Based on a collaboration with NTU Singapore, a new paper on leveraging the more faithful tracking of input uncertainty in the context of Quadcopter Unmanned Aerial Vehicle (UAV) control has been accepted for publication in the IEEE/ASME Transactions on Mechatronics.
An early access copy is available via the DOI here:
"Input Uncertainty Sensitivity Enhanced Non-Singleton Fuzzy Logic Controllers for Long-Term Navigation of Quadrotor UAVs": https://doi.org/10.1109/TMECH.2018.2810947
fuzzycreator is a toolkit for automatic generation and analysis of fuzzy sets from data. It facilitates the creation of both conventional and non-conventional (non-normal and non-convex) type-1, interval type-2 and general type-2 (zSlices-based) fuzzy sets from data. These fuzzy sets may then be analysed and compared through a series of tools and measures (included in the toolkit), such as evaluating their similarity and distance.
It is now available through the LUCID website at www.lucidresearch.org/software.html
Detailed documentation is available within the toolkit and a high-level overview will be available soon.
To appear in January but available open access here: https://www.researchgate.net/publication/308076698_Interval_Type-2_Fuzzy_Decision_Making
The paper by Saeed Alqahtani and Bob John has just been accepted for presentation at SSCI 2016.
Abstract—The use of Internet has been increasing day by day and the internet traffic is exponentially increasing. The services providers such as web services providers, email services providers, and cloud service providers have to deal with millions of users per second; and thus, the level of threats to their growing networks is also very high. To deal with this much number of users is a big challenge but detection and prevention of such kinds of threats is even more challenging and vital. This is due to the fact that those threats might cause a severe loss to the service providers in terms of privacy leakage or unavailability of the services to the users. To incorporate this issue, several Intrusion Detections Systems (IDS) have been developed that differ in their detection capabilities, performance and accuracy. In this study, we have used SNORT and SURICATA as well-known IDS systems that are used worldwide. The aim of this paper is to analytically compare the functionality, working and the capability of these two IDS systems in order to detect the intrusions and different kinds of cyber-attacks within M yCloud network. Furthermore, this study also proposes a Fuzzy-Logic engine based on these two IDSs in order to enhances the performance and accuracy of these two systems in terms of increased accuracy, specificity, sensitivity and reduced false alarms. Several experiments in this compatrative study have been conducted by using and testing ISCX dataset, which results that fuzzy logic based IDS outperforms IDS alone whereas FL-SnortIDS system outperforms FL-SuricataIDS.
You can download here
The paper "Measuring Agreement on Linguistic Expressions in Medical Treatment Scenarios" by Javier Navarro, Christian Wagner, Uwe Aickelin, Lynsey Green and Robert Ashford has been accepted to the 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016) which will be held in Athens, Greece in December 2016. This paper comes from a study made in collaboration with the East Midlands Sarcoma Service, Nottingham University Hospitals.
Tajul and Chao have released a new version of FuzzyR! https://CRAN.R-project.org/package=FuzzyR
Bob John has just had an article accepted in the International Journal of Approximate Reasoning.
This paper concerns itself with decision making under uncertainty and the
consideration of risk. Type-1 fuzzy logic by its (essentially) crisp nature
is limited in modelling decision making as there is no uncertainty in the
membership function. We are interested in the role that interval type-2 fuzzy
sets might play in enhancing decision making. Previous work by Bellman and
Zadeh considered decision making to be based on goals and constraint. They
deployed type-1 fuzzy sets. This paper extends this notion to interval type-2
fuzzy sets and presents a new approach to using interval type-2 fuzzy sets
in a decision making situation taking into account the risk associated with
the decision making. The explicit consideration of risk levels increases the
solution space of the decision process and thus enables better decisions. We
explain the new approach and provide two examples to show how this new
Runners-up in both Best Regular and Best Student Paper in Fuzz-IEEE 2016!
At this years WCCI 2016 conference, LUCID papers came second in both the Best Student and Best Regular Paper category! While it is not the top spot, we are very proud for the work to be recognised in the face of strong competition of 350 accepted papers. The papers are:
2nd in Best Student Paper Category:
Measuring the Similarity between zSlices General Type-2 Fuzzy Sets with Non-Normal Secondary
Josie McCulloch and Christian Wagner
University of Nottingham, United Kingdom
2nd in Best Regular Paper Category:
A Similarity-based Inference Engine for Non-Singleton Fuzzy Logic Systems
Christian Wagner, Amir Pourabdollah, Josie McCulloch, Robert John and Jonathan M. Garibaldi
University of Nottingham, United Kingdom
The representation of type-2 fuzzy sets is a hot research topic. Christian Wagner and Hani Hagras introduced the notion of zslices. Mendel also introduced alpha planes. Bob John's paper with Hussam Hamrawi and Simon Coupland on type-2 alpha cuts has just been accepted in IEEE Transactions on Fuzzy Systems. The paper is here. Type-2 fuzzy logic systems make use of type-2 fuzzy sets. To be able to deliver useful type-2 fuzzy logic applications we need to be able to perform meaningful operations on these sets. These operations should also be practically tractable. However, type-2 fuzzy sets suffer the shortcoming of being complex by definition. Indeed, the third dimension, which is the source of extra parameters, is in itself the origin of extra computational cost. The quest for a representation that allow practical systems to be implemented is the motivation for our work. In this paper we define the alpha-cut decomposition theorem for type- 2 fuzzy sets which is a new representation analogous to the alpha-cut representation of type-1 fuzzy sets and the extension principle.