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: This paper presents a new approach for setting
frequency relays based on the dynamic of power system. A
simplified model of the power system based on the load-frequency
control loop will be developed to be used instead of the complete
model of the power system. The effects of the equipments and their
responses on the frequency variations of the power plant will be
investigated and then a method for adaptive settings of frequency
relays will be explained. The proposed method will be investigated
by analyzing a simplified model of a power plant by MATLAB
software.
Abstract: Biomass is becoming a large renewable resource for
power generation; it is involved in higher frequency in
environmentally clean processes, and even it is used for biofuels
preparation. On the other hand, hydrogen – other energy source – can
be produced in a variety of methods including gasification of
biomass. In this study, the production of hydrogen by gasification of
biomass waste is examined. This work explores the production of a
gaseous mixture with high power potential from Amazonas´ specie
known as copoazu, using a counter-flow fixed-bed bioreactor.
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: The development of renewable energies - particularly energy from wind, water, solar power and biomass - is a central aim of the European Commission's energy policy. There are several reasons for this choice: renewable energies are sustainable, nonpolluting, widely available and clean. Increasing the share of renewable energy in the energy balance enhances sustainability. It also helps to improve the security of energy supply by reducing the Community's growing dependence on imported energy sources.In this paper it was studied the possibility to realize three photovoltaic systems in the Italian Natural Park “Gola della Rossa e di Frasassi". The first photovoltaic system is a grid-connected system for Services and Documentation Center of Castelletta with a nominal power of about 6 kWp. The second photovoltaic system is a grid-connected integrated system on the ticket office-s roof with a nominal power of about 4 kWp. The third project is set up by five grid-connected systems integrated on the roofs of the bungalows in Natural Park-s tourist camping with a nominal power of about 10 kWp. The electricity which is generated by all these plants is purchased according to the Italian program called “Conto Energia". Economical analysis and the amount of the avoided CO2 emissions are elaborated for these photovoltaic systems.
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: 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: 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: This paper presents methodologies for developing an
intelligent CAD system assisting in analysis and design of
reconfigurable special machines. It describes a procedure for
determining feasibility of utilizing these machines for a given part
and presents a model for developing an intelligent CAD system. The
system analyzes geometrical and topological information of the given
part to determine possibility of the part being produced by
reconfigurable special machines from a technical point of view. Also
feasibility of the process from a economical point of view is
analyzed. Then the system determines proper positioning of the part
considering details of machining features and operations needed.
This involves determination of operation types, cutting tools and the
number of working stations needed. Upon completion of this stage
the overall layout of the machine and machining equipment required
are determined.
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: 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: The European countries that during the past two
decades based their exchange rate regimes on currency board
arrangement (CBA) are usually analysed from the perspective of
corner solution choice’s stabilisation effects. There is an open
discussion on the positive and negative background of a strict
exchange rate regime choice, although it should be seen as part of the
transition process towards the monetary union membership. The
focus of the paper is on the Baltic countries that after two decades of
a rigid exchange rate arrangement and strongly influenced by global
crisis are finishing their path towards the euro zone. Besides the
stabilising capacity, the CBA is highly vulnerable regime, with
limited developing potential. The rigidity of the exchange rate (and
monetary) system, despite the ensured credibility, do not leave
enough (or any) space for the adjustment and/or active crisis
management. Still, the Baltics are in a process of recovery, with fiscal
consolidation measures combined with (painful and politically
unpopular) measures of internal devaluation. Today, two of them
(Estonia and Latvia) are members of euro zone, fulfilling their
ultimate transition targets, but de facto exchanging one fixed regime
with another.
The paper analyses the challenges for the CBA in unstable
environment since the fixed regimes rely on imported stability and
are sensitive to external shocks. With limited monetary instruments,
these countries were oriented to the fiscal policies and used a
combination of internal devaluation and tax policy measures. Despite
their rather quick recovery, our second goal is to analyse the long
term influence that the measures had on the national economy.
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: 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.