5,308 research outputs found
A methodology for the selection of new technologies in the aviation industry
The purpose of this report is to present a technology selection methodology to
quantify both tangible and intangible benefits of certain technology
alternatives within a fuzzy environment. Specifically, it describes an
application of the theory of fuzzy sets to hierarchical structural analysis and
economic evaluations for utilisation in the industry. The report proposes a
complete methodology to accurately select new technologies. A computer based
prototype model has been developed to handle the more complex fuzzy
calculations. Decision-makers are only required to express their opinions on
comparative importance of various factors in linguistic terms rather than exact
numerical values. These linguistic variable scales, such as ‘very high’, ‘high’,
‘medium’, ‘low’ and ‘very low’, are then converted into fuzzy numbers, since it
becomes more meaningful to quantify a subjective measurement into a range rather
than in an exact value. By aggregating the hierarchy, the preferential weight of
each alternative technology is found, which is called fuzzy appropriate index.
The fuzzy appropriate indices of different technologies are then ranked and
preferential ranking orders of technologies are found. From the economic
evaluation perspective, a fuzzy cash flow analysis is employed. This deals
quantitatively with imprecision or uncertainties, as the cash flows are modelled
as triangular fuzzy numbers which represent ‘the most likely possible value’,
‘the most pessimistic value’ and ‘the most optimistic value’. By using this
methodology, the ambiguities involved in the assessment data can be effectively
represented and processed to assure a more convincing and effective decision-
making process when selecting new technologies in which to invest. The prototype
model was validated with a case study within the aviation industry that ensured
it was properly configured to meet the
An efficient -means-type algorithm for clustering datasets with incomplete records
The -means algorithm is arguably the most popular nonparametric clustering
method but cannot generally be applied to datasets with incomplete records. The
usual practice then is to either impute missing values under an assumed
missing-completely-at-random mechanism or to ignore the incomplete records, and
apply the algorithm on the resulting dataset. We develop an efficient version
of the -means algorithm that allows for clustering in the presence of
incomplete records. Our extension is called -means and reduces to the
-means algorithm when all records are complete. We also provide
initialization strategies for our algorithm and methods to estimate the number
of groups in the dataset. Illustrations and simulations demonstrate the
efficacy of our approach in a variety of settings and patterns of missing data.
Our methods are also applied to the analysis of activation images obtained from
a functional Magnetic Resonance Imaging experiment.Comment: 21 pages, 12 figures, 3 tables, in press, Statistical Analysis and
Data Mining -- The ASA Data Science Journal, 201
Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE
ANFIS is applicable in modeling of key parameters when investigating the performance and functionality of wireless networks. The need to save both capital and operational expenditure in the management of wireless networks cannot be over-emphasized. Automation of network operations is a veritable means of achieving the necessary reduction in CAPEX and OPEX. To this end, next generations networks such WiMAX and 3GPP LTE and LTE-Advanced provide support for self-optimization, self-configuration and self-healing to minimize human-to-system interaction and hence reap the attendant benefits of automation. One of the most important optimization tasks is load balancing as it affects network operation right from planning through the lifespan of the network. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practically implementable at the current state of technology. Furthermore, most of the techniques proposed employ iterative algorithm, which in itself is not computationally efficient. This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE. Three key performance indicators (i.e. number of satisfied user, virtual load and fairness distribution index) are used to adjust hysteresis task of load balancing
Comparison of fuzzy clustering methods in economic freedom ranking in Asia-Pacific
Economic freedom can be defined as freedom in which individuals can perform their economic activities freely without being exposed to the pressures and constraints. The aim of the studies on the classification of countries according to their economic freedoms is to determine the place of the countries in the world or in the continent where they are located. In this way, the status of the countries with sustainable growth and high welfare is determined. In this study, it is aimed to rank Asian countries according to economic freedom data. In contrast to many classifications and sorting studies, the present study attempts to determine the best sorting method by comparing multiple methods. As a result of the economic freedoms published by the Heritage Foundation every year, the conditions of Asian countries between 2015-2019 were determined. Fuzzy C-Means, Gath-Geva and Gustafson-Kessel methods, which are the three most commonly used methods, were used in the fuzzy clustering analysis. The results obtained from all fuzzy clustering methods were compared and interpreted with the results of the Heritage Foundation year by year. According to all analysis results, it can be said that the Fuzzy C-means method is more successful for Economic Freedom data and classification studies. According to the Fuzzy C-Means method, the three best Asian countries were Hong Kong, New Zealand and Australia respectivel
On the geometric mean method for incomplete pairwise comparisons
When creating the ranking based on the pairwise comparisons very often, we
face difficulties in completing all the results of direct comparisons. In this
case, the solution is to use the ranking method based on the incomplete PC
matrix. The article presents the extension of the well known geometric mean
method for incomplete PC matrices. The description of the methods is
accompanied by theoretical considerations showing the existence of the solution
and the optimality of the proposed approach.Comment: 15 page
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