Abstract: In this paper, we present a new algorithm for clustering data in large datasets using image processing approaches. First the dataset is mapped into a binary image plane. The synthesized image is then processed utilizing efficient image processing techniques to cluster the data in the dataset. Henceforth, the algorithm avoids exhaustive search to identify clusters. The algorithm considers only a small set of the data that contains critical boundary information sufficient to identify contained clusters. Compared to available data clustering techniques, the proposed algorithm produces similar quality results and outperforms them in execution time and storage requirements.
Abstract: A key to success of high quality software development
is to define valid and feasible requirements specification. We have
proposed a method of model-driven requirements analysis using
Unified Modeling Language (UML). The main feature of our method
is to automatically generate a Web user interface mock-up from UML
requirements analysis model so that we can confirm validity of
input/output data for each page and page transition on the system by
directly operating the mock-up. This paper proposes a support method
to check the validity of a data life cycle by using a model checking tool
“UPPAAL" focusing on CRUD (Create, Read, Update and Delete).
Exhaustive checking improves the quality of requirements analysis
model which are validated by the customers through automatically
generated mock-up. The effectiveness of our method is discussed by a
case study of requirements modeling of two small projects which are a
library management system and a supportive sales system for text
books in a university.
Abstract: Electrical Discharge Machine (EDM) is especially
used for the manufacturing of 3-D complex geometry and hard
material parts that are extremely difficult-to-machine by conventional
machining processes. In this paper authors review the research work
carried out in the development of die-sinking EDM within the past
decades for the improvement of machining characteristics such as
Material Removal Rate, Surface Roughness and Tool Wear Ratio. In
this review various techniques reported by EDM researchers for
improving the machining characteristics have been categorized as
process parameters optimization, multi spark technique, powder
mixed EDM, servo control system and pulse discriminating. At the
end, flexible machine controller is suggested for Die Sinking EDM to
enhance the machining characteristics and to achieve high-level
automation. Thus, die sinking EDM can be integrated with Computer
Integrated Manufacturing environment as a need of agile
manufacturing systems.
Abstract: The mixture formation prior to the ignition process
plays as a key element in the diesel combustion. Parametric studies of
mixture formation and ignition process in various injection parameter
has received considerable attention in potential for reducing
emissions. Purpose of this study is to clarify the effects of injection
pressure on mixture formation and ignition especially during ignition
delay period, which have to be significantly influences throughout the
combustion process and exhaust emissions. This study investigated
the effects of injection pressure on diesel combustion fundamentally
using rapid compression machine. The detail behavior of mixture
formation during ignition delay period was investigated using the
schlieren photography system with a high speed camera. This method
can capture spray evaporation, spray interference, mixture formation
and flame development clearly with real images. Ignition process and
flame development were investigated by direct photography method
using a light sensitive high-speed color digital video camera. The
injection pressure and air motion are important variable that strongly
affect to the fuel evaporation, endothermic and prolysis process
during ignition delay. An increased injection pressure makes spray tip
penetration longer and promotes a greater amount of fuel-air mixing
occurs during ignition delay. A greater quantity of fuel prepared
during ignition delay period thus predominantly promotes more rapid
heat release.
Abstract: A long-term campaign for monitoring the
concentration of atmospheric Particulate Matter (PM) was conducted
at multiple sites located in the center and suburbs of the Tokyo
Metropolitan Area in Japan. The concentration of fine PM has shown a
declining trend over the last two decades. A positive matrix
factorization model elucidated that the contribution of combustion
sources was drastically reduced. In Japan, the regulations on vehicle
exhaust emissions were phased in and gradually tightened over the last
two decades, which has triggered a notable reduction in PM emissions
from automobiles and has contributed to the mitigation of the problem
of fine PM pollution.
Abstract: In a travelling wave thermoacoustic device, the
regenerator sandwiched between a pair of (hot and cold) heat
exchangers constitutes the so-called thermoacoustic core, where the
thermoacoustic energy conversion from heat to acoustic power takes
place. The temperature gradient along the regenerator caused by the
two heat exchangers excites and maintains the acoustic wave in the
resonator. The devices are called travelling wave thermoacoustic
systems because the phase angle difference between the pressure and
velocity oscillation is close to zero in the regenerator. This paper
presents the construction and testing of a thermoacoustic engine
equipped with a ceramic regenerator, made from a ceramic material
that is usually used as catalyst substrate in vehicles- exhaust systems,
with fine square channels (900 cells per square inch). The testing
includes the onset temperature difference (minimum temperature
difference required to start the acoustic oscillation in an engine), the
acoustic power output, thermal efficiency and the temperature profile
along the regenerator.
