Abstract: The policies governing the business of any
organization are well reflected in her business rules. The business
rules are implemented by data validation techniques, coded during
the software development process. Any change in business
policies results in change in the code written for data validation
used to enforce the business policies. Implementing the change in
business rules without changing the code is the objective of this
paper. The proposed approach enables users to create rule sets at
run time once the software has been developed. The newly defined
rule sets by end users are associated with the data variables for
which the validation is required. The proposed approach facilitates
the users to define business rules using all the comparison
operators and Boolean operators. Multithreading is used to
validate the data entered by end user against the business rules
applied. The evaluation of the data is performed by a newly
created thread using an enhanced form of the RPN (Reverse Polish
Notation) algorithm.
Abstract: This study proposes a novel recommender system to
provide the advertisements of context-aware services. Our proposed
model is designed to apply a modified collaborative filtering (CF)
algorithm with regard to the several dimensions for the personalization
of mobile devices – location, time and the user-s needs type. In
particular, we employ a classification rule to understand user-s needs
type using a decision tree algorithm. In addition, we collect primary
data from the mobile phone users and apply them to the proposed
model to validate its effectiveness. Experimental results show that the
proposed system makes more accurate and satisfactory advertisements
than comparative systems.
Abstract: The present paper concerns with the influence of fiber
packing on the transverse plastic properties of metal matrix
composites. A micromechanical modeling procedure is used to
predict the effective mechanical properties of composite materials at
large tensile and compressive deformations. Microstructure is
represented by a repeating unit cell (RUC). Two fiber arrays are
considered including ideal square fiber packing and random fiber
packing defined by random sequential algorithm. The
micromechanical modeling procedure is implemented for
graphite/aluminum metal matrix composite in which the
reinforcement behaves as elastic, isotropic solids and the matrix is
modeled as an isotropic elastic-plastic solid following the von Mises
criterion with isotropic hardening and the Ramberg-Osgood
relationship between equivalent true stress and logarithmic strain.
The deformation is increased to a considerable value to evaluate both
elastic and plastic behaviors of metal matrix composites. The yields
strength and true elastic-plastic stress are determined for
graphite/aluminum composites.
Abstract: IEEE 802.15.4a impulse radio-time hopping ultra wide
band (IR-TH UWB) physical layer, due to small duty cycle and very
short pulse widths is robust against multipath propagation. However,
scattering and reflections with the large number of obstacles in indoor
channel environments, give rise to dense multipath fading. It imposes
serious problem to optimum Rake receiver architectures, for which
very large number of fingers are needed. Presence of strong noise
also affects the reception of fine pulses having extremely low power
spectral density. A robust SRake receiver for IEEE 802.15.4a IRTH
UWB in dense multipath and additive white Gaussian noise
(AWGN) is proposed to efficiently recover the weak signals with
much reduced complexity. It adaptively increases the signal to noise
(SNR) by decreasing noise through a recursive least square (RLS)
algorithm. For simulation, dense multipath environment of IEEE
802.15.4a industrial non line of sight (NLOS) is employed. The power
delay profile (PDF) and the cumulative distribution function (CDF)
for the respective channel environment are found. Moreover, the error
performance of the proposed architecture is evaluated in comparison
with conventional SRake and AWGN correlation receivers. The
simulation results indicate a substantial performance improvement
with very less number of Rake fingers.
Abstract: Scheduling algorithm is a key technology in satellite
switching system with input-buffer. In this paper, a new scheduling
algorithm and its realization are proposed. Based on Crossbar
switching fabric, the algorithm adopts serial scheduling strategy and
adjusts the output port arbitrating strategy for the better equity of every
port. Consequently, it increases the matching probability. The
algorithm can greatly reduce the scheduling delay and cell loss rate.
The analysis and simulation results by OPNET show that the proposed
algorithm has the better performance than others in average delay and
cell loss rate, and has the equivalent complexity. On the basis of these
results, the hardware realization and simulation based on FPGA are
completed, which validate the feasibility of the new scheduling
algorithm.
Abstract: In this paper, we propose a novel fast search algorithm for short MPEG video clips from video database. This algorithm is based on the adjacent pixel intensity difference quantization (APIDQ) algorithm, which had been reliably applied to human face recognition previously. An APIDQ histogram is utilized as the feature vector of the frame image. Instead of fully decompressed video frames, partially decoded data, namely DC images are utilized. Combined with active search [4], a temporal pruning algorithm, fast and robust video search can be realized. The proposed search algorithm has been evaluated by 6 hours of video to search for given 200 MPEG video clips which each length is 15 seconds. Experimental results show the proposed algorithm can detect the similar video clip in merely 80ms, and Equal Error Rate (ERR) of 3 % is achieved, which is more accurately and robust than conventional fast video search algorithm.
