Abstract: This paper presents an adaptive framework for
modelling financial markets using equity risk premiums, risk free
rates and volatilities. The recorded economic factors are initially
used to train four adaptive filters for a certain limited period of time
in the past. Once the systems are trained, the adjusted coefficients
are used for modelling and prediction of an important financial
market index. Two different approaches based on least mean squares
(LMS) and recursive least squares (RLS) algorithms are investigated.
Performance analysis of each method in terms of the mean squared
error (MSE) is presented and the results are discussed. Computer
simulations carried out using recorded data show MSEs of 4% and
3.4% for the next month prediction using LMS and RLS adaptive
algorithms, respectively. In terms of twelve months prediction, RLS
method shows a better tendency estimation compared to the LMS
algorithm.
Abstract: This paper provides a comparative study on the
performances of standard PID and adaptive PID controllers tested on
travel angle of a 3-Degree-of-Freedom (3-DOF) Quanser bench-top
helicopter. Quanser, a well-known manufacturer of educational
bench-top helicopter has developed Proportional Integration
Derivative (PID) controller with Linear Quadratic Regulator (LQR)
for all travel, pitch and yaw angle of the bench-top helicopter. The
performance of the PID controller is relatively good; however, its
performance could also be improved if the controller is combined
with adaptive element. The objective of this research is to design
adaptive PID controller and then compare the performances of the
adaptive PID with the standard PID. The controller design and test is
focused on travel angle control only. Adaptive method used in this
project is self-tuning controller, which controller’s parameters are
updated online. Two adaptive algorithms those are pole-placement
and deadbeat have been chosen as the method to achieve optimal
controller’s parameters. Performance comparisons have shown that
the adaptive (deadbeat) PID controller has produced more desirable
performance compared to standard PID and adaptive (poleplacement).
The adaptive (deadbeat) PID controller attained very fast
settling time (5 seconds) and very small percentage of overshoot (5%
to 7.5%) for 10° to 30° step change of travel angle.
Abstract: WiFi has become an essential technology that is widely used nowadays. It is famous due to its convenience to be used with mobile devices. This is especially true for Internet users worldwide that use WiFi connections. There are many location based services that are available nowadays which uses Wireless Fidelity (WiFi) signal fingerprinting. A common example that is gaining popularity in this era would be Foursquare. In this work, the WiFi signal would be used to estimate the user or client’s location. Similar to GPS, fingerprinting method needs a floor plan to increase the accuracy of location estimation. Still, the factor of inconsistent WiFi signal makes the estimation defer at different time intervals. Given so, an adaptive method is needed to obtain the most accurate signal at all times. WiFi signals are heavily distorted by external factors such as physical objects, radio frequency interference, electrical interference, and environmental factors to name a few. Due to these factors, this work uses a method of reducing the signal noise and estimation using the Nearest Neighbour based on past activities of the signal to increase the signal accuracy up to more than 80%. The repository yet increases the accuracy by using Artificial Neural Network (ANN) pattern matching. The repository acts as the server cum support of the client side application decision. Numerous previous works has adapted the methods of collecting signal strengths in the repository over the years, but mostly were just static. In this work, proposed solutions on how the adaptive method is done to match the signal received to the data in the repository are highlighted. With the said approach, location estimation can be done more accurately. Adaptive update allows the latest location fingerprint to be stored in the repository. Furthermore, any redundant location fingerprints are removed and only the updated version of the fingerprint is stored in the repository. How the location estimation of the user can be predicted would be highlighted more in the proposed solution section. After some studies on previous works, it is found that the Artificial Neural Network is the most feasible method to deploy in updating the repository and making it adaptive. The Artificial Neural Network functions are to do the pattern matching of the WiFi signal to the existing data available in the repository.
Abstract: In this article, an adaptive least-squares mixed finite element method is studied for pseudo-parabolic integro-differential equations. The solutions of least-squares mixed weak formulation and mixed finite element are proved. A posteriori error estimator is constructed based on the least-squares functional and the posteriori errors are obtained.
