Abstract: Supply chains are the backbone of trade and
commerce. Their logistics use different transport corridors on regular
basis for operational purpose. The international supply chain
transport corridors include different infrastructure elements (e.g.
weighbridge, package handling equipments, border clearance
authorities, and so on). This paper presents the use of multi-agent
systems (MAS) to model and simulate some aspects of transportation
corridors, and in particular the area of weighbridge resource
optimization for operational profit. An underlying multi-agent model
provides a means of modeling the relationships among stakeholders
in order to enable coordination in a transport corridor environment.
Simulations of the costs of container unloading, reloading, and
waiting time for queuing up tracks have been carried out using data
sets. Results of the simulation provide the potential guidance in
making decisions about optimal service resource allocation in a trade
corridor.
Abstract: This research aims to develop an algorithm to
generate a schedule of multiple cranes that will maximize load
throughputs in anodizing operation. The algorithm proposed utilizes
an enumerative strategy to search for constant time between
successive loads and crane covering range over baths. The computer
program developed is able to generate a near-optimal crane schedule
within reasonable times, i.e. within 10 minutes. Its results are
compared with existing solutions from an aluminum extrusion
industry. The program can be used to generate crane schedules for
mixed products, thus allowing mixed-model line balancing to
improve overall cycle times.
Abstract: Nowadays, the use of renewable energy sources has been increasingly great because of the cost increase and public demand for clean energy sources. One of the fastest growing sources is wind energy. In this paper, Wind Diesel Hybrid System (WDHS) comprising a Diesel Generator (DG), a Wind Turbine Generator (WTG), the Consumer Load, a Battery-based Energy Storage System (BESS), and a Dump Load (DL) is used. Voltage is controlled by Diesel Generator; the frequency is controlled by BESS and DL. The BESS elimination is an efficient way to reduce maintenance cost and increase the dynamic response. Simulation results with graphs for the frequency of Power System, active power, and the battery power are presented for load changes. The controlling parameters are optimized by using Imperialist Competitive Algorithm (ICA). The simulation results for the BESS/no BESS cases are compared. Results show that in no BESS case, the frequency control is more optimal than the BESS case by using ICA.
Abstract: This paper reports the development and application of a 2D1 depth-averaged model. The main goal of this contribution is to apply the depth averaged equations to a wind park model in which the treatment of the geometry, introduced on the mathematical model by the mass and momentum source terms. The depth-averaged model will be used in future to find the optimal position of wind turbines in the wind park. κ − ε and 2D LES turbulence models were consider in this article. 2D CFD2 simulations for one hill was done to check the depth-averaged model in practise.
Abstract: Image segmentation and color identification is an
important process used in various emerging fields like intelligent
robotics. A method is proposed for the manipulator to grasp and place
the color object into correct location. The existing methods such as
PSO, has problems like accelerating the convergence speed and
converging to a local minimum leading to sub optimal performance.
To improve the performance, we are using watershed algorithm and
for color identification, we are using EPSO. EPSO method is used to
reduce the probability of being stuck in the local minimum. The
proposed method offers the particles a more powerful global
exploration capability. EPSO methods can determine the particles
stuck in the local minimum and can also enhance learning speed as
the particle movement will be faster.
Abstract: This study examines the feasibility of indirect solar
desalination in oil producing countries in the Middle East and North
Africa (MENA) region. It relies on value engineering (VE) and costbenefit
with sensitivity analyses to identify optimal coupling
configurations of desalination and solar energy technologies. A
comparative return on investment was assessed as a function of water
costs for varied plant capacities (25,000 to 75,000 m3/day), project
lifetimes (15 to 25 years), and discount rates (5 to 15%) taking into
consideration water and energy subsidies, land cost as well as
environmental externalities in the form of carbon credit related to
greenhouse gas (GHG) emissions reduction. The results showed
reverse osmosis (RO) coupled with photovoltaic technologies (PVs)
as the most promising configuration, robust across different prices for
Brent oil, discount rates, as well as different project lifetimes.
