Abstract: This paper investigates the problem of exponential stability for a class of uncertain discrete-time stochastic neural network with time-varying delays. By constructing a suitable Lyapunov-Krasovskii functional, combining the stochastic stability theory, the free-weighting matrix method, a delay-dependent exponential stability criteria is obtained in term of LMIs. Compared with some previous results, the new conditions obtain in this paper are less conservative. Finally, two numerical examples are exploited to show the usefulness of the results derived.
Abstract: This paper looks at transgender identities and the law in the context of marriage. It particularly focuses on the role of language and definition in classifying transgendered individuals into a legal category. Two lines of cases in transgender jurisprudence are examined. The former cases decided the definition of 'man' and 'woman' on the basis of biological criteria while the latter cases held that biological factors should not be the sole criterion for defining a man or a woman. Three categories were found to classify transgender people, namely male, female and "monstrous". Since transgender people challenge the core gender distinction that the law stresses, they are often regarded as problematic and monstrous which caused them to be subjected to severe legal consequences. This paper discusses these issues by analyzing and comparing different cases in transgender jurisprudence as well as examining how these issues play out in contemporary Hong Kong.
Abstract: The aim of this studywas toinvestigate the effect
ofrunning classification (sprint, middle, and long distance)and two
distances on blood lactate (BLa), heart rate (HR), and rating of
perceived exertion (RPE) Borg scale ratings in collegiate athletes. On
different days, runners (n = 15) ran 400m and 1600m at a five min
mile pace, followed by a two min 6mph jog, and a two min 3mph
walk as part of the cool down. BLa, HR, and RPE were taken at
baseline, post-run, plus 2 and 4 min recovery times. The middle and
long distance runners exhibited lower BLa concentrations than sprint
runners after two min of recovery post 400 m runs, immediately after,
and two and four min recovery periods post 1600 m runs. When
compared to sprint runners, distance runners may have exhibited the
ability to clear BLa more quickly, particularly after running 1600 m.
Abstract: The approach based on the wavelet transform has
been widely used for image denoising due to its multi-resolution
nature, its ability to produce high levels of noise reduction and the
low level of distortion introduced. However, by removing noise, high
frequency components belonging to edges are also removed, which
leads to blurring the signal features. This paper proposes a new
method of image noise reduction based on local variance and edge
analysis. The analysis is performed by dividing an image into 32 x 32
pixel blocks, and transforming the data into wavelet domain. Fast
lifting wavelet spatial-frequency decomposition and reconstruction is
developed with the advantages of being computationally efficient and
boundary effects minimized. The adaptive thresholding by local
variance estimation and edge strength measurement can effectively
reduce image noise while preserve the features of the original image
corresponding to the boundaries of the objects. Experimental results
demonstrate that the method performs well for images contaminated
by natural and artificial noise, and is suitable to be adapted for
different class of images and type of noises. The proposed algorithm
provides a potential solution with parallel computation for real time
or embedded system application.
Abstract: Classification is an interesting problem in functional
data analysis (FDA), because many science and application problems
end up with classification problems, such as recognition, prediction,
control, decision making, management, etc. As the high dimension
and high correlation in functional data (FD), it is a key problem to
extract features from FD whereas keeping its global characters, which
relates to the classification efficiency and precision to heavens. In this
paper, a novel automatic method which combined Genetic Algorithm
(GA) and classification algorithm to extract classification features is
proposed. In this method, the optimal features and classification model
are approached via evolutional study step by step. It is proved by
theory analysis and experiment test that this method has advantages in
improving classification efficiency, precision and robustness whereas
using less features and the dimension of extracted classification
features can be controlled.
Abstract: In this paper a new Joint Adaptive Block Matching
Search (JABMS) algorithm is proposed to generate motion vector
and search a best match macro block by classifying the motion vector
movement based on prediction error. Diamond Search (DS)
algorithm generates high estimation accuracy when motion vector is
small and Adaptive Rood Pattern Search (ARPS) algorithm can
handle large motion vector but is not very accurate. The proposed
JABMS algorithm which is capable of considering both small and
large motions gives improved estimation accuracy and the
computational cost is reduced by 15.2 times compared with
Exhaustive Search (ES) algorithm and is 1.3 times less compared
with Diamond search algorithm.
Abstract: In this paper, we proposed the robust mobile object
detection method for light effect in the night street image block based
updating reference background model using block state analysis.
Experiment image is acquired sequence color video from steady
camera. When suddenly appeared artificial illumination, reference
background model update this information such as street light, sign
light. Generally natural illumination is change by temporal, but
artificial illumination is suddenly appearance. So in this paper for
exactly detect artificial illumination have 2 state process. First process
is compare difference between current image and reference
background by block based, it can know changed blocks. Second
process is difference between current image-s edge map and reference
background image-s edge map, it possible to estimate illumination at
any block. This information is possible to exactly detect object,
artificial illumination and it was generating reference background
more clearly. Block is classified by block-state analysis. Block-state
has a 4 state (i.e. transient, stationary, background, artificial
illumination). Fig. 1 is show characteristic of block-state respectively
[1]. Experimental results show that the presented approach works well
in the presence of illumination variance.
