Abstract: The “conveyor belt" as a product represents a
complex high performance component with a wide range of different
applications. Further development of these highly complex
components demands an integration of new technologies and new
enhanced materials. In this context nanostructured fillers appear to
have a more promising effect on the performance of the conveyor
belt composite than conventional micro-scaled fillers.
Within the project “DotTrans" nanostructured fillers, for example
silicon dioxide, are used to optimize performance parameters of
conveyor belt systems. The objective of the project includes
operating parameters like energy consumption or friction
characteristics as well as adaptive parameters like cut or wear
resistance.
Abstract: Sedimentation process resulting from soil erosion in
the water basin especially in arid and semi-arid where poor
vegetation cover in the slope of the mountains upstream could
contribute to sediment formation. The consequence of sedimentation
not only makes considerable change in the morphology of the river
and the hydraulic characteristics but would also have a major
challenge for the operation and maintenance of the canal network
which depend on water flow to meet the stakeholder-s requirements.
For this reason mathematical modeling can be used to simulate the
effective factors on scouring, sediment transport and their settling
along the waterways. This is particularly important behind the
reservoirs which enable the operators to estimate the useful life of
these hydraulic structures. The aim of this paper is to simulate the
sedimentation and erosion in the eastern and western water intake
structures of the Dez Diversion weir using GSTARS-3 software. This
is done to estimate the sedimentation and investigate the ways in
which to optimize the process and minimize the operational
problems. Results indicated that the at the furthest point upstream of
the diversion weir, the coarser sediment grains tended to settle. The
reason for this is the construction of the phantom bridge and the
outstanding rocks just upstream of the structure. The construction of
these along the river course has reduced the momentum energy
require to push the sediment loads and make it possible for them to
settle wherever the river regime allows it. Results further indicated a
trend for the sediment size in such a way that as the focus of study
shifts downstream the size of grains get smaller and vice versa. It
was also found that the finding of the GSTARS-3 had a close
proximity with the sets of the observed data. This suggests that the
software is a powerful analytical tool which can be applied in the
river engineering project with a minimum of costs and relatively
accurate results.
Abstract: The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to resort to the trial and error approach. This paper describes the process of developing three layer feed-forward large neural networks for short-term load forecasting and then presents a heuristic search algorithm for performing an important task of this process, i.e. optimal networks structure design. Particle Swarm Optimization (PSO) is used to develop the optimum large neural network structure and connecting weights for one-day ahead electric load forecasting problem. PSO is a novel random optimization method based on swarm intelligence, which has more powerful ability of global optimization. Employing PSO algorithms on the design and training of ANNs allows the ANN architecture and parameters to be easily optimized. The proposed method is applied to STLF of the local utility. Data are clustered due to the differences in their characteristics. Special days are extracted from the normal training sets and handled separately. In this way, a solution is provided for all load types, including working days and weekends and special days. The experimental results show that the proposed method optimized by PSO can quicken the learning speed of the network and improve the forecasting precision compared with the conventional Back Propagation (BP) method. Moreover, it is not only simple to calculate, but also practical and effective. Also, it provides a greater degree of accuracy in many cases and gives lower percent errors all the time for STLF problem compared to BP method. Thus, it can be applied to automatically design an optimal load forecaster based on historical data.
Abstract: An attempt has been made to investigate the
machinability of zirconia toughened alumina (ZTA) inserts while
turning AISI 4340 steel. The insert was prepared by powder
metallurgy process route and the machining experiments were
performed based on Response Surface Methodology (RSM) design
called Central Composite Design (CCD). The mathematical model of
flank wear, cutting force and surface roughness have been developed
using second order regression analysis. The adequacy of model has
been carried out based on Analysis of variance (ANOVA) techniques.
It can be concluded that cutting speed and feed rate are the two most
influential factor for flank wear and cutting force prediction. For
surface roughness determination, the cutting speed & depth of cut
both have significant contribution. Key parameters effect on each
response has also been presented in graphical contours for choosing
the operating parameter preciously. 83% desirability level has been
achieved using this optimized condition.
Abstract: Two optimized strategies were successfully established
to develop biomolecule-based magnetic nanoassemblies.
Streptavidin-coated and amine-coated magnetic nanoparticles were
chosen as model scaffolds onto which double-stranded DNA and
human immunoglobulin G were specifically conjugated in succession,
using biotin-streptavidin interaction or covalent cross-linkers. The
success of this study opens the prospect of developing selective and
sensitive nanoparticle-based structures for diagnostics or drug
delivery.
