Abstract: In recent years, copulas have become very popular in
financial research and actuarial science as they are more flexible in
modelling the co-movements and relationships of risk factors as compared
to the conventional linear correlation coefficient by Pearson.
However, a precise estimation of the copula parameters is vital in
order to correctly capture the (possibly nonlinear) dependence structure
and joint tail events. In this study, we employ two optimization
heuristics, namely Differential Evolution and Threshold Accepting to
tackle the parameter estimation of multivariate t distribution models
in the EML approach. Since the evolutionary optimizer does not rely
on gradient search, the EML approach can be applied to estimation of
more complicated copula models such as high-dimensional copulas.
Our experimental study shows that the proposed method provides
more robust and more accurate estimates as compared to the IFM
approach.
Abstract: In this paper, we present a robust and secure
algorithm for watermarking, the watermark is first transformed into
the frequency domain using the discrete wavelet transform (DWT).
Then the entire DWT coefficient except the LL (Band) discarded,
these coefficients are permuted and encrypted by specific mixing.
The encrypted coefficients are inserted into the most significant
spectral components of the stego-image using a chaotic system. This
technique makes our watermark non-vulnerable to the attack (like
compression, and geometric distortion) of an active intruder, or due
to noise in the transmission link.
Abstract: Chua’s circuit is one of the most important electronic devices that are used for Chaos and Bifurcation studies. A central role of secure communication is devoted to it. Since the adaptive control is used vastly in the linear systems control, here we introduce a new trend of application of adaptive method in the chaos controlling field. In this paper, we try to derive a new adaptive control scheme for Chua’s circuit controlling because control of chaos is often very important in practical operations. The novelty of this approach is for sake of its robustness against the external perturbations which is simulated as an additive noise in all measured states and can be generalized to other chaotic systems. Our approach is based on Lyapunov analysis and the adaptation law is considered for the feedback gain. Because of this, we have named it NAFT (Nonlinear Adaptive Feedback Technique). At last, simulations show the capability of the presented technique for Chua’s circuit.
Abstract: Image convolution similar to the receptive fields
found in mammalian visual pathways has long been used in
conventional image processing in the form of Gabor masks.
However, no VLSI implementation of parallel, multi-layered pulsed
processing has been brought forward which would emulate this
property. We present a technical realization of such a pulsed image
processing scheme. The discussed IC also serves as a general testbed
for VLSI-based pulsed information processing, which is of interest
especially with regard to the robustness of representing an analog
signal in the phase or duration of a pulsed, quasi-digital signal, as
well as the possibility of direct digital manipulation of such an
analog signal. The network connectivity and processing properties
are reconfigurable so as to allow adaptation to various processing
tasks.
Abstract: The first generation of Mobile Agents based Intrusion
Detection System just had two components namely data collection
and single centralized analyzer. The disadvantage of this type of
intrusion detection is if connection to the analyzer fails, the entire
system will become useless. In this work, we propose novel hybrid
model for Mobile Agent based Distributed Intrusion Detection
System to overcome the current problem. The proposed model has
new features such as robustness, capability of detecting intrusion
against the IDS itself and capability of updating itself to detect new
pattern of intrusions. In addition, our proposed model is also capable
of tackling some of the weaknesses of centralized Intrusion Detection
System models.
Abstract: This paper examines many mathematical methods for
molding the hourly price forward curve (HPFC); the model will be
constructed by numerous regression methods, like polynomial
regression, radial basic function neural networks & a furrier series.
Examination the models goodness of fit will be done by means of
statistical & graphical tools. The criteria for choosing the model will
depend on minimize the Root Mean Squared Error (RMSE), using the
correlation analysis approach for the regression analysis the optimal
model will be distinct, which are robust against model
misspecification. Learning & supervision technique employed to
determine the form of the optimal parameters corresponding to each
measure of overall loss. By using all the numerical methods that
mentioned previously; the explicit expressions for the optimal model
derived and the optimal designs will be implemented.
Abstract: The impact of the information revolution is double
edged. While it is applauded for its versatility and performance
robustness and acclaimed for making life smooth and easy, on the
other hand people are concerned about its dark side especially to
younger generations. The education system should extend its
educating role beyond the school to home. Parents should be included
in forming the policies of Internet use as well as in the curriculum
delivery. This paper discusses how curriculum can be instrumental in
addressing social and ethical issues resulted from the Internet.