Abstract: The paper relates to a catalyst, comprising copperchromium
spinel, coated on carrier γ-Al2O3. The effect of preparation
conditions on the active component composition and activity
behavior of the catalysts is discussed. It was found that the activity of
carbon monoxide, DME, formaldehyde and methanol oxidation
reaches a maximum at an active component content of 20 – 30 wt. %.
Temperature calcination at 500oC seems to be optimal for the γ–
alumina supported CuO-Cr2O3 catalysts for CO, DME, formaldehyde
and methanol oxidation. A three months industrial experiment was
carried out to elucidate the changes in the catalyst composition
during industrial exploitation of the catalyst and the main reasons for
catalyst deactivation.
It was concluded that the CuO–Cr2O3/γ–alumina supported
catalysts have enhanced activity toward CO, DME, formaldehyde
and methanol oxidation and that these catalysts are suitable for
industrial application. The main reason for catalyst deactivation
seems to be the deposition of iron and molybdenum, coming from the
main reactor, on the active component surface.
Abstract: A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresholds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in image and vision processing. In Neural networks, radial basis functions are often used for interpolation in multidimensional space. A Radial Basis function is a function, which has built into it a distance criterion with respect to a centre. Radial basis functions have often been applied in the area of neural networks where they may be used as a replacement for the sigmoid hidden layer transfer characteristic in multi-layer perceptron. This paper aims to present exhaustive results of using RBF units in a complex-valued neural network model that uses the back-propagation algorithm (called 'Complex-BP') for learning. Our experiments results demonstrate the effectiveness of a Radial basis function in a complex valued neural network in image recognition over a real valued neural network. We have studied and stated various observations like effect of learning rates, ranges of the initial weights randomly selected, error functions used and number of iterations for the convergence of error on a neural network model with RBF units. Some inherent properties of this complex back propagation algorithm are also studied and discussed.
Abstract: The alterations in pancreas gland secretion hormones
following an aerobic and exhausting exercise was the purpose of this
study. Sixteen healthy men participated in the study. The blood
samples of these participants were taken in four stages under fasting
condition. The first sample was taken before Bruce exhausting and
aerobic test, the second sample was taken after Bruce exercise and
the third and forth stages samples were taken 24 and 48 hours after
the exercises respectively. The final results indicated that a strenuous
aerobic exercise can have a significant effect on glucagon and insulin
concentration of blood serum. The increase in blood serum insulin
was higher after 24 and 48 hours. It seems that an intensive exercise
has little effect on changes in glucagon concentration of blood serum.
Also, disorder in secretion in glucagon and insulin concentration of
serum disturbs athletes- exercise.
Abstract: Because of importance of energy, optimization of
power generation systems is necessary. Gas turbine cycles are
suitable manner for fast power generation, but their efficiency is
partly low. In order to achieving higher efficiencies, some
propositions are preferred such as recovery of heat from exhaust
gases in a regenerator, utilization of intercooler in a multistage
compressor, steam injection to combustion chamber and etc.
However thermodynamic optimization of gas turbine cycle, even
with above components, is necessary. In this article multi-objective
genetic algorithms are employed for Pareto approach optimization of
Regenerative-Intercooling-Gas Turbine (RIGT) cycle. In the multiobjective
optimization a number of conflicting objective functions
are to be optimized simultaneously. The important objective
functions that have been considered for optimization are entropy
generation of RIGT cycle (Ns) derives using Exergy Analysis and
Gouy-Stodola theorem, thermal efficiency and the net output power
of RIGT Cycle. These objectives are usually conflicting with each
other. The design variables consist of thermodynamic parameters
such as compressor pressure ratio (Rp), excess air in combustion
(EA), turbine inlet temperature (TIT) and inlet air temperature (T0).
At the first stage single objective optimization has been investigated
and the method of Non-dominated Sorting Genetic Algorithm
(NSGA-II) has been used for multi-objective optimization.
