Abstract: In this article we explore how computer assisted exercises may allow for bridging the traditional gap between theory and practice in professional education. To educate officers able to master the complexity of the battlefield the Norwegian Military Academy needs to develop a learning environment that allows for creating viable connections between the educational environment and the field of practice. In response to this challenge we explore the conditions necessary to make computer assisted training systems (CATS) a useful tool to create structural similarities between an educational context and the field of military practice. Although, CATS may facilitate work procedures close to real life situations, this case do demonstrate how professional competence also must build on viable learning theories and environments. This paper explores the conditions that allow for using simulators to facilitate professional competence from within an educational setting. We develop a generic didactic model that ascribes learning to participation in iterative cycles of action and reflection. The development of this model is motivated by the need to develop an interdisciplinary professional education rooted in the pattern of military practice.
Abstract: Fundamental sensor-motor couplings form the backbone
of most mobile robot control tasks, and often need to be implemented
fast, efficiently and nevertheless reliably. Machine learning
techniques are therefore often used to obtain the desired sensor-motor
competences.
In this paper we present an alternative to established machine
learning methods such as artificial neural networks, that is very fast,
easy to implement, and has the distinct advantage that it generates
transparent, analysable sensor-motor couplings: system identification
through nonlinear polynomial mapping.
This work, which is part of the RobotMODIC project at the
universities of Essex and Sheffield, aims to develop a theoretical understanding
of the interaction between the robot and its environment.
One of the purposes of this research is to enable the principled design
of robot control programs.
As a first step towards this aim we model the behaviour of the
robot, as this emerges from its interaction with the environment, with
the NARMAX modelling method (Nonlinear, Auto-Regressive, Moving
Average models with eXogenous inputs). This method produces
explicit polynomial functions that can be subsequently analysed using
established mathematical methods.
In this paper we demonstrate the fidelity of the obtained NARMAX
models in the challenging task of robot route learning; we present a
set of experiments in which a Magellan Pro mobile robot was taught
to follow four different routes, always using the same mechanism to
obtain the required control law.
Abstract: The use of new technologies such internet (e-mail, chat
rooms) and cell phones has steeply increased in recent years.
Especially among children and young people, use of technological
tools and equipments is widespread. Although many teachers and
administrators now recognize the problem of school bullying, few are
aware that students are being harassed through electronic
communication. Referred to as electronic bullying, cyber bullying, or
online social cruelty, this phenomenon includes bullying through email,
instant messaging, in a chat room, on a website, or through
digital messages or images sent to a cell phone. Cyber bullying is
defined as causing deliberate/intentional harm to others using internet
or other digital technologies. It has a quantitative research design nd
uses relational survey as its method. The participants consisted of
300 secondary school students in the city of Konya, Turkey. 195
(64.8%) participants were female and 105 (35.2%) were male. 39
(13%) students were at grade 1, 187 (62.1%) were at grade 2 and 74
(24.6%) were at grade 3. The “Cyber Bullying Question List"
developed by Ar─▒cak (2009) was given to students. Following
questions about demographics, a functional definition of cyber
bullying was provided. In order to specify students- human values,
“Human Values Scale (HVS)" developed by Dilmaç (2007) for
secondary school students was administered. The scale consists of 42
items in six dimensions. Data analysis was conducted by the primary
investigator of the study using SPSS 14.00 statistical analysis
software. Descriptive statistics were calculated for the analysis of
students- cyber bullying behaviour and simple regression analysis was
conducted in order to test whether each value in the scale could
explain cyber bullying behaviour.
Abstract: The clinical usefulness of heart rate variability is
limited to the range of Holter monitoring software available. These
software algorithms require a normal sinus rhythm to accurately
acquire heart rate variability (HRV) measures in the frequency
domain. Premature ventricular contractions (PVC) or more
commonly referred to as ectopic beats, frequent in heart failure,
hinder this analysis and introduce ambiguity. This investigation
demonstrates an algorithm to automatically detect ectopic beats by
analyzing discrete wavelet transform coefficients. Two techniques
for filtering and replacing the ectopic beats from the RR signal are
compared. One technique applies wavelet hard thresholding
techniques and another applies linear interpolation to replace ectopic
cycles. The results demonstrate through simulation, and signals
acquired from a 24hr ambulatory recorder, that these techniques can
accurately detect PVC-s and remove the noise and leakage effects
produced by ectopic cycles retaining smooth spectra with the
minimum of error.
