Fuzzy axiomatic design extension for managing model selection paradigm in decision science

The model selection paradigm is one of the focused themes within decision science. This paper addresses the consistent solution of model selection issue on the basis of the fuzzy axiomatic design (FAD) methodology. Moreover, the developed FAD–based model selection interface (FAD–MSI) is performed over the critical ship management processes in order to assign suitable (multiple criteria decision-making) MCDM techniques even if commonly utilized hybrid approaches. The outcomes of this study encourage the maritime practitioners for the further researches towards analytical modelling of ship management processes.     

Data mining for adaptive learning sequence in English language instruction

The purpose of this paper is to propose an adaptive system analysis for optimizing learning sequences. The analysis employs a decision tree algorithm, based on students’ profiles, to discover the most adaptive learning sequences for a particular teaching content. The profiles were created on the basis of pretesting and posttesting, and from a set of five student characteristics: gender, personality type, cognitive style, learning style, and the students’ grades from the previous semester. This paper address the problem of adhering to a fixed learning sequence in the traditional method of teaching English, and recommend a rule for setting up an optimal learning sequence for facilitating students’ learning processes and for maximizing their learning outcome. By using the technique proposed in this paper, teachers will be able both to lower the cost of teaching and to achieve an optimally adaptive learning sequence for students. The results show that the power of the adaptive learning sequence lies in the way it takes into account students’ personal characteristics and performance; for this reason, it constitutes an important innovation in the field of Teaching English as a Second Language (TESL).     

Development of a stable localized visual inspection system for underwater structures

To avert potential crisis from Japan’s aging infrastructure and declining birth rate, the Japanese Government is planning to introduce robotic technology for the inspection of social infrastructure (such as pipes, dams, and bridges). Recording underwater positions is a difficult task for human divers who undertake conventional dam inspections. This study presents the Anchor Diver 5.2 system for efficient and effectual dam inspection. Anchor Diver 5.2 is based on an extended-tether-maneuvered remotely operated vehicle (ROV) equipped with cameras. The ROV is lowered into water by a hoist system from a boat and implements a visual survey of the concrete underwater structure. To improve the visibility of the ROV in murky and cloudy water, a novel concept named Water Loupe is proposed. In addition, a simple boat-fixing method is proposed to provide a stable base on the water surface, and the underwater position of the ROV, which cannot be accessed by global positioning systems, is recorded using a feasible localization method. Finally, the developed system was evaluated in field experiments conducted in the Amagase Dam, Japan, and its merits and problems are discussed.     

Upgrade of a current research information system with ontologically supported semantic search engine

Harmonising the metadata format alone does not solve the issue of efficient access to relevant information in heterogeneous environments, when different systems use different content, contextual and semantic concepts for certain entities. One such type of heterogeneous systems are also Current Research Information Systems (CRIS), which store their data primarily in local relational databases, using different formats and various local concepts.In this article, we study the possibilities and propose a new ontologically supported semantic search engine (OSSSE) which, in addition to the harmonisation of the metadata format among local CRIS systems, also ensures that the meaning of data and/or concepts that belong to various metadata entities are also harmonised. A special model of ontological infrastructure was designed, and dedicated test ontology was created alongside with a new simplified algorithm for creating ontology, the basis of which is the distinction between new and already existing classes in terms of content. Finally, we evaluated the proposed OSSSE model using a simulation of the search process on the base of 41,113 real searches within SICRIS. The obtained results show that regardless of the search situation, the proposed OSSSE is always at least as efficient as a search without ontological support in terms of precision, while recall remains the same; the improvement has been shown to be statistically significant with a high confidence interval (p<0.005).The proposed OSSSE model is able to solve the issue of harmonizing the data where different heterogeneous systems use different content, contextual and semantic concepts, which is the case in many advanced expert systems. In this manner, the more the search is carried out based on the properties described by the supporting ontology, the more the infrastructure can help a searcher. The proposed concepts, ontological infrastructure and the designed semantic search engine may well help to improve search precision in several information retrieval systems.     

Homogenization-based multiscale crack modelling: From micro-diffusive damage to macro-cracks

The existence of a representative volume element (RVE) for a class of quasi-brittle materials having a random heterogeneous microstructure in tensile, shear and mixed mode loading is demonstrated by deriving traction–separation relations, which are objective with respect to RVE size. A computational homogenization based multiscale crack modelling framework, implemented in an FE² setting, for quasi-brittle solids with complex random microstructure is presented. The objectivity of the macroscopic response to the micro-sample size is shown by numerical simulations. Therefore, a homogenization scheme, which is objective with respect to macroscopic discretization and microscopic sample size, is devised. Numerical examples including a comparison with direct numerical simulation are given to demonstrate the performance of the proposed method.     

