Abstract: In an era of knowledge explosion, the growth of data
increases rapidly day by day. Since data storage is a limited resource,
how to reduce the data space in the process becomes a challenge issue.
Data compression provides a good solution which can lower the
required space. Data mining has many useful applications in recent
years because it can help users discover interesting knowledge in large
databases. However, existing compression algorithms are not
appropriate for data mining. In [1, 2], two different approaches were
proposed to compress databases and then perform the data mining
process. However, they all lack the ability to decompress the data to
their original state and improve the data mining performance. In this
research a new approach called Mining Merged Transactions with the
Quantification Table (M2TQT) was proposed to solve these problems.
M2TQT uses the relationship of transactions to merge related
transactions and builds a quantification table to prune the candidate
itemsets which are impossible to become frequent in order to improve
the performance of mining association rules. The experiments show
that M2TQT performs better than existing approaches.
Abstract: Monitoring the tool flank wear without affecting the
throughput is considered as the prudent method in production
technology. The examination has to be done without affecting the
machining process. In this paper we proposed a novel work that is
used to determine tool flank wear by observing the sound signals
emitted during the turning process. The work-piece material we used
here is steel and aluminum and the cutting insert was carbide
material. Two different cutting speeds were used in this work. The
feed rate and the cutting depth were constant whereas the flank wear
was a variable. The emitted sound signal of a fresh tool (0 mm flank
wear) a slightly worn tool (0.2 -0.25 mm flank wear) and a severely
worn tool (0.4mm and above flank wear) during turning process were
recorded separately using a high sensitive microphone. Analysis
using Singular Value Decomposition was done on these sound
signals to extract the feature sound components. Observation of the
results showed that an increase in tool flank wear correlates with an
increase in the values of SVD features produced out of the sound
signals for both the materials. Hence it can be concluded that wear
monitoring of tool flank during turning process using SVD features
with the Fuzzy C means classification on the emitted sound signal is
a potential and relatively simple method.
Abstract: This paper aims at to develop a robust optimization methodology for the mechatronic modules of machine tools by considering all important characteristics from all structural and control domains in one single process. The relationship between these two domains is strongly coupled. In order to reduce the disturbance caused by parameters in either one, the mechanical and controller design domains need to be integrated. Therefore, the concurrent integrated design method Design For Control (DFC), will be employed in this paper. In this connect, it is not only applied to achieve minimal power consumption but also enhance structural performance and system response at same time. To investigate the method for integrated optimization, a mechatronic feed drive system of the machine tools is used as a design platform. Pro/Engineer and AnSys are first used to build the 3D model to analyze and design structure parameters such as elastic deformation, nature frequency and component size, based on their effects and sensitivities to the structure. In addition, the robust controller,based on Quantitative Feedback Theory (QFT), will be applied to determine proper control parameters for the controller. Therefore, overall physical properties of the machine tool will be obtained in the initial stage. Finally, the technology of design for control will be carried out to modify the structural and control parameters to achieve overall system performance. Hence, the corresponding productivity is expected to be greatly improved.
Abstract: The purpose of study was to design and construction
the semi-automatic sliced ginger machine for reduce production times
in sheet and slice ginger procedure furthermore, reduced amount of
labor of slides and cutting method. Take consider into clean and safety of workers and consumers. The principle of machines, used 1
horsepower motor, rotation speed of sliced blade 967 rpm, the diameter of sliced dish 310 mm, consists of 2 blades for sheet cutting
ginger and the power from motor which transfer to rotate the sliced blade roller, rotation speed 440 rpm. The slice cutter roller was sliced
ginger from sheet ginger to line ginger. The conveyer could
adjustment level of motors, used to the beginning area that sheet
ginger was transference to the roller for sheet and sliced cutting in next process. The cover of sliced cutting had channel for 1 tuber of
ginger. The semi-automatic sliced ginger machine could produced sheet ginger 81.8 kg/h (6.2 times of labor) and line ginger 17.9 kg/h
(2.5 times of labor) compare with, labor work could produced sheet
ginger 13.2 kg/h and line ginger 7.1 kg/h, and when timekeeper, the
total times of semi auto machine 30.86 kg/h and labor 4.6 kg/h, there
for the semi auto machine was 6.7 times of labor. The semiautomatic
sliced ginger machine convenient, easy for use and
maintain, in addition to reduce fatigue of body and seriousness from
works; must be used high skill, and protection accident in slicing
procedure. Beside, machine could used with other vegetables for
example potato, carrot .etc
Abstract: The pulp and paper mill effluent is one of the high
