structures. We also provide a brief review of the work done in the field to solve the contact resistance problem.
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: This research aims to study employment trends in
printing industry for prepress support by Suan Sunandha University
Fund. The objectives of this research are to explain the trends of the
employment in Thai Printing Industry for prepress in Bangkok and
the description of different personnel that prepress entrepreneur need
and also the problems of employment.
The population of prepress entrepreneurs is about 100
organizations in the area of Bangkok. The questionnaires has been
taken and analyzed with SPSS program by using the average
percentage and standard deviation.
This research is multiple case studies. The conceptual framework
is developed on the basis of the open systems theory.
The research result show that
1. The most of prepress entrepreneur have trend to choose the
employee by any sex, the age 25-29 years old, bachelor degree
and have 1-2 years experience.
2. The most problems are the understanding in job,
communication/relation and the understanding in new
technology.
3. The trends aims to employment in 1-3 years have 57.8% for
prepress industry in Bangkok.
This research suggests that:
1. Thai printing industry for prepress in Bangkok need quality
employee that expert in printing technology.
2. Prepress entrepreneur should have agreement to development
with university for practice the employee.
3. Prepress entrepreneur should support personal to fulfill the
knowledge.
Abstract: Wheeled Mobile Robots (WMRs) are built with their
Wheels- drive machine, Motors. Depend on their desire design of
WMR, Technicians made used of DC Motors for motion control. In
this paper, the author would like to analyze how to choose DC motor
to be balance with their applications of especially for WMR.
Specification of DC Motor that can be used with desire WMR is to
be determined by using MATLAB Simulink model. Therefore, this
paper is mainly focus on software application of MATLAB and
Control Technology. As the driving system of DC motor, a
Peripheral Interface Controller (PIC) based control system is
designed including the assembly software technology and H-bridge
control circuit. This Driving system is used to drive two DC gear
motors which are used to control the motion of WMR. In this
analyzing process, the author mainly focus the drive system on
driving two DC gear motors that will control with Differential Drive
technique to the Wheeled Mobile Robot . For the design analysis of
Motor Driving System, PIC16F84A is used and five inputs of sensors
detected data are tested with five ON/OFF switches. The outputs of
PIC are the commands to drive two DC gear motors, inputs of Hbridge
circuit .In this paper, Control techniques of PIC
microcontroller and H-bridge circuit, Mechanism assignments of
WMR are combined and analyzed by mainly focusing with the
“Modeling and Simulink of DC Motor using MATLAB".
Abstract: System identification is the process of creating
models of dynamic process from input- output signals. The aim of
system identification can be identified as “ to find a model with
adjustable parameters and then to adjust them so that the predicted
output matches the measured output". This paper presents a method
of modeling and simulating with system identification to achieve the
maximum fitness for transformation function. First by using
optimized KLM equivalent circuit for PVDF piezoelectric transducer
and assuming different inputs including: sinuside, step and sum of
sinusides, get the outputs, then by using system identification
toolbox in MATLAB, we estimate the transformation function from
inputs and outputs resulted in last program. Then compare the fitness
of transformation function resulted from using ARX,OE(Output-
Error) and BJ(Box-Jenkins) models in system identification toolbox
and primary transformation function form KLM equivalent circuit.
Abstract: A new approach for timestamp ordering problem in
serializable schedules is presented. Since the number of users using
databases is increasing rapidly, the accuracy and needing high
throughput are main topics in database area. Strict 2PL does not
allow all possible serializable schedules and so does not result high
throughput. The main advantages of the approach are the ability to
enforce the execution of transaction to be recoverable and the high
achievable performance of concurrent execution in central databases.
Comparing to Strict 2PL, the general structure of the algorithm is
simple, free deadlock, and allows executing all possible serializable
schedules which results high throughput. Various examples which
include different orders of database operations are discussed.
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: Fast forecasting of stock market prices is very important for
strategic planning. In this paper, a new approach for fast forecasting of
stock market prices is presented. Such algorithm uses new high speed
time delay neural networks (HSTDNNs). The operation of these
networks relies on performing cross correlation in the frequency
domain between the input data and the input weights of neural
networks. It is proved mathematically and practically that the number
of computation steps required for the presented HSTDNNs is less
than that needed by traditional time delay neural networks
(TTDNNs). Simulation results using MATLAB confirm the
theoretical computations.