Optimization procedures are performed for two and three objective
functions and the results are compared for RIGT Cycle. In order to
investigate the optimal thermodynamic behavior of two objectives,
different set, each including two objectives of output parameters, are
considered individually. For each set Pareto front are depicted. The
sets of selected decision variables based on this Pareto front, will
cause the best possible combination of corresponding objective
functions. There is no superiority for the points on the Pareto front
figure, but they are superior to any other point. In the case of three
objective optimization the results are given in tables.
Abstract: Smoke from domestic wood burning has been
identified as a major contributor to air pollution, motivating detailed
emission measurements under controlled conditions. A series of
experiments was performed to characterise the emissions from wood
combustion in a fireplace and in a woodstove of two common species
of trees grown in Spain: Pyrenean oak (Quercus pyrenaica) and
black poplar (Populus nigra). Volatile organic compounds (VOCs) in
the exhaust emissions were collected in Tedlar bags, re-sampled in
sorbent tubes and analysed by thermal desorption-gas
chromatography-flame ionisation detection. Pyrenean oak presented
substantially higher emissions in the woodstove than in the fireplace,
for the majority of compounds. The opposite was observed for
poplar. Among the 45 identified species, benzene and benzenerelated
compounds represent the most abundant group, followed by
oxygenated VOCs and aliphatics. Emission factors obtained in this
study are generally of the same order than those reported for
residential experiments in the USA.
Abstract: The bypass exhaust system of a 160 MW combined cycle has been modeled and analyzed using numerical simulation in 2D prospective. Analysis was carried out using the commercial numerical simulation software, FLUENT 6.2. All inputs were based on the technical data gathered from working conditions of a Siemens V94.2 gas turbine, installed in the Yazd power plant. This paper deals with reduction of pressure drop in bypass exhaust system using turning vanes mounted in diverter box in order to alleviate turbulent energy dissipation rate above diverter box. The geometry of such turning vanes has been optimized based on the flow pattern at diverter box inlet. The results show that the use of optimized turning vanes in diverter box can improve the flow pattern and eliminate vortices around sharp edges just before the silencer. Furthermore, this optimization could decrease the pressure drop in bypass exhaust system and leads to higher plant efficiency.
Abstract: As a part of the development of a numerical method of
close capture exhausts systems for machining devices, a test rig
recreating a situation similar to a grinding operation, but in a
perfectly controlled environment, is used. The properties of the
obtained spray of solid particles are initially characterized using
particle tracking velocimetry (PTV), in order to obtain input and
validation parameters for numerical simulations. The dispersion of a
tracer gas (SF6) emitted simultaneously with the particle jet is then
studied experimentally, as the dispersion of such a gas is
representative of that of finer particles, whose aerodynamic response
time is negligible. Finally, complete modeling of the test rig is
achieved to allow comparison with experimental results and thus to
progress towards validation of the models used to describe a twophase
flow generated by machining operation.
Abstract: Existing experiences indicate that one of the most
prominent reasons that some ERP implementations fail is related to
selecting an improper ERP package. Among those important factors
resulting in inappropriate ERP selections, one is to ignore preliminary
activities that should be done before the evaluation of ERP packages.
Another factor yielding these unsuitable selections is that usually
organizations employ prolonged and costly selection processes in
such extent that sometimes the process would never be finalized
or sometimes the evaluation team might perform many key final
activities in an incomplete or inaccurate way due to exhaustion, lack
of interest or out-of-date data. In this paper, a systematic approach
that recommends some activities to be done before and after the
main selection phase is introduced for choosing an ERP package. On
the other hand, the proposed approach has utilized some ideas that
accelerates the selection process at the same time that reduces the
probability of an erroneous final selection.
Abstract: A systematic and exhaustive method based on the group
structure of a unitary Lie algebra is proposed to generate an enormous
number of quantum codes. With respect to the algebraic structure,
the orthogonality condition, which is the central rule of generating
quantum codes, is proved to be fully equivalent to the distinguishability
of the elements in this structure. In addition, four types of
quantum codes are classified according to the relation of the codeword
operators and some initial quantum state. By linking the unitary Lie
algebra with the additive group, the classical correspondences of some
of these quantum codes can be rendered.
