Abstract: Electromagnetic interference (EMI) is one of the
serious problems in most electrical and electronic appliances
including fluorescent lamps. The electronic ballast used to regulate
the power flow through the lamp is the major cause for EMI. The
interference is because of the high frequency switching operation of
the ballast. Formerly, some EMI mitigation techniques were in
practice, but they were not satisfactory because of the hardware
complexity in the circuit design, increased parasitic components and
power consumption and so on. The majority of the researchers have
their spotlight only on EMI mitigation without considering the other
constraints such as cost, effective operation of the equipment etc. In
this paper, we propose a technique for EMI mitigation in fluorescent
lamps by integrating Frequency Modulation and Evolutionary
Programming. By the Frequency Modulation technique, the
switching at a single central frequency is extended to a range of
frequencies, and so, the power is distributed throughout the range of
frequencies leading to EMI mitigation. But in order to meet the
operating frequency of the ballast and the operating power of the
fluorescent lamps, an optimal modulation index is necessary for
Frequency Modulation. The optimal modulation index is determined
using Evolutionary Programming. Thereby, the proposed technique
mitigates the EMI to a satisfactory level without disturbing the
operation of the fluorescent lamp.
Abstract: A Vehicle-s door wireing harness arrangement structure is provided. In vehicle-s door wiring harness(W/H) system is more toward to arrange a passenger compartment than a hinge and a weatherstrip. This article gives some insight into the dimensioning process, with special focus on large deflection analysis of wiring harness(W/H) in vehicle-s door structures for durability problem. An Finite elements analysis for door wiring harness(W/H) are used for residual stresses and dimensional stability with bending flexible. Durability test data for slim test specimens were compared with the numerical predicted fatigue life for verification. The final lifing of the component combines the effects of these microstructural features with the complex stress state arising from the combined service loading and residual stresses.
Abstract: A new paradigm for software design and development models software by its business process, translates the model into a process execution language, and has it run by a supporting execution engine. This process-oriented paradigm promotes modeling of software by less technical users or business analysts as well as rapid development. Since business process models may be shared by different organizations and sometimes even by different business domains, it is interesting to apply a technique used in traditional software component technology to design reusable business processes. This paper discusses an approach to apply a technique for software component fabrication to the design of process-oriented software units, called process components. These process components result from decomposing a business process of a particular application domain into subprocesses with an aim that the process components can be reusable in different process-based software models. The approach is quantitative because the quality of process component design is measured from technical features of the process components. The approach is also strategic because the measured quality is determined against business-oriented component management goals. A software tool has been developed to measure how good a process component design is, according to the required managerial goals and comparing to other designs. We also discuss how we benefit from reusable process components.
Abstract: This research proposes a methodology for patent-citation-based technology input-output analysis by applying the patent information to input-output analysis developed for the dependencies among different industries. For this analysis, a technology relationship matrix and its components, as well as input and technology inducement coefficients, are constructed using patent information. Then, a technology inducement coefficient is calculated by normalizing the degree of citation from certain IPCs to the different IPCs (International patent classification) or to the same IPCs. Finally, we construct a Dependency Structure Matrix (DSM) based on the technology inducement coefficient to suggest a useful application for this methodology.
Abstract: Wind power is among the most actively developing distributed generation (DG) technology. Majority of the wind power based DG technologies employ wind turbine induction generators (WTIG) instead of synchronous generators, for the technical advantages like: reduced size, increased robustness, lower cost, and increased electromechanical damping. However, dynamic changes of wind speed make the amount of active/reactive power injected/drawn to a WTIG embedded distribution network highly variable. This paper analyzes the effect of wind speed changes on the active and reactive power penetration to the wind energy embedded distribution network. Four types of wind speed changes namely; constant, linear change, gust change and random change of wind speed are considered in the analysis. The study is carried out by three-phase, non-linear, dynamic simulation of distribution system component models. Results obtained from the investigation are presented and discussed.
