Abstract: This paper describes the development of a 16-ports optical code division multiple access (OCDMA) encoder prototype based on Arrayed Waveguide Grating (AWG) and optical switches. It is potentially to provide a high security for data transmission due to all data will be transmitted in binary code form. The output signals from AWG are coded with a binary code that given to an optical switch before it signal modulate with the carrier and transmitted to the receiver. The 16-ports encoder used 16 double pole double throw (DPDT) toggle switches to control the polarization of voltage source from +5 V to -5 V for 16 optical switches. When +5 V is given, the optical switch will give code '1' and vice versa. The experimental results showed the insertion loss, crosstalk, uniformity, and optical signal-noise-ratio (OSNR) for the developed prototype are
Abstract: Because support interference corrections are not properly
understood, engineers mostly rely on expensive dummy measurements
or CFD calculations. This paper presents a method based on uncorrected wind tunnel measurements and fast calculation techniques
(it is a hybrid method) to calculate wall interference, support interference and residual interference (when e.g. a support member
closely approaches the wind tunnel walls) for any type of wind tunnel and support configuration. The method provides with a simple formula
for the calculation of the interference gradient. This gradient is
based on the uncorrected measurements and a successive calculation
of the slopes of the interference-free aerodynamic coefficients. For the latter purpose a new vortex-lattice routine is developed that corrects
the slopes for viscous effects. A test case of a measurement on a wing proves the value of this hybrid method as trends and orders of
magnitudes of the interference are correctly determined.
Abstract: Water quality is a subject of ongoing concern.
Deterioration of water quality has initiated serious management
efforts in many countries. This study endeavors to automatically
classify water quality. The water quality classes are evaluated using 6
factor indices. These factors are pH value (pH), Dissolved Oxygen
(DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen
(NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (TColiform).
The methodology involves applying data mining
techniques using multilayer perceptron (MLP) neural network
models. The data consisted of 11 sites of canals in Dusit district in
Bangkok, Thailand. The data is obtained from the Department of
Drainage and Sewerage Bangkok Metropolitan Administration
during 2007-2011. The results of multilayer perceptron neural
network exhibit a high accuracy multilayer perception rate at 96.52%
in classifying the water quality of Dusit district canal in Bangkok
Subsequently, this encouraging result could be applied with plan and
management source of water quality.
Abstract: The paper presents a modelling methodology for
small scale multi-source renewable energy systems. Using historical
site-specific weather data, the relationships of cost, availability and
energy form are visualised as a function of the sizing of photovoltaic
arrays, wind turbines, and battery capacity. The specific dependency
of each site on its own particular weather patterns show that unique
solutions exist for each site. It is shown that in certain cases the
capital component cost can be halved if the desired theoretical
demand availability is reduced from 100% to 99%.
Abstract: The study on the tree growth for four species groups of commercial timber in Koh Kong province, Cambodia-s tropical rainforest is described. The simulation for these four groups had been successfully developed in the 5-year interval through year-60. Data were obtained from twenty permanent sample plots in the duration of thirteen years. The aim for this study was to develop stand table simulation system of tree growth by the species group. There were five steps involved in the development of the tree growth simulation: aggregate the tree species into meaningful groups by using cluster analysis; allocate the trees in the diameter classes by the species group; observe the diameter movement of the species group. The diameter growth rate, mortality rate and recruitment rate were calculated by using some mathematical formula. Simulation equation had been created by combining those parameters. Result showed the dissimilarity of the diameter growth among species groups.
Abstract: A new analytical model is developed which provides
close-formed solutions for both transient indoor and envelope
temperature changes in buildings. Time-dependent boundary
temperature is presented as Fourier series which can approximate real
weather conditions. The final close-formed solutions are simple,
concise, and comprehensive. The model was compared with
numerical results and good accuracy was obtained. The model can
be used as design and control guidelines in engineering applications
for analysing mechanical heat transfer properties for buildings.
