Abstract: Series of experimental tests were conducted on a
section of a 660 kW wind turbine blade to measure the pressure
distribution of this model oscillating in plunging motion. In order to
minimize the amount of data required to predict aerodynamic loads
of the airfoil, a General Regression Neural Network, GRNN, was
trained using the measured experimental data. The network once
proved to be accurate enough, was used to predict the flow behavior
of the airfoil for the desired conditions.
Results showed that with using a few of the acquired data, the
trained neural network was able to predict accurate results with
minimal errors when compared with the corresponding measured
values. Therefore with employing this trained network the
aerodynamic coefficients of the plunging airfoil, are predicted
accurately at different oscillation frequencies, amplitudes, and angles
of attack; hence reducing the cost of tests while achieving acceptable
accuracy.
Abstract: This paper investigates the application of Particle Swarm Optimization (PSO) technique for coordinated design of a Power System Stabilizer (PSS) and a Thyristor Controlled Series Compensator (TCSC)-based controller to enhance the power system stability. The design problem of PSS and TCSC-based controllers is formulated as a time domain based optimization problem. PSO algorithm is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved; stability performance of the system is improved. To compare the capability of PSS and TCSC-based controller, both are designed independently first and then in a coordinated manner for individual and coordinated application. The proposed controllers are tested on a weakly connected power system. The eigenvalue analysis and non-linear simulation results are presented to show the effectiveness of the coordinated design approach over individual design. The simulation results show that the proposed controllers are effective in damping low frequency oscillations resulting from various small disturbances like change in mechanical power input and reference voltage setting.
Abstract: The classical temporal scan statistic is often used to
identify disease clusters. In recent years, this method has become as a
very popular technique and its field of application has been notably
increased. Many bioinformatic problems have been solved with this
technique. In this paper a new scan fuzzy method is proposed. The
behaviors of classic and fuzzy scan techniques are studied with
simulated data. ROC curves are calculated, being demonstrated the
superiority of the fuzzy scan technique.
Abstract: This research is aimed to describe the application of robust regression and its advantages over the least square regression method in analyzing financial data. To do this, relationship between earning per share, book value of equity per share and share price as price model and earning per share, annual change of earning per share and return of stock as return model is discussed using both robust and least square regressions, and finally the outcomes are compared. Comparing the results from the robust regression and the least square regression shows that the former can provide the possibility of a better and more realistic analysis owing to eliminating or reducing the contribution of outliers and influential data. Therefore, robust regression is recommended for getting more precise results in financial data analysis.
Abstract: InGaAsN and GaAsN epitaxial layers with similar
nitrogen compositions in a sample were successfully grown on a
GaAs (001) substrate by solid source molecular beam epitaxy. An
electron cyclotron resonance nitrogen plasma source has been used to
generate atomic nitrogen during the growth of the nitride layers. The
indium composition changed from sample to sample to give
compressive and tensile strained InGaAsN layers. Layer
characteristics have been assessed by high-resolution x-ray
diffraction to determine the relationship between the lattice constant
of the GaAs1-yNy layer and the fraction x of In. The objective was to
determine the In fraction x in an InxGa1-xAs1-yNy epitaxial layer which
exactly cancels the strain present in a GaAs1-yNy epitaxial layer with
the same nitrogen content when grown on a GaAs substrate.
Abstract: Classification is an important topic in machine learning
and bioinformatics. Many datasets have been introduced for
classification tasks. A dataset contains multiple features, and the quality of features influences the classification accuracy of the dataset.
The power of classification for each feature differs. In this study, we
suggest the Classification Influence Index (CII) as an indicator of classification power for each feature. CII enables evaluation of the
features in a dataset and improved classification accuracy by transformation of the dataset. By conducting experiments using CII
and the k-nearest neighbor classifier to analyze real datasets, we confirmed that the proposed index provided meaningful improvement
of the classification accuracy.
Abstract: The notions of prime(semiprime) fuzzy h-ideal(h-biideal,
h-quasi-ideal) in Γ-hemiring are introduced and some of their
characterizations are obtained by using "belongingness(∈)" and
"quasi - coincidence(q)". Cartesian product of prime(semiprime)
fuzzy h-ideals of Γ-hemirings are also investigated.
Abstract: Malay Folk Literature in early childhood education
served as an important agent in child development that involved
emotional, thinking and language aspects. Up to this moment not
much research has been carried out in Malaysia particularly in the
teaching and learning aspects nor has there been an effort to publish
“big books." Hence this article will discuss the stance taken by
university undergraduate students, teachers and parents in evaluating
Malay Folk Literature in early childhood education to be used as big
books. The data collated and analyzed were taken from 646
respondents comprising 347 undergraduates and 299 teachers. Results
of the study indicated that Malay Folk Literature can be absorbed into
teaching and learning for early childhood with a mean of 4.25 while it
can be in big books with a mean of 4.14. Meanwhile the highest mean
value required for placing Malay Folk Literature genre as big books in
early childhood education rests on exemplary stories for
undergraduates with mean of 4.47; animal fables for teachers with a
mean of 4.38. The lowest mean value of 3.57 is given to lipurlara
stories. The most popular Malay Folk Literature found suitable for
early children is Sang Kancil and the Crocodile, followed by Bawang
Putih Bawang Merah. Pak Padir, Legends of Mahsuri, Origin of
Malacca, and Origin of Rainbow are among the popular stories as
well. Overall the undergraduates show a positive attitude toward all
the items compared to teachers. The t-test analysis has revealed a non
significant relationship between the undergraduate students and
teachers with all the items for the teaching and learning of Malay Folk
Literature.
