Abstract: Prediction of highly non linear behavior of suspended
sediment flow in rivers has prime importance in the field of water
resources engineering. In this study the predictive performance of
two Artificial Neural Networks (ANNs) namely, the Radial Basis
Function (RBF) Network and the Multi Layer Feed Forward (MLFF)
Network have been compared. Time series data of daily suspended
sediment discharge and water discharge at Pari River was used for
training and testing the networks. A number of statistical parameters
i.e. root mean square error (RMSE), mean absolute error (MAE),
coefficient of efficiency (CE) and coefficient of determination (R2)
were used for performance evaluation of the models. Both the models
produced satisfactory results and showed a good agreement between
the predicted and observed data. The RBF network model provided
slightly better results than the MLFF network model in predicting
suspended sediment discharge.
Abstract: Software security testing is an important means to ensure software security and trustiness. This paper first mainly discusses the definition and classification of software security testing, and investigates methods and tools of software security testing widely. Then it analyzes and concludes the advantages and disadvantages of various methods and the scope of application, presents a taxonomy of security testing tools. Finally, the paper points out future focus and development directions of software security testing technology.
Abstract: The development of information and communication
technology, the increased use of the internet, as well as the effects of
the recession within the last years, have lead to the increased use of
cloud computing based solutions, also called on-demand solutions.
These solutions offer a large number of benefits to organizations as
well as challenges and risks, mainly determined by data visualization
in different geographic locations on the internet. As far as the specific
risks of cloud environment are concerned, data security is still
considered a peak barrier in adopting cloud computing. The present
study offers an approach upon ensuring the security of cloud data,
oriented towards the whole data life cycle. The final part of the study
focuses on the assessment of data security in the cloud, this
representing the bases in determining the potential losses and the
premise for subsequent improvements and continuous learning.
Abstract: This paper describes the authorization system
architecture for Pervasive Grid environment. It discusses the
characteristics of classical authorization system and requirements of
the authorization system in pervasive grid environment as well.
Based on our analysis of current systems and taking into account the
main requirements of such pervasive environment, we propose new
authorization system architecture as an extension of the existing grid
authorization mechanisms. This architecture not only supports user
attributes but also context attributes which act as a key concept for
context-awareness thought. The architecture allows authorization of
users dynamically when there are changes in the pervasive grid
environment. For this, we opt for hybrid authorization method that
integrates push and pull mechanisms to combine the existing grid
authorization attributes with dynamic context assertions. We will
investigate the proposed architecture using a real testing environment
that includes heterogeneous pervasive grid infrastructures mapped
over multiple virtual organizations. Various scenarios are described
in the last section of the article to strengthen the proposed mechanism
with different facilities for the authorization procedure.
Abstract: In this paper, an ultrasonic technique is proposed to
predict oil content in a fresh palm fruit. This is accomplished by
measuring the attenuation based on ultrasonic transmission mode.
Several palm fruit samples with known oil content by Soxhlet
extraction (ISO9001:2008) were tested with our ultrasonic
measurement. Amplitude attenuation data results for all palm samples
were collected. The Feedforward Neural Networks (FNNs) are
applied to predict the oil content for the samples. The Root Mean
Square Error (RMSE) and Mean Absolute Error (MAE) of the FNN
model for predicting oil content percentage are 7.6186 and 5.2287
with the correlation coefficient (R) of 0.9193.
Abstract: Security risk models have been successful in estimating the likelihood of attack for simple security threats. However, modeling complex system and their security risk is even a challenge. Many methods have been proposed to face this problem. Often difficult to manipulate, and not enough all-embracing they are not as famous as they should with administrators and deciders. We propose in this paper a new tool to model big systems on purpose. The software, takes into account attack threats and security strength.
Abstract: The counting process of cell colonies is always a long
and laborious process that is dependent on the judgment and ability
of the operator. The judgment of the operator in counting can vary in
relation to fatigue. Moreover, since this activity is time consuming it
can limit the usable number of dishes for each experiment. For these
purposes, it is necessary that an automatic system of cell colony
counting is used. This article introduces a new automatic system of
counting based on the elaboration of the digital images of cellular
colonies grown on petri dishes. This system is mainly based on the
algorithms of region-growing for the recognition of the regions of
interest (ROI) in the image and a Sanger neural net for the
characterization of such regions. The better final classification is
supplied from a Feed-Forward Neural Net (FF-NN) and confronted
with the K-Nearest Neighbour (K-NN) and a Linear Discriminative
Function (LDF). The preliminary results are shown.