Abstract: Artificial Bee Colony (ABC) algorithm is a relatively new swarm intelligence technique for clustering. It produces higher
quality clusters compared to other population-based algorithms but with poor energy efficiency, cluster quality consistency and typically slower in convergence speed. Inspired by energy saving foraging behavior of natural honey bees this paper presents a Quality and Quantity Aware Artificial Bee Colony (Q2ABC) algorithm to improve quality of cluster identification, energy efficiency and convergence speed of the original ABC. To evaluate the performance of Q2ABC algorithm, experiments were conducted on a suite of ten benchmark UCI datasets. The results demonstrate Q2ABC outperformed ABC and K-means algorithm in the quality of clusters delivered.
Abstract: Avoiding learning failures in mathematics e-learning environments caused by emotional problems in students with autism has become an important topic for combining of special education with information and communications technology. This study presents an adaptive emotional adjustment model in mathematics e-learning for students with autism, emphasizing the lack of emotional perception in mathematics e-learning systems. In addition, an emotion classification for students with autism was developed by inducing emotions in mathematical learning environments to record changes in the physiological signals and facial expressions of students. Using these methods, 58 emotional features were obtained. These features were then processed using one-way ANOVA and information gain (IG). After reducing the feature dimension, methods of support vector machines (SVM), k-nearest neighbors (KNN), and classification and regression trees (CART) were used to classify four emotional categories: baseline, happy, angry, and anxious. After testing and comparisons, in a situation without feature selection, the accuracy rate of the SVM classification can reach as high as 79.3-%. After using IG to reduce the feature dimension, with only 28 features remaining, SVM still has a classification accuracy of 78.2-%. The results of this research could enhance the effectiveness of eLearning in special education.
Abstract: A gene network gives the knowledge of the regulatory
relationships among the genes. Each gene has its activators and
inhibitors that regulate its expression positively and negatively
respectively. Genes themselves are believed to act as activators and
inhibitors of other genes. They can even activate one set of genes and
inhibit another set. Identifying gene networks is one of the most
crucial and challenging problems in Bioinformatics. Most work done
so far either assumes that there is no time delay in gene regulation or
there is a constant time delay. We here propose a Dynamic Time-
Lagged Correlation Based Method (DTCBM) to learn the gene
networks, which uses time-lagged correlation to find the potential
gene interactions, and then uses a post-processing stage to remove
false gene interactions to common parents, and finally uses dynamic
correlation thresholds for each gene to construct the gene network.
DTCBM finds correlation between gene expression signals shifted in
time, and therefore takes into consideration the multi time delay
relationships among the genes. The implementation of our method is
done in MATLAB and experimental results on Saccharomyces
cerevisiae gene expression data and comparison with other methods
indicate that it has a better performance.
Abstract: In this research work, poly (acrylonitrile-butadienestyrene)/
polypropylene (ABS/PP) blends were processed by melt
compounding in a twin-screw extruder. Upgrading of the thermal
characteristics of the obtained materials was attempted by the
incorporation of organically modified montmorillonite (OMMT), as
well as, by the addition of two types of compatibilizers;
polypropylene grafted with maleic anhydride (PP-g-MAH) and ABS
grafted with maleic anhydride (ABS-g-MAH). The effect of the
above treatments was investigated separately and in combination.
Increasing the PP content in ABS matrix seems to increase the
thermal stability of their blend and the glass transition temperature
(Tg) of SAN phase of ABS. From the other part, the addition of ABS
to PP promotes the formation of its β-phase, which is maximum at 30
wt% ABS concentration, and increases the crystallization temperature
(Tc) of PP. In addition, it increases the crystallization rate of PP.The
β-phase of PP in ABS/PP blends is reduced by the addition of
compatibilizers or/and organoclay reinforcement. The incorporation
of compatibilizers increases the thermal stability of PP and reduces
its melting (ΔΗm) and crystallization (ΔΗc) enthalpies. Furthermore it
decreases slightly the Tgs of PP and SAN phases of ABS/PP blends.
Regarding the storage modulus of the ABS/PP blends, it presents a
change in their behavior at about 10°C and return to their initial
behavior at ~110°C. The incorporation of OMMT to no compatibilized
and compatibilized ABS/PP blends enhances their storage modulus.
