Abstract: This paper presents Genetic Algorithm (GA) based
approach for the allocation of FACTS (Flexible AC Transmission
System) devices for the improvement of Power transfer capacity in an
interconnected Power System. The GA based approach is applied on
IEEE 30 BUS System. The system is reactively loaded starting from
base to 200% of base load. FACTS devices are installed in the
different locations of the power system and system performance is
noticed with and without FACTS devices. First, the locations, where
the FACTS devices to be placed is determined by calculating active
and reactive power flows in the lines. Genetic Algorithm is then
applied to find the amount of magnitudes of the FACTS devices. This
approach of GA based placement of FACTS devices is tremendous
beneficial both in terms of performance and economy is clearly
observed from the result obtained.
Abstract: This paper proposes an effective algorithm approach to hybrid control systems combining fuzzy logic and conventional control techniques of controlling the speed of induction motor assumed to operate in high-performance drives environment. The introducing of fuzzy logic in the control systems helps to achieve good dynamical response, disturbance rejection and low sensibility to parameter variations and external influences. Some fundamentals of the fuzzy logic control are preliminary illustrated. The developed control algorithm is robust, efficient and simple. It also assures precise trajectory tracking with the prescribed dynamics. Experimental results have shown excellent tracking performance of the proposed control system, and have convincingly demonstrated the validity and the usefulness of the hybrid fuzzy controller in high-performance drives with parameter and load uncertainties. Satisfactory performance was observed for most reference tracks.
Abstract: Despite so many years- development, the mainstream of workflow solutions from IT industries has not made ad-hoc workflow-support easy or inexpensive in MIS. Moreover, most of academic approaches tend to make their resulted BPM (Business Process Management) more complex and clumsy since they used to necessitate modeling workflow. To cope well with various ad-hoc or casual requirements on workflows while still keeping things simple and inexpensive, the author puts forth first the TSM design pattern that can provide a flexible workflow control while minimizing demand of predefinitions and modeling workflow, which introduces a generic approach for building BPM in workflow-aware MISs (Management Information Systems) with low development and running expenses.
Abstract: Glomerular filtration rate (GFR) is a measure of
kidney function. It is usually estimated from serum concentrations of
cystatin C or creatinine although there has been considerable debate
in the literature about (i) the best equation to use and (ii) the
variability in the correlation between the concentrations of creatinine
and cystatin C. The equations for GFR can be written in a general
form and from these I calculate the error of the GFR estimates
associated with analyte measurement error. These show that the
error of the GFR estimates is such that it is not possible to distinguish
between the equations over much of the concentration range of either
analyte. The general forms of the equations are also used to derive
an expression for the concentration of cystatin C as a function of the
concentration of creatinine. This equation shows that these analyte
concentrations are not linearly related. Clinical reports of cystatin C
and creatinine concentration are consistent with the expression
derived.
Abstract: An implant elicits a biological response in the
surrounding tissue which determines the acceptance and long-term
function of the implant. Dental implants have become one of the
main therapy methods in clinic after teeth lose. A successful implant
is in contact with bone and soft tissue represent by fibroblasts. In our
study we focused on the interaction between six different chemically
and physically modified titanium implants (Tis-MALP, Tis-O, Tis-
OA, Tis-OPAAE, Tis-OZ, Tis-OPAE) with alveolar fibroblasts as
well as with five type of microorganisms (S. epidermis, S.mutans, S.
gordonii, S. intermedius, C.albicans). The analysis of microorganism
adhesion was determined by CFU (colony forming unite) and biofilm
formation. The presence of α3β1 and vinculin expression on alveolar
fibroblasts was demonstrated using phospho specific cell based
ELISA (PACE). Alveolar fibroblasts have the highest expression of
these proteins on Tis-OPAAE and Tis-OPAE. It corresponds with
results from bacterial adhesion and biofilm formation and it was
related to the lowest production of collagen I by alveolar fibroblasts
on Tis-OPAAE titanium disc.
Abstract: The Aggregate Production Plan (APP) is a schedule of
the organization-s overall operations over a planning horizon to
satisfy demand while minimizing costs. It is the baseline for any
further planning and formulating the master production scheduling,
resources, capacity and raw material planning. This paper presents a
methodology to model the Aggregate Production Planning problem,
which is combinatorial in nature, when optimized with Genetic
Algorithms. This is done considering a multitude of constraints of
contradictory nature and the optimization criterion – overall cost,
made up of costs with production, work force, inventory, and
subcontracting. A case study of substantial size, used to develop the
model, is presented, along with the genetic operators.
Abstract: The paper investigates the potential of support vector
machines and Gaussian process based regression approaches to
model the oxygen–transfer capacity from experimental data of
multiple plunging jets oxygenation systems. The results suggest the
utility of both the modeling techniques in the prediction of the
overall volumetric oxygen transfer coefficient (KLa) from operational
parameters of multiple plunging jets oxygenation system. The
correlation coefficient root mean square error and coefficient of
determination values of 0.971, 0.002 and 0.945 respectively were
achieved by support vector machine in comparison to values of
0.960, 0.002 and 0.920 respectively achieved by Gaussian process
regression. Further, the performances of both these regression
approaches in predicting the overall volumetric oxygen transfer
coefficient was compared with the empirical relationship for multiple
plunging jets. A comparison of results suggests that support vector
machines approach works well in comparison to both empirical
relationship and Gaussian process approaches, and could successfully
be employed in modeling oxygen-transfer.
