Abstract: Importance of software quality is increasing leading to development of new sophisticated techniques, which can be used in constructing models for predicting quality attributes. One such technique is Artificial Neural Network (ANN). This paper examined the application of ANN for software quality prediction using Object- Oriented (OO) metrics. Quality estimation includes estimating maintainability of software. The dependent variable in our study was maintenance effort. The independent variables were principal components of eight OO metrics. The results showed that the Mean Absolute Relative Error (MARE) was 0.265 of ANN model. Thus we found that ANN method was useful in constructing software quality model.
Abstract: To evaluate genetic variation of wheat (Triticum
aestivum) affected by heat and drought stress on eight Australian
wheat genotypes that are parents of Doubled Haploid (HD) mapping
populations at the vegetative stage, the water stress experiment was
conducted at 65% field capacity in growth room. Heat stress
experiment was conducted in the research field under irrigation over
summer. Result show that water stress decreased dry shoot weight
and RWC but increased osmolarity and means of Fv/Fm values in all
varieties except for Krichauff. Krichauff and Kukri had the
maximum RWC under drought stress. Trident variety was shown
maximum WUE, osmolarity (610 mM/Kg), dry mater, quantum yield
and Fv/Fm 0.815 under water stress condition. However, the
recovery of quantum yield was apparent between 4 to 7 days after
stress in all varieties. Nevertheless, increase in water stress after that
lead to strong decrease in quantum yield. There was a genetic
variation for leaf pigments content among varieties under heat stress.
Heat stress decreased significantly the total chlorophyll content that
measured by SPAD. Krichauff had maximum value of Anthocyanin
content (2.978 A/g FW), chlorophyll a+b (2.001 mg/g FW) and
chlorophyll a (1.502 mg/g FW). Maximum value of chlorophyll b
(0.515 mg/g FW) and Carotenoids (0.234 mg/g FW) content
belonged to Kukri. The quantum yield of all varieties decreased
significantly, when the weather temperature increased from 28 ÔùªC to
36 ÔùªC during the 6 days. However, the recovery of quantum yield
was apparent after 8th day in all varieties. The maximum decrease
and recovery in quantum yield was observed in Krichauff. Drought
and heat tolerant and moderately tolerant wheat genotypes were
included Trident, Krichauff, Kukri and RAC875. Molineux, Berkut
and Excalibur were clustered into most sensitive and moderately
sensitive genotypes. Finally, the results show that there was a
significantly genetic variation among the eight varieties that were
studied under heat and water stress.
Abstract: The paper focuses on the enhanced stiffness modeling
of robotic manipulators by taking into account influence of the external force/torque acting upon the end point. It implements the
virtual joint technique that describes the compliance of manipulator elements by a set of localized six-dimensional springs separated by
rigid links and perfect joints. In contrast to the conventional
formulation, which is valid for the unloaded mode and small
displacements, the proposed approach implicitly assumes that the loading leads to the non-negligible changes of the manipulator posture and corresponding amendment of the Jacobian. The
developed numerical technique allows computing the static
equilibrium and relevant force/torque reaction of the manipulator for
any given displacement of the end-effector. This enables designer
detecting essentially nonlinear effects in elastic behavior of
manipulator, similar to the buckling of beam elements. It is also proposed the linearization procedure that is based on the inversion of
the dedicated matrix composed of the stiffness parameters of the
virtual springs and the Jacobians/Hessians of the active and passive
joints. The developed technique is illustrated by an application example that deals with the stiffness analysis of a parallel
manipulator of the Orthoglide family
Abstract: Detection of incipient abnormal events is important to
improve safety and reliability of machine operations and reduce losses
caused by failures. Improper set-ups or aligning of parts often leads to
severe problems in many machines. The construction of prediction
models for predicting faulty conditions is quite essential in making
decisions on when to perform machine maintenance. This paper
presents a multivariate calibration monitoring approach based on the
statistical analysis of machine measurement data. The calibration
model is used to predict two faulty conditions from historical reference
data. This approach utilizes genetic algorithms (GA) based variable
selection, and we evaluate the predictive performance of several
prediction methods using real data. The results shows that the
calibration model based on supervised probabilistic principal
component analysis (SPPCA) yielded best performance in this work.
By adopting a proper variable selection scheme in calibration models,
the prediction performance can be improved by excluding
non-informative variables from their model building steps.
