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: 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: In this paper, SFQ (Start Time Fair Queuing)
algorithm is analyzed when this is applied in computer networks to
know what kind of behavior the traffic in the net has when different
data sources are managed by the scheduler. Using the NS2 software
the computer networks were simulated to be able to get the graphs
showing the performance of the scheduler. Different traffic sources
were introduced in the scripts, trying to establish the real scenario.
Finally the results were that depending on the data source, the traffic
can be affected in different levels, when Constant Bite Rate is
applied, the scheduler ensures a constant level of data sent and
received, but the truth is that in the real life it is impossible to ensure
a level that resists the changes in work load.
Abstract: In this paper a simple terrain evaluation method for
hexapod robot is introduced. This method is based on feet coordinate
evaluation when all are on the ground. Depending on the feet
coordinate differences the local terrain evaluation is possible. Terrain
evaluation is necessary for right gait selection and/or body position
correction. For terrain roughness evaluation three planes are plotted:
two of them as definition points use opposite feet coordinates, third
coincides with the robot body plane. The leaning angle of body plane
is evaluated measuring gravity force using three-axis accelerometer.
Terrain roughness evaluation method is based on angle estimation
between normal vectors of these planes. Aim of this work is to
present a simple method for embedded robot controller, allowing to
find the best further movement settings.
Abstract: With increasing number of wireless devices like
laptops, Wi-Fi Web Cams, network extenders, etc., a new kind of
problems appeared, mostly related to poor Wi-Fi throughput or
communication problems. In this paper an investigation on wireless
networks and it-s saturation in Vilnius City and its surrounding is
presented, covering the main problems of wireless saturation and
network load during day. Also an investigation on wireless channel
selection and noise levels were made, showing the impact of
neighbor AP to signal and noise levels and how it changes during the
day.
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.
Abstract: The rising price of fossil fuels, government incentives
and growing public aware-ness for the need to implement sustainable
energy supplies has resulted in a large in-crease in solar panel
installations across the country. For many sites the most eco-nomical
solar panel installation uses existing, southerly facing rooftops.
Adding solar panels to an existing roof typically means increased
loads that must be borne by the building-s structural elements. The
structural design professional is responsible for ensuring a new solar
panel installation is properly supported by an existing structure and
configured to maximize energy generation.
Abstract: In this paper the application of rule mining in order to
review the effective factors on supplier selection is reviewed in the
following three sections 1) criteria selecting and information
gathering 2) performing association rule mining 3) validation and
constituting rule base. Afterwards a few of applications of rule base
is explained. Then, a numerical example is presented and analyzed
by Clementine software. Some of extracted rules as well as the
results are presented at the end.
Abstract: This paper aims at numerically analysing the effect
of an active flow control (AFC) by a vortex generator jet (VGJ)
submerged in a boundary layer via Chimera Grids and Detached-
Eddy Simulation (DES). The performance of DES results are
judged against Reynolds-Averaged Navier-Stokes (RANS) and
compared with the experiments that showed an unsteady vortex
motion downstream of VGJ. Experimental results showed that
the mechanism of embedding logitudinal vortex structure in the
main stream flow is quite effective in increasing the near wall
momentum of separated aircraft wing. In order to simulate such
a flow configuration together with the VGJ, an efficient numerical
approach is required. This requirement is fulfilled by performing
the DES simulation over the flat plate using the DLR TAU Code.
The DES predictions identify the vortex region via smooth hybrid
length scale and predict the unsteady vortex motion observed in
the experiments. The DES results also showed that the sufficient
grid refinement in the vortex region resolves the turbulent scales
downstream of the VGJ, the spatial vortex core postion and nondimensional
momentum coefficient RVx .
Abstract: Purpose: To develop a method for automatic segmentation of adipose and muscular tissue in thighs from magnetic resonance images. Materials and methods: Thirty obese women were scanned on a Siemens Impact Expert 1T resonance machine. 1500 images were finally used in the tests. The developed segmentation method is a recursive and multilevel process that makes use of several concepts such as shaped histograms, adaptative thresholding and connectivity. The segmentation process was implemented in Matlab and operates without the need of any user interaction. The whole set of images were segmented with the developed method. An expert radiologist segmented the same set of images following a manual procedure with the aid of the SliceOmatic software (Tomovision). These constituted our 'goal standard'. Results: The number of coincidental pixels of the automatic and manual segmentation procedures was measured. The average results were above 90 % of success in most of the images. Conclusions: The proposed approach allows effective automatic segmentation of MRIs from thighs, comparable to expert manual performance.
Abstract: The industrial process of the sugar cane crystallization produces a residual that still contains a lot of soluble sucrose and the objective of the factory is to improve its extraction. Therefore, there are substantial losses justifying the search for the optimization of the process. Crystallization process studied on the industrial site is based on the “three massecuites process". The third step of this process constitutes the final stage of exhaustion of the sucrose dissolved in the mother liquor. During the process of the third step of crystallization (Ccrystallization), the phase that is studied and whose control is to be improved, is the growing phase (crystal growth phase). The study of this process on the industrial site is a problem in its own. A control scheme is proposed to improve the standard PID control law used in the factory. An auto-tuning PID controller based on instantaneous linearization of a neural network is then proposed.
Abstract: Customer-supplier collaboration enables firms to
achieve greater success than acting independently. Nevertheless, not
many firms have fully utilized the potential of collaboration. This
paper presents organizational and human related success factors for
collaboration in manufacturing supply chains in casting industry. Our
research approach was a case study including multiple cases. Data
was gathered by interviews and group discussions in two different
research projects. In the first research project we studied seven firms
and in the second five. It was found that the success factors are
interrelated, in other words, organizational and human factors
together enable success but not any of them alone. Some of the found
success factors are a culture of following agreements, and a speed of
informing the partner about changes affecting to the product or the
delivery chain.