Abstract: We introduce an extended resource leveling model that abstracts real life projects that consider specific work ranges for each resource. Contrary to traditional resource leveling problems this model considers scarce resources and multiple objectives: the minimization of the project makespan and the leveling of each resource usage over time. We formulate this model as a multiobjective optimization problem and we propose a multiobjective genetic algorithm-based solver to optimize it. This solver consists in a two-stage process: a main stage where we obtain non-dominated solutions for all the objectives, and a postprocessing stage where we seek to specifically improve the resource leveling of these solutions. We propose an intelligent encoding for the solver that allows including domain specific knowledge in the solving mechanism. The chosen encoding proves to be effective to solve leveling problems with scarce resources and multiple objectives. The outcome of the proposed solvers represent optimized trade-offs (alternatives) that can be later evaluated by a decision maker, this multi-solution approach represents an advantage over the traditional single solution approach. We compare the proposed solver with state-of-art resource leveling methods and we report competitive and performing results.
Abstract: Wireless Sensor Network is Multi hop Self-configuring
Wireless Network consisting of sensor nodes. The deployment of
wireless sensor networks in many application areas, e.g., aggregation
services, requires self-organization of the network nodes into clusters.
Efficient way to enhance the lifetime of the system is to partition the
network into distinct clusters with a high energy node as cluster head.
The different methods of node clustering techniques have appeared in
the literature, and roughly fall into two families; those based on the
construction of a dominating set and those which are based solely on
energy considerations. Energy optimized cluster formation for a set
of randomly scattered wireless sensors is presented. Sensors within a
cluster are expected to be communicating with cluster head only. The
energy constraint and limited computing resources of the sensor nodes
present the major challenges in gathering the data. In this paper we
propose a framework to study how partially correlated data affect the
performance of clustering algorithms. The total energy consumption
and network lifetime can be analyzed by combining random geometry
techniques and rate distortion theory. We also present the relation
between compression distortion and data correlation.
Abstract: Nowadays, the importance of energy saving is clearance to everyone. By attention to increasing price of fuels and also the problems of environment pollutions, there are the most efforts for using fuels littler and more optimum in everywhere. This essay studies optimizing of gas consumption in gas-burner space heaters. In oven of each gas-burner space heaters there is two snags to prevent the hot air (the result of combustion of natural gas) to go out of oven of the gas-burner space heaters directly without delivering its heat to the space of favorite environment like a room. These snags cause a excess circulating that helps hot air deliver its heat to the space of favorite environment. It means the exhaust air temperature will be decreased then when there are no snags. This is the aim of this essay to use maximum potential energy of the natural gas to make heat. In this study, by the help of a finite volume software (FLUENT) consumption of the gas-burner space heaters is simulated and optimized. At the end of this writing, by comparing the results of software and experimental results, it will be proved the authenticity of this method.
Abstract: The authors present an algorithm for order reduction of linear time invariant dynamic systems using the combined advantages of the eigen spectrum analysis and the error minimization by particle swarm optimization technique. Pole centroid and system stiffness of both original and reduced order systems remain same in this method to determine the poles, whereas zeros are synthesized by minimizing the integral square error in between the transient responses of original and reduced order models using particle swarm optimization technique, pertaining to a unit step input. It is shown that the algorithm has several advantages, e.g. the reduced order models retain the steady-state value and stability of the original system. The algorithm is illustrated with the help of two numerical examples and the results are compared with the other existing techniques.
Abstract: In recent years, response surface methodology (RSM) has
brought many attentions of many quality engineers in different
industries. Most of the published literature on robust design
methodology is basically concerned with optimization of a single
response or quality characteristic which is often most critical to
consumers. For most products, however, quality is multidimensional,
so it is common to observe multiple responses in an experimental
situation. Through this paper interested person will be familiarize
with this methodology via surveying of the most cited technical
papers.
It is believed that the proposed procedure in this study can resolve
a complex parameter design problem with more than two responses.
It can be applied to those areas where there are large data sets and a
number of responses are to be optimized simultaneously. In addition,
the proposed procedure is relatively simple and can be implemented
easily by using ready-made standard statistical packages.
Abstract: A new tool path planning method for 5-axis flank
milling of a globoidal indexing cam is developed in this paper. The
globoidal indexing cam is a practical transmission mechanism due
to its high transmission speed, accuracy and dynamic performance.
Machining the cam profile is a complex and precise task. The profile
surface of the globoidal cam is generated by the conjugate contact
motion of the roller. The generated complex profile surface is usually
machined by 5-axis point-milling method. The point-milling method
is time-consuming compared with flank milling. The tool path for
5-axis flank milling of globoidal cam is developed to improve the
cutting efficiency. The flank milling tool path is globally optimized
according to the minimum zone criterion, and high accuracy is
guaranteed. The computational example and cutting simulation finally
validate the developed method.
