Abstract: Knowledge of an organization does not merely reside
in structured form of information and data; it is also embedded in
unstructured form. The discovery of such knowledge is particularly
difficult as the characteristic is dynamic, scattered, massive and
multiplying at high speed. Conventional methods of managing
unstructured information are considered too resource demanding and
time consuming to cope with the rapid information growth.
In this paper, a Multi-faceted and Automatic Knowledge
Elicitation System (MAKES) is introduced for the purpose of
discovery and capture of organizational knowledge. A trial
implementation has been conducted in a public organization to
achieve the objective of decision capture and navigation from a
number of meeting minutes which are autonomously organized,
classified and presented in a multi-faceted taxonomy map in both
document and content level. Key concepts such as critical decision
made, key knowledge workers, knowledge flow and the relationship
among them are elicited and displayed in predefined knowledge
model and maps. Hence, the structured knowledge can be retained,
shared and reused.
Conducting Knowledge Management with MAKES reduces work
in searching and retrieving the target decision, saves a great deal of
time and manpower, and also enables an organization to keep pace
with the knowledge life cycle. This is particularly important when
the amount of unstructured information and data grows extremely
quickly. This system approach of knowledge management can
accelerate value extraction and creation cycles of organizations.
Abstract: Symbolic Circuit Analysis (SCA) is a technique used
to generate the symbolic expression of a network. It has become a
well-established technique in circuit analysis and design. The
symbolic expression of networks offers excellent way to perform
frequency response analysis, sensitivity computation, stability
measurements, performance optimization, and fault diagnosis. Many
approaches have been proposed in the area of SCA offering different
features and capabilities. Numerical Interpolation methods are very
common in this context, especially by using the Fast Fourier
Transform (FFT). The aim of this paper is to present a method for
SCA that depends on the use of Wavelet Transform (WT) as a
mathematical tool to generate the symbolic expression for large
circuits with minimizing the analysis time by reducing the number of
computations.
Abstract: Nowadays, Multimedia Communication has been developed and improved rapidly in order to enable users to communicate between each other over the Internet. In general, the multimedia communication consists of audio and video communication. However, this paper focuses on audio streams. The audio translation between protocols is a very critical issue due to solving the communication problems between any two protocols, as well as it enables people around the world to talk with each other at anywhere and anytime even they use different protocols. In this paper, a proposed method for an audio translation module between two protocols has been presented. These two protocols are InterAsterisk eXchange Protocol (IAX) and Real Time Switching Control Protocol (RSW), which they are widely used to provide two ways audio transfer feature. The result of this work is to introduce possibility of interworking together.
Abstract: Missing data is a persistent problem in almost all
areas of empirical research. The missing data must be treated very
carefully, as data plays a fundamental role in every analysis.
Improper treatment can distort the analysis or generate biased results.
In this paper, we compare and contrast various imputation techniques
on missing data sets and make an empirical evaluation of these
methods so as to construct quality software models. Our empirical
study is based on NASA-s two public dataset. KC4 and KC1. The
actual data sets of 125 cases and 2107 cases respectively, without
any missing values were considered. The data set is used to create
Missing at Random (MAR) data Listwise Deletion(LD), Mean
Substitution(MS), Interpolation, Regression with an error term and
Expectation-Maximization (EM) approaches were used to compare
the effects of the various techniques.
Abstract: As computer network technology becomes
increasingly complex, it becomes necessary to place greater
requirements on the validity of developing standards and the
resulting technology. Communication networks are based on large
amounts of protocols. The validity of these protocols have to be
proved either individually or in an integral fashion. One strategy for
achieving this is to apply the growing field of formal methods.
Formal methods research defines systems in high order logic so that
automated reasoning can be applied for verification. In this research
we represent and implement a formerly announced multicast protocol
in Prolog language so that certain properties of the protocol can be
verified. It is shown that by using this approach some minor faults in
the protocol were found and repaired. Describing the protocol as
facts and rules also have other benefits i.e. leads to a process-able
knowledge. This knowledge can be transferred as ontology between
systems in KQML format. Since the Prolog language can increase its
knowledge base every time, this method can also be used to learn an
intelligent network.
Abstract: In this work, we present an automatic vehicle detection
system for airborne videos using combined features. We propose a
pixel-wise classification method for vehicle detection using Dynamic
Bayesian Networks. In spite of performing pixel-wise classification,
relations among neighboring pixels in a region are preserved in the
feature extraction process. The main novelty of the detection scheme is
that the extracted combined features comprise not only pixel-level
information but also region-level information. Afterwards, tracking is
performed on the detected vehicles. Tracking is performed using
efficient Kalman filter with dynamic particle sampling. Experiments
were conducted on a wide variety of airborne videos. We do not
assume prior information of camera heights, orientation, and target
object sizes in the proposed framework. The results demonstrate
flexibility and good generalization abilities of the proposed method on
a challenging dataset.
