Abstract: Lighvan cheese is basically made from sheep milk in
the area of Sahand mountainside which is located in the North West
of Iran. The main objective of this study was to investigate the effect
of enterococci isolated from traditional Lighvan cheese on the quality
of Iranian UF white during ripening. The experimental design was
split plot based on randomized complete blocks, main plots were four
types of starters and subplots were different ripening durations.
Addition of Enterococcus spp. did not significantly (P
Abstract: People detection from images has a variety of applications such as video surveillance and driver assistance system, but is still a challenging task and more difficult in crowded environments such as shopping malls in which occlusion of lower parts of human body often occurs. Lack of the full-body information requires more effective features than common features such as HOG. In this paper, new features are introduced that exploits global self-symmetry (GSS) characteristic in head-shoulder patterns. The features encode the similarity or difference of color histograms and oriented gradient histograms between two vertically symmetric blocks. The domain-specific features are rapid to compute from the integral images in Viola-Jones cascade-of-rejecters framework. The proposed features are evaluated with our own head-shoulder dataset that, in part, consists of a well-known INRIA pedestrian dataset. Experimental results show that the GSS features are effective in reduction of false alarmsmarginally and the gradient GSS features are preferred more often than the color GSS ones in the feature selection.
Abstract: Human heart valves diseased by congenital heart
defects, rheumatic fever, bacterial infection, cancer may cause stenosis
or insufficiency in the valves. Treatment may be with medication but
often involves valve repair or replacement (insertion of an artificial
heart valve). Bileaflet mechanical heart valves (BMHVs) are widely
implanted to replace the diseased heart valves, but still suffer from
complications such as hemolysis, platelet activation, tissue
overgrowth and device failure. These complications are closely related
to both flow characteristics through the valves and leaflet dynamics. In
this study, the physiological flow interacting with the moving leaflets
in a bileaflet mechanical heart valve (BMHV) is simulated with a
strongly coupled implicit fluid-structure interaction (FSI) method
which is newly organized based on the Arbitrary-Lagrangian-Eulerian
(ALE) approach and the dynamic mesh method (remeshing) of
FLUENT. The simulated results are in good agreement with previous
experimental studies. This study shows the applicability of the present
FSI model to the complicated physics interacting between fluid flow
and moving boundary.
Abstract: Entrepreneurs are important for national labour markets and economies in that they contribute significantly to economic growth as well as provide the majority of jobs and create new ones. According to the Global Entrepreneurship Monitor’s “Report on Women and Entrepreneurship”, investment in women’s entrepreneurship is an important way to exponentially increase the impact of new venture creation finding ways to empower women’s participation and success in entrepreneurship are critical for more sustainable and successful economic development. Our results confirm that they are still differences between men and women entrepreneurs The reasons seems to be the lack of specific business skills, the less extensive social network, and the lack of identification patterns among women. Those differences can be explained by the fact that women still have fewer opportunities to make a career. If this is correct, we can predict an increasing proportion of women among entrepreneurs in the next years. Concerning the development of a favorable environment for developing and enhancing women entrepreneurship activities, our results show the insertion in a network and the role of a model doubtless represent elements determining in the choice to launch an entrepreneurship activity, as well as a precious resource for the success of her company.
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: Face and facial expressions play essential roles in
interpersonal communication. Most of the current works on the facial
expression recognition attempt to recognize a small set of the
prototypic expressions such as happy, surprise, anger, sad, disgust
and fear. However the most of the human emotions are
communicated by changes in one or two of discrete features. In this
paper, we develop a facial expressions synthesis system, based on the
facial characteristic points (FCP's) tracking in the frontal image
sequences. Selected FCP's are automatically tracked using a crosscorrelation
based optical flow. The proposed synthesis system uses a
simple deformable facial features model with a few set of control
points that can be tracked in original facial image sequences.
Abstract: Ever increasing capacities of contemporary storage devices
inspire the vision to accumulate (personal) information without
the need of deleting old data over a long time-span. Hence the target
of SemanticLIFE project is to create a Personal Information Management
system for a human lifetime data. One of the most important
characteristics of the system is its dedication to retrieve information
in a very efficient way. By adopting user demands regarding the
reduction of ambiguities, our approach aims at a user-oriented and
yet powerful enough system with a satisfactory query performance.
We introduce the query system of SemanticLIFE, the Virtual Query
System, which uses emerging Semantic Web technologies to fulfill
users- requirements.