Abstract: Phytophthora cinnamomi (P. c) is a plant pathogenic
oomycete that is capable of damaging plants in commercial production
systems and natural ecosystems worldwide. The most common
methods for the detection and diagnosis of P. c infection are
expensive, elaborate and time consuming. This study was carried out
to examine whether species specific and life cycle specific volatile
organic compounds (VOCs) can be absorbed by solid-phase
microextraction fibers and detected by gas chromatography that are
produced by P. c and another oomycete Pythium dissotocum. A
headspace solid-phase microextraction (HS-SPME) together with gas
chromatography (GC) method was developed and optimized for the
identification of the VOCs released by P. c. The optimized parameters
included type of fiber, exposure time, desorption temperature and
desorption time. Optimization was achieved with the analytes of P.
c+V8A and V8A alone. To perform the HS-SPME, six types of fiber
were assayed and compared: 7μm Polydimethylsiloxane (PDMS),
100μm Polydimethylsiloxane (PDMS), 50/30μm
Divinylbenzene/CarboxenTM/Polydimethylsiloxane
DVB/CAR/PDMS), 65μm Polydimethylsiloxane/Divinylbenzene
(PDMS/DVB), 85μm Polyacrylate (PA) fibre and 85μm CarboxenTM/
Polydimethylsiloxane (Carboxen™/PDMS). In a comparison of the
efficacy of the fibers, the bipolar fiber DVB/CAR/PDMS had a higher
extraction efficiency than the other fibers. An exposure time of 16h
with DVB/CAR/PDMS fiber in the sample headspace was enough to
reach the maximum extraction efficiency. A desorption time of 3min
in the GC injector with the desorption temperature of 250°C was
enough for the fiber to desorb the compounds of interest. The chromatograms and morphology study confirmed that the VOCs from
P. c+V8A had distinct differences from V8A alone, as did different
life cycle stages of P. c and different taxa such as Pythium dissotocum.
The study proved that P. c has species and life cycle specific VOCs,
which in turn demonstrated the feasibility of this method as means of
Abstract: This paper presents a novel approach for the design of
microwave circuits using Adaptive Network Fuzzy Inference
Optimizer (ANFIO). The method takes advantage of direct synthesis
of subsections of the amplifier using very fast and accurate ANFIO
models based on exact simulations using ADS. A mapping from
course space to fine space known as space mapping is also used. The
proposed synthesis approach takes into account the noise and
scattering parameters due to parasitic elements to achieve optimal
results. The overall ANFIO system is capable of designing different
LNAs at different noise and scattering criteria. This approach offers
significantly reduced time in the design of microwave amplifiers
within the validity range of the ANFIO system. The method has been
proven to work efficiently for a 2.4GHz LNA example. The S21 of
10.1 dB and noise figure (NF) of 2.7 dB achieved for ANFIO while
S21 of 9.05 dB and NF of 2.6 dB achieved for ANN.
Abstract: In the current decade, wireless sensor networks are
emerging as a peculiar multi-disciplinary research area. By this
way, energy efficiency is one of the fundamental research themes
in the design of Medium Access Control (MAC) protocols for
wireless sensor networks. Thus, in order to optimize the energy
consumption in these networks, a variety of MAC protocols are
available in the literature. These schemes were commonly evaluated
under simple network density and a few results are published on
their robustness in realistic network-s size. We, in this paper, provide
an analytical study aiming to highlight the energy waste sources in
wireless sensor networks. Then, we experiment three energy efficient
hybrid CSMA/CA based MAC protocols optimized for wireless
sensor networks: Sensor-MAC (SMAC), Time-out MAC (TMAC)
and Traffic aware Energy Efficient MAC (TEEM). We investigate
these protocols with different network densities in order to discuss
the end-to-end performances of these schemes (i.e. in terms of energy
efficiency, delay and throughput). Through Network Simulator (NS-
2) implementations, we explore the behaviors of these protocols with
respect to the network density. In fact, this study may help the multihops
sensor networks designers to design or select the MAC layer
which matches better their applications aims.
Abstract: Acute toxicity of nano SiO2, ZnO, MCM-41 (Meso
pore silica), Cu, Multi Wall Carbon Nano Tube (MWCNT), Single
Wall Carbon Nano Tube (SWCNT) , Fe (Coated) to bacteria Vibrio
fischeri using a homemade luminometer , was evaluated. The values
of the nominal effective concentrations (EC), causing 20% and 50%
inhibition of biouminescence, using two mathematical models at two
times of 5 and 30 minutes were calculated. Luminometer was
designed with Photomultiplier (PMT) detector. Luminol
chemiluminescence reaction was carried out for the calibration graph.