P2P soft security: On evolutionary dynamics of P2P incentive mechanism

This paper thoroughly investigates the evolutionary dynamics of soft security mechanism, namely, reciprocity-based incentive mechanism, in P2P systems based on Evolutionary Game Theory (EGT). By soft security mechanism, it means social control mechanisms to overcome peers’ selfish (rational) behaviors, and encourage cooperation in P2P systems. Specifically, there exist three strategies in P2P systems: always cooperative (ALLC), always defect (ALLD) and reciprocator (R). Instead of existing work which take it for granted that, like ALLC users, R users did not bear any information-seeking cost, we assume small reciprocation cost, and study generalized mutation-selection dynamics. Our contributions are threefold: firstly, we prove and illustrate that, in a well-mixed P2P structure, ALLD is the only strict Nash equilibrium; secondly, we infer the specific condition under which evolution dynamics exhibits rock-scissors-paper oscillation in a structured P2P population. That is, the population cycles from ALLD to R to ALLC and back to ALLD; finally, we theoretically illustrate that the intensity of selection plays an important role in the evolutionary dynamics of P2P incentive mechanism. That is, when the intensity of selection is relatively weak and reciprocation cost limits to zero, the time average can be mostly concentrated on reciprocator. In brief, considering the existence of reciprocation cost and the small mutation in P2P incentive mechanisms, unlike existing work, it is impossible to simply achieve the “absolute cooperative” in P2P incentive mechanisms. On the other hand, stochastic evolution in P2P incentive mechanism with finite population and network structure still favor reciprocation.     

3D block-based medial axis transform and chessboard distance transform based on dominance

Traditionally, the block-based medial axis transform (BB-MAT) and the chessboard distance transform (CDT) were usually viewed as two completely different image computation problems, especially for three dimensional (3D) space. In fact, there exist some equivalent properties between them. The relationship between both of them is first derived and proved in this paper. One of the significant properties is that CDT for 3D binary image V is equal to BB-MAT for image V' where it denotes the inverse image of V. In a parallel algorithm, a cost is defined as the product of the time complexity and the number of processors used. The main contribution of this work is to reduce the costs of 3D BB-MAT and 3D CDT problems proposed by Wang [65]. Based on the reverse-dominance technique which is redefined from dominance concept, we achieve the computation of the 3D CDT problem by implementing the 3D BB-MAT algorithm first. For a 3D binary image of size N³, our parallel algorithm can be run in O(logN) time using N³ processors on the concurrent read exclusive write (CREW) parallel random access machine (PRAM) model to solve both 3D BB-MAT and 3D CDT problems, respectively. The presented results for the cost are reduced in comparison with those of Wang's. To the best of our knowledge, this work is the lowest costs for the 3D BB-MAT and 3D CDT algorithms known. In parallel algorithms, the running time can be divided into computation time and communication time. The experimental results of the running, communication and computation times for the different problem sizes are implemented in an HP Superdome with SMP/CC-NUMA (symmetric multiprocessor/cache coherent non-uniform memory access) architecture. We conclude that the parallel computer (i.e., SMP/CC-NUMA architecture or cluster system) is more suitable for solving problems with a large amount of input size.     

Automated discovery of local search heuristics for satisfiability testing

The development of successful metaheuristic algorithms such as local search for a difficult problem such as satisfiability testing (SAT) is a challenging task. We investigate an evolutionary approach to automating the discovery of new local search heuristics for SAT. We show that several well-known SAT local search algorithms such as Walksat and Novelty are composite heuristics that are derived from novel combinations of a set of building blocks. Based on this observation, we developed CLASS, a genetic programming system that uses a simple composition operator to automatically discover SAT local search heuristics. New heuristics discovered by CLASS are shown to be competitive with the best Walksat variants, including Novelty+. Evolutionary algorithms have previously been applied to directly evolve a solution for a particular SAT instance. We show that the heuristics discovered by CLASS are also competitive with these previous, direct evolutionary approaches for SAT. We also analyze the local search behavior of the learned heuristics using the depth, mobility, and coverage metrics proposed by Schuurmans and Southey.