Type-2 fuzzy ontology-based semantic knowledge ... - Semantic Scholar

Information Sciences 295 (2015) 441–464

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Type-2 fuzzy ontology-based semantic knowledge for collision avoidance of autonomous underwater vehicles Farman Ali, Eun Kyoung Kim, Yong-Gi Kim ⇑ Department of Computer Science and Engineering Research Institute (ERI), Gyeongsang National University, Jinju, Gyeongnam 660-701, Republic of Korea

a r t i c l e

i n f o

Article history: Received 19 January 2014 Received in revised form 17 September 2014 Accepted 3 October 2014 Available online 22 October 2014 Keywords: Type-2 fuzzy ontology Autonomous vehicle Knowledge representation Information retrieval Ontology-based type-2 fuzzy inference layer

a b s t r a c t The volume of obstacles encountered in the marine environment is rapidly increasing, which makes the development of collision avoidance systems more challenging. Several fuzzy ontology-based simulators have been proposed to provide a virtual platform for the analysis of maritime missions. However, due to the simulators’ limitations, ontologybased knowledge cannot be utilized to evaluate maritime robot algorithms and to avoid collisions. The existing simulators must be equipped with smart semantic domain knowledge to provide an efficient framework for the decision-making system of AUVs. This article presents type-2 fuzzy ontology-based semantic knowledge (T2FOBSK) and a simulator for marine users that will reduce experimental time and the cost of marine robots and will evaluate algorithms intelligently. The system reformulates the user’s query to extract the positions of AUVs and obstacles and convert them to a proper format for the simulator. The simulator uses semantic knowledge to calculate the degree of collision risk and to avoid obstacles. The available type-1 fuzzy ontology-based approach cannot extract intensively blurred data from the hazy marine environment to offer actual solutions. Therefore, we propose a type-2 fuzzy ontology to provide accurate information about collision risk and the marine environment during real-time marine operations. Moreover, the type-2 fuzzy ontology is designed using Protégé OWL-2 tools. The DL query and SPARQL query are used to evaluate the ontology. The distance to closest point of approach (DCPA), time to closest point of approach (TCPA) and variation of compass degree (VCD) are used to calculate the degree of collision risk between AUVs and obstacles. The experimental and simulation results show that the proposed architecture is highly efficient and highly productive for marine missions and the real-time decision-making system of AUVs. 2014 Elsevier Inc. All rights reserved.

1. Introduction With the rapid increase in the number of obstacles encountered in the hazardous maritime environment, it is evident that autonomous underwater vehicles (AUVs) cannot efficiently handle maritime information across various platforms for collision risk computation. The fundamental task of any autonomous vehicle is to gather precise information in an uncertain environment for decision-making. An AUV collects information using sonar during marine missions, but this information is further processed offline. A decision-making system and collision risk computation system provide a high level of autonomy for AUVs, which require a high level of knowledge representation. This knowledge representation is required for ⇑ Corresponding author. Tel.: +82 55 772 1384. E-mail addresses: [email protected] (F. Ali), [email protected] (Y.-G. Kim). http://dx.doi.org/10.1016/j.ins.2014.10.013 0020-0255/ 2014 Elsevier Inc. All rights reserved.

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F. Ali et al. / Information Sciences 295 (2015) 441–464

decision-making, analyzing environmental conditions, collision risk calculation, and detecting temporal and spatial obstacles. To make autonomous decisions, the adopted knowledge representation platform is responsible for providing the same level of information as that issued by the operator. The performance analysis of the knowledge representation for a decisionmaking system is a complex task in real-time systems. Thus, these marine real-time systems require experts in advanced technology. Generally, it is almost impossible to repeat sea tasks due to uncertain and varying environmental conditions. Thus, a simulator is used to find an effective way to test decision-making knowledge representation within in a short period. At the decision level, most proposed robotic systems use classical ontology for knowledge representation. However, this ontology addresses only crisp data and cannot be used to obtain profitable results from blurred data. To address this issue, researchers have integrated fuzzy logic with crisp ontologies to propose precise solutions. Type-1 fuzzy logic (T1FL) with classical ontology is used in [15] to efficiently represent semantic knowledge for marine robot algorithms and develop a simulator for algorithm evaluation. However, there are certain technical limitations that affect the system results. The semantic knowledge approaches that are currently available are not able to provide detailed marine information; they only extract AUV and obstacle positions from natural language input queries for navigation. Moreover, the ideal simulator requires a collision risk computation system with which a user can easily compute the risk of an AUV colliding with various obstacles. The simulator also requires knowledge representation and intelligent algorithms to expedite computation such that a non-expert marine user can easily calculate the degree of collision risk. The available systems are becoming ineffective because type-1 fuzzy ontology (T1FO)-based systems can extract information only to a limited extent. Additionally, the marine environment is so fuzzy and collision risk computation is so fraught with uncertainties that T1FL-based systems cannot determine membership functions precisely. The use of type-2 fuzzy logic (T2FL) with crisp ontology is the preferable option for making autonomous vehicles adjustable, interoperable and recyclable with other knowledge-based system. To address the aforementioned issues, a type-2 fuzzy ontology-based semantic knowledge (T2FOBSK) system is proposed for a simulator to avoid the collision of AUVs. The proposed system is composed of T2FL and an ontology to develop an intelligent knowledge representation-based simulator for current marine users that reduces experimental time and the cost of marine robots. The simulator is used to represent domain expert and sonar data in a structured form for collision risk computation and decision-making. The proposed semantic knowledge-based system not only extracts the positions of AUVs and obstacles from a natural language input query but also provides detailed information about the marine environment. The proposed simulator uses mainframe model technology integrated with semantic knowledge to remotely examine tasks. The simulator is further equipped with techniques for collision risk computation and dynamic path planning to provide a preanalysis tool for marine users and also facilitates the user in analyzing marine algorithms through natural language queries. The proposed system is further outfitted with a case-based reasoning ontology for storing navigation cases. The cased-based reasoning ontology system retrieves cases similar to the current navigation situation from the ontology and then selects the most similar case. This process avoids the computation-heavy tasks of creating a new solution each time. Real-time system studies show that T1FL cannot handle uncertainties in the system adequately. Therefore, T2FL is used to determine membership functions with precision and to represent the uncertainty in model variables. Furthermore, the proposed T2FO-based system extracts the required information from comprehensive data of vague origin. If the required data do not exist, the proposed system provides facility in extracting the data based on semantic knowledge using a query. This is the first type of work mainly concerned with T2FO-based knowledge representation for the collision risk computation system of AUVs. This approach uses semantic knowledge representation to calculate the degree of collision risk of AUVs during marine missions. The goal of this article is to help researchers easily understand the entire process and enable them to aid in the development of new systems. The rest of this paper is structured as follows: section 2 reviews previous related works. In this section, we discuss knowledge representation and type-2 fuzzy ontology. Section 3 briefly explains type-2 fuzzy sets, the structure of type-2 fuzzy ontology, and the distance to closest point of approach (DCPA), time to closest point of approach (TCPA), and variation of compass degree (VCD). The proposed architecture, which provides knowledge representation for autonomous underwater vehicles, is explained in section 4. Finally, section 5 evaluates type-2 fuzzy ontologies and presents the experiment and the results obtained. 2. Related work At present, knowledge representation and ontology based on derived information for intelligent decision-making systems are considered to be the most popular topics of robotics research. The increasing complexity of the marine environment makes collision avoidance increasingly challenging. Currently, knowledge representation can provide information in the form of labels. Ontology is intended to present these knowledge labels and defines the formal and explicit specifications of shared concepts and knowledge [36]. Detailed examples based on the abovementioned definitions are presented in [4,44,19]. Over the past few years, ontology has been widely used by many researchers and industries in different fields of robotics. An ontology for storing and exploiting domain knowledge for the development of virtual environments is presented in [15]. The authors show that the combination of virtual environment and fuzzy scene independent ontology allows for the development of a semantic knowledge-based simulator that can be used by non-expert marine users to test algorithms easily. However, the fuzzy ontology cannot extract efficient information from blurred data. The simulator still requires semantic knowledge for degree of collision risk calculation which plays an important role in collision avoidance and path planning. A spatial formal ontological structure for remodeling the navigational capabilities of robots and robot ontology for knowledge representation and action generation by symbols is presented in [5,17]. These classical ontologies


