Ranking Fuzzy Cognitive Map based scenarios using ELECTRE III: Applied on housing market

Policy makers often deal with a wide range of alternative probable future states for the entity they work for – a country, for example. To strive for the most desirable state, the policy makers must evaluate and rank all probable future scenarios. To this end, ‘scenario methods’ gained recent popularity are increasingly being employed. Nevertheless, currently, decisions made based on insight gained from scenarios are not made in an integrated systematic process. Current variants of the method help study the role of any research concept individually; thus, they do not provide a complete picture of the research situation. A more suitable variant of the method for today's world should provide a holistic view of the research situation by modelling possible links among research concepts. This paper, introduces a stepwise methodology that can guide the building, developing, and ranking of possible future scenarios, having factored in the possible causal interrelations among research concepts. The method is enriched with a combination of Fuzzy Cognitive Maps, a widely used soft computing method, and ELECTRE III, a popular method of Multi Attribute Decision Making. This paper also presents the results of the application of the proposed methodology for Iran's housing market, highlighting the advantages of the proposed methodology.     

Performance optimization of current focusing and virtual electrode strategies in retinal implants

The electrode configuration in an implanted visual prosthesis array affects the spatial electric field distribution within the retina, contributing to current focusing and virtual electrode (VE) stimulation strategies. In this paper, a finite element model incorporating various electrode configurations was used to study the interaction between electrode size and electrode-to-cell distance in current focusing and VE stimulation paradigms. The electrode array unit comprises an active electrode, six flanking return electrodes and a distant monopolar return. A quasi-monopolar (QMP) fraction is defined as the proportion of current which can be preferentially returned through the distant return, in comparison with the more adjacent flanking electrodes. The simulation results indicate that current focusing and VE strategies can be optimized by tuning the QMP fraction. The QMP fraction is adjusted to optimize the electric field spread based on retinal ganglion cell (RGC) density in the degenerate retina, thereby offsetting the effect of inhomogeneous distribution of surviving RGCs and leading to a uniform stimulation paradigm across electrodes. Importantly, there is negligible difference in functional performance across electrode configurations for distances less than the electrode diameter, implying that the stimulation mode does not significantly affect activation threshold or activated retinal area for electrode diameters greater than the retinal thickness. Furthermore, the QMP fraction has a significant effect on VE performance, defined by activation threshold and activated retinal area, when threshold current is evenly divided between two adjacent active electrodes.     

Thermal decomposition of potassium permanganate: Thermogravimetry,differential scanning calorimetry and hot-stage microscopy studies

Thermogravimetry (TG), differential scanning calorimetry (d.s.c.), and hot-stage microscopy (HSM) techniques have been applied in a study of the thermal decomposition of potassium permanganate. Single crystals of potassium permanganate and ground samples are studied at heating rates from 0.62 to 10 °C min^?1, and results compared with previous isothermal studies. Comparison of theoretical and experimental weight loss is made, for both forms of potassium permangante, and a possible mechanism based on diffusion chain theory and nucleation suggested.     

Audiovisual emotion recognition using ANOVA feature selection method and multi-classifier neural networks

To make human–computer interaction more naturally and friendly, computers must enjoy the ability to understand human’s affective states the same way as human does. There are many modals such as face, body gesture and speech that people use to express their feelings. In this study, we simulate human perception of emotion through combining emotion-related information using facial expression and speech. Speech emotion recognition system is based on prosody features, mel-frequency cepstral coefficients (a representation of the short-term power spectrum of a sound) and facial expression recognition based on integrated time motion image and quantized image matrix, which can be seen as an extension to temporal templates. Experimental results showed that using the hybrid features and decision-level fusion improves the outcome of unimodal systems. This method can improve the recognition rate by about 15 % with respect to the speech unimodal system and by about 30 % with respect to the facial expression system. By using the proposed multi-classifier system that is an improved hybrid system, recognition rate would increase up to 7.5 % over the hybrid features and decision-level fusion with RBF, up to 22.7 % over the speech-based system and up to 38 % over the facial expression-based system.     

