Abstract: The fractal-shaped orifices are assumed to have a
significant effect on the pressure drop downstream pipe flow due to
their edge self-similarity shape which enhances the mixing
properties. Here, we investigate the pressure drop after these fractals
using a digital micro-manometer at different stations downstream a
turbulent flow pipe then a direct comparison has been made with the
pressure drop measured from regular orifices with the same flow
area. Our results showed that the fractal-shaped orifices have a
significant effect on the pressure drop downstream the flow. Also
the pressure drop measured across the fractal-shaped orifices is
noticed to be lower that that from ordinary orifices of the same flow
areas. This result could be important in designing piping systems
from point of view of losses consideration with the same flow
control area. This is promising to use the fractal-shaped orifices as
flowmeters as they can sense the pressure drop across them
accurately with minimum losses than the regular ones.
Abstract: This paper explores the scalability issues associated
with solving the Named Entity Recognition (NER) problem using
Support Vector Machines (SVM) and high-dimensional features. The
performance results of a set of experiments conducted using binary
and multi-class SVM with increasing training data sizes are
examined. The NER domain chosen for these experiments is the
biomedical publications domain, especially selected due to its
importance and inherent challenges. A simple machine learning
approach is used that eliminates prior language knowledge such as
part-of-speech or noun phrase tagging thereby allowing for its
applicability across languages. No domain-specific knowledge is
included. The accuracy measures achieved are comparable to those
obtained using more complex approaches, which constitutes a
motivation to investigate ways to improve the scalability of multiclass
SVM in order to make the solution more practical and useable.
Improving training time of multi-class SVM would make support
vector machines a more viable and practical machine learning
solution for real-world problems with large datasets. An initial
prototype results in great improvement of the training time at the
expense of memory requirements.
Abstract: The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive for solving problems with multiple and conflicting objectives. This paper uses an elitist multiobjective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm (NSGA), for solving the dynamic shortest path routing problem in computer networks. A priority-based encoding scheme is proposed for population initialization. Elitism ensures that the best solution does not deteriorate in the next generations. Results for a sample test network have been presented to demonstrate the capabilities of the proposed approach to generate well-distributed pareto-optimal solutions of dynamic routing problem in one single run. The results obtained by NSGA are compared with single objective weighting factor method for which Genetic Algorithm (GA) was applied.
Abstract: Dynamic location referencing method is an important technology to shield map differences. These method references objects of the road network by utilizing condensed selection of its real-world geographic properties stored in a digital map database, which overcomes the defections existing in pre-coded location referencing methods. The high attributes completeness requirements and complicated reference point selection algorithm are the main problems of recent researches. Therefore, a dynamic location referencing algorithm combining intersection points selected at the extremities compulsively and road link points selected according to link partition principle was proposed. An experimental system based on this theory was implemented. The tests using Beijing digital map database showed satisfied results and thus verified the feasibility and practicability of this method.
Abstract: The influence of flakes from biologically activated
hull-less barley grain and malt extract on chemical composition of
yoghurt was studied.
Pasteurized milk, freeze-dried yoghurt culture YF-L811 (Chr.
Hansen, Denmark), flakes from biologically activated hull-less
barley grain (Latvia) and malt extract (Ilgezeem, Latvia) were used
for experiments. Yoghurt samples with and without flakes from
biologically activated hull-less barley grain and malt extract were
analyzed for content of total solids, total proteins, fats, amino acids
and riboflavin.
The addition of flakes from biologically activated hull-less barley
grain and malt extract allowed increase of nutritional value of
yoghurt samples. There was obtained the increase of total proteins
(p>0.05) and the decrease of fat (p>0.05). The presence of flakes
from biologically activated hull-less barley grain and malt extract in
yoghurt samples provided significant increase of amino acids amount
(p
Abstract: This paper presents two different sequential switching hybrid-modulation strategies and implemented for cascaded multilevel inverters. Hybrid modulation strategies represent the combinations of Fundamental-frequency pulse width modulation (FFPWM) and Multilevel sinusoidal-modulation (MSPWM) strategies, and are designed for performance of the well-known Alternative Phase opposition disposition (APOD), Phase shifted carrier (PSC). The main characteristics of these modulations are the reduction of switching losses with good harmonic performance, balanced power loss dissipation among the devices with in a cell, and among the series-connected cells. The feasibility of these modulations is verified through spectral analysis, power loss analysis and simulation.