Abstract: The presented work is motivated by a French law
regarding nuclear waste management. A new conceptual Accelerator
Driven System (ADS) designed for the Minor Actinides (MA)
transmutation has been assessed by numerical simulation. The
MUltiple Spallation Target (MUST) ADS combines high thermal power (up to 1.4 GWth) and high specific power. A 30 mA and 1
GeV proton beam is divided into three secondary beams transmitted on three liquid lead-bismuth spallation targets. Neutron and thermalhydraulic
simulations have been performed with the code MURE, based on the Monte-Carlo transport code MCNPX. A methodology has been developed to define characteristic of the MUST ADS concept according to a specific transmutation scenario. The reference
scenario is based on a MA flux (neptunium, americium and curium)
providing from European Fast Reactor (EPR) and a plutonium multireprocessing
strategy is accounted for. The MUST ADS reference
concept is a sodium cooled fast reactor. The MA fuel at equilibrium is mixed with MgO inert matrix to limit the core reactivity and
improve the fuel thermal conductivity. The fuel is irradiated over five
years. Five years of cooling and two years for the fuel fabrication are
taken into account. The MUST ADS reference concept burns about 50% of the initial MA inventory during a complete cycle. In term of
mass, up to 570 kg/year are transmuted in one concept. The methodology to design the MUST ADS and to calculate fuel
composition at equilibrium is precisely described in the paper. A detailed fuel evolution analysis is performed and the reference scenario is compared to a scenario where only americium transmutation is performed.
Abstract: Springback is a significant problem in the sheet metal
forming process. When the tools are released after the stage of
forming, the product springs out, because of the action of the internal
stresses. In many cases the deviation of form is too large and the
compensation of the springback is necessary. The precise prediction
of the springback of product is increasingly significant for the design
of the tools and for compensation because of the higher ratio of the
yield stress to the elastic modulus.
The main object in this paper was to study the effect of the
anisotropy on the springback for three directions of rolling: 0°, 45°
and 90°. At the same time, we highlighted the influence of three
different metallic materials: Aluminum, Steel and Galvanized steel.
The original of our purpose consist on tests which are ensured by
adapting a U-type stretching-bending device on a tensile testing
machine, where we studied and quantified the variation of the
springback according to the direction of rolling. We also showed the
role of lubrication in the reduction of the springback.
Moreover, in this work, we have studied important characteristics
in deep drawing process which is a springback. We have presented
defaults that are showed in this process and many parameters
influenced a springback.
Finally, our results works lead us to understand the influence of
grains orientation with different metallic materials on the springback
and drawing some conclusions how to concept deep drawing tools. In
addition, the conducted work represents a fundamental contribution
in the discussion the industry application.
Abstract: Researches show that probability-statistical methods application, especially at the early stage of the aviation Gas Turbine Engine (GTE) technical condition diagnosing, when the flight information has property of the fuzzy, limitation and uncertainty is unfounded. Hence the efficiency of application of new technology Soft Computing at these diagnosing stages with the using of the Fuzzy Logic and Neural Networks methods is considered. According to the purpose of this problem training with high accuracy of fuzzy multiple linear and non-linear models (fuzzy regression equations) which received on the statistical fuzzy data basis is made. For GTE technical condition more adequate model making dynamics of skewness and kurtosis coefficients- changes are analysed. Researches of skewness and kurtosis coefficients values- changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes- dynamics of GTE work parameters allows drawing conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines' technical condition. Researches of correlation coefficients values- changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. At the information sufficiency is offered to use recurrent algorithm of aviation GTE technical condition identification (Hard Computing technology is used) on measurements of input and output parameters of the multiple linear and non-linear generalised models at presence of noise measured (the new recursive Least Squares Method (LSM)). The developed GTE condition monitoring system provides stageby- stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine technical condition was made.
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: With a surge of stream processing applications novel
techniques are required for generation and analysis of association
rules in streams. The traditional rule mining solutions cannot handle
streams because they generally require multiple passes over the data
and do not guarantee the results in a predictable, small time. Though
researchers have been proposing algorithms for generation of rules
from streams, there has not been much focus on their analysis.
We propose Association rule profiling, a user centric process for
analyzing association rules and attaching suitable profiles to them
depending on their changing frequency behavior over a previous
snapshot of time in a data stream.
Association rule profiles provide insights into the changing nature
of associations and can be used to characterize the associations. We
discuss importance of characteristics such as predictability of
linkages present in the data and propose metric to quantify it. We
also show how association rule profiles can aid in generation of user
specific, more understandable and actionable rules.
The framework is implemented as SUPAR: System for Usercentric
Profiling of Association Rules in streaming data. The
proposed system offers following capabilities:
i) Continuous monitoring of frequency of streaming item-sets
and detection of significant changes therein for association rule
profiling.
ii) Computation of metrics for quantifying predictability of
associations present in the data.
iii) User-centric control of the characterization process: user
can control the framework through a) constraint specification and b)
non-interesting rule elimination.
Abstract: It is important to remove manganese from water
because of its effects on human and the environment. Human
activities are one of the biggest contributors for excessive manganese
concentration in the environment. The proposed method to remove
manganese in aqueous solution by using adsorption as in carbon
nanotubes (CNT) at different parameters: The parameters are CNT
dosage, pH, agitation speed and contact time. Different pHs are pH
6.0, pH 6.5, pH 7.0, pH 7.5 and pH 8.0, CNT dosages are 5mg,
6.25mg, 7.5mg, 8.75mg or 10mg, contact time are 10 min, 32.5 min,
55 min, 87.5 min and 120 min while the agitation speeds are 100rpm,
150rpm, 200rpm, 250rpm and 300rpm. The parameters chosen for
experiments are based on experimental design done by using Central
Composite Design, Design Expert 6.0 with 4 parameters, 5 levels and
2 replications. Based on the results, condition set at pH 7.0, agitation
speed of 300 rpm, 7.5mg and contact time 55 minutes gives the
highest removal with 75.5%. From ANOVA analysis in Design
Expert 6.0, the residual concentration will be very much affected by
pH and CNT dosage. Initial manganese concentration is 1.2mg/L
while the lowest residual concentration achieved is 0.294mg/L,
which almost satisfy DOE Malaysia Standard B requirement.