Environmental externalities and subsidies analysis revealed that a
16% reduction in existing subsidy on water tariffs would ensure
economic viability. Additionally, while land costs affect investment
attractiveness, the viability of RO coupled with PV remains possible
for a land purchase cost
Abstract: Optimal feeding, including optimal micronutrient
intake, becomes one of the ways to overcome the long-term
consequences of undernutrition. Macronutrient and micronutrient
intake were important to a rapid growth and development of young
children. The study objective was to assess macro and micronutrient
intake and its adequacy in children aged 12-23 months. This survey
was a cross-sectional study, involving 83 caregivers with children
aged 12-23 months old in Senen Sub-district, Central Jakarta selected
through simple random sampling. Data on nutrient intake was
obtained through interview using single 24-hour recall. Repeated 24-
hour recall to sub-sample was done to estimate the proportion of
nutrient inadequacy. The highest prevalence of nutrient inadequacy
was iron (52.4%), followed by vitamin C (30.9%) and zinc (28.8%).
Almost 12% children had inadequate energy intake. More than half
of children (62.6%) were anemic (25.3% were severely anemic).
Micronutrient inadequacy, especially iron, was more problematic
than macronutrient inadequacy in the study area.
Abstract: Development of new generation bio-tribological,
multilayer coatings opens an avenue for fabrication of future hightech
functional surfaces. In the presented work, nano-composite,
Cr/CrN+[Cr/ a-C:H implanted by metallic nanocrystals] multilayer
coatings have been developed for surface protection of medical tools.
Thin films were fabricated by a hybrid Pulsed Laser Deposition
technique. Complex microstructure analysis of nanomultilayer
coatings, subjected to mechanical and biological tests, were
performed by means of transmission electron microscopy (TEM).
Microstructure characterization revealed the layered arrangement of
Cr23C6 nanoparticles in multilayer structure. Influence of deposition
conditions on bio-tribological properties of the coatings was studied.
The bio-tests were used as a screening tool for the analyzed
nanomultilayer coatings before they could be deposited on medical
tools. Bio-medical tests were done using fibroblasts. The mechanical
properties of the coatings were investigated by means of a ball-ondisc
mechanical test. The micro hardness was done using Berkovich
indenter. The scratch adhesion test was done using Rockwell
indenter. From the bio-tribological point of view, the optimal
properties had the C106_1 material.
Abstract: This paper presents an optimization method for
reducing the number of input channels and the complexity of the
feed-forward NARX neural network (NN) without compromising the
accuracy of the NN model. By utilizing the correlation analysis
method, the most significant regressors are selected to form the input
layer of the NN structure. An application of vehicle dynamic model
identification is also presented in this paper to demonstrate the
optimization technique and the optimal input layer structure and the
optimal number of neurons for the neural network is investigated.
Abstract: In a practical power system, the power plants are not
located at the same distance from the center of loads and their fuel
costs are different. Also, under normal operating conditions, the
generation capacity is more than the total load demand and losses.
Thus, there are many options for scheduling generation. In an
interconnected power system, the objective is to find the real and
reactive power scheduling of each power plant in such a way as to
minimize the operating cost. This means that the generator’s real and
reactive powers are allowed to vary within certain limits so as to meet
a particular load demand with minimum fuel cost. This is called
optimal power flow problem. In this paper, Economic Load Dispatch
(ELD) of real power generation is considered. Economic Load
Dispatch (ELD) is the scheduling of generators to minimize total
operating cost of generator units subjected to equality constraint of
power balance within the minimum and maximum operating limits of
the generating units. In this paper, genetic algorithms are considered.
ELD solutions are found by solving the conventional load flow
equations while at the same time minimizing the fuel costs.
Abstract: Super steel materials play a vital role in the
construction and fabrication of structural, piping and pipeline
components. In assuring the integrity of onshore and offshore
operating systems, they enable life cycle costs to be minimized. In
this context, Duplex stainless steel (DSS) material related welding on
constructions and fabrications plays a significant role in maintaining
and assuring integrity at an optimal expenditure over the life cycle of
production and process systems as well as associated structures. In
DSS welding, factors such as gap geometry, shielding gas supply
rate, welding current, and type of the welding process are vital to the
final joint performance. Hence, an experimental investigation has
been performed using an engineering robust design approach
(ERDA) to investigate the optimal settings that generate optimal
super DSS (i.e. UNS S32750) joint performance. This manuscript
illustrates the mathematical approach and experimental design,
optimal parameter settings and results of the verification experiment.
