Electro-copolymerisation of indene and styrene

Electroinitiated cationic copolymerisation of indene and styrene was investigated by constant potential electrolysis. Effects of copolymerisation potential and temperature on the copolymer composition and the reactivity ratios are discussed. The reactivity ratios of the monomers were calculated according to the integrated Lewis-Mayo equation.     

3D Hair sketching for real-time dynamic & key frame animations

Physically based simulation of human hair is a well studied and well known problem. But the “pure” physically based representation of hair (and other animation elements) is not the only concern of the animators, who want to “control” the creation and animation phases of the content. This paper describes a sketch-based tool, with which a user can both create hair models with different styling parameters and produce animations of these created hair models using physically and key frame-based techniques. The model creation and animation production tasks are all performed with direct manipulation techniques in real-time.     

Fuzzy rule interpolation for multidimensional input spaces in determining d50c of hydrocyclones

Fuzzy rule-based systems have been very popular in many engineering applications. In mineral engineering, fuzzy rules are normally constructed using some fuzzy rule extraction techniques to establish the determination model in predicting the d50c of hydrocyclones. However, when generating fuzzy rules from the available information, it may result in a sparse fuzzy rule base. The use of more than one input variable is also common in hydrocyclone data analysis. This paper examines the application of fuzzy interpolation to resolve the problems using sparse fuzzy rule bases, and to perform analysis of fuzzy interpolation in multidimensional input spaces.     

A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory

The Journal of Supercomputing - In recent years, deep artificial neural networks can have better forecasting performance than many other artificial neural networks. The long short-term memory...     

CFAR processing with switching exponential smoothers for nonhomogeneous environments

Conventional constant false alarm rate (CFAR) methods use a fixed number of cells to estimate the background variance. For homogeneous environments, it is desirable to increase the number of cells, at the cost of increased computation and memory requirements, in order to improve the estimation performance. For nonhomogeneous environments, it is desirable to use less number of cells in order to reduce the number of false alarms around the clutter edges. In this work, we present a solution with two exponential smoothers (first order IIR filters) having different time-constants to leverage the conflicting requirements of homogeneous and nonhomogeneous environments. The system is designed to use the filter having the large time-constant in homogeneous environments and to promptly switch to the filter having the small time constant once a clutter edge is encountered. The main advantages of proposed Switching IIR CFAR method are computational simplicity, small memory requirement (in comparison to windowing based methods) and its good performance in homogeneous environments (due to the large time-constant smoother) and rapid adaptation to clutter edges (due to the small time-constant smoother).     

Simultaneous afterclearing and decolorisation by ozonation after disperse dyeing of polyester

A set of trials have been conducted to examine the efficiency of ozonation on afterclearing of disperse dyed poly(ethylene terephthalate) fibres. Ozonation was performed in the cooled dyebath after the completion of the dyeing cycle. The ozone concentration used was 12.8 ± 0.3 mg/min at a gas flow rate of 400 ml/min. The results indicate that 3 min ozonation time is appropriate to achieve wash fastness results comparable to conventional reduction clearing. These results were obtained with simultaneous dyebath decolorisation ratios up to 67% and without significant colour yield ( K/S value) losses of the dyed fabric. Ozonation periods exceeding 3 min caused significant colour yield ( K/S value) losses, although dyebath decolorisation ratios increased up to 82% and wash fastness properties further improved. The chemical oxygen demand of the dyeing process decreased up to 62% by the ozonation afterclearing. The advantages of the ozonation afterclearing process are savings in terms of water, energy and time and reduction in environmental load.     

A modified genetic algorithm for forecasting fuzzy time series

Fuzzy time series approaches are used when observations of time series contain uncertainty. Moreover, these approaches do not require the assumptions needed for traditional time series approaches. Generally, fuzzy time series methods consist of three stages, namely, fuzzification, determination of fuzzy relations, and defuzzification. Artificial intelligence algorithms are frequently used in these stages with genetic algorithms being the most popular of these algorithms owing to their rich operators and good performance. However, the mutation operator of a GA may cause some negative results in the solution set. Thus, we propose a modified genetic algorithm to find optimal interval lengths and control the effects of the mutation operator. The results of applying our new approach to real datasets show superior forecasting performance when compared with those obtained by other techniques.     

Enhanced user performance in an image gallery application with a mobile autostereoscopic touch display

In this study, we explored how stereoscopic depth affects performance and user experience in a mobile device with an autostereoscopic touch display. Participants conducted a visual search task with an image gallery application on three layouts with different depth ranges. The task completion times were recorded, and the participants were asked to rate their experiences. The results revealed that the image search times were facilitated by a mild depth effect and that too great a depth slowed search times and decreased user-experience ratings.     

Fuzzy multiattribute evaluation of R&D projects using a real options valuation model

R&D project selection decision is very important in two ways. First, in many organizations, R&D budget represents huge investment. Project selection decisions could be thought with the strategic objectives and plans of the firm. Second, R&D projects' organizational returns are multidimensional in nature and risky in terms of projected outcome. Real options approach helps to calculate this risky side of the selection process. This paper considers that multidimensional side of the R&D project selection process. Another consideration is the vagueness in the evaluation process. The fuzzy analytic hierarchy process, which takes monetary (fuzzy real option value) and nonmonetary (capability, success probability, trends, etc.) criteria into account, is used to make this selection among alternative R&D projects. A real case study is given to illustrate the application of the proposed approach. © 2008 Wiley Periodicals, Inc.     

The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting

Computational Economics - Looking at the artificial neural networks’ literature, most of the studies started with feedforward artificial neural networks and the training of many feedforward...