Abstract: This study suggests how an order-receiving company
can avoid disclosing schedule information on unit tasks to the
order-placing company when carrying out a collaborative project on
the value chain in an order-oriented industry. Specifically, it suggests
methods for keeping schedule information confidential, and
categorizes potential situations by inter-task dependency. Lastly, an
approach to select the most optimal non-disclosure method is
discussed. With the methods for not disclosing work-related
information suggested in the study, order-receiving companies can
logically deal with political issues relating to the question of whether
or not to disclose information upon the execution of a collaborative
project in cooperation with an order-placing firm. Moreover,
order-placing companies can monitor undistorted information, while
respecting the legitimate rights of an order-receiving company.
Therefore, it is fair to say that the suggestions made in this study will
contribute to the smooth operation of collaborative intercompany
projects.
Abstract: Most of the collision warning systems currently
available in the automotive market are mainly designed to warn
against imminent rear-end and lane-changing collisions. No collision
warning system is commercially available to warn against imminent
turning collisions at intersections, especially for left-turn collisions
when a driver attempts to make a left-turn at either a signalized or
non-signalized intersection, conflicting with the path of other
approaching vehicles traveling on the opposite-direction traffic
stream. One of the major factors that lead to left-turn collisions is the
human error and misjudgment of the driver of the turning vehicle
when perceiving the speed and acceleration of other vehicles
traveling on the opposite-direction traffic stream; therefore, using a
properly-designed collision warning system will likely reduce, or
even eliminate, this type of collisions by reducing human error. This
paper introduces perceptual framework for a proposed collision
warning system that can detect imminent left-turn collisions at
intersections. The system utilizes a commercially-available detection
sensor (either a radar sensor or a laser detector) to detect approaching
vehicles traveling on the opposite-direction traffic stream and
calculate their speeds and acceleration rates to estimate the time-tocollision
and compare that time to the time required for the turning
vehicle to clear the intersection. When calculating the time required
for the turning vehicle to clear the intersection, consideration is given
to the perception-reaction time of the driver of the turning vehicle,
which is the time required by the driver to perceive the message
given by the warning system and react to it by engaging the throttle.
A regression model was developed to estimate perception-reaction
time based on age and gender of the driver of the host vehicle.
Desired acceleration rate selected by the driver of the turning vehicle,
when making the left-turn movement, is another human factor that is
considered by the system. Another regression model was developed
to estimate the acceleration rate selected by the driver of the turning
vehicle based on driver-s age and gender as well as on the location
and speed of the nearest approaching vehicle along with the
maximum acceleration rate provided by the mechanical
characteristics of the turning vehicle. By comparing time-to-collision
with the time required for the turning vehicle to clear the intersection,
the system displays a message to the driver of the turning vehicle
when departure is safe. An application example is provided to
illustrate the logic algorithm of the proposed system.
Abstract: One of the important steps in a safety and risk management system is the economical evaluation of occupational accident and diseases costs in order to decrease accidents from reoccurring in the workplace. This study proposed a plausible method for calculating occupational accident costs and illnesses in work place. This method design for cost estimation takes into account both the personnel, organizational level as well as the community level especially intended for an Iranian work place. The research indicates that a using systematic method for calculating costs which also provides risk evaluation can help managers to plan correctly the investment in health and safety measures. Using this method is that not only is it comprehensive, easy and practical and could be applied in practice by a manager within a short period of time but it also shows the importance of accident costs as well as calculates the real cost of an accident and illnesses.
Abstract: The paper presents the service learning project titled
DicDucFac (idea-leadership-product), that was planned and
conducted by the team of information sciences students. It was
planned as a workshop dealing with the application of modern social
media (Facebook, YouTube, Gmail) for the purposes of selfpromotion,
free advertising via social networks and marketing own
ideas and/or products in the virtual world. The workshop was
organized for highly-skilled computer literate unemployed youth.
These youth, as final beneficiaries, will be able to apply what they
learned in this workshop to “the real world“, increasing their chances
for employment and self-employment. The results of the project
reveal that the basic, active-learning principles embodied in our
teaching approach allow students to learn more effectively and gain
essential life skills (from computer applications to teamwork) that
can only be learned by doing. It also shows that our students received
the essentials of professional ethics and citizenship through direct,
personal engagement in professional activities and the life of the
community.
