Type-2 fuzzy ontology-based semantic knowledge for collision avoidance of autonomous underwater vehicles

Ali, Farman; Kim, Eun Kyoung; Kim, Yong-Gi

doi:10.1016/j.ins.2014.10.013

Cited by 75 publications

(31 citation statements)

References 40 publications

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“…For CBR systems, many studies have utilized fuzzy ontology for case base representation and fuzzy retrieval processes [21,48]. Alexopoulos et al [21] proposed a fuzzy ontology-based CBR system using fuzzy algebra.…”

Section: Regarding the Role Of Fuzzy Ontology In Cbrmentioning

confidence: 99%

“…Alexopoulos et al [21] proposed a fuzzy ontology-based CBR system using fuzzy algebra. Ali et al [48] proposed a type-2 fuzzy ontology-based CBR system for collision avoidance of autonomous underwater vehicles. Fuzzy ontology can enhance CBR in different ways such as physician can more easily define experience cases using natural-like language, cases can be indexed more efficiently, and fuzzy-semantic retrieval algorithms can be implemented.…”

Section: Regarding the Role Of Fuzzy Ontology In Cbrmentioning

confidence: 99%

“…Creating a fuzzy case-base ontology from a fuzzy case-base database is supported by methodologies [40], languages [63], tools [48], and reasoners [60]. These fuzzy ontologies add vagueness to the KI-CBR systems.…”

Section: The Fuzzy-semantic Case Representationmentioning

confidence: 99%

“…Second, checking correctness, accuracy, and completeness is typically manual [48]. Our Domain experts have validated the correctness, accuracy, and completeness of the built case-base fuzzy ontology.…”

Section: Evaluation Of the Proposed Cbr Systemmentioning

confidence: 99%

See 3 more Smart Citations

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

El–Sappagh

Elmogy

Riad

2015

Artificial Intelligence in Medicine

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Section: Regarding the Role Of Fuzzy Ontology In Cbrmentioning

confidence: 99%

Section: Regarding the Role Of Fuzzy Ontology In Cbrmentioning

confidence: 99%

Section: The Fuzzy-semantic Case Representationmentioning

confidence: 99%

Section: Evaluation Of the Proposed Cbr Systemmentioning

confidence: 99%

See 2 more Smart Citations

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

El–Sappagh

Elmogy

Riad

2015

Artificial Intelligence in Medicine

View full text Add to dashboard Cite

“…In another research, Mashadi and Majidi (2014) have proposed the global optimal path planning of an autonomous vehicle for overtaking a moving obstacle, by performing a double lanechange maneuver after detecting it in a proper distance ahead. Ali et al (2015) has proposed a method to prevent the underwater robots from colliding with obstacles. In this method, they used type-2 fuzzy and ontology-based semantic knowledge for simulation, identifying obstacles and avoiding collisions.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Unmanned Underwater Vehicle in 3D Space with Obstacles Using Spline-Imperialist Competitive Algorithm and Optimal Interval Type-2 Fuzzy Logic Controller

Zakeri

Farahat

Moezi

et al. 2016

Lat. Am. j. solids struct.

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In this research, generation of a short and smooth path in three-dimensional space with obstacles for guiding an Unmanned Underwater Vehicle (UUV) without collision is investigated. This is done by utilizing spline technique, in which the spline control points positions are determined by Imperialist Competitive Algorithm (ICA) in three-dimensional space such that the shortest possible path from the starting point to the target point without colliding with obstacles is achieved. Furthermore, for guiding the UUV in the generated path, an Interval Type-2 Fuzzy Logic Controller (IT2FLC), the coefficients of which are optimized by considering an objective function that includes quadratic terms of the input forces and state error of the system, is used. Selecting such objective function reduces the control error and also the force applied to the UUV, which consequently leads to reduction of energy consumption. Therefore, by using a special method, desired signals of UUV state are obtained from generated three-dimensional optimal path such that tracking these signals by the controller leads to the tracking of this path by UUV. In this paper, the dynamical model of the UUV, entitled as "mUUV-WJ-1", is derived and its hydrodynamic coefficients are calculated by CFD in order to be used in the simulations. For simulation by the method presented in this study, three environments with different obstacles are intended in order to check the performance of the IT2FLC controller in generating optimal paths for guiding the UUV. In this article, in addition to ICA, Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) are also used for generation of the paths and the results are compared with each other. The results show the appropriate performance of ICA rather than ABC and PSO. Moreover, to evaluate the performance of the IT2FLC, optimal Type-1 Fuzzy Logic Controller (T1FLC) and Proportional Integrator Differentiator (PID) controller are designed and applied to the UUV and compared with each other. The simulation results show the superiority of the IT2FLC than the other two controllers.

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Using Fuzzy Ontology to Improve Similarity Assessment: Method and Evaluation

Safaeipour

Zarandi

Bastani

2017

Int. J. Intell. Syst.

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Assessing semantic similarity is a fundamental requirement for many AI applications. Crisp ontology (CO) is one of the knowledge representation tools that can be used for this purpose. Thanks to the development of semantic web, CO‐based similarity assessment has become a popular approach in recent years. However, in the presence of vague information, CO cannot consider uncertainty of relations between concepts. On the other hand, fuzzy ontology (FO) can effectively process uncertainty of concepts and their relations. This paper aims at proposing an approach for assessing concept similarity based on FO. The proposed approach incorporates fuzzy relation composition in combination with an edge counting approach to assess the similarity. Accordingly, proposed measure relies on taxonomical features of an ontology in combination with statistical features of concepts. Furthermore, an evaluation approach for the FO‐based similarity measure named as FOSE is proposed. Considering social network data, proposed similarity measure is evaluated using FOSE. The evaluation results prove the dominance of proposed approach over its respective CO‐based measure.

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Type-2 fuzzy ontology-based semantic knowledge for collision avoidance of autonomous underwater vehicles

Cited by 75 publications

References 40 publications

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

A fuzzy-ontology-oriented case-based reasoning framework for semantic diabetes diagnosis

Path Planning for Unmanned Underwater Vehicle in 3D Space with Obstacles Using Spline-Imperialist Competitive Algorithm and Optimal Interval Type-2 Fuzzy Logic Controller

Using Fuzzy Ontology to Improve Similarity Assessment: Method and Evaluation

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