Abstract: Three similar negative differential resistance (NDR)
profiles with both high peak to valley current density ratio (PVCDR)
value and high peak current density (PCD) value in unity resonant
tunneling electronic circuit (RTEC) element is developed in this paper.
The PCD values and valley current density (VCD) values of the three
NDR curves are all about 3.5 A and 0.8 A, respectively. All PV values
of NDR curves are 0.40 V, 0.82 V, and 1.35 V, respectively. The VV
values are 0.61 V, 1.07 V, and 1.69 V, respectively. All PVCDR
values reach about 4.4 in three NDR curves. The PCD value of 3.5 A
in triple PVCDR RTEC element is better than other resonant
tunneling devices (RTD) elements. The high PVCDR value is
concluded the lower VCD value about 0.8 A. The low VCD value is
achieved by suitable selection of resistors in triple PVCDR RTEC
element. The low PV value less than 1.35 V possesses low power
dispersion in triple PVCDR RTEC element. The designed multiple
value logical level (MVLL) system using triple PVCDR RTEC
element provides equidistant logical level. The logical levels of
MVLL system are about 0.2 V, 0.8 V, 1.5 V, and 2.2 V from low
voltage to high voltage and then 2.2 V, 1.3 V, 0.8 V, and 0.2 V from
high voltage back to low voltage in half cycle of sinusoid wave. The
output level of four levels MVLL system is represented in 0.3 V, 1.1 V,
1.7 V, and 2.6 V, which satisfies the NMP condition of traditional
two-bit system. The remarkable logical characteristic of improved
MVLL system with paralleled capacitor are with four significant
stable logical levels about 220 mV, 223 mV, 228 mV, and 230 mV.
The stability and articulation of logical levels of improved MVLL
system are outstanding. The average holding time of improved MVLL
system is approximately 0.14 μs. The holding time of improved
MVLL system is fourfold than of basic MVLL system. The function of
additional capacitor in the improved MVLL system is successfully
discovered.
Abstract: This paper presents a new approach for the protection
of Thyristor-Controlled Series Compensator (TCSC) line using
Support Vector Machine (SVM). One SVM is trained for fault
classification and another for section identification. This method use
three phase current measurement that results in better speed and
accuracy than other SVM based methods which used single phase
current measurement. This makes it suitable for real-time protection.
The method was tested on 10,000 data instances with a very wide
variation in system conditions such as compensation level, source
impedance, location of fault, fault inception angle, load angle at
source bus and fault resistance. The proposed method requires only
local current measurement.
Abstract: Ontology-based modelling of multi-formatted
software application content is a challenging area in content
management. When the number of software content unit is huge and
in continuous process of change, content change management is
important. The management of content in this context requires
targeted access and manipulation methods. We present a novel
approach to deal with model-driven content-centric information
systems and access to their content. At the core of our approach is an
ontology-based semantic annotation technique for diversely
formatted content that can improve the accuracy of access and
systems evolution. Domain ontologies represent domain-specific
concepts and conform to metamodels. Different ontologies - from
application domain ontologies to software ontologies - capture and
model the different properties and perspectives on a software content
unit. Interdependencies between domain ontologies, the artifacts and
the content are captured through a trace model. The annotation traces
are formalised and a graph-based system is selected for the
representation of the annotation traces.
Abstract: Regression testing is a maintenance activity applied to
modified software to provide confidence that the changed parts are
correct and that the unchanged parts have not been adversely affected
by the modifications. Regression test selection techniques reduce the
cost of regression testing, by selecting a subset of an existing test
suite to use in retesting modified programs. This paper presents the
first general regression-test-selection technique, which based on code
and allows selecting test cases for any programs written in any
programming language. Then it handles incomplete program. We
also describe RTSDiff, a regression-test-selection system that
implements the proposed technique. The results of the empirical
studied that performed in four programming languages java, C#, Cµ
and Visual basic show that the efficiency and effective in reducing
the size of test suit.
Abstract: Image watermarking has become an important tool for
intellectual property protection and authentication. In this paper a
watermarking technique is suggested that incorporates two
watermarks in a host image for improved protection and robustness.