polluting effluent amongst the effluents obtained from polluting
industries. All the available methods for treatment of pulp and paper
mill effluent have certain drawbacks. The coagulation is one of the
cheapest process for treatment of various organic effluents. Thus, the
removal of chemical oxygen demand (COD) and colour of paper mill
effluent is studied using coagulation process. The batch coagulation
process was performed using various coagulants like: aluminium
chloride, poly aluminium chloride and copper sulphate. The initial
pH of the effluent (Coagulation pH) has tremendous effect on COD
and colour removal. Poly aluminium chloride (PAC) as coagulant
reduced COD to 84 % and 92 % of colour was removed at an
optimum pH 5 and coagulant dose of 8 ml l-1. With aluminium
chloride at an optimum pH = 4 and coagulant dose of 5 g l-1, 74 %
COD and 86 % colour removal were observed. The results using
copper sulphate as coagulant (a less commercial coagulant) were
encouraging. At an optimum pH 6 and mass loading of 5 g l-1, 76 %
COD reduction and 78 % colour reduction were obtained. It was also
observed that after addition of coagulant, the pH of the effluent
decreases. The decrease in pH was highest for AlCl3, which was
followed by PAC and CuSO4. Significant amount of COD reductions
was obtained by coagulation process. Since the coagulation process
is the first stage for treatment of effluent and some of the coagulant
cations usually remain in the treated effluents. Thus, cation like
copper may be one of the good catalyst for second stage of treatment
process like wet oxidation. The copper has been found to be good
oxidation catalyst then iron and aluminum.
Abstract: In this paper, an intelligent automatic parking control method is proposed. First, the dynamical equation of the rear parking control is derived. Then a fuzzy logic control is proposed to perform the parking planning process. Further, a rear neural network is proposed for the steering control. Through the simulations and experiments, the intelligent auto-parking mode controllers have been shown to achieve the demanded goals with satisfactory control performance and to guarantee the system robustness under parametric variations and external disturbances. To improve some shortcomings and limitations in conventional parking mode control and further to reduce consumption time and prime cost.
Abstract: A novel and versatile numerical technique to solve a self-stress equilibrium state is adopted herein as a form-finding procedure for an irregular tensegrity structure. The numerical form-finding scheme of a tensegrity structure uses only the connectivity matrix and prototype tension coefficient vector as the initial guess solution. Any information on the symmetrical geometry or other predefined initial structural conditions is not necessary to get the solution in the form-finding process. An eight-node initial condition example is presented to demonstrate the efficiency and robustness of the proposed method in the form-finding of an irregular tensegrity structure. Based on the conception from the form-finding of an eight-node irregular tensegrity structure, a monumental object is designed by considering the real world situation such as self-weight, wind and earthquake loadings.
Abstract: A multilayer self organizing neural neural network
(MLSONN) architecture for binary object extraction, guided by a beta
activation function and characterized by backpropagation of errors
estimated from the linear indices of fuzziness of the network output
states, is discussed. Since the MLSONN architecture is designed to
operate in a single point fixed/uniform thresholding scenario, it does
not take into cognizance the heterogeneity of image information in
the extraction process. The performance of the MLSONN architecture
with representative values of the threshold parameters of the beta
activation function employed is also studied. A three layer bidirectional
self organizing neural network (BDSONN) architecture
comprising fully connected neurons, for the extraction of objects from
a noisy background and capable of incorporating the underlying image
context heterogeneity through variable and adaptive thresholding,
is proposed in this article. The input layer of the network architecture
represents the fuzzy membership information of the image scene to
be extracted. The second layer (the intermediate layer) and the final
layer (the output layer) of the network architecture deal with the self
supervised object extraction task by bi-directional propagation of the
network states. Each layer except the output layer is connected to the
next layer following a neighborhood based topology. The output layer
neurons are in turn, connected to the intermediate layer following
similar topology, thus forming a counter-propagating architecture
with the intermediate layer. The novelty of the proposed architecture
is that the assignment/updating of the inter-layer connection weights
are done using the relative fuzzy membership values at the constituent
neurons in the different network layers. Another interesting feature
of the network lies in the fact that the processing capabilities of
the intermediate and the output layer neurons are guided by a beta
activation function, which uses image context sensitive adaptive
thresholding arising out of the fuzzy cardinality estimates of the
different network neighborhood fuzzy subsets, rather than resorting to
fixed and single point thresholding. An application of the proposed
architecture for object extraction is demonstrated using a synthetic
and a real life image. The extraction efficiency of the proposed
network architecture is evaluated by a proposed system transfer index
characteristic of the network.