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address only crisp data and cannot be used to obtain profitable results from blurred data. The combination of fuzzy logic and classical ontology has gained popularity among research groups over the past couple of years. However, the combination of a fuzzy logic controller with classical ontology for robots confronts linguistic and uncertainty problems, especially in hazardous environments. As a solution, type-2 fuzzy logic is a suitable technology for addressing these uncertainties. Currently, developing ontologies have very paradoxical requirements for AUVs. Ontologies should be effective for knowledge representation and corresponding information extraction. Several collision avoidance techniques have been proposed for AUVs, such as an intelligent collision avoidance system [29], a 3D global dynamic window approach [48], a behaviorbased approach [35] and an RVC model based on fuzzy logic [22]. Each technique has its own advantages and uses fuzzy logic to gain better performance in vague information processes. Researchers have presented a system that takes data from radar and calculates the degree of collision risk among ships from a vehicle traffic service (VTS) center to facilitate the VTS crew and ship captain [12]. However, the authors used 2D techniques to calculate DCPA and TCPA among all ships from the VTS center. The 2D technique is only suitable for the surface area of the ocean. Additionally, the previous fuzzy logic system cannot determine the exact degree of collision risk and is completely dependent on VTS center data to avoid collisions among ships. It is difficult to collect complete statistics about an unpredictable environment and tackle the aforementioned issues in a non-tethered system [1,10]. The above-mentioned techniques require domain knowledge to provide high-level statistics and intelligently address the problems associated with marine environments. The notion of an ontology-based information system for retrieving and combining various plain text documents, image captions and tables suggests that a user cannot find an exact answer about football, for instance, using Google without a knowledge base [11]. Nevertheless, uncertainties exist (e.g., the penalty has some linguistic terms miss, done, repeat), which require fuzzy logic. Similarly, the idea of supporting government transformation and the evolution of an ontology-based government knowledge repository is presented to improve the relationship between government and the challenges confronted by citizens [47]. The authors also present detailed information indicating that people demand the same level of services from government as they do from the private sector. A meeting scheduling system based on fuzzy meeting scheduling ontology and personal ontology is proposed in [23]. The authors used the ontology model and fuzzy personal ontology to support a genetic fuzzy agent for meeting scheduling domain knowledge and provided a mechanism for next meeting learning. The ontology-based fuzzy case-based reasoning (CBR) support system for a ship’s collision avoidance highlights the advantage of fuzzy ontology in avoiding the cumbersome tasks of creating a new solution each time for ships [39]. This idea is mentioned only for academic purposes. A proposal for a fuzzy ontology-based framework to address linguistic values of fuzzy concepts in supply-chain management is discussed in [56]. This paper makes information sharing and gaining convenient, which is the main activity of supply-chain management. The idea of a granular fuzzy rule-based system for granular computing is introduced in [40]. In this approach, fuzzy rules are linked to a reduction process to reduce the number of rules and enhance the readability of the resulting rule base. The idea of knowledge management in fuzzy modeling is presented in [41]. In this paper, fuzzy models are used as sources of knowledge and associated with various group pursuits to launch modeling outcomes of a global character. The results culminate in the form of a granular fuzzy model that reflects the diversity of the available sources of knowledge. Type-2 fuzzy sets form an intuitively appealing generalization of interval-valued fuzzy sets [6,16,42]. A type-2 fuzzy set handles ambiguity and imprecision better than a type-1 fuzzy set. A type-2 fuzzy personal ontology for a meeting scheduling system is demonstrated in [24]. This paper presents T2FO to ease an organization’s meeting scheduling process and suggests some references to attendees for the meeting hosts. A fuzzy markup language (FML)-based type-2 fuzzy ontology for ‘computer go knowledge’ is proposed in [27]. This paper presents the idea of ‘computer go knowledge’, including an FML conversion mechanism, a type-2 fuzzy set inference mechanism and a type-2 fuzzy set construction for inferring the winning rate of games. An article on a type-2 fuzzy ontology and diet recommendation for a diabetic patient introduces the T2FO creation steps and recommends a diet plan on a daily basis according to individual demographic information [26]. This proposal applies the T2FO to the diabetes and nutrition domains to develop a T2FS-based intelligent ontological agent for diabetic-diet recommendation. The design of the computer diet assessment system is presented in [25]. In this article, FML is introduced to design a knowledge base and a type-2 fuzzy ontology for intelligent decision-making. A novel approach to automating the personalized flight ticket booking domain and associated ontologies using a multi-agent system to exploit their combined strengths is proposed in [13]. In this approach, customers enter the objectives that they desire to achieve. The system automatically searches customer information regarding ticketing. Ontologies are already being used in robots, such as the ontology for representing a worldwide database in which robots can store and share information regarding concepts among multiple robots. This project is ongoing and is mostly concerned with sharing and environment knowledge representation. Ontology for representing scientific knowledge between different robots is proposed in [50]. The authors show that ontology cannot be used directly for code invention and operation. It is a fact that classical ontology can only extract plain sets of data and cannot obtain the desired results from blurred resources of data. This limitation occurs because most of the information is in an imprecise format. To resolve this issue, type-1 fuzzy logic is integrated with classical ontology. The integration of both technologies has gained popularity due to its capability to address hazy information. However, it well known that type-1 fuzzy logic with an ontology-based system can extract vague data to some extent. However, it cannot perfectly address intensively blurred information. Type-2 fuzzy logic and its integration with crisp ontology represents one solution to the above-mentioned problems. The type-2 fuzzy grammar is introduced to extent the original work of Lee and Zadeh and analyze grammar evolution and language acquisition in [2]. In this paper, an ant colony optimization algorithm is used to reproduce the common interactions in a communicating society and simulate the results that uphold the given approach’s validity, as well as to provide an

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explanation of how the simulation operates. A review of type-2 fuzzy logic-based applications in pattern recognition and classification is presented in [33]. In this article, the authors demonstrate the most successful applications of T2FL in the field of pattern recognition, clustering and classification, in which the approach has helped to enhance results over those obtained by T1FL. The idea of age-structured population growth based on an interval type fuzzy cellular structure is proposed in [43]. This paper uses T2FL to evaluate the variation in mortality and emigration rates and to handle the effects caused by environmental heterogeneity on a population. The optimal design of type-2 and type-1 fuzzy tracking controllers for autonomous mobile robots is introduced in [32]. This article highlights the optimization method applied to solve the motion problem via a torque controller based on fuzzy logic theory. The main objective of this paper is the demonstration of a fuzzy controller in a chemical paradigm for searching for the optimal parameters. The idea of embedding a high-speed interval type-2 fuzzy controller for a real plant in an FPGA is presented in [45]. The article shows that type-2 fuzzy logic inference systems can be used in different systems that require high speed processing. This article also shows that the Kernik–Mendel (KM) technique can be effective if it is appropriately applied using a suitable combination of hardware and software. Type-2 fuzzy sets are employed to develop a model that efficiently captures the factor of uncertainty in [3]. In this approach, a type-2 fuzzy set provides additional degrees of freedom for the model’s uncertainties and establishes an evolutionary optimization tool, which results in efficient ‘‘If–Then’’ rules. A new methodology involving type-2 fuzzy models that exploit the uncertainty of non-numeric membership functions is proposed in [34]. This new approach demonstrates originality in the sense that it provides a systematic method for producing interval type-2 fuzzy systems. The above-described studies were examined in depth, and it was concluded that the studies provide very interesting frameworks regarding knowledge representation and ontology for a decision-making system of robots. Most of the proposed frameworks use classical ontology and type-1 fuzzy logic, which cannot address intensively blurred data. The proposed type2 fuzzy ontology-based semantic knowledge simulator of AUVs represents a novel attempt to develop an automatic decision support system. The simulator is recycled to test marine algorithms through natural language query. In this system, type-2 fuzzy ontology is used to provide major contributions; for example, ontology is implemented to provide higher levels of data representation for the simulator to make decision-making techniques intelligent; ontology is recycled to design an accurate knowledge representation system of marine environments; and CBR is applied to store navigation cases. CBR retrieves cases similar to the current navigation situation from ontology and then selects the most similar case. This process avoids the computation-heavy task of creating a new solution each time. A fuzzy marine lexical ontology, domain ontology, personal ontology, case-based reasoning ontology, knowledge-based and rule-based ontology and type-2 fuzzy inference mechanism are developed. These ontologies are combined to form a type-2 fuzzy ontology that represents a high level of knowledge in an intelligent AUV decision-making system. 3. Type-2 fuzzy set and ontology Type-2 fuzzy ontology is the main part of the proposed system. Before properly illustrating type-2 fuzzy ontology we must explain fuzzy set theory and type-2 fuzzy logic. 3.1. Type-2 fuzzy set Fuzzy set theory was invented by Lofti Zadeh in 1965 to extract vague and blurred concepts [54]. The theory was first developed for controlling a steam engine [30]. The extension of type-1 fuzzy sets (T1FS) to type-2 fuzzy sets (T2FS) was made by Zadeh in 1975 ten year later [55]. e characterized by a membership function l ðx; lÞ, where x 2 X; l 2 J # ½0; 1 is expressed as follows A type-2 fuzzy set A x eA [13,31,57].

e ¼ fððx; lÞ; l ðx; lÞÞj8x 2 X 8l 2 J # ½0; 1g A x e A

ð1Þ

where 0 6 lA ðx; lÞ 6 1 and is typically written as

e¼ A

Z

Z

x2X

l2Jx

lðx; lÞ=ðx; lÞJx # ½0; 1

ð2Þ

RR

indicates union over all admissible x and l. From Eqs. (1) and (2), Jx # [0, 1] is a limitation that is equal to e where Jx # [0, 1] for x 2 X. 0 6 le ðx; lÞ 6 1 for type-1 membership functions and Jx indicates primary membership of A where

A

If x = x0 then, for each value of x, we have the following.

l Ae ðx0 Þ ¼

X

l 2 Jx0 f x0 ðlÞ=l; for l 2 Jx0 # ½0; 1 and x0 2 x [ e ¼ J x0 ¼ fðx; lÞ : l 2 J x0 # ½0; 1g FOU A

ð3Þ ð4Þ

8x2X

e 0 Þ and J 0 is the primary membership of x0 . There are two In Eqs. (3) and (4), the secondary membership function is l Aðx x e T1FS membership functions that bound FOU A , a lower membership function (LMF) denoted by le ðxÞj8x 2 X and an upper A membership function (UMF) denoted by le ðxÞj8x 2 X [31], where A


leA ðxÞ FOU Ae 8x 2 X

445

ð5Þ

and

leA ðxÞ FOU Ae 8x 2 X

ð6Þ

For interval T2FS,

Jx ¼

leA ðxÞ; leA ðxÞ ; 8x 2 X

ð7Þ

Let us consider the collision risk degree (CRD) which is defined by fuzzy sets. These fuzzy sets include positive small DCPA, positive medium small DCPA, positive medium DCPA, positive medium big DCPA, positive big DCPA, positive small TCPA, positive medium small TCPA, positive medium TCPA, positive medium big TCPA, positive big TCPA, positive small VCD, positive medium small VCD, positive medium VCD, positive medium big VCD and positive big VCD. Similarly, the fuzzy variables DCPA, TCPA and VCD are also defined by fuzzy sets, which are low speed, high speed, medium speed, positive small direction, medium direction and positive big direction. In addition, there is uncertainty in each fuzzy variable so we cannot determine the membership degree as a crisp number in [0, 1]. In this case, we are unable to fuzzify the crisp input as type-1 fuzzy set, because type-1 membership functions (MFs) cannot fully represent the uncertainty associated with these fuzzy variables. Therefore, we use type-2 fuzzy set. A type-2 fuzzy set is characterized by fuzzy MFs, i.e. the membership degree for each element of this set is a fuzzy set in [0, 1], unlike type-1 set where the membership degree is a crisp value which is shown in Fig. 1a. MFs establish a relationship between numerical values and linguistic labels. The interval of type-2 fuzzy MFs is created by two type-1 MFs which are shown in Fig. 1b. The figure presents the interval of fuzzy membership values ranging from 0.38 to 0.6. The UMF and LMF are fully enclosed by the shaded region. It represents the maximum and minimum value of l for each x. The shaded region is called the footprint of uncertainty (FOU), which is the aggregation of all the primary membership functions [52]. U represents the area between the UMF and LMF. Triangular MFs used in this work which are defined by 4 linear functions and 5 points (a, b, c, d and e) as illustrated in the Eqs. (8)–(10).

