Abstract: This paper presents an application of Artificial Neural Network (ANN) to forecast actual cost of a project based on the earned value management system (EVMS). For this purpose, some projects randomly selected based on the standard data set , and it is produced necessary progress data such as actual cost ,actual percent complete , baseline cost and percent complete for five periods of project. Then an ANN with five inputs and five outputs and one hidden layer is trained to produce forecasted actual costs. The comparison between real and forecasted data show better performance based on the Mean Absolute Percentage Error (MAPE) criterion. This approach could be applicable to better forecasting the project cost and result in decreasing the risk of project cost overrun, and therefore it is beneficial for planning preventive actions.
Abstract: Saturated hydraulic conductivity is one of the soil
hydraulic properties which is widely used in environmental studies
especially subsurface ground water. Since, its direct measurement is
time consuming and therefore costly, indirect methods such as
pedotransfer functions have been developed based on multiple linear
regression equations and neural networks model in order to estimate
saturated hydraulic conductivity from readily available soil
properties e.g. sand, silt, and clay contents, bulk density, and organic
matter. The objective of this study was to develop neural networks
(NNs) model to estimate saturated hydraulic conductivity from
available parameters such as sand and clay contents, bulk density,
van Genuchten retention model parameters (i.e. r
θ , α , and n) as well
as effective porosity. We used two methods to calculate effective
porosity: : (1) eff s FC φ =θ -θ , and (2) inf φ =θ -θ eff s , in which s
θ is
saturated water content, FC θ is water content retained at -33 kPa
matric potential, and inf θ is water content at the inflection point.
Total of 311 soil samples from the UNSODA database was divided
into three groups as 187 for the training, 62 for the validation (to
avoid over training), and 62 for the test of NNs model. A commercial
neural network toolbox of MATLAB software with a multi-layer
perceptron model and back propagation algorithm were used for the
training procedure. The statistical parameters such as correlation
coefficient (R2), and mean square error (MSE) were also used to
evaluate the developed NNs model. The best number of neurons in
the middle layer of NNs model for methods (1) and (2) were
calculated 44 and 6, respectively. The R2 and MSE values of the test
phase were determined for method (1), 0.94 and 0.0016, and for
method (2), 0.98 and 0.00065, respectively, which shows that method
(2) estimates saturated hydraulic conductivity better than method (1).
Abstract: We have studied the migration of a charged permeable aggregate in electrolyte under the influence of an axial electric field and pressure gradient. The migration of the positively charged aggregate leads to a deformation of the anionic cloud around it. The hydrodynamics of the aggregate is governed by the interaction of electroosmotic flow in and around the particle, hydrodynamic friction and electric force experienced by the aggregate. We have computed the non-linear Nernest-Planck equations coupled with the Dracy- Brinkman extended Navier-Stokes equations and Poisson equation for electric field through a finite volume method. The permeability of the aggregate enable the counterion penetration. The penetration of counterions depends on the volume charge density of the aggregate and ionic concentration of electrolytes at a fixed field strength. The retardation effect due to the double layer polarization increases the drag force compared to an uncharged aggregate. Increase in migration sped from the electrophretic velocity of the aggregate produces further asymmetry in charge cloud and reduces the electric body force exerted on the particle. The permeability of the particle have relatively little influence on the electric body force when Double layer is relatively thin. The impact of the key parameters of electrokinetics on the hydrodynamics of the aggregate is analyzed.
Abstract: A multilayer self organizing neural neural network
(MLSONN) architecture for binary object extraction, guided by a beta
activation function and characterized by backpropagation of errors
estimated from the linear indices of fuzziness of the network output
states, is discussed. Since the MLSONN architecture is designed to
operate in a single point fixed/uniform thresholding scenario, it does
not take into cognizance the heterogeneity of image information in
the extraction process. The performance of the MLSONN architecture
with representative values of the threshold parameters of the beta
activation function employed is also studied. A three layer bidirectional
self organizing neural network (BDSONN) architecture
comprising fully connected neurons, for the extraction of objects from
a noisy background and capable of incorporating the underlying image
context heterogeneity through variable and adaptive thresholding,
is proposed in this article. The input layer of the network architecture
represents the fuzzy membership information of the image scene to
be extracted. The second layer (the intermediate layer) and the final
layer (the output layer) of the network architecture deal with the self
supervised object extraction task by bi-directional propagation of the
network states. Each layer except the output layer is connected to the
next layer following a neighborhood based topology. The output layer
neurons are in turn, connected to the intermediate layer following
similar topology, thus forming a counter-propagating architecture
with the intermediate layer. The novelty of the proposed architecture
is that the assignment/updating of the inter-layer connection weights
are done using the relative fuzzy membership values at the constituent
neurons in the different network layers. Another interesting feature
of the network lies in the fact that the processing capabilities of
the intermediate and the output layer neurons are guided by a beta
activation function, which uses image context sensitive adaptive
thresholding arising out of the fuzzy cardinality estimates of the
different network neighborhood fuzzy subsets, rather than resorting to
fixed and single point thresholding. An application of the proposed
architecture for object extraction is demonstrated using a synthetic
and a real life image. The extraction efficiency of the proposed
network architecture is evaluated by a proposed system transfer index
characteristic of the network.