Abstract: ZnO nanostructures including nanowires, nanorods,
and nanoneedles were successfully deposited on GaAs substrates,
respectively, by simple two-step chemical method for the first time. A
ZnO seed layer was firstly pre-coated on the O2-plasma treated
substrate by sol-gel process, followed by the nucleation of ZnO
nanostructures through hydrothermal synthesis. Nanostructures with
different average diameter (15-250 nm), length (0.9-1.8 μm), density
(0.9-16×109 cm-2) were obtained via adjusting the growth time and
concentration of precursors. From the reflectivity spectra, we
concluded ordered and taper nanostructures were preferential for
photovoltaic applications. ZnO nanoneedles with an average diameter
of 106 nm, a moderate length of 2.4 μm, and the density of 7.2×109
cm-2 could be synthesized in the concentration of 0.04 M for 18 h.
Integrated with the nanoneedle array, the power conversion efficiency
of single junction solar cell was increased from 7.3 to 12.2%,
corresponding to a 67% improvement.
Abstract: A method is presented for using thermo-mechanical fatigue analysis as a tool in the design of automotive heat exchangers. Use of infra-red thermography to measure the real thermal history in the heat exchanger reduces the time necessary for calculating design parameters and improves prediction accuracy. Thermal shocks are the primary cause of heat exchanger damage. Thermo-mechanical simulation is based on the mean behavior of the aluminum tubes used in the heat exchanger. An energetic fatigue criterion is used to detect critical zones.
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: In this paper, optimal generation expansion planning (GEP) is investigated considering purchase prices, profits of independent power producers (IPPs) and reliability criteria using a new method based on hybrid coded Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). In this approach, optimal purchase price of each IPP is obtained by HCGA and reliability criteria are calculated by PSO technique. It should be noted that reliability criteria and the rate of carbon dioxide (CO2) emission have been considered as constraints of the GEP problem. Finally, the proposed method has been tested on the case study system. The results evaluation show that the proposed method can simply obtain optimal purchase prices of IPPs and is a fast method for calculation of reliability criteria in expansion planning. Also, considering the optimal purchase prices and profits of IPPs in generation expansion planning are caused that the expansion costs are decreased and the problem is solved more exactly.
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: This work aims to describe the process of developing
services and applications of seamless communication within a
Telecom Italia long-term research project, which takes as central aim
the design of a wearable communication device. In particular, the
objective was to design a wrist phone integrated into everyday life of
people in full transparency. The methodology used to design the
wristwatch was developed through several subsequent steps also
involving the Personas Layering Framework. The data collected in
this phases have been very useful for designing an improved version
of the first two concepts of wrist phone going to change aspects
related to the four critical points expressed by the users.
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: Texture classification is an important image processing
task with a broad application range. Many different techniques for
texture classification have been explored. Using sparse approximation
as a feature extraction method for texture classification is a relatively
new approach, and Skretting et al. recently presented the Frame
Texture Classification Method (FTCM), showing very good results on
classical texture images. As an extension of that work the FTCM is
here tested on a real world application as detection of abnormalities
in mammograms. Some extensions to the original FTCM that are
useful in some applications are implemented; two different smoothing
techniques and a vector augmentation technique. Both detection of
microcalcifications (as a primary detection technique and as a last
stage of a detection scheme), and soft tissue lesions in mammograms
are explored. All the results are interesting, and especially the results
using FTCM on regions of interest as the last stage in a detection
scheme for microcalcifications are promising.
Abstract: The aim of this paper is to study in depth some methodological aspects of social interventation, focusing on desirable passage from social maternage method to peer advocacy method. For this purpose, we intend analyze social and organizative components, that affect operator's professional action and that are part of his psychological environment, besides the physical and social one. In fact, operator's interventation should not be limited to a pure supply of techniques, nor to take shape as improvised action, but “full of good purposes".
Abstract: Inconel 718, a nickel based super-alloy is an
extensively used alloy, accounting for about 50% by weight of
materials used in an aerospace engine, mainly in the gas turbine
compartment. This is owing to their outstanding strength and
oxidation resistance at elevated temperatures in excess of 5500 C.
Machining is a requisite operation in the aircraft industries for the
manufacture of the components especially for gas turbines. This
paper is concerned with optimization of the surface roughness when
turning Inconel 718 with cermet inserts. Optimization of turning
operation is very useful to reduce cost and time for machining. The
approach is based on Response Surface Method (RSM). In this work,
second-order quadratic models are developed for surface roughness,
considering the cutting speed, feed rate and depth of cut as the cutting
parameters, using central composite design. The developed models
are used to determine the optimum machining parameters. These
optimized machining parameters are validated experimentally, and it
is observed that the response values are in reasonable agreement with
the predicted values.
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.