Abstract: The fault detection and diagnosis of complicated
production processes is one of essential tasks needed to run the process
safely with good final product quality. Unexpected events occurred in
the process may have a serious impact on the process. In this work,
triangular representation of process measurement data obtained in an
on-line basis is evaluated using simulation process. The effect of using
linear and nonlinear reduced spaces is also tested. Their diagnosis
performance was demonstrated using multivariate fault data. It has
shown that the nonlinear technique based diagnosis method produced
more reliable results and outperforms linear method. The use of
appropriate reduced space yielded better diagnosis performance. The
presented diagnosis framework is different from existing ones in that it
attempts to extract the fault pattern in the reduced space, not in the
original process variable space. The use of reduced model space helps
to mitigate the sensitivity of the fault pattern to noise.
Abstract: Reinforced concrete has good durability and excellent structural performance. But there are cases of early deterioration due to a number of factors, one prominent factor being corrosion of steel reinforcement. The process of corrosion sets in due to ingress of moisture, oxygen and other ingredients into the body of concrete, which is unsound, permeable and absorbent. Cracks due to structural and other causes such as creep, shrinkage, etc also allow ingress of moisture and other harmful ingredients and thus accelerate the rate of corrosion. There are several interactive factors both external and internal, which lead to corrosion of reinforcement and ultimately failure of structures. Suitable addition of mineral admixture like silica fume (SF) in concrete improves the strength and durability of concrete due to considerable improvement in the microstructure of concrete composites, especially at the transition zone. Secondary reinforcement in the form of fibre is added to concrete, which provides three dimensional random reinforcement in the entire mass of concrete. Reinforced concrete beams of size 0.1 m X 0.15 m and length 1m have been cast using M 35 grade of concrete. The beams after curing process were subjected to corrosion process by impressing an external Direct Current (Galvanostatic Method) for a period of 15 days under stressed and unstressed conditions. The corroded beams were tested by applying two point loads to determine the ultimate load carrying capacity and cracking pattern and the results of specimens were compared with that of the companion specimens. Gravimetric method is used to quantify corrosion that has occurred.
Abstract: Reactive powder concretes (RPC) are characterized by
particle diameter not exceeding 600 μm and having very high
compressive and tensile strengths. This paper describes a new
generation of micro concrete, which has an initial, as well as a final,
high physicomechanical performance. To achieve this, we replaced
the Portland cement (15% by weight) by materials rich in Silica (Slag
and Dune Sand).
The results obtained from tests carried out on RPC show that
compressive and tensile strengths increase when adding the additions,
thus improving the compactness of mixtures via filler and pozzolanic
effect.
With a reduction of the aggregate phase in the RPC and the
abundance of dune sand (south Algeria) and slag (industrial byproduct
of blast furnace), the use of the RPC will allow Algeria to
fulfil economical as well as ecological requirements.
Abstract: One of the most basic functions of control engineers is
tuning of controllers. There are always several process loops in the
plant necessitate of tuning. The auto tuned Proportional Integral
Derivative (PID) Controllers are designed for applications where
large load changes are expected or the need for extreme accuracy and
fast response time exists. The algorithm presented in this paper is
used for the tuning PID controller to obtain its parameters with a
minimum computing complexity. It requires continuous analysis of
variation in few parameters, and let the program to do the plant test
and calculate the controller parameters to adjust and optimize the
variables for the best performance. The algorithm developed needs
less time as compared to a normal step response test for continuous
tuning of the PID through gain scheduling.
Abstract: The aim of this investigation is to study the
performance of the new generation of the PVD coated grade and to
map the influence of cutting conditions on the tool life in milling of
ADI (Austempered Ductile Iron). The results show that chipping is
the main wear mechanism which determines the tool life in dry
condition and notch wear in wet condition for this application. This
due to the different stress mechanisms and preexisting cracks in the
coating. The wear development shows clearly that the new PVD
coating (C20) has the best ability to delay the chipping growth. It
was also found that a high content of Al in the new coating (C20)
was especially favorable compared to a TiAlN multilayer with lower
Al content (C30) or CVD coating. This is due to fine grains and low
compressive stress level in the coating which increase the coating
ability to withstand the mechanical and thermal impact. It was also
found that the use of coolant decreases the tool life with 70-80%
compare to dry milling.
