Abstract: One of the major causes of voltage instability is the reactive power limit of the system. Improving the system's reactive power handling capacity via Flexible AC transmission System (FACTS) devices is a remedy for prevention of voltage instability and hence voltage collapse. In this paper, the effects of SVC and STATCOM in Static Voltage Stability Margin Enhancement will be studied. AC and DC representations of SVC and STATCOM are used in the continuation power flow process in static voltage stability study. The IEEE-14 bus system is simulated to test the increasing loadability. It is found that these controllers significantly increase the loadability margin of power systems.
Abstract: Pressure wave velocity in a hydraulic system was
determined using piezo pressure sensors without removing fluid from
the system. The measurements were carried out in a low pressure
range (0.2 – 6 bar) and the results were compared with the results of
other studies. This method is not as accurate as measurement with
separate measurement equipment, but the fluid is in the actual
machine the whole time and the effect of air is taken into
consideration if air is present in the system. The amount of air is
estimated by calculations and comparisons between other studies.
This measurement equipment can also be installed in an existing
machine and it can be programmed so that it measures in real time.
Thus, it could be used e.g. to control dampers.
Abstract: This article gives a short preview of the new software
created especially for palletizing process in automated production
systems. Each chapter of this article is about problem solving in
development of modules in Java programming language. First part
describes structure of the software, its modules and data flow
between them. Second part describes all deployment methods, which
are implemented in the software. Next chapter is about twodimensional
editor created for manipulation with objects in each
layer of the load and gives calculations for collision control. Module
of virtual reality used for three-dimensional preview and creation of
the load is described in the fifth chapter. The last part of this article
describes communication and data flow between control system of
the robot, vision system and software.
Abstract: The tubes in an Ammonia primary reformer furnace
operate close to the limits of materials technology in terms of the
stress induced as a result of very high temperatures, combined with
large differential pressures across the tube wall. Operation at tube
wall temperatures significantly above design can result in a rapid
increase in the number of tube failures, since tube life is very
sensitive to the absolute operating temperature of the tube. Clearly it
is important to measure tube wall temperatures accurately in order to
prevent premature tube failure by overheating.. In the present study,
the catalyst tubes in an Ammonia primary reformer has been modeled
taking into consideration heat, mass and momentum transfer as well
as reformer characteristics.. The investigations concern the effects of
tube characteristics and superficial tube wall temperatures on of the
percentage of heat flux, unconverted methane and production of
Hydrogen for various values of steam to carbon ratios. The results
show the impact of catalyst tubes length and diameters on the
performance of operating parameters in ammonia primary reformers.
Abstract: A two-parameter fatigue model explicitly accounting for the cyclic as well as the mean stress was used to fit static and fatigue data available in literature concerning carbon fiber reinforced composite laminates subjected tension-tension fatigue. The model confirms the strength–life equal rank assumption and predicts reasonably the probability of failure under cyclic loading. The model parameters were found by best fitting procedures and required a minimum of experimental tests.
Abstract: In this paper back-propagation artificial neural
network (BPANN) is employed to predict the limiting drawing ratio
(LDR) of the deep drawing process. To prepare a training set for
BPANN, some finite element simulations were carried out. die and
punch radius, die arc radius, friction coefficient, thickness, yield
strength of sheet and strain hardening exponent were used as the
input data and the LDR as the specified output used in the training of
neural network. As a result of the specified parameters, the program
will be able to estimate the LDR for any new given condition.
Comparing FEM and BPANN results, an acceptable correlation was
found.
Abstract: This paper describes about dynamic reconfiguration to
miniaturize arithmetic circuits in general-purpose processor. Dynamic
reconfiguration is a technique to realize required functions by
changing hardware construction during operation. The proposed
arithmetic circuit performs floating-point arithmetic which is
frequently used in science and technology. The data format is
floating-point based on IEEE754. The proposed circuit is designed
using VHDL, and verified the correct operation by simulations and
experiments.
Abstract: Cross layer optimization based on utility functions has
been recently studied extensively, meanwhile, numerous types of
utility functions have been examined in the corresponding literature.
However, a major drawback is that most utility functions take a fixed
mathematical form or are based on simple combining, which can
not fully exploit available information. In this paper, we formulate a
framework of cross layer optimization based on Adaptively Weighted
Utility Functions (AWUF) for fairness balancing in OFDMA networks.
