Abstract: An adaptive software reliability prediction model
using evolutionary connectionist approach based on Recurrent Radial
Basis Function architecture is proposed. Based on the currently
available software failure time data, Fuzzy Min-Max algorithm is
used to globally optimize the number of the k Gaussian nodes. The
corresponding optimized neural network architecture is iteratively
and dynamically reconfigured in real-time as new actual failure time
data arrives. The performance of our proposed approach has been
tested using sixteen real-time software failure data. Numerical results
show that our proposed approach is robust across different software
projects, and has a better performance with respect to next-steppredictability
compared to existing neural network model for failure
time prediction.
Abstract: Employing a recently introduced unified adaptive filter
theory, we show how the performance of a large number of important
adaptive filter algorithms can be predicted within a general framework
in nonstationary environment. This approach is based on energy conservation
arguments and does not need to assume a Gaussian or white
distribution for the regressors. This general performance analysis can
be used to evaluate the mean square performance of the Least Mean
Square (LMS) algorithm, its normalized version (NLMS), the family
of Affine Projection Algorithms (APA), the Recursive Least Squares
(RLS), the Data-Reusing LMS (DR-LMS), its normalized version
(NDR-LMS), the Block Least Mean Squares (BLMS), the Block
Normalized LMS (BNLMS), the Transform Domain Adaptive Filters
(TDAF) and the Subband Adaptive Filters (SAF) in nonstationary
environment. Also, we establish the general expressions for the
steady-state excess mean square in this environment for all these
adaptive algorithms. Finally, we demonstrate through simulations that
these results are useful in predicting the adaptive filter performance.
Abstract: The use of the mechanical simulation (in particular the finite element analysis) requires the management of assumptions in order to analyse a real complex system. In finite element analysis (FEA), two modeling steps require assumptions to be able to carry out the computations and to obtain some results: the building of the physical model and the building of the simulation model. The simplification assumptions made on the analysed system in these two steps can generate two kinds of errors: the physical modeling errors (mathematical model, domain simplifications, materials properties, boundary conditions and loads) and the mesh discretization errors. This paper proposes a mesh adaptive method based on the use of an h-adaptive scheme in combination with an error estimator in order to choose the mesh of the simulation model. This method allows us to choose the mesh of the simulation model in order to control the cost and the quality of the finite element analysis.
Abstract: Business Process Modeling (BPM) is the first and
most important step in business process management lifecycle. Graph
based formalism and rule based formalism are the two most
predominant formalisms on which process modeling languages are
developed. BPM technology continues to face challenges in coping
with dynamic business environments where requirements and goals
are constantly changing at the execution time. Graph based
formalisms incur problems to react to dynamic changes in Business
Process (BP) at the runtime instances. In this research, an adaptive
and flexible framework based on the integration between Object
Oriented diagramming technique and Petri Net modeling language is
proposed in order to support change management techniques for
BPM and increase the representation capability for Object Oriented
modeling for the dynamic changes in the runtime instances. The
proposed framework is applied in a higher education environment to
achieve flexible, updatable and dynamic BP.
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: 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: 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: In this paper by using the port-controlled Hamiltonian
(PCH) systems theory, a full-order nonlinear controlled model is first
developed. Then a nonlinear passivity-based robust adaptive control
(PBRAC) of switched reluctance motor in the presence of external
disturbances for the purpose of torque ripple reduction and
characteristic improvement is presented. The proposed controller
design is separated into the inner loop and the outer loop controller.
In the inner loop, passivity-based control is employed by using
energy shaping techniques to produce the proper switching function.
The outer loop control is employed by robust adaptive controller to
determine the appropriate Torque command. It can also overcome the
inherent nonlinear characteristics of the system and make the whole
system robust to uncertainties and bounded disturbances. A 4KW 8/6
SRM with experimental characteristics that takes magnetic saturation
into account is modeled, simulation results show that the proposed
scheme has good performance and practical application prospects.
Abstract: In this paper, we present a method named Signal Level
Matrix (SLM) which can improve the accuracy and stability of active
RFID indoor positioning system. Considering the accuracy and cost,
we use uniform distribution mode to set up and separate the
overlapped signal covering areas, in order to achieve preliminary
location setting. Then, based on the proposed SLM concept and the
characteristic of the signal strength value that attenuates as the
distance increases, this system cross-examines the distribution of
adjacent signals to locate the users more accurately. The experimental
results indicate that the adaptive positioning method proposed in this
paper could improve the accuracy and stability of the positioning
system effectively and satisfyingly.
