Abstract: Despite various methods that exist in software risk management, software projects have a high rate of failure. When complexity and size of the projects are increased, managing software development becomes more difficult. In these projects the need for more analysis and risk assessment is vital. In this paper, a classification for software risks is specified. Then relations between these risks using risk tree structure are presented. Analysis and assessment of these risks are done using probabilistic calculations. This analysis helps qualitative and quantitative assessment of risk of failure. Moreover it can help software risk management process. This classification and risk tree structure can apply to some software tools.
Abstract: Sixteen female Holstein calves allocated in three
treatments including: 1: control, 2: fed raw fiber concentrate (RFC)
for 45 days and 3: fed RFC for 90 days. RFC supplement (Vitacel®
200) was added to milk immediately before feeding (10 g/L milk).
Withers height and body weights of calves were measured monthly.
Individual dry matter intake was recorded daily. Blood samples were
taken monthly. The result showed that calves consumed RFC had
significantly greater weaning and final body weight. Treatment effect
on dry matter intake was not significant (p>0.05). Calves fed RFC
had better feed efficiency. Withers height of calves fed RFC were
taller than the control group (p
Abstract: The objective of this paper is to present the
development of the frame of Chulalongkorn University team in TSAE
Auto Challenge Student Formula and Student Formula SAE
Competition of Japan. Chulalongkorn University's SAE team, has
established since year 2003, joined many competitions since year 2006
and became the leading team in Thailand. Through these 5 years, space
frame was the most selected and developed year by year through six
frame designs. In this paper, the discussions on the conceptual design
of these frames are introduced, focusing on the mass and torsional
stiffness improvement. The torsional stiffness test was performed on
the real used frames and the results are compared. It can be seen that
the 2010-2011 frame is firstly designed based on the analysis and
experiment that considered the required mass and torsional stiffness.
From the torsional stiffness results, it can be concluded that the frames
were developed including the decreasing of mass and the increasing
torsional stiffness by applying many techniques.
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: This paper focuses on cost and profit analysis of
single-server Markovian queuing system with two priority classes. In
this paper, functions of total expected cost, revenue and profit of the
system are constructed and subjected to optimization with respect to
its service rates of lower and higher priority classes. A computing
algorithm has been developed on the basis of fast converging
numerical method to solve the system of non linear equations formed
out of the mathematical analysis. A novel performance measure of
cost and profit analysis in view of its economic interpretation for the
system with priority classes is attempted to discuss in this paper. On
the basis of computed tables observations are also drawn to enlighten
the variational-effect of the model on the parameters involved
therein.
Abstract: The frequency contents of the non-stationary
signals vary with time. For proper characterization of such
signals, a smart time-frequency representation is necessary.
Classically, the STFT (short-time Fourier transform) is
employed for this purpose. Its limitation is the fixed timefrequency
resolution. To overcome this drawback an enhanced
STFT version is devised. It is based on the signal driven
sampling scheme, which is named as the cross-level sampling.
It can adapt the sampling frequency and the window function
(length plus shape) by following the input signal local
variations. This adaptation results into the proposed technique
appealing features, which are the adaptive time-frequency
resolution and the computational efficiency.
Abstract: The study of the stress distribution on a hollow
cylindrical fiber placed in a composite material is considered in this
work and an analytical solution for this stress distribution has been
constructed. Finally some parameters such as fiber-s thickness and
fiber-s length are considered and their effects on the distribution of
stress have been investigated. For finding the governing relations,
continuity equations for the axisymmetric problem in cylindrical
coordinate (r,o,z) are considered. Then by assuming some conditions
and solving the governing equations and applying the boundary
conditions, an equation relates the stress applied to the representative
volume element with the stress distribution on the fiber has been
found.
Abstract: A new strategy for oriented immobilization of proteins was proposed. The strategy contains two steps. The first step is to search for a docking site away from the active site on the protein surface. The second step is trying to find a ligand that is able to grasp the targeted site of the protein. To avoid ligand binding to the active site of protein, the targeted docking site is selected to own opposite charges to those near the active site. To enhance the ligand-protein binding, both hydrophobic and electrostatic interactions need to be included. The targeted docking site should therefore contain hydrophobic amino acids. The ligand is then selected through the help of molecular docking simulations. The enzyme α-amylase derived from Aspergillus oryzae (TAKA) was taken as an example for oriented immobilization. The active site of TAKA is surrounded by negatively charged amino acids. All the possible hydrophobic sites on the surface of TAKA were evaluated by the free energy estimation through benzene docking. A hydrophobic site on the opposite side of TAKA-s active site was found to be positive in net charges. A possible ligand, 3,3-,4,4- – Biphenyltetra- carboxylic acid (BPTA), was found to catch TAKA by the designated docking site. Then, the BPTA molecules were grafted onto silica gels and measured the affinity of TAKA adsorption and the specific activity of thereby immobilized enzymes. It was found that TAKA had a dissociation constant as low as 7.0×10-6 M toward the ligand BPTA on silica gel. The increase in ionic strength has little effect on the adsorption of TAKA, which indicated the existence of hydrophobic interaction between ligands and proteins. The specific activity of the immobilized TAKA was compared with the randomly adsorbed TAKA on primary amine containing silica gel. It was found that the orderly immobilized TAKA owns a specific activity twice as high as the one randomly adsorbed by ionic interaction.
