Abstract: The operating control parameters of injection
flushing type of electrical discharge machining process on stainless
steel 304 workpiece with copper tools are being optimized
according to its individual machining characteristic i.e. material
removal rate (MRR). Lower MRR during EDM machining process
may decrease its- machining productivity. Hence, the quality
characteristic for MRR is set to higher-the-better to achieve the
optimum machining productivity. Taguchi method has been used
for the construction, layout and analysis of the experiment for each
of the machining characteristic for the MRR. The use of Taguchi
method in the experiment saves a lot of time and cost of preparing
and machining the experiment samples. Therefore, an L18
Orthogonal array which was the fundamental component in the
statistical design of experiments has been used to plan the
experiments and Analysis of Variance (ANOVA) is used to
determine the optimum machining parameters for this machining
characteristic. The control parameters selected for this
optimization experiments are polarity, pulse on duration, discharge
current, discharge voltage, machining depth, machining diameter
and dielectric liquid pressure. The result had shown that the higher
the discharge voltage, the higher will be the MRR.
Abstract: Nowadays increasingly the population makes use of
Information Technology (IT). As such, in recent year the Portuguese
government increased its focus on using the IT for improving
people-s life and began to develop a set of measures to enable the
modernization of the Public Administration, and so reducing the gap
between Public Administration and citizens.Thus the Portuguese
Government launched the Simplex Program. However these
SIMPLEX eGov measures, which have been implemented over the
years, present a serious challenge: how to forecast its impact on
existing Information Systems Architecture (ISA). Thus, this research
is focus in addressing the problem of automating the evaluation of the
actual impact of implementation an eGovSimplification and
Modernization measures in the Information Systems Architecture. To
realize the evaluation we proposes a Framework, which is supported
by some key concepts as: Quality Factors, ISA modeling,
Multicriteria Approach, Polarity Profile and Quality Metrics
Abstract: This study aimed at assessing whether and to what extent moral judgment and behaviour were: 1. situation-dependent; 2. selectively dependent on cognitive and affective components; 3. influenced by gender and age; 4. reciprocally congruent. In order to achieve these aims, four different types of moral dilemmas were construed and five types of thinking were presented for each of them – representing five possible ways to evaluate the situation. The judgment criteria included selfishness, altruism, sense of justice, and the conflict between selfishness and the two moral issues. The participants were 250 unpaid volunteers (50% male; 50% female) belonging to two age-groups: young people and adults. The study entailed a 2 (gender) x 2 (age-group) x 5 (type of thinking) x 4 (situation) mixed design: the first two variables were betweensubjects, the others were within-subjects. Results have shown that: 1. moral judgment and behaviour are at least partially affected by the type of situations and by interpersonal variables such as gender and age; 2. moral reasoning depends in a similar manner on cognitive and affective factors; 3. there is not a gender polarity between the ethic of justice and the ethic of cure/ altruism; 4. moral reasoning and behavior are perceived as reciprocally congruent even though their congruence decreases with a more objective assessment. Such results were discussed in the light of contrasting theories on morality.
Abstract: In the present work, Pulsed Electro Acoustic (PEA)
technique was adopted to understand the space charge dynamics in
elastomeric material. It is observed that the polarity of the applied
DC voltage voltage and its magnitude alters the space charge
dynamics in insulation structure. It is also noticed that any addition
of compound to the base material/processing technique have
characteristic variation in the space charge injection process. It could
be concluded based on the present work that the plasticizer could
inject heterocharges into the insulation medium. Also it is realized
that space charge magnitude is less with the addition of plasticizer. In
the PEA studies, it is observed that local electric field in the
insulating material can be much more than applied electric field due
to space charge formation. One of the important conclusions arrived
at based on PEA technique is that one could understand the safe
operating electric field of an insulation material and the charge trap
sites.
Abstract: Censored Production Rule is an extension of standard
production rule, which is concerned with problems of reasoning with
incomplete information, subject to resource constraints and problem
of reasoning efficiently with exceptions. A CPR has a form: IF A
(Condition) THEN B (Action) UNLESS C (Censor), Where C is the
exception condition. Fuzzy CPR are obtained by augmenting
ordinary fuzzy production rule “If X is A then Y is B with an
exception condition and are written in the form “If X is A then Y is B
Unless Z is C. Such rules are employed in situation in which the
fuzzy conditional statement “If X is A then Y is B" holds frequently
and the exception condition “Z is C" holds rarely. Thus “If X is A
then Y is B" part of the fuzzy CPR express important information
while the unless part acts only as a switch that changes the polarity of
“Y is B" to “Y is not B" when the assertion “Z is C" holds. The
proposed approach is an attempt to discover fuzzy censored
production rules from set of discovered fuzzy if then rules in the
form:
A(X)  B(Y) || C(Z).
