Abstract: This is an application research presenting the
improvement of production quality using the six sigma solutions and
the analyses of benefit-cost ratio. The case of interest is the
production of tile-concrete. Such production has faced with the
problem of high nonconforming products from an inappropriate
surface coating and had low process capability based on the strength
property of tile. Surface coating and tile strength are the most critical
to quality of this product. The improvements followed five stages of
six sigma solutions. After the improvement, the production yield was
improved to 80% as target required and the defective products from
coating process was remarkably reduced from 29.40% to 4.09%. The
process capability based on the strength quality was increased from
0.87 to 1.08 as customer oriented. The improvement was able to save
the materials loss for 3.24 millions baht or 0.11 million dollars. The
benefits from the improvement were analyzed from (1) the reduction
of the numbers of non conforming tile using its factory price for
surface coating improvement and (2) the materials saved from the
increment of process capability. The benefit-cost ratio of overall
improvement was high as 7.03. It was non valuable investment in
define, measure, analyses and the initial of improve stages after that
it kept increasing. This was due to there were no benefits in define,
measure, and analyze stages of six sigma since these three stages
mainly determine the cause of problem and its effects rather than
improve the process. The benefit-cost ratio starts existing in the
improve stage and go on. Within each stage, the individual benefitcost
ratio was much higher than the accumulative one as there was an
accumulation of cost since the first stage of six sigma. The
consideration of the benefit-cost ratio during the improvement
project helps make decisions for cost saving of similar activities
during the improvement and for new project. In conclusion, the
determination of benefit-cost ratio behavior through out six sigma
implementation period provides the useful data for managing quality
improvement for the optimal effectiveness. This is the additional
outcome from the regular proceeding of six sigma.
Abstract: Above Elbow Prosthesis is one of the most commonly
amputated or missing limbs. The research is done for modelling
techniques of upper limb prosthesis and design of high torque, light
weight and compact in size elbow actuator. The purposed actuator
consists of a DC motor, planetary gear set and a harmonic drive. The
calculations show that the actuator is good enough to be used in real
life powered prosthetic upper limb or rehabilitation exoskeleton.
Abstract: The objective of this paper is to study the electrical
resistivity complexity between field and laboratory measurement, in
order to improve the effectiveness of data interpretation for
geophysical ground resistivity survey. The geological outcrop in
Penang, Malaysia with an obvious layering contact was chosen as the
study site. Two dimensional geoelectrical resistivity imaging were
used in this study to maps the resistivity distribution of subsurface,
whereas few subsurface sample were obtained for laboratory
advance. In this study, resistivity of samples in original conditions is
measured in laboratory by using time domain low-voltage technique,
particularly for granite core sample and soil resistivity measuring set
for soil sample. The experimentation results from both schemes are
studied, analyzed, calibrated and verified, including basis and
correlation, degree of tolerance and characteristics of substance.
Consequently, the significant different between both schemes is
explained comprehensively within this paper.
Abstract: Parallel programming models exist as an abstraction
of hardware and memory architectures. There are several parallel
programming models in commonly use; they are shared memory
model, thread model, message passing model, data parallel model,
hybrid model, Flynn-s models, embarrassingly parallel computations
model, pipelined computations model. These models are not specific
to a particular type of machine or memory architecture. This paper
expresses the model program for concurrent approach to data parallel
model through java programming.
Abstract: Histogram equalization is often used in image enhancement, but it can be also used in auto exposure. However, conventional histogram equalization does not work well when many pixels are concentrated in a narrow luminance range.This paper proposes an auto exposure method based on 2-way histogram equalization. Two cumulative distribution functions are used, where one is from dark to bright and the other is from bright to dark. In this paper, the proposed auto exposure method is also designed and implemented for image signal processors with full-HD images.
Abstract: The issue of unintentional islanding in PV grid
interconnection still remains as a challenge in grid-connected
photovoltaic (PV) systems. This paper discusses the overview of
popularly used anti-islanding detection methods, practically applied
in PV grid-connected systems. Anti-islanding methods generally can
be classified into four major groups, which include passive methods,
active methods, hybrid methods and communication base methods.
Active methods have been the preferred detection technique over the
years due to very small non-detected zone (NDZ) in small scale
distribution generation. Passive method is comparatively simpler
than active method in terms of circuitry and operations. However, it
suffers from large NDZ that significantly reduces its performance.
Communication base methods inherit the advantages of active and
passive methods with reduced drawbacks. Hybrid method which
evolved from the combination of both active and passive methods
has been proven to achieve accurate anti-islanding detection by many
researchers. For each of the studied anti-islanding methods, the
operation analysis is described while the advantages and
disadvantages are compared and discussed. It is difficult to pinpoint a
generic method for a specific application, because most of the
methods discussed are governed by the nature of application and
system dependent elements. This study concludes that the setup and
operation cost is the vital factor for anti-islanding method selection in
order to achieve minimal compromising between cost and system
quality.