Abstract: A novel idea presented in this paper is to combine
multihop routing with single-frequency networks (SFNs) for a
broadcasting scenario. An SFN is a set of multiple nodes that transmit
the same data simultaneously, resulting in transmitter macrodiversity.
Two of the most important performance factors of multihop
networks, node reachability and routing robustness, are analyzed.
Simulation results show that our proposed SFN-D routing algorithm
improves the node reachability by 37 percentage points as compared
to non-SFN multihop routing. It shows a diversity gain of 3.7 dB,
meaning that 3.7 dB lower transmission powers are required for the
same reachability. Even better results are possible for larger
networks. If an important node becomes inactive, this algorithm can
find new routes that a non-SFN scheme would not be able to find.
Thus, two of the major problems in multihopping are addressed;
achieving robust routing as well as improving node reachability or
reducing transmission power.
Abstract: A dissimilarity measure between the empiric
characteristic functions of the subsamples associated to the different
classes in a multivariate data set is proposed. This measure can be
efficiently computed, and it depends on all the cases of each class. It
may be used to find groups of similar classes, which could be joined
for further analysis, or it could be employed to perform an
agglomerative hierarchical cluster analysis of the set of classes. The
final tree can serve to build a family of binary classification models,
offering an alternative approach to the multi-class SVM problem. We
have tested this dendrogram based SVM approach with the oneagainst-
one SVM approach over four publicly available data sets,
three of them being microarray data. Both performances have been
found equivalent, but the first solution requires a smaller number of
binary SVM models.
Abstract: In this paper, we propose a dynamic TDMA slot
reservation (DTSR) protocol for cognitive radio ad hoc networks.
Quality of Service (QoS) guarantee plays a critically important role
in such networks. We consider the problem of providing QoS
guarantee to users as well as to maintain the most efficient use of
scarce bandwidth resources. According to one hop neighboring
information and the bandwidth requirement, our proposed protocol
dynamically changes the frame length and the transmission schedule.
A dynamic frame length expansion and shrinking scheme that
controls the excessive increase of unassigned slots has been
proposed. This method efficiently utilizes the channel bandwidth by
assigning unused slots to new neighboring nodes and increasing the
frame length when the number of slots in the frame is insufficient to
support the neighboring nodes. It also shrinks the frame length when
half of the slots in the frame of a node are empty. An efficient slot
reservation protocol not only guarantees successful data
transmissions without collisions but also enhance channel spatial
reuse to maximize the system throughput. Our proposed scheme,
which provides both QoS guarantee and efficient resource utilization,
be employed to optimize the channel spatial reuse and maximize the
system throughput. Extensive simulation results show that the
proposed mechanism achieves desirable performance in multichannel
multi-rate cognitive radio ad hoc networks.
Abstract: The public sector holds large amounts of data of
various areas such as social affairs, economy, or tourism. Various
initiatives such as Open Government Data or the EU Directive on
public sector information aim to make these data available for public
and private service providers. Requirements for the provision of
public sector data are defined by legal and organizational
frameworks. Surprisingly, the defined requirements hardly cover
security aspects such as integrity or authenticity.
In this paper we discuss the importance of these missing
requirements and present a concept to assure the integrity and
authenticity of provided data based on electronic signatures. We
show that our concept is perfectly suitable for the provisioning of
unaltered data. We also show that our concept can also be extended
to data that needs to be anonymized before provisioning by
incorporating redactable signatures. Our proposed concept enhances
trust and reliability of provided public sector data.
Abstract: Recently, lots of researchers are attracted to retrieving
multimedia database by using some impression words and their values.
Ikezoe-s research is one of the representatives and uses eight pairs of
opposite impression words. We had modified its retrieval interface and
proposed '2D-RIB' in the previous work. The aim of the present paper
is to improve his/her satisfaction level to the retrieval result in the
2D-RIB. Our method is to extend the 2D-RIB. One of our extensions is
to define and introduce the following two measures: 'melody
goodness' and 'general acceptance'. Another extension is three types
of customization menus. The result of evaluation using a pilot system
is as follows. Both of these two measures 'melody goodness'
and -general acceptance- can contribute to the improvement.
Moreover, it is effective if we introduce the customization menu
which enables a retrieval person to reduce the strictness level of
retrieval condition in an impression pair based on his/her need.
Abstract: Coherent and incoherent scattering cross section measurements have been carried out using a HPGe detector on elements in the range of Z = 13 - 50 using 241Am gamma rays. The cross sections have been derived by comparing the net count rate obtained from the Compton peak of aluminium with the corresponding peak of the target. The measured cross sections for the coherent and incoherent processes are compared with theoretical values and earlier reported values. Our results are in agreement with the theoretical values.