Abstract: This paper observed the interhemispheric conjugate
auroral activity occurred on 21 September 2009. The GPS derived
ionospheric total electron content (TEC) during a weak substorm
interval recorded at interhemispheric conjugate points at Husafell in
Iceland and Syowa in Antarctica is investigated to look at their
signatures on the auroral features. Selection of all-sky camera (ASC)
images and keogram at Tjörnes and Syowa during the interval
00:47:54 – 00:50:14 UT on 21 September 2009 found that the auroral
activity had exerted their influence on the GPS TEC as a
consequence of varying interplanetary magnetic field (IMF) By
polarity.
Abstract: The application of a high frequency signal injection method as speed and position observer in PMSM drives has been a research focus. At present, the precision of this method is nearly good as that of ten-bit encoder. But there are some questions for estimating position polarity. Based on high frequency signal injection, this paper presents a method to compensate position polarity for permanent magnet synchronous motor (PMSM). Experiments were performed to test the effectiveness of the proposed algorithm and results present the good performance.
Abstract: This paper deals optimized model to investigate the
effects of peak current, pulse on time and pulse off time in EDM performance on material removal rate of titanium alloy utilizing copper tungsten as electrode and positive polarity of the electrode. The experiments are carried out on Ti6Al4V. Experiments were
conducted by varying the peak current, pulse on time and pulse off time. A mathematical model is developed to correlate the influences of these variables and material removal rate of workpiece. Design of
experiments (DOE) method and response surface methodology
(RSM) techniques are implemented. The validity test of the fit and adequacy of the proposed models has been carried out through
analysis of variance (ANOVA). The obtained results evidence that as
the material removal rate increases as peak current and pulse on time
increases. The effect of pulse off time on MRR changes with peak ampere. The optimum machining conditions in favor of material removal rate are verified and compared. The optimum machining
conditions in favor of material removal rate are estimated and verified with proposed optimized results. It is observed that the developed model is within the limits of the agreeable error (about
4%) when compared to experimental results. This result leads to desirable material removal rate and economical industrial machining to optimize the input parameters.
Abstract: In this paper, we are presenting a new type of pointing interface for computers which provides mouse functionalities with near surface haptic feedback. Further, it can be configured as a haptic display where users may feel the basic geometrical shapes in the GUI by moving the finger on top of the device surface. These functionalities are achieved by tracking three dimensional positions of the neodymium magnet using Hall Effect sensors grid and generating like polarity haptic feedback using an electromagnet array. This interface brings the haptic sensations to the 3D space where previously it is felt only on top of the buttons of the haptic mouse implementations.
Abstract: Conventionally the selection of parameters depends
intensely on the operator-s experience or conservative technological
data provided by the EDM equipment manufacturers that assign
inconsistent machining performance. The parameter settings given by
the manufacturers are only relevant with common steel grades. A
single parameter change influences the process in a complex way.
Hence, the present research proposes artificial neural network (ANN)
models for the prediction of surface roughness on first commenced
Ti-15-3 alloy in electrical discharge machining (EDM) process. The
proposed models use peak current, pulse on time, pulse off time and
servo voltage as input parameters. Multilayer perceptron (MLP) with
three hidden layer feedforward networks are applied. An assessment
is carried out with the models of distinct hidden layer. Training of the
models is performed with data from an extensive series of
experiments utilizing copper electrode as positive polarity. The
predictions based on the above developed models have been verified
with another set of experiments and are found to be in good
agreement with the experimental results. Beside this they can be
exercised as precious tools for the process planning for EDM.
Abstract: A New features are extracted and compared to
improve the prediction of protein-protein interactions. The basic idea
is to select and use the best set of features from the Tensor matrices
that are produced by the frequency vectors of the protein sequences.
Three set of features are compared, the first set is based on the
indices that are the most common in the interacting proteins, the
second set is based on the indices that tend to be common in the
interacting and non-interacting proteins, and the third set is
constructed by using random indices. Moreover, three encoding
strategies are compared; that are based on the amino asides polarity,
structure, and chemical properties. The experimental results indicate
that the highest accuracy can be obtained by using random indices
with chemical properties encoding strategy and support vector
machine.