Abstract: Categorical data based on description of the
agricultural landscape imposed some mathematical and analytical
limitations. This problem however can be overcome by data
transformation through coding scheme and the use of non-parametric
multivariate approach. The present study describes data
transformation from qualitative to numerical descriptors. In a
collection of 103 random soil samples over a 60 hectare field,
categorical data were obtained from the following variables: levels of
nitrogen, phosphorus, potassium, pH, hue, chroma, value and data on
topography, vegetation type, and the presence of rocks. Categorical
data were coded, and Spearman-s rho correlation was then calculated
using PAST software ver. 1.78 in which Principal Component
Analysis was based. Results revealed successful data transformation,
generating 1030 quantitative descriptors. Visualization based on the
new set of descriptors showed clear differences among sites, and
amount of variation was successfully measured. Possible applications
of data transformation are discussed.
Abstract: Supply chain consists of all stages involved, directly
or indirectly, includes all functions involved in fulfilling a customer
demand. In two stage transportation supply chain problem,
transportation costs are of a significant proportion of final product
costs. It is often crucial for successful decisions making approaches
in two stage supply chain to explicit account for non-linear
transportation costs. In this paper, deterministic demand and finite
supply of products was considered. The optimized distribution level
and the routing structure from the manufacturing plants to the
distribution centres and to the end customers is determined using
developed mathematical model and solved by proposed particle
swarm optimization based genetic algorithm. Numerical analysis of
the case study is carried out to validate the model.
Abstract: In the modern manufacturing systems, the use of
thermal cutting techniques using oxyfuel, plasma and laser have
become indispensable for the shape forming of high quality complex
components; however, the conventional chip removal production
techniques still have its widespread space in the manufacturing
industry. Both these types of machining operations require the
positioning of end effector tool at the edge where the cutting process
commences. This repositioning of the cutting tool in every machining
operation is repeated several times and is termed as non-productive
time or airtime motion. Minimization of this non-productive
machining time plays an important role in mass production with high
speed machining. As, the tool moves from one region to the other by
rapid movement and visits a meticulous region once in the whole
operation, hence the non-productive time can be minimized by
synchronizing the tool movements. In this work, this problem is
being formulated as a general travelling salesman problem (TSP) and
a genetic algorithm approach has been applied to solve the same. For
improving the efficiency of the algorithm, the GA has been
hybridized with a noble special heuristic and simulating annealing
(SA). In the present work a novel heuristic in the combination of GA
has been developed for synchronization of toolpath movements
during repositioning of the tool. A comparative analysis of new Meta
heuristic techniques with simple genetic algorithm has been
performed. The proposed metaheuristic approach shows better
performance than simple genetic algorithm for minimization of nonproductive
toolpath length. Also, the results obtained with the help of
hybrid simulated annealing genetic algorithm (HSAGA) are also
found better than the results using simple genetic algorithm only.
Abstract: In intensity modulated radiation therapy (IMRT)
treatment planning, beam angles are usually preselected on the basis of
experience and intuition. Therefore, getting an appropriate beam
configuration needs a very long time. Based on the present situation,
the paper puts forward beam orientation optimization using ant colony
optimization (ACO). We use ant colony optimization to select the
beam configurations, after getting the beam configuration using
Conjugate Gradient (CG) algorithm to optimize the intensity profiles.
Combining with the information of the effect of pencil beam, we can
get the global optimal solution accelerating. In order to verify the
feasibility of the presented method, a simulated and clinical case was
tested, compared with dose-volume histogram and isodose line
between target area and organ at risk. The results showed that the
effect was improved after optimizing beam configurations. The
optimization approach could make treatment planning meet clinical
requirements more efficiently, so it had extensive application
perspective.
Abstract: Computer worm detection is commonly performed by
antivirus software tools that rely on prior explicit knowledge of the
worm-s code (detection based on code signatures). We present an
approach for detection of the presence of computer worms based on
Artificial Neural Networks (ANN) using the computer's behavioral
measures. Identification of significant features, which describe the
activity of a worm within a host, is commonly acquired from security
experts. We suggest acquiring these features by applying feature
selection methods. We compare three different feature selection
techniques for the dimensionality reduction and identification of the
most prominent features to capture efficiently the computer behavior
in the context of worm activity. Additionally, we explore three
different temporal representation techniques for the most prominent
features. In order to evaluate the different techniques, several
computers were infected with five different worms and 323 different
features of the infected computers were measured. We evaluated
each technique by preprocessing the dataset according to each one
and training the ANN model with the preprocessed data. We then
evaluated the ability of the model to detect the presence of a new
computer worm, in particular, during heavy user activity on the
infected computers.
