Abstract: The ability of UML to handle the modeling process of complex industrial software applications has increased its popularity to the extent of becoming the de-facto language in serving the design purpose. Although, its rich graphical notation naturally oriented towards the object-oriented concept, facilitates the understandability, it hardly successes to report all domainspecific aspects in a satisfactory way. OCL, as the standard language for expressing additional constraints on UML models, has great potential to help improve expressiveness. Unfortunately, it suffers from a weak formalism due to its poor semantic resulting in many obstacles towards the build of tools support and thus its application in the industry field. For this reason, many researches were established to formalize OCL expressions using a more rigorous approach. Our contribution join this work in a complementary way since it focuses specifically on OCL predefined properties which constitute an important part in the construction of OCL expressions. Using formal methods, we mainly succeed in expressing rigorously OCL predefined functions.
Abstract: This paper is motivated by the aspect of uncertainty in
financial decision making, and how artificial intelligence and soft
computing, with its uncertainty reducing aspects can be used for
algorithmic trading applications that trade in high frequency.
This paper presents an optimized high frequency trading system that
has been combined with various moving averages to produce a hybrid
system that outperforms trading systems that rely solely on moving
averages. The paper optimizes an adaptive neuro-fuzzy inference
system that takes both the price and its moving average as input,
learns to predict price movements from training data consisting of
intraday data, dynamically switches between the best performing
moving averages, and performs decision making of when to buy or
sell a certain currency in high frequency.
Abstract: Interpretation of aerial images is an important task in
various applications. Image segmentation can be viewed as the essential
step for extracting information from aerial images. Among many
developed segmentation methods, the technique of clustering has been
extensively investigated and used. However, determining the number
of clusters in an image is inherently a difficult problem, especially
when a priori information on the aerial image is unavailable. This
study proposes a support vector machine approach for clustering
aerial images. Three cluster validity indices, distance-based index,
Davies-Bouldin index, and Xie-Beni index, are utilized as quantitative
measures of the quality of clustering results. Comparisons on the
effectiveness of these indices and various parameters settings on the
proposed methods are conducted. Experimental results are provided
to illustrate the feasibility of the proposed approach.
Abstract: Models are placed by modeling paradigm at the center of development process. These models are represented by languages, like UML the language standardized by the OMG which became necessary for development. Moreover the ontology engineering paradigm places ontologies at the center of development process; in this paradigm we find OWL the principal language for knowledge representation. Building ontologies from scratch is generally a difficult task. The bridging between UML and OWL appeared on several regards such as the classes and associations. In this paper, we have to profit from convergence between UML and OWL to propose an approach based on Meta-Modelling and Graph Grammars and registered in the MDA architecture for the automatic generation of OWL ontologies from UML class diagrams. The transformation is based on transformation rules; the level of abstraction in these rules is close to the application in order to have usable ontologies. We illustrate this approach by an example.
Abstract: In this paper, an analytical modeling is presentated to
describe the channel noise in GME SGT/CGT MOSFET, based on
explicit functions of MOSFETs geometry and biasing conditions for
all channel length down to deep submicron and is verified with the
experimental data. Results shows the impact of various parameters
such as gate bias, drain bias, channel length ,device diameter and gate
material work function difference on drain current noise spectral
density of the device reflecting its applicability for circuit design
applications.
Abstract: Property investment in the real estate industry has a
high risk due to the uncertainty factors that will affect the decisions
made and high cost. Analytic hierarchy process has existed for some
time in which referred to an expert-s opinion to measure the
uncertainty of the risk factors for the risk analysis. Therefore,
different level of experts- experiences will create different opinion
and lead to the conflict among the experts in the field. The objective
of this paper is to propose a new technique to measure the uncertainty
of the risk factors based on multidimensional data model and data
mining techniques as deterministic approach. The propose technique
consist of a basic framework which includes four modules: user,
technology, end-user access tools and applications. The property
investment risk analysis defines as a micro level analysis as the
features of the property will be considered in the analysis in this
paper.
Abstract: Results in one field necessarily give insight into the
others, and all have much potential for scientific and technological
application. The Hadamard-transform technique once been applied to
the spectrometry also has its use in the SNR Enhancement of OTDR.
In this report, a new set of code (Simplex-codes) is discussed and
where the addition gain of SNR come from is implied.
Abstract: Target tracking and localization are important applications
in wireless sensor networks. In these applications, sensor nodes
collectively monitor and track the movement of a target. They have
limited energy supplied by batteries, so energy efficiency is essential
for sensor networks. Most existing target tracking protocols need to
wake up sensors periodically to perform tracking. Some unnecessary
energy waste is thus introduced. In this paper, an energy efficient
protocol for target localization is proposed. In order to preserve
energy, the protocol fixes the number of sensors for target tracking,
but it retains the quality of target localization in an acceptable
level. By selecting a set of sensors for target localization, the other
sensors can sleep rather than periodically wake up to track the target.
Simulation results show that the proposed protocol saves a significant
amount of energy and also prolongs the network lifetime.