The fast pyrotechnic reaction of silicon with lead oxides: Differential scanning calorimetry and hot-stage microscopy studies

Differential scanning calorimetry has been applied in the study of the fast pyrotechnic reaction of silicon with (a) PbO; and (b) Pb₃O₄ in powder form. Samples were heated up to 725 °C at heating rates from 0.3 to 160 °C min^?1 under a nitrogen atmosphere. Hot-stage microscopy was used to observe phase changes up to 1000 °C at heating rates of 0.1–100 °C min^?1, and X-ray analysis to identify products. The DSC studies of both compositions indicate two exothermic peaks at 548–667 °C and 667–726 °C, corresponding to surface and bulk reaction. The use of PbO with a smaller particle size results in a larger first and smaller second peak indicating enhanced surface activity. For Pb₃O₄/Si compositions, reaction is principally between Si and PbO, resulting from the decomposition of PbO. At a heating rate of 100 °C min ^?1 both compositions ignite above 700 °C. Hot-stage microscopy correlates phase and colour changes with temperature of occurrence of peaks in the DSC studies.     

Electromagnetic scattering from a buried cylinder in layered media with rough interfaces

A solution to scattering from a cylinder buried arbitrarily in layered media with rough interfaces based on extended boundary condition method (EBCM) and scattering matrix technique is developed. The reflection and transmission matrices of arbitrary rough interfaces as well as an isolated single cylinder are constructed using EBCM and recursive T-matrix algorithm, respectively. The cylinder/rough surface interactions are taken into account by applying the generalized scattering matrix technique. The scattering matrix technique is used to cascade reflection and transmission matrices from individual systems (i.e., rough surfaces or cylinders) in order to obtain the scattering pattern from the overall system. Bistatic scattering coefficients are then obtained by incoherently averaging the power computed from the resulting Floquet modes of the overall system. In numerical simulations, the bistatic scattering coefficients are first validated by comparing the simulation results with the existing solutions which are the limiting cases including scattering from two-interface rough surfaces without any buried object and from a buried cylinder beneath a single rough surface. Subsequently, the numerical simulations of scattering from a buried cylinder in layered rough surfaces are performed to investigate the relative importance and sensitivity of various physical parameters of layered rough surfaces to incoherent scattering coefficients. Results show layered rough interfaces can significantly alter the scattering behaviors of a buried cylinder.     

Principal manifolds and probabilistic subspaces for visualrecognition

Investigates the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques - principal component analysis (PCA), independent component analysis (ICA) and nonlinear kernel PCA (KPCA) - are examined and tested in a visual recognition experiment using 1,800+ facial images from the "FERET" (FacE REcognition Technology) database. We compare the recognition performance of nearest-neighbor matching with each principal manifold representation to that of a maximum a-posteriori (MAP) matching rule using a Bayesian similarity measure derived from dual probabilistic subspaces. The experimental results demonstrate the simplicity, computational economy and performance superiority of the Bayesian subspace method over principal manifold techniques for visual matching     

A genetic based disk scheduling method to decrease makespan and missed tasks

Disk scheduling is an operating system process to service disk requests. It has an important role in QOS guarantee of soft real-time environments such as video-on-demand and multimedia servers. Since now, some disk scheduling algorithms have been proposed to schedule disk requests in an optimized manner. Most of these methods try to minimize makespan by decreasing the number of disk head seeks as one of the slowest operations in modern computers and crucial for system performance because it usually takes some milli-seconds. In this paper, we propose a new disk scheduling method based on genetic algorithm that considers makespan and number of missed tasks simultaneously. In the proposed method, a new coding scheme is presented which employs simple GA procedures such as crossover and mutation and a penalty function in fitness. To get the best performance of the proposed method, its parameters such as number of chromosomes in initial population, mutation, and crossover probabilities, etc have been adjusted by applying it on some sample problems. The algorithm has been tested on several problems and its results were compared with well-known related methods. Experimental results showed that the proposed method worked very well and excelled most related works in terms of miss ratio and average seeks.     

Parameters Estimation for the New Four-Parameter Nonlinear Muskingum Model Using the Particle Swarm Optimization

The Muskingum model is a popular method for flood routing in river engineering. This model has several parameters, which should be estimated. Most of the techniques have applied to estimate these parameters to reduce the distance between observed flow and estimated flows. In this paper, for the first time, the parameters of a novel form of the nonlinear Muskingum model are estimated by the Particle Swarm Optimization (PSO) algorithm. The new Muskingum model, which have four parameters, is applied for three benchmark examples and one real case in Iran. The sum of the squared (SSQ) or absolute (SAD) deviations between the observed and estimated outflows was considered as objective functions. The results showed that although the new Muskingum model became more complex but this model by using PSO technique can improve the fit to observed flow especially in multiple-peak hydrographs.