Abstract: It has been proven that early establishment of
microbial flora in digestive tract of ruminants, has a beneficial effect
on their health condition and productivity. A probiotic compound,
made from five bacteria isolated from adult bovine cattle, was dosed
to 15 Holstein newborn calves in order to measure its capacity of
improving body weight gain and reduce diarrhea incidence. The test
was performed in the municipality of Cajicá (Colombia), at 2580
m.a.s.l., throughout rainy season, with environmental temperature
that oscillated between 4 to 25 °C. Five calves were allotted to
control (no addition of probiotic). Treatments 1, and 2 (5 calves per
group) received 10 ml Probiotic mix 1 and 2, respectively. Probiotic
mixes 1 and 2 where similar in microbial composition but different in
production process. Probiotics were added to the morning milk and
dosed on a daily basis by a month and then on a weekly basis for
three additional months. Diarrhea incidence was measured by
observance of number of animals affected in each group; each animal
was weighed up on a daily basis for obtaining weight gain and rumen
fluid samples were extracted with oro-esophageal catheter for
determining level of fiber and grain consumption.
Abstract: A number of previous studies were rarely considered
the effects of transient non-uniform balloon expansion on evaluation
of the properties and behaviors of stents during stent expansion, nor
did they determine parameters to maximize the performances driven
by mechanical characteristics. Therefore, in order to fully understand
the mechanical characteristics and behaviors of stent, it is necessary to
consider a realistic modeling of transient non-uniform balloon-stent
expansion. The aim of the study is to propose design parameters
capable of improving the ability of vascular stent through a
comparative study of seven commercial stents using finite element
analyses of a realistic transient non-uniform balloon-stent expansion
process. In this study, seven representative commercialized stents were
evaluated by finite element (FE) analysis in terms of the criteria based
on the itemized list of Food and Drug Administration (FDA) and
European Standards (prEN). The results indicate that using stents
composed of opened unit cells connected by bend-shaped link
structures and controlling the geometrical and morphological features
of the unit cell strut or the link structure at the distal ends of stent may
improve mechanical characteristics of stent. This study provides a
better method at the realistic transient non-uniform balloon-stent
expansion by investigating the characteristics, behaviors, and
parameters capable of improving the ability of vascular stent.
Abstract: In this paper back-propagation artificial neural
network (BPANN) is employed to predict the limiting drawing ratio
(LDR) of the deep drawing process. To prepare a training set for
BPANN, some finite element simulations were carried out. die and
punch radius, die arc radius, friction coefficient, thickness, yield
strength of sheet and strain hardening exponent were used as the
input data and the LDR as the specified output used in the training of
neural network. As a result of the specified parameters, the program
will be able to estimate the LDR for any new given condition.
Comparing FEM and BPANN results, an acceptable correlation was
found.
Abstract: In this paper we present a Feed-Foward Neural
Networks Autoregressive (FFNN-AR) model with genetic algorithms
training optimization in order to predict the gross domestic product
growth of six countries. Specifically we propose a kind of weighted
regression, which can be used for econometric purposes, where the
initial inputs are multiplied by the neural networks final optimum
weights from input-hidden layer of the training process. The
forecasts are compared with those of the ordinary autoregressive
model and we conclude that the proposed regression-s forecasting
results outperform significant those of autoregressive model.
Moreover this technique can be used in Autoregressive-Moving
Average models, with and without exogenous inputs, as also the
training process with genetics algorithms optimization can be
replaced by the error back-propagation algorithm.
Abstract: In this research, a systematic investigation was carried out to determine the optimum conditions of HDS reactor. Moreover, a suitable model was developed for a rigorous RTO (real time optimization) loop of HDS (Hydro desulfurization) process. A systematic experimental series was designed based on CCD (Central Composite design) and carried out in the related pilot plant to tune the develop model. The designed variables in the experiments were Temperature, LHSV and pressure. However, the hydrogen over fresh feed ratio was remained constant. The ranges of these variables were respectively equal to 320-380ºC, 1- 21/hr and 50-55 bar. a power law kinetic model was also developed for our further research in the future .The rate order and activation energy , power of reactant concentration and frequency factor of this model was respectively equal to 1.4, 92.66 kJ/mol and k0=2.7*109 .
