Abstract: The analysis of Acoustic Emission (AE) signal
generated from metal cutting processes has often approached
statistically. This is due to the stochastic nature of the emission
signal as a result of factors effecting the signal from its generation
through transmission and sensing. Different techniques are applied in
this manner, each of which is suitable for certain processes. In metal
cutting where the emission generated by the deformation process is
rather continuous, an appropriate method for analysing the AE signal
based on the root mean square (RMS) of the signal is often used and
is suitable for use with the conventional signal processing systems.
The aim of this paper is to set a strategy in tool failure detection in
turning processes via the statistic analysis of the AE generated from
the cutting zone. The strategy is based on the investigation of the
distribution moments of the AE signal at predetermined sampling.
The skews and kurtosis of these distributions are the key elements in
the detection. A normal (Gaussian) distribution has first been
suggested then this was eliminated due to insufficiency. The so
called Beta distribution was then considered, this has been used with
an assumed β density function and has given promising results with
regard to chipping and tool breakage detection.
Abstract: This article presents new current-mode oscillator circuits using CDTAs which is designed from block diagram. The proposed circuits consist of two CDTAs and two grounded capacitors. The condition of oscillation and the frequency of oscillation can be adjusted by electronic method. The circuits have high output impedance and use only grounded capacitors without any external resistor which is very appropriate to future development into an integrated circuit. The results of PSPICE simulation program are corresponding to the theoretical analysis.
Abstract: Intelligent systems based on machine learning
techniques, such as classification, clustering, are gaining wide spread
popularity in real world applications. This paper presents work on
developing a software system for predicting crop yield, for example
oil-palm yield, from climate and plantation data. At the core of our
system is a method for unsupervised partitioning of data for finding
spatio-temporal patterns in climate data using kernel methods which
offer strength to deal with complex data. This work gets inspiration
from the notion that a non-linear data transformation into some high
dimensional feature space increases the possibility of linear
separability of the patterns in the transformed space. Therefore, it
simplifies exploration of the associated structure in the data. Kernel
methods implicitly perform a non-linear mapping of the input data
into a high dimensional feature space by replacing the inner products
with an appropriate positive definite function. In this paper we
present a robust weighted kernel k-means algorithm incorporating
spatial constraints for clustering the data. The proposed algorithm
can effectively handle noise, outliers and auto-correlation in the
spatial data, for effective and efficient data analysis by exploring
patterns and structures in the data, and thus can be used for
predicting oil-palm yield by analyzing various factors affecting the
yield.
Abstract: This article stands in the context of rural communities
in Brazil, where, like many others emerging countries, the
overwhelming increasing markets and the overcrowded cities are
leaving behind informal settlements based on obsolete agricultural
economies and techniques. The pilot project for the community of
Goiabeira reflects the attempt to imagine a development model that
privileges the actual improvement of living conditions, the education
and training, the social inclusion and participation of the dwellers of
rural communities. Through the inclusion of operative public space,
the aim is for them to become self-sustaining, encouraging the use of
local resources for appropriate architectural, ecological and energy
technologies and devices, that are efficient, affordable and foster
community participation, in the respect of the surrounding
environment.
Abstract: A Data Warehouses is a repository of information
integrated from source data. Information stored in data warehouse is
the form of materialized in order to provide the better performance
for answering the queries. Deciding which appropriated views to be
materialized is one of important problem. In order to achieve this
requirement, the constructing search space close to optimal is a
necessary task. It will provide effective result for selecting view to be
materialized. In this paper we have proposed an approach to reoptimize
Multiple View Processing Plan (MVPP) by using global
common subexpressions. The merged queries which have query
processing cost not close to optimal would be rewritten. The
experiment shows that our approach can help to improve the total
query processing cost of MVPP and sum of query processing cost
and materialized view maintenance cost is reduced as well after views
are selected to be materialized.
Abstract: Through the course of this paper we define Business Case Management and its characteristics, and highlight its link to knowledge workers. Business Case Management combines knowledge and process effectively, supporting the ad hoc and unpredictable nature of cases, and coordinate a range of other technologies to appropriately support knowledge-intensive processes. We emphasize the growing importance of knowledge workers and the current poor support for knowledge work automation. We also discuss the challenges in supporting this kind of knowledge work and propose a novel approach to overcome these challenges.
Abstract: A basic conceptual study of TCSC device on Simulink is a teaching aid and helps in understanding the rudiments of the topic. This paper thus stems out from basics of TCSC device and analyzes the impedance characteristics and associated single & multi resonance conditions. The Impedance characteristics curve is drawn for different values of inductance in MATLAB using M-files. The study is also helpful in estimating the appropriate inductance and capacitance values which have influence on multi resonance point in TCSC device. The capacitor voltage, line current, thyristor current and capacitor current waveforms are discussed briefly as simulation results. Simulink model of TCSC device is given and corresponding waveforms are analyzed. The subsidiary topics e.g. power oscillation damping, SSR mitigation and transient stability is also brought out.