Abstract: Stick models are widely used in studying the
behaviour of straight as well as skew bridges and viaducts subjected
to earthquakes while carrying out preliminary studies. The
application of such models to highly curved bridges continues to
pose challenging problems. A viaduct proposed in the foothills of the
Himalayas in Northern India is chosen for the study. It is having 8
simply supported spans @ 30 m c/c. It is doubly curved in horizontal
plane with 20 m radius. It is inclined in vertical plane as well. The
superstructure consists of a box section. Three models have been
used: a conventional stick model, an improved stick model and a 3D
finite element model. The improved stick model is employed by
making use of body constraints in order to study its capabilities. The
first 8 frequencies are about 9.71% away in the latter two models.
Later the difference increases to 80% in 50th mode. The viaduct was
subjected to all three components of the El Centro earthquake of May
1940. The numerical integration was carried out using the Hilber-
Hughes-Taylor method as implemented in SAP2000. Axial forces
and moments in the bridge piers as well as lateral displacements at
the bearing levels are compared for the three models. The maximum
difference in the axial forces and bending moments and
displacements vary by 25% between the improved and finite element
model. Whereas, the maximum difference in the axial forces,
moments, and displacements in various sections vary by 35%
between the improved stick model and equivalent straight stick
model. The difference for torsional moment was as high as 75%. It is
concluded that the stick model with body constraints to model the
bearings and expansion joints is not desirable in very sharp S curved
viaducts even for preliminary analysis. This model can be used only
to determine first 10 frequency and mode shapes but not for member
forces. A 3D finite element analysis must be carried out for
meaningful results.
Abstract: Data on 657 lactation from 163 Maltese goat,
collected over a 5-year period were analyzed by a mixed model to
estimate the variance components for heritability. The considered
lactation traits were: milk yield (MY) and lactation length (LL). Year,
parity and type of birth (single or twin) were significant sources of
variation for lactation length; on the other hand milk yield was
significantly influenced only by the year. The average MY was
352.34 kg and the average LL was 230 days. Estimates of heritability
were 0.21 and 0.15 for MY and LL respectively. These values
suggest there is low correlation between genotype and phenotype so
it may be difficult to evaluate animals directly on phenotype. So, the
genetic improvement of this breed may be quite slow without the
support of progeny test aimed to select Maltese breeders.
Abstract: The paper presents a complete discrete statistical framework, based on a novel vector quantization (VQ) front-end process. This new VQ approach performs an optimal distribution of VQ codebook components on HMM states. This technique that we named the distributed vector quantization (DVQ) of hidden Markov models, succeeds in unifying acoustic micro-structure and phonetic macro-structure, when the estimation of HMM parameters is performed. The DVQ technique is implemented through two variants. The first variant uses the K-means algorithm (K-means- DVQ) to optimize the VQ, while the second variant exploits the benefits of the classification behavior of neural networks (NN-DVQ) for the same purpose. The proposed variants are compared with the HMM-based baseline system by experiments of specific Arabic consonants recognition. The results show that the distributed vector quantization technique increase the performance of the discrete HMM system.
Abstract: Early supplier involvement (ESI) benefits new
product development projects several ways. Nevertheless, many castuser
companies do not know the advantages of ESI and therefore do
not utilize it. This paper presents reasons why to utilize ESI in
casting industry and how that can be done. Further, this paper
presents advantages and challenges related to ESI in casting industry,
and introduces a Casting-Network Collaboration Model. The model
presents practices for companies to build advantageous collaborative
relationships. More detailed, the model describes three levels for
company-network relationships in casting industry with different
degrees of collaboration, and requirements for operating in each
level. In our research, ESI was found to influence, for example, on
project time, component cost, and quality. In addition, challenges
related to ESI, such as, a lack of mutual trust and unawareness about
the advantages were found. Our research approach was a case study
including four cases.
Abstract: A way of generating millimeter wave I/Q signal using inductive resonator matched poly-phase filter is suggested. Normally the poly-phase filter generates quite accurate I/Q phase and magnitude but the loss of the filter is considerable due to series connection of passive RC components. This loss term directly increases system noise figure when the poly-phase filter is used in RF Front-end. The proposed matching method eliminates above mentioned loss and in addition provides gain on the passive filter. The working algorithm is illustrated by mathematical analysis. The generated I/Q signal is used in implementing millimeter wave phase shifter for the 60 GHz communication system to verify its effectiveness. The circuit is fabricated in 90 nm TSMC RF CMOS process under 1.2 V supply voltage. The measurement results showed that the suggested method improved gain by 6.5 dB and noise by 2.3 dB. The summary of the proposed I/Q generation is compared with previous works.