Therefore, further experiments must be done to remove manganese
from model water to the required standard (0.2 mg/L) with the initial
concentration set to 0.294 mg/L.
Abstract: This paper presents the effectiveness of artificial
intelligent technique to apply for pattern recognition and
classification of Partial Discharge (PD). Characteristics of PD signal
for pattern recognition and classification are computed from the
relation of the voltage phase angle, the discharge magnitude and the
repeated existing of partial discharges by using statistical and fractal
methods. The simplified fuzzy ARTMAP (SFAM) is used for pattern
recognition and classification as artificial intelligent technique. PDs
quantities, 13 parameters from statistical method and fractal method
results, are inputted to Simplified Fuzzy ARTMAP to train system
for pattern recognition and classification. The results confirm the
effectiveness of purpose technique.
Abstract: Recently in the field of bridges that are newly built or
repaired, fast construction is required more than ever. For these
reasons, precast prefabricated bridge that enables rapid construction is
actively discussed and studied today. In South Korea, it is called
modular bridge. Cross beam is an integral component of modular
bridge. It functions for load distribution, reduction of bending
moment, resistance of horizontal strength on lateral upper structure. In
this study, the structural characteristics of domestic and foreign cross
beam types were compared. Based on this, alternative cross beam
connection types suitable for modular bridge were selected. And
bulb-T girder specimens were fabricated with each type of connection.
The behavior of each specimen was analyzed under static loading, and
cross beam connection type which is expected to be best suited to
modular bridge proposed.
Abstract: This paper deals with the effect of a power transformer’s vector group on the basic voltage sag characteristics during unbalanced faults at a meshed or radial power network. Specifically, the propagation of voltage sags through a power transformer is studied with advanced short-circuit analysis. A smart method to incorporate this effect on analytical mathematical expressions is proposed. Based on this methodology, the positive effect of transformers of certain vector groups on the mitigation of the expected number of voltage sags per year (sag frequency) at the terminals of critical industrial customers can be estimated.
Abstract: This paper presents three-phase evolution search methodology to automatically design fuzzy logic controllers (FLCs) that can work in a wide range of operating conditions. These include varying load, parameter variations, and unknown external disturbances. The three-phase scheme consists of an exploration phase, an exploitation phase and a robustness phase. The first two phases search for FLC with high accuracy performances while the last phase aims at obtaining FLC providing the best compromise between the accuracy and robustness performances. Simulations were performed for direct-drive two-axis robot arm. The evolved FLC with the proposed design technique found to provide a very satisfactory performance under the wide range of operation conditions and to overcome problem associated with coupling and nonlinearities characteristics inherent to robot arms.
Abstract: The design of Class A and Class AB 2-stage X band
Power Amplifier is described in this report. This power amplifier is
part of a transceiver used in radar for monitoring iron characteristics
in a blast furnace. The circuit was designed using foundry WIN
Semiconductors. The specification requires 15dB gain in the linear
region, VSWR nearly 1 at input as well as at the output, an output
power of 10 dBm and good stable performance in the band 10.9-12.2
GHz. The design was implemented by using inter-stage
configuration, the Class A amplifier was chosen for driver stage i.e.
the first amplifier focusing on the gain and the output amplifier
conducted at Class AB with more emphasis on output power.
Abstract: In this paper a comprehensive model of a fossil fueled
power plant (FFPP) is developed in order to evaluate the
performance of a newly designed turbine follower controller.
Considering the drawbacks of previous works, an overall model is
developed to minimize the error between each subsystem model
output and the experimental data obtained at the actual power plant.
The developed model is organized in two main subsystems namely;
Boiler and Turbine. Considering each FFPP subsystem
characteristics, different modeling approaches are developed. For
economizer, evaporator, superheater and reheater, first order models
are determined based on principles of mass and energy conservation.
Simulations verify the accuracy of the developed models. Due to the
nonlinear characteristics of attemperator, a new model, based on a
genetic-fuzzy systems utilizing Pittsburgh approach is developed
showing a promising performance vis-à-vis those derived with other
methods like ANFIS. The optimization constraints are handled
utilizing penalty functions. The effect of increasing the number of
rules and membership functions on the performance of the proposed
model is also studied and evaluated. The turbine model is developed
based on the equation of adiabatic expansion. Parameters of all
evaluated models are tuned by means of evolutionary algorithms.
Based on the developed model a fuzzy PI controller is developed. It
is then successfully implemented in the turbine follower control
strategy of the plant. In this control strategy instead of keeping
control parameters constant, they are adjusted on-line with regard to
the error and the error rate. It is shown that the response of the
system improves significantly. It is also shown that fuel consumption
decreases considerably.