Triangular ðx; a; b; c; d; eÞ ¼ maxð0; minðz1 ; z2 ; eÞÞ xe z1 ðx; a; b; cÞ ¼ e ba dx z2 ðx; c; d; eÞ ¼ e dc

ð8Þ ð9Þ ð10Þ

The interval of type-2 fuzzy MF is represented for a set of q interval type-1 MFs located at points xi as illustrated in Fig. 1c. The centroid of type-2 MF is then calculated as the centroid of type-1 fuzzy MFs. The centroids of type-1fuzzy MF and type-2 fuzzy MF are defined by Eqs. (11) and (12) respectively.

Pq i¼1 lðxi Þ xi P q i¼1 lðxi Þ Pq Pq i¼1 l ðxi Þ xi i¼1 l ðxi Þ xi P P ; ½cl ; cr ¼ q q i¼1 l ðxi Þ i¼1 l ðxi Þ

c¼

ð11Þ ð12Þ

where l ðxi Þ and l ðxi Þ are the values of UMF and LMF which minimize and maximize the weighted average [31]. The UMF = Triangular (x, 1, 0, 0, 1, 1) and LMF = Triangular (x, 0.7, 0, 0, 0.7, 1) as illustrated in Fig. 1c. The intervals of type-2 fuzzy MF for a fuzzy sets xi are as follows.

Fig. 1a. Type-1 fuzzy set.

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Fig. 1b. Type-2 fuzzy set.

Fig. 1c. Intervals of type-2 fuzzy MF for fuzzy sets.

I1 = [0, 0.3] at x1 = 0.7. I2 = [0.34, 0.55] at x2 = 0.5. I3 = [0.65, 0.73] at x3 = 0.2. I4 = [0.3, 0.41] at x4 = 0.5.

In general, a type-2 fuzzy logic system consists of five components. Those are fuzzifier, type-2 fuzzy rule base, fuzzy inference engine, type-reducer and defuzzifier. The fuzzifier maps a crisp data into type-2 fuzzy sets. The type-2 fuzzy rule base contains ‘‘If–Then’’ rules which are same as T1FS, but its antecedents and consequents are represented by T2FS. The inference engine assigns T2FS inputs to T2FS outputs using the rules of fuzzy rule base and operators (union and intersection). The type-reducer transforms the type-2 fuzzy output sets to type-1 fuzzy output sets. The defuzzifier further transforms the type-2 fuzzy set into a crisp value which is the average of right end point and left end point of the type-reduced set. 3.2. Structure of type-2 fuzzy ontology An ontology is an unambiguous and formal arrangement of a shared conceptualization of a specific domain in a machinereadable and human-understandable format [24]. Essentially, an ontology is developed to share a common understanding of domain knowledge among people and software for the purpose of reusing the domain’s classes instead of remodeling them [14,53]. Ontology is a branch of metaphysics concerning the study of existence. An ontology is a model that characterizes knowledge as a set of concepts and relations between concepts in a single domain. Ontology has been defined as the study of what types of things exist and what things there are in the universe. An ontology is made up of four main components; axioms, instances, concepts, and relationships. In the proposed ontology, the domain concepts (classes) and their properties, values and relationships are the focus. An ontology is a hierarchical structure of classes for defining constraint properties on its values. Resource Description Framework (RDF) and Web Ontology Language (OWL) are standard languages that are used for writing ontologies. OWL [37] is a language specifically developed to design ontology that was conceived by the World Wide Web Consortium (W3C). Indeed, OWL is a standard and powerful language for ontology representation. Following the successful use of OWL, the W3C launched an extended version of OWL with various new features, called OWL-2. These new features, such as annotations, data representation, Pellet Reasoner, RacerPro Reasoner, Hermit Reasoner and Fact++ Reasoner increased the expressivity level of OWL. Mathematically, an ontology can be expressed as follows [13].

e ¼ ðC; P; R; V; V c Þ O

ð13Þ


447

where C, P, R, V, and Vc represent concepts, properties of concepts, relationship among concepts, values of concepts, and constraint values of properties, respectively. Currently, ontology plays a vital role within the robot engineering domain. An ontology can be classified as domain knowledge or application level knowledge for the development of system solutions. Domain ontology is applied to transfer scientific knowledge between different robotic groups to extract precise information and make the decision-making system more intelligent. However, most researchers design ontologies based on their own criteria. Thus, there is no common way to form an ontology. In classical ontology, the concept’s value is crisp, but most real-time systems feature fuzzy terms [28,38]. Classical ontology cannot yield the required results from blurred resources of data due to its limited functionality. Therefore, type-2 fuzzy logic with crisp ontology is used to protect a system from imprecise information and infer the exact membership degree of collision risk. Triangular T2 fuzzy set figures are used in the proposed paper. The upper bound of FOU (A) is called UMF (A), and the lower bound of FOU (A) is called LMF (A), as shown in Fig. 1b. The fuzzy variables, fuzzy sets, and T2Fs are recycled to construct the concepts and relations of T2FO. For example, a fuzzy variable has some fuzzy sets to represent a fuzzy concept. T1Fs and T2Fs are applied to make T1Fs and T2Fs stacks. The main differences between these two stacks are the properties of concepts in these stacks. The T2Fs stack is an extension of the T1Fs stack. The concepts in the T1Fs stacks are T1FL sets, whereas the concepts in T2Fs are the T2FL sets accumulated from T1FL sets in the T1Fs stack. The relationship between these two stacks represents an association. The relations and utility of the T2FO stacks are shown in Fig. 2, which highlights the generic structure of T2FO-based semantic knowledge. There are five stacks the domain stack, category stack, concept and relation stack, T1Fs stack and T2Fs stack. The domain stack is defined by domain experts; it characterizes the domain name of the ontology for incorporation with different categories. The category stacks describe several categories. Every category is composed of various fuzzy concepts and relations to create relationships among them. In the T1Fs stack, there are type-1 fuzzy set concepts, which use the properties of fuzzy classes to find the membership value of it. The T2Fs is produced by joining some type-1 fuzzy sets in the type2 fuzzy set stack. A step-by-step procedure is used to define the design of T2FO in section 4. The development process of the fuzzy ontology is divided into phases. At first, a simple ontology is developed using Protégé OWL-2 tools and then the fuzzy

Fig. 2. Structure of type-2 fuzzy ontology.

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terms are incorporated into the ontology by using the fuzzy OWL-2 plug-in of Protégé to address blurred terms within a real time system [8,9]. 3.3. Calculations of DCPA, TCPA and VCD To precisely understand all ontology knowledge associated with the collision risk computation system, it is important to know certain usable factors, such as the distance to closest point of approach (DCPA) and time to closest point of approach (TCPA), of the collision risk computation system, which is used to determine the degree of collision risk between AUVs and obstacle in the ocean. The DCPA verifies whether an AUV will be in collision with an obstacle or not based on proximity, whereas the TCPA determines the closest passing time between the AUV and an obstacle. The variation of compass degree (VCD) is an important factor used to precisely measure the collision risk in maritime systems. The proposed system extracts the input vector information of the AUV and obstacle from semantic knowledge and then computes the obstacle’s DCPA, TCPA and VCD. These are the inputs of the collision risk computation system used to compute collision risk using type-2 fuzzy logic. Fig. 3 shows an AUV labeled A, that is moving towards the northeast and an obstacle B that is moving towards the southeast. Both AUV A and obstacle B are moving with different speed(s) along different trajectories (h). P indicates the distance closest point between A and B in the figure. To calculate the DCPA and TCPA between A and B, we draw a projection vector in the opposite direction of A at position (xt ; yt ; zt ) and a position vector for B at position (xobt ; yobt ; yobt ) [21,12]. After touching the end points of the two projection vectors, a slope vector is drawn to compute the relationship between A and B. The entire scenario is clearly demonstrated in Fig. 3. Mathematically, the DCPA between AUV A and obstacle B can be calculated by using the following equations [21]. xobt ¼ obstacle B speed cos a0 yobt ¼ obstacle B speed cos b0 zobt ¼ obstacle B speed cos c0 xt ¼ ðSpeed cos aÞ yt ¼ ðSpeed cos bÞ zt ¼ ðSpeed cos cÞ L ¼ xt þ xobt M ¼ yt þ yobt N ¼ zt þ zobt qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi V ¼ jLj2 þ jMj2 þ jNj2

ð14Þ

ð15Þ

ð16Þ

ð17Þ

where (xobt ; yobt ; zobt ) are the end point coordinates of obstacle B and (at, bt, ct) are the end point coordinates of A. V is the velocity, and L, M, and N indicate the sum of the end point coordinates vectors of A and B and (a0, b0, c0) is the trajectory (h) of the AUV. A linear equation is used to determine the DCPA and TCPA as follows. x xobt y yobt z zobt ¼ ¼ L M N ðLxobt þ Myobt þ Nzobt Þ t¼ L2 þ M 2 þ N 2 Px ¼ X obt þ Lt Py ¼ Y obt þ Mt Pz ¼ Z obt þ Nt qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi DCPA ¼ P2x þ P2y þ P2z

ð18Þ ð19Þ ð20Þ

ð21Þ

Fig. 3. Calculation of DCPA and TCPA.


449

Here, (x; y; z) is the starting position of A, and Px þ Py þ Pz are the coordinates of P. P is the closest point along a straight line to the origin. After the calculation of the DCPA calculation, the TCPA can be calculated. According to Fig. 3, the TCPA is the time required for A to reach B.