Abstract: This paper was aimed at developing a computer aided
design and manufacturing system for spatial cylindrical cams. In the
proposed system, a milling tool with a diameter smaller than that of the
roller, instead of the standard cutter for traditional machining process,
was used to generate the tool path for spatial cams. To verify the
feasibility of the proposed method, a multi-axis machining simulation
software was further used to simulate the practical milling operation of
spatial cams. It was observed from computer simulation that the tool
path of small-sized cutter were within the motion range of a standard
cutter, no occurrence of overcutting. Examination of a finished cam
component clearly verifies the accuracy of the tool path generated for
small-sized milling tool. It is believed that the use of small-sized cutter
for the machining of the spatial cylindrical cams can generate a better
surface morphology with higher accuracy. The improvement in
efficiency and cost for the manufacturing of the spatial cylindrical cam
can be expected through the proposed method.
Abstract: The importance of machining process in today-s
industry requires the establishment of more practical approaches to
clearly represent the intimate and severe contact on the tool-chipworkpiece
interfaces. Mathematical models are developed using the
measured force signals to relate each of the tool-chip friction
components on the rake face to the operating cutting parameters in
rough turning operation using multilayers coated carbide inserts.
Nonlinear modeling proved to have high capability to detect the
nonlinear functional variability embedded in the experimental data.
While feedrate is found to be the most influential parameter on the
friction coefficient and its related force components, both cutting
speed and depth of cut are found to have slight influence. Greater
deformed chip thickness is found to lower the value of friction
coefficient as the sliding length on the tool-chip interface is reduced.
Abstract: Perturbed-Chain Statistical Association Fluid Theory (PC-SAFT) equation of state (EOS) is a modified SAFT EOS with three pure component specific parameters: segment number (m), diameter (σ) and energy (ε). These PC-SAFT parameters need to be determined for each component under the conditions of interest by fitting experimental data, such as vapor pressure, density or heat capacity. PC-SAFT parameters for propane, ethylene and hydrogen in supercritical region were successfully estimated by fitting experimental density data available in literature. The regressed PCSAFT parameters were compared with the literature values by means of estimating pure component density and calculating average absolute deviation between the estimated and experimental density values. PC-SAFT parameters available in literature especially for ethylene and hydrogen estimated density in supercritical region reasonably well. However, the regressed PC-SAFT parameters performed better in supercritical region than the PC-SAFT parameters from literature.
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: Human identification at a distance has recently gained
growing interest from computer vision researchers. Gait recognition
aims essentially to address this problem by identifying people based
on the way they walk [1]. Gait recognition has 3 steps. The first step
is preprocessing, the second step is feature extraction and the third
one is classification. This paper focuses on the classification step that
is essential to increase the CCR (Correct Classification Rate).
Multilayer Perceptron (MLP) is used in this work. Neural Networks
imitate the human brain to perform intelligent tasks [3].They can
represent complicated relationships between input and output and
acquire knowledge about these relationships directly from the data
[2]. In this paper we apply MLP NN for 11 views in our database and
compare the CCR values for these views. Experiments are performed
with the NLPR databases, and the effectiveness of the proposed
method for gait recognition is demonstrated.
Abstract: The paper deals with the estimation of amplitude and phase of an analogue multi-harmonic band-limited signal from irregularly spaced sampling values. To this end, assuming the signal fundamental frequency is known in advance (i.e., estimated at an independent stage), a complexity-reduced algorithm for signal reconstruction in time domain is proposed. The reduction in complexity is achieved owing to completely new analytical and summarized expressions that enable a quick estimation at a low numerical error. The proposed algorithm for the calculation of the unknown parameters requires O((2M+1)2) flops, while the straightforward solution of the obtained equations takes O((2M+1)3) flops (M is the number of the harmonic components). It is applied in signal reconstruction, spectral estimation, system identification, as well as in other important signal processing problems. The proposed method of processing can be used for precise RMS measurements (for power and energy) of a periodic signal based on the presented signal reconstruction. The paper investigates the errors related to the signal parameter estimation, and there is a computer simulation that demonstrates the accuracy of these algorithms.