In the linear calibration range, the correlation coefficients and
coefficient of Variation (CV) were 0.988 and 3.21% respectively
which demonstrate the accuracy and reproducibility of the instrument
that are suitable. The important part of this research depends on how
to optimize the best condition for maximum bioluminescence. The
culture of Vibrio fischeri with optimal conditions in liquid media,
were stirring at 120 rpm at a temperature of 150C to 180C and were
incubated for 24 to 72 hours while solid medium was held at 180C
and for 48 hours. Suspension of nanoparticles ZnO, after 30 min
contact time to bacteria Vibrio fischeri, showed the highest toxicity
while SiO2 nanoparticles showed the lowest toxicity. After 5 min
exposure time, the toxicity of ZnO was the strongest and MCM-41
was the weakest toxicant component.
Abstract: The purpose of the present work was to study the
production and process parameters optimization for the synthesis of
cellulase from Trichoderma viride in solid state fermentation (SSF)
using an agricultural wheat straw as substrates; as fungal conversion
of lignocellulosic biomass for cellulase production is one among the
major increasing demand for various biotechnological applications.
An optimization of process parameters is a necessary step to get
higher yield of product. Several kinetic parameters like pretreatment,
extraction solvent, substrate concentration, initial moisture content,
pH, incubation temperature and inoculum size were optimized for
enhanced production of third most demanded industrially important
cellulase. The maximum cellulase enzyme activity 398.10±2.43
μM/mL/min was achieved when proximally analyzed lignocellulosic
substrate wheat straw inocubated at 2% HCl as pretreatment tool
along with distilled water as extraction solvent, 3% substrate
concentration 40% moisture content with optimum pH 5.5 at 45°C
incubation temperature and 10% inoculum size.
Abstract: In this paper we are interested in classification problems
with a performance constraint on error probability. In such
problems if the constraint cannot be satisfied, then a rejection option
is introduced. For binary labelled classification, a number of SVM
based methods with rejection option have been proposed over the
past few years. All of these methods use two thresholds on the SVM
output. However, in previous works, we have shown on synthetic data
that using thresholds on the output of the optimal SVM may lead to
poor results for classification tasks with performance constraint. In
this paper a new method for supervised classification with rejection
option is proposed. It consists in two different classifiers jointly
optimized to minimize the rejection probability subject to a given
constraint on error rate. This method uses a new kernel based linear
learning machine that we have recently presented. This learning
machine is characterized by its simplicity and high training speed
which makes the simultaneous optimization of the two classifiers
computationally reasonable. The proposed classification method with
rejection option is compared to a SVM based rejection method
proposed in recent literature. Experiments show the superiority of
the proposed method.
Abstract: Today air-core coils (ACC) are a viable alternative to
ferrite-core coils in a range of applications due to their low induction
effect. An analytical study was carried out and the results were used as
a guide to understand the relationship between the magnet-coil
distance and the resulting attractive magnetic force. Four different
ACC models were fabricated for experimental study. The variation in
the models included the dimensions, the number of coil turns and the
current supply to the coil. Comparison between the analytical and
experimental results for all the models shows an average discrepancy
of less than 10%. An optimized ACC design was selected for the
scanner which can provide maximum magnetic force.
Abstract: Three service providers in competition, try to optimize
their quality of service / content level and their service access
price. But, they have to deal with uncertainty on the consumers-
preferences. To reduce their uncertainty, they have the opportunity
to buy information and to build alliances. We determine the Shapley
value which is a fair way to allocate the grand coalition-s revenue
between the service providers. Then, we identify the values of β
(consumers- sensitivity coefficient to the quality of service / contents)
for which allocating the grand coalition-s revenue using the Shapley
value guarantees the system stability. For other values of β, we prove
that it is possible for the regulator to impose a per-period interest rate
maximizing the market coverage under equal allocation rules.
Abstract: In this paper, a joint source-channel coding (JSCC) scheme for time-varying channels is presented. The proposed scheme uses hierarchical framework for both source encoder and transmission via QAM modulation. Hierarchical joint source channel codes with hierarchical QAM constellations are designed to track the channel variations which yields to a higher throughput by adapting certain parameters of the receiver to the channel variation. We consider the problem of still image transmission over time-varying channels with channel state information (CSI) available at 1) receiver only and 2) both transmitter and receiver being informed about the state of the channel. We describe an algorithm that optimizes hierarchical source codebooks by minimizing the distortion due to source quantizer and channel impairments. Simulation results, based on image representation, show that, the proposed hierarchical system outperforms the conventional schemes based on a single-modulator and channel optimized source coding.