Abstract: A Variable Structure Model Reference Adaptive Controller using state variables is proposed for a class of multi input-multi output systems. Adaptation law is of variable structure type and switching functions is designed based on stability requirements. Global exponential stability is proved based on Lyapunov criterion. Transient behavior is analyzed using sliding mode control and shows perfect model following at a finite time.
Abstract: QoS Routing aims to find paths between senders and
receivers satisfying the QoS requirements of the application which
efficiently using the network resources and underlying routing
algorithm to be able to find low-cost paths that satisfy given QoS
constraints. The problem of finding least-cost routing is known to be
NP hard or complete and some algorithms have been proposed to
find a near optimal solution. But these heuristics or algorithms either
impose relationships among the link metrics to reduce the complexity
of the problem which may limit the general applicability of the
heuristic, or are too costly in terms of execution time to be applicable
to large networks. In this paper, we analyzed two algorithms namely
Characterized Delay Constrained Routing (CDCR) and Optimized
Delay Constrained Routing (ODCR). The CDCR algorithm dealt an
approach for delay constrained routing that captures the trade-off
between cost minimization and risk level regarding the delay
constraint. The ODCR which uses an adaptive path weight function
together with an additional constraint imposed on the path cost, to
restrict search space and hence ODCR finds near optimal solution in
much quicker time.
Abstract: The aim of this paper is to discuss a low-cost methodology that can predict traffic flow conflicts and quantitatively rank crash expectancies (based on relative probability) for various traffic facilities. This paper focuses on the application of statistical distributions to model traffic flow and Monte Carlo techniques to simulate traffic and discusses how to create a tool in order to predict the possibility of a traffic crash. A low-cost data collection methodology has been discussed for the heterogeneous traffic flow that exists and a GIS platform has been proposed to thematically represent traffic flow from simulations and the probability of a crash. Furthermore, discussions have been made to reflect the dynamism of the model in reference to its adaptability, adequacy, economy, and efficiency to ensure adoption.
Abstract: In this paper, the performance of two adaptive
observers applied to interconnected systems is studied. The
nonlinearity of systems can be written in a fractional form. The first
adaptive observer is an adaptive sliding mode observer for a Lipchitz
nonlinear system and the second one is an adaptive sliding mode
observer having a filtered error as a sliding surface. After comparing
their performances throughout the inverted pendulum mounted on a
car system, it was shown that the second one is more robust to
estimate the state.
Abstract: This paper presents an adaptive feedback linearization approach to derive helicopter. Ideal feedback linearization is defined for the cases when the system model is known. Adaptive feedback linearization is employed to get asymptotically exact cancellation for the inherent uncertainty in the knowledge of the given parameters of system. The control algorithm is implemented using the feedback linearization technique and adaptive method. The controller parameters are unknown where an adaptive control law aims to drive them towards their ideal values for providing perfect model matching between the reference model and the closed-loop plant model. The converged parameters of controller would then provide good estimates for the unknown plant parameters.
Abstract: The mobile systems are powered by batteries.
Reducing the system power consumption is a key to increase its
autonomy. It is known that mostly the systems are dealing with time
varying signals. Thus, we aim to achieve power efficiency by smartly
adapting the system processing activity in accordance with the input
signal local characteristics. It is done by completely rethinking the
processing chain, by adopting signal driven sampling and processing.
In this context, a signal driven filtering technique, based on the level
crossing sampling is devised. It adapts the sampling frequency and
the filter order by analysing the input signal local variations. Thus, it
correlates the processing activity with the signal variations. It leads
towards a drastic computational gain of the proposed technique
compared to the classical one.