Abstract: A study was conducted in greenhouse environment to
determine the response of five tissue-cultured date palm cultivars, Al-
Ahamad, Nabusaif, Barhee, Khalas, and Kasab to irrigation water
salinity of 1.6, 5, 10, or 20 dS/ m. The salinity level of 1.6dS/m, was
used as a control. The effects of high salinity on plant survival were
manifested at 360 days after planting (DAP) onwards. Three
cultivars, Khalas, Kasab and Barhee were able to tolerate 10 dS/m
salinity level at 24 months after the start of study. Khalas tolerated
the highest salinity level of 20 dS/ m and 'Nabusaif' was found to be the
least tolerant cv. The average heights of palms and the number of
fronds were decreased with increasing salinity levels as time
progressed.
Abstract: This paper focuses on a critical component of the situational awareness (SA), the control of autonomous vertical flight for tactical unmanned aerial vehicle (TUAV). With the SA strategy, we proposed a two stage flight control procedure using two autonomous control subsystems to address the dynamics variation and performance requirement difference in initial and final stages of flight trajectory for an unmanned helicopter model with coaxial rotor and ducted fan configuration. This control strategy for chosen model of TUAV has been verified by simulation of hovering maneuvers using software package Simulink and demonstrated good performance for fast stabilization of engines in hovering, consequently, fast SA with economy in energy can be asserted during search-and-rescue operations.
Abstract: Software project effort estimation is frequently seen
as complex and expensive for individual software engineers.
Software production is in a crisis. It suffers from excessive costs.
Software production is often out of control. It has been suggested that
software production is out of control because we do not measure.
You cannot control what you cannot measure. During last decade, a
number of researches on cost estimation have been conducted. The
metric-set selection has a vital role in software cost estimation
studies; its importance has been ignored especially in neural network
based studies. In this study we have explored the reasons of those
disappointing results and implemented different neural network
models using augmented new metrics. The results obtained are
compared with previous studies using traditional metrics. To be able
to make comparisons, two types of data have been used. The first
part of the data is taken from the Constructive Cost Model
(COCOMO'81) which is commonly used in previous studies and the
second part is collected according to new metrics in a leading
international company in Turkey. The accuracy of the selected
metrics and the data samples are verified using statistical techniques.
The model presented here is based on Multi-Layer Perceptron
(MLP). Another difficulty associated with the cost estimation studies
is the fact that the data collection requires time and care. To make a
more thorough use of the samples collected, k-fold, cross validation
method is also implemented. It is concluded that, as long as an
accurate and quantifiable set of metrics are defined and measured
correctly, neural networks can be applied in software cost estimation
studies with success
Abstract: On the basis of the linearized Phillips-Herffron model of a single-machine power system, a novel method for designing unified power flow controller (UPFC) based output feedback controller is presented. The design problem of output feedback controller for UPFC is formulated as an optimization problem according to with the time domain-based objective function which is solved by iteration particle swarm optimization (IPSO) that has a strong ability to find the most optimistic results. To ensure the robustness of the proposed damping controller, the design process takes into account a wide range of operating conditions and system configurations. The simulation results prove the effectiveness and robustness of the proposed method in terms of a high performance power system. The simulation study shows that the designed controller by Iteration PSO performs better than Classical PSO in finding the solution.
Abstract: The security of their network remains the priorities of almost all companies. Existing security systems have shown their limit; thus a new type of security systems was born: honeypots. Honeypots are defined as programs or intended servers which have to attract pirates to study theirs behaviours. It is in this context that the leurre.com project of gathering about twenty platforms was born. This article aims to specify a model of honeypots attack. Our model describes, on a given platform, the evolution of attacks according to theirs hours. Afterward, we show the most attacked services by the studies of attacks on the various ports. It is advisable to note that this article was elaborated within the framework of the research projects on honeyspots within the LABTIC (Laboratory of Information Technologies and Communication).
Abstract: An approach to develop the FPGA of a flexible key
RSA encryption engine that can be used as a standard device in the
secured communication system is presented. The VHDL modeling of
this RSA encryption engine has the unique characteristics of
supporting multiple key sizes, thus can easily be fit into the systems
that require different levels of security. A simple nested loop addition
and subtraction have been used in order to implement the RSA
operation. This has made the processing time faster and used
comparatively smaller amount of space in the FPGA. The hardware
design is targeted on Altera STRATIX II device and determined that
the flexible key RSA encryption engine can be best suited in the
device named EP2S30F484C3. The RSA encryption implementation
has made use of 13,779 units of logic elements and achieved a clock
frequency of 17.77MHz. It has been verified that this RSA
encryption engine can perform 32-bit, 256-bit and 1024-bit
encryption operation in less than 41.585us, 531.515us and 790.61us
respectively.
Abstract: In this paper we present a novel approach for face image coding. The proposed method makes a use of the features of video encoders like motion prediction. At first encoder selects appropriate prototype from the database and warps it according to features of encoding face. Warped prototype is placed as first I frame. Encoding face is placed as second frame as P frame type. Information about features positions, color change, selected prototype and data flow of P frame will be sent to decoder. The condition is both encoder and decoder own the same database of prototypes. We have run experiment with H.264 video encoder and obtained results were compared to results achieved by JPEG and JPEG2000. Obtained results show that our approach is able to achieve 3 times lower bitrate and two times higher PSNR in comparison with JPEG. According to comparison with JPEG2000 the bitrate was very similar, but subjective quality achieved by proposed method is better.
