Abstract: In this research work, investigations are carried out on
Continuous Wave (CW) Nd:YAG laser welding system after
preliminary experimentation to understand the influencing parameters
associated with laser welding of AISI 304. The experimental
procedure involves a series of laser welding trials on AISI 304
stainless steel sheets with various combinations of process parameters
like beam power, beam incident angle and beam incident angle. An
industrial 2 kW CW Nd:YAG laser system, available at Welding
Research Institute (WRI), BHEL Tiruchirappalli, is used for
conducting the welding trials for this research. After proper tuning of
laser beam, laser welding experiments are conducted on AISI 304
grade sheets to evaluate the influence of various input parameters on
weld bead geometry i.e. bead width (BW) and depth of penetration
(DOP). From the laser welding results, it is noticed that the beam
power and welding speed are the two influencing parameters on
depth and width of the bead. Three dimensional finite element
simulation of high density heat source have been performed for laser
welding technique using finite element code ANSYS for predicting
the temperature profile of laser beam heat source on AISI 304
stainless steel sheets. The temperature dependent material properties
for AISI 304 stainless steel are taken into account in the simulation,
which has a great influence in computing the temperature profiles.
The latent heat of fusion is considered by the thermal enthalpy of
material for calculation of phase transition problem. A Gaussian
distribution of heat flux using a moving heat source with a conical
shape is used for analyzing the temperature profiles. Experimental
and simulated values for weld bead profiles are analyzed for stainless
steel material for different beam power, welding speed and beam
incident angle. The results obtained from the simulation are
compared with those from the experimental data and it is observed
that the results of numerical analysis (FEM) are in good agreement
with experimental results, with an overall percentage of error
estimated to be within ±6%.
Abstract: Data mining (DM) is the process of finding and extracting frequent patterns that can describe the data, or predict unknown or future values. These goals are achieved by using various learning algorithms. Each algorithm may produce a mining result completely different from the others. Some algorithms may find millions of patterns. It is thus the difficult job for data analysts to select appropriate models and interpret the discovered knowledge. In this paper, we describe a framework of an intelligent and complete data mining system called SUT-Miner. Our system is comprised of a full complement of major DM algorithms, pre-DM and post-DM functionalities. It is the post-DM packages that ease the DM deployment for business intelligence applications.
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: 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: 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: Information and communication technology (ICT) has
become, within a very short time, one of the basic building blocks of
modern society. Many countries now understanding the importance
of ICT and mastering the basic skills and concepts of it as part of the
core of education. Organizations, experts and practitioners in the
education sector increasingly recognizing the importance of ICT in
supporting educational improvement and reform. This paper
addresses the convergence of ICT and education. When two
technologies are converging to each other, together they will generate
some great opportunities and challenges. This paper focuses on these
issues. In introduction section, it explains the ICT, education, and
ICT-enhanced education. In next section it describes need of ICT in
education, relationship between ICT skills and education, and stages
of teaching learning process. The next two sections describe
opportunities and challenges in integrating ICT in education. Finally
the concluding section summaries the idea and its usefulness.
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: 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: For high-speed control of robots, a good knowledge of system modelling is necessary to obtain the desired bandwidth. In this paper, we present a cartesian robot with a pan/tilt unit in end-effector (5 dof). This robot is implemented with powerful direct drive AC induction machines. The dynamic model, parameter identification and model validation of the robot are studied (including actuators). This work considers the cartesian robot coupled and non linear (contrary to normal considerations for this type of robots). The mechanical and control architecture proposed in this paper is efficient for industrial and research application in which high speed, well known model and very high accuracy are required.
Abstract: This paper reviews various approaches that have been
used for the modeling and simulation of large-scale engineering
systems and determines their appropriateness in the development of a
RICS modeling and simulation tool. Bond graphs, linear graphs,
block diagrams, differential and difference equations, modeling
languages, cellular automata and agents are reviewed. This tool
should be based on linear graph representation and supports symbolic
programming, functional programming, the development of noncausal
models and the incorporation of decentralized approaches.
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: 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: In recent years, the research in wireless sensor
network has increased steadily, and many studies were focusing on
reducing energy consumption of sensor nodes to extend their lifetimes.
In this paper, the issue of energy consumption is investigated and two
adaptive mechanisms are proposed to extend the network lifetime.
This study uses high-energy-first scheme to determine cluster heads
for data transmission. Thus, energy consumption in each cluster is
balanced and network lifetime can be extended. In addition, this study
uses cluster merging and dynamic routing mechanisms to further
reduce energy consumption during data transmission. The simulation
results show that the proposed method can effectively extend the
lifetime of wireless sensor network, and it is suitable for different base
station locations.
Abstract: In the present study, changes of morphology and
mechanical characteristics in the lumbar vertebrae of the
ovariectomised (OVX) rat were investigated. In previous researches,
there were many studies about morphology like volume fraction and
trabecular thickness based on Micro - Computed Tomography (Micro
- CT). However, detecting and tracking long-term changes in the
trabecular bone of the lumbar vertebrae for the OVX rat were few. For
this study, one female Sprague-Dawley rat was used: an OVX rat. The
4th Lumbar of the OVX rat was subjected to in-vivo micro-CT.
Detecting and tracking long-term changes could be investigated in the
trabecular bone of the lumbar vertebrae for an OVX rat using in-vivo
micro-CT. An OVX rat was scanned at week 0 (just before surgery), at
week 4, at week 8, week 16, week 22 and week 56 after surgery. Finite
element (FE) analysis was used to investigate mechanical
characteristics of the lumbar vertebrae for an OVX rat. When the OVX
rat (at week 56) was compared with the OVX rat (at week 0), volume
fraction was decreased by 80% and effective modulus was decreased
by 75%.
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: Development of artificial neural network (ANN) for
prediction of aluminum workpieces' surface roughness in ultrasonicvibration
assisted turning (UAT) has been the subject of the present
study. Tool wear as the main cause of surface roughness was also
investigated. ANN was trained through experimental data obtained
on the basis of full factorial design of experiments. Various
influential machining parameters were taken into consideration. It
was illustrated that a multilayer perceptron neural network could
efficiently model the surface roughness as the response of the
network, with an error less than ten percent. The performance of the
trained network was verified by further experiments. The results of
UAT were compared with the results of conventional turning
experiments carried out with similar machining parameters except for
the vibration amplitude whence considerable reduction was observed
in the built-up edge and the surface roughness.
Abstract: Years of extensive research in the field of speech
processing for compression and recognition in the last five decades,
resulted in a severe competition among the various methods and
paradigms introduced. In this paper we include the different representations
of speech in the time-frequency and time-scale domains
for the purpose of compression and recognition. The examination of
these representations in a variety of related work is accomplished.
In particular, we emphasize methods related to Fourier analysis
paradigms and wavelet based ones along with the advantages and
disadvantages of both approaches.