Abstract: This work presents a new phonetic transcription system based on a tree of hierarchical pronunciation rules expressed as context-specific grapheme-phoneme correspondences. The tree is automatically inferred from a phonetic dictionary by incrementally analyzing deeper context levels, eventually representing a minimum set of exhaustive rules that pronounce without errors all the words in the training dictionary and that can be applied to out-of-vocabulary words. The proposed approach improves upon existing rule-tree-based techniques in that it makes use of graphemes, rather than letters, as elementary orthographic units. A new linear algorithm for the segmentation of a word in graphemes is introduced to enable outof- vocabulary grapheme-based phonetic transcription. Exhaustive rule trees provide a canonical representation of the pronunciation rules of a language that can be used not only to pronounce out-of-vocabulary words, but also to analyze and compare the pronunciation rules inferred from different dictionaries. The proposed approach has been implemented in C and tested on Oxford British English and Basic English. Experimental results show that grapheme-based rule trees represent phonetically sound rules and provide better performance than letter-based rule trees.
Abstract: The aim of this paper is to explore the security issues
that significantly affect the performance of Mobile Adhoc Networks
(MANET)and limit the services provided to their intended users. The
MANETs are more vulnerable to Distributed Denial of Service
attacks (DDoS) because of their properties like shared medium,
dynamic topologies etc. A DDoS attack is a coordinated attempt
made by malicious users to flood the victim network with the large
amount of data such that the resources of the victim network are
exhausted resulting in the deterioration of the network performance.
This paper highlights the effects of different types of DDoS attacks
in MANETs and categorizes them according to their behavior.
Abstract: In this study, we developed a model to predict the
temperature and the pressure variation in an internal combustion
engine operated in HCCI (Homogeneous charge compression ignition)
mode. HCCI operation begins from aspirating of homogeneous charge
mixture through intake valve like SI (Spark ignition) engine and the
premixed charge is compressed until temperature and pressure of
mixture reach autoignition point like diesel engine. Combustion phase
was described by double-Wiebe function. The single zone model
coupled with an double-Wiebe function were performed to simulated
pressure and temperature between the period of IVC (Inlet valve close)
and EVO (Exhaust valve open). Mixture gas properties were
implemented using STANJAN and transfer the results to main model.
The model has considered the engine geometry and enables varying in
fuelling, equivalence ratio, manifold temperature and pressure. The
results were compared with the experiment and showed good
correlation with respect to combustion phasing, pressure rise, peak
pressure and temperature. This model could be adapted and use to
control start of combustion for HCCI engine.
Abstract: Spectrum is a scarce commodity, and considering the spectrum scarcity faced by the wireless-based service providers led to high congestion levels. Technical inefficiencies from pooled, since all networks share a common pool of channels, exhausting the available channels will force networks to block the services. Researchers found that cognitive radio (CR) technology may resolve the spectrum scarcity. A CR is a self-configuring entity in a wireless networking that senses its environment, tracks changes, and frequently exchanges information with their networks. However, CRN facing challenges and condition become worst while tracks changes i.e. reallocation of another under-utilized channels while primary network user arrives. In this paper, channels or resource reallocation technique based on DNA-inspired computing algorithm for CRN has been proposed.
Abstract: Wireless sensor networks can be used to measure and monitor many challenging problems and typically involve in monitoring, tracking and controlling areas such as battlefield monitoring, object tracking, habitat monitoring and home sentry systems. However, wireless sensor networks pose unique security challenges including forgery of sensor data, eavesdropping, denial of service attacks, and the physical compromise of sensor nodes. Node in a sensor networks may be vanished due to power exhaustion or malicious attacks. To expand the life span of the sensor network, a new node deployment is needed. In military scenarios, intruder may directly organize malicious nodes or manipulate existing nodes to set up malicious new nodes through many kinds of attacks. To avoid malicious nodes from joining the sensor network, a security is required in the design of sensor network protocols. In this paper, we proposed a security framework to provide a complete security solution against the known attacks in wireless sensor networks. Our framework accomplishes node authentication for new nodes with recognition of a malicious node. When deployed as a framework, a high degree of security is reachable compared with the conventional sensor network security solutions. A proposed framework can protect against most of the notorious attacks in sensor networks, and attain better computation and communication performance. This is different from conventional authentication methods based on the node identity. It includes identity of nodes and the node security time stamp into the authentication procedure. Hence security protocols not only see the identity of each node but also distinguish between new nodes and old nodes.