Abstract: The purpose of this paper is to investigate the adjust-
ment of solar shading devices in office buildings in two different
seasons by occupants, and its influence on the lighting control and
indoor illuminance levels. The results show that occupants take
inappropriate measures both in reducing solar radiation in summer
and in admitting solar gains in winter, resulting in an increase in
lighting energy and a reduction in indoor illuminance. Therefore,
movable shading devices, controlled automatically, are suitable for
building applications to reduce energy consumption.
Abstract: In this paper, we present a comparative study between two computer vision systems for objects recognition and tracking, these algorithms describe two different approach based on regions constituted by a set of pixels which parameterized objects in shot sequences. For the image segmentation and objects detection, the FCM technique is used, the overlapping between cluster's distribution is minimized by the use of suitable color space (other that the RGB one). The first technique takes into account a priori probabilities governing the computation of various clusters to track objects. A Parzen kernel method is described and allows identifying the players in each frame, we also show the importance of standard deviation value research of the Gaussian probability density function. Region matching is carried out by an algorithm that operates on the Mahalanobis distance between region descriptors in two subsequent frames and uses singular value decomposition to compute a set of correspondences satisfying both the principle of proximity and the principle of exclusion.
Abstract: The performance and the plasma created by a pulsed
magnetoplasmadynamic thruster for small satellite application is
studied to understand better the ablation and plasma propagation
processes occurring during the short-time discharge. The results can
be applied to improve the quality of the thruster in terms of efficiency,
and to tune the propulsion system to the needs required by the satellite
mission. Therefore, plasma measurements with a high-speed camera
and induction probes, and performance measurements of mass bit
and impulse bit were conducted. Values for current sheet propagation
speed, mean exhaust velocity and thrust efficiency were derived from
these experimental data. A maximum in current sheet propagation
was found by the high-speed camera measurements for a medium
energy input and confirmed by the induction probes. A quasilinear
tendency between the mass bit and the energy input, the current
action integral respectively, was found, as well as a linear tendency
between the created impulse and the discharge energy. The highest
mean exhaust velocity and thrust efficiency was found for the highest
energy input.
Abstract: Short Message Service (SMS) has grown in
popularity over the years and it has become a common way of
communication, it is a service provided through General System
for Mobile Communications (GSM) that allows users to send text
messages to others.
SMS is usually used to transport unclassified information, but
with the rise of mobile commerce it has become a popular tool for
transmitting sensitive information between the business and its
clients. By default SMS does not guarantee confidentiality and
integrity to the message content.
In the mobile communication systems, security (encryption)
offered by the network operator only applies on the wireless link.
Data delivered through the mobile core network may not be
protected. Existing end-to-end security mechanisms are provided
at application level and typically based on public key
cryptosystem.
The main concern in a public-key setting is the authenticity of
the public key; this issue can be resolved by identity-based (IDbased)
cryptography where the public key of a user can be derived
from public information that uniquely identifies the user.
This paper presents an encryption mechanism based on the IDbased
scheme using Elliptic curves to provide end-to-end security
for SMS. This mechanism has been implemented over the standard
SMS network architecture and the encryption overhead has been
estimated and compared with RSA scheme. This study indicates
that the ID-based mechanism has advantages over the RSA
mechanism in key distribution and scalability of increasing
security level for mobile service.
Abstract: Quantum computation using qubits made of two component Bose-Einstein condensates (BECs) is analyzed. We construct a general framework for quantum algorithms to be executed using the collective states of the BECs. The use of BECs allows for an increase of energy scales via bosonic enhancement, resulting in two qubit gate operations that can be performed at a time reduced by a factor of N, where N is the number of bosons per qubit. We illustrate the scheme by an application to Deutsch-s and Grover-s algorithms, and discuss possible experimental implementations. Decoherence effects are analyzed under both general conditions and for the experimental implementation proposed.
Abstract: In order to evaluation the effects of soil organic
matter and biofertilizer on chickpea quality and biological
nitrogen fixation, field experiments were carried out in 2007
and 2008 growing seasons. In this research the effects of
different strategies for soil fertilization were investigated on
grain yield and yield component, minerals, organic compounds
and cooking time of chickpea. Experimental units were
arranged in split-split plots based on randomized complete
blocks with three replications. Main plots consisted of (G1):
establishing a mixed vegetation of Vicia panunica and
Hordeum vulgare and (G2): control, as green manure levels.
Also, five strategies for obtaining the base fertilizer
requirement including (N1): 20 t.ha-1 farmyard manure; (N2):
10 t.ha-1 compost; (N3): 75 kg.ha-1 triple super phosphate;
(N4): 10 t.ha-1 farmyard manure + 5 t.ha-1 compost and (N5):
10 t.ha-1 farmyard manure + 5 t.ha-1 compost + 50 kg.ha-1
triple super phosphate were considered in sub plots.
