Abstract: In the last decades, a number of robust fuzzy clustering algorithms have been proposed to partition data sets affected by noise and outliers. Robust fuzzy C-means (robust-FCM) is certainly one of the most known among these algorithms. In robust-FCM, noise is modeled as a separate cluster and is characterized by a prototype that has a constant distance δ from all data points. Distance δ determines the boundary of the noise cluster and therefore is a critical parameter of the algorithm. Though some approaches have been proposed to automatically determine the most suitable δ for the specific application, up to today an efficient and fully satisfactory solution does not exist. The aim of this paper is to propose a novel method to compute the optimal δ based on the analysis of the distribution of the percentage of objects assigned to the noise cluster in repeated executions of the robust-FCM with decreasing values of δ . The extremely encouraging results obtained on some data sets found in the literature are shown and discussed.
Abstract: The primary objective of the paper is to propose a new method for solving assignment problem under uncertain situation. In the classical assignment problem (AP), zpqdenotes the cost for assigning the qth job to the pth person which is deterministic in nature. Here in some uncertain situation, we have assigned a cost in the form of composite relative degree Fpq instead of and this replaced cost is in the maximization form. In this paper, it has been solved and validated by the two proposed algorithms, a new mathematical formulation of IVIF assignment problem has been presented where the cost has been considered to be an IVIFN and the membership of elements in the set can be explained by positive and negative evidences. To determine the composite relative degree of similarity of IVIFS the concept of similarity measure and the score function is used for validating the solution which is obtained by Composite relative similarity degree method. Further, hypothetical numeric illusion is conducted to clarify the method’s effectiveness and feasibility developed in the study. Finally, conclusion and suggestion for future work are also proposed.
Abstract: Many people regard food events as part of gastronomic tourism and important in enhancing visitors’ experiences. Realizing the importance and contribution of food events to a country’s economy, the Malaysia government is undertaking greater efforts to promote such tourism activities to international tourists. Among other food events, the Ramadan bazaar is a unique food culture event, which receives significant attention from the Malaysia Ministry of Tourism. This study reports the empirical investigation into the international tourists’ perceptions, attraction towards the Ramadan bazaar and willingness in disseminating the information. Using the Ramadan bazaar at Kampung Baru, Kuala Lumpur as the data collection setting, results revealed that the Ramadan bazaar attributes (food and beverages, events and culture) significantly influenced the international tourist attraction to such a bazaar. Their high level of experience and satisfaction positively influenced their willingness to disseminate information. The positive response among the international tourists indicates that the Ramadan bazaar as gastronomic tourism can be used in addition to other tourism products as a catalyst to generate and boost the local economy. The related authorities that are closely associated with the tourism industry therefore should not ignore this indicator but continue to take proactive action in promoting the gastronomic event as one of the major tourist attractions.
Abstract: In this paper, we discuss the egalitarianism solution (ES) and center-of-gravity of the imputation-set value (CIV) for bicooperative games, which can be seen as the extensions of the solutions for traditional games given by Dutta and Ray [1] and Driessen and Funaki [2]. Furthermore, axiomatic systems for the given values are proposed. Finally, a numerical example is offered to illustrate the player ES and CTV.
Abstract: This paper presents an application of the improved
QFD method for determining the specifications of kitchen utensils
rack. By using the improved method, the subjective nature in original
QFD was reduced; particularly in defining the relationship between
customer requirement and engineering characteristics. The regression
analysis that was used for obtaining the relationship functions
between customer requirement and engineering characteristics also
accommodated the inaccurateness of the competitive assessment
results. The improved method which is represented in the form of a
mathematical model had become a formal guidance to allocate the
resource for improving the specifications of kitchen utensils rack.
The specifications obtained had led to the achievement of the highest
feasible customer satisfaction.
Abstract: Wind turbine should be controlled to capture maximum
wind energy and to prevent the turbine from being stalled. To achieve
those two goals, wind turbine controller controls torque on generator
and limits input torque from wind by pitching blade. Usually, torque
on generator is controlled using inverter torque set point. However,
verifying a control algorithm in actual wind turbine needs a lot of
efforts to test and the actual wind turbine could be broken while testing
a control algorithm. So, several software have developed and
commercialized by Garrad Hassan, GH Bladed, and NREL, FAST.
Even though, those programs can simulate control system modeling
with subroutines or DLLs. However, those simulation programs are
not able to emulate detailed generator or PMSG. In this paper, a small
size wind turbine simulator is developed with induction motor and
small size drive train. The developed system can simulate wind turbine
control algorithm in the region before rated power.