Abstract: Failure in mastery of motor skills proficiency during
childhood has been seen as a detrimental factor for children to be
physically active. Lack of motor skills proficiency tends to reduce
children’s competency and confidence level to participate in physical
activity. As a consequence of less participation in physical activity,
children will turn to be overweight and obese. It has been suggested
that children who master motor skill proficiency will be more
involved in physical activity thus preventing them from being
overweight. Obesity has become a serious childhood health issues
worldwide. Previous studies have found that children who were
overweight and obese were generally less active however these
studies focused on one gender. This study aims to compare motor
skill proficiency of underweight, normal-weight, overweight and
obese young boys as well as to determine the relationship between
motor skills proficiency and body composition. 112 boys aged
between 8 to 10 years old participated in this study. Participants were
assigned to four groups; underweight, normal-weight, overweight and
obese using BMI-age percentile chart for children. Bruininks-
Oseretsky Test Second Edition-Short Form was administered to
assess their motor skill proficiency. Meanwhile, body composition
was determined by the skinfold thickness measurement. Result
indicated that underweight and normal children were superior in
motor skills proficiency compared to overweight and obese children
(p < 0.05). A significant strong inverse correlation between motor
skills proficiency and body composition (r = -0.849) is noted. The
findings of this study could be explained by non-contributory mass
that carried by overweight and obese children leads to biomechanical
movement inefficiency which will become detrimental to motor skills
proficiency. It can be concluded that motor skills proficiency is
inversely correlated with body composition.
Abstract: Focusing on the environmental issues, including the reduction of scrap and consumer residuals, along with the benefiting from the economic value during the life cycle of goods/products leads the companies to have an important competitive approach. The aim of this paper is to present a new mixed nonlinear facility locationallocation model in recycling collection networks by considering multi-echelon, multi-suppliers, multi-collection centers and multifacilities in the recycling network. To make an appropriate decision in reality, demands, returns, capacities, costs and distances, are regarded uncertain in our model. For this purpose, a fuzzy mathematical programming-based possibilistic approach is introduced as a solution methodology from the recent literature to solve the proposed mixed-nonlinear programming model (MNLP). The computational experiments are provided to illustrate the applicability of the designed model in a supply chain environment and to help the decision makers to facilitate their analysis.
Abstract: Falls are the primary cause of accidents in people over
the age of 65, and frequently lead to serious injuries. Since the early
detection of falls is an important step to alert and protect the aging
population, a variety of research on detecting falls was carried out
including the use of accelerators, gyroscopes and tilt sensors. In
exiting studies, falls were detected using an accelerometer with
errors. In this study, the proposed method for detecting falls was to
use two accelerometers to reject wrong falls detection. As falls are
accompanied by the acceleration of gravity and rotational motion, the
falls in this study were detected by using the z-axial acceleration
differences between two sites. The falls were detected by calculating
the difference between the analyses of accelerometers placed on two
different positions on the chest of the subject. The parameters of the
maximum difference of accelerations (diff_Z) and the integration of
accelerations in a defined region (Sum_diff_Z) were used to form the
fall detection algorithm. The falls and the activities of daily living
(ADL) could be distinguished by using the proposed parameters
without errors in spite of the impact and the change in the positions
of the accelerometers. By comparing each of the axial accelerations,
the directions of falls and the condition of the subject afterwards
could be determined.In this study, by using two accelerometers
without errors attached to two sites to detect falls, the usefulness of
the proposed fall detection algorithm parameters, diff_Z and
Sum_diff_Z, were confirmed.
Abstract: Over the past few years, companies in developing
countries have implemented enterprise resource planning (ERP)
systems. Regardless of the various benefits of the ERP system, its
adoption and implementation have not been without problems. Many
companies have assigned considerable organizational resources to
their ERP projects, but have encountered unexpected challenges.
Neglecting a number of important factors in ERP projects might lead
to failure instead of success. User satisfaction is among those factors
that has a major influence on ERP implementation success. So, this
paper intends to investigate the key factors that create ERP users-
satisfaction and to discover whether ERP users- satisfaction varies
among different users- profiles. The study was conducted using a
survey questionnaire which was distributed to ERP users in Iranian
organizations. A total of 384 responses were collected and analyzed.
The findings indicated that younger ERP users tend to be more
satisfied with ERP systems. Furthermore, ERP users with more
experiences in IT and also more educated users have more
satisfaction with ERP softwares. However, the study found no
satisfaction differences between men and women users.
Abstract: The continued interest in the use of distributed generation in recent years is leading to the growth in number of distributed generators connected to distribution networks. Steady state voltage rise resulting from the connection of these generators can be a major obstacle to their connection at lower voltage levels. The present electric distribution network is designed to keep the customer voltage within tolerance limit. This may require a reduction in connectable generation capacity, under utilization of appropriate generation sites. Thus distribution network operators need a proper voltage regulation method to allow the significant integration of distributed generation systems to existing network. In this work a voltage rise problem in a typical distribution system has been studied. A method for voltage regulation of distribution system with multiple DG system by coordinated operation distributed generator, capacitor and OLTC has been developed. A sensitivity based analysis has been carried out to determine the priority for individual generators in multiple DG environment. The effectiveness of the developed method has been evaluated under various cases through simulation results.