Abstract: In the last decade digital watermarking procedures have
become increasingly applied to implement the copyright protection
of multimedia digital contents distributed on the Internet. To this
end, it is worth noting that a lot of watermarking procedures
for images and videos proposed in literature are based on spread
spectrum techniques. However, some scepticism about the robustness
and security of such watermarking procedures has arisen because
of some documented attacks which claim to render the inserted
watermarks undetectable. On the other hand, web content providers
wish to exploit watermarking procedures characterized by flexible and
efficient implementations and which can be easily integrated in their
existing web services frameworks or platforms. This paper presents
how a simple spread spectrum watermarking procedure for MPEG-2
videos can be modified to be exploited in web contexts. To this end,
the proposed procedure has been made secure and robust against some
well-known and dangerous attacks. Furthermore, its basic scheme
has been optimized by making the insertion procedure adaptive with
respect to the terminals used to open the videos and the network transactions
carried out to deliver them to buyers. Finally, two different
implementations of the procedure have been developed: the former
is a high performance parallel implementation, whereas the latter is
a portable Java and XML based implementation. Thus, the paper
demonstrates that a simple spread spectrum watermarking procedure,
with limited and appropriate modifications to the embedding scheme,
can still represent a valid alternative to many other well-known and
more recent watermarking procedures proposed in literature.
Abstract: In this paper, a model for an information retrieval
system is proposed which takes into account that knowledge about
documents and information need of users are dynamic. Two
methods are combined, one qualitative or symbolic and the other
quantitative or numeric, which are deemed suitable for many
clustering contexts, data analysis, concept exploring and
knowledge discovery. These two methods may be classified as
inductive learning techniques. In this model, they are introduced to
build “long term" knowledge about past queries and concepts in a
collection of documents. The “long term" knowledge can guide
and assist the user to formulate an initial query and can be
exploited in the process of retrieving relevant information. The
different kinds of knowledge are organized in different points of
view. This may be considered an enrichment of the exploration
level which is coherent with the concept of document/query
structure.
Abstract: Heating systems are a necessity for regions which
brace extreme cold weather throughout the year. To maintain a comfortable temperature inside a given place, heating systems
making use of- Hydronic boilers- are used. The principle of a single
pipe system serves as a base for their working. It is mandatory for these heating systems to control the room temperature, thus
maintaining a warm environment. In this paper, the concept of regulation of the room temperature over a wide range is established
by using an Adaptive Fuzzy Controller (AFC). This fuzzy controller automatically detects the changes in the outside temperatures and
correspondingly maintains the inside temperature to a palatial value. Two separate AFC's are put to use to carry out this function: one to
determine the quantity of heat needed to reach the prospective temperature required and to set the desired temperature; the other to control the position of the valve, which is directly proportional to the
error between the present room temperature and the user desired temperature. The fuzzy logic controls the position of the valve as per
the requirement of the heat. The amount by which the valve opens or closes is controlled by 5 knob positions, which vary from minimum to maximum, thereby regulating the amount of heat flowing through the valve. For the given test system data, different de-fuzzifier
methods have been implemented and the results are compared. In order to validate the effectiveness of the proposed approach, a fuzzy controller has been designed by obtaining a test data from a real time
system. The simulations are performed in MATLAB and are verified with standard system data. The proposed approach can be implemented for real time applications.
Abstract: This paper focuses on reducing the power consumption
of wireless sensor networks. Therefore, a communication protocol
named LEACH (Low-Energy Adaptive Clustering Hierarchy) is modified.
We extend LEACHs stochastic cluster-head selection algorithm
by a modifying the probability of each node to become cluster-head
based on its required energy to transmit to the sink. We present
an efficient energy aware routing algorithm for the wireless sensor
networks. Our contribution consists in rotation selection of clusterheads
considering the remoteness of the nodes to the sink, and then,
the network nodes residual energy. This choice allows a best distribution
of the transmission energy in the network. The cluster-heads
selection algorithm is completely decentralized. Simulation results
show that the energy is significantly reduced compared with the
previous clustering based routing algorithm for the sensor networks.
Abstract: Due to some reasons, observed images are degraded which are mainly caused by noise. Recently image denoising using the wavelet transform has been attracting much attention. Waveletbased approach provides a particularly useful method for image denoising when the preservation of edges in the scene is of importance because the local adaptivity is based explicitly on the values of the wavelet detail coefficients. In this paper, we propose several methods of noise removal from degraded images with Gaussian noise by using adaptive wavelet threshold (Bayes Shrink, Modified Bayes Shrink and Normal Shrink). The proposed thresholds are simple and adaptive to each subband because the parameters required for estimating the threshold depend on subband data. Experimental results show that the proposed thresholds remove noise significantly and preserve the edges in the scene.