Abstract: The goal of this paper is to find Wardrop equilibrium
in transport networks at case of uncertainty situations, where the
uncertainty comes from lack of information. We use simulation tool
to find the equilibrium, which gives only approximate solution, but
this is sufficient for large networks as well. In order to take the
uncertainty into account we have developed an interval-based
procedure for finding the paths with minimal cost using the
Dempster-Shafer theory. Furthermore we have investigated the users-
behaviors using game theory approach, because their path choices
influence the costs of the other users- paths.
Abstract: This paper describes a method to improve the robustness of a face recognition system based on the combination of two compensating classifiers. The face images are preprocessed by the appearance-based statistical approaches such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). LDA features of the face image are taken as the input of the Radial Basis Function Network (RBFN). The proposed approach has been tested on the ORL database. The experimental results show that the LDA+RBFN algorithm has achieved a recognition rate of 93.5%
Abstract: A full six degrees of freedom (6-DOF) flight dynamics
model is proposed for the accurate prediction of short and long-range
trajectories of high spin and fin-stabilized projectiles via atmospheric
flight to final impact point. The projectiles is assumed to be both rigid
(non-flexible), and rotationally symmetric about its spin axis launched
at low and high pitch angles. The mathematical model is based on the
full equations of motion set up in the no-roll body reference frame and
is integrated numerically from given initial conditions at the firing
site. The projectiles maneuvering motion depends on the most
significant force and moment variations, in addition to wind and
gravity. The computational flight analysis takes into consideration the
Mach number and total angle of attack effects by means of the
variable aerodynamic coefficients. For the purposes of the present
work, linear interpolation has been applied from the tabulated database
of McCoy-s book. The developed computational method gives
satisfactory agreement with published data of verified experiments and
computational codes on atmospheric projectile trajectory analysis for
various initial firing flight conditions.
Abstract: Gradual patterns have been studied for many years as
they contain precious information. They have been integrated in
many expert systems and rule-based systems, for instance to reason
on knowledge such as “the greater the number of turns, the greater
the number of car crashes”. In many cases, this knowledge has been
considered as a rule “the greater the number of turns → the greater
the number of car crashes” Historically, works have thus been
focused on the representation of such rules, studying how implication
could be defined, especially fuzzy implication. These rules were
defined by experts who were in charge to describe the systems they
were working on in order to turn them to operate automatically. More
recently, approaches have been proposed in order to mine databases
for automatically discovering such knowledge. Several approaches
have been studied, the main scientific topics being: how to determine
what is an relevant gradual pattern, and how to discover them as
efficiently as possible (in terms of both memory and CPU usage).
However, in some cases, end-users are not interested in raw level
knowledge, and are rather interested in trends. Moreover, it may be
the case that no relevant pattern can be discovered at a low level of
granularity (e.g. city), whereas some can be discovered at a higher
level (e.g. county). In this paper, we thus extend gradual pattern
approaches in order to consider multiple level gradual patterns. For
this purpose, we consider two aggregation policies, namely
horizontal and vertical.
Abstract: In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the components of the EEG signal (δ, θ, α, β and γ) and the Parseval-s theorem are employed to extract the percentage distribution of energy features of the EEG signal at different resolution levels. Second, the neural network (NN) classifies these extracted features to identify the EEGs type according to the percentage distribution of energy features. The performance of the proposed algorithm has been evaluated using in total 300 EEG signals. The results showed that the proposed classifier has the ability of recognizing and classifying EEG signals efficiently.
Abstract: This paper presents the development techniques
for a complete autonomous design model of an advanced train
control system and gives a new approach for the
implementation of multi-agents based system. This research
work proposes to develop a novel control system to enhance
the efficiency of the vehicles under constraints of various
conditions, and contributes in stability and controllability
issues, considering relevant safety and operational
requirements with command control communication and
various sensors to avoid accidents. The approach of speed
scheduling, management and control in local and distributed
environment is given to fulfill the dire needs of modern trend
and enhance the vehicles control systems in automation. These
techniques suggest the state of the art microelectronic
technology with accuracy and stability as forefront goals.