Under this framework, a two-step allocation algorithm is
provided as a sub-optimal solution, whose control parameters can be
updated in real-time to accommodate instantaneous QoS constrains.
The simulation results show that the proposed algorithm achieves
high throughput while balancing the fairness among multiple users.
Abstract: State-of-the-art methods for secondary structure (Porter, Psi-PRED, SAM-T99sec, Sable) and solvent accessibility (Sable, ACCpro) predictions use evolutionary profiles represented by the position specific scoring matrix (PSSM). It has been demonstrated that evolutionary profiles are the most important features in the feature space for these predictions. Unfortunately applying PSSM matrix leads to high dimensional feature spaces that may create problems with parameter optimization and generalization. Several recently published suggested that applying feature extraction for the PSSM matrix may result in improvements in secondary structure predictions. However, none of the top performing methods considered here utilizes dimensionality reduction to improve generalization. In the present study, we used simple and fast methods for features selection (t-statistics, information gain) that allow us to decrease the dimensionality of PSSM matrix by 75% and improve generalization in the case of secondary structure prediction compared to the Sable server.
Abstract: In this study, a vibration analysis was carried out of
symmetric angle-ply laminated composite plates with and without
square hole when subjected to compressive loads, numerically. A
buckling analysis is also performed to determine the buckling load of
laminated plates. For each fibre orientation, the compression load is
taken equal to 50% of the corresponding buckling load. In the
analysis, finite element method (FEM) was applied to perform
parametric studies, the effects of degree of orthotropy and stacking
sequence upon the fundamental frequencies and buckling loads are
discussed. The results show that the presence of a constant
compressive load tends to reduce uniformly the natural frequencies
for materials which have a low degree of orthotropy. However, this
reduction becomes non-uniform for materials with a higher degree of
orthotropy.
Abstract: This paper discusses the approach of real-time
controlling of the energy management system using the data
acquisition tool of LabVIEW. The main idea of this inspiration was
to interface the Station (PC) with the system and publish the data on
internet using LabVIEW. In this venture, controlling and switching of
3 phase AC loads are effectively and efficiently done. The phases are
also sensed through devices. In case of any failure the attached
generator starts functioning automatically. The computer sends
command to the system and system respond to the request. The
modern feature is to access and control the system world-wide using
world wide web (internet). This controlling can be done at any time
from anywhere to effectively use the energy especially in developing
countries where energy management is a big problem. In this system
totally integrated devices are used to operate via remote location.
Abstract: In this paper we present a Feed-Foward Neural
Networks Autoregressive (FFNN-AR) model with genetic algorithms
training optimization in order to predict the gross domestic product
growth of six countries. Specifically we propose a kind of weighted
regression, which can be used for econometric purposes, where the
initial inputs are multiplied by the neural networks final optimum
weights from input-hidden layer of the training process. The
forecasts are compared with those of the ordinary autoregressive
model and we conclude that the proposed regression-s forecasting
results outperform significant those of autoregressive model.
Moreover this technique can be used in Autoregressive-Moving
Average models, with and without exogenous inputs, as also the
training process with genetics algorithms optimization can be
replaced by the error back-propagation algorithm.
Abstract: loss of feedwater accident is one of the frequently sever accidents in steam boiler facilities. It threatens the system structural integrity and generates serious hazards and economic loses. The safety analysis of the thermal installations, based extensively on the numeric simulation. The simulation analysis using realistic computer codes like Relap5/Mod3.2 will help understand steam boiler thermal-hydraulic behavior during normal and abnormal conditions. In this study, we are interested on the evaluation of the radiant steam boiler assessment and response to loss-of-feedwater accident. Pressure, temperature and flow rate profiles are presented in various steam boiler system components. The obtained results demonstrate the importance and capability of the Relap5/Mod3.2 code in the thermal-hydraulic analysis of the steam boiler facilities.
Abstract: This paper describes a newly designed decentralized
nonlinear control strategy to control a robot manipulator. Based on the
concept of the nonlinear state feedback theory and decentralized
concept is developed to improve the drawbacks in previous works
concerned with complicate intelligent control and low cost effective
sensor. The control methodology is derived in the sense of Lyapunov
theorem so that the stability of the control system is guaranteed. The
decentralized algorithm does not require other joint angle and velocity
information. Individual Joint controller is implemented using a digital
processor with nearly actuator to make it possible to achieve good
dynamics and modular. Computer simulation result has been
conducted to validate the effectiveness of the proposed control scheme
under the occurrence of possible uncertainties and different reference
trajectories. The merit of the proposed control system is indicated in
comparison with a classical control system.