Abstract: A framework to estimate the state of dynamically
varying environment where data are generated from heterogeneous
sources possessing partial knowledge about the environment is presented.
This is entirely derived within Dempster-Shafer and Evidence
Filtering frameworks. The belief about the current state is expressed
as belief and plausibility functions. An addition to Single Input
Single Output Evidence Filter, Multiple Input Single Output Evidence
Filtering approach is introduced. Variety of applications such as
situational estimation of an emergency environment can be developed
within the framework successfully. Fire propagation scenario is used
to justify the proposed framework, simulation results are presented.
Abstract: Process capability index Cpk is the most widely
used index in making managerial decisions since it provides bounds
on the process yield for normally distributed processes. However,
existent methods for assessing process performance which
constructed by statistical inference may unfortunately lead to fine
results, because uncertainties exist in most real-world applications.
Thus, this study adopts fuzzy inference to deal with testing of Cpk .
A brief score is obtained for assessing a supplier’s process instead of
a severe evaluation.
Abstract: Carbon disulfide is widely used for the production of
viscose rayon, rubber, and other organic materials and it is a
feedstock for the synthesis of sulfuric acid. The objective of this
paper is to analyze possibilities for efficient production of CS2 from
sour natural gas reformation (H2SMR) (2H2S+CH4 =CS2 +4H2) .
Also, the effect of H2S to CH4 feed ratio and reaction temperature on
carbon disulfide production is investigated numerically in a
reforming reactor. The chemical reaction model is based on an
assumed Probability Density Function (PDF) parameterized by the
mean and variance of mixture fraction and β-PDF shape. The results
show that the major factors influencing CS2 production are reactor
temperature. The yield of carbon disulfide increases with increasing
H2S to CH4 feed gas ratio (H2S/CH4≤4). Also the yield of C(s)
increases with increasing temperature until the temperature reaches
to 1000°K, and then due to increase of CS2 production and
consumption of C(s), yield of C(s) drops with further increase in the
temperature. The predicted CH4 and H2S conversion and yield of
carbon disulfide are in good agreement with result of Huang and TRaissi.
Abstract: In comparison to the original SVM, which involves a
quadratic programming task; LS–SVM simplifies the required
computation, but unfortunately the sparseness of standard SVM is
lost. Another problem is that LS-SVM is only optimal if the training
samples are corrupted by Gaussian noise. In Least Squares SVM
(LS–SVM), the nonlinear solution is obtained, by first mapping the
input vector to a high dimensional kernel space in a nonlinear
fashion, where the solution is calculated from a linear equation set. In
this paper a geometric view of the kernel space is introduced, which
enables us to develop a new formulation to achieve a sparse and
robust estimate.
Abstract: This paper considers the problem of finding low cost
chip set for a minimum cost partitioning of a large logic circuits. Chip
sets are selected from a given library. Each chip in the library has a
different price, area, and I/O pin. We propose a low cost chip set
selection algorithm. Inputs to the algorithm are a netlist and a chip
information in the library. Output is a list of chip sets satisfied with
area and maximum partitioning number and it is sorted by cost. The
algorithm finds the sorted list of chip sets from minimum cost to
maximum cost. We used MCNC benchmark circuits for experiments.
The experimental results show that all of chip sets found satisfy the
multiple partitioning constraints.
Abstract: The utilize of renewable energy sources becomes
more crucial and fascinatingly, wider application of renewable
energy devices at domestic, commercial and industrial levels is not
only affect to stronger awareness but also significantly installed
capacities. Moreover, biomass principally is in form of woods and
converts to be energy for using by humans for a long time.
Gasification is a process of conversion of solid carbonaceous fuel
into combustible gas by partial combustion. Many gasified models
have various operating conditions because the parameters kept in
each model are differentiated. This study applied the experimental
data including three inputs variables including biomass consumption;
temperature at combustion zone and ash discharge rate and gas flow
rate as only one output variable. In this paper, response surface
methods were applied for identification of the gasified system
equation suitable for experimental data. The result showed that linear
model gave superlative results.
Abstract: Sparse representation which can represent high dimensional
data effectively has been successfully used in computer vision
and pattern recognition problems. However, it doesn-t consider the
label information of data samples. To overcome this limitation,
we develop a novel dimensionality reduction algorithm namely
dscriminatively regularized sparse subspace learning(DR-SSL) in this
paper. The proposed DR-SSL algorithm can not only make use of
the sparse representation to model the data, but also can effective
employ the label information to guide the procedure of dimensionality
reduction. In addition,the presented algorithm can effectively deal
with the out-of-sample problem.The experiments on gene-expression
data sets show that the proposed algorithm is an effective tool for
dimensionality reduction and gene-expression data classification.