Furthermoree four levels of biofertilizers consisted of (B1):
Bacillus lentus + Pseudomonas putida; (B2): Trichoderma
harzianum; (B3): Bacillus lentus + Pseudomonas putida +
Trichoderma harzianum; and (B4): control (without
biofertilizers) were arranged in sub-sub plots. Results showed
that integrating biofertilizers (B3) and green manure (G1)
produced the highest grain yield. The highest amounts of yield
were obtained in G1×N5 interaction. Comparison of all 2-way
and 3-way interactions showed that G1N5B3 was determined
as the superior treatment. Significant increasing of N, P2O5,
K2O, Fe and Mg content in leaves and grains emphasized on
superiority of mentioned treatment because each one of these
nutrients has an approved role in chlorophyll synthesis and
photosynthesis abilities of the crops. The combined application
of compost, farmyard manure and chemical phosphorus (N5)
in addition to having the highest yield, had the best grain
quality due to high protein, starch and total sugar contents, low
crude fiber and reduced cooking time.
Abstract: This paper presents application artificial intelligent (AI) techniques, namely artificial neural network (ANN), adaptive neuro fuzzy interface system (ANFIS), to estimate the real power transfer between generators and loads. Since these AI techniques adopt supervised learning, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to estimate the power transfer using AI techniques. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of both AI methods compared to that of the MNE method. The mean squared error of the estimate of ANN and ANFIS power transfer allocation methods are 1.19E-05 and 2.97E-05, respectively. Furthermore, when compared to MNE method, ANN and ANFIS methods computes generator contribution to loads within 20.99 and 39.37msec respectively whereas the MNE method took 360msec for the calculation of same real power transfer allocation.
Abstract: Predicting short term wind speed is essential in order
to prevent systems in-action from the effects of strong winds. It also
helps in using wind energy as an alternative source of energy, mainly
for Electrical power generation. Wind speed prediction has
applications in Military and civilian fields for air traffic control,
rocket launch, ship navigation etc. The wind speed in near future
depends on the values of other meteorological variables, such as
atmospheric pressure, moisture content, humidity, rainfall etc. The
values of these parameters are obtained from a nearest weather
station and are used to train various forms of neural networks. The
trained model of neural networks is validated using a similar set of
data. The model is then used to predict the wind speed, using the
same meteorological information. This paper reports an Artificial
Neural Network model for short term wind speed prediction, which
uses back propagation algorithm.
Abstract: Spatial trends are one of the valuable patterns in geo
databases. They play an important role in data analysis and
knowledge discovery from spatial data. A spatial trend is a regular
change of one or more non spatial attributes when spatially moving
away from a start object. Spatial trend detection is a graph search
problem therefore heuristic methods can be good solution. Artificial
immune system (AIS) is a special method for searching and
optimizing. AIS is a novel evolutionary paradigm inspired by the
biological immune system. The models based on immune system
principles, such as the clonal selection theory, the immune network
model or the negative selection algorithm, have been finding
increasing applications in fields of science and engineering.
In this paper, we develop a novel immunological algorithm based
on clonal selection algorithm (CSA) for spatial trend detection. We
are created neighborhood graph and neighborhood path, then select
spatial trends that their affinity is high for antibody. In an
evolutionary process with artificial immune algorithm, affinity of
low trends is increased with mutation until stop condition is satisfied.
Abstract: This study determines the effect of naked and heparinbased
super-paramagnetic iron oxide nanoparticles on the human
cancer cell lines of A2780. Doxorubicin was used as the anticancer
drug, entrapped in the SPIO-NPs. This study aimed to decorate
nanoparticles with heparin, a molecular ligand for 'active' targeting
of cancerous cells and the application of modified-nanoparticles in
cancer treatment. The nanoparticles containing the anticancer drug
DOX were prepared by a solvent evaporation and emulsification
cross-linking method. The physicochemical properties of the
nanoparticles were characterized by various techniques, and uniform
nanoparticles with an average particle size of 110±15 nm with high
encapsulation efficiencies (EE) were obtained. Additionally, a
sustained release of DOX from the SPIO-NPs was successful.
Cytotoxicity tests showed that the SPIO-DOX-HP had higher cell
toxicity than the individual HP and confocal microscopy analysis
confirmed excellent cellular uptake efficiency. These results indicate
that HP based SPIO-NPs have potential uses as anticancer drug
carriers and also have an enhanced anticancer effect.
Abstract: In this paper, the application of neural networks to study the design of short-term load forecasting (STLF) Systems for Illam state located in west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model STLF systems was used. Our study based on MLP was trained and tested using three years (2004-2006) data. The results show that MLP network has the minimum forecasting error and can be considered as a good method to model the STLF systems.
Abstract: DNA shuffling is a powerful method used for in vitro
evolute molecules with specific functions and has application in areas
such as, for example, pharmaceutical, medical and agricultural
research. The success of such experiments is dependent on a variety
of parameters and conditions that, sometimes, can not be properly
pre-established. Here, two computational models predicting DNA
shuffling results is presented and their use and results are evaluated
against an empirical experiment. The in silico and in vitro results
show agreement indicating the importance of these two models and
motivating the study and development of new models.