Special issue : Computational intelligence models for image processing and information reasoning

Computational Intelligence (CI) models comprise robust computing methodologies with a high level of machine learning quotient. CI models, in general, are useful for designing computerized intelligent systems/machines that possess useful characteristics mimicking human behaviors and capabilities in solving complex tasks, e.g., learning, adaptation, and evolution. Examples of some popular CI models include fuzzy systems, artificial neural networks, evolutionary algorithms, multi-agent systems, decision trees, rough set theory, knowledge-based systems, and hybrid of these models. This special issue highlights how different computational intelligence models, coupled with other complementary techniques, can be used to handle problems encountered in image processing and information reasoning.

Optimal effort investment for overcoming the weakest point: new insights from a computational model of neuromuscular adaptation

The occurrence of so-called sticking points in a lift is pervasive in weight training practice. Biomechanically complex exercises often exhibit multi modal variation of effective force exerted against the load as a function of the elevation and velocity of the load. This results in a variety of possible loci for the occurrence of sticking points and makes the problem of designing the optimal training strategy to overcome them challenging. In this article a case founded on theoretical grounds is made against a purely empirical method. It is argued that the nature of the problem considered and the wide range of variables involved limit the generality of conclusions which can be drawn from experimental studies alone. Instead an alternative is described, whereby a recently proposed mathematical model of neuromuscular adaptation is employed in a series of computer simulations. These are used to examine quantitatively the effects of differently targeted partial range of motion (ROM) training approaches. Counter-intuitively and in contrast to common training practices, the key novel insight inferred from the obtained results is that in some cases the most effective approach for improving performance in an exercise with a sticking point at a particular point in the ROM is to improve force production capability at a different and possibly remote position in the lift. In the context of the employed model, this result is explained by changes in the neuromuscular and biomechanical environment for force production.

Hybrid prediction method for solar power using different computational intelligence algorithms

Computational Intelligence (CI) holds the key to the development of smart grid to overcome the challenges of planning and optimization through accurate prediction of Renewable Energy Sources (RES). This paper presents an architectural framework for the construction of hybrid intelligent predictor for solar power. This research investigates the applicabil- ity of heterogeneous regression algorithms for 6 hour ahead solar power availability forecasting using historical data from Rockhampton, Australia. Real life solar radiation data is collected across six years with hourly resolution from 2005 to 2010. We observe that the hybrid prediction method is suitable for a reliable smart grid energy management. Prediction reliability of the proposed hybrid prediction method is carried out in terms of prediction error performance based on statistical and graphical methods. The experimental results show that the proposed hybrid method achieved acceptable prediction accuracy. This potential hybrid model is applicable as a local predictor for any proposed hybrid method in real life application for 6 hours in advance prediction to ensure constant solar power supply in the smart grid operation.

Linguistic computational model based on 2-tuples and intervals

Herrera and Mart́inez initiated a 2-tuple fuzzy linguistic representation model for computing with words.Moreover, Wang and Hao further developed a new 2-tuple fuzzy linguistic representation model to deal with the linguistic term sets that are not uniformly and symmetrically distributed. This study proposes another linguistic computational model based on 2-tuples and intervals, which we call an interval version of the 2-tuple fuzzy linguistic representation model. The proposed model possesses three steps: 1) interval numerical scale; 2) computation based on interval numbers; and 3) a generalized inverse operation of the interval numerical scale. The first step transforms linguistic terms into interval numbers, based on which the second step is executed with output as an interval number. Finally, this number is then mapped into the interval of the linguistic 2-tuples by the generalized inverse operation. This study also generalizes the numerical scale approach, presented in the Wang and Hao model, to set the interval numerical scale, by considering the context where semantics of linguistic terms are defined by interval type-2 fuzzy sets (IT2 FSs). In order to compare the proposed model with the existing linguistic computational model based on IT2 FSs, we have conducted extensive simulations. The simulations demonstrate that the results obtained by our proposal are consistent with the results of the linguistic computational model based on IT2 FSs (in some sense) in a vast majority of cases.

Review of trust and machine ethics research: towards a bio-inspired computational model of ethical trust (CMET)

Recent advances in the fields of robotics, cyborg development, moral psychology, trust, multi agent-based systems and socionics have raised the need for a better understanding of ethics, moral reasoning, judgment and decision-making within the system of man and machines. Here we seek to understand key research questions concerning the interplay of ethical trust at the individual level and the social moral norms at the collective end. We review salient works in the fields of trust and machine ethics research, underscore the importance and the need for a deeper understanding of ethical trust at the individual level and the development of collective social moral norms. Drawing upon the recent findings from neural sciences on mirror-neuron system (MNS) and social cognition, we present a bio-inspired Computational Model of Ethical Trust (CMET) to allow investigations of the interplay of ethical trust and social moral norms.

Interplay of ethical trust and social moral norms : environment modelling and computational mechanisms in agent-based social simulation

In many agent-based models theoretical and computational mechanisms are needed for model abstraction and design. However, it can be challenging to arrive at the appropriate mechanisms and models. This research on the interplay of ethical trust and social moral norms addresses that challenge via an analytical framework on the spread of moral norms, the modelling of social environment and the selection of spread mechanisms as applied to agent-based social simulation. We describe the mechanism alignment mapping, two forms of interaction modelling between the social environment and agents, and the results obtained from the simulation of our computational model. These results provide an insight into how the agent-based paradigm can be applied as a technique of investigation for normative moral processes in computational social sciences.

A review of computational fluid dynamics applications in pressure-driven membrane filtration

Ongoing advances in computational performance and numerics have led to computational fluid dynamics (CFD) becoming a ubiquitous modelling tool. However, CFD methods have only been adopted to simulate pressure-driven membrane filtration systems relatively recently. This paper reviews various approaches to describing the behaviour of these systems using CFD, beginning with the hydrodynamics of membrane channels, including discussion of laminar, turbulent, and transition flow regimes, with reference to the effects of osmotic pressure, concentration polarisation, and cake formation. The use of CFD in describing mass transfer through the membrane itself is then discussed, followed by some concluding comments on commercial membrane simulation packages and future research directions in membrane CFD. © 2013 Springer Science+Business Media Dordrecht.

Spectroscopic and computational characterizations of alkaline-earth- and heavy-metal-exchanged natrolites

Synchrotron infrared (IR) and micro-Raman spectra of natrolites containing alkaline-earth ions (Ca2+, Sr2+, and Ba2+) and heavy metals (Cd2+, Pb2+, and Ag+) as extra-framework cations (EFCs) were measured under ambient conditions. Complementing our previous spectroscopic investigations of natrolites with monovalent alkali metal (Li+, Na+, K+, Rb +, and Cs+) EFCs, we establish a correlation between the redshifts of the frequencies of the 4-ring and helical 8-ring units and the size of the EFCs in natrolite. Through ab initio calculations we have derived structural models of Ca2+- and Ag+-exchanged natrolites with hydrogen atoms, and found that the frequency shifts in the H - O - H bending mode and the differences in the O - H stretching vibration modes can be correlated with the orientations of the water molecules along the natrolite channel. Assuming that the members of a solid solution series behave as an ideal mixture, we will be able to use spectroscopy to probe compositions. Deviation from ideal behavior might indicate the occurrence of phase separation on various length scales. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.