Abstract: This paper presents an experimental investigation of
transformer dielectric response and solid insulation water content.
The dielectric response was carried out on the base of Hybrid
Frequency Dielectric Spectroscopy and Polarization Current
measurements method (FDS &PC). The calculation of the water
content in paper is based on the water content in oil and the obtained
equilibrium curves. A reference measurements were performed at
equilibrium conditions for water content in oil and paper of
transformer at different stable temperatures (25, 50, 60 and 70°C) to
prepare references to evaluate the insulation behavior at the not
equilibrium conditions. Some measurements performed at the
different simulated normal working modes of transformer operation
at the same temperature where the equilibrium conditions. The
obtained results show that when transformer temperature is mach
more than the its ambient temperature, the transformer temperature
decreases immediately after disconnecting the transformer from the
network and this temperature reduction influences the transformer
insulation condition in the measuring process. In addition to the oil
temperature at the near places to the sensors, the temperature
uniformity in transformer which can be changed by a big change in
the load of transformer before the measuring time will influence the
result. The investigations have shown that the extremely influence of
the time between disconnecting the transformer and beginning the
measurements on the results. And the online monitoring for water
content in paper measurements, on the basis of the oil water content
on line monitoring and the obtained equilibrium curves. The
measurements where performed continuously and for about 50 days
without any disconnection in the prepared the adiabatic room.
Abstract: Image fusion aims to enhance the perception
of a scene by combining important information captured by
different sensors. Dual-Tree Complex Wavelet (DT-CWT) has been
thouroughly investigated for image fusion, since it takes advantages
of approximate shift invariance and direction selectivity. But it can
only handle limited direction information. To allow a more flexible
directional expansion for images, we propose a novel fusion scheme,
referred to as complex contourlet transform (CCT). It successfully
incorporates directional filter banks (DFB) into DT-CWT. As a result
it efficiently deal with images containing contours and textures,
whereas it retains the property of shift invariance. Experimental
results demonstrated that the method features high quality fusion
performance and can facilitate many image processing applications.
Abstract: Performance of a dual maximal ratio combining
receiver has been analyzed for M-ary coherent and non-coherent
modulations over correlated Nakagami-m fading channels with nonidentical
and arbitrary fading parameter. The classical probability
density function (PDF) based approach is used for analysis.
Expressions for outage probability and average symbol error
performance for M-ary coherent and non-coherent modulations have
been obtained. The obtained results are verified against the special
case published results and found to be matching. The effect of the
unequal fading parameters, branch correlation and unequal input
average SNR on the receiver performance has been studied.
Abstract: Recent widespread use of information and
communication technology has greatly changed information security
risks that businesses and institutions encounter. Along with this
situation, in order to ensure security and have confidence in electronic
trading, it has become important for organizations to take competent
information security measures to provide international confidence that
sensitive information is secure. Against this backdrop, the approach to
information security checking has come to an important issue, which
is believed to be common to all countries. The purpose of this paper is
to introduce the new system of information security checking program
in Korea and to propose synthetic information security
countermeasures under domestic circumstances in order to protect
physical equipment, security management and technology, and the
operation of security check for securing services on ISP(Internet
Service Provider), IDC(Internet Data Center), and
e-commerce(shopping malls, etc.)
Abstract: Pattern recognition is the research area of Artificial
Intelligence that studies the operation and design of systems that
recognize patterns in the data. Important application areas are image
analysis, character recognition, fingerprint classification, speech
analysis, DNA sequence identification, man and machine
diagnostics, person identification and industrial inspection. The
interest in improving the classification systems of data analysis is
independent from the context of applications. In fact, in many
studies it is often the case to have to recognize and to distinguish
groups of various objects, which requires the need for valid
instruments capable to perform this task. The objective of this article
is to show several methodologies of Artificial Intelligence for data
classification applied to biomedical patterns. In particular, this work
deals with the realization of a Computer-Aided Detection system
(CADe) that is able to assist the radiologist in identifying types of
mammary tumor lesions. As an additional biomedical application of
the classification systems, we present a study conducted on blood
samples which shows how these methods may help to distinguish
between carriers of Thalassemia (or Mediterranean Anaemia) and
healthy subjects.