Abstract: The 2008 Candlelight Protests of Korea was very
significant to portray the political environment among the South
Korean youth. Many challenges and new advanced technologies have
driven the youth community to be engaged in the political arena that
has shifted them from traditional Korean youth to a very greater
community. Due to historical perspective with the people of North
Korea, the young generation has embraced different view of ethnic
nationalism. This study examines the youth involvement in politics in
line with their level of acceptance the practice of democracy. The
increase usage of new media has shown great results in the survey
results whereby the youth used as a platform to gain political
information and brought higher degree of their sociopolitical interests
among them. Furthermore, the rise of nationalism and patriotism will
be discussed in this paper to the dynamism of the political approaches
used by the Korea government
Abstract: Quantitative trait loci (QTL) experiments have yielded
important biological and biochemical information necessary for
understanding the relationship between genetic markers and
quantitative traits. For many years, most QTL algorithms only
allowed one observation per genotype. Recently, there has been an
increasing demand for QTL algorithms that can accommodate more
than one observation per genotypic distribution. The Bayesian
hierarchical model is very flexible and can easily incorporate this
information into the model. Herein a methodology is presented that
uses a Bayesian hierarchical model to capture the complexity of the
data. Furthermore, the Markov chain Monte Carlo model composition
(MC3) algorithm is used to search and identify important markers. An
extensive simulation study illustrates that the method captures the
true QTL, even under nonnormal noise and up to 6 QTL.
Abstract: Video-on-demand (VOD) is designed by using content delivery networks (CDN) to minimize the overall operational cost and to maximize scalability. Estimation of the viewing pattern (i.e., the relationship between the number of viewings and the ranking of VOD contents) plays an important role in minimizing the total operational cost and maximizing the performance of the VOD systems. In this paper, we have analyzed a large body of commercial VOD viewing data and found that the viewing rank distribution fits well with the parabolic fractal distribution. The weighted linear model fitting function is used to estimate the parameters (coefficients) of the parabolic fractal distribution. This paper presents an analytical basis for designing an optimal hierarchical VOD contents distribution system in terms of its cost and performance.
Abstract: This paper presents a novel genetic algorithm, termed
the Optimum Individual Monogenetic Algorithm (OIMGA) and
describes its hardware implementation. As the monogenetic strategy
retains only the optimum individual, the memory requirement is
dramatically reduced and no crossover circuitry is needed, thereby
ensuring the requisite silicon area is kept to a minimum.
Consequently, depending on application requirements, OIMGA
allows the investigation of solutions that warrant either larger GA
populations or individuals of greater length. The results given in this
paper demonstrate that both the performance of OIMGA and its
convergence time are superior to those of existing hardware GA
implementations. Local convergence is achieved in OIMGA by
retaining elite individuals, while population diversity is ensured by
continually searching for the best individuals in fresh regions of the
search space.
Abstract: We report a computational study of the spreading
dynamics of a viral infection in a complex (scale-free) network. The
final epidemic size distribution (FESD) was found to be unimodal or
bimodal depending on the value of the basic reproductive
number R0 . The FESDs occurred on time-scales long enough for
intermediate-time epidemic size distributions (IESDs) to be important
for control measures. The usefulness of R0 for deciding on the
timeliness and intensity of control measures was found to be limited
by the multimodal nature of the IESDs and by its inability to inform
on the speed at which the infection spreads through the population. A
reduction of the transmission probability at the hubs of the scale-free
network decreased the occurrence of the larger-sized epidemic events
of the multimodal distributions. For effective epidemic control, an
early reduction in transmission at the index cell and its neighbors was
essential.
Abstract: Data clustering is an important data exploration technique
with many applications in data mining. We present an enhanced
version of the well known single link clustering algorithm. We will
refer to this algorithm as DCBOR. The proposed algorithm alleviates
the chain effect by removing the outliers from the given dataset.