Abstract: Nonlinear system identification is becoming an important tool which can be used to improve control performance. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for controlling a car. The vehicle must follow a predefined path by supervised learning. Backpropagation gradient descent method was performed to train the ANFIS system. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in controlling the non linear system.
Abstract: In this work, we present a novel active learning approach
for learning a visual object detection system. Our system
is composed of an active learning mechanism as wrapper around
a sub-algorithm which implement an online boosting-based learning
object detector. In the core is a combination of a bootstrap procedure
and a semi automatic learning process based on the online boosting
procedure. The idea is to exploit the availability of classifier during
learning to automatically label training samples and increasingly
improves the classifier. This addresses the issue of reducing labeling
effort meanwhile obtain better performance. In addition, we propose
a verification process for further improvement of the classifier.
The idea is to allow re-update on seen data during learning for
stabilizing the detector. The main contribution of this empirical study
is a demonstration that active learning based on an online boosting
approach trained in this manner can achieve results comparable or
even outperform a framework trained in conventional manner using
much more labeling effort. Empirical experiments on challenging data
set for specific object deteciton problems show the effectiveness of
our approach.
Abstract: Wind catchers are traditional natural ventilation
systems attached to buildings in order to ventilate the indoor air. The
most common type of wind catcher is four sided one which is
capable to catch wind in all directions. CFD simulation is the perfect
way to evaluate the wind catcher performance. The accuracy of CFD
results is the issue of concern, so sensitivity analyses is crucial to
find out the effect of different settings of CFD on results. This paper
presents a series of 3D steady RANS simulations for a generic
isolated four-sided wind catcher attached to a room subjected to wind
direction ranging from 0º to 180º with an interval of 45º. The CFD
simulations are validated with detailed wind tunnel experiments. The
influence of an extensive range of computational parameters is
explored in this paper, including the resolution of the computational
grid, the size of the computational domain and the turbulence model.
This study found that CFD simulation is a reliable method for wind
catcher study, but it is less accurate in prediction of models with non
perpendicular wind directions.
Abstract: This research focus on the intrusion detection system (IDS) development which using artificial immune system (AIS) with population based incremental learning (PBIL). AIS have powerful distinguished capability to extirpate antigen when the antigen intrude into human body. The PBIL is based on past learning experience to adjust new learning. Therefore we propose an intrusion detection system call PBIL-AIS which combine two approaches of PBIL and AIS to evolution computing. In AIS part we design three mechanisms such as clonal selection, negative selection and antibody level to intensify AIS performance. In experimental result, our PBIL-AIS IDS can capture high accuracy when an intrusion connection attacks.
Abstract: The electromagnetic spectrum is a natural resource
and hence well-organized usage of the limited natural resources is the
necessities for better communication. The present static frequency
allocation schemes cannot accommodate demands of the rapidly
increasing number of higher data rate services. Therefore, dynamic
usage of the spectrum must be distinguished from the static usage to
increase the availability of frequency spectrum. Cognitive radio is not
a single piece of apparatus but it is a technology that can incorporate
components spread across a network. It offers great promise for
improving system efficiency, spectrum utilization, more effective
applications, reduction in interference and reduced complexity of
usage for users. Cognitive radio is aware of its environmental,
internal state, and location, and autonomously adjusts its operations
to achieve designed objectives. It first senses its spectral environment
over a wide frequency band, and then adapts the parameters to
maximize spectrum efficiency with high performance. This paper
only focuses on the analysis of Bit-Error-Rate in cognitive radio by
using Particle Swarm Optimization Algorithm. It is theoretically as
well as practically analyzed and interpreted in the sense of
advantages and drawbacks and how BER affects the efficiency and
performance of the communication system.