Abstract: It is established that the instantaneous heart rate (HR) of healthy humans keeps on changing. Analysis of heart rate variability (HRV) has become a popular non invasive tool for assessing the activities of autonomic nervous system. Depressed HRV has been found in several disorders, like diabetes mellitus (DM) and coronary artery disease, characterised by autonomic nervous dysfunction. A new technique, which searches for pattern repeatability in a time series, is proposed specifically for the analysis of heart rate data. These set of indices, which are termed as pattern repeatability measure and pattern repeatability ratio are compared with approximate entropy and sample entropy. In our analysis, based on the method developed, it is observed that heart rate variability is significantly different for DM patients, particularly for patients with diabetic foot ulcer.
Abstract: Due to memory leaks, often-valuable system memory
gets wasted and denied for other processes thereby affecting the
computational performance. If an application-s memory usage
exceeds virtual memory size, it can leads to system crash. Current
memory leak detection techniques for clusters are reactive and
display the memory leak information after the execution of the
process (they detect memory leak only after it occur).
This paper presents a Dynamic Memory Monitoring Agent
(DMMA) technique. DMMA framework is a dynamic memory leak
detection, that detects the memory leak while application is in
execution phase, when memory leak in any process in the cluster is
identified by DMMA it gives information to the end users to enable
them to take corrective actions and also DMMA submit the affected
process to healthy node in the system. Thus provides reliable service
to the user. DMMA maintains information about memory
consumption of executing processes and based on this information
and critical states, DMMA can improve reliability and
efficaciousness of cluster computing.
Abstract: This paper describes the application of a model predictive controller to the problem of batch reactor temperature control. Although a great deal of work has been done to improve reactor throughput using batch sequence control, the control of the actual reactor temperature remains a difficult problem for many operators of these processes. Temperature control is important as many chemical reactions are sensitive to temperature for formation of desired products. This controller consist of two part (1) a nonlinear control method GLC (Global Linearizing Control) to create a linear model of system and (2) a Model predictive controller used to obtain optimal input control sequence. The temperature of reactor is tuned to track a predetermined temperature trajectory that applied to the batch reactor. To do so two input signals, electrical powers and the flow of coolant in the coil are used. Simulation results show that the proposed controller has a remarkable performance for tracking reference trajectory while at the same time it is robust against noise imposed to system output.
Abstract: The proposed Multimedia Pronunciation Learning
Management System (MPLMS) in this study is a technology with
profound potential for inducing improvement in pronunciation
learning. The MPLMS optimizes the digitised phonetic symbols with
the integration of text, sound and mouth movement video. The
components are designed and developed in an online management
system which turns the web to a dynamic user-centric collection of
consistent and timely information for quality sustainable learning.
The aim of this study is to design and develop the MPLMS which
serves as an innovative tool to improve English pronunciation. This
paper discusses the iterative methodology and the three-phase Alessi
and Trollip model in the development of MPLMS. To align with the
flexibility of the development of educational software, the iterative
approach comprises plan, design, develop, evaluate and implement is
followed. To ensure the instructional appropriateness of MPLMS, the
instructional system design (ISD) model of Alessi and Trollip serves
as a platform to guide the important instructional factors and process.
It is expected that the results of future empirical research will support
the efficacy of MPLMS and its place as the premier pronunciation
learning system.
Abstract: This paper presents the simulation the results of
electric field and potential distributions along surface of silicone
rubber polymer insulators. Near the same leakage distance subjected
to 15 kV in 50 cycle salt fog ageing test, alternate sheds silicone
rubber polymer insulator showed better contamination performance
than straight sheds silicone rubber polymer insulator. Severe surface
ageing was observed on the straight sheds insulator. The objective of
this work is to elucidate that electric field distribution along straight
sheds insulator higher than alternate shed insulator in salt fog ageing
test. Finite element method (FEM) is adopted for this work. The
simulation results confirmed the experimental data, as well.