So this algorithm provides outlier detection and data clustering
simultaneously. This algorithm does not need to update the distance
matrix, since the algorithm depends on merging the most k-nearest
objects in one step and the cluster continues grow as long as possible
under specified condition. So the algorithm consists of two phases;
at the first phase, it removes the outliers from the input dataset. At
the second phase, it performs the clustering process. This algorithm
discovers clusters of different shapes, sizes, densities and requires
only one input parameter; this parameter represents a threshold for
outlier points. The value of the input parameter is ranging from 0 to
1. The algorithm supports the user in determining an appropriate
value for it. We have tested this algorithm on different datasets
contain outlier and connecting clusters by chain of density points,
and the algorithm discovers the correct clusters. The results of
our experiments demonstrate the effectiveness and the efficiency of
DCBOR.
Abstract: Compare to western cultures, women who smoke in Korea are not tolerated. Korean people are prejudiced against women smoking. In spite of the relative prevalence of sexual equality in South Korea, women too often feel obliged to confine their smoking to only a few public spaces, such as designated smoking rooms, coffee shops or pubs. Korean Confucianism classifies people according to gender and social status. According to Confucian culture, cigarettes convey clear social meanings as well as reinforcing status, age and gender, beyond personal preferences. For these reasons, the significant of people smoking in Korea varies according to their gender. This study will determine reasons for the ongoing sexual discrimination against female Korean smokers thorough analyzing Korean films. Since film is a medium reflects social phenomenon. Roland Barthes- Mythology Theory will be used to analyze films.
Abstract: IEEE has designed 802.11i protocol to address the
security issues in wireless local area networks. Formal analysis is
important to ensure that the protocols work properly without having
to resort to tedious testing and debugging which can only show the
presence of errors, never their absence. In this paper, we present
the formal verification of an abstract protocol model of 802.11i.
We translate the 802.11i protocol into the Strand Space Model and
then prove the authentication property of the resulting model using
the Strand Space formalism. The intruder in our model is imbued
with powerful capabilities and repercussions to possible attacks are
evaluated. Our analysis proves that the authentication of 802.11i is
not compromised in the presented model. We further demonstrate
how changes in our model will yield a successful man-in-the-middle
attack.
Abstract: Hemorrhage Disease of Grass Carp (HDGC) is a kind
of commonly occurring illnesses in summer, and the extremely high
death rate result in colossal losses to aquaculture. As the complex
connections among each factor which influences aquiculture diseases,
there-s no quit reasonable mathematical model to solve the problem at
present.A BP neural network which with excellent nonlinear mapping
coherence was adopted to establish mathematical model;
Environmental factor, which can easily detected, such as breeding
density, water temperature, pH and light intensity was set as the main
analyzing object. 25 groups of experimental data were used for
training and test, and the accuracy of using the model to predict the
trend of HDGC was above 80%. It is demonstrated that BP neural
network for predicating diseases in HDGC has a particularly
objectivity and practicality, thus it can be spread to other aquiculture
disease.
Abstract: There have been various methods created based on the regression ideas to resolve the problem of data set containing censored observations, i.e. the Buckley-James method, Miller-s method, Cox method, and Koul-Susarla-Van Ryzin estimators. Even though comparison studies show the Buckley-James method performs better than some other methods, it is still rarely used by researchers mainly because of the limited diagnostics analysis developed for the Buckley-James method thus far. Therefore, a diagnostic tool for the Buckley-James method is proposed in this paper. It is called the renovated Cook-s Distance, (RD* i ) and has been developed based on the Cook-s idea. The renovated Cook-s Distance (RD* i ) has advantages (depending on the analyst demand) over (i) the change in the fitted value for a single case, DFIT* i as it measures the influence of case i on all n fitted values Yˆ∗ (not just the fitted value for case i as DFIT* i) (ii) the change in the estimate of the coefficient when the ith case is deleted, DBETA* i since DBETA* i corresponds to the number of variables p so it is usually easier to look at a diagnostic measure such as RD* i since information from p variables can be considered simultaneously. Finally, an example using Stanford Heart Transplant data is provided to illustrate the proposed diagnostic tool.