Abstract: In this paper we present two novel 1-bit full adder
cells in dynamic logic style. NP-CMOS (Zipper) and Multi-Output
structures are used to design the adder blocks. Characteristic of
dynamic logic leads to higher speeds than the other standard static
full adder cells. Using HSpice and 0.18┬Ám CMOS technology
exhibits a significant decrease in the cell delay which can result in a
considerable reduction in the power-delay product (PDP). The PDP
of Multi-Output design at 1.8v power supply is around 0.15 femto
joule that is 5% lower than conventional dynamic full adder cell and
at least 21% lower than other static full adders.
Abstract: This paper develops driver reaction-time models for
car-following analysis based on human factors. The reaction time
was classified as brake-reaction time (BRT) and
acceleration/deceleration reaction time (ADRT). The BRT occurs
when the lead vehicle is barking and its brake light is on, while the
ADRT occurs when the driver reacts to adjust his/her speed using the
gas pedal only. The study evaluates the effect of driver
characteristics and traffic kinematic conditions on the driver reaction
time in a car-following environment. The kinematic conditions
introduced urgency and expectancy based on the braking behaviour
of the lead vehicle at different speeds and spacing. The kinematic
conditions were used for evaluating the BRT and are classified as
normal, surprised, and stationary. Data were collected on a driving
simulator integrated into a real car and included the BRT and ADRT
(as dependent variables) and driver-s age, gender, driving experience,
driving intensity (driving hours per week), vehicle speed, and
spacing (as independent variables). The results showed that there was
a significant difference in the BRT at normal, surprised, and
stationary scenarios and supported the hypothesis that both urgency
and expectancy had significant effects on BRT. Driver-s age, gender,
speed, and spacing were found to be significant variables for the
BRT in all scenarios. The results also showed that driver-s age and
gender were significant variables for the ADRT. The research
presented in this paper is part of a larger project to develop a driversensitive
in-vehicle rear-end collision warning system.
Abstract: In this paper, a clustering algorithm named KHarmonic
means (KHM) was employed in the training of Radial
Basis Function Networks (RBFNs). KHM organized the data in
clusters and determined the centres of the basis function. The popular
clustering algorithms, namely K-means (KM) and Fuzzy c-means
(FCM), are highly dependent on the initial identification of elements
that represent the cluster well. In KHM, the problem can be avoided.
This leads to improvement in the classification performance when
compared to other clustering algorithms. A comparison of the
classification accuracy was performed between KM, FCM and KHM.
The classification performance is based on the benchmark data sets:
Iris Plant, Diabetes and Breast Cancer. RBFN training with the KHM
algorithm shows better accuracy in classification problem.
Abstract: The spin (ms) and orbital (mo) magnetic moment of
the antiferromagnetic NiO and MnO have been studied in the local
spin density approximation (LSDA+U) within full potential linear
muffin-tin orbital (FP-LMTO method with in the coulomb interaction
U varying from 0 to 10eV, exchange interaction J, from 0 to 1.0eV,
and volume compression VC in range of 0 to 80%. Our calculated
results shown that the spin magnetic moments and the orbital
magnetic moments increase linearly with increasing U and J. While
the interesting behaviour appears when volume compression is
greater than 70% for NiO and 50% for MnO at which ms collapses.
Further increase of volume compression to be at 80% leads to the
disappearance of both magnetic moments.
Abstract: De novo genome assembly is always fragmented. Assembly fragmentation is more serious using the popular next generation sequencing (NGS) data because NGS sequences are shorter than the traditional Sanger sequences. As the data throughput of NGS is high, the fragmentations in assemblies are usually not the result of missing data. On the contrary, the assembled sequences, called contigs, are often connected to more than one other contigs in a complicated manner, leading to the fragmentations. False connections in such complicated connections between contigs, named a contig graph, are inevitable because of repeats and sequencing/assembly errors. Simplifying a contig graph by removing false connections directly improves genome assembly. In this work, we have developed a tool, SIMGraph, to resolve ambiguous connections between contigs using NGS data. Applying SIMGraph to the assembly of a fungus and a fish genome, we resolved 27.6% and 60.3% ambiguous contig connections, respectively. These results can reduce the experimental efforts in resolving contig connections.
Abstract: Linear stability of wake-shear layers in two-phase
shallow flows is analyzed in the present paper. Stability analysis is
based on two-dimensional shallow water equations. It is assumed that
the fluid contains uniformly distributed solid particles. No dynamic
interaction between the carrier fluid and particles is expected in the
initial moment. Linear stability curves are obtained for different
values of the particle loading parameter, the velocity ratio and the
velocity deficit. It is shown that the increase in the velocity ratio
destabilizes the flow. The particle loading parameter has a stabilizing
effect on the flow. The role of the velocity deficit is also
destabilizing: the increase of the velocity deficit leads to less stable
flow.