Abstract: Evolvable hardware (EHW) is a developing field that
applies evolutionary algorithm (EA) to automatically design circuits,
antennas, robot controllers etc. A lot of research has been done in this
area and several different EAs have been introduced to tackle
numerous problems, as scalability, evolvability etc. However every
time a specific EA is chosen for solving a particular task, all its
components, such as population size, initialization, selection
mechanism, mutation rate, and genetic operators, should be selected
in order to achieve the best results. In the last three decade the
selection of the right parameters for the EA-s components for solving
different “test-problems" has been investigated. In this paper the
behaviour of mutation rate for designing logic circuits, which has not
been done before, has been deeply analyzed. The mutation rate for an
EHW system modifies the number of inputs of each logic gates, the
functionality (for example from AND to NOR) and the connectivity
between logic gates. The behaviour of the mutation has been
analyzed based on the number of generations, genotype redundancy
and number of logic gates for the evolved circuits. The experimental
results found provide the behaviour of the mutation rate during
evolution for the design and optimization of simple logic circuits.
The experimental results propose the best mutation rate to be used for
designing combinational logic circuits. The research presented is
particular important for those who would like to implement a
dynamic mutation rate inside the evolutionary algorithm for evolving
digital circuits. The researches on the mutation rate during the last 40
years are also summarized.
Abstract: In this paper, a new adaptive Fourier decomposition
(AFD) based time-frequency speech analysis approach is proposed.
Given the fact that the fundamental frequency of speech signals often
undergo fluctuation, the classical short-time Fourier transform (STFT)
based spectrogram analysis suffers from the difficulty of window size
selection. AFD is a newly developed signal decomposition theory. It is
designed to deal with time-varying non-stationary signals. Its
outstanding characteristic is to provide instantaneous frequency for
each decomposed component, so the time-frequency analysis becomes
easier. Experiments are conducted based on the sample sentence in
TIMIT Acoustic-Phonetic Continuous Speech Corpus. The results
show that the AFD based time-frequency distribution outperforms the
STFT based one.
Abstract: Fault-proneness of a software module is the
probability that the module contains faults. To predict faultproneness
of modules different techniques have been proposed which
includes statistical methods, machine learning techniques, neural
network techniques and clustering techniques. The aim of proposed
study is to explore whether metrics available in the early lifecycle
(i.e. requirement metrics), metrics available in the late lifecycle (i.e.
code metrics) and metrics available in the early lifecycle (i.e.
requirement metrics) combined with metrics available in the late
lifecycle (i.e. code metrics) can be used to identify fault prone
modules using Genetic Algorithm technique. This approach has been
tested with real time defect C Programming language datasets of
NASA software projects. The results show that the fusion of
requirement and code metric is the best prediction model for
detecting the faults as compared with commonly used code based
model.
Abstract: Mushrooms are a group of fleshy macroscopic fungi.
They have been valued throughout the world as both edible and
medicine. They are highly nutritious with good amount of quality
proteins, vitamins and minerals. An edible mushroom, Calocybe
indica was selected to validate its nutritional and medicinal
properties. Since tissue damage in hyperglycemia has been related to
oxidative stress, we evaluated the enzymatic and non-enzymatic
antioxidant status in the serum, liver and kidney since they are the
target organs in diabetic complications. From the results, increased
oxidative stress and decreased antioxidants might be related to the
causation of diabetes mellitus. The treatment in the diabetic rats with
the Calocybe indica showed an increase in the antioxidant system
and decrease in the production of free radicals. The mushrooms
which contain antioxidant phytochemicals has potential free radical
scavenging capacity and hence can induce the antioxidant system in
the body significantly reduces the generated free radicals thereby
maintaining the normal levels of the antioxidants
Abstract: Procurement is an important component in the field of
operating resource management and e-procurement is the golden key
to optimizing the supply chains system. Global firms are optimistic
on the level of savings that can be achieved through full
implementation of e-procurement strategies. E-procurement is an
Internet-based business process for obtaining materials and services
and managing their inflow into the organization. In this paper, the
subjects of supply chains and e-procurement and its benefits to
organizations have been studied. Also, e-procurement in construction
and its drivers and barriers have been discussed and a framework of
supplier selection in an e-procurement environment has been
demonstrated. This paper also has addressed critical success factors
in adopting e-procurement in supply chains.
