Abstract: OEE has been used in many industries as measure of
performance. However due to limitations of original OEE, it has been
modified by various researchers. OEE for mining application is
special version of classic equation, carries these limitation over. In
this paper it has been aimed to modify the OEE for mining
application by introducing the weights to the elements of it and
termed as Mine Production index (MPi). As a special application of
new index MPishovel has been developed by authors. This can be used
for evaluating the shovel effectiveness. Based on analysis, utilization
followed by performance and availability were ranked in this order.
To check the applicability of this index, a case study was done on
four electrical and one hydraulic shovel in a Swedish mine. The
results shows that MPishovel can evaluate production effectiveness of
shovels and can determine effectiveness values in optimistic view
compared to OEE. MPi with calculation not only give the
effectiveness but also can predict which elements should be focused
for improving the productivity.
Abstract: Class cohesion is a key object-oriented software
quality attribute that is used to evaluate the degree of relatedness of
class attributes and methods. Researchers have proposed several class
cohesion measures. However, the effect of considering the special
methods (i.e., constructors, destructors, and access and delegation
methods) in cohesion calculation is not thoroughly theoretically
studied for most of them. In this paper, we address this issue for three
popular connectivity-based class cohesion measures. For each of the
considered measures we theoretically study the impact of including
or excluding special methods on the values that are obtained by
applying the measure. This study is based on analyzing the
definitions and formulas that are proposed for the measures. The
results show that including/excluding special methods has a
considerable effect on the obtained cohesion values and that this
effect varies from one measure to another. For each of the three
connectivity-based measures, the proposed theoretical study
recommended excluding the special methods in cohesion
measurement.
Abstract: The purpose of this research is to study of consumer
perception and understanding consumer buying behavior that related
between satisfied and factors affecting the purchasing. Methodology
can be classified between qualitative and quantitative approaches for
the qualitative research were interviews from middlemen who bought
organic vegetables, and middlemen related to production and
marketing system. A questionnaire was utilized as a tool to collect
data. Statistics utilized in this research included frequency,
percentage, mean, standard deviation, and multiple regression
analysis. The result show the reason to decision buying motives is
Fresh products of organic vegetables is the most significant factor on
individuals’ income, with a b of –.143, t = –2.470, the price of
organic vegetables is the most significant factor on individuals’
income, with a b of .176, t = 2.561, p value = .011. The results show
that most people with higher income think about the organic products
are expensive and have negative attitudes towards organic vegetable
as individuals with low and medium income level. Therefore,
household income had a significant influence on the purchasing
decision.
Abstract: This research is aimed to develop the online-class
scheduling management system and improve as a complex problem
solution, this must take into consideration in various conditions and
factors. In addition to the number of courses, the number of students
and a timetable to study, the physical characteristics of each class
room and regulations used in the class scheduling must also be taken
into consideration. This system is developed to assist management in
the class scheduling for convenience and efficiency. It can provide
several instructors to schedule simultaneously. Both lecturers and
students can check and publish a timetable and other documents
associated with the system online immediately. It is developed in a
web-based application. PHP is used as a developing tool. The
database management system was MySQL. The tool that is used for
efficiency testing of the system is questionnaire. The system was
evaluated by using a Black-Box testing. The sample was composed
of 2 groups: 5 experts and 100 general users. The average and the
standard deviation of results from the experts were 3.50 and 0.67.
The average and the standard deviation of results from the general
users were 3.54 and 0.54. In summary, the results from the research
indicated that the satisfaction of users were in a good level.
Therefore, this system could be implemented in an actual workplace
and satisfy the users’ requirement effectively.
Abstract: The main objective of this study was to identify
factors and conditions that motivated and encouraged students
towards the math class and the factors that made this class an
attractive and lovely one. To do this end, questionnaires consisting of
15 questions were distributed among 85 math teachers working in
schools of Zahedan. Having collected and reviewed these
questionnaires, it was shown that doing activity in math class
(activity of students while teaching) and previous math teachers'
behaviors have had much impact on encouraging the students
towards mathematics. Separation of educational classroom of
mathematics from the main classroom (which is decorated with crafts
created by students themselves with regard to math book including
article, wall newspaper, figures and formulas), peers, size and
appearance of math book, first grade teachers in each educational
level, among whom the Elementary first grade teachers had more
importance and impact, were among the most influential and
important factors in this regard. Then, school environment, family,
conducting research related to mathematics, its application in daily
life and other courses and studying the history of mathematics were
categorized as important factors that would increase the students’
interest in mathematics.
