Abstract: Gluconic acid is one of interesting chemical products
in industries such as detergents, leather, photographic, textile, and
especially in food and pharmaceutical industries. Fermentation is an
advantageous process to produce gluconic acid. Mathematical
modeling is important in the design and operation of fermentation
process. In fact, kinetic data must be available for modeling. The
kinetic parameters of gluconic acid production by Aspergillus niger
in batch culture was studied in this research at initial substrate
concentration of 150, 200 and 250 g/l. The kinetic models used were
logistic equation for growth, Luedeking-Piret equation for gluconic
acid formation, and Luedeking-Piret-like equation for glucose
consumption. The Kinetic parameters in the model were obtained by
minimizing non linear least squares curve fitting.
Abstract: The electrical substation components are often subject to degradation due to over-voltage or over-current, caused by a short circuit or a lightning. A particular interest is given to the circuit breaker, regarding the importance of its function and its dangerous failure. This component degrades gradually due to the use, and it is also subject to the shock process resulted from the stress of isolating the fault when a short circuit occurs in the system. In this paper, based on failure mechanisms developments, the wear out of the circuit breaker contacts is modeled. The aim of this work is to evaluate its reliability and consequently its residual lifetime. The shock process is based on two random variables such as: the arrival of shocks and their magnitudes. The arrival of shocks was modeled using homogeneous Poisson process (HPP). By simulation, the dates of short-circuit arrivals were generated accompanied with their magnitudes. The same principle of simulation is applied to the amount of cumulative wear out contacts. The objective reached is to find the formulation of the wear function depending on the number of solicitations of the circuit breaker.
Abstract: In this paper, a novel scheme is proposed for Ownership Identification and Color Image Authentication by deploying Cryptography & Digital Watermarking. The color image is first transformed from RGB to YST color space exclusively designed for watermarking. Followed by color space transformation, each channel is divided into 4×4 non-overlapping blocks with selection of central 2×2 sub-blocks. Depending upon the channel selected two to three LSBs of each central 2×2 sub-block are set to zero to hold the ownership, authentication and recovery information. The size & position of sub-block is important for correct localization, enhanced security & fast computation. As YS ÔèÑ T so it is suitable to embed the recovery information apart from the ownership and authentication information, therefore 4×4 block of T channel along with ownership information is then deployed by SHA160 to compute the content based hash that is unique and invulnerable to birthday attack or hash collision instead of using MD5 that may raise the condition i.e. H(m)=H(m'). For recovery, intensity mean of 4x4 block of each channel is computed and encoded upto eight bits. For watermark embedding, key based mapping of blocks is performed using 2DTorus Automorphism. Our scheme is oblivious, generates highly imperceptible images with correct localization of tampering within reasonable time and has the ability to recover the original work with probability of near one.
Abstract: Grobner basis calculation forms a key part of computational
commutative algebra and many other areas. One important
ramification of the theory of Grobner basis provides a means to solve
a system of non-linear equations. This is why it has become very
important in the areas where the solution of non-linear equations is
needed, for instance in algebraic cryptanalysis and coding theory. This
paper explores on a parallel-distributed implementation for Grobner
basis calculation over GF(2). For doing so Buchberger algorithm is
used. OpenMP and MPI-C language constructs have been used to
implement the scheme. Some relevant results have been furnished
to compare the performances between the standalone and hybrid
(parallel-distributed) implementation.
Abstract: The main objective of this work is to provide a fault detection and isolation based on Markov parameters for residual generation and a neural network for fault classification. The diagnostic approach is accomplished in two steps: In step 1, the system is identified using a series of input / output variables through an identification algorithm. In step 2, the fault is diagnosed comparing the Markov parameters of faulty and non faulty systems. The Artificial Neural Network is trained using predetermined faulty conditions serves to classify the unknown fault. In step 1, the identification is done by first formulating a Hankel matrix out of Input/ output variables and then decomposing the matrix via singular value decomposition technique. For identifying the system online sliding window approach is adopted wherein an open slit slides over a subset of 'n' input/output variables. The faults are introduced at arbitrary instances and the identification is carried out in online. Fault residues are extracted making a comparison of the first five Markov parameters of faulty and non faulty systems. The proposed diagnostic approach is illustrated on benchmark problems with encouraging results.
Abstract: Investigation of sandy clay behavior is important since
urban development demands mean that sandy clay areas are
increasingly encountered, especially for transportation
infrastructures. This paper presents the results of the finite element
analysis of the direct shear test (under three vertical loading 44, 96
and 192 kPa) and discusses the effects of different parameters such as
cohesion, friction angle and Young's modulus on the shear strength of
sandy clay. The numerical model was calibrated against the
experimental results of large-scale direct shear tests. The results have
shown that the shear strength was increased with increase in friction
angle and cohesion. However, the shear strength was not influenced
by raising the friction angle at normal stress of 44 kPa. Also, the
effect of different young's modulus factors on stress-strain curve was
investigated.