Abstract: Security has been an important issue and concern in the
smart home systems. Smart home networks consist of a wide range of
wired or wireless devices, there is possibility that illegal access to
some restricted data or devices may happen. Password-based
authentication is widely used to identify authorize users, because this
method is cheap, easy and quite accurate. In this paper, a neural
network is trained to store the passwords instead of using verification
table. This method is useful in solving security problems that
happened in some authentication system. The conventional way to
train the network using Backpropagation (BPN) requires a long
training time. Hence, a faster training algorithm, Resilient
Backpropagation (RPROP) is embedded to the MLPs Neural
Network to accelerate the training process. For the Data Part, 200
sets of UserID and Passwords were created and encoded into binary
as the input. The simulation had been carried out to evaluate the
performance for different number of hidden neurons and combination
of transfer functions. Mean Square Error (MSE), training time and
number of epochs are used to determine the network performance.
From the results obtained, using Tansig and Purelin in hidden and
output layer and 250 hidden neurons gave the better performance. As
a result, a password-based user authentication system for smart home
by using neural network had been developed successfully.
Abstract: At the previous study of new metal gasket, contact
width and contact stress were important design parameter for
optimizing metal gasket performance. However, the range of contact
stress had not been investigated thoroughly. In this study, we
conducted a gasket design optimization based on an elastic and plastic
contact stress analysis considering forming effect using FEM. The
gasket model was simulated by using two simulation stages which is
forming and tightening simulation. The optimum design based on an
elastic and plastic contact stress was founded. Final evaluation was
determined by helium leak quantity to check leakage performance of
both type of gaskets. The helium leak test shows that a gasket based
on the plastic contact stress design better than based on elastic stress
design.
Abstract: Employing a recently introduced unified adaptive filter
theory, we show how the performance of a large number of important
adaptive filter algorithms can be predicted within a general framework
in nonstationary environment. This approach is based on energy conservation
arguments and does not need to assume a Gaussian or white
distribution for the regressors. This general performance analysis can
be used to evaluate the mean square performance of the Least Mean
Square (LMS) algorithm, its normalized version (NLMS), the family
of Affine Projection Algorithms (APA), the Recursive Least Squares
(RLS), the Data-Reusing LMS (DR-LMS), its normalized version
(NDR-LMS), the Block Least Mean Squares (BLMS), the Block
Normalized LMS (BNLMS), the Transform Domain Adaptive Filters
(TDAF) and the Subband Adaptive Filters (SAF) in nonstationary
environment. Also, we establish the general expressions for the
steady-state excess mean square in this environment for all these
adaptive algorithms. Finally, we demonstrate through simulations that
these results are useful in predicting the adaptive filter performance.
Abstract: This article provides partial evaluation index and its
standard of sports aerobics, including the following 12 indexes: health
vitality, coordination, flexibility, accuracy, pace, endurance, elasticity,
self-confidence, form, control, uniformity and musicality. The
three-layer BP artificial neural network model including input layer,
hidden layer and output layer is established. The result shows that the
model can well reflect the non-linear relationship between the
performance of 12 indexes and the overall performance. The predicted
value of each sample is very close to the true value, with a relative
error fluctuating around of 5%, and the network training is successful.
It shows that BP network has high prediction accuracy and good
generalization capacity if being applied in sports aerobics performance
evaluation after effective training.
Abstract: This paper considers the control of the longitudinal
flight dynamics of an F-16 aircraft. The primary design objective
is model-following of the pitch rate q, which is the preferred
system for aircraft approach and landing. Regulation of the aircraft
velocity V (or the Mach-hold autopilot) is also considered, but
as a secondary objective. The problem is challenging because the
system is nonlinear, and also non-affine in the input. A sliding
mode controller is designed for the pitch rate, that exploits the
modal decomposition of the linearized dynamics into its short-period
and phugoid approximations. The inherent robustness of the SMC
design provides a convenient way to design controllers without gain
scheduling, with a steady-state response that is comparable to that
of a conventional polynomial based gain-scheduled approach with
integral control, but with improved transient performance. Integral
action is introduced in the sliding mode design using the recently
developed technique of “conditional integrators", and it is shown that
robust regulation is achieved with asymptotically constant exogenous
signals, without degrading the transient response. Through extensive
simulation on the nonlinear multiple-input multiple-output (MIMO)
longitudinal model of the F-16 aircraft, it is shown that the conditional
integrator design outperforms the one based on the conventional linear
control, without requiring any scheduling.