Abstract: In the planning point of view, it is essential to have
mode choice, due to the massive amount of incurred in transportation
systems. The intercity travellers in Libya have distinct features, as
against travellers from other countries, which includes cultural and
socioeconomic factors. Consequently, the goal of this study is to
recognize the behavior of intercity travel using disaggregate models,
for projecting the demand of nation-level intercity travel in Libya.
Multinomial Logit Model for all the intercity trips has been
formulated to examine the national-level intercity transportation in
Libya. The Multinomial logit model was calibrated using nationwide
revealed preferences (RP) and stated preferences (SP) survey. The
model was developed for deference purpose of intercity trips (work,
social and recreational). The variables of the model have been
predicted based on maximum likelihood method. The data needed for
model development were obtained from all major intercity corridors
in Libya. The final sample size consisted of 1300 interviews. About
two-thirds of these data were used for model calibration, and the
remaining parts were used for model validation. This study, which is
the first of its kind in Libya, investigates the intercity traveler’s
mode-choice behavior. The intercity travel mode-choice model was
successfully calibrated and validated. The outcomes indicate that, the
overall model is effective and yields higher precision of estimation.
The proposed model is beneficial, due to the fact that, it is receptive
to a lot of variables, and can be employed to determine the impact of
modifications in the numerous characteristics on the need for various
travel modes. Estimations of the model might also be of valuable to
planners, who can estimate possibilities for various modes and
determine the impact of unique policy modifications on the need for
intercity travel.
Abstract: Exploding concentrated underwater charges to
damage underwater structures such as ship hulls is a part of naval
warfare strategies. Adding small amounts of foreign particles (like
clay or silica) of nanosize significantly improves the engineering
properties of the polymers. In the present work the clay in terms 1, 2
and 3 percent by weight was surface treated with a suitable silane
agent. The hybrid nanocomposite was prepared by the hand lay-up
technique. Mathematical regression models have been employed for
theoretical prediction. This will result in considerable savings in terms of project time, effort and cost.
Abstract: Tackling emergency situations is performed based on emergency scenarios. These scenarios do not have a uniform form in the Czech Republic. They are unstructured and developed primarily in the text form. This does not allow solving emergency situations efficiently. For this reason, the paper aims at defining a Process Oriented Architecture to support and thus to improve tackling emergency situations in the Czech Republic. The innovative Process Oriented Architecture is based on the Workflow Reference Model while taking into account the options of Business Process Management Suites for the implementation of process oriented emergency scenarios. To verify the proposed architecture the Proof of Concept has been used which covers the reception of an emergency event at the district emergency operations centre. Within the particular implementation of the proposed architecture the Bonita Open Solution has been used. The architecture created in this way is suitable not only for emergency management, but also for educational purposes.
Abstract: Sediment formation and its transport along the river course is considered as important hydraulic consideration in river engineering. Their impact on the morphology of rivers on one hand and important considerations of which in the design and construction of the hydraulic structures on the other has attracted the attention of experts in arid and semi-arid regions. Under certain conditions where the momentum energy of the flow stream reaches a specific rate, the sediment materials start to be transported with the flow. This can usually be analyzed in two different categories of suspended and bed load materials. Sedimentation phenomenon along the waterways and the conveyance of vast volume of materials into the canal networks can potentially influence water abstraction in the intake structures. This can pose a serious threat to operational sustainability and water delivery performance in the canal networks. The situation is serious where ineffective watershed management (poor vegetation cover in the water basin) is the underlying cause of soil erosion which feeds the materials into the waterways that intern would necessitate comprehensive study. The present paper aims to present an analytical investigation of the sediment process in the waterways on one hand and estimation of the sediment load transport into the lined canals using the SHARC software on the other. For this reason, the paper focuses on the comparative analysis of the hydraulic behaviors of the Sabilli main canal that feeds the pumping station with that of the Western canal in the Greater Dezful region to identify effective factors in sedimentation and ways of mitigating their impact on water abstraction in the canal systems. The method involved use of observational data available in the Dezful Dastmashoon hydrometric station along a 6 km waterway of the Sabilli main canal using the SHARC software to estimate the suspended load concentration and bed load materials. Results showed the transport of a significant volume of sediment loads from the waterways into the canal system which is assumed to have arisen from the absence of stilling basin on one hand and the gravity flow on the other has caused serious challenges. This is contrary to what occurs in the Sabilli canal, where the design feature which incorporates a settling basin just before the pumping station is the major cause of reduced sediment load transport into the canal system.Results showed that modification of the present design features by constructing a settling basin just upstream of the western intake structure can considerably reduce the entry of sediment materials into the canal system. Not only this can result in the sustainability of the hydraulic structures but can also improve operational performance of water conveyance and distribution system, all of which are the pre-requisite to secure reliable and equitable water delivery regime for the command area.