Abstract: An application framework provides a reusable
design and implementation for a family of software systems.
Frameworks are introduced to reduce the cost of a product line
(i.e., family of products that share the common features). Software
testing is a time consuming and costly ongoing activity during the
application software development process. Generating reusable test
cases for the framework applications at the framework
development stage, and providing and using the test cases to test
part of the framework application whenever the framework is used
reduces the application development time and cost considerably.
Framework Interface Classes (FICs) are classes introduced by
the framework hooks to be implemented at the application
development stage. They can have reusable test cases generated at
the framework development stage and provided with the
framework to test the implementations of the FICs at the
application development stage. In this paper, we conduct a case
study using thirteen applications developed using three
frameworks; one domain oriented and two application oriented.
The results show that, in general, the percentage of the number of
FICs in the applications developed using domain frameworks is, on
average, greater than the percentage of the number of FICs in the
applications developed using application frameworks.
Consequently, the reduction of the application unit testing time
using the reusable test cases generated for domain frameworks is,
in general, greater than the reduction of the application unit testing
time using the reusable test cases generated for application
frameworks.
Abstract: It is well known that enhancing interfacial adhesion
between inorganic filler and matrix resin in a composite lead to
favorable properties such as excellent mechanical properties, high
thermal resistance, prominent electric insulation, low expansion
coefficient, and so on. But it should be avoided that much excess of
coupling agent is reacted due to a negative impact of their final
composite-s properties. There is no report to achieve classification of
the bonding state excepting investigation of coating layer thickness.
Therefore, the analysis of the bonding state of the coupling agent
reacted with the filler surface such as BN particles with less functional
group and silica particles having much functional group was
performed by thermal gravimetric analysis and pyrolysis GC/MS. The
reacted number of functional groups on the silane-coupling agent was
classified as a result of the analysis. Thus, we succeeded in classifying
the reacted number of the functional groups as a result of this study.
Abstract: In this paper we introduce a new unit test technique
called déjà-vu object. Déjà-vu objects replace real objects used by
classes under test, allowing the execution of isolated unit tests. A
déjà-vu object is able to observe and record the behaviour of a real
object during real sessions, and to replace it during unit tests,
returning previously recorded results. Consequently déjà-vu object
technique can be useful when a bottom-up development and testing
strategy is adopted. In this case déjà-vu objects can increase test
portability and test source code readability. At the same time they
can reduce the time spent by programmers to develop test code and
the risk of incompatibility during the switching between déjà-vu and
production code.
Abstract: E-services have significantly changed the way of
doing business in recent years. We can, however, observe poor use of
these services. There is a large gap between supply and actual eservices
usage. This is why we started a project to provide an
environment that will encourage the use of e-services. We believe
that only providing e-service does not automatically mean consumers
would use them. This paper shows the origins of our project and its
current position. We discuss the decision of using semantic web
technologies and their potential to improve e-services usage. We also
present current knowledge base and its real-world classification. In the paper, we discuss further work to be done in the project. Current
state of the project is promising.
Abstract: This paper derives some new sufficient conditions for
the stability of a class of neutral-type neural networks with discrete
time delays by employing a suitable Lyapunov functional. The
obtained conditions can be easily verified as they can be expressed
in terms of the network parameters only. It is shown that the results
presented in this paper for neutral-type delayed neural networks establish
a new set of stability criteria, and therefore can be considered
as the alternative results to the previously published literature results.
A numerical example is also given to demonstrate the applicability
of our proposed stability criterion.
Abstract: Biclustering is a very useful data mining technique for
identifying patterns where different genes are co-related based on a
subset of conditions in gene expression analysis. Association rules
mining is an efficient approach to achieve biclustering as in
BIMODULE algorithm but it is sensitive to the value given to its
input parameters and the discretization procedure used in the
preprocessing step, also when noise is present, classical association
rules miners discover multiple small fragments of the true bicluster,
but miss the true bicluster itself. This paper formally presents a
generalized noise tolerant bicluster model, termed as μBicluster. An
iterative algorithm termed as BIDENS based on the proposed model
is introduced that can discover a set of k possibly overlapping
biclusters simultaneously. Our model uses a more flexible method to
partition the dimensions to preserve meaningful and significant
biclusters. The proposed algorithm allows discovering biclusters that
hard to be discovered by BIMODULE. Experimental study on yeast,
human gene expression data and several artificial datasets shows that
our algorithm offers substantial improvements over several
previously proposed biclustering algorithms.