Abstract: Robustness is one of the primary performance criteria for an Intelligent Video Surveillance (IVS) system. One of the key factors in enhancing the robustness of dynamic video analysis is,providing accurate and reliable means for shadow detection. If left undetected, shadow pixels may result in incorrect object tracking and classification, as it tends to distort localization and measurement information. Most of the algorithms proposed in literature are computationally expensive; some to the extent of equalling computational requirement of motion detection. In this paper, the homogeneity property of shadows is explored in a novel way for shadow detection. An adaptive division image (which highlights homogeneity property of shadows) analysis followed by a relatively simpler projection histogram analysis for penumbra suppression is the key novelty in our approach.
Abstract: In this paper, the two-dimension differential transformation method (DTM) is employed to obtain the closed form solutions of the three famous coupled partial differential equation with physical interest namely, the coupled Korteweg-de Vries(KdV) equations, the coupled Burgers equations and coupled nonlinear Schrödinger equation. We begin by showing that how the differential transformation method applies to a linear and non-linear part of any PDEs and apply on these coupled PDEs to illustrate the sufficiency of the method for this kind of nonlinear differential equations. The results obtained are in good agreement with the exact solution. These results show that the technique introduced here is accurate and easy to apply.
Abstract: Buyer coalition with a combination of items is a group of buyers joining together to purchase a combination of items with a larger discount. The primary aim of existing buyer coalition with a combination of items research is to generate a large total discount. However, the aim is hard to achieve because this research is based on the assumption that each buyer completely knows other buyers- information or at least one buyer knows other buyers- information in a coalition by exchange of information. These assumption contrast with the real world environment where buyers join a coalition with incomplete information, i.e., they concerned only with their expected discounts. Therefore, this paper proposes a new buyer community coalition formation with a combination of items scheme, called the Community Compromised Combinatorial Coalition scheme, under such an environment of incomplete information. In order to generate a larger total discount, after buyers who want to join a coalition propose their minimum required saving, a coalition structure that gives a maximum total retail prices is formed. Then, the total discount division of the coalition is divided among buyers in the coalition depending on their minimum required saving and is a Pareto optimal. In mathematical analysis, we compare concepts of this scheme with concepts of the existing buyer coalition scheme. Our mathematical analysis results show that the total discount of the coalition in this scheme is larger than that in the existing buyer coalition scheme.
Abstract: Nonlinear propagation of ion-acoustic waves in a selfgravitating
dusty plasma consisting of warm positive ions,
isothermal two-temperature electrons and negatively charged dust
particles having charge fluctuations is studied using the reductive
perturbation method. It is shown that the nonlinear propagation of
ion-acoustic waves in such plasma can be described by an uncoupled
third order partial differential equation which is a modified form of
the usual Korteweg-deVries (KdV) equation. From this nonlinear
equation, a new type of solution for the ion-acoustic wave is
obtained. The effects of two-temperature electrons, gravity and dust
charge fluctuations on the ion-acoustic solitary waves are discussed
with possible applications.
Abstract: The modern queueing theory is one of the powerful
tools for a quantitative and qualitative analysis of communication systems, computer networks, transportation systems, and many other technical systems. The paper is designated to the analysis of queueing
systems, arising in the networks theory and communications theory
(called open queueing network). The authors of this research in the
sphere of queueing theory present the theorem about the law of the iterated logarithm (LIL) for the queue length of a customers in open
queueing network and its application to the mathematical model of
the open message switching system.
Abstract: The main objective developed in this paper is to find a
graphic technique for modeling, simulation and diagnosis of the
industrial systems. This importance is much apparent when it is about
a complex system such as the nuclear reactor with pressurized water
of several form with various several non-linearity and time scales. In
this case the analytical approach is heavy and does not give a fast
idea on the evolution of the system. The tool Bond Graph enabled us
to transform the analytical model into graphic model and the
software of simulation SYMBOLS 2000 specific to the Bond Graphs
made it possible to validate and have the results given by the
technical specifications. We introduce the analysis of the problem
involved in the faults localization and identification in the complex
industrial processes. We propose a method of fault detection applied
to the diagnosis and to determine the gravity of a detected fault. We
show the possibilities of application of the new diagnosis approaches
to the complex system control. The industrial systems became
increasingly complex with the faults diagnosis procedures in the
physical systems prove to become very complex as soon as the
systems considered are not elementary any more. Indeed, in front of
this complexity, we chose to make recourse to Fault Detection and
Isolation method (FDI) by the analysis of the problem of its control
and to conceive a reliable system of diagnosis making it possible to
apprehend the complex dynamic systems spatially distributed applied
to the standard pressurized water nuclear reactor.