Abstract: The effect of seed inoculation by VA- mycorrhiza and
different levels of phosphorus fertilizer on growth and yield of
sunflower (Azargol cultivar) was studied in experiment farm of
Islamic Azad University, Karaj Branch during 2008 growing season.
The experiment treatments were arranged in factorial based on a
complete randomized block design with three replications. Four
phosphorus fertilizer levels of 25%, 50% 75% and 100% P
recommended with two levels of Mycorrhiza: with and without
Mycorrhiza (control) were assigned in a factorial combination.
Results showed that head diameter, number of seeds in head, seed
yield and oil yield were significantly higher in inoculated plants than
in non-inoculated plants. Head diameter, number of seeds in head,
1000 seeds weight, biological yield, seed yield and oil yield increased
with increasing P level above 75% P recommended in non-inoculated
plants, whereas no significant difference was observed between 75%
and 100% P recommended. The positive effect of mycorrhizal
inoculation decreased with increasing P levels due to decreased
percent root colonization at higher P levels. According to the results
of this experiment, application of mycorrhiza in present of 50% P
recommended had an appropriate performance and could increase
seed yield and oil production to an acceptable level, so it could be
considered as a suitable substitute for chemical phosphorus fertilizer
in organic agricultural systems.
Abstract: Super-quadrics can represent a set of implicit surfaces,
which can be used furthermore as primitive surfaces to construct a
complex object via Boolean set operations in implicit surface
modeling. In fact, super-quadrics were developed to create a
parametric surface by performing spherical product on two parametric
curves and some of the resulting parametric surfaces were also
represented as implicit surfaces. However, because not every
parametric curve can be redefined implicitly, this causes only implicit
super-elliptic and super-hyperbolic curves are applied to perform
spherical product and so only implicit super-ellipsoids and
hyperboloids are developed in super-quadrics. To create implicit
surfaces with more diverse shapes than super-quadrics, this paper
proposes an implicit representation of spherical product, which
performs spherical product on two implicit curves like super-quadrics
do. By means of the implicit representation, many new implicit curves
such as polygonal, star-shaped and rose-shaped curves can be used to
develop new implicit surfaces with a greater variety of shapes than
super-quadrics, such as polyhedrons, hyper-ellipsoids, superhyperboloids
and hyper-toroids containing star-shaped and roseshaped
major and minor circles. Besides, the newly developed implicit
surfaces can also be used to define new primitive implicit surfaces for
constructing a more complex implicit surface in implicit surface
modeling.
Abstract: An adaptive software reliability prediction model
using evolutionary connectionist approach based on Recurrent Radial
Basis Function architecture is proposed. Based on the currently
available software failure time data, Fuzzy Min-Max algorithm is
used to globally optimize the number of the k Gaussian nodes. The
corresponding optimized neural network architecture is iteratively
and dynamically reconfigured in real-time as new actual failure time
data arrives. The performance of our proposed approach has been
tested using sixteen real-time software failure data. Numerical results
show that our proposed approach is robust across different software
projects, and has a better performance with respect to next-steppredictability
compared to existing neural network model for failure
time prediction.
Abstract: Recently the usefulness of Concept Abduction, a novel non-monotonic inference service for Description Logics (DLs), has been argued in the context of ontology-based applications such as semantic matchmaking and resource retrieval. Based on tableau calculus, a method has been proposed to realize this reasoning task in ALN, a description logic that supports simple cardinality restrictions as well as other basic constructors. However, in many ontology-based systems, the representation of ontology would require expressive formalisms for capturing domain-specific constraints, this language is not sufficient. In order to increase the applicability of the abductive reasoning method in such contexts, we would like to present in the scope of this paper an extension of the tableaux-based algorithm for dealing with concepts represented inALCQ, the description logic that extends ALN with full concept negation and quantified number restrictions.
Abstract: Fuzzy logic can be used when knowledge is
incomplete or when ambiguity of data exists. The purpose of
this paper is to propose a proactive fuzzy set- based model for
reacting to the risk inherent in investment activities relative to
a complete view of portfolio management. Fuzzy rules are
given where, depending on the antecedents, the portfolio size
may be slightly or significantly decreased or increased. The
decision maker considers acceptable bounds on the proportion
of acceptable risk and return. The Fuzzy Controller model
allows learning to be achieved as 1) the firing strength of each
rule is measured, 2) fuzzy output allows rules to be updated,
and 3) new actions are recommended as the system continues
to loop. An extension is given to the fuzzy controller that
evaluates potential financial loss before adjusting the
portfolio. An application is presented that illustrates the
algorithm and extension developed in the paper.