Abstract: The triumph of inductive neuro-stimulation since its rediscovery in the 1980s has been quite spectacular. In lots of branches ranging from clinical applications to basic research this system is absolutely indispensable. Nevertheless, the basic knowledge about the processes underlying the stimulation effect is still very rough and rarely refined in a quantitative way. This seems to be not only an inexcusable blank spot in biophysics and for stimulation prediction, but also a fundamental hindrance for technological progress. The already very sophisticated devices have reached a stage where further optimization requires better strategies than provided by simple linear membrane models of integrate-and-fire style. Addressing this problem for the first time, we suggest in the following text a way for virtual quantitative analysis of a stimulation system. Concomitantly, this ansatz seems to provide a route towards a better understanding by using nonlinear signal processing and taking the nerve as a filter that is adapted for neuronal magnetic stimulation. The model is compact and easy to adjust. The whole setup behaved very robustly during all performed tests. Exemplarily a recent innovative stimulator design known as cTMS is analyzed and dimensioned with this approach in the following. The results show hitherto unforeseen potentials.
Abstract: Compensating physiological motion in the context
of minimally invasive cardiac surgery has become an attractive
issue since it outperforms traditional cardiac procedures offering
remarkable benefits. Owing to space restrictions, computer vision
techniques have proven to be the most practical and suitable solution.
However, the lack of robustness and efficiency of existing methods
make physiological motion compensation an open and challenging
problem. This work focusses on increasing robustness and efficiency
via exploration of the classes of 1−and 2−regularized optimization,
emphasizing the use of explicit regularization. Both approaches are
based on natural features of the heart using intensity information.
Results pointed out the 1−regularized optimization class as the best
since it offered the shortest computational cost, the smallest average
error and it proved to work even under complex deformations.
Abstract: This paper identifies five key design characteristics of
production scheduling software systems in printed circuit board (PCB) manufacturing. The authors consider that, in addition to an effective scheduling engine, a scheduling system should be able to
process a preventative maintenance calendar, to give the user the
flexibility to handle data using a variety of electronic sources, to run
simulations to support decision-making, and to have simple and
customisable graphical user interfaces. These design considerations
were the result of a review of academic literature, the evaluation of
commercial applications and a compilation of requirements of a PCB manufacturer. It was found that, from those systems that were evaluated, those that effectively addressed all five characteristics
outlined in this paper were the most robust of all and could be used in
PCB manufacturing.
Abstract: This paper describes a method to improve the robustness of a face recognition system based on the combination of two compensating classifiers. The face images are preprocessed by the appearance-based statistical approaches such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). LDA features of the face image are taken as the input of the Radial Basis Function Network (RBFN). The proposed approach has been tested on the ORL database. The experimental results show that the LDA+RBFN algorithm has achieved a recognition rate of 93.5%
Abstract: Multicarrier code-division multiple-access is one of the
effective techniques to gain its multiple access capability, robustness
against fading, and to mitigate the ISI. In this paper, we propose an
improved mulcarrier CDMA system with adaptive subchannel
allocation. We analyzed the performance of our proposed system in
frequency selective fading environment with narrowband interference
existing and compared it with that of parallel transmission over many
subchannels (namely, conventional MC-CDMA scheme) and
DS-CDMA system. Simulation results show that adaptive subchannel
allocation scheme, when used in conventional multicarrier CDMA
system, the performance will be greatly improved.
Abstract: State-dependent Riccati equation based controllers are
becoming increasingly popular because of having attractive
properties like optimality, stability and robustness. This paper focuses
on the design of a roll autopilot for a fin stabilized and canard
controlled 122mm artillery rocket using state-dependent Riccati
equation technique. Initial spin is imparted to rocket during launch
and it quickly decays due to straight tail fins. After the spin phase, the
roll orientation of rocket is brought to zero with the canard deflection
commands generated by the roll autopilot. Roll autopilot has been
developed by considering uncoupled roll, pitch and yaw channels.
The canard actuator is modeled as a second-order nonlinear system.
Elements of the state weighing matrix for Riccati equation have been
chosen to be state dependent to exploit the design flexibility offered
by the Riccati equation technique. Simulation results under varying
conditions of flight demonstrate the wide operating range of the
proposed autopilot.
Abstract: Intelligent systems based on machine learning
techniques, such as classification, clustering, are gaining wide spread
popularity in real world applications. This paper presents work on
developing a software system for predicting crop yield, for example
oil-palm yield, from climate and plantation data. At the core of our
system is a method for unsupervised partitioning of data for finding
spatio-temporal patterns in climate data using kernel methods which
offer strength to deal with complex data. This work gets inspiration
from the notion that a non-linear data transformation into some high
dimensional feature space increases the possibility of linear
separability of the patterns in the transformed space. Therefore, it
simplifies exploration of the associated structure in the data. Kernel
methods implicitly perform a non-linear mapping of the input data
into a high dimensional feature space by replacing the inner products
with an appropriate positive definite function. In this paper we
present a robust weighted kernel k-means algorithm incorporating
spatial constraints for clustering the data. The proposed algorithm
can effectively handle noise, outliers and auto-correlation in the
spatial data, for effective and efficient data analysis by exploring
patterns and structures in the data, and thus can be used for
predicting oil-palm yield by analyzing various factors affecting the
yield.
