Abstract: In this paper sensitivity analysis is performed for
reliability evaluation of power systems. When examining the
reliability of a system, it is useful to recognize how results
change as component parameters are varied. This knowledge
helps engineers to understand the impact of poor data, and
gives insight on how reliability can be improved. For these
reasons, a sensitivity analysis can be performed. Finally, a real
network was used for testing the presented method.
Abstract: The paper discusses optimising work on a method of processing ceramic / metal composite coatings for various applications and is based on preliminary work on processing anodes for solid oxide fuel cells (SOFCs). The composite coating is manufactured by the electroless co-deposition of nickel and yttria stabilised zirconia (YSZ) simultaneously on to a ceramic substrate. The effect on coating characteristics of substrate surface treatments and electroless nickel bath parameters such as pH and agitation methods are also investigated. Characterisation of the resulting deposit by scanning electron microscopy (SEM) and energy dispersive X-ray analysis (EDXA) is also discussed.
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: 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.
Abstract: Avionic software architecture has transit from a
federated avionics architecture to an integrated modular avionics
(IMA) .ARINC 653 (Avionics Application Standard Software Interface) is a software specification for space and time partitioning in
Safety-critical avionics Real-time operating systems. Methods to transform the abstract avionics application logic function to the
executable model have been brought up, however with less
consideration about the code generating input and output model specific for ARINC 653 platform and inner-task synchronous dynamic
interaction order sequence. In this paper, we proposed an
AADL-based model-driven design methodology to fulfill the purpose
to automatically generating Cµ executable model on ARINC 653 platform from the ARINC653 architecture which defined as AADL653 in order to facilitate the development of the avionics software constructed on ARINC653 OS. This paper presents the
mapping rules between the AADL653 elements and the elements in
Cµ language, and define the code generating rules , designs an automatic C µ code generator .Then, we use a case to illustrate our
approach. Finally, we give the related work and future research directions.
Abstract: Experiments were carried out at the Latvia State
Institute of Fruit-Growing in 2011. Fresh-cut minimally processed
apple and pear mixed salad were packed by passive modified
atmosphere (MAP) in PP containers, which were hermetically sealed
by breathable conventional BOPP PropafreshTM P2GAF, and Amcor
Agrifresh films. Biodegradable NatureFlexTM NVS INNOVIA Films
and VC999 BioPack PLA films coated with a barrier of pure silicon
oxide (SiOx) were used to compare the fresh-cut produce quality
with this packed in conventional packaging films. Samples were cold
stored at temperature +4.0±0.5 °C up to 10 days. The quality of salad
was evaluated by physicochemical properties – weight losses,
moisture, firmness, the effect of packaging modes on the colour,
dynamics in headspace atmosphere concentration (CO2 and O2),
titratable acidity values, as well as by microbiological contamination
(yeasts, moulds and total bacteria count) of salads, analyzing before
packaging and after 2, 4, 6, 8, and 10 storage days.
Abstract: Psoriasis is a chronic inflammatory skin condition
which affects 2-3% of population around the world. Psoriasis Area
and Severity Index (PASI) is a gold standard to assess psoriasis
severity as well as the treatment efficacy. Although a gold standard,
PASI is rarely used because it is tedious and complex. In practice,
PASI score is determined subjectively by dermatologists, therefore
inter and intra variations of assessment are possible to happen even
among expert dermatologists. This research develops an algorithm to
assess psoriasis lesion for PASI scoring objectively. Focus of this
research is thickness assessment as one of PASI four parameters
beside area, erythema and scaliness. Psoriasis lesion thickness is
measured by averaging the total elevation from lesion base to lesion
surface. Thickness values of 122 3D images taken from 39 patients
are grouped into 4 PASI thickness score using K-means clustering.
Validation on lesion base construction is performed using twelve
body curvature models and show good result with coefficient of
determinant (R2) is equal to 1.
Abstract: Many research works are carried out on the analysis of
traces in a digital learning environment. These studies produce large
volumes of usage tracks from the various actions performed by a
user. However, to exploit these data, compare and improve
performance, several issues are raised. To remedy this, several works
deal with this problem seen recently. This research studied a series of
questions about format and description of the data to be shared. Our
goal is to share thoughts on these issues by presenting our experience
in the analysis of trace-based log files, comparing several approaches
used in automatic classification applied to e-learning platforms.
Finally, the obtained results are discussed.
Abstract: One of the essential sectors of Myanmar economy is
agriculture which is sensitive to climate variation. The most
important climatic element which impacts on agriculture sector is
rainfall. Thus rainfall prediction becomes an important issue in
agriculture country. Multi variables polynomial regression (MPR)
provides an effective way to describe complex nonlinear input output
relationships so that an outcome variable can be predicted from the
other or others. In this paper, the modeling of monthly rainfall
prediction over Myanmar is described in detail by applying the
polynomial regression equation. The proposed model results are
compared to the results produced by multiple linear regression model
(MLR). Experiments indicate that the prediction model based on
MPR has higher accuracy than using MLR.
Abstract: This work proposes an approach to address automatic
text summarization. This approach is a trainable summarizer, which
takes into account several features, including sentence position,
positive keyword, negative keyword, sentence centrality, sentence
resemblance to the title, sentence inclusion of name entity, sentence
inclusion of numerical data, sentence relative length, Bushy path of
the sentence and aggregated similarity for each sentence to generate
summaries. First we investigate the effect of each sentence feature on
the summarization task. Then we use all features score function to
train genetic algorithm (GA) and mathematical regression (MR)
models to obtain a suitable combination of feature weights. The
proposed approach performance is measured at several compression
rates on a data corpus composed of 100 English religious articles.