Abstract: For the electrical metrics that describe photovoltaic
cell performance are inherently multivariate in nature, use of a
univariate, or one variable, statistical process control chart can have
important limitations. Development of a comprehensive process
control strategy is known to be significantly beneficial to reducing
process variability that ultimately drives up the manufacturing cost
photovoltaic cells. The multivariate moving average or MMA chart,
is applied to the electrical metrics of photovoltaic cells to illustrate
the improved sensitivity on process variability this method of control
charting offers. The result show the ability of the MMA chart to
expand to as any variables as needed, suggests an application
with multiple photovoltaic electrical metrics being used in
concert to determine the processes state of control.
Abstract: We describe a novel method for removing noise (in wavelet domain) of unknown variance from microarrays. The method is based on a smoothing of the coefficients of the highest subbands. Specifically, we decompose the noisy microarray into wavelet subbands, apply smoothing within each highest subband, and reconstruct a microarray from the modified wavelet coefficients. This process is applied a single time, and exclusively to the first level of decomposition, i.e., in most of the cases, it is not necessary a multirresoltuion analysis. Denoising results compare favorably to the most of methods in use at the moment.
Abstract: We consider a typical problem in the assembly of
printed circuit boards (PCBs) in a two-machine flow shop system to
simultaneously minimize the weighted sum of weighted tardiness and
weighted flow time. The investigated problem is a group scheduling
problem in which PCBs are assembled in groups and the interest is to
find the best sequence of groups as well as the boards within each
group to minimize the objective function value. The type of setup
operation between any two board groups is characterized as carryover
sequence-dependent setup time, which exactly matches with the real
application of this problem. As a technical constraint, all of the
boards must be kitted before the assembly operation starts (kitting
operation) and by kitting staff. The main idea developed in this paper
is to completely eliminate the role of kitting staff by assigning the
task of kitting to the machine operator during the time he is idle
which is referred to as integration of internal (machine) and external
(kitting) setup times. Performing the kitting operation, which is a
preparation process of the next set of boards while the other boards
are currently being assembled, results in the boards to continuously
enter the system or have dynamic arrival times. Consequently, a
dynamic PCB assembly system is introduced for the first time in the
assembly of PCBs, which also has characteristics similar to that of
just-in-time manufacturing. The problem investigated is
computationally very complex, meaning that finding the optimal
solutions especially when the problem size gets larger is impossible.
Thus, a heuristic based on Genetic Algorithm (GA) is employed. An
example problem on the application of the GA developed is
demonstrated and also numerical results of applying the GA on
solving several instances are provided.
Abstract: The main aim of this study is to identify the most
influential variables that cause defects on the items produced by a
casting company located in Turkey. To this end, one of the items
produced by the company with high defective percentage rates is
selected. Two approaches-the regression analysis and decision treesare
used to model the relationship between process parameters and
defect types. Although logistic regression models failed, decision tree
model gives meaningful results. Based on these results, it can be
claimed that the decision tree approach is a promising technique for
determining the most important process variables.
Abstract: These days wireless local area networks has become
very popular, when the initial IEEE802.11 is the standard for
providing wireless connectivity to automatic machinery, equipment
and stations that require rapid deployment, which may be portable,
handheld or which may be mounted on moving vehicles within a
local area. IEEE802.11 Wireless local area network is a sharedmedium
communication network that transmits information over
wireless links for all IEEE802.11 stations in its transmission range to
receive. When a user is moving from one location to another, how
the other user knows about the required station inside WLAN. For
that we designed and implemented a system to locate a mobile user
inside the wireless local area network based on RSSI with the help of
four specially designed architectures. These architectures are based
on statistical or we can say manual configuration of mapping and
radio map of indoor and outdoor location with the help of available
Sniffer based and cluster based techniques. We found a better
location of a mobile user in WLAN. We tested this work in indoor
and outdoor environments with different locations with the help of
Pamvotis, a simulator for WLAN.
Abstract: This paper aims to select the optimal location and
setting parameters of TCSC (Thyristor Controlled Series
Compensator) controller using Particle Swarm Optimization (PSO)
and Genetic Algorithm (GA) to mitigate small signal oscillations in a
multimachine power system. Though Power System Stabilizers
(PSSs) are prime choice in this issue, installation of FACTS device
has been suggested here in order to achieve appreciable damping of
system oscillations. However, performance of any FACTS devices
highly depends upon its parameters and suitable location in the
power network. In this paper PSO as well as GA based techniques are
used separately and compared their performances to investigate this
problem. The results of small signal stability analysis have been
represented employing eigenvalue as well as time domain response in
face of two common power system disturbances e.g., varying load
and transmission line outage. It has been revealed that the PSO based
TCSC controller is more effective than GA based controller even
during critical loading condition.
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.