Abstract: Transportation of long turbine blades from one place
to another is a difficult process. Hence a feasibility study of
modularization of wind turbine blade was taken from structural
standpoint through finite element analysis. Initially, a non-segmented
blade is modeled and its structural behavior is evaluated to serve as
reference. The resonant, static bending and fatigue tests are simulated
in accordance with IEC61400-23 standard for comparison purpose.
The non-segmented test blade is separated at suitable location based
on trade off studies and the segments are joined with an innovative
double strap bonded joint configuration. The adhesive joint is
modeled by adopting cohesive zone modeling approach in ANSYS.
The developed blade model is analyzed for its structural response
through simulation. Performances of both the blades are found to be
similar, which indicates that, efficient segmentation of the long blade
is possible which facilitates easy transportation of the blades and on
site reassembling. The location selected for segmentation and
adopted joint configuration has resulted in an efficient segmented
blade model which proves the methodology adopted for segmentation
was quite effective. The developed segmented blade appears to be the
viable alternative considering its structural response specifically in
fatigue within considered assumptions.
Abstract: Background: Worldwide, at least 2.8 million people
die each year as a result of being overweight or obese, and 35.8
million (2.3%) of global DALYs are caused by overweight or
obesity. Obesity is acknowledged as one of the burning public
health problems reducing life expectancy and quality of life. The
body composition analysis of the university population is essential
in assessing the nutritional status, as well as the risk of developing
diseases associated with abnormal body fat content so as to make
nutritional recommendations. Objectives: The main aim was to
determine the prevalence of obesity and overweight in University
students using Anthropometric analysis and BIA methods. Material
and Methods: In this cross-sectional study, 283 university students
participated. The body composition analysis was undertaken by
using mainly: i) Anthropometric Measurement: Height, Weight,
BMI, waist circumference, hip circumference and skin fold
thickness, ii) Bio-electrical impedance was used for analysis of
body fat mass, fat percent and visceral fat which was measured by
Tanita SC-330P Professional Body Composition Analyzer. The
data so collected were compiled in MS Excel and analyzed for
males and females using SPSS 16. Results and Discussion: The
mean age of the male (n= 153) studied subjects was 25.37 ±2.39
years and females (n=130) was 22.53 ±2.31. The data of BIA
revealed very high mean fat per cent of the female subjects i.e.
30.3±6.5 per cent whereas mean fat per cent of the male subjects
was 15.60±6.02 per cent indicating a normal body fat range. The
findings showed high visceral fat of both males (12.92±3.02) and
females (16.86±4.98). BMI, BF% and WHR were higher among
females, and BMI was higher among males. The most evident
correlation was verified between BF% and WHR for female
students (r=0.902; p
Abstract: This article presents two methods for the
compensation of harmonics generated by a nonlinear load. The first is
the classic method P-Q. The second is the controller by modern
method of artificial intelligence specifically fuzzy logic. Both
methods are applied to a shunt Active Power Filter (sAPF) based on a
three-phase voltage converter at five levels NPC topology. In
calculating the harmonic currents of reference, we use the algorithm
P-Q and pulse generation, we use the intersective PWM. For
flexibility and dynamics, we use fuzzy logic. The results give us clear
that the rate of Harmonic Distortion issued by fuzzy logic is better
than P-Q.
Abstract: This research paper presents highly optimized barrel
shifter at 22nm Hi K metal gate strained Si technology node. This
barrel shifter is having a unique combination of static and dynamic
body bias which gives lowest power delay product. This power delay
product is compared with the same circuit at same technology node
with static forward biasing at ‘supply/2’ and also with normal reverse
substrate biasing and still found to be the lowest. The power delay
product of this barrel sifter is .39362X10-17J and is lowered by
approximately 78% to reference proposed barrel shifter at 32nm bulk
CMOS technology. Power delay product of barrel shifter at 22nm Hi
K Metal gate technology with normal reverse substrate bias is
2.97186933X10-17J and can be compared with this design’s PDP of
.39362X10-17J. This design uses both static and dynamic substrate
biasing and also has approximately 96% lower power delay product
compared to only forward body biased at half of supply voltage. The
NMOS model used are predictive technology models of Arizona state
university and the simulations to be carried out using HSPICE
simulator.