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: 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: 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: In this paper, an estimation accuracy of multiple moving
talker tracking using a microphone array is improved. The tracking
can be achieved by the adaptive method in which two algorithms are integrated, namely, the PAST (Projection Approximation Subspace
Tracking) algorithm and the IPLS (Interior Point Least Square) algorithm. When either talker begins to speak again after a silent
period, an appropriate feasible region for an evaluation function of
the IPLS algorithm might not be set. Then, the tracking fails due to the incorrect updating. Therefore, if an increment of the number of
active talkers is detected, the feasible region must be reset. Then, a low cost realization is required for the high speed tracking and a high
accuracy realization is desired for the precise tracking. In this paper,
the directions roughly estimated using the delayed-sum-array method
are used for the resetting. Several results of experiments performed in
an actual room environment show the effectiveness of the proposed method.
Abstract: Subdivision surfaces were applied to the entire
meshes in order to produce smooth surfaces refinement from coarse
mesh. Several schemes had been introduced in this area to provide a
set of rules to converge smooth surfaces. However, to compute and
render all the vertices are really inconvenient in terms of memory
consumption and runtime during the subdivision process. It will lead
to a heavy computational load especially at a higher level of
subdivision. Adaptive subdivision is a method that subdivides only at
certain areas of the meshes while the rest were maintained less
polygons. Although adaptive subdivision occurs at the selected areas,
the quality of produced surfaces which is their smoothness can be
preserved similar as well as regular subdivision. Nevertheless,
adaptive subdivision process burdened from two causes; calculations
need to be done to define areas that are required to be subdivided and
to remove cracks created from the subdivision depth difference
between the selected and unselected areas. Unfortunately, the result
of adaptive subdivision when it reaches to the higher level of
subdivision, it still brings the problem with memory consumption.
This research brings to iterative process of adaptive subdivision to
improve the previous adaptive method that will reduce memory
consumption applied on triangular mesh. The result of this iterative
process was acceptable better in memory and appearance in order to
produce fewer polygons while it preserves smooth surfaces.
Abstract: The batch nature limits the standard kernel principal component analysis (KPCA) methods in numerous applications, especially for dynamic or large-scale data. In this paper, an efficient adaptive approach is presented for online extraction of the kernel principal components (KPC). The contribution of this paper may be divided into two parts. First, kernel covariance matrix is correctly updated to adapt to the changing characteristics of data. Second, KPC are recursively formulated to overcome the batch nature of standard KPCA.This formulation is derived from the recursive eigen-decomposition of kernel covariance matrix and indicates the KPC variation caused by the new data. The proposed method not only alleviates sub-optimality of the KPCA method for non-stationary data, but also maintains constant update speed and memory usage as the data-size increases. Experiments for simulation data and real applications demonstrate that our approach yields improvements in terms of both computational speed and approximation accuracy.
Abstract: In this paper we present an adaptive method for image
compression that is based on complexity level of the image. The
basic compressor/de-compressor structure of this method is a multilayer
perceptron artificial neural network. In adaptive approach
different Back-Propagation artificial neural networks are used as
compressor and de-compressor and this is done by dividing the
image into blocks, computing the complexity of each block and then
selecting one network for each block according to its complexity
value. Three complexity measure methods, called Entropy, Activity
and Pattern-based are used to determine the level of complexity in
image blocks and their ability in complexity estimation are evaluated
and compared. In training and evaluation, each image block is
assigned to a network based on its complexity value. Best-SNR is
another alternative in selecting compressor network for image blocks
in evolution phase which chooses one of the trained networks such
that results best SNR in compressing the input image block. In our
evaluations, best results are obtained when overlapping the blocks is
allowed and choosing the networks in compressor is based on the
Best-SNR. In this case, the results demonstrate superiority of this
method comparing with previous similar works and JPEG standard
coding.
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: With data centers, end-users can realize the pervasiveness of services that will be one day the cornerstone of our lives. However, data centers are often classified as computing systems that consume the most amounts of power. To circumvent such a problem, we propose a self-adaptive weighted sum methodology that jointly optimizes the performance and power consumption of any given data center. Compared to traditional methodologies for multi-objective optimization problems, the proposed self-adaptive weighted sum technique does not rely on a systematical change of weights during the optimization procedure. The proposed technique is compared with the greedy and LR heuristics for large-scale problems, and the optimal solution for small-scale problems implemented in LINDO. the experimental results revealed that the proposed selfadaptive weighted sum technique outperforms both of the heuristics and projects a competitive performance compared to the optimal solution.