Abstract: In this research work, neural networks were applied to
classify two types of hip joint implants based on the relative hip joint
implant side speed and three components of each ground reaction
force. The condition of walking gait at normal velocity was used and
carried out with each of the two hip joint implants assessed. Ground
reaction forces’ kinetic temporal changes were considered in the first
approach followed but discarded in the second one. Ground reaction
force components were obtained from eighteen patients under such
gait condition, half of which had a hip implant type I-II, whilst the
other half had the hip implant, defined as type III by Orthoload®.
After pre-processing raw gait kinetic data and selecting the time
frames needed for the analysis, the ground reaction force components
were used to train a MLP neural network, which learnt to distinguish
the two hip joint implants in the abovementioned condition. Further
to training, unknown hip implant side and ground reaction force
components were presented to the neural networks, which assigned
those features into the right class with a reasonably high accuracy for
the hip implant type I-II and the type III. The results suggest that
neural networks could be successfully applied in the performance
assessment of hip joint implants.
Abstract: Audio-lingual Method (ALM) is a teaching approach
that is claimed that ineffective for teaching second/foreign languages.
Because some linguists and second/foreign language teachers believe
that ALM is a rote learning style. However, this study is done on a
belief that ALM will be able to solve Thais’ English speaking
problem. This paper aims to report the findings on teaching English
speaking to adult learners with an “adapted ALM”, one distinction of
which is to use Thai as the medium language of instruction.
The participants are consisted of 9 adult learners. They were
allowed to speak English more freely using both the materials
presented in the class and their background knowledge of English. At
the end of the course, they spoke English more fluently, more
confidently, to the extent that they applied what they learnt both in
and outside the class.
Abstract: Estimation of a proportion has many applications in
economics and social studies. A common application is the estimation
of the low income proportion, which gives the proportion of people
classified as poor into a population. In this paper, we present this
poverty indicator and propose to use the logistic regression estimator
for the problem of estimating the low income proportion. Various
sampling designs are presented. Assuming a real data set obtained
from the European Survey on Income and Living Conditions, Monte
Carlo simulation studies are carried out to analyze the empirical
performance of the logistic regression estimator under the various
sampling designs considered in this paper. Results derived from
Monte Carlo simulation studies indicate that the logistic regression
estimator can be more accurate than the customary estimator under
the various sampling designs considered in this paper. The stratified
sampling design can also provide more accurate results.
Abstract: This paper aims at introducing finite automata theory,
the different ways to describe regular languages and create a program
to implement the subset construction algorithms to convert
nondeterministic finite automata (NFA) to deterministic finite
automata (DFA). This program is written in c++ programming
language. The program reads FA 5tuples from text file and then
classifies it into either DFA or NFA. For DFA, the program will read
the string w and decide whether it is acceptable or not. If accepted, the
program will save the tracking path and point it out. On the other hand,
when the automation is NFA, the program will change the Automation
to DFA so that it is easy to track and it can decide whether the w exists
in the regular language or not.
Abstract: The paper presents the results of clusterization by
Kohonen self-organizing maps (SOM) applied for analysis of array of
Raman spectra of multi-component solutions of inorganic salts, for
determination of types of salts present in the solution. It is
demonstrated that use of SOM is a promising method for solution of
clusterization and classification problems in spectroscopy of multicomponent
objects, as attributing a pattern to some cluster may be
used for recognition of component composition of the object.
Abstract: Frequent pattern mining is the process of finding a
pattern (a set of items, subsequences, substructures, etc.) that occurs
frequently in a data set. It was proposed in the context of frequent
itemsets and association rule mining. Frequent pattern mining is used
to find inherent regularities in data. What products were often
purchased together? Its applications include basket data analysis,
cross-marketing, catalog design, sale campaign analysis, Web log
(click stream) analysis, and DNA sequence analysis. However, one of
the bottlenecks of frequent itemset mining is that as the data increase
the amount of time and resources required to mining the data
increases at an exponential rate. In this investigation a new algorithm
is proposed which can be uses as a pre-processor for frequent itemset
mining. FASTER (FeAture SelecTion using Entropy and Rough sets)
is a hybrid pre-processor algorithm which utilizes entropy and roughsets
to carry out record reduction and feature (attribute) selection
respectively. FASTER for frequent itemset mining can produce a
speed up of 3.1 times when compared to original algorithm while
maintaining an accuracy of 71%.