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: Mining sequential patterns from large customer transaction databases has been recognized as a key research topic in database systems. However, the previous works more focused on mining sequential patterns at a single concept level. In this study, we introduced concept hierarchies into this problem and present several algorithms for discovering multiple-level sequential patterns based on the hierarchies. An experiment was conducted to assess the performance of the proposed algorithms. The performances of the algorithms were measured by the relative time spent on completing the mining tasks on two different datasets. The experimental results showed that the performance depends on the characteristics of the datasets and the pre-defined threshold of minimal support for each level of the concept hierarchy. Based on the experimental results, some suggestions were also given for how to select appropriate algorithm for a certain datasets.
Abstract: In recent years, scanning probe atomic force
microscopy SPM AFM has gained acceptance over a wide spectrum
of research and science applications. Most fields focuses on physical,
chemical, biological while less attention is devoted to manufacturing
and machining aspects. The purpose of the current study is to assess
the possible implementation of the SPM AFM features and its
NanoScope software in general machining applications with special
attention to the tribological aspects of cutting tool. The surface
morphology of coated and uncoated as-received carbide inserts is
examined, analyzed, and characterized through the determination of
the appropriate scanning setting, the suitable data type imaging
techniques and the most representative data analysis parameters
using the MultiMode SPM AFM in contact mode. The NanoScope
operating software is used to capture realtime three data types
images: “Height", “Deflection" and “Friction". Three scan sizes are
independently performed: 2, 6, and 12 μm with a 2.5 μm vertical
range (Z). Offline mode analysis includes the determination of three
functional topographical parameters: surface “Roughness", power
spectral density “PSD" and “Section". The 12 μm scan size in
association with “Height" imaging is found efficient to capture every
tiny features and tribological aspects of the examined surface. Also,
“Friction" analysis is found to produce a comprehensive explanation
about the lateral characteristics of the scanned surface. Configuration
of many surface defects and drawbacks has been precisely detected
and analyzed.
Abstract: Several studies have shown the association between
ambient particulate matter (PM) and adverse health effects and
climate change, thus highlighting the need to limit the anthropogenic
sources of PM. PM Exposure is commonly monitored as mass
concentration of PM10 (particle aerodynamic diameter < 10μm) or
PM2.5 (particle aerodynamic diameter < 2.5μm), although increasing
toxicity with decreasing aerodynamic diameter has been reported due
to increased surface area and enhanced chemical reactivity with other
species. Additionally, the light scattering properties of PM increases
with decreasing size. Hence, it is important to study the chemical
characterization of finer fraction of the particulate matter and to
identify their sources so that they can be controlled appropriately to a
large extent at the sources before reaching to the receptors.
Abstract: Sedimentation is a hydraulic phenomenon that is
emerging as a serious challenge in river engineering. When the flow
reaches a certain state that gather potential energy, it shifts the
sediment load along channel bed. The transport of such materials can
be in the form of suspended and bed loads. The movement of these
along the river course and channels and the ways in which this could
influence the water intakes is considered as the major challenges for
sustainable O&M of hydraulic structures. This could be very serious
in arid and semi-arid regions like Iran, where inappropriate watershed
management could lead to shifting a great deal of sediments into the
reservoirs and irrigation systems. This paper aims to investigate
sedimentation in the Western Canal of Dez Diversion Weir in Iran,
identifying factors which influence the process and provide ways in
which to mitigate its detrimental effects by using the SHARC
Software.
For the purpose of this paper, data from the Dezful water authority
and Dezful Hydrometric Station pertinent to a river course of about 6
Km were used.
Results estimated sand and silt bed loads concentrations to be 193
ppm and 827ppm respectively. Given the available data on average
annual bed loads and average suspended sediment loads of 165ppm
and 837ppm, there was a significant statistical difference (16%)
between the sand grains, whereas no significant difference (1.2%)
was find in the silt grain sizes. One explanation for such finding
being that along the 6 Km river course there was considerable
meandering effects which explains recent shift in the hydraulic
behavior along the stream course under investigation. The sand
concentration in downstream relative to present state of the canal
showed a steep descending curve. Sediment trapping on the other
hand indicated a steep ascending curve. These occurred because the
diversion weir was not considered in the simulation model.
Abstract: In a product development process, understanding the functional behavior of the system, the role of components in achieving functions and failure modes if components/subsystem fails its required function will help develop appropriate design validation and verification program for reliability assessment. The integration of these three issues will help design and reliability engineers in identifying weak spots in design and planning future actions and testing program. This case study demonstrate the advantage of unascertained theory described in the subjective cognition uncertainty, and then applies blind number (BN) theory in describing the uncertainty of the mechanical system failure process and the same time used the same theory in bringing out another mechanical reliability system model. The practical calculations shows the BN Model embodied the characters of simply, small account of calculation but betterforecasting capability, which had the value of macroscopic discussion to some extent.
