Anbar Journal of Engineering Sciences

In this research a simply supported beam is used as a master structure with unknown number of
attachments (fuzzy substructure) which is modeled as a system of 1-DOF attachments. Two types
of attachments models were studied, namely 1-DOF mass attachment model and 1-DOF massspring attachment model. It is shown that the effect of attachments on the master structure natural
frequencies when modeled as (mass-spring substructure) is larger than that when modeled as
(mass substructure) for the same attachment mass. Engineering Statistics and normal distribution
were used to find the values of the attachments to be added to the simply supported beam to
improve the dynamical properties of the master structure and to find the best distribution of the
attachment. The results also show that the distribution of the additional substructure can produce
a great change in the natural frequencies so that the proposed statistical approach can be used to
find the best distribution of attachments and number, value and location of the additional
substructure

This paper specifies a proposed improvement model of Data Encryption Standard (DES)
which may be used to protect sensitive data. Protection of data during transmission may be
necessary to maintain the confidentiality and integrity of the transformation represented by
data. Instead of expansion step in each round which made by copying 16 bit from 32 bits data
in each right side of the standard algorithm, the unused 8-bits as a key (sometimes it is used
for error detection and correction purposes, or it is possible to generate an additional 8-bits
with the 56-bits standard key) in the first starting round with the other 8-neglected bits from
each of 16 round in the key algorithm will be used, and take the same locations of the
expanded data. As a result, the complexity to cryptanalysis of the secured data has been
increased. The proposed method was more active and reliable than standard conventional
DES, where it can be switched to the system at any round for working with original DES
algorithm, which means that an additional security has been added

The feasibility of using an Artificial Neural Network (ANN) for controlling
time- varying dynamical system is presented. The direct adjusting of neural controller
by direct adaptive control (DAC) is available, by using the error between output of
plant and desired input. The finite recurrent back propagation (FRBP) is used in the
learning process, because the ability of this method to capture the nonlinearly and
overcome the problem of time varying system. Hybrid controller structure used in this
paper, where the parameters of classical controller are adjusted with time at specified
freezing points for time varying dynamical system, and summed the outputs of two
controllers and enter to the plant, identify of system by ANN to get the optimal initial
condition for neuro controller.
A single channel for Spacecraft model is used as an example in this paper,
satisfactory results are obtained, which explain the ability of recurrent neural network
(RNN) to identify time varying dynamical system and overcome for all its problem
and explain the ability of this structure of hybrid neuro controller to use with time
varying dynamical system

Minor component analysis (MCA) of lower dimensional data is related to many signal
processing applications. MCA strives to extract the "minor" direction in the data space where the
variance of the data is minimal, identify the way for dimension reduction and data compression.
In this paper neural networks are used to estimate the minor component of signal. This
component is used to determine the Direction of Arrival Estimation (DOA) of incident signals.
These signals are considered to be emitted from their emission sources .The neural networks
knowing “Hebbian-networks” are used to estimate the minor component directions from signal
subspace. Narrow band signals are considered here and strike an array composed of M sensors.
Simulation results are introduced to shown the performance of the adaptive neural networks to
estimate signal components, a comparison of the results obtained from classical method and
MCA method, is presented which shows the performance of MCA over classical methods, to
estimate exact signal direction from noise subspace.

Determinations of unsaturated soil parameters using experimental procedures are time
consuming and difficult. In recent years, the soil–water characteristic curve (SWCC) has
become an important tool in the interpretation of the engineering behavior of unsaturated
soils. Difficulties associated with determining such parameters have justified the use of
indirect determination. This paper presents the general nature of the SWCC for soils with
different plasticity limits, index and gradation, in terms of gravimetric water content and
degree of saturation versus soil matric suction from Anbar governorate. In order to investigate
possible relationships between the plasticity limits, index, percent passing no.200 and SWCC,
7 type of soils were tested to find its SWCC experimentally and compared the result with the
curves obtained from different model presented in the literature. The objectives of the paper
were to check the validity of these models with the experimental results. The results shows a
good agreement and to present a simple method for inferring the SWCC for soils, taking into
account the liquid limit, plastic limit, plasticity index and percent of fines passing sieve
no.200