Abstract: This paper addresses the problems encountered by conventional distance relays when protecting double-circuit transmission lines. The problems arise principally as a result of the mutual coupling between the two circuits under different fault conditions; this mutual coupling is highly nonlinear in nature. An adaptive protection scheme is proposed for such lines based on application of artificial neural network (ANN). ANN has the ability to classify the nonlinear relationship between measured signals by identifying different patterns of the associated signals. One of the key points of the present work is that only current signals measured at local end have been used to detect and classify the faults in the double circuit transmission line with double end infeed. The adaptive protection scheme is tested under a specific fault type, but varying fault location, fault resistance, fault inception angle and with remote end infeed. An improved performance is experienced once the neural network is trained adequately, which performs precisely when faced with different system parameters and conditions. The entire test results clearly show that the fault is detected and classified within a quarter cycle; thus the proposed adaptive protection technique is well suited for double circuit transmission line fault detection & classification. Results of performance studies show that the proposed neural network-based module can improve the performance of conventional fault selection algorithms.
Abstract: The C3 plants are frequently suffering from exposure
to high temperature stress which limits the growth and yield of these
plants. This study seeks to clarify the physiological mechanisms of
heat tolerance in relation to oxidative stress in C3 species. Fifteen C3
species were exposed to prolonged moderately high temperature
stress 36/30°C for 40 days in a growth chamber. Chlorophyll
fluorescence (Fv/Fm) showed great difference among species at 40
days of the stress. The species showed decreases in Fv/Fm and
increases in malondialdehyde (MDA) content under stress condition
as well as negative correlation between Fv/Fm and MDA (r = -0.61*)
at 40 days of the stress. Hydrogen peroxide (H2O2) content before
and after stress in addition to its response under stress showed great
differences among species. The results suggest that the difference in
heat tolerance among C3 species is closely associated with the ability
to suppress oxidative damage but not with the content of reactive
oxygen species (ROS) which is regulated by complex network.
Abstract: The objective of positioning the fixture elements in
the fixture is to make the workpiece stiff, so that geometric errors in
the manufacturing process can be reduced. Most of the work for
optimal fixture layout used the minimization of the sum of the nodal
deflection normal to the surface as objective function. All deflections
in other direction have been neglected. We propose a new method for
fixture layout optimization in this paper, which uses the element
strain energy. The deformations in all the directions have been
considered in this way. The objective function in this method is to
minimize the sum of square of element strain energy. Strain energy
and stiffness are inversely proportional to each other. The
optimization problem is solved by the sequential quadratic
programming method. Three different kinds of case studies are
presented, and results are compared with the method using nodal
deflections as objective function to verify the propose method.
Abstract: The paper presents an analysis of linkages and
structures of co-operation and their intensity like the potential for the
establishment of clusters in the Central and Eastern (Pannonian)
Croatian. Starting from the theoretical elaboration of the need for
entrepreneurs to organize through the cluster model and the terms of
their self-actualization, related to the importance of traditional values
in terms of benefits, social capital and assess where the company now
is, in order to prove the need to create their own identity in terms of
clustering. The institutional dimensions of social capital where the
public sector has the best role in creating the social structure of
clusters, and social dimensions of social capital in terms of trust,
cooperation and networking will be analyzed to what extent the trust
and coherency are present between companies in the Brod posavina
and Pozega slavonia County, expressed through the readiness of
inclusion in clusters in the NUTS II region - Central and Eastern
(Pannonian) Croatia, as a homogeneous economic entity, with
emphasis on limiting factors that stand in the way of greater
competitiveness.