TCPA ¼

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 xob þ y2ob þ z2ob DCPA2 V

ð22Þ

In the above equation, V is velocity and xob ; yob ; zob represent geographic coordinates. To calculate the degree of collision risk between the AUV and obstacle, DCPA and TCPA have to be determined. If there is more than one obstacle, the same approach is used between the AUV and each of the obstacles. The VCD plays a vital role in measuring the exact collision risk degree in the ocean environment. It is important to calculate the bearing between the AUV and all obstacles before calculating VCD. A bearing is the angle between the forward direction of the AUV and the direction from the AUV to the obstacle; it is calculated in degrees [12]. The equation used to measure the bearing via the Pythagorean theorem is as follows.

VCD ¼ jBearingi1 Bearingi j

ð23Þ

At the end of this stage, all of the required inputs are obtained, which can be further used to determine the degree of collision risk among the AUV and obstacles and to produce a semantic knowledge for the intelligent collision risk computation system. 4. Development of semantic knowledge based on type-2 fuzzy ontology An ontology is the knowledge model of a domain that is organized in a hierarchical order. This concept is mostly used in a knowledge model of the decision-making process and collision risk computation system of an AUV. All previous studies on fuzzy ontology have been limited to and designed for academic purposes only. The core of our proposed system is based on T2FO, which creates intelligent knowledge and rules for the decision-making system. This term concerns the types of tasks and entities that exist in the proposed system. For an AUV and obstacle, the ontology of the system can automatically suggest the collision risk degree and location of the moving obstacle. The system is further equipped with obstacle avoidance techniques and CBR to avoid the computation-heavy tasks of creating a new solution each time. The proposed system can be recycled as an intelligent decision- making tool for determining the degree of collision risk and avoiding obstacles. This

Fig. 4. A graphical view of semantic knowledge based on type-2 fuzzy ontology for the collision risk computation system.

450


Fig. 5. A graphical view of an arbitrary query to SPARQL translation, providing output to the collision risk computation system.

approach can handle any type of real world situation that is related to the ocean environment. Protégé 4.3 with OWL is used to develop ontologies. It is a highly trusted tool in the field of information engineering for constructing a semi-automatic ontology. Domain experts and data collection from the real ocean environment are the two main actors that can help accelerate the representation process of domain knowledge. AUV ocean environment information is gathered from previous research, domain experts and the Internet and classified manually. The main purpose of the proposed T2FO is to extract required information from domain knowledge to determine the correct degree of collision risk, avoid similar cases and


451

provide knowledge representation for the decision-making system. Fig. 4 shows the architecture of the proposed system. For simplicity the architecture is divided into three effective layers. In the next section, we will clarify the internal operation of each layer. 4.1. Initial interaction layer The initial interactive layer (IIL) consists of two interactive components, sound navigation and ranging (sonar) and the query interface. These two components are used to facilitate the AUV’s internal decision-making system. Sonar is recycled to detect underwater objects and verify the water depth by creating sounds and measuring their return after being reflected. In the proposed system, the AUV employs sonar to detect obstacles during navigation and collect data about obstacle positions in the ocean environment. Sonar technology is usually not associated with ontology directly. Therefore, the marine ontology needs to be designed manually. The marine ontology efficiently shares the ocean information. The marine data are obtained from a wide range of sources and are heterogeneous; which is why it is difficult to share data precisely without an ontology. The data include various types of marine information processed in the form of variables and properties, such as sea depth, light condition, rocky environment, weather conditions, vessel information, AUV starting point S, goal point G, current positions of obstacles, type of obstacles (moving or static), direction, and speed. Each class has its own properties or relations, which provide instructions for each task. The second part of the initial interaction layer is the query interface. The strength of IIL lies in its natural language query processing interface. The query interface allows the user to enter mission requirements through multiple dialog sessions. The AUV extracts the start and end positions from the query interface and then retrieves ocean environment information from the marine ontology, such as rocky environment and sea depth, to navigate intelligently. The four components of the query interface play key roles in accessing the external ontology knowledge base. The conversion of a natural language query to a SPARQL query is performed by using these four components, ‘‘sonar interface’’, ‘‘lexical analyzer’’, ‘‘grammar checker’’, and ‘‘SPARQL generator’’ [15]. To extract the AUV and obstacle information from domain knowledge, it is important that a query be in a human-and machine-understandable format. The proposed system receives the entire query in its natural state and then uses the abovementioned components to translate it into a SPARQL structure. A simple scenario demonstrating the SPARQL language processing techniques is presented to help the reader easily understand the procedure.

Find the degree of collision risk, AUV_Start_position (X, Y, Z), AUV_ End_Position (X, Y, Z), obstacle_1_position (X, Y, Z), obstacle_2_position (X, Y, Z), obstacle_3_ position (Z, Y, X), and Avoid obstacle

The query finds the degree of collision risk between the AUV, which has start position (0.0, 0.0, 0.0) and end position (13.0, 13.0, 0.0), faces obstacle_1 with position (4.0, 4.0, 0.0), obstacle_2 with position (10.0, 5.0, 4.0), and obstacle_3 with position (6.0, 8.5, 2.0) and avoids all obstacles from the start to end position. The translation of natural language to a SPARQL query is a very difficult task. At first, the natural language is parsed and then translated to a SPARQL query. The Stanford parser [46] is used for natural language parsing. When the query interface receives the user query, the NLP function of the backend algorithm obtains the natural language query, converts the query into small chunks, and then sorts them for further processing. Wordnet 2.0 [51] is used for lexical analysis such as synonym matching and stop-word reduction in natural language. A lexical ontology is used to define a synonym repository for the lexical analyzer. During the term construction and filtering phase, the lexical analyzer ontology consults with the domain ontology to select the right terms that can produce language triples. The query knowledge and lexical ontology terms are combined to find the next set of possible terms. The lexical ontology contains relevant knowledge about all small lexical terms and includes the grammar for all formulated queries. Ungrammatical terms in the query cannot be accepted by the system. Thus, first the terms are defined by rules, which can be easily processed further. The query is displayed as a set of triples after processing the createSparql generator components. Once the query is completed, the SPARQL generator converts the query to a SPARQL query statement and forwards the result to the collision risk computation layer for further processing. The overall query translation procedure is illustrated in Fig. 5. The precision and recall statistics generated during the experimental phase are briefly explained. 4.2. Semantic knowledge base layer (SKBL) The second layer of the proposed system is the semantic knowledge base layer (SKBL), which is the core part of the proposed system. The layer consists of a T2FO component, which has five sub-components; domain ontology, lexical ontology, marine ontology, fuzzy-CBR ontology, and knowledge-based and rule-based ontology. The strength of the proposed system is T2FO, which not only provides knowledge for decision-making but also assists in obtaining the required information about the ocean environment from ontologies using SPARQL and DL queries. The domain ontology describes domain knowledge and also creates a relationship between ontologies. This section explains the modeling process of the marine and domain

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ontology. The literature indicates that previous studies on fuzzy semantic knowledge-based maritime systems could not solve real-world domain problems. The potency of our system is the T2FL with domain ontology, which describes the precise domain knowledge of the marine system. The seven steps followed to develop the domain ontology for the AUV are listed below, which cover all aspects of ontology modeling [15].

Determine the domain and scope of the ontology. Consider reusing existing ontologies. Enumerate important terms in the ontology. Define the classes and the class hierarchy. Define the properties of classes. Define the facet of the slots. Create instances.

Protégé 4.3 with OWL-2 is a highly trusted tool for developing ontologies. During the development phase of ontologies, each step is discussed with domain experts to achieve accuracy and precision in the experiment. Fig. 6 shows the relationship of ontology terms. The OntoGraf tab of Protégé is used to develop this graph. Fuzzy set theory techniques are employed in pre-modeled ontology to demonstrate the vague information in the ontology. T2FO is divided into steps to achieve efficiency in creating the ontology. At first, a crisp domain ontology is developed, and then Protégé Reasoner tools such as Pellet, FaCT++ and RACER are applied to evaluate the efficiency of the ontology. These tools automatically create inference results on behalf of binding terms in the ontology. After the development of a classical ontology, the fuzzy OWL plug-in of Protégé is used to create a fuzzy ontology [9]. This tool is used for creating fuzzy data types, fuzzy concepts, fuzzy modifiers, and fuzzy axioms. The fuzzy and classical ontologies have the same classes, properties and axioms, but the difference is that all concepts have blurry term values in the case of a crisp ontology [12]. Table 1 shows the parameters of the type-2 fuzzy membership functions as well as the linguistic term values of fuzzy variables. The fuzzy variables are DCPA, TCPA, VCD, and COD, whereas the linguistic terms are positive small (PS), positive medium small (PMS), positive medium (PM), positive medium big (PMB), and positive big (PB). Every linguistic term has a UMF and LMF to define its boundary. For example, consider the first entry in Table 1. The entry indicates that the fuzzy variable DCPA has a linguistic term PS. This linguistic term is represented by parameters (LMF, M, M, and UMF) that have certain values to define the term’s boundary on the x-axis, as shown in Fig. 1b. Fig. 1b represents a triangular T2 fuzzy set, with an upper bound of the FOU (A), which is called UMF (A), and a lower bound of the FOU (A), which is called LMF (A). The PS is characterized by the following parameters on the x-axis.

M; Rg ¼ f½UMFL ; LMFL ; ½M; M; ½M; M; ½LMFR ; UMFR g fL; M;

Fig. 6. Relationships among ontology terms: the graph generated by the Onto Graf Tab in protégé owl.