Abstract: A gene network gives the knowledge of the regulatory
relationships among the genes. Each gene has its activators and
inhibitors that regulate its expression positively and negatively
respectively. Genes themselves are believed to act as activators and
inhibitors of other genes. They can even activate one set of genes and
inhibit another set. Identifying gene networks is one of the most
crucial and challenging problems in Bioinformatics. Most work done
so far either assumes that there is no time delay in gene regulation or
there is a constant time delay. We here propose a Dynamic Time-
Lagged Correlation Based Method (DTCBM) to learn the gene
networks, which uses time-lagged correlation to find the potential
gene interactions, and then uses a post-processing stage to remove
false gene interactions to common parents, and finally uses dynamic
correlation thresholds for each gene to construct the gene network.
DTCBM finds correlation between gene expression signals shifted in
time, and therefore takes into consideration the multi time delay
relationships among the genes. The implementation of our method is
done in MATLAB and experimental results on Saccharomyces
cerevisiae gene expression data and comparison with other methods
indicate that it has a better performance.
Abstract: Green supply chain management is an increasingly recognized practice among companies that are seeking to improve environmental performance. Of particular concern is how to arouse organizational awareness and put green activities into practice in
order to enhance manufacturing performances. This paper investigates the correlation of green supply chain practices and
manufacturing performances in Malaysian certified MS ISO 14000 manufacturing firms. The findings shows that green supply chain
practices which that can be denominated product recycling, environmental compliance and optimization have significant influence to some of the manufacturing performances.
Abstract: This paper presents an effective traffic lights detection
method at the night-time. First, candidate blobs of traffic lights are
extracted from RGB color image. Input image is represented on the
dominant color domain by using color transform proposed by Ruta,
then red and green color dominant regions are selected as candidates.
After candidate blob selection, we carry out shape filter for noise
reduction using information of blobs such as length, area, area of
boundary box, etc. A multi-class classifier based on SVM (Support
Vector Machine) applies into the candidates. Three kinds of features
are used. We use basic features such as blob width, height, center
coordinate, area, area of blob. Bright based stochastic features are also
used. In particular, geometric based moment-s values between
candidate region and adjacent region are proposed and used to improve
the detection performance. The proposed system is implemented on
Intel Core CPU with 2.80 GHz and 4 GB RAM and tested with the
urban and rural road videos. Through the test, we show that the
proposed method using PF, BMF, and GMF reaches up to 93 % of
detection rate with computation time of in average 15 ms/frame.
Abstract: Protein residue contact map is a compact
representation of secondary structure of protein. Due to the
information hold in the contact map, attentions from researchers in
related field were drawn and plenty of works have been done
throughout the past decade. Artificial intelligence approaches have
been widely adapted in related works such as neural networks,
genetic programming, and Hidden Markov model as well as support
vector machine. However, the performance of the prediction was not
generalized which probably depends on the data used to train and
generate the prediction model. This situation shown the importance
of the features or information used in affecting the prediction
performance. In this research, support vector machine was used to
predict protein residue contact map on different combination of
features in order to show and analyze the effectiveness of the
features.
Abstract: Enterprise applications are complex systems that are hard to develop and deploy in organizations. Although software application development tools, frameworks, methodologies and patterns are rapidly developing; many projects fail by causing big costs. There are challenging issues that programmers and designers face with while working on enterprise applications. In this paper, we present the three of the significant issues: Architectural, technological and performance. The important subjects in each issue are pointed out and recommendations are given. In architectural issues the lifecycle, meta-architecture, guidelines are pointed out. .NET and Java EE platforms are presented in technological issues. The importance of performance, measuring performance and profilers are explained in performance issues.