Abstract: A Rice Sheller is used for obtaining polished white
rice from paddy. There are about 3000 Rice Shellers in Punjab and
50000 in India. During the process of shelling lot of dust is emitted
from different unit operations like paddy silo, paddy shaker, bucket
elevators, huskers, paddy separator etc. These dust emissions have
adverse effect on the health of the workers and the wear and tear of
the shelling machinery is fast. All the dust emissions spewing out of
these unit operations of a rice Sheller were contained by providing
suitable hoods and enclosures while ensuring their workability. These
were sucked by providing an induced draft fan followed by a high
efficiency cyclone separator that has got an overall dust collection
efficiency of more than 90%. This cyclone separator replaced two
cyclone separators and a filter bag house, which the Rice Sheller was
already having. The dust concentration in the stack after the
installation of cyclone separator is well within the stipulated
standards. Besides controlling pollution, there is improvement in the
quality of products like bran and the life of shelling machinery has
enhanced. The payback period of this technology is less than four
shelling months.
Abstract: The output error of the globoidal cam mechanism can
be considered as a relevant indicator of mechanism performance,
because it determines kinematic and dynamical behavior of
mechanical transmission. Based on the differential geometry and the
rigid body transformations, the mathematical model of surface
geometry of the globoidal cam is established. Then we present the
analytical expression of the output error (including the transmission
error and the displacement error along the output axis) by considering
different manufacture and assembly errors. The effects of the center
distance error, the perpendicular error between input and output axes
and the rotational angle error of the globoidal cam on the output error
are systematically analyzed. A globoidal cam mechanism which is
widely used in automatic tool changer of CNC machines is applied for
illustration. Our results show that the perpendicular error and the
rotational angle error have little effects on the transmission error but
have great effects on the displacement error along the output axis. This
study plays an important role in the design, manufacture and assembly
of the globoidal cam mechanism.
Abstract: The design of high pressure water jet based polishing
equipment and its fabrication conducted in this study is reported
herein, together with some preliminary test results for assessing its
applicability for HMA surface polishing. This study also provides
preliminary findings concerning the test variables, such as the
rotational speed, the water jet pressure, the abrasive agent used, and
the impact angel that were experimentally investigated in this study. The preliminary findings based on four trial tests (two on large
slab specimens and two on small size gyratory compacted
specimens), however, indicate that both friction and texture values
tend to increase with the polishing durations for two combinations of
pressure and rotation speed of the rotary deck. It seems that the more
polishing action the specimen is subjected to; the aggregate edges are
created such that the surface texture values are increased with the
accompanied increase in friction values. It may be of interest (but
which is outside the scope of this study) to investigate if the similar
trend exist for HMA prepared with aggregate source that is sand and
gravel.
Abstract: Human beings have the ability to make logical
decisions. Although human decision - making is often optimal, it is
insufficient when huge amount of data is to be classified. Medical
dataset is a vital ingredient used in predicting patient’s health
condition. In other to have the best prediction, there calls for most
suitable machine learning algorithms. This work compared the
performance of Artificial Neural Network (ANN) and Decision Tree
Algorithms (DTA) as regards to some performance metrics using
diabetes data. WEKA software was used for the implementation of
the algorithms. Multilayer Perceptron (MLP) and Radial Basis
Function (RBF) were the two algorithms used for ANN, while
RegTree and LADTree algorithms were the DTA models used. From
the results obtained, DTA performed better than ANN. The Root
Mean Squared Error (RMSE) of MLP is 0.3913 that of RBF is
0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206
respectively.
Abstract: In Electric Power Steering (EPS), spoke type
Brushless AC (BLAC) motors offer distinct advantages over other
electric motor types in terms torque smoothness, reliability and
efficiency. This paper deals with the shape optimization of spoke
type BLAC motor, in order to reduce cogging torque. This paper
examines 3 steps skewing rotor angle, optimizing rotor core edge and
rotor overlap length for reducing cogging torque in spoke type BLAC
motor. The methods were applied to existing machine designs and
their performance was calculated using finite- element analysis
(FEA). Prototypes of the machine designs were constructed and
experimental results obtained. It is shown that the FEA predicted the
cogging torque to be nearly reduce using those methods.