Abstract: In this study, the effects of machining parameters on
specific energy during surface grinding of 6061Al-SiC35P
composites are investigated. Vol% of SiC, feed and depth of cut were
chosen as process variables. The power needed for the calculation of
the specific energy is measured from the two watt meter method.
Experiments are conducted using standard RSM design called Central
composite design (CCD). A second order response surface model was
developed for specific energy. The results identify the significant
influence factors to minimize the specific energy. The confirmation
results demonstrate the practicability and effectiveness of the
proposed approach.
Abstract: This study proposes a basic molecular formula for all
proteins. A total of 10,739 proteins belonging to 9 different protein
groups classified on the basis of their functions were selected
randomly. They included enzymes, storage proteins, hormones,
signalling proteins, structural proteins, transport proteins,
immunoglobulins or antibodies, motor proteins and receptor proteins.
After obtaining the protein molecular formula using the ProtParam
tool, the H/C, N/C, O/C, and S/C ratios were determined for each
randomly selected sample. In this case, H, N, O, and S coefficients
were specified per carbon atom. Surprisingly, the results
demonstrated that H, N, O, and S coefficients for all 10,739 proteins
are similar and highly correlated. This study demonstrates that
despite differences in the structure and function, all known proteins
have a similar basic molecular formula CnH1.58 ± 0.015nN0.28 ± 0.005nO0.30
± 0.007nS0.01 ± 0.002n. The total correlation between all coefficients was
found to be 0.9999.
Abstract: This paper proposes an effective adaptation learning
algorithm based on artificial neural networks for speed control of an
induction motor assumed to operate in a high-performance drives
environment. The structure scheme consists of a neural network
controller and an algorithm for changing the NN weights in order that
the motor speed can accurately track of the reference command. This
paper also makes uses a very realistic and practical scheme to
estimate and adaptively learn the noise content in the speed load
torque characteristic of the motor. The availability of the proposed
controller is verified by through a laboratory implementation and
under computation simulations with Matlab-software. The process is
also tested for the tracking property using different types of reference
signals. The performance and robustness of the proposed control
scheme have evaluated under a variety of operating conditions of the
induction motor drives. The obtained results demonstrate the
effectiveness of the proposed control scheme system performances,
both in steady state error in speed and dynamic conditions, was found
to be excellent and those is not overshoot.
Abstract: Automated discovery of hierarchical structures in
large data sets has been an active research area in the recent past.
This paper focuses on the issue of mining generalized rules with crisp
hierarchical structure using Genetic Programming (GP) approach to
knowledge discovery. The post-processing scheme presented in this
work uses flat rules as initial individuals of GP and discovers
hierarchical structure. Suitable genetic operators are proposed for the
suggested encoding. Based on the Subsumption Matrix(SM), an
appropriate fitness function is suggested. Finally, Hierarchical
Production Rules (HPRs) are generated from the discovered
hierarchy. Experimental results are presented to demonstrate the
performance of the proposed algorithm.
Abstract: As a tool for human spatial cognition and thinking, the map has been playing an important role. Maps are perhaps as fundamental to society as language and the written word. Economic and social development requires extensive and in-depth understanding of their own living environment, from the scope of the overall global to urban housing. This has brought unprecedented opportunities and challenges for traditional cartography . This paper first proposed the concept of scaleless-map and its basic characteristics, through the analysis of the existing multi-scale representation techniques. Then some strategies are presented for automated mapping compilation. Taking into account the demand of automated map compilation, detailed proposed the software - WJ workstation must have four technical features, which are generalization operators, symbol primitives, dynamically annotation and mapping process template. This paper provides a more systematic new idea and solution to improve the intelligence and automation of the scaleless cartography.
Abstract: Gas chromatography (GC) is the most widely used
technique in analytical chemistry. However, GC has high initial cost
and requires frequent maintenance. This paper examines the
feasibility and potential of using a neural network model as an
alternative whenever GC is unvailable. It can also be part of system
verification on the performance of GC for preventive maintenance
activities. It shows the performance of MultiLayer Perceptron (MLP)
with Backpropagation structure. Results demonstrate that neural
network model when trained using this structure provides an
adequate result and is suitable for this purpose. cm.