Abstract: Data Warehousing tools have become very popular and currently many of them have moved to Web-based user interfaces to make it easier to access and use the tools. The next step is to enable these tools to be used within a portal framework. The portal framework consists of pages having several small windows that contain individual data warehouse query results. There are several issues that need to be considered when designing the architecture for a portal enabled data warehouse query tool. Some issues need special techniques that can overcome the limitations that are imposed by the nature of data warehouse queries. Issues such as single sign-on, query result caching and sharing, customization, scheduling and authorization need to be considered. This paper discusses such issues and suggests an architecture to support data warehouse queries within Web portal frameworks.
Abstract: Let a and b be nonnegative integers with 2 ≤ a < b, and
let G be a Hamiltonian graph of order n with n ≥ (a+b−4)(a+b−2)
b−2 .
An [a, b]-factor F of G is called a Hamiltonian [a, b]-factor if F
contains a Hamiltonian cycle. In this paper, it is proved that G has a
Hamiltonian [a, b]-factor if |NG(X)| > (a−1)n+|X|−1
a+b−3 for every nonempty
independent subset X of V (G) and δ(G) > (a−1)n+a+b−4
a+b−3 .
Abstract: Hysteresis phenomenon has been observed in the
operations of both horizontal-axis and vertical-axis wind turbines
(HAWTs and VAWTs). In this study, wind tunnel experiments were
applied to investigate the characters of hysteresis phenomena between
the angular speed and the external resistance of electrical loading
during the operation of a Darrieus type VAWT. Data of output voltage,
output current, angular speed of wind turbine under different wind
speeds are measured and analyzed. Results show that the range of
external resistance changes with the wind speed. The range decreases
as the wind speed increases following an exponential decay form.
Experiments also indicate that the maximum output power of wind
turbines is always inside the range where hysteresis happened. These
results provide an important reference to the design of output control
system of wind turbines.
Abstract: This paper presents the development and application of an adaptive neuro fuzzy inference system (ANFIS) based intelligent hybrid neuro fuzzy controller for automatic generation control (AGC) of two-area interconnected thermal power system with reheat non linearity. The dynamic response of the system has been studied for 1% step load perturbation in area-1. The performance of the proposed neuro fuzzy controller is compared against conventional proportional-integral (PI) controller, state feedback linear quadratic regulator (LQR) controller and fuzzy gain scheduled proportionalintegral (FGSPI) controller. Comparative analysis demonstrates that the proposed intelligent neuro fuzzy controller is the most effective of all in improving the transients of frequency and tie-line power deviations against small step load disturbances. Simulations have been performed using Matlab®.
Abstract: This paper addresses an efficient technique to embed and detect digital fingerprint code. Orthogonal modulation method is a straightforward and widely used approach for digital fingerprinting but shows several limitations in computational cost and signal efficiency. Coded modulation method can solve these limitations in theory. However it is difficult to perform well in practice if host signals are not available during tracing colluders, other kinds of attacks are applied, and the size of fingerprint code becomes large. In this paper, we propose a hybrid modulation method, in which the merits of or-thogonal modulation and coded modulation method are combined so that we can achieve low computational cost and high signal efficiency. To analyze the performance, we design a new fingerprint code based on GD-PBIBD theory and modulate this code into images by our method using spread-spectrum watermarking on frequency domain. The results show that the proposed method can efficiently handle large fingerprint code and trace colluders against averaging attacks.