Abstract: One of the most important tasks in urban remote
sensing is the detection of impervious surfaces (IS), such as roofs and
roads. However, detection of IS in heterogeneous areas still remains
one of the most challenging tasks. In this study, detection of concrete
roof using an object-based approach was proposed. A new rule-based
classification was developed to detect concrete roof tile. This
proposed rule-based classification was applied to WorldView-2
image and results showed that the proposed rule has good potential to
predict concrete roof material from WorldView-2 images, with 85%
accuracy.
Abstract: Boron-gypsum is a waste which occurs in the boric
acid production process. In this study, the boron content of this waste
is evaluated for the use in synthesis of magnesium borates and such
evaluation of this kind of waste is useful more than storage or
disposal. Magnesium borates, which are a sub-class of boron
minerals, are useful additive materials for the industries due to their
remarkable thermal and mechanical properties. Magnesium borates
were obtained hydrothermally at different temperatures. Novelty of
this study is the search of the solution density effects to magnesium
borate synthesis process for the increasing the possibility of borongypsum
usage as a raw material. After the synthesis process, products
are subjected to XRD and FT-IR to identify and characterize their
crystal structure, respectively.
Abstract: A generalized vortex lattice method for complex
lifting surfaces with flap and aileron deflection is formulated. The
method is not restricted by the linearized theory assumption and
accounts for all standard geometric lifting surface parameters:
camber, taper, sweep, washout, dihedral, in addition to flap and
aileron deflection. Thickness is not accounted for since the physical
lifting body is replaced by a lattice of panels located on the mean
camber surface. This panel lattice setup and the treatment of different
wake geometries is what distinguish the present work form the
overwhelming majority of previous solutions based on the vortex
lattice method. A MATLAB code implementing the proposed
formulation is developed and validated by comparing our results to
existing experimental and numerical ones and good agreement is
demonstrated. It is then used to study the accuracy of the widely used
classical vortex-lattice method. It is shown that the classical approach
gives good agreement in the clean configuration but is off by as much
as 30% when a flap or aileron deflection of 30° is imposed. This
discrepancy is mainly due the linearized theory assumption
associated with the conventional method. A comparison of the effect
of four different wake geometries on the values of aerodynamic
coefficients was also carried out and it is found that the choice of the
wake shape had very little effect on the results.
Abstract: One of the major goals of Spoken Dialog Systems
(SDS) is to understand what the user utters.
In the SDS domain, the Spoken Language Understanding (SLU)
Module classifies user utterances by means of a pre-definite
conceptual knowledge. The SLU module is able to recognize only the
meaning previously included in its knowledge base. Due the vastity
of that knowledge, the information storing is a very expensive
process.
Updating and managing the knowledge base are time-consuming
and error-prone processes because of the rapidly growing number of
entities like proper nouns and domain-specific nouns. This paper
proposes a solution to the problem of Name Entity Recognition
(NER) applied to a SDS domain. The proposed solution attempts to
automatically recognize the meaning associated with an utterance by
using the PANKOW (Pattern based Annotation through Knowledge
On the Web) method at runtime.
The method being proposed extracts information from the Web to
increase the SLU knowledge module and reduces the development
effort. In particular, the Google Search Engine is used to extract
information from the Facebook social network.
Abstract: This paper presents the voltage problem location
classification using performance of Least Squares Support Vector
Machine (LS-SVM) and Learning Vector Quantization (LVQ) in
electrical power system for proper voltage problem location
implemented by IEEE 39 bus New- England. The data was collected
from the time domain simulation by using Power System Analysis
Toolbox (PSAT). Outputs from simulation data such as voltage, phase
angle, real power and reactive power were taken as input to estimate
voltage stability at particular buses based on Power Transfer Stability
Index (PTSI).The simulation data was carried out on the IEEE 39 bus
test system by considering load bus increased on the system. To verify
of the proposed LS-SVM its performance was compared to Learning
Vector Quantization (LVQ). The results showed that LS-SVM is faster
and better as compared to LVQ. The results also demonstrated that the
LS-SVM was estimated by 0% misclassification whereas LVQ had
7.69% misclassification.