Abstract: Voltage flicker is a disturbance in electrical power
systems. The reason for this disturbance is mainly the large nonlinear
loads such as electric arc furnaces. Synchronous static
compensator (STATCOM) is considered as a proper technique to
mitigate the voltage flicker. Application of more suitable and precise
power electronic converter leads to a more precise performance of the
compensator. In this paper a three-level 12-pulse voltage source
inverter (VSI) with a 12-terminal transformer connected to the ac
system is studied and the obtained results are compared with the
performance of a STATCOM using a simple two-level VSI and an
optimal and more precise performance of the proposed scheme is
achieved.
Abstract: Electrocardiogram (ECG) data compression algorithm
is needed that will reduce the amount of data to be transmitted, stored
and analyzed, but without losing the clinical information content. A
wavelet ECG data codec based on the Set Partitioning In Hierarchical
Trees (SPIHT) compression algorithm is proposed in this paper. The
SPIHT algorithm has achieved notable success in still image coding.
We modified the algorithm for the one-dimensional (1-D) case and
applied it to compression of ECG data.
By this compression method, small percent root mean square
difference (PRD) and high compression ratio with low
implementation complexity are achieved. Experiments on selected
records from the MIT-BIH arrhythmia database revealed that the
proposed codec is significantly more efficient in compression and in
computation than previously proposed ECG compression schemes.
Compression ratios of up to 48:1 for ECG signals lead to acceptable
results for visual inspection.
Abstract: Since after the historical moment of Malaysia
Independence Day on the year of 1957, the government had been trying hard in order to find the most efficient methods in learning.
However, it is hard to actually access and evaluate students whom will then be called an excellent student. It because in our realtime
student who excellent is only excel in academic. This evaluation
become a problem because it not balance in our real life interm of to get an excellent student in whole area in their involvement of curiculum and co-curiculum. To overcome this scenario, we
proposed a method called Student Idol to evaluate student through
three categories which are academic, co-curiculum and leadership.
All the categories have their own merit point. Using this method, student will be evaluated more accurate compared to the previously.
So, teacher can easily evaluate their student without having any emotion factor, relation factor and others. As conclustion this method will helps student evaluation more accurate and valid.
Abstract: The state of the art in instructional design for
computer-assisted learning has been strongly influenced by advances
in information technology, Internet and Web-based systems. The
emphasis of educational systems has shifted from training to
learning. The course delivered has also been changed from large
inflexible content to sequential small chunks of learning objects. The
concepts of learning objects together with the advanced technologies
of Web and communications support the reusability, interoperability,
and accessibility design criteria currently exploited by most learning
systems. These concepts enable just-in-time learning. We propose to
extend theses design criteria further to include the learnability
concept that will help adapting content to the needs of learners. The
learnability concept offers a better personalization leading to the
creation and delivery of course content more appropriate to
performance and interest of each learner. In this paper we present a
new framework of learning environments containing knowledge
discovery as a tool to automatically learn patterns of learning
behavior from learners' profiles and history.
Abstract: Named Entity Recognition (NER) aims to classify each word of a document into predefined target named entity classes and is now-a-days considered to be fundamental for many Natural Language Processing (NLP) tasks such as information retrieval, machine translation, information extraction, question answering systems and others. This paper reports about the development of a NER system for Bengali and Hindi using Support Vector Machine (SVM). Though this state of the art machine learning technique has been widely applied to NER in several well-studied languages, the use of this technique to Indian languages (ILs) is very new. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the four different named (NE) classes, such as Person name, Location name, Organization name and Miscellaneous name. We have used the annotated corpora of 122,467 tokens of Bengali and 502,974 tokens of Hindi tagged with the twelve different NE classes 1, defined as part of the IJCNLP-08 NER Shared Task for South and South East Asian Languages (SSEAL) 2. In addition, we have manually annotated 150K wordforms of the Bengali news corpus, developed from the web-archive of a leading Bengali newspaper. We have also developed an unsupervised algorithm in order to generate the lexical context patterns from a part of the unlabeled Bengali news corpus. Lexical patterns have been used as the features of SVM in order to improve the system performance. The NER system has been tested with the gold standard test sets of 35K, and 60K tokens for Bengali, and Hindi, respectively. Evaluation results have demonstrated the recall, precision, and f-score values of 88.61%, 80.12%, and 84.15%, respectively, for Bengali and 80.23%, 74.34%, and 77.17%, respectively, for Hindi. Results show the improvement in the f-score by 5.13% with the use of context patterns. Statistical analysis, ANOVA is also performed to compare the performance of the proposed NER system with that of the existing HMM based system for both the languages.