Abstract: This paper considers the problem of scheduling maintenance actions for identical aircraft gas turbine engines. Each one of the turbines consists of parts which frequently require replacement. A finite inventory of spare parts is available and all parts are ready for replacement at any time. The inventory consists of both new and refurbished parts. Hence, these parts have different field lives. The goal is to find a replacement part sequencing that maximizes the time that the aircraft will keep functioning before the inventory is replenished. The problem is formulated as an identical parallel machine scheduling problem where the minimum completion time has to be maximized. Two models have been developed. The first one is an optimization model which is based on a 0-1 linear programming formulation, while the second one is an approximate procedure which consists in decomposing the problem into several two-machine subproblems. Each subproblem is optimally solved using the first model. Both models have been implemented using Lingo and have been tested on two sets of randomly generated data with up to 150 parts and 10 turbines. Experimental results show that the optimization model is able to solve only instances with no more than 4 turbines, while the decomposition procedure often provides near-optimal solutions within a maximum CPU time of 3 seconds.
Abstract: This paper concerns a formal model to help the
simulation of agent societies where institutional roles and
institutional links can be specified operationally. That is, this paper
concerns institutional roles that can be specified in terms of a minimal behavioral capability that an agent should have in order to
enact that role and, thus, to perform the set of institutional functions that role is responsible for. Correspondingly, the paper concerns
institutional links that can be specified in terms of a minimal
interactional capability that two agents should have in order to, while
enacting the two institutional roles that are linked by that institutional
link, perform for each other the institutional functions supported by
that institutional link. The paper proposes a cognitive architecture
approach to institutional roles and institutional links, that is, an approach in which a institutional role is seen as an abstract cognitive
architecture that should be implemented by any concrete agent (or set of concrete agents) that enacts the institutional role, and in which
institutional links are seen as interactions between the two abstract
cognitive agents that model the two linked institutional roles. We
introduce a cognitive architecture for such purpose, called the
Institutional BCC (IBCC) model, which lifts Yoav Shoham-s BCC
(Beliefs-Capabilities-Commitments) agent architecture to social
contexts. We show how the resulting model can be taken as a means
for a cognitive architecture account of institutional roles and
institutional links of agent societies. Finally, we present an example
of a generic scheme for certain fragments of the social organization
of agent societies, where institutional roles and institutional links are
given in terms of the model.
Abstract: The purpose of this study was to investigate the effect
of combining Real Experimentation (RE) With Virtual
Experimentation (VE) on students- conceptual understanding of
photo electric effect. To achieve this, a pre–post comparison study
design was used that involved 46 undergraduate students. Two
groups were set up for this study. Participants in the control group
used RE to learn photo electric effect, whereas, participants in the
experimental group used RE in the first part of the curriculum and
VE in another part. Achievement test was given to the groups
before and after the application as pre-test and post test. The
independent samples t- test, one way Anova and Tukey HSD test
were used for testing the data obtained from the study.
According to the results of analyzes, the experimental group
was found more successful than the control group.
Abstract: In conventional reliability assessment, the reliability data of system components are treated as crisp values. The collected data have some uncertainties due to errors by human beings/machines or any other sources. These uncertainty factors will limit the understanding of system component failure due to the reason of incomplete data. In these situations, we need to generalize classical methods to fuzzy environment for studying and analyzing the systems of interest. Fuzzy set theory has been proposed to handle such vagueness by generalizing the notion of membership in a set. Essentially, in a Fuzzy Set (FS) each element is associated with a point-value selected from the unit interval [0, 1], which is termed as the grade of membership in the set. A Vague Set (VS), as well as an Intuitionistic Fuzzy Set (IFS), is a further generalization of an FS. Instead of using point-based membership as in FS, interval-based membership is used in VS. The interval-based membership in VS is more expressive in capturing vagueness of data. In the present paper, vague set theory coupled with conventional Lambda-Tau method is presented for reliability analysis of repairable systems. The methodology uses Petri nets (PN) to model the system instead of fault tree because it allows efficient simultaneous generation of minimal cuts and path sets. The presented method is illustrated with the press unit of the paper mill.