A watermark, in form of a PN sequence (will be called the secondary
watermark), is embedded in the wavelet domain of a primary
watermark before being embedded in the host image. The technique
has been tested using Lena image as a host and the camera man as
the primary watermark. The embedded PN sequence was detectable
through correlation among other five sequences where a PSNR of
44.1065 dB was measured. Furthermore, to test the robustness of the
technique, the watermarked image was exposed to four types of
attacks, namely compression, low pass filtering, salt and pepper noise
and luminance change. In all cases the secondary watermark was
easy to detect even when the primary one is severely distorted.
Abstract: This research focuses on micro-enterprise of
Malaysian Malays that are involved in very small-scaled business
activities. Among them include food stall and burger stall operators,
night market hawkers, grocery store operators as well as construction
and small service activities works. The study seeks to explore why
some micro-entrepreneurs still lag in entrepreneurship and what
needs to be rectified. This quantitative study is conducted on 173
Malay micro-enterprise owners (MEOs) and 58 Malay failed microenterprise
owners (FMEOs) involved in all range of businesses
throughout the state of Perak, Malaysia. The main aims are to
identify the gaps between the failed micro-enterprise owners
(FMEOs) and existing micro-enterprise owners (MEOs) and the
problems faced among FMEOs. The results reveal that the MEOs had
strong motivations and better marketing approaches as compared to
FMEOs. Furthermore, the FMEOs failed in the business ventures
mainly due to lack of management, sales and marketing skills and
poor competitive abilities to keep up with rivals.
Abstract: In the meat processing industry the substitution of
meat with non-meat ingredients is considered an important strategy
for reducing overall production costs. The main purpose of the
current research was to evaluate differences in physical-chemical
composition of cooked sausage with different legumes additions.
Peas (Pisum sativum), beans (Phaseolus vulgaris) and lentil (Lens
culinaris) were used in preparation of sausages. The legumes at
proportion of 20% of the total weight of meat were added in
sausages. The whole ingredients were mixed, filled into casing,
compressed, cooked and cooled. After storage the samples were
sensory evaluated. The sensory evaluation was carried out using the
nine point hedonic scale and line scale. Sausages without legumes
flour was used as control sample. The main conclusion of the current
research the legumes flour can be successfully used for cooked
sausages production.
Abstract: An application framework provides a reusable design
and implementation for a family of software systems. Application
developers extend the framework to build their particular
applications using hooks. Hooks are the places identified to show
how to use and customize the framework. Hooks define the
Framework Interface Classes (FICs) and their possible specifications,
which helps in building reusable test cases for the implementations of
these classes. This paper introduces a novel technique called all
paths-state to generate state-based test cases to test the FICs at class
level. The technique is experimentally evaluated. The empirical
evaluation shows that all paths-state technique produces test cases
with a high degree of coverage for the specifications of the
implemented FICs comparing to test cases generated using round-trip
path and all-transition techniques.
Abstract: The development and use of mobile devices as well as its integration within education systems to deliver electronic contents and to support real-time communications was the focus of this research. In order to investigate the software engineering issues in using mobile devices a research on electronic content was initiated. The Developed MP3 mobile software solution was developed as a prototype for testing and developing a strategy for designing a usable m-learning environment. The mobile software solution was evaluated using mobile device using the link: http://projects.seeu.edu.mk/mlearn. The investigation also tested the correlation between the two mobile learning indicators: electronic content and attention, based on the Task Based learning instructional method. The mobile software solution ''M-Learn“ was developed as a prototype for testing the approach and developing a strategy for designing usable m-learning environment. The proposed methodology is about what learning modeling approach is more appropriate to use when developing mobile learning software.
Abstract: We propose photo-BJMOSFET (Bipolar Junction Metal-Oxide-Semiconductor Field Effect Transistor) fabricated on SOI film. ITO film is adopted in the device as gate electrode to reduce light absorption. I-V characteristics of photo-BJMOSFET obtained in dark (dark current) and under 570nm illumination (photo current) are studied furthermore to achieve high photo-to-dark-current contrast ratio. Two variables in the calculation were the channel length and the thickness of the film which were set equal to six different values, i.e., L=2, 4, 6, 8, 10, and 12μm and three different values, i.e., dsi =100, 200 and 300nm, respectively. The results indicate that the greatest photo-to-dark-current contrast ratio is achieved with L=10μm and dsi=200 nm at VGK=0.6V.
