Multi-objective fuzzy rule-based prediction and uncertainty quantification of aircraft taxi time

The ever growing air traffic demand and highly connected air transportation networks put considerable pressure for the sector to optimise air traffic management (ATM) related performances and develop robust ATM systems. Recent efforts made in accurate aircraft taxi time prediction have shown significant advancement in generating more efficient taxi routes and schedules, benefiting other key airside operations, such as runway sequencing and gate assignment. However, little study has been devoted to quantification of uncertainty associated with taxiing aircraft. Routes and schedules generated based on deterministic and accurate taxi time prediction for an aircraft may not be resilient under uncertainties due to factors such as varying weather conditions, operational scenarios and pilot behaviours, impairing system-wide performance as taxi delays can propagate throughout the network. Therefore, the primary aim of this paper is to utilise multi-objective fuzzy rule-based systems to better quantify such uncertainties based on historic aircraft taxiing data. Preliminary results reveals that the proposed approach can capture uncertainty in a more informative way, and hence represents a promising tool to further develop robust taxi planning to reduce delays due to uncertain taxi times

Bibliometric Analysis of Firefly Algorithm Applications in the Field of Wireless Sensor Networks

Wireless Sensor Network is a network of wireless sensor nodes that are capable of sensing information from their surroundings and transmit the sensed information to data collection point known as a base station. Applications of wireless sensor networks are large in number and forest fire detection, landslide monitoring, etc. are few applications to note. The research challenges in wireless sensor networks is the transmission of data from the sensor node to the base station in an energy-efficient manner and network life prolongation. Cluster-based routing techniques are extensively adopted to address this research challenge. Researchers have used different metaheuristic and soft computing techniques for designing such energy-efficient routing techniques. In the literature, a lot of survey article on cluster-based routing methods are available, but there is no bibliometric analysis conducted so far. Hence in this research article, bibliometric study with the focus on the firefly algorithm and its applications in wireless sensor network is undertaken. The purpose of this article is to explore the nature of research conducted concerning to authors, the connection between keywords, the importance of journals and scope for further research in soft computing based clustered routing methods. A detailed bibliometric analysis is carried out by collecting the details of published articles from the Scopus database. In this article, the collected data is articulated in terms of yearly document statistics, key affiliations of authors, contributing geographical locations, subject area statistics, author-keyword mapping, and many more essential aspects of bibliometric analysis. The conducted study helped in understanding that there is a vast scope for the research community to perform research work concerning firefly algorithm applications in the field of wireless sensor networks

fuzzycreator: a python-based toolkit for automatically generating and analysing data-driven fuzzy sets

This paper presents a toolkit for automatic generation and analysis of fuzzy sets (FS) from data. Toolkits are vital for the wider dissemination, accessibility and implementation of theoretic work and applications on FSs. There are currently several toolkits in the literature that focus on knowledge representation and fuzzy inference, but there are few that focus on the automatic generation and comparison of FSs. As there are several methods of constructing FSs from data, it is important to have the tools to use these methods. This paper presents an open-source, python-based toolkit, named fuzzycreator, that facilitates the creation of both conventional and non-conventional (non-normal and non-convex) type-1, interval type-2 and general type-2 FSs from data. These FSs may then be analysed and compared through a series of tools and measures (included in the toolkit), such as evaluating their similarity and distance. An overview of the key features of the toolkit are given and demonstrations which provide rapid access to cutting-edge methodologies in FSs to both expert and non-expert users

SyFSeL: generating synthetic fuzzy sets made simple

Empirical tests can help determine if methods developed for fuzzy sets work correctly. However, finding a large enough data set with suitable properties to conduct thorough tests can be challenging. This paper presents a new library named SyFSeL (Synthetic Fuzzy Set Library) which automatically generates synthetic fuzzy sets with specified characteristics and fuzzy set type. SyFSeL generates as many sets as desired, with adjustable parameters to enable users to emulate real data. Generated fuzzy sets are exported so users can import them into their own fuzzy systems software. SyFSeL can also create graphical plots of the generated sets, examples of which are shown in this paper. The library is cross-platform and open-source under the GNU General Public License, and users are free to develop upon and adapt the code. However, SyFSeL has been designed so that no understanding of the code is required to use it

Jaya optimization algorithm with GPU acceleration

Optimization methods allow looking for an optimal value given a specific function within a constrained or unconstrained domain. These methods are useful for a wide range of scientific and engineering applications. Recently, a new optimization method called Jaya has generated growing interest because of its simplicity and efficiency. In this paper, we present the Jaya GPU-based parallel algorithms we developed and analyze both parallel performance and optimization performance using a well-known benchmark of unconstrained functions. Results indicate that parallel Jaya implementation achieves significant speed-up for all benchmark functions, obtaining speed-ups of up to 190×, without affecting optimization performance.This research was supported by the Spanish Ministry of Economy and Competitiveness under Grant TIN2015-66972-C5-4-R, co-financed by FEDER funds (MINECO/FEDER/UE)

Hierarchical Fuzzy Systems: Interpretability and Complexity

Hierarchical fuzzy systems (HFSs) have been regarded as a useful solution for overcoming the major issues in fuzzy logic systems (FLSs), i.e., rule explosion due to the increase in the number of input variables. In HFS, the standard FLS are reformed into a low-dimensional FLS subsystem network. Moreover, the rules in HFS usually have antecedents with fewer variables than the rules in standard FLS with equivalent functions, because the number of input variables in each subsystem is less. Consequently, HFSs manage to decrease rule explosion, which minimises complexity and improves model interpretability. Nevertheless, the issues related to the question of “Does the complexity reduction of HFSs that have multiple subsystems, layers and different topologies really improve their interpretability?” are not clear and persist. In this paper, a comparison focusing on interpretability and complexity is made between two HFS’ topologies: parallel and serial. A detailed measurement of the interpretability and complexity with different configurations for both topologies is provided. This comparative study aims to examine the correlation between interpretability and complexity in HFS

SyFSeL: generating synthetic fuzzy sets made simple

Empirical tests can help determine if methods developed for fuzzy sets work correctly. However, finding a large enough data set with suitable properties to conduct thorough tests can be challenging. This paper presents a new library named SyFSeL (Synthetic Fuzzy Set Library) which automatically generates synthetic fuzzy sets with specified characteristics and fuzzy set type. SyFSeL generates as many sets as desired, with adjustable parameters to enable users to emulate real data. Generated fuzzy sets are exported so users can import them into their own fuzzy systems software. SyFSeL can also create graphical plots of the generated sets, examples of which are shown in this paper. The library is cross-platform and open-source under the GNU General Public License, and users are free to develop upon and adapt the code. However, SyFSeL has been designed so that no understanding of the code is required to use it