Abstract: The paper presents new results concerning selection of
optimal information fusion formula for ensembles of C-OTDR
channels. The goal of information fusion is to create an integral
classificator designed for effective classification of seismoacoustic
target events. The LPBoost (LP-β and LP-B variants), the Multiple
Kernel Learning, and Weighing of Inversely as Lipschitz Constants
(WILC) approaches were compared. The WILC is a brand new
approach to optimal fusion of Lipschitz Classifiers Ensembles.
Results of practical usage are presented.
Abstract: Fabric textures are very common in our daily life.
However, the representation of fabric textures has never been explored
from neuroscience view. Theoretical studies suggest that primary
visual cortex (V1) uses a sparse code to efficiently represent natural
images. However, how the simple cells in V1 encode the artificial
textures is still a mystery. So, here we will take fabric texture as
stimulus to study the response of independent component analysis that
is established to model the receptive field of simple cells in V1. We
choose 140 types of fabrics to get the classical fabric textures as
materials. Experiment results indicate that the receptive fields of
simple cells have obvious selectivity in orientation, frequency and
phase when drifting gratings are used to determine their tuning
properties. Additionally, the distribution of optimal orientation and
frequency shows that the patch size selected from each original fabric
image has a significant effect on the frequency selectivity.
Abstract: In this contribution two approaches for calculating
optimal trajectories for highly automated vehicles are presented and
compared. The first one is based on a non-linear vehicle model, used
for evaluation. The second one is based on a simplified model and
can be implemented on a current ECU. In usual driving situations
both approaches show very similar results.
Abstract: A simple adaptive voice activity detector (VAD) is
implemented using Gabor and gammatone atomic decomposition of
speech for high Gaussian noise environments. Matching pursuit is
used for atomic decomposition, and is shown to achieve optimal
speech detection capability at high data compression rates for low
signal to noise ratios. The most active dictionary elements found by
matching pursuit are used for the signal reconstruction so that the
algorithm adapts to the individual speakers dominant time-frequency
characteristics. Speech has a high peak to average ratio enabling
matching pursuit greedy heuristic of highest inner products to isolate
high energy speech components in high noise environments. Gabor
and gammatone atoms are both investigated with identical
logarithmically spaced center frequencies, and similar bandwidths.
The algorithm performs equally well for both Gabor and gammatone
atoms with no significant statistical differences. The algorithm
achieves 70% accuracy at a 0 dB SNR, 90% accuracy at a 5 dB SNR
and 98% accuracy at a 20dB SNR using 30d B SNR as a reference
for voice activity.
Abstract: The problems arising from unbalanced data sets
generally appear in real world applications. Due to unequal class
distribution, many researchers have found that the performance of
existing classifiers tends to be biased towards the majority class. The
k-nearest neighbors’ nonparametric discriminant analysis is a method
that was proposed for classifying unbalanced classes with good
performance. In this study, the methods of discriminant analysis are
of interest in investigating misclassification error rates for classimbalanced
data of three diabetes risk groups. The purpose of this
study was to compare the classification performance between
parametric discriminant analysis and nonparametric discriminant
analysis in a three-class classification of class-imbalanced data of
diabetes risk groups. Data from a project maintaining healthy
conditions for 599 employees of a government hospital in Bangkok
were obtained for the classification problem. The employees were
divided into three diabetes risk groups: non-risk (90%), risk (5%),
and diabetic (5%). The original data including the variables of
diabetes risk group, age, gender, blood glucose, and BMI were
analyzed and bootstrapped for 50 and 100 samples, 599 observations
per sample, for additional estimation of the misclassification error
rate. Each data set was explored for the departure of multivariate
normality and the equality of covariance matrices of the three risk
groups. Both the original data and the bootstrap samples showed nonnormality
and unequal covariance matrices. The parametric linear
discriminant function, quadratic discriminant function, and the
nonparametric k-nearest neighbors’ discriminant function were
performed over 50 and 100 bootstrap samples and applied to the
original data. Searching the optimal classification rule, the choices of
prior probabilities were set up for both equal proportions (0.33: 0.33:
0.33) and unequal proportions of (0.90:0.05:0.05), (0.80: 0.10: 0.10)
and (0.70, 0.15, 0.15). The results from 50 and 100 bootstrap samples
indicated that the k-nearest neighbors approach when k=3 or k=4 and
the defined prior probabilities of non-risk: risk: diabetic as 0.90:
0.05:0.05 or 0.80:0.10:0.10 gave the smallest error rate of
misclassification. The k-nearest neighbors approach would be
suggested for classifying a three-class-imbalanced data of diabetes
risk groups.