Abstract: In recent years, the development of e-learning is very
rapid. E-learning is an attractive and efficient way for computer
education. Student interaction and collaboration also plays an
important role in e-learning. In this paper, a collaborative web-based
e-learning environment is presented. A wide range of interactive and
collaborative methods are integrated into a web-based environment.
This e-learning environment is designed for information security
curriculum.
Abstract: Steady state experiments have been conducted for
natural and mixed convection heat transfer, from five different sized
protruding discrete heat sources, placed at the bottom position on a
PCB and mounted on a vertical channel. The characteristic length (
Lh ) of heat sources vary from 0.005 to 0.011 m. The study has been
done for different range of Reynolds number and modified Grashof
number. From the experiment, the surface temperature distribution
and the Nusselt number of discrete heat sources have been obtained
and the effects of Reynold number and Richardson number on them
have been discussed. The objective is to find the rate of heat
dissipation from heat sources, by placing them at the bottom position
on a PCB and to compare both modes of cooling of heat sources.
Abstract: A concept of switched beam antennas consisting of
2×2 rectangular array spaced by λ/4 accompanied with a null locating
has been proposed in the previous work. In this letter, the
performance evaluations of its prototype are presented. The benefits
of using proposed system have been clearly measured in term of
signal quality, throughput and delays. Also, the impact of position
shift which mesh router is not located on the expected beam direction
has also been investigated.
Abstract: Portuguese diet has been gradually diverging from the basic principles of healthy eating, leading to an unbalanced dietary pattern which, associated with increasing sedentary lifestyle, has a negative impact on public health. The main objective of this work was to characterize the dietary habits of university students in Viseu, Portugal. The study consisted of a sample of 80 university students, aged between 18 and 28 years. Anthropometric data (weight (kg) and height (m)) were collected and Body Mass Index (BMI) was calculated. The dietary habits were assessed through a three-day food record and the software Medpoint was used to convert food into energy and nutrients. The results showed that students present a normal body mass index. Female university students made a higher number of daily meals than male students, and these last skipped breakfast more frequently. The values of average daily intake of energy, macronutrients and calcium were higher in males. The food pattern was characterized by a predominant consumption of meat, cereal, fats and sugar. Dietary intake of dairy products, fruits, vegetables and legumes does not meet the recommendations, revealing inadequate food habits such as hypoglycemic, hyperprotein and hyperlipidemic diet. Our findings suggest that preventive interventions should be focus in promoting healthy eating habits and physical activity in adulthood.
Abstract: Processing the data by computers and performing
reasoning tasks is an important aim in Computer Science. Semantic
Web is one step towards it. The use of ontologies to enhance the
information by semantically is the current trend. Huge amount of
domain specific, unstructured on-line data needs to be expressed in
machine understandable and semantically searchable format.
Currently users are often forced to search manually in the results
returned by the keyword-based search services. They also want to use
their native languages to express what they search. In this paper, an
ontology-based automated question answering system on software
test documents domain is presented. The system allows users to enter
a question about the domain by means of natural language and
returns exact answer of the questions. Conversion of the natural
language question into the ontology based query is the challenging
part of the system. To be able to achieve this, a new algorithm
regarding free text to ontology based search engine query conversion
is proposed. The algorithm is based on investigation of suitable
question type and parsing the words of the question sentence.
Abstract: The use of solar control film on windows as one of
solar passive strategies for building have becoming important and is
gaining recognition. Malaysia located close to equator is having
warm humid climate with long sunshine hours and abundant solar
radiation throughout the year. Hence, befitting solar control on
windows is absolutely necessary to capture the daylight whilst
moderating thermal impact and eliminating glare problems. This is
one of the energy efficient strategies to achieve thermal and visual
comfort in buildings. Therefore, this study was carried out to
investigate the effect of window solar controls on thermal and visual
performance of naturally ventilated buildings. This was conducted via
field data monitoring using a test building facility. Four types of
window glazing systems were used with three types of solar control
films. Data were analysed for thermal and visual impact with
reference to thermal and optical characteristics of the films. Results
show that for each glazing system, the surface temperature of
windows are influenced by the Solar Energy Absorption property, the
indoor air temperature are influenced by the Solar Energy
Transmittance and Solar Energy Reflectance, and the daylighting by
Visible Light Transmission and Shading Coefficient. Further
investigations are underway to determine the mathematical relation
between thermal energy and visual performance with the thermal and
optical characteristics of solar control films.
