Sustainability ranking of desalination plants using Mamdani Fuzzy Logic Inference Systems

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability

Learning in Description Logics with Fuzzy Concrete Domains

Description Logics (DLs) are a family of logic-based Knowledge Representation (KR) formalisms, which are particularly suitable for representing incomplete yet precise structured knowledge. Several fuzzy extensions of DLs have been proposed in the KR field in order to handle imprecise knowledge which is particularly pervading in those domains where entities could be better described in natural language. Among the many approaches to fuzzification in DLs, a simple yet interesting one involves the use of fuzzy concrete domains. In this paper, we present a method for learning within the KR framework of fuzzy DLs. The method induces fuzzy DL inclusion axioms from any crisp DL knowledge base. Notably, the induced axioms may contain fuzzy concepts automatically generated from numerical concrete domains during the learning process. We discuss the results obtained on a popular learning problem in comparison with state-of-the-art DL learning algorithms, and on a test bed in order to evaluate the classification performance

Software Reuse across Robotic Platforms: Limiting the effects of diversity

Robots have diverse capabilities and complex interactions with their environment. Software development for robotic platforms is time consuming due to the complex nature of the tasks to be performed. Such an environment demands sound software engineering practices to produce high quality software. However software engineering in the robotics domain fails to facilitate any significant level of software reuse or portability. This paper identifies the major issues limiting software reuse in the robotics domain. Lack of standardisation, diversity of robotic platforms, and the subtle effects of environmental interaction all contribute to this problem. It is then shown that software components, fuzzy logic, and related techniques can be used together to address this problem. While complete software reuse is not possible, it is demonstrated that significant levels of software reuse can be obtained. Without an acceptable level of reuse or portability, software engineering in the robotics domain will not be able to meet the demands of a rapidly developing field. The work presented in this paper demonstrates a method for supporting software reuse across robotic platforms and hence facilitating improved software engineering practices