Abstract: The paper presents coupled electromagnetic and
thermal field analysis of busbar system (of rectangular cross-section
geometry) submitted to short circuit conditions. The laboratory model
was validated against both analytical solution and experimental
observations. The considered problem required the computation of
the detailed distribution of the power losses and the heat transfer
modes. In this electromagnetic and thermal analysis, different
definitions of electric busbar heating were considered and compared.
The busbar system is a three phase one and consists of aluminum,
painted aluminum and copper busbar. The solution to the coupled
field problem is obtained using the finite element method and the
QuickField™ program. Experiments have been carried out using two
different approaches and compared with computed results.
Abstract: Computer networks are essential part in computerbased
information systems. The performance of these networks has a
great influence on the whole information system. Measuring the
usability criteria and customers satisfaction on small computer
network is very important. In this article, an effective approach for
measuring the usability of business network in an information system
is introduced. The usability process for networking provides us with a
flexible and a cost-effective way to assess the usability of a network
and its products. In addition, the proposed approach can be used to
certify network product usability late in the development cycle.
Furthermore, it can be used to help in developing usable interfaces
very early in the cycle and to give a way to measure, track, and
improve usability. Moreover, a new approach for fast information
processing over computer networks is presented. The entire data are
collected together in a long vector and then tested as a one input
pattern. Proposed fast time delay neural networks (FTDNNs) use
cross correlation in the frequency domain between the tested data and
the input weights of neural networks. It is proved mathematically and
practically that the number of computation steps required for the
presented time delay neural networks is less than that needed by
conventional time delay neural networks (CTDNNs). Simulation
results using MATLAB confirm the theoretical computations.
Abstract: Information Retrieval has the objective of studying
models and the realization of systems allowing a user to find the
relevant documents adapted to his need of information. The
information search is a problem which remains difficult because the
difficulty in the representing and to treat the natural languages such
as polysemia. Intentional Structures promise to be a new paradigm to
extend the existing documents structures and to enhance the different
phases of documents process such as creation, editing, search and
retrieval. The intention recognition of the author-s of texts can reduce
the largeness of this problem. In this article, we present intentions
recognition system is based on a semi-automatic method of
extraction the intentional information starting from a corpus of text.
This system is also able to update the ontology of intentions for the
enrichment of the knowledge base containing all possible intentions
of a domain. This approach uses the construction of a semi-formal
ontology which considered as the conceptualization of the intentional
information contained in a text. An experiments on scientific
publications in the field of computer science was considered to
validate this approach.
Abstract: Image target detection and tracking methods based on
target information such as intensity, shape model, histogram and
target dynamics have been proven to be robust to target model
variations and background clutters as shown by recent researches.
However, no definitive answer has been given to occluded target by
counter measure or limited field of view(FOV). In this paper, we
will present a novel tracking method using filtering and computational
geometry. This paper has two central goals: 1) to deal with vulnerable
target measurements; and 2) to maintain target tracking out of FOV
using non-target-originated information. The experimental results,
obtained with airborne images, show a robust tracking ability with
respect to the existing approaches. In exploring the questions of target
tracking, this paper will be limited to consideration of airborne image.
Abstract: Accurately predicting non-peak traffic is crucial to
daily traffic for all forecasting models. In the paper, least squares
support vector machines (LS-SVMs) are investigated to solve such a
practical problem. It is the first time to apply the approach and analyze
the forecast performance in the domain. For comparison purpose, two
parametric and two non-parametric techniques are selected because of
their effectiveness proved in past research. Having good
generalization ability and guaranteeing global minima, LS-SVMs
perform better than the others. Providing sufficient improvement in
stability and robustness reveals that the approach is practically
promising.
Abstract: Urbanization and related anthropogenic modifications
cause extent of habitat fragmentation and directly lead to decline of
local biodiversity. Conservation biologists advocate corridor creation
as one approach to rescue biodiversity. Here we examine the utility of
roads as corridors in preserving plant diversity by investigating
roadside vegetation in Yellow River Delta (YRD), China. We
examined the spatio-temporal distribution pattern of plant species
richness, diversity and composition along roadside. The results
suggest that roads, as dispersal conduits, increase occurrence
probability of new settlers to a new area, meanwhile, roads accumulate
the greater propagule pressure and favourable survival condition
during operation phase. As a result, more species, including native and
alien plants, non- halophyte and halophyte species, threatened and
cosmopolitic species, were found prosperous at roadside. Roadside
may be a refuge for more species, and the pattern of vegetation
distribution is affected by road age and the distance from road verge.