Abstract: This paper presents a modified version of the
maximum urgency first scheduling algorithm. The maximum
urgency algorithm combines the advantages of fixed and dynamic
scheduling to provide the dynamically changing systems with
flexible scheduling. This algorithm, however, has a major
shortcoming due to its scheduling mechanism which may cause a
critical task to fail. The modified maximum urgency first scheduling
algorithm resolves the mentioned problem. In this paper, we propose
two possible implementations for this algorithm by using either
earliest deadline first or modified least laxity first algorithms for
calculating the dynamic priorities. These two approaches are
compared together by simulating the two algorithms. The earliest
deadline first algorithm as the preferred implementation is then
recommended. Afterwards, we make a comparison between our
proposed algorithm and maximum urgency first algorithm using
simulation and results are presented. It is shown that modified
maximum urgency first is superior to maximum urgency first, since it
usually has less task preemption and hence, less related overhead. It
also leads to less failed non-critical tasks in overloaded situations.
Abstract: Based on the classical algorithm LSQR for solving (unconstrained) LS problem, an iterative method is proposed for the least-squares like-minimum-norm symmetric solution of AXB+CYD=E. As the application of this algorithm, an iterative method for the least-squares like-minimum-norm biymmetric solution of AXB=E is also obtained. Numerical results are reported that show the efficiency of the proposed methods.
Abstract: Characteristics of ad hoc networks and even their existence depend on the nodes forming them. Thus, services and applications designed for ad hoc networks should adapt to this dynamic and distributed environment. In particular, multicast algorithms having reliability and scalability requirements should abstain from centralized approaches. We aspire to define a reliable and scalable multicast protocol for ad hoc networks. Our target is to utilize epidemic techniques for this purpose. In this paper, we present a brief survey of epidemic algorithms for reliable multicasting in ad hoc networks, and describe formulations and analytical results for simple epidemics. Then, P2P anti-entropy algorithm for content distribution and our prototype simulation model are described together with our initial results demonstrating the behavior of the algorithm.
Abstract: Biological data has several characteristics that strongly differentiate it from typical business data. It is much more complex, usually large in size, and continuously changes. Until recently business data has been the main target for discovering trends, patterns or future expectations. However, with the recent rise in biotechnology, the powerful technology that was used for analyzing business data is now being applied to biological data. With the advanced technology at hand, the main trend in biological research is rapidly changing from structural DNA analysis to understanding cellular functions of the DNA sequences. DNA chips are now being used to perform experiments and DNA analysis processes are being used by researchers. Clustering is one of the important processes used for grouping together similar entities. There are many clustering algorithms such as hierarchical clustering, self-organizing maps, K-means clustering and so on. In this paper, we propose a clustering algorithm that imitates the ecosystem taking into account the features of biological data. We implemented the system using an Ant-Colony clustering algorithm. The system decides the number of clusters automatically. The system processes the input biological data, runs the Ant-Colony algorithm, draws the Topic Map, assigns clusters to the genes and displays the output. We tested the algorithm with a test data of 100 to1000 genes and 24 samples and show promising results for applying this algorithm to clustering DNA chip data.
Abstract: The capturing of gel electrophoresis image represents
the output of a DNA computing algorithm. Before this image is being
captured, DNA computing involves parallel overlap assembly (POA)
and polymerase chain reaction (PCR) that is the main of this
computing algorithm. However, the design of the DNA
oligonucleotides to represent a problem is quite complicated and is
prone to errors. In order to reduce these errors during the design stage
before the actual in-vitro experiment is carried out; a simulation
software capable of simulating the POA and PCR processes is
developed. This simulation software capability is unlimited where
problem of any size and complexity can be simulated, thus saving
cost due to possible errors during the design process. Information
regarding the DNA sequence during the computing process as well as
the computing output can be extracted at the same time using the
simulation software.
Abstract: Recently, distributed generation technologies have received much attention for the potential energy savings and reliability assurances that might be achieved as a result of their widespread adoption. Fueling the attention have been the possibilities of international agreements to reduce greenhouse gas emissions, electricity sector restructuring, high power reliability requirements for certain activities, and concern about easing transmission and distribution capacity bottlenecks and congestion. So it is necessary that impact of these kinds of generators on distribution feeder reconfiguration would be investigated. This paper presents an approach for distribution reconfiguration considering Distributed Generators (DGs). The objective function is summation of electrical power losses A Tabu search optimization is used to solve the optimal operation problem. The approach is tested on a real distribution feeder.