Abstract: Green supply chain management is an increasingly recognized practice among companies that are seeking to improve environmental performance. Of particular concern is how to arouse organizational awareness and put green activities into practice in
order to enhance manufacturing performances. This paper investigates the correlation of green supply chain practices and
manufacturing performances in Malaysian certified MS ISO 14000 manufacturing firms. The findings shows that green supply chain
practices which that can be denominated product recycling, environmental compliance and optimization have significant influence to some of the manufacturing performances.
Abstract: The advances in multimedia and networking technologies
have created opportunities for Internet pirates, who can easily
copy multimedia contents and illegally distribute them on the Internet,
thus violating the legal rights of content owners. This paper describes
how a simple and well-known watermarking procedure based on a
spread spectrum method and a watermark recovery by correlation can
be improved to effectively and adaptively protect MPEG-2 videos
distributed on the Internet. In fact, the procedure, in its simplest
form, is vulnerable to a variety of attacks. However, its security
and robustness have been increased, and its behavior has been
made adaptive with respect to the video terminals used to open
the videos and the network transactions carried out to deliver them
to buyers. In fact, such an adaptive behavior enables the proposed
procedure to efficiently embed watermarks, and this characteristic
makes the procedure well suited to be exploited in web contexts,
where watermarks usually generated from fingerprinting codes have
to be inserted into the distributed videos “on the fly", i.e. during the
purchase web transactions.
Abstract: Annotation of a protein sequence is pivotal for the understanding of its function. Accuracy of manual annotation provided by curators is still questionable by having lesser evidence strength and yet a hard task and time consuming. A number of computational methods including tools have been developed to tackle this challenging task. However, they require high-cost hardware, are difficult to be setup by the bioscientists, or depend on time intensive and blind sequence similarity search like Basic Local Alignment Search Tool. This paper introduces a new method of assigning highly correlated Gene Ontology terms of annotated protein sequences to partially annotated or newly discovered protein sequences. This method is fully based on Gene Ontology data and annotations. Two problems had been identified to achieve this method. The first problem relates to splitting the single monolithic Gene Ontology RDF/XML file into a set of smaller files that can be easy to assess and process. Thus, these files can be enriched with protein sequences and Inferred from Electronic Annotation evidence associations. The second problem involves searching for a set of semantically similar Gene Ontology terms to a given query. The details of macro and micro problems involved and their solutions including objective of this study are described. This paper also describes the protein sequence annotation and the Gene Ontology. The methodology of this study and Gene Ontology based protein sequence annotation tool namely extended UTMGO is presented. Furthermore, its basic version which is a Gene Ontology browser that is based on semantic similarity search is also introduced.
Abstract: Text data mining is a process of exploratory data
analysis. Classification maps data into predefined groups or classes.
It is often referred to as supervised learning because the classes are
determined before examining the data. This paper describes proposed
radial basis function Classifier that performs comparative crossvalidation
for existing radial basis function Classifier. The feasibility
and the benefits of the proposed approach are demonstrated by means
of data mining problem: direct Marketing. Direct marketing has
become an important application field of data mining. Comparative
Cross-validation involves estimation of accuracy by either stratified
k-fold cross-validation or equivalent repeated random subsampling.
While the proposed method may have high bias; its performance
(accuracy estimation in our case) may be poor due to high variance.
Thus the accuracy with proposed radial basis function Classifier was
less than with the existing radial basis function Classifier. However
there is smaller the improvement in runtime and larger improvement
in precision and recall. In the proposed method Classification
accuracy and prediction accuracy are determined where the
prediction accuracy is comparatively high.