Abstract: In the recent past Learning Classifier Systems have
been successfully used for data mining. Learning Classifier System
(LCS) is basically a machine learning technique which combines
evolutionary computing, reinforcement learning, supervised or
unsupervised learning and heuristics to produce adaptive systems. A
LCS learns by interacting with an environment from which it
receives feedback in the form of numerical reward. Learning is
achieved by trying to maximize the amount of reward received. All
LCSs models more or less, comprise four main components; a finite
population of condition–action rules, called classifiers; the
performance component, which governs the interaction with the
environment; the credit assignment component, which distributes the
reward received from the environment to the classifiers accountable
for the rewards obtained; the discovery component, which is
responsible for discovering better rules and improving existing ones
through a genetic algorithm. The concatenate of the production rules
in the LCS form the genotype, and therefore the GA should operate
on a population of classifier systems. This approach is known as the
'Pittsburgh' Classifier Systems. Other LCS that perform their GA at
the rule level within a population are known as 'Mitchigan' Classifier
Systems. The most predominant representation of the discovered
knowledge is the standard production rules (PRs) in the form of IF P
THEN D. The PRs, however, are unable to handle exceptions and do
not exhibit variable precision. The Censored Production Rules
(CPRs), an extension of PRs, were proposed by Michalski and
Winston that exhibit variable precision and supports an efficient
mechanism for handling exceptions. A CPR is an augmented
production rule of the form: IF P THEN D UNLESS C, where
Censor C is an exception to the rule. Such rules are employed in
situations, in which conditional statement IF P THEN D holds
frequently and the assertion C holds rarely. By using a rule of this
type we are free to ignore the exception conditions, when the
resources needed to establish its presence are tight or there is simply
no information available as to whether it holds or not. Thus, the IF P
THEN D part of CPR expresses important information, while the
UNLESS C part acts only as a switch and changes the polarity of D
to ~D. In this paper Pittsburgh style LCSs approach is used for
automated discovery of CPRs. An appropriate encoding scheme is
suggested to represent a chromosome consisting of fixed size set of
CPRs. Suitable genetic operators are designed for the set of CPRs
and individual CPRs and also appropriate fitness function is proposed
that incorporates basic constraints on CPR. Experimental results are
presented to demonstrate the performance of the proposed learning
classifier system.
Abstract: Knowledge is indispensable but voluminous knowledge becomes a bottleneck for efficient processing. A great challenge for data mining activity is the generation of large number of potential rules as a result of mining process. In fact sometimes result size is comparable to the original data. Traditional data mining pruning activities such as support do not sufficiently reduce the huge rule space. Moreover, many practical applications are characterized by continual change of data and knowledge, thereby making knowledge voluminous with each change. The most predominant representation of the discovered knowledge is the standard Production Rules (PRs) in the form If P Then D. Michalski & Winston proposed Censored Production Rules (CPRs), as an extension of production rules, that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: If P Then D Unless C, where C (Censor) is an exception to the rule. Such rules are employed in situations in which the conditional statement 'If P Then D' holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence, are tight or there is simply no information available as to whether it holds or not. Thus the 'If P Then D' part of the CPR expresses important information while the Unless C part acts only as a switch changes the polarity of D to ~D. In this paper a scheme based on Dempster-Shafer Theory (DST) interpretation of a CPR is suggested for discovering CPRs from the discovered flat PRs. The discovery of CPRs from flat rules would result in considerable reduction of the already discovered rules. The proposed scheme incrementally incorporates new knowledge and also reduces the size of knowledge base considerably with each episode. Examples are given to demonstrate the behaviour of the proposed scheme. The suggested cumulative learning scheme would be useful in mining data streams.
Abstract: The operating control parameters of injection
flushing type of electrical discharge machining process on stainless
steel 304 workpiece using copper tools are being optimized
according to its individual machining characteristic i.e. Electrode
Wear Ratio (EWR). Higher EWR would give bad dimensional
precision for the EDM machined workpiece because of high
electrode wear. Hence, the quality characteristic for EWR is set to
lower-the-better to achieve the optimum dimensional precision for
the machined workpiece. Taguchi method has been used for the
construction, layout and analysis of the experiment for EWR
machining characteristic. The use of Taguchi method in the
experiment saves a lot of time and cost of preparing and machining
the experiment samples. Therefore, an L18 Orthogonal array
which was the fundamental component in the statistical design of
experiments has been used to plan the experiments and Analysis of
Variance (ANOVA) is used to determine the optimum machining
parameters for this machining characteristic. The control
parameters selected for this optimization experiments are polarity,
pulse on duration, discharge current, discharge voltage, machining
depth, machining diameter and dielectric liquid pressure. The
result had shown that negative polarity machining parameter
setting will decreases EWR.