Abstract: Measurements of radioactivity in the environment is of great importance to monitor and control the levels of radiation to which man is exposed directly or indirectly. It is necessary to show that regardless of working or being close to nuclear power plants, people are daily in contact with some amount of radiation from the actual environment and food that are ingested, contradicting the view of most of them. The aim of this study was to analyze the rate of natural and artificial radiation from radionuclides present in cement, soil and fertilizers used in Sergipe State – Brazil. The radionuclide activitiesmeasured all samples arebelow the Brazilian limit of the exclusion and exemption criteria from the requirement of radiation protection.It was detected Be-7 in organic fertilizers that means a short interval between the brewing processes for use in agriculture. It was also detected an unexpected Cs-137 in some samples; however its activities does not represent risk for the population. Th-231 was also found in samples of soil and cement in the state of Sergipe that is an unprecedented result.
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: In this study, a classification-based video
super-resolution method using artificial neural network (ANN) is
proposed to enhance low-resolution (LR) to high-resolution (HR)
frames. The proposed method consists of four main steps:
classification, motion-trace volume collection, temporal adjustment,
and ANN prediction. A classifier is designed based on the edge
properties of a pixel in the LR frame to identify the spatial information.
To exploit the spatio-temporal information, a motion-trace volume is
collected using motion estimation, which can eliminate unfathomable
object motion in the LR frames. In addition, temporal lateral process is
employed for volume adjustment to reduce unnecessary temporal
features. Finally, ANN is applied to each class to learn the complicated
spatio-temporal relationship between LR and HR frames. Simulation
results show that the proposed method successfully improves both
peak signal-to-noise ratio and perceptual quality.
Abstract: The evolution of information and communication
technology has made a very powerful support for the improvement of
online learning platforms in creation of courses. This paper presents a
study that attempts to explore new web architecture for creating an
adaptive online learning system to profiles of learners, using the Web
as a source for the automatic creation of courses for the online
training platform. This architecture will reduce the time and decrease
the effort performed by the drafters of the current e-learning
platform, and direct adaptation of the Web content will greatly enrich
the quality of online training courses.
Abstract: Direct Torque Control is a control technique in AC
drive systems to obtain high performance torque control. The
conventional DTC drive contains a pair of hysteresis comparators.
DTC drives utilizing hysteresis comparators suffer from high torque
ripple and variable switching frequency. The most common solution
to those problems is to use the space vector depends on the reference
torque and flux. In this Paper The space vector modulation technique
(SVPWM) is applied to 2 level inverter control in the proposed
DTC-based induction motor drive system, thereby dramatically
reducing the torque ripple. Then the controller based on space vector
modulation is designed to be applied in the control of Induction
Motor (IM) with a three-level Inverter. This type of Inverter has
several advantages over the standard two-level VSI, such as a greater
number of levels in the output voltage waveforms, Lower dV/dt, less
harmonic distortion in voltage and current waveforms and lower
switching frequencies. This paper proposes a general SVPWM
algorithm for three-level based on standard two-level SVPWM. The
proposed scheme is described clearly and simulation results are
reported to demonstrate its effectiveness. The entire control scheme is
implemented with Matlab/Simulink.
Abstract: Radiolabeled cyclic RGD peptides targeting integrin αvβ3 are reported as promising agents for the early diagnosis of metastatic tumors. With an aim to improve tumor uptake and retention of the peptide, cyclic RGD peptide dimer E[c (RGDfK)] 2 (E = Glutamic acid, f = phenyl alanine, K = lysine) coupled to the bifunctional chelator DOTA was custom synthesized and radiolabelled with 68Ga. Radiolabelling of cyclic RGD peptide dimer with 68Ga was carried out using HEPES buffer and biological evaluation of the complex was done in nude mice bearing HT29 tumors.
Abstract: In this paper, a novel method for a biometric system based on the ECG signal is proposed, using spectral coefficients computed through linear predictive coding (LPC). ECG biometric systems have traditionally incorporated characteristics of fiducial points of the ECG signal as the feature set. These systems have been shown to contain loopholes and thus a non-fiducial system allows for tighter security. In the proposed system, incorporating non-fiducial features from the LPC spectrum produced a segment and subject recognition rate of 99.52% and 100% respectively. The recognition rates outperformed the biometric system that is based on the wavelet packet decomposition (WPD) algorithm in terms of recognition rates and computation time. This allows for LPC to be used in a practical ECG biometric system that requires fast, stringent and accurate recognition.
Abstract: In this paper, an artificial neural network simulator is
employed to carry out diagnosis and prognosis on electric motor as
rotating machinery based on predictive maintenance. Vibration data
of the primary failed motor including unbalance, misalignment and
bearing fault were collected for training the neural network. Neural
network training was performed for a variety of inputs and the motor
condition was used as the expert training information. The main
purpose of applying the neural network as an expert system was to
detect the type of failure and applying preventive maintenance. The
advantage of this study is for machinery Industries by providing
appropriate maintenance that has an essential activity to keep the
production process going at all processes in the machinery industry.
Proper maintenance is pivotal in order to prevent the possible failures
in operating system and increase the availability and effectiveness of
a system by analyzing vibration monitoring and developing expert
system.