ð24Þ


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Table 1 Parameters of the type-2 fuzzy set. Fuzzy variable

Linguistic Term

f LMFL ; MLMFL ; MLMFR ; LMFR ; UMFL ; MUMFL ; MUMFR ; UMFR g

DCPA

PS PMS PM PMB PB PS PMS PM PMB PB PS PMS PM PMB PB NB NM NS PS PMS PM PMB PB

{[0, 0, .5, 0.9], [0, 0, 0.5, 1.1]} {[0.6, 1, 1, 1.4], [0.4, 1, 1, 1.6]} {[1.1, 1.5, 1.5, 1.9], [0.9, 1.5, 1.5, 2.1]} {[1.6, 2, 2, 2.4], [1.4, 2, 2, 2.6]} {[2.1, 2.5, 3, 3], [1.9, 2.5, 3, 3]} {[0, 0, 5, 10], [0, 0, 5, 14]} {[8.5, 12, 12, 16], [4, 12, 12, 20]} {[14, 18, 18, 22], [12, 18, 18, 26]} {[20, 24, 24, 28], [18, 24, 24, 32]} {[26, 30, 35, 35], [24, 30, 35, 35]} {[5, 5, 0, 6], [-5, 5, 0, 9]} {[1.5, 7.5, 7.5, 13.5], [-1.5, 7.5, 7.5, 16.5]} {[9, 15, 15, 21], [6, 15, 15, 24]} {[16.5, 22.5, 22.5, 28.5], [13.5, 22.5, 22.5, 31]} {[24, 30, 35, 35], [21, 30, 35, 35]} {[1.5, 1.5, 1, 0.7], [-1.5, 1.5, 1, 0.5]} {[0.9, 0.6, 0.6, 0.3], [-1, 0.6, 0.6, 0.1]} {[0.5, 0.2, 0, 0], [-0.7, 0.2, 0, 0]} {[0, 0, 0.2, 0.3], [0, 0, 0.2, 0.5]} {[0.3, 0.4, 0.4,0.56], [0.19, 0.4, 0.4, 0.7]} {[0.5, 0.6, 0.6, 0.7], [0.34, 0.6, 0.6, 0.9]} {[0.64, 0.8, 0.8,0.96], [0.56, 0.8, 0.8, 1.1]} {[0.84, 1, 1, 1], [0.74, 1, 1.5, 1.5]}

TCPA

VCD

COD

These membership function values are assigned to the deserved class (linguistic term) using the fuzzy OWL plug-in. The fuzzy OWL plug-in of Protégé shows the annotations, pellet Reasoner and feature menu. The annotation of the fuzzy OWL plug-in is used to define fuzzy concepts. Adding the annotation is a very difficult manual process. However, the fuzzy OWL tab makes this task very easy. The annotation functionality is assigned to each fuzzy data type. The fuzzy OWL tab easily converts crisp ontology to fuzzy ontology and capably handles the fuzzy terms in the domain ontology. A class of obstacle can be described in a fuzzy format as follows: obstacle P$ hassize size_small. Similarly, an obstacle that has a very small size can be described as follows: very (obstacle P$ hassize size_small). Several Reasoners are available in Protégé 4.3; these Reasoners, such as Pellet, FaCT++, Hermit, RacerPro, Snorocket and DeLorean Reasoner, are used to evaluate the efficiency of an ontology. DeLorean is a very popular Reasoner. The DeLorean Reasoner is used to obtain inference results from an ontology [7]. It first converts fuzzy ontology into classical ontology internally and then produces the inference results [13]. The fuzzy ontology and crisp ontology have the same classes, instances, properties and axioms, but in the case of the crisp ontology, all of the concept values are blurry terms. However, our experiment requires the development of T2FO, which is the advanced form of T1FO. The architecture of the T2FO-based simulator for the collision risk computation system is shown in Fig. 7. The first row represents the architecture domain name of the ontology. The second row shows high-level domain category terms (sonar and obstacle). These terms are further subdivided into ontologies. The obstacle class has many subclasses, such as moving obstacles, static obstacles, AUVs, large fish, mountains and rocks. All of these classes are stored in the marine ontology with each obstacle’s own personal ontology. Each personal ontology provides knowledge about obstacles, such as ‘name’, ’size’, ‘speed’, ‘direction’, ‘condition’, ‘bearing’, ‘destination’, and ‘location’. Each class is defined by the main properties hasSmallQuality, hasBigQuality, hasAUVoperated. The properties describe the size of obstacles and indicate that the AUV should not be a fish; it should move independently (hasAUVoperated). The domain ontology represents knowledge of the existing application domain. It captures analyzed information such as ‘‘moving obstacle’’, ‘‘static obstacle’’, ‘‘is-a’’ sub-class of ‘‘obstacle’’ and ‘‘position_ change’’, which is a property of ‘‘moving obstacle’’ (this means that a moving obstacle must change its position (x, y, z)). The AUV class of the domain ontology has child classes such as DCPA, TCPA, and VCD, which are calculated using the collision risk computation layer. During calculation, the collision risk computation system interacts with the marine ontology to obtain the correct position of the AUV and obstacles. At the same time, the system will also contact the knowledge and rule-based ontology. The knowledge and rule-based ontology is used to construct the important knowledge base and rule base of the collision risk computation system. During the degree of collision risk calculation, the type-2 fuzzy inference layer interacts with the knowledge and rule-based ontology to use the rule for every fuzzy variable. The knowledgebased component illustrates the fuzzy concepts, fuzzy variables, fuzzy terms and fuzzy set membership functions, whereas the rule-based component explains the fuzzy rule sets, such as the antecedent and consequence rules. There is one output fuzzy variable (Collision risk degree (CRD)), 162 fuzzy rules (e.g., R1: if DCPA is PS, TCPA is PS, and VCD is PS, then DOC is PB) and three input fuzzy variables (TCPA, VCD, and DCPA) in the knowledge-based and rule-based ontology. The third and fourth rows in Fig. 8 highlight intensive fuzzy terminologies. Fuzzy variable, such as DCPA, TCPA and VCD, are located in the concept and relation stack to successfully store the necessary information about the degree of collision risk. Moreover, various fuzzy linguistic terms are located in the type-1 fuzzy sets stack. The fuzzy variable DCPA has five linguistic terms, PS,

454


Fig. 7. Type-2 fuzzy collision risk computation ontology.

PMS, PM, PMB and PB. Now, the fuzzy decision knows each of the linguistic terms of DCPA, TCPA and VCD. The fuzzy decision class links individuals (instances) to linguistic terms, which can be easily extracted using DL queries. Instances are located in the type-2 fuzzy sets stack, including ‘‘COD_ PS,’’ ‘‘COD_ PMS,’’ ‘‘COD_ PM,’’ ‘‘COD_ PMB’’, and ‘‘COD_ PB,’’ which are defined to describe the degree of collision for obstacles by using the linguistic terms of DCPA, TCPA and VCD. The fuzzy OWL plug-in generates the type-1 fuzzy sets based on these fuzzy classes and properties. The fuzzy OWL tab of Protégé cannot handle intensively blurred data. Therefore, T1FO is imported into a simple text editor to develop a T2FO-based knowledge representation. The important fuzzy terminologies and their associations are declared manually. The connected concepts are separated and categorized into crisp, partly fuzzy, full fuzzy and intensively fuzzy ontologies. During the development phase of T2FO, it is noted that OWL-2 is the most useful rule-defining language, and the DeLorean Reasoner is used as an information extraction tool. More specifically, the DeLorean Reasoner is used to produce type-2 fuzzy inference results [13]. In the CBR ontology, the cases are displayed as concepts, and instances and their attributes are the relations or properties of variables. The relation attributes yield the values of the instance, which is defined in the domain ontology. There are five fuzzy variables in the fuzzy CBR ontology; TCPA, DCPA, VCD, and target obstacle distance (TOD). The TOD is used to indicate the distance between the AUV and the target obstacle. The fuzzy variables DCPA and TCPA are used to measure collision risk in dangerous areas. The fuzzy CBR ontology stores all cases that can be reused in a new navigation situation. The CBR system retrieves similar cases during the navigation and finds the most similar case in the ontology. Furthermore, the fuzzy variables VCD, TCPA, and TOD are used to alter the solution by finding the most similar case among all stored cases in the ontology. Initially, the VCD, TCPA, and DCPA values are utilized in the fuzzy inference layer. The fuzzy inference layer receives all the


455

Fig. 8. Structure of the type-2 fuzzy inference layer.

inputs to compute the collision risk and index it in the CBR system. The indexing task is very important for retrieving similar cases from the ontology. When systems retrieve similar cases from an ontology, the result of the retrieved case will be the input for the obstacle avoidance and navigation system. The following is a very simple equation for calculating similarity [49].

SimilarityðT; SÞ ¼

n X

f ðT i ; Si Þ wi

ð25Þ

i¼1

where T represents target and S represents sources. T and S are the number of attributes for each case; i is an individual attribute from 1 to n, f is a similarity function for each attribute i in every case of T and S; and wi represents the importance weighting attributes of i. The similarity function is repeated to grade every case relative to the target. Most systems typically normalize the similarity by zero and one (where one represents an exact match and zero represents dissimilarity). The proposed system assigns three fuzzy variables, VCD, TOD, and TCPA to the CBR system, as indicated by the following equation.

SimilarityðT CRD ; SðVCD;TOD;TCPAÞ Þ ¼

n X f ðT DoCi ; SðVCDi ;TODi ;TCPAi Þ Þ wi

ð26Þ

i¼1

In the above equation, various compass degrees, target obstacle distances, and times to closest point of approach are the source fuzzy variables for finding a similar case in the case-based reasoning ontology, whereas the degree of collision risk is the target fuzzy variable. If the same case is present in the ontology, then the system does not need to calculate the collision risk degree but instead directly assign the CRD fuzzy variable value from the case-based reasoning ontology to the navigation and collision avoidance system. The proposed system uses DL queries to check for a similar case in the ontology and extract the required result from it. The DL query method is discussed in the experimental results section. During the practical implementation of the type-2 fuzzy collision risk computation ontology, certain technical challenges are confronted due to a lack of established technologies. 4.3. Collision risk computation layer based on type-2 fuzzy logic The collision risk computation layer uses all ontologies’ information, as well as the SPARQL query to retrieve the AUV information from the marine ontology. This information is assigned to the simulator to compute the collision risk with different obstacles in the maritime environment. The marine and domain ontologies make the system determine a safe navigation route when the AUV is at risk of collision with obstacles. The fuzzy inference layer is a subpart of the collision risk computation layer, which is connected to T2FO. The fuzzy inference layer is the fuzzy rule-based and decision-making model for the AUV and is a very useful framework for fuzzy set theory that is used to map input to outputs. The fuzzy inference layer is connected to three main components: the rule-based and knowledge-based ontology, the marine ontology and the mechanism for calculating VCD, DCPA, and TCPA. The most interesting feature of the fuzzy inference layer is that it takes the input as a fuzzy value and also a crisp value but the result is always in the fuzzy format. The information described in the