Abstract: We have previously introduced an ultrasonic imaging
approach that combines harmonic-sensitive pulse sequences with a
post-beamforming quadratic kernel derived from a second-order
Volterra filter (SOVF). This approach is designed to produce images
with high sensitivity to nonlinear oscillations from microbubble
ultrasound contrast agents (UCA) while maintaining high levels of
noise rejection. In this paper, a two-step algorithm for computing the
coefficients of the quadratic kernel leading to reduction of tissue
component introduced by motion, maximizing the noise rejection and
increases the specificity while optimizing the sensitivity to the UCA
is presented. In the first step, quadratic kernels from individual
singular modes of the PI data matrix are compared in terms of their
ability of maximize the contrast to tissue ratio (CTR). In the second
step, quadratic kernels resulting in the highest CTR values are
convolved. The imaging results indicate that a signal processing
approach to this clinical challenge is feasible.
Abstract: A simple mobile engine-driven pneumatic paddy
collector made of locally available materials using local
manufacturing technology was designed, fabricated, and tested for
collecting and bagging of paddy dried on concrete pavement. The
pneumatic paddy collector had the following major components:
radial flat bladed type centrifugal fan, power transmission system,
bagging area, frame and the conveyance system. Results showed
significant differences on the collecting capacity, noise level, and fuel
consumption when rotational speed of the air mover shaft was varied.
Other parameters such as collecting efficiency, air velocity,
augmented cracked grain percentage, and germination rate were not
significantly affected by varying rotational speed of the air mover
shaft. The pneumatic paddy collector had a collecting efficiency of
99.33 % with a collecting capacity of 2685.00 kg/h at maximum
rotational speed of centrifugal fan shaft of about 4200 rpm. The
machine entailed an investment cost of P 62,829.25. The break-even
weight of paddy was 510,606.75 kg/yr at a collecting cost of 0.11
P/kg of paddy. Utilizing the machine for 400 hours per year
generated an income of P 23,887.73. The projected time needed to
recover cost of the machine based on 2685 kg/h collecting capacity
was 2.63 year.
Abstract: There have been different approaches to compute the
analytic instantaneous frequency with a variety of background reasoning
and applicability in practice, as well as restrictions. This paper presents an adaptive Fourier decomposition and (α-counting) based
instantaneous frequency computation approach. The adaptive Fourier
decomposition is a recently proposed new signal decomposition
approach. The instantaneous frequency can be computed through the so called mono-components decomposed by it. Due to the fast energy
convergency, the highest frequency of the signal will be discarded by the adaptive Fourier decomposition, which represents the noise of
the signal in most of the situation. A new instantaneous frequency
definition for a large class of so-called simple waves is also proposed
in this paper. Simple wave contains a wide range of signals for which
the concept instantaneous frequency has a perfect physical sense.
The α-counting instantaneous frequency can be used to compute the highest frequency for a signal. Combination of these two approaches one can obtain the IFs of the whole signal. An experiment is demonstrated the computation procedure with promising results.
Abstract: This paper describes an optimal approach for feature
subset selection to classify the leaves based on Genetic Algorithm
(GA) and Kernel Based Principle Component Analysis (KPCA). Due
to high complexity in the selection of the optimal features, the
classification has become a critical task to analyse the leaf image
data. Initially the shape, texture and colour features are extracted
from the leaf images. These extracted features are optimized through
the separate functioning of GA and KPCA. This approach performs
an intersection operation over the subsets obtained from the
optimization process. Finally, the most common matching subset is
forwarded to train the Support Vector Machine (SVM). Our
experimental results successfully prove that the application of GA
and KPCA for feature subset selection using SVM as a classifier is
computationally effective and improves the accuracy of the classifier.