Abstract: This study reveals that anti-immigrant policies in
Europe result from a process of securitization, and that, within this
process, radical right parties have been formulating discourses and
approaches through a construction process by using some common
security themes. These security themes can be classified as national
security, economic security, cultural security and internal security.
The frequency with which radical right parties use these themes may
vary according to the specific historical, social and cultural
characteristics of a particular country.
Abstract: Blood pulse is an important human physiological signal commonly used for the understanding of the individual physical health. Current methods of non-invasive blood pulse sensing require direct contact or access to the human skin. As such, the performances of these devices tend to vary with time and are subjective to human body fluids (e.g. blood, perspiration and skin-oil) and environmental contaminants (e.g. mud, water, etc). This paper proposes a simulation model for the novel method of non-invasive acquisition of blood pulse using the disturbance created by blood flowing through a localized magnetic field. The simulation model geometry represents a blood vessel, a permanent magnet, a magnetic sensor, surrounding tissues and air in 2-dimensional. In this model, the velocity and pressure fields in the blood stream are described based on Navier-Stroke equations and the walls of the blood vessel are assumed to have no-slip condition. The blood assumes a parabolic profile considering a laminar flow for blood in major artery near the skin. And the inlet velocity follows a sinusoidal equation. This will allow the computational software to compute the interactions between the magnetic vector potential generated by the permanent magnet and the magnetic nanoparticles in the blood. These interactions are simulated based on Maxwell equations at the location where the magnetic sensor is placed. The simulated magnetic field at the sensor location is found to assume similar sinusoidal waveform characteristics as the inlet velocity of the blood. The amplitude of the simulated waveforms at the sensor location are compared with physical measurements on human subjects and found to be highly correlated.
Abstract: The objective of this paper is to estimate realistic
principal extrusion process parameters by means of artificial neural
network. Conventionally, finite element analysis is used to derive
process parameters. However, the finite element analysis of the
extrusion model does not consider the manufacturing process
constraints in its modeling. Therefore, the process parameters
obtained through such an analysis remains highly theoretical.
Alternatively, process development in industrial extrusion is to a
great extent based on trial and error and often involves full-size
experiments, which are both expensive and time-consuming. The
artificial neural network-based estimation of the extrusion process
parameters prior to plant execution helps to make the actual extrusion
operation more efficient because more realistic parameters may be
obtained. And so, it bridges the gap between simulation and real
manufacturing execution system. In this work, a suitable neural
network is designed which is trained using an appropriate learning
algorithm. The network so trained is used to predict the
manufacturing process parameters.
Abstract: Electrospinning is a broadly used technology to obtain
polymeric nanofibers ranging from several micrometers down to
several hundred nanometers for a wide range of applications. It offers
unique capabilities to produce nanofibers with controllable porous
structure. With smaller pores and higher surface area than regular
fibers, electrospun fibers have been successfully applied in various
fields, such as, nanocatalysis, tissue engineering scaffolds, protective
clothing, filtration, biomedical, pharmaceutical, optical electronics,
healthcare, biotechnology, defense and security, and environmental
engineering. In this study, polyurethane nanofibers were obtained
under different electrospinning parameters. Fiber morphology and
diameter distribution were investigated in order to understand them
as a function of process parameters.
Abstract: Statistical learning theory was developed by Vapnik. It
is a learning theory based on Vapnik-Chervonenkis dimension. It also
has been used in learning models as good analytical tools. In general, a
learning theory has had several problems. Some of them are local
optima and over-fitting problems. As well, statistical learning theory
has same problems because the kernel type, kernel parameters, and
regularization constant C are determined subjectively by the art of
researchers. So, we propose an evolutionary statistical learning theory
to settle the problems of original statistical learning theory.
Combining evolutionary computing into statistical learning theory,
our theory is constructed. We verify improved performances of an
evolutionary statistical learning theory using data sets from KDD cup.
Abstract: An optical fiber Fabry-Perot interferometer (FFPI) is
proposed and demonstrated for dynamic measurements in a
mechanical vibrating target. A polishing metal with a low reflectance
value adhered to a mechanical vibrator was excited via a function
generator at various excitation frequencies. Output interference
fringes were generated by modulating the reference and sensing
signal at the output arm. A fringe-counting technique was used for
interpreting the displacement information on the dedicated computer.
The fiber interferometer has been found the capability of the
displacement measurements of 1.28 μm – 96.01 μm. A commercial
displacement sensor was employed as a reference sensor for
investigating the measurement errors from the fiber sensor. A
maximum percentage measurement error of approximately 1.59 %
was obtained.