Abstract: This paper introduces a novel design for boring bar with enhanced damping capability. The principle followed in the design phase was to enhance the damping capability minimizing the loss in static stiffness through implementation of composite material interfaces. The newly designed tool has been compared to a conventional tool. The evaluation criteria were the dynamic characteristics, frequency and damping ratio, of the machining system, as well as the surface roughness of the machined workpieces. The use of composite material in the design of damped tool has been demonstrated effective. Furthermore, the autoregressive moving average (ARMA) models presented in this paper take into consideration the interaction between the elastic structure of the machine tool and the cutting process and can therefore be used to characterize the machining system in operational conditions.
Abstract: In the Equivalent Transformation (ET) computation
model, a program is constructed by the successive accumulation of
ET rules. A method by meta-computation by which a correct ET
rule is generated has been proposed. Although the method covers a
broad range in the generation of ET rules, all important ET rules
are not necessarily generated. Generation of more ET rules can be
achieved by supplementing generation methods which are specialized
for important ET rules. A Specialization-by-Equation (Speq) rule is
one of those important rules. A Speq rule describes a procedure in
which two variables included in an atom conjunction are equalized
due to predicate constraints. In this paper, we propose an algorithm
that systematically and recursively generate Speq rules and discuss
its effectiveness in the synthesis of ET programs. A Speq rule is
generated based on proof of a logical formula consisting of given
atom set and dis-equality. The proof is carried out by utilizing some
ET rules and the ultimately obtained rules in generating Speq rules.
Abstract: The purpose of this study is to investiagte the use of
the ecommerce website in Indonesia as a developing country. The
ecommerce website has been identified having the significant impact
on business activities in particular solving the geographical problem
for islanded countries likes Indonesia. Again, website is identified as
a crucial marketing tool. This study presents the effect of quality and
features on the use and user satisfaction employing ecommerce
websites. Survey method for 115 undergraduate students of
Management Department in Andalas University who are attending
Management Information Systems (SIM) class have been
undertaken. The data obtained is analyzed using Structural Equation
Modeling (SEM) using SmartPLS program. This result found that
quality of system and information, feature as well satisfaction
influencing the use ecommerce website in Indonesia contexts.
Abstract: Naïve Bayes classifiers are simple probabilistic
classifiers. Classification extracts patterns by using data file with a set
of labeled training examples and is currently one of the most
significant areas in data mining. However, Naïve Bayes assumes the
independence among the features. Structural learning among the
features thus helps in the classification problem. In this study, the use
of structural learning in Bayesian Network is proposed to be applied
where there are relationships between the features when using the
Naïve Bayes. The improvement in the classification using structural
learning is shown if there exist relationship between the features or
when they are not independent.
Abstract: Indian subcontinent has a plethora of traditional
medicine systems that provide promising solutions to lifestyle
disorders in an 'all natural way'. Spices and oilseeds hold
prominence in Indian cuisine hence the focus of the current study
was to evaluate the bioactive molecules from Linum usitatissinum
(LU), Lepidium sativum (LS), Nigella sativa (NS) and Guizotia
abyssinica (GA) seeds. The seeds were characterized for functional
lipids like omega-3 fatty acid, antioxidant capacity, phenolic
compounds, dietary fiber and anti-nutritional factors. Analysis of the
seeds revealed LU and LS to be a rich source of α-linolenic acid
(41.85 ± 0.33%, 26.71 ± 0.63%), an omega 3 fatty acid (using
GCMS). While studying antioxidant potential NS seeds demonstrated
highest antioxidant ability (61.68 ± 0.21 TEAC/ 100 gm DW) due to
the presence of phenolics and terpenes as assayed by the Mass
spectral analysis. When screened for anti-nutritional factor
cyanogenic glycoside, LS seeds showed content as high as 1674 ± 54
mg HCN / kg. GA is a probable good source of a stable vegetable oil
(SFA: PUFA 1:2.3). The seeds showed diversified bioactive profile
and hence further studies to use different bio molecules in tandem for
the development of a possible 'nutraceutical cocktail' have been
initiated..
Abstract: Microstrip lines, widely used for good reason, are
broadband in frequency and provide circuits that are compact and
light in weight. They are generally economical to produce since they
are readily adaptable to hybrid and monolithic integrated circuit (IC)
fabrication technologies at RF and microwave frequencies. Although,
the existing EM simulation models used for the synthesis and
analysis of microstrip lines are reasonably accurate, they are
computationally intensive and time consuming. Neural networks
recently gained attention as fast and flexible vehicles to microwave
modeling, simulation and optimization. After learning and
abstracting from microwave data, through a process called training,
neural network models are used during microwave design to provide
instant answers to the task learned.This paper presents simple and
accurate ANN models for the synthesis and analysis of Microstrip
lines to more accurately compute the characteristic parameters and
the physical dimensions respectively for the required design
specifications.