Abstract: In this paper, we propose a system for preventing gas
risks through the use of wireless communication modules and
intelligent gas safety appliances. Our system configuration consists of
an automatic extinguishing system, detectors, a wall-pad, and a
microcomputer controlled micom gas meter to monitor gas flow and
pressure as well as the occurrence of earthquakes. The automatic fire
extinguishing system checks for both combustible gaseous leaks and
monitors the environmental temperature, while the detector array
measures smoke and CO gas concentrations. Depending on detected
conditions, the micom gas meter cuts off an inner valve and generates
a warning, the automatic fire-extinguishing system cuts off an external
valve and sprays extinguishing materials, or the sensors generate
signals and take further action when smoke or CO are detected.
Information on intelligent measures taken by the gas safety appliances
and sensors are transmitted to the wall-pad, which in turn relays this as
real time data to a server that can be monitored via an external network
(BcN) connection to a web or mobile application for the management
of gas safety. To validate this smart-home gas management system, we
field-tested its suitability for use in Korean apartments under several
scenarios.
Abstract: To understand the friction stir welding process, it is
very important to know the nature of the material flow in and around
the tool. The process is a combination of both thermal as well as
mechanical work i.e. it is a coupled thermo-mechanical process.
Numerical simulations are very much essential in order to obtain a
complete knowledge of the process as well as the physics underlying
it. In the present work a model based approach is adopted in order to
study material flow. A thermo-mechanical based CFD model is
developed using a Finite Element package, Comsol Multiphysics.
The fluid flow analysis is done. The model simultaneously predicts
shear strain fields, shear strain rates and shear stress over the entire
workpiece for the given conditions. The flow fields generated by the
streamline plot give an idea of the material flow. The variation of
dynamic viscosity, velocity field and shear strain fields with various
welding parameters is studied. Finally the result obtained from the
above mentioned conditions is discussed elaborately and concluded.
Abstract: This paper presents a rank correlation curve. The
traditional correlation coefficient is valid for both continuous
variables and for integer variables using rank statistics. Since
the correlation coefficient has already been established in rank
statistics by Spearman, such a calculation can be extended to
the correlation curve.
This paper presents two survey questions. The survey
collected non-continuous variables. We will show weak to
moderate correlation. Obviously, one question has a negative
effect on the other. A review of the qualitative literature
can answer which question and why. The rank correlation
curve shows which collection of responses has a positive
slope and which collection of responses has a negative slope.
Such information is unavailable from the flat, ”first-glance”
correlation statistics.
Abstract: Waste load allocation (WLA) policies may use multiobjective
optimization methods to find the most appropriate and
sustainable solutions. These usually intend to simultaneously
minimize two criteria, total abatement costs (TC) and environmental
violations (EV). If other criteria, such as inequity, need for
minimization as well, it requires introducing more binary
optimizations through different scenarios. In order to reduce the
calculation steps, this study presents value index as an innovative
decision making approach. Since the value index contains both the
environmental violation and treatment costs, it can be maximized
simultaneously with the equity index. It implies that the definition of
different scenarios for environmental violations is no longer required.
Furthermore, the solution is not necessarily the point with minimized
total costs or environmental violations. This idea is testified for Haraz
River, in north of Iran. Here, the dissolved oxygen (DO) level of river
is simulated by Streeter-Phelps equation in MATLAB software. The
WLA is determined for fish farms using multi-objective particle
swarm optimization (MOPSO) in two scenarios. At first, the trade-off
curves of TC-EV and TC-Inequity are plotted separately as the
conventional approach. In the second, the Value-Equity curve is
derived. The comparative results show that the solutions are in a
similar range of inequity with lower total costs. This is due to the
freedom of environmental violation attained in value index. As a
result, the conventional approach can well be replaced by the value
index particularly for problems optimizing these objectives. This
reduces the process to achieve the best solutions and may find better
classification for scenario definition. It is also concluded that decision
makers are better to focus on value index and weighting its contents
to find the most sustainable alternatives based on their requirements.