Abstract: At present, increased concerns about global
environmental problems have magnified the importance of
sustainability management. To move towards sustainability,
companies need to look at everything from a holistic perspective in
order to understand the interconnections between economic growth
and environmental and social sustainability. This paper aims to gain
an understanding of key determinants that drive sustainability
management and barriers that hinder its development. It employs
semi-structured interviews with key informants, site observation and
documentation. The informants are production, marketing and
environmental managers of the leading wine producer, which aims to
become an Asia-s leader in wine & wine based products. It is found
that corporate image and top management leadership are the primary
factors influencing the adoption of sustainability management. Lack
of environmental knowledge and inefficient communication are
identified as barriers.
Abstract: This paper focuses on creating a component model of information system under uncertainty. The paper identifies problem in current approach of component modeling and proposes fuzzy tool, which will work with vague customer requirements and propose components of the resulting component model. The proposed tool is verified on specific information system and results are shown in paper. After finding suitable sub-components of the resulting component model, the component model is visualised by tool.
Abstract: Privacy issues commonly discussed among
researchers, practitioners, and end-users in pervasive healthcare.
Pervasive healthcare systems are applications that can support
patient-s need anytime and anywhere. However, pervasive healthcare
raises privacy concerns since it can lead to situations where patients
may not be aware that their private information is being shared and
becomes vulnerable to threat. We have systematically analyzed the
privacy issues and present a summary in tabular form to show the
relationship among the issues. The six issues identified are medical
information misuse, prescription leakage, medical information
eavesdropping, social implications for the patient, patient difficulties
in managing privacy settings, and lack of support in designing
privacy-sensitive applications. We narrow down the issues and chose
to focus on the issue of 'lack of support in designing privacysensitive
applications' by proposing a privacy-sensitive architecture
specifically designed for pervasive healthcare monitoring systems.
Abstract: Basel III (or the Third Basel Accord) is a global
regulatory standard on bank capital adequacy, stress testing and
market liquidity risk agreed upon by the members of the Basel
Committee on Banking Supervision in 2010-2011, and scheduled to
be introduced from 2013 until 2018. Basel III is a comprehensive set
of reform measures. These measures aim to; (1) improve the banking
sector-s ability to absorb shocks arising from financial and economic
stress, whatever the source, (2) improve risk management and
governance, (3) strengthen banks- transparency and disclosures.
Similarly the reform target; (1) bank level or micro-prudential,
regulation, which will help raise the resilience of individual banking
institutions to periods of stress. (2) Macro-prudential regulations,
system wide risk that can build up across the banking sector as well
as the pro-cyclical implication of these risks over time. These two
approaches to supervision are complementary as greater resilience at
the individual bank level reduces the risk system wide shocks.
Macroeconomic impact of Basel III; OECD estimates that the
medium-term impact of Basel III implementation on GDP growth is
in the range -0,05 percent to -0,15 percent per year. On the other hand
economic output is mainly affected by an increase in bank lending
spreads as banks pass a rise in banking funding costs, due to higher
capital requirements, to their customers. Consequently the estimated
effects on GDP growth assume no active response from monetary
policy. Basel III impact on economic output could be offset by a
reduction (or delayed increase) in monetary policy rates by about 30
to 80 basis points. The aim of this paper is to create a framework
based on the recent regulations in order to prevent financial crises.
Thus the need to overcome the global financial crisis will contribute
to financial crises that may occur in the future periods. In the first
part of the paper, the effects of the global crisis on the banking
system examine the concept of financial regulations. In the second
part; especially in the financial regulations and Basel III are analyzed.
The last section in this paper explored the possible consequences of
the macroeconomic impacts of Basel III.
Abstract: This paper presents the use of a newly created network
structure known as a Self-Delaying Dynamic Network (SDN) to
create a high resolution image from a set of time stepped input
frames. These SDNs are non-recurrent temporal neural networks
which can process time sampled data. SDNs can store input data
for a lifecycle and feature dynamic logic based connections between
layers. Several low resolution images and one high resolution image
of a scene were presented to the SDN during training by a Genetic
Algorithm. The SDN was trained to process the input frames in order
to recreate the high resolution image. The trained SDN was then used
to enhance a number of unseen noisy image sets. The quality of high
resolution images produced by the SDN is compared to that of high
resolution images generated using Bi-Cubic interpolation. The SDN
produced images are superior in several ways to the images produced
using Bi-Cubic interpolation.