Abstract: Consumer-to-Consumer (C2C) E-commerce has been
growing at a very high speed in recent years. Since identical or
nearly-same kinds of products compete one another by relying on
keyword search in C2C E-commerce, some sellers describe their
products with spam keywords that are popular but are not related to
their products. Though such products get more chances to be retrieved
and selected by consumers than those without spam keywords,
the spam keywords mislead the consumers and waste their time.
This problem has been reported in many commercial services like
ebay and taobao, but there have been little research to solve this
problem. As a solution to this problem, this paper proposes a method
to classify whether keywords of a product are spam or not. The
proposed method assumes that a keyword for a given product is
more reliable if the keyword is observed commonly in specifications
of products which are the same or the same kind as the given
product. This is because that a hierarchical category of a product
in general determined precisely by a seller of the product and so is
the specification of the product. Since higher layers of the hierarchical
category represent more general kinds of products, a reliable degree
is differently determined according to the layers. Hence, reliable
degrees from different layers of a hierarchical category become
features for keywords and they are used together with features only
from specifications for classification of the keywords. Support Vector
Machines are adopted as a basic classifier using the features, since
it is powerful, and widely used in many classification tasks. In
the experiments, the proposed method is evaluated with a golden
standard dataset from Yi-han-wang, a Chinese C2C E-commerce,
and is compared with a baseline method that does not consider
the hierarchical category. The experimental results show that the
proposed method outperforms the baseline in F1-measure, which
proves that spam keywords are effectively identified by a hierarchical
category in C2C E-commerce.
Abstract: In this paper the issue of dimensionality reduction is
investigated in finger vein recognition systems using kernel Principal
Component Analysis (KPCA). One aspect of KPCA is to find the
most appropriate kernel function on finger vein recognition as there
are several kernel functions which can be used within PCA-based
algorithms. In this paper, however, another side of PCA-based
algorithms -particularly KPCA- is investigated. The aspect of
dimension of feature vector in PCA-based algorithms is of
importance especially when it comes to the real-world applications
and usage of such algorithms. It means that a fixed dimension of
feature vector has to be set to reduce the dimension of the input and
output data and extract the features from them. Then a classifier is
performed to classify the data and make the final decision. We
analyze KPCA (Polynomial, Gaussian, and Laplacian) in details in
this paper and investigate the optimal feature extraction dimension in
finger vein recognition using KPCA.
Abstract: Biological conversion of biomass to methane has
received increasing attention in recent years. Grasses have been
explored for their potential anaerobic digestion to methane. In this
review, extensive literature data have been tabulated and classified.
The influences of several parameters on the potential of these
feedstocks to produce methane are presented. Lignocellulosic
biomass represents a mostly unused source for biogas and ethanol
production. Many factors, including lignin content, crystallinity of
cellulose, and particle size, limit the digestibility of the hemicellulose
and cellulose present in the lignocellulosic biomass. Pretreatments
have used to improve the digestibility of the lignocellulosic biomass.
Each pretreatment has its own effects on cellulose, hemicellulose and
lignin, the three main components of lignocellulosic biomass. Solidstate
anaerobic digestion (SS-AD) generally occurs at solid
concentrations higher than 15%. In contrast, liquid anaerobic
digestion (AD) handles feedstocks with solid concentrations between
0.5% and 15%. Animal manure, sewage sludge, and food waste are
generally treated by liquid AD, while organic fractions of municipal
solid waste (OFMSW) and lignocellulosic biomass such as crop
residues and energy crops can be processed through SS-AD. An
increase in operating temperature can improve both the biogas yield
and the production efficiency, other practices such as using AD
digestate or leachate as an inoculant or decreasing the solid content
may increase biogas yield but have negative impact on production
efficiency. Focus is placed on substrate pretreatment in anaerobic
digestion (AD) as a means of increasing biogas yields using today’s
diversified substrate sources.
Abstract: Bureaucracy reform program drives Indonesian
government to change their management to enhance their
organizational performance. Information technology became one of
strategic plan that organization tried to improve. Knowledge
management system is one of information system that supporting
knowledge management implementation in government which
categorized as people perspective, because this system has high
dependency in human interaction and participation. Strategic plan for
developing knowledge management system can be determine using
some of information system strategic methods. This research
conducted to define type of strategic method of information system,
stage of activity each method, strength and weakness. Literature
review methods used to identify and classify strategic methods of
information system, differentiate method type, categorize common
activities, strength and weakness. Result of this research are
determine and compare six strategic information system methods,
Balanced Scorecard and Risk Analysis believe as common strategic
method that usually used and have the highest excellence strength.