Abstract: In this paper we consider a nonlinear feedback control
called augmented automatic choosing control (AACC) using the
gradient optimization automatic choosing functions for nonlinear
systems. Constant terms which arise from sectionwise linearization
of a given nonlinear system are treated as coefficients of a stable
zero dynamics. Parameters included in the control are suboptimally
selected by expanding a stable region in the sense of Lyapunov
with the aid of the genetic algorithm. This approach is applied to
a field excitation control problem of power system to demonstrate
the splendidness of the AACC. Simulation results show that the new
controller can improve performance remarkably well.
Abstract: Because of excellent properties, people has paid more
attention to SPIHI algorithm, which is based on the traditional wavelet
transformation theory, but it also has its shortcomings. Combined the
progress in the present wavelet domain and the human's visual
characteristics, we propose an improved algorithm based on human
visual characteristics of SPIHT in the base of analysis of SPIHI
algorithm. The experiment indicated that the coding speed and quality
has been enhanced well compared to the original SPIHT algorithm,
moreover improved the quality of the transmission cut off.
Abstract: Drilling is the most common machining operation and it forms the highest machining cost in many manufacturing activities including automotive engine production. The outcome of this operation depends upon many factors including utilization of proper cutting tool geometry, cutting tool material and the type of coating used to improve hardness and resistance to wear, and also cutting parameters. With the availability of a large array of tool geometries, materials and coatings, is has become a challenging task to select the best tool and cutting parameters that would result in the lowest machining cost or highest profit rate. This paper describes an algorithm developed to help achieve good performances in drilling operations by automatically determination of proper cutting tools and cutting parameters. It also helps determine machining sequences resulting in minimum tool changes that would eventually reduce machining time and cost where multiple tools are used.
Abstract: In this paper the performance of unified power flow
controller is investigated in controlling the flow of po wer over the
transmission line. Voltage sources model is utilized to study the
behaviour of the UPFC in regulating the active, reactive power and
voltage profile. This model is incorporated in Newton Raphson
algorithm for load flow studies. Simultaneous method is employed
in which equations of UPFC and the power balance equations of
network are combined in to one set of non-linear algebraic equations.
It is solved according to the Newton raphson algorithm. Case studies
are carried on standard 5 bus network. Simulation is done in Matlab.
The result of network with and without using UPFC are compared in
terms of active and reactive power flows in the line and active and
reactive power flows at the bus to analyze the performance of UPFC.
Abstract: Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recorded from the C3 and C4 locations. Principle features extracted through principle component analysis of the segmented EEG are analyzed using two novel classification algorithms using Elman recurrent neural network and functional link neural network. Performance of both classifiers is evaluated using a particle swarm optimization training algorithm; results are also compared with the conventional back propagation training algorithm. EEG motor imagery recorded from two subjects is used in the offline analysis. From overall classification performance it is observed that the BP algorithm has higher average classification of 93.5%, while the PSO algorithm has better training time and maximum classification. The proposed methods promises to provide a useful alternative general procedure for motor imagery classification
Abstract: A parallel block method based on Backward
Differentiation Formulas (BDF) is developed for the parallel solution
of stiff Ordinary Differential Equations (ODEs). Most common
methods for solving stiff systems of ODEs are based on implicit
formulae and solved using Newton iteration which requires repeated
solution of systems of linear equations with coefficient matrix, I -
hβJ . Here, J is the Jacobian matrix of the problem. In this paper,
the matrix operations is paralleled in order to reduce the cost of the
iterations. Numerical results are given to compare the speedup and
efficiency of parallel algorithm and that of sequential algorithm.
Abstract: This paper aims to develop a NOx emission model of
an acid gas incinerator using Nelder-Mead least squares support
vector regression (LS-SVR). Malaysia DOE is actively imposing the
Clean Air Regulation to mandate the installation of analytical
instrumentation known as Continuous Emission Monitoring System
(CEMS) to report emission level online to DOE . As a hardware
based analyzer, CEMS is expensive, maintenance intensive and often
unreliable. Therefore, software predictive technique is often
preferred and considered as a feasible alternative to replace the
CEMS for regulatory compliance. The LS-SVR model is built based
on the emissions from an acid gas incinerator that operates in a LNG
Complex. Simulated Annealing (SA) is first used to determine the
initial hyperparameters which are then further optimized based on the
performance of the model using Nelder-Mead simplex algorithm.
The LS-SVR model is shown to outperform a benchmark model
based on backpropagation neural networks (BPNN) in both training
and testing data.
Abstract: Clustering is a very well known technique in data mining. One of the most widely used clustering techniques is the k-means algorithm. Solutions obtained from this technique are dependent on the initialization of cluster centers. In this article we propose a new algorithm to initialize the clusters. The proposed algorithm is based on finding a set of medians extracted from a dimension with maximum variance. The algorithm has been applied to different data sets and good results are obtained.