Abstract: Comparison of two approaches for the simulation of
the dynamic behaviour of a permanent magnet linear actuator is
presented. These are full coupled model, where the electromagnetic
field, electric circuit and mechanical motion problems are solved
simultaneously, and decoupled model, where first a set of static
magnetic filed analysis is carried out and then the electric circuit and
mechanical motion equations are solved employing bi-cubic spline
approximations of the field analysis results. The results show that the
proposed decoupled model is of satisfactory accuracy and gives more
flexibility when the actuator response is required to be estimated for
different external conditions, e.g. external circuit parameters or
mechanical loads.
Abstract: Embedded systems need to respect stringent real
time constraints. Various hardware components included in such
systems such as cache memories exhibit variability and therefore
affect execution time. Indeed, a cache memory access from an
embedded microprocessor might result in a cache hit where the
data is available or a cache miss and the data need to be fetched
with an additional delay from an external memory. It is therefore
highly desirable to predict future memory accesses during
execution in order to appropriately prefetch data without incurring
delays. In this paper, we evaluate the potential of several artificial
neural networks for the prediction of instruction memory
addresses. Neural network have the potential to tackle the nonlinear
behavior observed in memory accesses during program
execution and their demonstrated numerous hardware
implementation emphasize this choice over traditional forecasting
techniques for their inclusion in embedded systems. However,
embedded applications execute millions of instructions and
therefore millions of addresses to be predicted. This very
challenging problem of neural network based prediction of large
time series is approached in this paper by evaluating various neural
network architectures based on the recurrent neural network
paradigm with pre-processing based on the Self Organizing Map
(SOM) classification technique.
Abstract: The objectives of this research are to produce
prototype coconut oil based solvent offset printing inks and to
analyze a basic quality of printing work derived from coconut oil
based solvent offset printing inks, by mean of bringing coconut oil
for producing varnish and bringing such varnish to produce black
offset printing inks. Then, analysis of qualities i.e. CIELAB value,
density value, and dot gain value of printing work from coconut oil
based solvent offset printing inks which printed on gloss-coated
woodfree paper weighs 130 grams were done. The research result of
coconut oil based solvent offset printing inks indicated that the
suitable varnish formulation is using 51% of coconut oil, 36% of
phenolic resin, and 14% of solvent oil 14%, while the result of
producing black offset ink displayed that the suitable formula of
printing ink is using varnish mixed with 20% of coconut oil, and the
analyzing printing work of coconut oil based solvent offset printing
inks which printed on paper, the results were as follows: CIELAB
value of black offset printing ink is at L* = 31.90, a* = 0.27, and b* =
1.86, density value is at 1.27 and dot gain value was high at mid tone
area of image area.
Abstract: This paper presents an application of level sets for the segmentation of abdominal and thoracic aortic aneurysms in CTA
datasets. An important challenge in reliably detecting aortic is the
need to overcome problems associated with intensity
inhomogeneities. Level sets are part of an important class of methods
that utilize partial differential equations (PDEs) and have been extensively applied in image segmentation. A kernel function in the
level set formulation aids the suppression of noise in the extracted
regions of interest and then guides the motion of the evolving contour
for the detection of weak boundaries. The speed of curve evolution
has been significantly improved with a resulting decrease in segmentation time compared with previous implementations of level
sets, and are shown to be more effective than other approaches in
coping with intensity inhomogeneities. We have applied the Courant
Friedrichs Levy (CFL) condition as stability criterion for our algorithm.
Abstract: In this paper, a mathematical model of human immunodeficiency
virus (HIV) is utilized and an optimization problem is
proposed, with the final goal of implementing an optimal 900-day
structured treatment interruption (STI) protocol. Two type of commonly
used drugs in highly active antiretroviral therapy (HAART),
reverse transcriptase inhibitors (RTI) and protease inhibitors (PI), are
considered. In order to solving the proposed optimization problem an
adaptive memetic algorithm with population management (AMAPM)
is proposed. The AMAPM uses a distance measure to control the
diversity of population in genotype space and thus preventing the
stagnation and premature convergence. Moreover, the AMAPM uses
diversity parameter in phenotype space to dynamically set the population
size and the number of crossovers during the search process.
Three crossover operators diversify the population, simultaneously.
The progresses of crossover operators are utilized to set the number
of each crossover per generation. In order to escaping the local optima
and introducing the new search directions toward the global optima,
two local searchers assist the evolutionary process. In contrast to
traditional memetic algorithms, the activation of these local searchers
is not random and depends on both the diversity parameters in
genotype space and phenotype space. The capability of AMAPM in
finding optimal solutions compared with three popular metaheurestics
is introduced.