Abstract: Research has suggested that implicit learning tasks
may rely on episodic processing to generate above chance
performance on the standard classification tasks. The current
research examines the invariant features task (McGeorge and Burton,
1990) and argues that such episodic processing is indeed important.
The results of the experiment suggest that both rejection and
similarity strategies are used by participants in this task to
simultaneously reject unfamiliar items and to accept (falsely) familiar
items. Primarily these decisions are based on the presence of low or
high frequency goal based features of the stimuli presented in the
incidental learning phase. It is proposed that a goal based analysis of
the incidental learning task provides a simple step in understanding
which features of the episodic processing are most important for
explaining the match between incidental, implicit learning and test
performance.
Abstract: In many applications, magnetic suspension systems
are required to operate over large variations in air gap. As a result,
the nonlinearities inherent in most types of suspensions have a
significant impact on performance. Specifically, it may be difficult to
design a linear controller which gives satisfactory performance,
stability, and disturbance rejection over a wide range of operating
points. in this paper an optimal controller based on discontinuous
mathematical model of the system for an electromagnetic suspension
system which is applied in magnetic trains has been designed .
Simulations show that the new controller can adapt well to the
variance of suspension mass and gap, and keep its dynamic
performance, thus it is superior to the classic controller.
Abstract: Software security testing is an important means to ensure software security and trustiness. This paper first mainly discusses the definition and classification of software security testing, and investigates methods and tools of software security testing widely. Then it analyzes and concludes the advantages and disadvantages of various methods and the scope of application, presents a taxonomy of security testing tools. Finally, the paper points out future focus and development directions of software security testing technology.
Abstract: In this paper, a necessary and sufficient coefficient are given for functions in a class of complex valued meromorphic harmonic univalent functions of the form f = h + g using Salagean operator. Furthermore, distortion theorems, extreme points, convolution condition and convex combinations for this family of meromorphic harmonic functions are obtained.
Abstract: Insulation used in transformer is mostly oil pressboard insulation. Insulation failure is one of the major causes of catastrophic failure of transformers. It is established that partial discharges (PD) cause insulation degradation and premature failure of insulation. Online monitoring of PDs can reduce the risk of catastrophic failure of transformers. There are different techniques of partial discharge measurement like, electrical, optical, acoustic, opto-acoustic and ultra high frequency (UHF). Being non invasive and non interference prone, acoustic emission technique is advantageous for online PD measurement. Acoustic detection of p.d. is based on the retrieval and analysis of mechanical or pressure signals produced by partial discharges. Partial discharges are classified according to the origin of discharges. Their effects on insulation deterioration are different for different types. This paper reports experimental results and analysis for classification of partial discharges using acoustic emission signal of laboratory simulated partial discharges in oil pressboard insulation system using three different electrode systems. Acoustic emission signal produced by PD are detected by sensors mounted on the experimental tank surface, stored on an oscilloscope and fed to computer for further analysis. The measured AE signals are analyzed using discrete wavelet transform analysis and wavelet packet analysis. Energy distribution in different frequency bands of discrete wavelet decomposed signal and wavelet packet decomposed signal is calculated. These analyses show a distinct feature useful for PD classification. Wavelet packet analysis can sort out any misclassification arising out of DWT in most cases.
Abstract: This paper describes the authorization system
architecture for Pervasive Grid environment. It discusses the
characteristics of classical authorization system and requirements of
the authorization system in pervasive grid environment as well.
Based on our analysis of current systems and taking into account the
main requirements of such pervasive environment, we propose new
authorization system architecture as an extension of the existing grid
authorization mechanisms. This architecture not only supports user
attributes but also context attributes which act as a key concept for
context-awareness thought. The architecture allows authorization of
users dynamically when there are changes in the pervasive grid
environment. For this, we opt for hybrid authorization method that
integrates push and pull mechanisms to combine the existing grid
authorization attributes with dynamic context assertions. We will
investigate the proposed architecture using a real testing environment
that includes heterogeneous pervasive grid infrastructures mapped
over multiple virtual organizations. Various scenarios are described
in the last section of the article to strengthen the proposed mechanism
with different facilities for the authorization procedure.
Abstract: Noise causes significant sensibility changes on a human. This study investigated the effect of five different noises on electroencephalogram (EEG) and subjective evaluation. Six human subjects were exposed to classic piano, ocean wave, alarm in army, ambulance, mosquito noise and EEG data were collected during the experimental session. Alpha band activity in the mosquito noise was smaller than that in the classic piano. Alpha band activity decreased 43.4 ± 8.2 % in the mosquito noise. On the other hand, Beta band activity in the mosquito noise was greater than that in the classic piano. Beta band activity increased 60.1 ± 10.7 % in the mosquito noise. The advances from this study may aid the product design process with human sensibility engineering. This result may provide useful information in designing a human-oriented product to avoid the stress.