Abstract: Repetitive control and feedback dithering modulation
are applied to a single-phase voltage source inverter, with an aim to
eliminate harmonics and stabilize the inverter under load variations.
The proposed control and modulation scheme comprise multiple loops
of feedback, which helps improve inverter performance and
robustness. Experimental results show that the designed inverter
exhibits very low distortion at its output with THD of about 0.3%
under different load variations.
Abstract: This paper presents a robust method to detect obstacles in stereo images using shadow removal technique and color information. Stereo vision based obstacle detection is an algorithm that aims to detect and compute obstacle depth using stereo matching and disparity map. The proposed advanced method is divided into three phases, the first phase is detecting obstacles and removing shadows, the second one is matching and the last phase is depth computing. We propose a robust method for detecting obstacles in stereo images using a shadow removal technique based on color information in HIS space, at the first phase. In this paper we use Normalized Cross Correlation (NCC) function matching with a 5 × 5 window and prepare an empty matching table τ and start growing disparity components by drawing a seed s from S which is computed using canny edge detector, and adding it to τ. In this way we achieve higher performance than the previous works [2,17]. A fast stereo matching algorithm is proposed that visits only a small fraction of disparity space in order to find a semi-dense disparity map. It works by growing from a small set of correspondence seeds. The obstacle identified in phase one which appears in the disparity map of phase two enters to the third phase of depth computing. Finally, experimental results are presented to show the effectiveness of the proposed method.
Abstract: The study aimed to evaluated the reproductive performance response to short term oestrus synchronization during the transition period. One hundred and sixty-five indigenous multiparous non-lactating goats were subdivided into the following six treatment groups for oestrus synchronization: NT control Group (N= 30), Fe-21d, FGA vaginal sponge for 21days+eCG at 19thd; FPe- 11d, FGA 11d + PGF2α and eCG at 9th d; FPe-10d, FGA 10d+ PGF2α and eCG at 8th d; FPe-9d, FGA 9d +PGF2α and eCG at 7thd; PFe-5d, PGF2α at d0 + FGA 5d + eCG at 5thd. The goats were natural mated (1 male/6 females). Fecundity rates (n. births /n. females treated x 100) were statistically higher (P < 0.05) in short term FPe-9d (157.9%), FPe- 11d (115.4%), FPe-10d (111.1%) and PFe-5d (107.7%) groups compared to the NT control Group (66.7%).
Abstract: Little attention has been paid to information
transmission between the portfolios of large stocks and small stocks in the Korean stock market. This study investigates the return and volatility transmission mechanisms between large and small stocks in
the Korea Exchange (KRX). This study also explores whether bad news in the large stock market leads to a volatility of the small stock
market that is larger than the good news volatility of the large stock market. By employing the Granger causality test, we found
unidirectional return transmissions from the large stocks to medium
and small stocks. This evidence indicates that pat information about
the large stocks has a better ability to predict the returns of the medium and small stocks in the Korean stock market. Moreover, by using the
asymmetric GARCH-BEKK model, we observed the unidirectional relationship of asymmetric volatility transmission from large stocks to
the medium and small stocks. This finding suggests that volatility in
the medium and small stocks following a negative shock in the large
stocks is larger than that following a positive shock in the large stocks.
Abstract: The primary purpose of this article is an attempt to
find the implication of globalization on education. Globalization has
an important role as a process in the economical, political, cultural
and technological dimensions in the life of the contemporary human
being and has been affected by it. Education has its effects in this
procedure and while influencing it through educating global citizens
having universal human features and characteristics, has been
influenced by this phenomenon too. Nowadays, the role of education
is not just to develop in the students the knowledge and skills
necessary for the new kinds of jobs. If education wants to help
students be prepared of the new global society, it has to make them
engaged productive and critical citizens for the global era, so that
they can reflect about their roles as key actors in a dynamic often
uneven, matrix of economic and cultural exchanges. If education
wants to reinforce and raise the national identity, the value system
and the children and teenagers, it should make them ready for living
in the global era of this century. The used method in this research is
documentary and analyzing the documents. Studies in this field show
globalization has influences on the processes of the production,
distribution and consuming of knowledge. The happening of this
event in the information era has not only provide the necessary
opportunities for the exchanges of education worldwide but also has
privileges for the developing countries which enables them to
strengthen educational bases of their society and have an important
step toward their future.
Abstract: In today's world where everything is rapidly changing
and information technology is high in development, many features of culture, society, politic and economy has changed. The advent of
information technology and electronic data transmission lead to easy communication and fields like e-learning and e-commerce, are
accessible for everyone easily. One of these technologies is virtual
training. The "quality" of such kind of education systems is critical. 131 questionnaires were prepared and distributed among university
student in Toba University. So the research has followed factors that affect the quality of learning from the perspective of staff, students, professors and this type of university. It is concluded that the important factors in virtual training are the quality of professors, the
quality of staff, and the quality of the university. These mentioned factors were the most prior factors in this education system and
necessary for improving virtual training.