Abstract: Cognitive Science appeared about 40 years ago,
subsequent to the challenge of the Artificial Intelligence, as common
territory for several scientific disciplines such as: IT, mathematics,
psychology, neurology, philosophy, sociology, and linguistics. The
new born science was justified by the complexity of the problems
related to the human knowledge on one hand, and on the other by the
fact that none of the above mentioned sciences could explain alone
the mental phenomena. Based on the data supplied by the
experimental sciences such as psychology or neurology, models of
the human mind operation are built in the cognition science. These
models are implemented in computer programs and/or electronic
circuits (specific to the artificial intelligence) – cognitive systems –
whose competences and performances are compared to the human
ones, leading to the psychology and neurology data reinterpretation,
respectively to the construction of new models. During these
processes if psychology provides the experimental basis, philosophy
and mathematics provides the abstraction level utterly necessary for
the intermission of the mentioned sciences.
The ongoing general problematic of the cognitive approach
provides two important types of approach: the computational one,
starting from the idea that the mental phenomenon can be reduced to
1 and 0 type calculus operations, and the connection one that
considers the thinking products as being a result of the interaction
between all the composing (included) systems. In the field of
psychology measurements in the computational register use classical
inquiries and psychometrical tests, generally based on calculus
methods. Deeming things from both sides that are representing the
cognitive science, we can notice a gap in psychological product
measurement possibilities, regarded from the connectionist
perspective, that requires the unitary understanding of the quality –
quantity whole. In such approach measurement by calculus proves to
be inefficient. Our researches, deployed for longer than 20 years,
lead to the conclusion that measuring by forms properly fits to the
connectionism laws and principles.
Abstract: Instead of traditional (nominal) classification we investigate
the subject of ordinal classification or ranking. An enhanced
method based on an ensemble of Support Vector Machines (SVM-s)
is proposed. Each binary classifier is trained with specific weights
for each object in the training data set. Experiments on benchmark
datasets and synthetic data indicate that the performance of our
approach is comparable to state of the art kernel methods for
ordinal regression. The ensemble method, which is straightforward
to implement, provides a very good sensitivity-specificity trade-off
for the highest and lowest rank.
Abstract: Road industry has challenged the prospect of ecoconstruction. Pavements may fit within the framework of sustainable development. Hence, research implements assessments of conventional pavements impacts on environment in use of life cycle approach. To meet global, and often national, targets on pollution control, newly introduced pavement designs are under study. This is the case of Cyprus demonstration, which occurred within EcoLanes project work. This alternative pavement differs on concrete layer reinforced with tire recycling product. Processing of post-consumer tires produces steel fibers improving strength capacity against cracking. Thus maintenance works are relevantly limited in comparison to flexible pavement. This enables to be more ecofriendly, referenced to current study outputs. More specific, proposed concrete pavement life cycle processes emits 15 % less air pollutants and consumes 28 % less embodied energy than those of the asphalt pavement. In addition there is also a reduction on costs by 0.06 %.
Abstract: Automatic methods of detecting changes through
satellite imaging are the object of growing interest, especially
beca²use of numerous applications linked to analysis of the Earth’s
surface or the environment (monitoring vegetation, updating maps,
risk management, etc...). This work implemented spatial analysis
techniques by using images with different spatial and spectral
resolutions on different dates. The work was based on the principle
of control charts in order to set the upper and lower limits beyond
which a change would be noted. Later, the a contrario approach was
used. This was done by testing different thresholds for which the
difference calculated between two pixels was significant. Finally,
labeled images were considered, giving a particularly low difference
which meant that the number of “false changes” could be estimated
according to a given limit.
Abstract: Evolutionary Programming (EP) represents a
methodology of Evolutionary Algorithms (EA) in which mutation is
considered as a main reproduction operator. This paper presents a
novel EP approach for Artificial Neural Networks (ANN) learning.
The proposed strategy consists of two components: the self-adaptive,
which contains phenotype information and the dynamic, which is
described by genotype. Self-adaptation is achieved by the addition of
a value, called the network weight, which depends on a total number
of hidden layers and an average number of neurons in hidden layers.
The dynamic component changes its value depending on the fitness
of a chromosome, exposed to mutation. Thus, the mutation step size
is controlled by two components, encapsulated in the algorithm,
which adjust it according to the characteristics of a predefined ANN
architecture and the fitness of a particular chromosome. The
comparative analysis of the proposed approach and the classical EP
(Gaussian mutation) showed, that that the significant acceleration of
the evolution process is achieved by using both phenotype and
genotype information in the mutation strategy.