Abstract: An accurate optimal design of laminated composite
structures may present considerable difficulties due to the complexity
and multi-modality of the functional design space. The Big Bang
– Big Crunch (BB-BC) optimization method is a relatively new
technique and has already proved to be a valuable tool for structural
optimization. In the present study the exceptional efficiency of the
method is demonstrated by an example of the lay-up optimization
of multilayered anisotropic cylinders based on a three-dimensional
elasticity solution. It is shown that, due to its simplicity and speed,
the BB-BC is much more efficient for this class of problems when
compared to the genetic algorithms.
Abstract: Multirate multimedia delivery applications in multihop Wireless Mesh Network (WMN) are data redundant and delay-sensitive, which brings a lot of challenges for designing efficient transmission systems. In this paper, we propose a new cross layer resource allocation scheme to minimize the receiver side distortion within the delay bound requirements, by exploring application layer Position and Value (P-V) diversity as well as the multihop Effective Capacity (EC). We specifically consider image transmission optimization here. First of all, the maximum supportable source traffic rate is identified by exploring the multihop Effective Capacity (EC) model. Furthermore, the optimal source coding rate is selected according to the P-V diversity of multirate media streaming, which significantly increases the decoded media quality. Simulation results show the proposed approach improved media quality significantly compared with traditional approaches under the same QoS requirements.
Abstract: Music segmentation is a key issue in music information
retrieval (MIR) as it provides an insight into the
internal structure of a composition. Structural information about
a composition can improve several tasks related to MIR such
as searching and browsing large music collections, visualizing
musical structure, lyric alignment, and music summarization.
The authors of this paper present the MTSSM framework, a twolayer
framework for the multi-track segmentation of symbolic
music. The strength of this framework lies in the combination of
existing methods for local track segmentation and the application
of global structure information spanning via multiple tracks.
The first layer of the MTSSM uses various string matching
techniques to detect the best candidate segmentations for each
track of a multi-track composition independently. The second
layer combines all single track results and determines the best
segmentation for each track in respect to the global structure of
the composition.
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: In this paper, a simple microfluidic device for monitoring algal cell behavior is proposed. An array of algal microwells is fabricated by PDMS soft-lithography using X-ray LIGA mold, placed on a glass substrate. Two layers of replicated PDMS and substrate are attached by oxygen plasma bonding, creating a microchannel for the microfluidic system. Algal cell are loaded into the microfluidic device, which provides positive charge on the bottom surface of wells. Algal cells, which are negative charged, can be attracted to the bottom of the wells via electrostatic interaction. By varying the concentration of algal cells in the loading suspension, it is possible to obtain wells with a single cell. Liquid medium for cells monitoring are flown continuously over the wells, providing nutrient and waste exchange between the well and the main flow. This device could lead to the uncovering of the quantitative biology of the algae, which is a key to effective and extensive algal utilizations in the field of biotechnology, food industry and bioenergy research and developments.
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: 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: The new architecture for quantum cellular
automata is offered. A QCA cell includes two layers nc-Si,
divided by a dielectric. Among themselves cells are connected
by the bridge from a conductive material. The comparison is
made between this and QCA, offered earlier by C. Lent's
group.
Abstract: The paper deals with the possibilities of modelling
vapour propagation of explosive substances in the FLUENT
software. With regard to very low tensions of explosive substance
vapours the experiment has been verified as exemplified by
mononitrotoluene. Either constant or time variable meteorological
conditions have been used for calculation. Further, it has been
verified that the eluent source may be time-dependent and may reflect
a real situation or the liberation rate may be constant. The execution
of the experiment as well as evaluation were clear and it could also
be used for modelling vapour and aerosol propagation of selected
explosive substances in the atmospheric boundary layer.
Abstract: There are a lot of extensions made to the classic model of multi-layer perceptron (MLP). A notable amount of them has been designed to hasten the learning process without considering the quality of generalization. The paper proposes a new MLP extension based on exploiting topology of the input layer of the network. Experimental results show the extended model to improve upon generalization capability in certain cases. The new model requires additional computational resources to compare to the classic model, nevertheless the loss in efficiency isn-t regarded to be significant.