Abstract: The aim of the present study was to investigate the
chemical and biological properties of local cowpea seed protein
cultivated in Gizan region. The results showed that the cowpea and
its products contain high level of protein (22.9-77.6%), high
carbohydrates (9.4-64.3%) and low fats (0.1-0.3%). The trypsin and
chymotrypsin activities were found to be 32.2 and 15.2 units,
respectively. These activities were not affected in both defatted and
protein concentrate whereas they were significantly reduced in
isolated protein and cooked samples. The phytate content of cooked
and concentrated cowpea samples varied from 0.25% -0.32%,
respectively. Tannin content was found to be 0.4% and 0.23% for
cooked and raw samples, respectively. The in vitro protein
digestibility was very high in cowpea seeds (75.04-78.76%). The
biological evaluation using rats showed that the group fed with
animal feed containing casein gain more weight than those fed with
that containing cowpea. However, the group fed with cooked cowpea
gain more weight than those fed with uncooked cowpea. On the
other hand, in vivo digestion showed high value (98.33%) among the
group consumed casein compared to other groups those consumed
cowpea contains feed. This could be attributed to low antinutritional
factors in casein contains feed compared to those of cowpea contains
feed because cooking significantly increased the digestion rate
(80.8% to 83.5%) of cowpea contains feed. Furthermore, the
biological evaluation was high (91.67%) of casein containing feed
compared to that of cowpea containing feed (80.83%-87.5%). The
net protein utilization (NPU) was higher (89.67%) in the group fed
with casein containing feed than that of cowpea containing feed
(56.33%-69.67%).
Abstract: Conceptualization strengthens intelligent systems in generalization skill, effective knowledge representation, real-time inference, and managing uncertain and indefinite situations in addition to facilitating knowledge communication for learning agents situated in real world. Concept learning introduces a way of abstraction by which the continuous state is formed as entities called concepts which are connected to the action space and thus, they illustrate somehow the complex action space. Of computational concept learning approaches, action-based conceptualization is favored because of its simplicity and mirror neuron foundations in neuroscience. In this paper, a new biologically inspired concept learning approach based on the probabilistic framework is proposed. This approach exploits and extends the mirror neuron-s role in conceptualization for a reinforcement learning agent in nondeterministic environments. In the proposed method, instead of building a huge numerical knowledge, the concepts are learnt gradually from rewards through interaction with the environment. Moreover the probabilistic formation of the concepts is employed to deal with uncertain and dynamic nature of real problems in addition to the ability of generalization. These characteristics as a whole distinguish the proposed learning algorithm from both a pure classification algorithm and typical reinforcement learning. Simulation results show advantages of the proposed framework in terms of convergence speed as well as generalization and asymptotic behavior because of utilizing both success and failures attempts through received rewards. Experimental results, on the other hand, show the applicability and effectiveness of the proposed method in continuous and noisy environments for a real robotic task such as maze as well as the benefits of implementing an incremental learning scenario in artificial agents.
Abstract: A multi-rate discrete-time model, whose response
agrees exactly with that of a continuous-time original at all sampling
instants for any sampling periods, is developed for a linear system,
which is assumed to have multiple real eigenvalues. The sampling
rates can be chosen arbitrarily and individually, so that their ratios
can even be irrational. The state space model is obtained as a
combination of a linear diagonal state equation and a nonlinear output
equation. Unlike the usual lifted model, the order of the proposed
model is the same as the number of sampling rates, which is less than
or equal to the order of the original continuous-time system. The
method is based on a nonlinear variable transformation, which can be
considered as a generalization of linear similarity transformation,
which cannot be applied to systems with multiple eigenvalues in
general. An example and its simulation result show that the proposed
multi-rate model gives exact responses at all sampling instants.
Abstract: In this study four Holstein steers with rumen fistula
fed 7 kg of dry matter (DM) of diets differing in concentrate to
alfalfa hay ratios as 60:40, 70:30, 80:20, and 90:10 in a 4 × 4 latin
square design. The pH of the ruminal fluid was measured before
the morning feeding (0.0 h) to 8 h post feeding. In this study, a
two-layered feed-forward neural network trained by the
Levenberg-Marquardt algorithm was used for modelling of ruminal
pH. The input variables of the network were time, concentrate to
alfalfa hay ratios (C/F), non fiber carbohydrate (NFC) and neutral
detergent fiber (NDF). The output variable was the ruminal pH.
The modeling results showed that there was excellent agreement
between the experimental data and predicted values, with a high
determination coefficient (R2 >0.96). Therefore, we suggest using
these model-derived biological values to summarize continuously
recorded pH data.