The results of the proposed approach are promising.
Abstract: In the paper an effective context based lossless coding
technique is presented. Three principal and few auxiliary contexts are
defined. The predictor adaptation technique is an improved CoBALP
algorithm, denoted CoBALP+. Cumulated predictor error combining
8 bias estimators is calculated. It is shown experimentally that
indeed, the new technique is time-effective while it outperforms the
well known methods having reasonable time complexity, and is
inferior only to extremely computationally complex ones.
Abstract: Text document categorization involves large amount
of data or features. The high dimensionality of features is a
troublesome and can affect the performance of the classification.
Therefore, feature selection is strongly considered as one of the
crucial part in text document categorization. Selecting the best
features to represent documents can reduce the dimensionality of
feature space hence increase the performance. There were many
approaches has been implemented by various researchers to
overcome this problem. This paper proposed a novel hybrid approach
for feature selection in text document categorization based on Ant
Colony Optimization (ACO) and Information Gain (IG). We also
presented state-of-the-art algorithms by several other researchers.
Abstract: The exact gain shape profile of erbium doped fiber
amplifiers (EDFA`s) are depends on fiber length and Er3 ion
densities. This paper optimized several of erbium doped fiber
parameters to obtain high performance characteristic at pump
wavelengths of λp= 980 nm and λs= 1550 nm for three different
pump powers. The maximum gain obtained for pump powers (10, 30
and 50mw) is nearly (19, 30 and 33 dB) at optimizations. The
required numerical aperture NA to obtain maximum gain becomes
less when pump power increased. The amplifier gain is increase
when Er+3doped near the center of the fiber core. The simulation has
been done by using optisystem 5.0 software (CAD for Photonics, a
license product of a Canadian based company) at 2.5 Gbps.
Abstract: In this paper, we propose a Connect6 solver which
adopts a hybrid approach based on a tree-search algorithm and image
processing techniques. The solver must deal with the complicated
computation and provide high performance in order to make real-time
decisions. The proposed approach enables the solver to be
implemented on a single Spartan-6 XC6SLX45 FPGA produced by
XILINX without using any external devices. The compact
implementation is achieved through image processing techniques to
optimize a tree-search algorithm of the Connect6 game. The tree
search is widely used in computer games and the optimal search brings
the best move in every turn of a computer game. Thus, many
tree-search algorithms such as Minimax algorithm and artificial
intelligence approaches have been widely proposed in this field.
However, there is one fundamental problem in this area; the
computation time increases rapidly in response to the growth of the
game tree. It means the larger the game tree is, the bigger the circuit
size is because of their highly parallel computation characteristics.
Here, this paper aims to reduce the size of a Connect6 game tree using
image processing techniques and its position symmetric property. The
proposed solver is composed of four computational modules: a
two-dimensional checkmate strategy checker, a template matching
module, a skilful-line predictor, and a next-move selector. These
modules work well together in selecting next moves from some
candidates and the total amount of their circuits is small. The details of
the hardware design for an FPGA implementation are described and
the performance of this design is also shown in this paper.
Abstract: Traditional Javanese classic dance is a valuable
inheritance in Java Indonesia. Nowadays, this treasure of culture is
no longer belonging to Javanese people only. Many art departments
from universities around the world already put this as a subject in
their curriculum. Nonetheless, dance is a practical skill. It needs to be
practices so often while accompanied by an instructor to get the right
technique. An interactive Computer Aided Instruction (iCAI) that can
interactively assist the student to practice is developed. By using this
software students can conduct a self practice in studio and get some
feedbacks from the software. This CAI is not intended to replace the
instructor, but to assist them in increasing the student fly-time in
practice.
Abstract: A direct adaptive controller for a class of unknown nonlinear discrete-time systems is presented in this article. The proposed controller is constructed by fuzzy rules emulated network (FREN). With its simple structure, the human knowledge about the plant is transferred to be if-then rules for setting the network. These adjustable parameters inside FREN are tuned by the learning mechanism with time varying step size or learning rate. The variation of learning rate is introduced by main theorem to improve the system performance and stabilization. Furthermore, the boundary of adjustable parameters is guaranteed through the on-line learning and membership functions properties. The validation of the theoretical findings is represented by some illustrated examples.
Abstract: Cytogenetic analysis still remains the gold standard method for prenatal diagnosis of trisomy 21 (Down syndrome, DS). Nevertheless, the conventional cytogenetic analysis needs live cultured cells and is too time-consuming for clinical application. In contrast, molecular methods such as FISH, QF-PCR, MLPA and quantitative Real-time PCR are rapid assays with results available in 24h. In the present study, we have successfully used a novel MGB TaqMan probe-based real time PCR assay for rapid diagnosis of trisomy 21 status in Down syndrome samples. We have also compared the results of this molecular method with corresponding results obtained by the cytogenetic analysis. Blood samples obtained from DS patients (n=25) and normal controls (n=20) were tested by quantitative Real-time PCR in parallel to standard G-banding analysis. Genomic DNA was extracted from peripheral blood lymphocytes. A high precision TaqMan probe quantitative Real-time PCR assay was developed to determine the gene dosage of DSCAM (target gene on 21q22.2) relative to PMP22 (reference gene on 17p11.2). The DSCAM/PMP22 ratio was calculated according to the formula; ratio=2 -ΔΔCT. The quantitative Real-time PCR was able to distinguish between trisomy 21 samples and normal controls with the gene ratios of 1.49±0.13 and 1.03±0.04 respectively (p value