Abstract: Heat transfer of leaves is a crucial factor in optimal
operation of metabolic functions in plants. In order to quantify this
phenomenon in different leaves and investigate the influence of leaf
shape on heat transfer, natural convection for pine, orange and olive
leaves was simulated as representatives of different groups of leaf
shapes. CFD techniques were used in this simulation with the
purpose to calculate heat transfer of leaves in similar environmental
conditions. The problem was simulated for steady state and threedimensional
conditions. From obtained results, it was concluded that
heat fluxes of all three different leaves are almost identical, however,
total rate of heat transfer have highest and lowest values for orange
leaves, and pine leaves, respectively.
Abstract: Roadway planning and design is a very complex
process involving five key phases before a project is completed;
planning, project development, final design, right-of-way, and
construction. The planning phase for a new roadway transportation
project is a very critical phase as it greatly affects all latter phases of
the project. A location study is usually performed during the
preliminary planning phase in a new roadway project. The objective
of the location study is to develop alignment alternatives that are cost
efficient considering land acquisition and construction costs. This
paper describes a methodology to develop optimal preliminary
roadway alignments utilizing spatial-data. Four optimization criteria
are taken into consideration; roadway length, land cost, land slope,
and environmental impacts. The basic concept of the methodology is
to convert the proposed project area into a grid, which represents the
search space for an optimal alignment. The aforementioned
optimization criteria are represented in each of the grid’s cells. A
spatial-data optimization technique is utilized to find the optimal
alignment in the search space based on the four optimization criteria.
Two case studies for new roadway projects in Duval County in the
State of Florida are presented to illustrate the methodology. The
optimization output alignments are compared to the proposed Florida
Department of Transportation (FDOT) alignments. The comparison is
based on right-of-way costs for the alignments. For both case studies,
the right-of-way costs for the developed optimal alignments were
found to be significantly lower than the FDOT alignments.
Abstract: This paper presents an optimal broadcast algorithm
for the hypercube networks. The main focus of the paper is the
effectiveness of the algorithm in the presence of many node faults.
For the optimal solution, our algorithm builds with spanning tree
connecting the all nodes of the networks, through which messages
are propagated from source node to remaining nodes. At any given
time, maximum n − 1 nodes may fail due to crashing. We show
that the hypercube networks are strongly fault-tolerant. Simulation
results analyze to accomplish algorithm characteristics under many
node faults. We have compared our simulation results between our
proposed method and the Fu’s method. Fu’s approach cannot tolerate
n − 1 faulty nodes in the worst case, but our approach can tolerate
n − 1 faulty nodes.
Abstract: Quality of Service (QoS) attributes as part of the
service description is an important factor for service attribute. It is not
easy to exactly quantify the weight of each QoS conditions since
human judgments based on their preference causes vagueness. As
web services selection requires optimization, evolutionary computing
based on heuristics to select an optimal solution is adopted. In this
work, the evolutionary computing technique Particle Swarm
Optimization (PSO) is used for selecting a suitable web services
based on the user’s weightage of each QoS values by optimizing the
QoS weight vector and thereby finding the best weight vectors for
best services that is being selected. Finally the results are compared
and analyzed using static inertia weight and deterministic inertia
weight of PSO.