Abstract: In this work, we derive two numerical schemes for
solving a class of nonlinear partial differential equations. The first
method is of second order accuracy in space and time directions, the
scheme is unconditionally stable using Von Neumann stability
analysis, the scheme produced a nonlinear block system where
Newton-s method is used to solve it. The second method is of fourth
order accuracy in space and second order in time. The method is
unconditionally stable and Newton's method is used to solve the
nonlinear block system obtained. The exact single soliton solution
and the conserved quantities are used to assess the accuracy and to
show the robustness of the schemes. The interaction of two solitary
waves for different parameters are also discussed.
Abstract: Wind power is among the most actively developing distributed generation (DG) technology. Majority of the wind power based DG technologies employ wind turbine induction generators (WTIG) instead of synchronous generators, for the technical advantages like: reduced size, increased robustness, lower cost, and increased electromechanical damping. However, dynamic changes of wind speed make the amount of active/reactive power injected/drawn to a WTIG embedded distribution network highly variable. This paper analyzes the effect of wind speed changes on the active and reactive power penetration to the wind energy embedded distribution network. Four types of wind speed changes namely; constant, linear change, gust change and random change of wind speed are considered in the analysis. The study is carried out by three-phase, non-linear, dynamic simulation of distribution system component models. Results obtained from the investigation are presented and discussed.
Abstract: In this paper, a robust digital image watermarking
scheme for copyright protection applications using the singular value
decomposition (SVD) is proposed. In this scheme, an entropy
masking model has been applied on the host image for the texture
segmentation. Moreover, the local luminance and textures of the host
image are considered for watermark embedding procedure to
increase the robustness of the watermarking scheme. In contrast to all
existing SVD-based watermarking systems that have been designed
to embed visual watermarks, our system uses a pseudo-random
sequence as a watermark. We have tested the performance of our
method using a wide variety of image processing attacks on different
test images. A comparison is made between the results of our
proposed algorithm with those of a wavelet-based method to
demonstrate the superior performance of our algorithm.
Abstract: A self tuning PID control strategy using reinforcement
learning is proposed in this paper to deal with the control of wind
energy conversion systems (WECS). Actor-Critic learning is used to
tune PID parameters in an adaptive way by taking advantage of the
model-free and on-line learning properties of reinforcement learning
effectively. In order to reduce the demand of storage space and to
improve the learning efficiency, a single RBF neural network is used
to approximate the policy function of Actor and the value function of
Critic simultaneously. The inputs of RBF network are the system
error, as well as the first and the second-order differences of error.
The Actor can realize the mapping from the system state to PID
parameters, while the Critic evaluates the outputs of the Actor and
produces TD error. Based on TD error performance index and
gradient descent method, the updating rules of RBF kernel function
and network weights were given. Simulation results show that the
proposed controller is efficient for WECS and it is perfectly
adaptable and strongly robust, which is better than that of a
conventional PID controller.
Abstract: In recent years, tuned mass damper (TMD) control systems for civil engineering structures have attracted considerable attention. This paper emphasizes on the application of particle swarm application (PSO) to design and optimize the parameters of the TMD control scheme for achieving the best results in the reduction of the building response under earthquake excitations. The Integral of the Time multiplied Absolute value of the Error (ITAE) based on relative displacement of all floors in the building is taken as a performance index of the optimization criterion. The problem of robustly TMD controller design is formatted as an optimization problem based on the ITAE performance index to be solved using the PSO technique which has a story ability to find the most optimistic results. An 11- story realistic building, located in the city of Rasht, Iran is considered as a test system to demonstrate effectiveness of the proposed method. The results analysis through the time-domain simulation and some performance indices reveals that the designed PSO based TMD controller has an excellent capability in reduction of the seismically excited example building.
Abstract: A hybrid feature based adaptive particle filter algorithm is presented for object tracking in real scenarios with static camera.
The hybrid feature is combined by two effective features: the Grayscale Arranging Pairs (GAP) feature and the color histogram feature. The GAP feature has high discriminative ability even under conditions of severe illumination variation and dynamic background
elements, while the color histogram feature has high reliability to identify the detected objects. The combination of two features covers the shortage of single feature. Furthermore, we adopt an updating
target model so that some external problems such as visual angles can be overcame well. An automatic initialization algorithm is introduced which provides precise initial positions of objects. The experimental
results show the good performance of the proposed method.