Abstract: The state of the art in instructional design for
computer-assisted learning has been strongly influenced by advances
in information technology, Internet and Web-based systems. The
emphasis of educational systems has shifted from training to
learning. The course delivered has also been changed from large
inflexible content to sequential small chunks of learning objects. The
concepts of learning objects together with the advanced technologies
of Web and communications support the reusability, interoperability,
and accessibility design criteria currently exploited by most learning
systems. These concepts enable just-in-time learning. We propose to
extend theses design criteria further to include the learnability
concept that will help adapting content to the needs of learners. The
learnability concept offers a better personalization leading to the
creation and delivery of course content more appropriate to
performance and interest of each learner. In this paper we present a
new framework of learning environments containing knowledge
discovery as a tool to automatically learn patterns of learning
behavior from learners' profiles and history.
Abstract: A clustering based technique has been developed and implemented for Short Term Load Forecasting, in this article. Formulation has been done using Mean Absolute Percentage Error (MAPE) as an objective function. Data Matrix and cluster size are optimization variables. Model designed, uses two temperature variables. This is compared with six input Radial Basis Function Neural Network (RBFNN) and Fuzzy Inference Neural Network (FINN) for the data of the same system, for same time period. The fuzzy inference system has the network structure and the training procedure of a neural network which initially creates a rule base from existing historical load data. It is observed that the proposed clustering based model is giving better forecasting accuracy as compared to the other two methods. Test results also indicate that the RBFNN can forecast future loads with accuracy comparable to that of proposed method, where as the training time required in the case of FINN is much less.
Abstract: Radio Frequency Identification (RFID) initially introduced
during WW-II, has revolutionized the world with its numerous
benefits and plethora of implementations in diverse areas ranging
from manufacturing to agriculture to healthcare to hotel management.
This work reviews the current research in this area with emphasis
on applications for supply chain management and to develop a
taxonomic framework to classify literature which will enable swift
and easy content analysis and also help identify areas for future
research.
Abstract: Color categorization is shared among members in a
society. This allows communication of color, especially when using
natural language such as English. Hence sociable robot, to live
coexist with human in human society, must also have the shared
color categorization. To achieve this, many works have been done
relying on modeling of human color perception and mathematical
complexities. In contrast, in this work, the computer as brain of the
robot learns color categorization through interaction with humans
without much mathematical complexities.
Abstract: We present on the method of inverse coherence matrix for the estimation of network connectivity from multivariate time series of a complex system. In a model system of coupled chaotic oscillators, it is shown that the inverse coherence matrix defined as the inverse of cross coherence matrix is proportional to the network connectivity. Therefore the inverse coherence matrix could be used for the distinction between the directly connected links from indirectly connected links in a complex network. We compare the result of network estimation using the method of the inverse coherence matrix with the results obtained from the coherence matrix and the partial coherence matrix.
Abstract: Optimal reactive power flow is an optimization problem
with one or more objective of minimizing the active power losses for
fixed generation schedule. The control variables are generator bus
voltages, transformer tap settings and reactive power output of the
compensating devices placed on different bus bars. Biogeography-
Based Optimization (BBO) technique has been applied to solve
different kinds of optimal reactive power flow problems subject
to operational constraints like power balance constraint, line flow
and bus voltages limits etc. BBO searches for the global optimum
mainly through two steps: Migration and Mutation. In the present
work, BBO has been applied to solve the optimal reactive power
flow problems on IEEE 30-bus and standard IEEE 57-bus power
systems for minimization of active power loss. The superiority of the
proposed method has been demonstrated. Considering the quality of
the solution obtained, the proposed method seems to be a promising
one for solving these problems.