456


Fig. 9. Fuzzifier outputs.

previous sections is involved in the inference layer processing, such as membership functions, if–then rules and logical operation. Fig. 8 shows the architecture of the inference layer based on type-2 fuzzy logic, which comprises five components, the fuzzifier, inference, type-reducer, defuzzifier and T2FO. After the computation of DCPA, TCPA, and VCD, all these variables are assigned to the fuzzifier. The fuzzifier takes crisp input and maps it onto an interval of type-2 fuzzy membership functions to create a type-1 fuzzy set interval. The following crisp inputs are assigned to the fuzzifier. The fuzzifier converts the crisp input to type-2 fuzzy intervals, as shown in Fig. 9. All these intervals are assigned to the inference component. The inference component uses the rule for type-1 fuzzy interval membership functions. There are six fuzzy rule variables in the ontology, the TCPA upper and lower membership values, the DCPA upper and lower membership values, and the VCD upper and lower membership values. The inference block assigns these membership values to the fuzzifier outputs using the rules or properties of the ontology with operations such as union and intersection. The fuzzy inference component extracts all information regarding these three variables (VCD, DCPA, and TCPA) using DL queries from T2FO. Each variable has five linguistic asset value variables, PS, PMS, PM, PMB and PB. The fuzzy variable rules in the case when VCD is PS, PMS, PM, PMB, and PB is shown in Tables 2–6. All these rules and linguistic values with the interval are stored in the knowledge and rule-based ontology as subclasses of fuzzy variables. The antecedents and consequence of the rules are expressed by the subclasses of the collision risk linguistic variable and its properties. Each subclass has an upper membership function and a lower membership function that contain values for collision risk calculation. All these rules are used in the following form.

Table 2 The fuzzy reasoning rule in the case in which VCD is PS for CRD. TCPA

DCPA PS PMS PM PMB PB

PS

PMS

PM

PMB

PB

PB PMB PMB PMB PMB

PMB PMB PMB PMB PM

PMB PMB PMB PM PM

PMB PMB PM PM PM

PMB PM PM PM PM

Table 3 The fuzzy reasoning rule in the case in which VCD is PMS for CRD. TCPA


PS

PMS

PM

PMB

PB

PB PMB PMB PMB PM

PMB PMB PMB PM PM

PMB PMB PM PM PM

PMB PM PM PM PMS

PM PM PM PMS PMS

Table 4 The fuzzy reasoning rule in the case in which VCD is PM for CRD. TCPA


PS

PMS

PM

PMB

PB

PMB PMB PMB PM PM

PMB PMB PM PM PM

PMB PM PM PM PMS

PM PM PM PMS PMS

PM PM PMS PMS PMS

457

F. Ali et al. / Information Sciences 295 (2015) 441–464 Table 5 The fuzzy reasoning rule in the case in which VCD is PMB for CRD. TCPA


PS

PMS

PM

PMB

PB

PMB PMB PM PM PM

PMB PM PM PM PMS

PM PM PM PMS PMS

PM PM PMS PMS PMS

PM PMS PMS PMS PS

Table 6 The fuzzy reasoning rule in the case in which VCD is PB for CRD. TCPA


PS

PMS

PM

PMB

PB

PMB PM PM PM PMS

PM PM PM PMS PMS

PM PM PMS PMS PMS

PM PMS PMS PMS PS

PM PMS PMS PS PS

Rule1 : IF x1 is A1 AND y1 is B1 THEN z1 is c1 Rule2 : IF x2 is A2 AND y2 is B2 THEN z2 is c2 Rule3 : IF x3 is A3 AND y3 is B3 THEN z3 is c3 ... Rulen : IF xn is An AND yn is Bn THEN zn is cn where x1 and y1 are the input variables and z1 is the output and control variable. A, B, and C are linguistic values for the linguistic variables x, y, and z respectively. The proposed ontology has three inputs and one output fuzzy variable, which can be expressed by the following linguistic rules.

Rule1 : If DCPA is PS; TCPA is PS; and VCD is PS; then DoC is PB Rule2 : If DCPA is PMS; TCPA is PS; and VCD is PS; then DoC is PMB Rule3 : If DCPA is PM; TCPA is PS; and VCD is PS; then DoC is PM Rule4 : If DCPA is PMB; TCPA is PS; and VCD is PS; then DoC is PMS Rule5 : If DCPA is PB; TCPA is PS; and VCD is PS; then DoC is PS The inference results are summarized in Fig. 10. Here, X represents the rule of the membership function and Y its firing value. After extracting the membership values and rules, the inference assigns the type-2 fuzzy interval outputs to the typereducer. The type-reducer transforms the type-2 fuzzy outputs to the type-1 fuzzy set membership function by using the Kernik Mendel algorithm (KMA), which is based on the calculation of the centroid. The output of the inferences X and Y are the input

Fig. 10. Inference outputs.

458


Fig. 11. Type reducer and defuzzifier output.

Fig. 12. Membership functions of DCPA, TCPA, VCD and CRD.


459

of the KMA. The defuzzifier calculates the average of the type-reducer left and right points to provide the result in the form of a value, which is called the degree of collision risk. The type-reducer and defuzzifier results are summarized in Fig. 11. The defuzzifier result is transferred to the collision avoidance unit. The collision avoidance unit features an intelligent algorithm that is based on the fuzzy BK product. This algorithm intelligently acquires the input values and finds the possible solution for avoiding obstacles. Fig. 12 shows the membership functions of DCPA, TCPA, and VCD which are selected as three input fuzzy variables and one output fuzzy variable (collision risk degree (CRD)) with membership functions. The type-1 fuzzy sets are shown as a thin line, whereas the interval type-2 fuzzy sets are shown as a thick line. 5. Experiment and results The performance evaluation of the proposed system is divided into two phases: ontology evaluation and the measurement of overall system efficiency. 5.1. Ontology evaluation Ontology evaluation is an essential task for measuring the quality of an ontology. It is a technical method and important for the approval and improvement of ontologies. It is important to determine whether the ontology built meets certain application requirements. Ontology evaluation is also essential because ontology information is automatically obtained from unusual resources that might be duplicated instances or might not be identical. An ontology can also be evaluated by presenting questions to specify the needs of a given application. The ontology evaluation biography is disintegrated. Most of the proposed system tackles the evaluation task more or less specifically. However, ontology evaluation is typically manual. The ontology classification framework provides ontology evaluation methods in the form of responses to questions [20]. Protégé OWL-2 is used to construct a valid instance in the required time using an input query of ontologies. The proposed system

Table 7 The output of evaluation query 1, 2 and 3. The output of evaluation query 1 and 2 Object Obstacle_2 Obstacle_3 AUV Start AUV end Obstacle_1 Obstacle_2 Obstacle_1 Obstacle_3

The output of evaluation query 3 Subject Moving (10.0, 5.0, 4.0) (0.0, 0.0, 0.0) (13.0, 13.0, 0.0) Static (6.0, 8.5, 2.0) (4.0, 4.0, 0.0) Static

CRD_PB (indicates that the collision risk degree linguistic variable PB is the output of the rule-based ontology)

Fig. 13. Screenshot of the AUV simulator.

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uses the Protégé DL query plug-in and SPARQL query plug-in for ontology evaluation. The syntax and semantics of the SPARQL query are defined for RDF. The SPARQL query plug-in possesses the ability to query the required and optional graph patterns. It retrieves obstacle and AUV information from ontologies. The DeLorean Reasoner obtains the inference results from ontologies. The SPARQL query is used in the following form. SELECT? Subject? object

WHERE f?subject rdfs : subClassOf ?objectg In the above query, the subject is the individual (instance) of the combined fuzzy variable classes of the object. To obtained fuzzy semantic information for the AUV and obstacle from the marine ontology, it is important to consider the AUV and obstacle positions as fuzzy linguistic variables and to define their values in ontologies. The SPARQL query extracts the AUV information using the following query. Evaluation query 1: SELECT? AUV_Start?AUV_End WHERE {? AUV_start rdfs:type?AUV_Start ? AUV_start: hasPosition?AUV_start_position ?AUV_End rdfs:type?AUV_End ?AUV_End:hasPosition?AUV_End_position} In the above query, the system attempts to extract the starting point and target point information of the AUV. Hasposition is a property or relation between the AUV_start and AUV_start_position classes. Similarly, the following evaluation query 2 is used to extract obstacle position, condition (static and moving), size, direction, etc. Evaluation query 2: SELECT? Obstacle? Obstacle_position WHERE {?Obstacle_1 rdfs:type?obstacle_1 ?Obstacle_1:hasPosition?obstacle1_position ?Obstacle_2 rdfs:type?obstacle_2 ?Obstacle_2:hasPosition?obstacle2_position ?Obstacle_3 rdfs:type?obstacle_3 ?Obstacle_3:hasPosition?obstacle3_position ?Obstacle_1 rdfs:type?obstacle_1 ?Obstacle_1:hasStatus?obstacle1_status ?Obstacle_2 rdfs:type?obstacle_2 ?Obstacle_2:hasStatus?obstacle2_status ?Obstacle_3 rdfs:type?obstacle_3 ?Obstacle_13:hasStatus?obstacle3_status} The knowledge and rule-based ontology is evaluated using DL queries. The knowledge and rule-based ontology contains all rules and knowledge for the collision risk computation layer. The fuzzifier engine transfers the values of the fuzzy variables (VCD, DCPA and TCPA) to the fuzzy inference unit. The inference unit extracts the fuzzy variable rules and knowledge from the ontology using the following DL query format. Evaluation query 3:

If only ðDCPA PS and TCPA PS and VCD PMSÞ In the above query, PS and PMS are linguistic terms of the fuzzy variables DCPA, TCPA, and VCD. All these classes have cumulatively one individual or instance or subclass, which can be extracted when the classes share a relation between one another. 5.2. Measurement of overall efficiency of proposed system During the development phase of the ontology, each step of the ontology is evaluated to compute the improvement level and compare the results. To measure the efficiency of the marine and AUV domain ontologies several queries are designed with the help of domain experts and verified using a Protégé SPARQL query. The SPARQL query retrieves data from OWL that can be applied in collision risk computation. The Pellet Reasoner and RacerPro are used to obtain the inference results from the ontology provided by Protégé version 4.3. Similarly, the knowledge-based and rule-based ontology is tested using the DL Reasoner. The Pellet, RacerPro and DL Reasoners are applied to parse the classical ontology and retrieve results. The proposed T1FO and T2FO have many blurred terms because marine environment information is intensively vague. The description logic Reasoner with vagueness (DeLorean) is the most valuable Reasoner; it is based on the Jena API and can generate reasoning from a fuzzy ontology [18]. The Reasoner is a type of reducer that converts T1FO and T2FO to classical ontology. Furthermore, the DL Reasoner and SPARQL query plug-in are used for different queries to analyze the performance of the merged ontology. First, a simple query is used to test the ontology results and then enhance the performance of the ontology. The ontology tester queries presented in section 5.1 are used for ontology evaluation. Evaluation query 1 retrieves AUV

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information whereas evaluation query 2 retrieves obstacle information from the marine ontology. The evaluation of both queries also represents an attempt to associatively test the results, as shown in Table 7. The output of this query is in the form of a subject and an object. The object represents the main classes (AUV, obstacles), whereas the subject represents the position and condition (moving and static) of the object. All these classes are interconnected with properties and relations. The results of the SPARQL query plug-in generator are manually assigned to the simulator for collision risk computation. The collision risk computation layer obtains the positions of the AUV and obstacle to calculate VCD, DCPA, and TCPA. During the degree of collision risk calculation, the fuzzy inference layer receives the rules from the knowledge and rule-based ontology using DL queries. The output of evaluation query 3 is also shown in Table 7. The results of this query indicate that, if DCPA is PS, TCPA is PS and VCD is PMS, then the collision risk degree (CRD) is PB. A real-time simulator is used to test the developed system, which receives input data from marine ontology manually [15]. Fig. 13 presents the graphical user interface of the simulator. The simulator display is divided into four sub-units, which are the AUV and obstacle statistic unit, the unit that stores the CRD between the AUV and obstacle, the unit that calculates DCPA, TCPA, and VCD, and the unit that loads the AUV ontology. The upper left side of the simulator display illustrates the AUV and obstacle statistics unit, which retrieves the x, y, and z coordinates, speed and angle from the marine ontology. The bottom left side describes the storage of the degree of collision between the AUV and obstacle, which stores the CRD for future use and also displays it. The unit that calculates DCPA, TCPA and VCD is shown in the upper right; this unit obtains the positions of the AUV and obstacle and calculates the DCPA, TCPA and VCD, along with the speed, angle and vector. There are well-known ways for efficiently measuring overall system performance, such as precision and recall. Here, the accuracy function and function measurement are also recycled to monitor the system performance. The proposed system has 5 scenarios. In each scenario, the number of obstacles varies; case 1 represents a scenario in which the AUV encounters only one obstacle. Similarly, cases 2, 3, 4, and 5 represent scenarios involving two, three, four, and five obstacles, respectively. Three levels are used to record the semantic knowledge system result during collision risk computation. First, a crisp ontology is used between the AUV and obstacles, and the SPARQL and DL Reasoner results, such as precision, recall, time, and accuracy, are recorded. Later, type-1 and type-2 fuzzy ontologies are used to compute the results. The following formulas are used to calculate the results.

Ae 100% Ae þ F e Ae 100% Recall ðRÞ ¼ Ae þ T e Ae þ T e 100% Accuracy ðAÞ ¼ Ae þ T e þ F e Execution Time ðETÞ Function measurement ðFMÞ ¼ 100% Accuracy ðAÞ

Precision ðPÞ ¼

ð27Þ ð28Þ ð29Þ ð30Þ

In the above equations, Ae is the total number of records, which is elicited from the maritime environment during collision risk calculation between the AUV and obstacles, and Te and Fe are the true and false elicited record elements respectively. The complete results of the true and false elicited elements of each scenario are displayed in Tables 8–10. To analyze the system, the average accuracy and the function measurement are computed at the completion of each case. The function measurement indicates the cost of the function in each case. The accurate result of the crisp ontology with T2FO is compared; in Table 8 The proposed system results in the case of a crisp ontology.

Case Case Case Case Case

1 2 3 4 5

Total elicited data from ontologies (Ae)

Elicited true elements (Te)

Elicited False elements (Fe)

Precision (P) %

Recall (R) %

Total execution time (T) in min

Accuracy (A) %

Function measure (FM) %

369 579 383 455 525

163 215 144 98 231

206 364 239 357 294

64.1 61.3 61.5 56.0 64.1

69.3 72.9 72.6 82.2 69.4

11.5 9.2 12.3 6.5 8.4

72.0 68.5 68.7 60.7 72.0

15.9 13.4 17.9 10.7 11.6

Table 9 The proposed system results in the case of T1FO.


1 2 3 4 5

Total Elicited data from ontologies (Ae)



Precision (P)%

Recall (R) %


Accuracy (A) %


369 579 383 455 525

195 323 209 212 340

174 256 174 243 185

67.9 69.3 68.7 65.1 73.9

65.0 64.1 64.6 68.7 60.0

7.3 10.5 8.2 5.1 9.0

76.3 77.8 77.2 73.3 82.3

9.5 13.3 10.6 6.9 10.9

462


Table 10 The proposed system results of T2FO.


1 2 3 4 5

Total elicited data from ontologies (Ae)



Precision (P) %

Recall (R) %


Accuracy (A) %


369 579 383 455 525

301 499 294 384 469

68 80 89 71 56

86.0 87.0 81.0 86.0 90.0

55.0 53.7 56.5 54.2 52.8

8.4 13.1 9.2 6.1 9.4

90.7 93.0 88.3 92.1 94.6

9.2 12.0 9.4 6.4 9.1

Fig. 14. A graphical representation of crisp, type-1, and type-2 fuzzy ontology performance.

Table 11 Comparison between existing and proposed system.


1 2 3 4 5

The increasing accuracy (%) in the fuzzy-based existed system

The increasing accuracy (%) in the T2FO-based proposed system

13.2 9.5 10.1 13.1 12.3

32.2 24.8 20.5 31.4 22.6

Fig. 15. A graphical representation of accuracy performance.

the case of the crisp ontology, the case 4 accuracy result is 60.7%, the precision is 56%, the recall is 88.2% and the function measurement is 10.7%. In the case of T2FO, the accuracy result increases to 92.1% and the precision increases to 86%, whereas the function measurement declines to 6.4 % and the recall to 54%. The total execution time is increased when the queries are executed in the T2FO-based system. The T2FO needs additional computational power for execution compared to that of the crisp ontology and that of T1FO. The recall, precision, accuracy and function measurement of each case are obtained from all ontologies to determine the averages thereof. Fig. 14 clearly demonstrates the performance of the proposed system, which is based on the crisp ontology, T1FO and T2FO. The figure shows that the average accuracy and precision increase significantly. The recall and function measurement decrease during the extraction of precise information in the case of the T2FO-based system. The proposed system results for each scenario are recorded and compared with those of the existing technique at the end of the experiments. The established techniques simply extract features from user queries to navigate the AUV from the starting point to the end point using a semantic knowledge-based simulator and then calculate the precision and cost of each function. We used 5 scenarios for better comparison, and in each scenario, the obstacles vary. Additionally, our


