Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.Comment: This paper is submitted to IEEE WCCI 2018 Conference for revie

Knowledge-based approach to risk analysis in the customs domain

The aim of this PhD project is to develop a fuzzy knowledge-based approach in support of risk analysis in the Customs domain. Focusing upon risk management and risk analysis in the Customs domain, this thesis explores the relationship of risk with uncertainty, fuzziness, vagueness, and imprecise knowledge and it analyses state of the art detection techniques for fraud and risk. Special focus is given to fuzzy logic, ontological engineering, and semantic modelling considering aspects such as the importance of human knowledge and semantic knowledge in the context of risk analysis for the Customs domain. An approach is presented combining the fuzzy modelling and reasoning with semantic modelling and ontologies. Fuzzy modelling and reasoning is explored in the context of risk analysis and detection in order to examine approximate human reasoning based on human knowledge. Ontologies and semantic modelling are explored as an approach to represent domain knowledge and concepts. The purpose is to enable easier communication and understanding as well as interoperability. Risk management is broader, multi-dimensional process involving a number of task, activities, and practises. The presented approach is focused on examining the analysis and detection of the risk, based on the outputs of the risk management process with the use of ontologies and fuzzy rule-based reasoning. An ontological architecture is developed in the context of the presented approach. It is considered that such architecture is possible to enable modularity, maintainability, re-usability, and extensibility and can also be extended or integrated with other ontologies. In addition, examples are discussed to illustrate representation of concepts at various levels (generic or specific) and the modelling of various semantics. Furthermore, fuzzy modelling and reasoning are investigated. This investigation consists of literature research and the use of a generic research prototype (examination of Mamdani and Sugeno model types). From theoretical research, fuzzy logic enables the expression of human knowledge with linguistic terms and it could simulate human reasoning in the context of risk analysis and detection. In addition, Hierarchical Fuzzy Systems (HFS) or Hybrid Hierarchical Fuzzy Controllers (HHFC) approaches can be used to manage complexity especially for complex domains. Linguistic fuzzy modelling (LFM) is an aspect that should be considered during fuzzy modelling. From the generic research prototype, fuzzy modelling with the use of ontologies is demonstrated together with their integration in the context of fuzzy rule-based reasoning. It is also considered that Mamdani type of fuzzy models is easier to express human knowledge since the output can be expressed with linguistic terms. However, Sugeno type of fuzzy model could be used from adaptive techniques for optimisation purposes

Entrainment Promoting Robot Based on Physiological Signals towards Human Cooperation

東京都立大学Tokyo Metropolitan University博士（情報科学）doctoral thesi

The Economic Application Approach of Fuzzy Logic Controller Type I and II for Second Order Linear Systems

During the last decades, Fuzzy Logic Controller (FLC) has been studied in many researchers. In this paper, the applications of FLC for second order linear systems are reviewed. However, FLC has a good performance for nonlinear systems as well. The main focus of this research is evaluating the FLC performance for linear case study. Also a comparison between FLC and PI controller has been studied. Moreover, both fuzzy type I and II are applied for evaluating the system performance. All the systems are reviews from the economic perspective. It means that these methods tried to decrease the cost function of the system. All the simulation and results are done in MATLAB environment

The Economic Application Approach of Fuzzy Logic Controller Type I and II for Second Order Linear Systems

During the last decades, Fuzzy Logic Controller (FLC) has been studied in many researchers. In this paper, the applications of FLC for second order linear systems are reviewed. However, FLC has a good performance for nonlinear systems as well. The main focus of this research is evaluating the FLC performance for linear case study. Also a comparison between FLC and PI controller has been studied. Moreover, both fuzzy type I and II are applied for evaluating the system performance. All the systems are reviews from the economic perspective. It means that these methods tried to decrease the cost function of the system. All the simulation and results are done in MATLAB environment