Abstract: This paper explores the changing trend in citizenship
norms among young citizens from various ethnic groups in Malaysia
and the extent to which it influences the participation of young
citizens in political and civil issues. Embedded in democratic
constitutions are the rights and freedoms that accompany citizenship,
and these rights and freedoms include participation. Participation in
democracies should go beyond voting; it should include taking part in
the governance process. The political process is not at risk even
though politics does not work as it did in the past. A national sample
of 1697 respondents between the ages of 21 and 40 years were
interviewed in January 2011. The findings show that respondents
embrace an engaged-citizenship norm more than they do the
traditional duty-citizen norm. Among the ethnic groups, the Chinese
show lower means in both citizenship norms compared with other
ethnic groups, namely, the Malays and the Indians. The duty-citizen
norm correlates higher with political participation than with civic
participation. On the other hand, the engaged-citizen norm correlates
higher with civic participation than with political participation.
Abstract: The electronically available Urdu data is in image form
which is very difficult to process. Printed Urdu data is the root cause
of problem. So for the rapid progress of Urdu language we need an
OCR systems, which can help us to make Urdu data available for the
common person. Research has been carried out for years to automata
Arabic and Urdu script. But the biggest hurdle in the development of
Urdu OCR is the challenge to recognize Nastalique Script which is
taken as standard for writing Urdu language. Nastalique script is
written diagonally with no fixed baseline which makes the script
somewhat complex. Overlap is present not only in characters but in
the ligatures as well. This paper proposes a method which allows
successful recognition of Nastalique Script.
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: In this work, propagation of uncertainty during calibration
process of TRANUS, an integrated land use and transport model
(ILUTM), has been investigated. It has also been examined, through a
sensitivity analysis, which input parameters affect the variation of the
outputs the most. Moreover, a probabilistic verification methodology
of calibration process, which equates the observed and calculated
production, has been proposed. The model chosen as an application is
the model of the city of Grenoble, France. For sensitivity analysis and
uncertainty propagation, Monte Carlo method was employed, and a
statistical hypothesis test was used for verification. The parameters of
the induced demand function in TRANUS, were assumed as uncertain
in the present case. It was found that, if during calibration, TRANUS
converges, then with a high probability the calibration process is
verified. Moreover, a weak correlation was found between the inputs
and the outputs of the calibration process. The total effect of the
inputs on outputs was investigated, and the output variation was found
to be dictated by only a few input parameters.
Abstract: Least Development Countries (LDC) like
Bangladesh, whose 25% revenue earning is achieved from Textile
export, requires producing less defective textile for minimizing
production cost and time. Inspection processes done on these
industries are mostly manual and time consuming. To reduce error
on identifying fabric defects requires more automotive and
accurate inspection process. Considering this lacking, this research
implements a Textile Defect Recognizer which uses computer
vision methodology with the combination of multi-layer neural
networks to identify four classifications of textile defects. The
recognizer, suitable for LDC countries, identifies the fabric defects
within economical cost and produces less error prone inspection
system in real time. In order to generate input set for the neural
network, primarily the recognizer captures digital fabric images by
image acquisition device and converts the RGB images into binary
images by restoration process and local threshold techniques.
Later, the output of the processed image, the area of the faulty
portion, the number of objects of the image and the sharp factor of
the image, are feed backed as an input layer to the neural network
which uses back propagation algorithm to compute the weighted
factors and generates the desired classifications of defects as an
output.
Abstract: Metal matrix composites have been increasingly used
as materials for components in automotive and aerospace industries
because of their improved properties compared with non-reinforced
alloys. During machining the selection of appropriate machining
parameters to produce job for desired surface roughness is of great
concern considering the economy of manufacturing process. In this
study, a surface roughness prediction model using fuzzy logic is
developed for end milling of Al-SiCp metal matrix composite
component using carbide end mill cutter. The surface roughness is
modeled as a function of spindle speed (N), feed rate (f), depth of cut
(d) and the SiCp percentage (S). The predicted values surface
roughness is compared with experimental result. The model predicts
average percentage error as 4.56% and mean square error as 0.0729.
It is observed that surface roughness is most influenced by feed rate,
spindle speed and SiC percentage. Depth of cut has least influence.
Abstract: This paper presents a new methodology to study power and energy consumption in mechatronic systems early in the development process. This new approach makes use of two modeling languages to represent and simulate embedded control software and electromechanical subsystems in the discrete event and continuous time domain respectively within a single co-model. This co-model enables an accurate representation of power and energy consumption and facilitates the analysis and development of both software and electro-mechanical subsystems in parallel. This makes the engineers aware of energy-wise implications of different design alternatives and enables early trade-off analysis from the beginning of the analysis and design activities.