Abstract: This paper presents a method for the optimal
allocation of Distributed generation in distribution systems. In this
paper, our aim would be optimal distributed generation allocation for
voltage profile improvement and loss reduction in distribution
network. Genetic Algorithm (GA) was used as the solving tool,
which referring two determined aim; the problem is defined and
objective function is introduced. Considering to fitness values
sensitivity in genetic algorithm process, there is needed to apply load
flow for decision-making. Load flow algorithm is combined
appropriately with GA, till access to acceptable results of this
operation. We used MATPOWER package for load flow algorithm
and composed it with our Genetic Algorithm. The suggested method
is programmed under MATLAB software and applied ETAP
software for evaluating of results correctness. It was implemented on
part of Tehran electricity distributing grid. The resulting operation of
this method on some testing system is illuminated improvement of
voltage profile and loss reduction indexes.
Abstract: BER analysis of Impulse Radio Ultra Wideband (IRUWB) pulse modulations over S-V channel model is proposed in this paper. The UWB pulse is Gaussian monocycle pulse modulated using Pulse Amplitude Modulation (PAM) and Pulse Position Modulation (PPM). The channel model is generated from a modified S-V model. Bit-error rate (BER) is measured over several of bit rates. The result shows that all modulation are appropriate for both LOS and NLOS channel, but PAM gives better performance in bit rates and SNR. Moreover, as standard of speed has been given for UWB, the communication is appropriate with high bit rates in LOS channel.
Abstract: This paper focuses on PSS/E modeling of wind farms
of Doubly-fed Induction Generator (DFIG) type and their impact on
issues of power system operation. Since Wind Turbine Generators
(WTG) don-t have the same characteristics as synchronous
generators, the appropriate modeling of wind farms is essential for
transmission system operators to analyze the best options of
transmission grid reinforcements as well as to evaluate the wind
power impact on reliability and security of supply. With the high
excepted penetration of wind power into the power system a
simultaneous loss of Wind Farm generation will put at risk power
system security and reliability. Therefore, the main wind grid code
requirements concern the fault ride through capability and frequency
operation range of wind turbines. In case of grid faults wind turbines
have to supply a definite reactive power depending on the
instantaneous voltage and to return quickly to normal operation.
Abstract: Teaching and learning about sustainability is a pedagogical endeavour with various innate difficulties and increased demands. Higher education has a dual role to play in addressing this challenge: to identify and explore innovative approaches and tools for addressing the complex and value-laden nature of sustainability in more meaningful ways, and to help teachers to integrate these approaches into their practice through appropriate professional development programs. The study reported here was designed and carried out within the context of a Masters course in Environmental Education. Eight teachers were collaboratively engaged in reconstructing a digital game microworld which was deliberately designed by the researchers to be questioned and evoke critical discussion on the idea of ‘sustainable city’. The study was based on the design-based research method. The findings indicate that the teachers’ involvement in processes of co-constructing the microworld initiated discussion and reflection upon the concepts of sustainability and sustainable lifestyles.
Abstract: CT assessment of postoperative spine is challenging in the presence of metal streak artifacts that could deteriorate the
quality of CT images. In this paper, we studied the influence of different acquisition parameters on the magnitude of metal streaking.
A water-bath phantom was constructed with metal insertion similar with postoperative spine assessment. The phantom was scanned with
different acquisition settings and acquired data were reconstructed
using various reconstruction settings. Standardized ROIs were defined within streaking region for image analysis. The result shows
increased kVp and mAs enhanced SNR values by reducing image
noise. Sharper kernel enhanced image quality compared to smooth
kernel, but produced more noise in the images with higher CT fluctuation. The noise between both kernels were significantly
different (P
Abstract: In contrast to existing methods which do not take into account multiconnectivity in a broad sense of this term, we develop mathematical models and highly effective combination (BIEM and FDM) numerical methods of calculation of stationary and quasistationary temperature field of a profile part of a blade with convective cooling (from the point of view of realization on PC). The theoretical substantiation of these methods is proved by appropriate theorems. For it, converging quadrature processes have been developed and the estimations of errors in the terms of A.Ziqmound continuity modules have been received. For visualization of profiles are used: the method of the least squares with automatic conjecture, device spline, smooth replenishment and neural nets. Boundary conditions of heat exchange are determined from the solution of the corresponding integral equations and empirical relationships. The reliability of designed methods is proved by calculation and experimental investigations heat and hydraulic characteristics of the gas turbine first stage nozzle blade.
Abstract: In DMVC, we have more than one options of sources available for construction of side information. The newer techniques make use of both the techniques simultaneously by constructing a bitmask that determines the source of every block or pixel of the side information. A lot of computation is done to determine each bit in the bitmask. In this paper, we have tried to define areas that can only be well predicted by temporal interpolation and not by multiview interpolation or synthesis. We predict that all such areas that are not covered by two cameras cannot be appropriately predicted by multiview synthesis and if we can identify such areas in the first place, we don-t need to go through the script of computations for all the pixels that lie in those areas. Moreover, this paper also defines a technique based on KLT to mark the above mentioned areas before any other processing is done on the side view.