Abstract: This paper introduces a temporal epistemic logic
CBCTL that updates agent-s belief states through communications
in them, based on computational tree logic (CTL). In practical
environments, communication channels between agents may not be
secure, and in bad cases agents might suffer blackouts. In this study,
we provide inform* protocol based on ACL of FIPA, and declare the
presence of secure channels between two agents, dependent on time.
Thus, the belief state of each agent is updated along with the progress
of time. We show a prover, that is a reasoning system for a given
formula in a given a situation of an agent ; if it is directly provable
or if it could be validated through the chains of communications, the
system returns the proof.
Abstract: Software Development Risks Identification (SDRI),
using Fault Tree Analysis (FTA), is a proposed technique to identify
not only the risk factors but also the causes of the appearance of the
risk factors in software development life cycle. The method is based
on analyzing the probable causes of software development failures
before they become problems and adversely affect a project. It uses
Fault tree analysis (FTA) to determine the probability of a particular
system level failures that are defined by A Taxonomy for Sources of
Software Development Risk to deduce failure analysis in which an
undesired state of a system by using Boolean logic to combine a
series of lower-level events. The major purpose of this paper is to use
the probabilistic calculations of Fault Tree Analysis approach to
determine all possible causes that lead to software development risk
occurrence
Abstract: The design of a modern aircraft is based on three pillars: theoretical results, experimental test and computational simulations.
As a results of this, Computational Fluid Dynamic (CFD) solvers are
widely used in the aeronautical field. These solvers require the correct
selection of many parameters in order to obtain successful results. Besides, the computational time spent in the simulation depends on
the proper choice of these parameters.
In this paper we create an expert system capable of making an
accurate prediction of the number of iterations and time required for the convergence of a computational fluid dynamic (CFD) solver.
Artificial neural network (ANN) has been used to design the expert system. It is shown that the developed expert system is capable of making an accurate prediction the number of iterations and time
required for the convergence of a CFD solver.
Abstract: MinC plays an important role in bacterial cell division
system by inhibiting FtsZ assembly. However, the molecular
mechanism of the action is poorly understood. E. coli MinC Nterminus
domain was purified and crystallized using 1.4 M sodium
citrate pH 6.5 as a precipitant. X-ray diffraction data was collected
and processed to 2.3 Å from a native crystal. The crystal belonged to
space group P212121, with the unit cell parameters a = 52.7, b = 54.0,
c = 64.7 Å. Assuming the presence of two molecules in the
asymmetric unit, the Matthews coefficient value is 1.94 Å3 Da-1,
which corresponds to a solvent content of 36.5%. The overall
structure of MinCN is observed as a dimer form through anti-parallel
ß-strand interaction.
Abstract: Reconfigurable optical add/drop multiplexers
(ROADMs) can be classified into three categories based on their
underlying switching technologies. Category I consists of a single
large optical switch; category II is composed of a number of small
optical switches aligned in parallel; and category III has a single
optical switch and only one wavelength being added/dropped. In this
paper, to evaluate the wavelength-routing capability of ROADMs of
category-II in dynamic optical networks,the dynamic traffic models
are designed based on Bernoulli, Poisson distributions for smooth
and regular types of traffic. Through Analytical and Simulation
results, the routing power of cat-II of ROADM networks for two
traffic models are determined.
Abstract: In this paper back-propagation artificial neural network
(BPANN) is employed to predict the deformation of the upsetting
process. To prepare a training set for BPANN, some finite element
simulations were carried out. The input data for the artificial neural
network are a set of parameters generated randomly (aspect ratio d/h,
material properties, temperature and coefficient of friction). The
output data are the coefficient of polynomial that fitted on barreling
curves. Neural network was trained using barreling curves generated
by finite element simulations of the upsetting and the corresponding
material parameters. This technique was tested for three different
specimens and can be successfully employed to predict the
deformation of the upsetting process
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: In this paper, we proposed a new routing protocol for
Unmanned Aerial Vehicles (UAVs) that equipped with directional
antenna. We named this protocol Directional Optimized Link State
Routing Protocol (DOLSR). This protocol is based on the well
known protocol that is called Optimized Link State Routing Protocol
(OLSR). We focused in our protocol on the multipoint relay (MPR)
concept which is the most important feature of this protocol. We
developed a heuristic that allows DOLSR protocol to minimize
the number of the multipoint relays. With this new protocol the
number of overhead packets will be reduced and the End-to-End
delay of the network will also be minimized. We showed through
simulation that our protocol outperformed Optimized Link State
Routing Protocol, Dynamic Source Routing (DSR) protocol and Ad-
Hoc On demand Distance Vector (AODV) routing protocol in
reducing the End-to-End delay and enhancing the overall
throughput. Our evaluation of the previous protocols was based
on the OPNET network simulation tool.