Abstract: Despite the advances made in various new
technologies, application of these technologies for agriculture still
remains a formidable task, as it involves integration of diverse
domains for monitoring the different process involved in agricultural
management. Advances in ambient intelligence technology represents
one of the most powerful technology for increasing the yield of
agricultural crops and to mitigate the impact of water scarcity,
climatic change and methods for managing pests, weeds and diseases.
This paper proposes a GPS-assisted, machine to machine solutions
that combine information collected by multiple sensors for the
automated management of paddy crops. To maintain the economic
viability of paddy cultivation, the various techniques used in
agriculture are discussed and a novel system which uses ambient
intelligence technique is proposed in this paper. The ambient
intelligence based agricultural system gives a great scope.
Abstract: This paper aims to study the effect of cold work
condition on the microstructure of Cu-1.5wt%Ti, and Cu-3.5wt%Ti
and hence mechanical properties. The samples under investigation
were machined, and solution heat treated. X-ray diffraction technique
is used to identify the different phases present after cold deformation
by compression and also different heat treatment and also measuring
the relative quantities of phases present. The metallographic
examination is used to study the microstructure of the samples. The
hardness measurements were used to indicate the change in
mechanical properties. The results are compared with the mechanical
properties obtained by previous workers. Experiments on cold
compression followed by aging of Cu-Ti alloys have indicated that
the most efficient hardening of the material results from continuous
precipitation of very fine particles within the matrix. These particles
were reported to be β`-type, Cu4Ti phase. The β`-β transformation
and particles coarsening within the matrix as well as long grain
boundaries were responsible for the overaging of Cu-1.5wt%Ti and
Cu-3.5wt%Ti alloys. It is well known that plate-like particles are β –
type, Cu3Ti phase. Discontinuous precipitation was found to start at
the grain boundaries and expand into grain interior. At the higher
aging temperature, a classic Widmanstätten morphology forms giving
rise to a coarse microstructure comprised of α and the equilibrium
phase β. Those results were confirmed by X-ray analysis, which
found that a few percent of Cu3Ti, β precipitates are formed during
aging at high temperature for long time for both Cu- Ti alloys (i.e.
Cu-1.5wt%Ti and Cu-3.5wt%Ti).
Abstract: Today, there is a large number of political transcripts
available on the Web to be mined and used for statistical analysis,
and product recommendations. As the online political resources are
used for various purposes, automatically determining the political
orientation on these transcripts becomes crucial. The methodologies
used by machine learning algorithms to do an automatic classification
are based on different features that are classified under categories
such as Linguistic, Personality etc. Considering the ideological
differences between Liberals and Conservatives, in this paper, the
effect of Personality traits on political orientation classification is
studied. The experiments in this study were based on the correlation
between LIWC features and the BIG Five Personality traits. Several
experiments were conducted using Convote U.S. Congressional-
Speech dataset with seven benchmark classification algorithms. The
different methodologies were applied on several LIWC feature sets
that constituted by 8 to 64 varying number of features that are
correlated to five personality traits. As results of experiments,
Neuroticism trait was obtained to be the most differentiating
personality trait for classification of political orientation. At the same
time, it was observed that the personality trait based classification
methodology gives better and comparable results with the related
work.
Abstract: Rapid Prototyping (RP) technologies enable physical
parts to be produced from various materials without depending on the
conventional tooling. Fused Deposition Modeling (FDM) is one of
the famous RP processes used at present. Tensile strength and
compressive strength resistance will be identified for different sample
structures and different layer orientations of ABS rapid prototype
solid models. The samples will be fabricated by a FDM rapid
prototyping machine in different layer orientations with variations in
internal geometrical structure. The 0° orientation where layers were
deposited along the length of the samples displayed superior strength
and impact resistance over all the other orientations. The anisotropic
properties were probably caused by weak interlayer bonding and
interlayer porosity.
Abstract: In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results.
Abstract: Obesity and osteoporosis are the two diseases whose
increasing prevalence and high impact on the global morbidity and
mortality, during the two recent decades, have gained a status of
major health threats worldwide. Obesity purports to affect the bone
metabolism through complex mechanisms. Debated data on the
connection between the bone mineral density and fracture prevalence
in the obese patients are widely presented in literature. There is
evidence that the correlation of weight and fracture risk is sitespecific.