463

proposed simulator is equipped with a collision risk calculation mechanism and uses the CBR technique. The proposed schemes not only use a crisp ontology and T1FO but also use T2FO to calculate the precision, recall, function measurement and accuracy. After completing all the experiments, we obtained an accuracy report to examine the level of improvement between the existing technique and the proposed system. The comparison results are shown in Table 11. It is quite clear from Fig. 15 that we can achieve considerable improvement in AUV collision computation and obstacle avoidance by utilizing the proposed T2FO-based semantic knowledge simulator. 6. Conclusions and future work In this article, the concept of a type-2 fuzzy ontology-based semantic knowledge simulator is proposed for AUVs to calculate the collision risk degree and avoid obstacles. The type-2 fuzzy logic with ontology allows for the creation of an intelligent knowledge model for the decision-making system of AUVs. The intelligent semantic knowledge based architecture of collision risk calculation and obstacle avoidance is evaluated using the simulator. The simulator is based on a type-2 fuzzy marine ontology and natural language processing to facilitate the calculation of the degree of collision risk using NLQ for marine users. Fuzzy ontology cannot accurately address blurred information because of its limited concept expressivity. However, type-2 fuzzy ontology technique has the ability to retrieve precise information from intensively blurred data. The type-2 fuzzy ontology system is developed using Protégé OWL tools. DL queries and SPARQL queries are used to retrieve information from different ontologies. Several experiments are designed to evaluate the efficiency and information retrieval system. During computation, the execution time, true elements, and false elements are noted and examined. This process is repeated for the crisp ontology, T1FO and T2FO. Extensive experimental results show that the proposed system is able to achieve a high level of performance for decision-making. As future work, we are looking forward to automating the type2 fuzzy ontology-based semantic knowledge layer and improving the performance of the system accordingly. Acknowledgment This work was supported by a Korean National Research Foundation (NRF) grant provided by the Korean Government (No. 2014R1A1A2053339). References [1] R.A. Aliev, W. Pedrycz, B.G. Guirimov, R.R. Aliev, U. Ihan, M. Babagil, S. Mammadli, Type-2 fuzzy neural networks with fuzzy clustering and differential evolution optimization, Inform. Sci. 181 (2011) 1591–1608. [2] J.P. Alvarado-Magaña, A.R. Díaz, J.R. Castro, O. Castillo, Type-2 fuzzy logic grammars in language evolution, Soft Comput. Appl. Optim., Control, Recog. (2013) 265–286. [3] A.M. Anvar, Intelligent navigation process for autonomous underwater vehicles (AUVs) using time-based fuzzy temporal reasoning, in: Fourth International Conference on Temporal Logic (TIME-ICTL), 2003, pp. 56–61. [4] F. Baader, D. Calvanese, D.L. McGuinness, D. Nardo, Patel-Schneider (Eds.), The Description logic Handbook: Theory, Implementation, and Applications, Cambridge University Press, 2003. ISBN 0-521-78176-0. [5] J. Bateman, S. Farrar, Modelling Models of Robot Navigation Using Formal Spatial Ontology, Lecture Notes in Computer Science, Springer-Verlag, Berlin, Germany, 2004. [6] N. Baklouti, A.M. Alimi, The geometric interval type-2 fuzzy logic approach in robotic mobile issue,in: FUZZ-IEEE, 2009. [7] F. Bobillo, M. Delgado, J. Gomez-Romero, DeLorean: a reasoner for fuzzy OWL 1.1, in: Proceedings of 4th International Workshop on Uncertainty Reasoning for the Semantic Web, 2008, pp. 35–44. [8] F. Bobillo, M. Delgado, J. Gómez, DeLorean A reasoner for fuzzy OWL 2, Expert Syst. Appl. 1 (2012) 258–272. [9] F. Bobillo, U. Straccia, Fuzzy ontology representation using OWL 2, Int. J. Approx. Reason. 52 (2011) 1073–1094. [10] D.P. Brutzman, Y. Kanayama, M.J. Zyda, Integrated simulation for rapid development of autonomous underwater vehicles, in: International Conference on Autonomous Underwater Vehicle Technology AUV, 1992, pp. 3–10. [11] P. Buitelaar, P. Cimiano, A. Frank, M. Hartung, S. Racioppa, Ontology-based information extraction and integration from heterogeneous data sources, Int. J. Hum.–Comp. Stud. 66 (2008) 759–788. [12] A.C. Bukhari, I. Tusseyeva, B.G. Lee, Y.G. Kim, An intelligent real-time multi-vessel collision risk assessment system from VTS view point based on fuzzy inference system, Expert Syst. Appl. 40 (2013) 1220–1230. [13] A.C. Bukhari, Y.G. Kim, Integration of a secure type-2 fuzzy ontology with a multi-agent platform: a proposal to automate the personalized flight ticket booking domain, Inform. Sci. 198 (2012) 24–47. [14] A.C. Bukhari, Y.G. Kim, Exploiting the heavyweight ontology with multi-agent system using vocal command system: a case study on e-mall, Int. J. Advance. Comput. Technol. 3 (2011) 233–241. [15] A.C. Bukhari, Y.G. Kim, A research on an intelligent multipurpose fuzzy semantic enhanced 3D virtual reality simulator for complex maritime missions, Appl. Intell. 2 (2013) 193–209. [16] Y.H. Chang, C.L. Chen, W.S. Chan, H.W. Lin, Type-2 fuzzy formation control for collision-freemulti-robot systems, Int. J. Fuzzy Syst. 15 (2013) 35–451. [17] S. Coradeschi, A. Saffiotti, Perceptual anchoring of symbols for action, in: Proceedings of the 17th (IJCAI) Conference, Seattle, WA, 2001, pp. 407–412. [18] Q. Guo, M. Zhang, A novel approach for multi-agent-based intelligent manufacturing system, Inform. Sci. 179 (2009) 3079–3090. [19] T. Gruber, What is an ontology, 2012 . [20] J. Hartmann, P. Spyns, A. Giboin, D. Maynard, R. Cuel, M.C. Suárez-Figueroa, Y. Sure, Methods for ontology evaluation, Knowledge Web Deliverable D1.2.3 (2004). [21] H. Jung, S.G. Kim, Y.G. Kim, Use of fuzzy technique for calculating degree of collision risk in obstacle avoidance of unmanned underwater vehicle, J. Kor. Inst. Intell. Syst. 21 (2011) 112–119. [22] S.G. Kim, Y.G. Kim, An autonomous navigation system for unmanned underwater vehicle, in: Underwater Vehicles, Intech, 2009, p. 582. [23] C.S. Lee, C.C. Jiang, T.C. Hsieh, A genetic agent using ontology model for meeting scheduling system, J. Inform. Sci. 176 (2006) 1131–1155. [24] C.S. Lee, M.H. Wang, M. Wu, C. Hsu, Y. Lin, S. Yen, A type-2 fuzzy personal ontology for meeting scheduling system, in: Proceeding of International Conference on Fuzzy Systems, 2010, pp. 1–8.

464


[25] C.S. Lee, M.H. Wang, G. Acampora, C. Hsu, H. Hagras, Diet assessment based on type-2 fuzzy ontology and fuzzy markup language, Int. J. Intell. Syst. 25 (2010) 1187–1216. [26] C.S. Lee, M.H. Wang, H. Hagras, A type-2 fuzzy ontology and its application to personal diabetic-diet recommendation, J. IEEE Trans. Fuzzy Syst. 18 (2010) 374–395. [27] C.S. Lee, M.H. Wang. Z.R. Yang, Y.J. Chen, H. Doghmen, O. Teytaud, FML-based type-2 fuzzy ontology for computer Go knowledge representation, in: Proceeding of International Conference on System Science and Engineering (ICSSE), 2010, pp. 63–68. [28] C.S. Lee, Wang, Ontology-based computational intelligent multi-agent and its application to CMMI assessment, Appl. Intell. 3 (2009) 203–219. [29] Y.I. Lee, Y.G. Kim, L.J. Kohout, An intelligent collision avoidance system for AUVs using fuzzy relational product, Inform. Sci. 158 (2004) 209–232. [30] E.H. Mamdani, S. Assilian, Anexperiment in linguistic synthesis with a fuzzy logic controller, Int. J. Man–Mach. Stud. 7 (1975) 1–13. [31] J.M. Mendel, Advances in type-2 fuzzy sets and systems, Inform. Sci. 177 (2007) 84–110. [32] P. Melin, L. Astudillo, O. Castillo, F. Valdez, M. Garcia, Optimal design of type-2 and type-1 fuzzy tracking controllers for autonomous mobile robots under perturbed torques using a new chemical optimization paradigm, Exp. Syst. Appl. 40 (2013) 3185–3195. [33] P. Melin, O. Castillo, A review on the applications of type-2 fuzzy logic in classification and pattern recognition, Exp. Syst. Appl. 40 (2013) 5413–5423. [34] P. Melin, O. Castillo, W. Pedrycz, Design of interval type-2 fuzzy models through optimal granularity allocation, Appl. Soft Comput. 11 (2011) 5590– 5601. [35] D. Nakhaeinia, B. Karasfi, A behavior-based approach for collision avoidance of mobile robots in unknown and dynamic environments, J. Intell. Fuzzy Syst. 24 (2013) 299–311. [36] F.N. Natalya, L.M. Deborah, Ontology Development 101: A Guide to Creating Your First Ontology . [37] S. Nasrolahi, M. Nikdast, M. Boroujerdi, The semantic web: a new approach for future world wide web, in: Proceedings of World Academy of Science, Engineering and Technology, 2009, pp. 1149–1154. [38] A. Paar, J. Reuter, J. Soldatos, K. Stamatis, L. Polymenakos, A formally specified ontology management API as a registry for ubiquitous computing systems, Appl. Intell. 1 (2009) 37–46. [39] G.K. Park, ontology-based fuzzy-CBR support system for ships collision avoidance, in: International Conference on Machine Learning and Cybernetics 2007, pp. 1845–1850. [40] W. Pedrycz, From fuzzy rule-based systems to granular fuzzy rule-based systems: a study in granular computing, Comb. Exper. Theory 271 (2012) 151–162. [41] W. Pedrycz, M. song, Granular fuzzy models: a study in knowledge management in fuzzy modeling, Int. J. Approx. Reason. 53 (2012) 1061–1079. [42] W. Pedrycz, Granular Computing: Analysis and Design of Intelligent Systems, CRC Press, Francis Taylor, Boca Raton, 2013. p. 51. [43] C.L. Ramírez, O. Castillo, P. Melin, A.R. Díaz, Simulation of the bird age-structured population growth based on an interval type-2 fuzzy cellular structure, Inform. Sci. 181 (2011) 519–535. [44] J. Schoening, Standard Upper Ontology Working Group, 2012 . [45] R. Sepúlveda, O. Montiel, O. Castillo, P. Melin, Embedding a high speed interval type-2 fuzzy controller for a real plant into an FPGA, Appl. Soft Comput. 12 (2012) 988–998. [46] ‘‘Standard-Parser’’ . [47] A.M. Sourouni, G. Kourlimpinis, S. Mouzakitis, D. Askounis, Towards the government transformation: an ontology-based government knowledge repository, J. Comp. Stand. Interf. 32 (2009) 44–53. [48] I. Tusseyeva, S.G. Kim, Y.G. Kim, 3D global dynamic window approach for navigation of autonomous underwater vehicles, Int. J. Fuzzy Logic Intell. Syst. 13 (2013) 91–99. [49] I. Watson, CBR is a methodology not a technology, Proc. Res. Develop. Exp. Syst. XV 12 (1999) 213–223. [50] R. Waibel, M. Beetz, J. Civera, J. Elfring, D. Galvez-Lopez, K. Haussermann, R. Janssen, J.M.M. Montiel, A. Perzylo, B. Schiessle, M. Tenorth, O. Zweigle, van de Molengraft, RoboEarth-a world wide web for robots, Robot. Autom. Magaz. 18 (2011) 69–82. [51] ‘‘WordNet’’, 2006 . [52] D. Wu, J.M. Mendel, Uncertainty measures for interval type-2 fuzzy sets, Inform. Sci. 177 (2007) 5378–5393. [53] P. Yan, Y. Zhao, C. Sanxing Ontology-based information content security analysis, in: Proceedings of Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008, pp. 479–483. [54] L.A. Zadeh, Fuzzy sets, Inform. Control 8 (1965) 338–353. [55] L.A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning, Inform. Sci. 8 (1975) 199–249. [56] J. Zhai, Q. Wang, M. Lv, Application of fuzzy ontology framework to information retrieval for SCM, in: Proceedings of International Symposiums on Information Processing (ISIP), 2008, pp. 173–177. [57] D. Zhai, J.M. Mendel, Uncertainty measures for general type-2 fuzzy sets, Inform. Sci. 3 (2011) 503–518.

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