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: One of the most famous techniques which affect the
efficiency of a production line is the assembly line balancing (ALB)
technique. This paper examines the balancing effect of a whole
production line of a real auto glass manufacturer in three steps. In the
first step, processing time of each activity in the workstations is
generated according to a practical approach. In the second step, the
whole production process is simulated and the bottleneck stations
have been identified, and finally in the third step, several
improvement scenarios are generated to optimize the system
throughput, and the best one is proposed. The main contribution of
the current research is the proposed framework which combines two
famous approaches including Assembly Line Balancing and
Optimization via Simulation technique (OvS). The results show that
the proposed framework could be applied in practical environments,
easily.
Abstract: This paper contains the description of argumentation
approach for the problem of inductive concept formation. It is
proposed to use argumentation, based on defeasible reasoning with
justification degrees, to improve the quality of classification models,
obtained by generalization algorithms. The experiment’s results on
both clear and noisy data are also presented.
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: Margin-Based Principle has been proposed for a long
time, it has been proved that this principle could reduce the
structural risk and improve the performance in both theoretical
and practical aspects. Meanwhile, feed-forward neural network is
a traditional classifier, which is very hot at present with a deeper
architecture. However, the training algorithm of feed-forward neural
network is developed and generated from Widrow-Hoff Principle that
means to minimize the squared error. In this paper, we propose
a new training algorithm for feed-forward neural networks based
on Margin-Based Principle, which could effectively promote the
accuracy and generalization ability of neural network classifiers
with less labelled samples and flexible network. We have conducted
experiments on four UCI open datasets and achieved good results
as expected. In conclusion, our model could handle more sparse
labelled and more high-dimension dataset in a high accuracy while
modification from old ANN method to our method is easy and almost
free of work.
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: In this work, we explore the capability of the mean
shift algorithm as a powerful preprocessing tool for improving the
quality of spatial data, acquired from airborne scanners, from densely
built urban areas. On one hand, high resolution image data corrupted
by noise caused by lossy compression techniques are appropriately
smoothed while at the same time preserving the optical edges and, on
the other, low resolution LiDAR data in the form of normalized
Digital Surface Map (nDSM) is upsampled through the joint mean
shift algorithm. Experiments on both the edge-preserving smoothing
and upsampling capabilities using synthetic RGB-z data show that the
mean shift algorithm is superior to bilateral filtering as well as to
other classical smoothing and upsampling algorithms. Application of
the proposed methodology for 3D reconstruction of buildings of a
pilot region of Athens, Greece results in a significant visual
improvement of the 3D building block model.
Abstract: Historical narration is an act that necessarily develops
and deforms history. This “translation” is examined within the
historical and political context of the 1930 Berlin film premiere of
“All Quiet on the Western Front,” a film based on Erich Maria
Remarque’s 1928 best-selling novel. Both the film and the novel
appeared during an era in which life was conceived of as innately
artistic. The emergence of this “aestheticization” of memory and
history enabled conservative propaganda of the period to denounce
all art that did not adhere conceptually to its political tenets, with “All
Quiet” becoming yet another of its “victims.”
Abstract: Microscopic simulation tool kits allow for
consideration of the two processes of railway operations and the
previous timetable production. Block occupation conflicts on both
process levels are often solved by using defined train priorities. These
conflict resolutions (dispatching decisions) generate reactionary
delays to the involved trains. The sum of reactionary delays is
commonly used to evaluate the quality of railway operations, which
describes the timetable robustness. It is either compared to an
acceptable train performance or the delays are appraised
economically by linear monetary functions. It is impossible to
adequately evaluate dispatching decisions without a well-founded
objective function. This paper presents a new approach for the
evaluation of dispatching decisions. The approach uses mode choice
models and considers the behaviour of the end-customers. These
models evaluate the reactionary delays in more detail and consider
other competing modes of transport. The new approach pursues the
coupling of a microscopic model of railway operations with the
macroscopic choice mode model. At first, it will be implemented for
railway operations process but it can also be used for timetable
production. The evaluation considers the possibility for the customer
to interchange to other transport modes. The new approach starts to
look at rail and road, but it can also be extended to air travel. The
result of mode choice models is the modal split. The reactions by the
end-customers have an impact on the revenue of the train operating
companies. Different purposes of travel have different payment
reserves and tolerances towards late running. Aside from changes to
revenues, longer journey times can also generate additional costs.