Abstract: This paper presents a new methodology to select test
cases from regression test suites. The selection strategy is based on
analyzing the dynamic behavior of the applications that written in
any programming language. Methods based on dynamic analysis are
more safe and efficient. We design a technique that combine the code
based technique and model based technique, to allow comparing the
object oriented of an application that written in any programming
language. We have developed a prototype tool that detect changes
and select test cases from test suite.
Abstract: Embedding Sustainability in technological curricula has become a crucial factor for educating engineers with competences in sustainability. The Technical University of Catalonia UPC, in 2008, designed the Sustainable Technology Excellence Program STEP 2015 in order to assure a successful Sustainability Embedding. This Program takes advantage of the opportunity that the redesign of all Bachelor and Master Degrees in Spain by 2010 under the European Higher Education Area framework offered. The STEP program goals are: to design compulsory courses in each degree; to develop the conceptual base and identify reference models in sustainability for all specialties at UPC; to create an internal interdisciplinary network of faculty from all the schools; to initiate new transdisciplinary research activities in technology-sustainability-education; to spread the know/how attained; to achieve international scientific excellence in technology-sustainability-education and to graduate the first engineers/architects of the new EHEA bachelors with sustainability as a generic competence. Specifically, in this paper authors explain their experience in leading the STEP program, and two examples are presented: Industrial Robotics subject and the curriculum for the School of Architecture.
Abstract: There are several approaches in trying to solve the
Quantitative 1Structure-Activity Relationship (QSAR) problem.
These approaches are based either on statistical methods or on
predictive data mining. Among the statistical methods, one should
consider regression analysis, pattern recognition (such as cluster
analysis, factor analysis and principal components analysis) or partial
least squares. Predictive data mining techniques use either neural
networks, or genetic programming, or neuro-fuzzy knowledge. These
approaches have a low explanatory capability or non at all. This
paper attempts to establish a new approach in solving QSAR
problems using descriptive data mining. This way, the relationship
between the chemical properties and the activity of a substance
would be comprehensibly modeled.
Abstract: In this research, a systematic investigation was carried out to determine the optimum conditions of HDS reactor. Moreover, a suitable model was developed for a rigorous RTO (real time optimization) loop of HDS (Hydro desulfurization) process. A systematic experimental series was designed based on CCD (Central Composite design) and carried out in the related pilot plant to tune the develop model. The designed variables in the experiments were Temperature, LHSV and pressure. However, the hydrogen over fresh feed ratio was remained constant. The ranges of these variables were respectively equal to 320-380ºC, 1- 21/hr and 50-55 bar. a power law kinetic model was also developed for our further research in the future .The rate order and activation energy , power of reactant concentration and frequency factor of this model was respectively equal to 1.4, 92.66 kJ/mol and k0=2.7*109 .
Abstract: We present a prototype interactive (hyper) map of strategic, tactical, and logistic options for Supply Chain Management. The map comprises an anthology of options, broadly classified within the strategic spectrum of efficiency versus responsiveness, and according to logistic and cross-functional drivers. They are exemplified by cases in diverse industries. We seek to get all these information and ideas organized to help supply chain managers identify effective choices for specific business environments. The key and innovative linkage we introduce is the configuration of competitive forces. Instead of going through seemingly endless and isolated cases and wondering how one can borrow from them, we aim to provide a guide by force comparisons. The premise is that best practices in a different industry facing similar forces may be a most productive resource in supply chain design and planning. A prototype template is demonstrated.
Abstract: The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. In this paper, a new feature extraction method using the combination of adaptive band pass filters and adaptive autoregressive (AAR) modelling is proposed and applied to the classification of right and left motor imagery signals extracted from the brain. The introduction of the adaptive bandpass filter improves the characterization process of the autocorrelation functions of the AAR models, as it enhances and strengthens the EEG signal, which is noisy and stochastic in nature. The experimental results on the Graz BCI data set have shown that by implementing the proposed feature extraction method, a LDA and SVM classifier outperforms other AAR approaches of the BCI 2003 competition in terms of the mutual information, the competition criterion, or misclassification rate.