Abstract: A solar powered air heating system using one ended evacuated tubes is experimentally investigated. A solar air heater containing forty evacuated tubes is used for heating purpose. The collector surface area is about 4.44 m2. The length and outer diameters of the outer glass tube and absorber tube are 1500, 47 and 37 mm, respectively. In this experimental setup, we have a header (heat exchanger) of square shape (190 mm x 190 mm). The length of header is 1500 mm. The header consists of a hollow pipe in the center whose diameter is 60 mm through which the air is made to flow. The experimental setup contains approximately 108 liters of water. Water is working as heat collecting medium which collects the solar heat falling on the tubes. This heat is delivered to the air flowing through the header pipe. This heat flow is due to natural convection and conduction. The outlet air temperature depends upon several factors along with air flow rate and solar radiation intensity. The study has been done for both up-flow and down-flow of air in header in similar weather conditions, at different flow rates. In the present investigations the study has been made to find the effect of intensity of solar radiations and flow rate of air on the out let temperature of the air with time and which flow is more efficient. The obtained results show that the system is highly effective for the heating in this region. Moreover, it has been observed that system is highly efficient for the particular flow rate of air. It was also observed that downflow configuration is more effective than up-flow condition at all flow rates due to lesser losses in down-flow. The results show that temperature differences of upper head and lower head, both of water and surface of pipes on the respective ends is lower in down-flow.
Abstract: This paper examines economic and Information and Communication Technology (ICT) development influence on recently increasing Internet purchases by individuals for European Union member states. After a growing trend for Internet purchases in EU27 was noticed, all possible regression analysis was applied using nine independent variables in 2011. Finally, two linear regression models were studied in detail. Conducted simple linear regression analysis confirmed the research hypothesis that the Internet purchases in analyzed EU countries is positively correlated with statistically significant variable Gross Domestic Product per capita (GDPpc). Also, analyzed multiple linear regression model with four regressors, showing ICT development level, indicates that ICT development is crucial for explaining the Internet purchases by individuals, confirming the research hypothesis.
Abstract: Nowadays, financial and economic crises are growing
more and reach more countries and sectors. These events have, as a
result, a considerable impact on the activities of the firms which think
unstable and in danger.
But besides this heavy uncertainty which weighs on the different
firms, the family firm, object of our research, is not only confronted
with these external difficulties but also with an internal challenge and
of size: that of transmission. Indeed, the transmission of an
organization from one generation to another can succeed as it can
fail; leaving considerable damage.
Our research registers as part of these problems since we tried to
understand relation between the behavior of two main actors of the
process of succession, predecessor and successor; and the success of
transmission.
Abstract: An important structuring mechanism for knowledge bases is building clusters based on the content of their knowledge objects. The objects are clustered based on the principle of maximizing the intraclass similarity and minimizing the interclass similarity. Clustering can also facilitate taxonomy formation, that is, the organization of observations into a hierarchy of classes that group similar events together. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and easy to understand systems is Hierarchical Production rule (HPRs) system. A HPR, a standard production rule augmented with generality and specificity information, is of the following form Decision If < condition> Generality Specificity . HPRs systems are capable of handling taxonomical structures inherent in the knowledge about the real world. In this paper, a set of related HPRs is called a cluster and is represented by a HPR-tree. This paper discusses an algorithm based on cumulative learning scenario for dynamic structuring of clusters. The proposed scheme incrementally incorporates new knowledge into the set of clusters from the previous episodes and also maintains summary of clusters as Synopsis to be used in the future episodes. Examples are given to demonstrate the behaviour of the proposed scheme. The suggested incremental structuring of clusters would be useful in mining data streams.
Abstract: In order to evaluation the effects of natural, biological
and chemical fertilizers on grain yield and chickpea quality, field
experiments were carried out in 2007 and 2008 growing seasons. In
this research the effects of different organic, chemical and biological
fertilizers were investigated on grain yield and quality of chickpea.
Experimental units were arranged in split-split plots based on
randomized complete blocks with three replications. The highest
amounts of yield and yield components were obtained in G1×N5
interaction. Significant increasing of N, P, K, Fe and Mg content in
leaves and grains emphasized on superiority of mentioned treatment
because each one of these nutrients has an approved role in
chlorophyll synthesis and photosynthesis ability of the crop. The
combined application of compost, farmyard manure and chemical
phosphorus (N5) had the best grain quality due to high protein, starch
and total sugar contents, low crude fiber and reduced cooking time.
Abstract: Unmanned aerial vehicles (UAVs) performing their
operations for a long time have been attracting much attention in
military and civil aviation industries for the past decade. The
applicable field of UAV is changing from the military purpose only to
the civil one. Because of their low operation cost, high reliability and
the necessity of various application areas, numerous development
programs have been initiated around the world. To obtain the optimal
solutions of the design variable (i.e., sectional airfoil profile, wing
taper ratio and sweep) for high performance of UAVs, both the lift and
lift-to-drag ratio are maximized whereas the pitching moment should
be minimized, simultaneously. It is found that the lift force and
lift-to-drag ratio are linearly dependent and a unique and dominant
solution are existed. However, a trade-off phenomenon is observed
between the lift-to-drag ratio and pitching moment. As the result of
optimization, sixty-five (65) non-dominated Pareto individuals at the
cutting edge of design spaces that are decided by airfoil shapes can be
obtained.