The costs are either time- or track-specific and arise from required
changes to rolling stock or train crew cycles. Only the variable values
are summarised in the contribution margin, which is the base for the
monetary evaluation of delays. The contribution margin is calculated
for different possible solutions to the same conflict. The conflict
resolution is optimised until the monetary loss becomes minimal. The
iterative process therefore determines an optimum conflict resolution
by monitoring the change to the contribution margin. Furthermore, a
monetary value of each dispatching decision can also be derived.
Abstract: Evolutionary optimization methods such as genetic
algorithms have been used extensively for the construction site layout
problem. More recently, ant colony optimization algorithms, which
are evolutionary methods based on the foraging behavior of ants,
have been successfully applied to benchmark combinatorial
optimization problems. This paper proposes a formulation of the site
layout problem in terms of a sequencing problem that is suitable for
solution using an ant colony optimization algorithm.
In the construction industry, site layout is a very important
planning problem. The objective of site layout is to position
temporary facilities both geographically and at the correct time such
that the construction work can be performed satisfactorily with
minimal costs and improved safety and working environment. During
the last decade, evolutionary methods such as genetic algorithms
have been used extensively for the construction site layout problem.
This paper proposes an ant colony optimization model for
construction site layout. A simple case study for a highway project is
utilized to illustrate the application of the model.
Abstract: The use of engineered nanomaterials has increased as
a result of their positive impact on many sectors of the economy,
including agriculture. Silver nanoparticles (AgNPs) are now used to
enhance seed germination, plant growth, and photosynthetic quantum
efficiency and as antimicrobial agents to control plant diseases. In
this study, we examined the effect of AgNP dosage on the seed
germination of three plant species: corn (Zea mays L.), watermelon
(Citrullus lanatus [Thunb.] Matsum. & Nakai) and zucchini
(Cucurbita pepo L.). This experiment was designed to study the
effect of AgNPs on germination percentage, germination rate, mean
germination time, root length and fresh and dry weight of seedlings
for the three species. Seven concentrations (0.05, 0.1, 0.5, 1, 1.5, 2
and 2.5 mg/ml) of AgNPs were examined at the seed germination
stage. The three species had different dose responses to AgNPs in
terms of germination parameters and the measured growth
characteristics. The germination rates of the three plants were
enhanced in response to AgNPs. Significant enhancement of the
germination percentage values was observed after treatment of the
watermelon and zucchini plants with AgNPs in comparison with
untreated seeds. AgNPs showed a toxic effect on corn root
elongation, whereas watermelon and zucchini seedling growth were
positively affected by certain concentrations of AgNPs. This study
showed that exposure to AgNPs caused both positive and negative
effects on plant growth and germination.
Abstract: This paper presents the development of a robot car
that can track the motion of an object by detecting its color through
an Android device. The employed computer vision algorithm uses the
OpenCV library, which is embedded into an Android application of a
smartphone, for manipulating the captured image of the object. The
captured image of the object is subjected to color conversion and is
transformed to a binary image for further processing after color
filtering. The desired object is clearly determined after removing
pixel noise by applying image morphology operations and contour
definition. Finally, the area and the center of the object are
determined so that object’s motion to be tracked. The smartphone
application has been placed on a robot car and transmits by Bluetooth
to an Arduino assembly the motion directives so that to follow
objects of a specified color. The experimental evaluation of the
proposed algorithm shows reliable color detection and smooth
tracking characteristics.