This study is aimed at determining the connection between
the bone mineral density (BMD) and trabecular bone score (TBS)
parameters in Ukrainian women suffering from obesity. We
examined 1025 40-89-year-old women, divided them into the groups
according to their body mass index: Group A included 360 women
with obesity whose BMI was ≥30 kg/m2, and Group B – 665 women
with no obesity and BMI of
Abstract: By the evolvement in technology, the way of
expressing opinions switched direction to the digital world. The
domain of politics, as one of the hottest topics of opinion mining
research, merged together with the behavior analysis for affiliation
determination in texts, which constitutes the subject of this paper.
This study aims to classify the text in news/blogs either as
Republican or Democrat with the minimum number of features. As
an initial set, 68 features which 64 were constituted by Linguistic
Inquiry and Word Count (LIWC) features were tested against 14
benchmark classification algorithms. In the later experiments, the
dimensions of the feature vector reduced based on the 7 feature
selection algorithms. The results show that the “Decision Tree”,
“Rule Induction” and “M5 Rule” classifiers when used with “SVM”
and “IGR” feature selection algorithms performed the best up to
82.5% accuracy on a given dataset. Further tests on a single feature
and the linguistic based feature sets showed the similar results. The
feature “Function”, as an aggregate feature of the linguistic category,
was found as the most differentiating feature among the 68 features
with the accuracy of 81% in classifying articles either as Republican
or Democrat.
Abstract: The 3D printing is a combination of digital technology, material science, intelligent manufacturing and control of opto-mechatronics systems. It is called the third industrial revolution from the view of the Economist Journal. A color 3D printing machine may provide the necessary support for high value-added industrial and commercial design, architectural design, personal boutique, and 3D artist’s creation. The main goal of this paper is to develop photo-curable color 3D manufacturing technology and system implementation. The key technologies include (1) Photo-curable color 3D additive manufacturing processes development and materials research (2) Piezo type ink-jet head control and Opto-mechatronics integration technique of the photo-curable color 3D laminated manufacturing system. The proposed system is integrated with single Piezo type ink-jet head with two individual channels for two primary UV light curable color resins which can provide for future colorful 3D printing solutions. The main research results are 16 grey levels and grey resolution of 75 dpi.
Abstract: Hybrid electric vehicles can reduce pollution and
improve fuel economy. Power-split hybrid electric vehicles (HEVs)
provide two power paths between the internal combustion engine
(ICE) and energy storage system (ESS) through the gears of an
electrically variable transmission (EVT). EVT allows ICE to operate
independently from vehicle speed all the time. Therefore, the ICE can
operate in the efficient region of its characteristic brake specific fuel
consumption (BSFC) map. The two-mode powertrain can operate in
input-split or compound-split EVT modes and in four different fixed
gear configurations. Power-split architecture is advantageous because
it combines conventional series and parallel power paths. This
research focuses on input-split and compound-split modes in the
two-mode power-split powertrain. Fuzzy Logic Control (FLC) for an
internal combustion engine (ICE) and PI control for electric machines
(EMs) are derived for the urban driving cycle simulation. These
control algorithms reduce vehicle fuel consumption and improve ICE
efficiency while maintaining the state of charge (SOC) of the energy
storage system in an efficient range.
Abstract: Distributed applications deployed on LEO satellites
and ground stations require substantial communication between
different members in a constellation to overcome the earth
coverage barriers imposed by GEOs. Applications running on LEO
constellations suffer the earth line-of-sight blockage effect. They
need adequate lab testing before launching to space. We propose
a scalable cloud-based network simulation framework to simulate
problems created by the earth line-of-sight blockage. The framework
utilized cloud IaaS virtual machines to simulate LEO satellites
and ground stations distributed software. A factorial ANOVA
statistical analysis is conducted to measure simulator overhead on
overall communication performance. The results showed a very low
simulator communication overhead. Consequently, the simulation
framework is proposed as a candidate for testing LEO constellations
with distributed software in the lab before space launch.