Abstract: Robot manipulators are highly coupled nonlinear
systems, therefore real system and mathematical model of dynamics
used for control system design are not same. Hence, fine-tuning of
controller is always needed. For better tuning fast simulation speed
is desired. Since, Matlab incorporates LAPACK to increase the speed
and complexity of matrix computation, dynamics, forward and
inverse kinematics of PUMA 560 is modeled on Matlab/Simulink in
such a way that all operations are matrix based which give very less
simulation time. This paper compares PID parameter tuning using
Genetic Algorithm, Simulated Annealing, Generalized Pattern Search
(GPS) and Hybrid Search techniques. Controller performances for all
these methods are compared in terms of joint space ITSE and
cartesian space ISE for tracking circular and butterfly trajectories.
Disturbance signal is added to check robustness of controller. GAGPS
hybrid search technique is showing best results for tuning PID
controller parameters in terms of ITSE and robustness.
Abstract: The incorporation of renewable energy sources for the sustainable electricity production is undertaking a more prominent role in electric power systems. Thus, it will be an indispensable incident that the characteristics of future power networks, their prospective stability for instance, get influenced by the imposed features of sustainable energy sources. One of the distinctive attributes of the sustainable energy sources is exhibiting the stochastic behavior. This paper investigates the impacts of this stochastic behavior on the small disturbance rotor angle stability in the upcoming electric power networks. Considering the various types of renewable energy sources and the vast variety of system configurations, the sensitivity analysis can be an efficient breakthrough towards generalizing the effects of new energy sources on the concept of stability. In this paper, the definition of small disturbance angle stability for future power systems and the iterative-stochastic way of its analysis are presented. Also, the effects of system parameters on this type of stability are described by performing a sensitivity analysis for an electric power test system.
Abstract: The induction hardening machines are utilized in
the industries which modify machine parts and tools needed to
achieve high ware resistance. This paper describes the model of
induction heating process design of inverter circuit and the results
of induction surface hardening of heating coil. In the design of
heating coil, the shape and the turn numbers of the coil are very
important design factors because they decide the overall operating
performance of induction heater including resonant frequency, Q
factor, efficiency and power factor. The performance will be tested
by experiments in some cases high frequency induction hardening
machine.
Abstract: The purposes of the paper are to know and improve
the legal awareness of Surakarta local government officer to Law
No.12/2006 on Citizenship in vanishing civic discrimination to
Chinese Indonesians. Some issues of the Chinese Indonesian are the
definitions of Indonesian citizens and native Indonesian people or
“warga negara Indonesia (WNI) asli" and their obligation to show
Indonesian Citizenship Certificate (SBKRI) in processing civil
documents. Legal awareness of Surakarta local government officer
can be categorized as “legal knowledge" only. They know the laws
but they do not implement it yet. Nevertheless, at least this research
has given the new awareness in citizenship law for the officers.
Abstract: This paper presents the development of a Bayesian
belief network classifier for prediction of graft status and survival
period in renal transplantation using the patient profile information
prior to the transplantation. The objective was to explore feasibility
of developing a decision making tool for identifying the most suitable
recipient among the candidate pool members. The dataset was
compiled from the University of Toledo Medical Center Hospital
patients as reported to the United Network Organ Sharing, and had
1228 patient records for the period covering 1987 through 2009. The
Bayes net classifiers were developed using the Weka machine
learning software workbench. Two separate classifiers were induced
from the data set, one to predict the status of the graft as either failed
or living, and a second classifier to predict the graft survival period.
The classifier for graft status prediction performed very well with a
prediction accuracy of 97.8% and true positive values of 0.967 and
0.988 for the living and failed classes, respectively. The second
classifier to predict the graft survival period yielded a prediction
accuracy of 68.2% and a true positive rate of 0.85 for the class
representing those instances with kidneys failing during the first year
following transplantation. Simulation results indicated that it is
feasible to develop a successful Bayesian belief network classifier for
prediction of graft status, but not the graft survival period, using the
information in UNOS database.
Abstract: Different types of Islamic debts have been
increasingly utilized as preferred means of debt funding by
Malaysian private firms in recent years. This study examines the
impact of Islamic debts announcement on private firms- stock
returns. Our sample includes forty five listed companies on Bursa
Malaysia involved in issuing of Islamic debts during 2005 to 2008.
The abnormal returns and cumulative average abnormal returns are
calculated and tested using standard event study methodology. The
results show that a significant, negative abnormal return occurs one
day before announcement date. This negative abnormal return is
representing market participant-s adverse attitude toward Islamic
private debt announcement during the research period.