Abstract: This paper introduces our first efforts of developing a
new team for RoboCup Middle Size Competition. In our robots we
have applied omni directional based mobile system with omnidirectional
vision system and fuzzy control algorithm to navigate
robots. The control architecture of MRL middle-size robots is a three
layered architecture, Planning, Sequencing, and Executing. It also
uses Blackboard system to achieve coordination among agents.
Moreover, the architecture should have minimum dependency on low
level structure and have a uniform protocol to interact with real
robot.
Abstract: The myoelectric signal (MES) is one of the Biosignals
utilized in helping humans to control equipments. Recent approaches
in MES classification to control prosthetic devices employing pattern
recognition techniques revealed two problems, first, the classification
performance of the system starts degrading when the number of
motion classes to be classified increases, second, in order to solve the
first problem, additional complicated methods were utilized which
increase the computational cost of a multifunction myoelectric
control system. In an effort to solve these problems and to achieve a
feasible design for real time implementation with high overall
accuracy, this paper presents a new method for feature extraction in
MES recognition systems. The method works by extracting features
using Wavelet Packet Transform (WPT) applied on the MES from
multiple channels, and then employs Fuzzy c-means (FCM)
algorithm to generate a measure that judges on features suitability for
classification. Finally, Principle Component Analysis (PCA) is
utilized to reduce the size of the data before computing the
classification accuracy with a multilayer perceptron neural network.
The proposed system produces powerful classification results (99%
accuracy) by using only a small portion of the original feature set.
Abstract: In this paper, a second order autoregressive (AR)
model is proposed to discriminate alcoholics using single trial
gamma band Visual Evoked Potential (VEP) signals using 3 different
classifiers: Simplified Fuzzy ARTMAP (SFA) neural network (NN),
Multilayer-perceptron-backpropagation (MLP-BP) NN and Linear
Discriminant (LD). Electroencephalogram (EEG) signals were
recorded from alcoholic and control subjects during the presentation
of visuals from Snodgrass and Vanderwart picture set. Single trial
VEP signals were extracted from EEG signals using Elliptic filtering
in the gamma band spectral range. A second order AR model was
used as gamma band VEP exhibits pseudo-periodic behaviour and
second order AR is optimal to represent this behaviour. This
circumvents the requirement of having to use some criteria to choose
the correct order. The averaged discrimination errors of 2.6%, 2.8%
and 11.9% were given by LD, MLP-BP and SFA classifiers. The
high LD discrimination results show the validity of the proposed
method to discriminate between alcoholic subjects.
Abstract: Toxoplasma gondii is an intracellular parasite capable
of infecting all nucleated cells in a diverse array of species.
Toxoplasma plaque assay have been described using Bacto Agar.
Because of its experimental advantages carboxymethyl cellulose
overlay, medium viscosity was choosing and the aim of this work
was to develop alternative method for formation of T. gondii plaques.
Tachyzoites were inoculated onto monolayers of Vero cells and
cultured at 37° C under 5 % CO2. The cultures were followed up by
microscopy inspection. Small plaques were visible by naphtol blue
stain 4 days after infection. Larger plaques could be observed by day
10 of culture. The carboxymethyl cellulose is a cheap reagent and the
methodology is easier, faster than assays under agar overlay. This is
the first description of the carboxymethyl cellulose overlay use for
obtaining the formation of T. gondii plaques and may be useful in
consequent obtaining tachyzoites for detailed studies.
Abstract: A biosensor based on glucose oxidase (GOx) immobilized onto nanoparticles zirconium oxide with polyethylene nanocomposite for glucose monitoring has been designed. The CTAB/PEG/ZrO2/GOx nanocomposite was deposited onto screen printed carbon paste (SPCE) electrode via spin coating technique. The properties of CTAB/PEG/ZrO2/GOx were study using scanning electron microscopy (SEM). The SPE modified with the CTAB/PEG/ZrO2/GOx showed electrocatalytical response to the oxidation of glucose when ferrocene carboxaldehyde was used as an artificial redox mediator, which was studied by cyclic voltammetry (CV). Several parameters such as working potential, effect of pH and effect of ZrO2/PEG layers that governed the analytical performance of the biosensor, have been studied. The biosensor was applied to detect glucose with a linear range of 0.4 to 2.0 mmol L−1 with good repetability and reproducibility.
Abstract: We study the dynamic response of a wind turbine
structure subjected to theoretical seismic motions, taking into account
the rotational component of ground shaking. Models are generated
for a shallow moderate crustal earthquake in the Madrid Region
(Spain). Synthetic translational and rotational time histories are
computed using the Discrete Wavenumber Method, assuming a point
source and a horizontal layered earth structure. These are used to
analyze the dynamic response of a wind turbine, represented by a
simple finite element model. Von Mises stress values at different
heights of the tower are used to study the dynamical structural
response to a set of synthetic ground motion time histories