Abstract: This study was aimed to investigate the machining
stability of a spindle tool with different preloaded amount. To this end,
the vibration tests were conducted on the spindle unit with different
preload to assess the dynamic characteristics and machining stability
of the milling machine. Current results demonstrate that the tool tip
frequency response characteristics and the machining stabilities in X
and Y direction are affected to change due to the different preload of
spindle bearings. As found from the results, a high preloaded spindle
tool shows higher limited cutting depth at mid position, while a spindle
with low preload shows a higher limited depth. This indicates that the
machining stability of a milling machine is affected to vary by the
spindle unit when it was assembled with different bearing preload.
Abstract: Aluminium matrix composites with alumina
reinforcements give superior mechanical & physical properties. Their
applications in several fields like automobile, aerospace, defense,
sports, electronics, bio-medical and other industrial purposes are
becoming essential for the last several decades. In the present work,
fabrication of hybrid composite was done by Stir casting technique
using Al 6061 as a matrix with alumina and silicon carbide (SiC) as
reinforcement materials. The weight percentage of alumina is varied
from 2 to 4% and the silicon carbide weight percentage is maintained
constant at 2%. Hardness and wear tests are performed in the as cast
and heat treated conditions. Age hardening treatment was performed
on the specimen with solutionizing at 550°C, aging at two
temperatures (150 and 200°C) for different time durations. Hardness
distribution curves are drawn and peak hardness values are recorded.
Hardness increase was very sensitive with respect to the decrease in
aging temperature. There was an improvement in wear resistance of
the peak aged material when aged at lower temperature. Also
increase in weight percent of alumina, increases wear resistance at
lower temperature but opposite behavior was seen when aged at
higher temperature.
Abstract: Software fault prediction models are created by using
the source code, processed metrics from the same or previous version
of code and related fault data. Some company do not store and keep
track of all artifacts which are required for software fault prediction.
To construct fault prediction model for such company, the training
data from the other projects can be one potential solution. Earlier we
predicted the fault the less cost it requires to correct. The training
data consists of metrics data and related fault data at function/module
level. This paper investigates fault predictions at early stage using the
cross-project data focusing on the design metrics. In this study,
empirical analysis is carried out to validate design metrics for cross
project fault prediction. The machine learning techniques used for
evaluation is Naïve Bayes. The design phase metrics of other projects
can be used as initial guideline for the projects where no previous
fault data is available. We analyze seven datasets from NASA
Metrics Data Program which offer design as well as code metrics.
Overall, the results of cross project is comparable to the within
company data learning.
Abstract: This research study aims to present a retrospective
study about speech recognition systems and artificial intelligence.
Speech recognition has become one of the widely used technologies,
as it offers great opportunity to interact and communicate with
automated machines. Precisely, it can be affirmed that speech
recognition facilitates its users and helps them to perform their daily
routine tasks, in a more convenient and effective manner. This
research intends to present the illustration of recent technological
advancements, which are associated with artificial intelligence.
Recent researches have revealed the fact that speech recognition is
found to be the utmost issue, which affects the decoding of speech. In
order to overcome these issues, different statistical models were
developed by the researchers. Some of the most prominent statistical
models include acoustic model (AM), language model (LM), lexicon
model, and hidden Markov models (HMM). The research will help in
understanding all of these statistical models of speech recognition.
Researchers have also formulated different decoding methods, which
are being utilized for realistic decoding tasks and constrained
artificial languages. These decoding methods include pattern
recognition, acoustic phonetic, and artificial intelligence. It has been
recognized that artificial intelligence is the most efficient and reliable
methods, which are being used in speech recognition.
Abstract: Due to the increasing efforts on saving our natural
environment a change in the structure of energy resources can be
observed - an increasing fraction of a renewable energy sources.
In many countries traditional underground coal mining loses its
significance but there are still countries, like Poland or Germany, in
which the coal based technologies have the greatest fraction in a total
energy production. This necessitates to make an effort to limit the
costs and negative effects of underground coal mining. The longwall
complex is as essential part of the underground coal mining. The
safety and the effectiveness of the work is strongly dependent of the
diagnostic state of powered roof supports.
The building of a useful and reliable diagnostic system requires
a lot of data. As the acquisition of a data of any possible operating
conditions it is important to have a possibility to generate a demanded
artificial working characteristics. In this paper a new approach of
modelling a leg pressure in the single unit of powered roof support.
The model is a result of the analysis of a typical working cycles.