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.     

Forecasting of Turkey's net electricity energy consumption on sectoral bases

In this study forecast of Turkey's net electricity energy consumption on sectoral basis until 2020 is explored. Artificial neural networks (ANN) is preferred as forecasting tool. The reasons behind choosing ANN are the ability of ANN to forecast future values of more than one variable at the same time and to model the nonlinear relation in the data structure. Founded forecast results by ANN are compared with official forecasts.     

Rapid analysis of polymer homologues and additives with SFE/SFC-MS coupling

A rapid and easy analysis method for polymers is presented. The method involves sample preparation by SFE, separation of the extracted compounds by SFC and simultaneous quantitative detection by FID, as well as identification of unknowns by MS. The applications illustrate how structural research work and routine polymer analysis can be done with this time saving method.     

Representation and uniformization of algebraic transductions

This paper explores different means of representation for algebraic transductions, i.e., word relations realized by pushdown transducers. The relevance of this work lies more in its point of view rather than any particular result. We are aiming at giving specific techniques for obtaining, or perhaps explaining, decompositions of algebraic (and incidentally, rational) relations, relying solely on their “machine” definition rather than some complex algebraic apparatus. From this point of view, we are hoping to have demystified the heavy formalism employed in the present literature. Some of the novelties of our work are: the use of “stack languages” and “embeddings,” which eliminate the need of arbitrary context-free languages in our characterizations, the study of uniformizations for algebraic transductions and the use of the so-called stack transductions for exposing the anatomy of pushdown transducers.This work was supported by the Natural Science and Engineering Research Council of Canada grants R220259 and OGP0041630.     

General solution of one-dimensional,time dependent coupled heat flow systems

A new, powerful method of analysis, involving the combined use of finite integral transform and finite element techniques, is presented for the solution of time dependent heat flow systems composed of many one-dimensional elements connected through the nodes. This method leads to an eigenvalue problem which is not of the conventional Sturm-Liouville type. A procedure for the determination of the eigenvalues is described. The solution obtained is in the form of an infinite series and contains quasi-steady and transient terms. The general solution obtained can be applied in the mathematical modelling of many engineering applications such as the determination of the penetration of the daily temperature cycle into buildings, the analysis of heat transfer in array of extended surfaces in compact heat exchangers, and many others.     

A note on double lacunary statistical $$\sigma$$-convergence of fuzzy numbers

Quite recently, Sava? (Appl Math Lett 21:134–141, 2008), defined the lacunary statistical analogue for double sequence \(X=\{X_{k,l}\}\) of fuzzy numbers as follows: a double sequence \(X=\{X_{k,l}\}\) is said to be lacunary P-statistically convergent to \(X_{0}\) provided that for each \(\epsilon >0\)

$ P-\lim_{r,s}\frac{1}{h_{r,s}}\left | \{(k,l)\in I_{r,s}: d(X_{k,l },X_0)\geq \epsilon\}\right|= 0. $

In this paper we introduce and study double lacunary \(\sigma\)-statistical convergence for sequence of fuzzy numbers and also we get some inclusion theorems.

     

Decentralized observers with consensus filters for distributed discrete-time linear systems

This paper presents a decentralized observer with a consensus filter for the state observation of discrete-time linear distributed systems. Each agent in the distributed system has an observer with a model of the plant that utilizes the set of locally available measurements, which may not make the full plant state detectable. This lack of detectability is overcome by utilizing a consensus filter that blends the state estimate of each agent with its neighbors’ estimates. It is proven that the state estimates of the proposed observer exponentially converge to the actual plant states under arbitrarily changing, but connected, communication and pseudo-connected sensing graph topologies. Except these connectivity properties, full knowledge of the sensing and communication graphs is not needed at the design time. As a byproduct, we obtained a result on the location of eigenvalues, i.e., the spectrum, of the Laplacian for a family of graphs with self-loops.     

A scalable algorithm for extraction and clustering of event-related pictures

The event detection problem, which is closely related to clustering, has gained a lot of attentions within event detection for textual documents. However, although image clustering is a problem that has been treated extensively in both Content-Based Image Retrieval (CBIR) and Text-Based Image Retrieval (TBIR) systems, event detection within image management is a relatively new area. Having this in mind, we propose a novel approach for event extraction and clustering of images, taking into account textual annotations, time and geographical positions. Our goal is to develop a clustering method based on the fact that an image may belong to an event cluster. Here, we stress the necessity of having an event clustering and cluster extraction algorithm that are both scalable and allow online applications. To achieve this, we extend a well-known clustering algorithm called Suffix Tree Clustering (STC), originally developed to cluster text documents using document snippets. The idea is that we consider an image along with its annotation as a document. Further, we extend it to also include time and geographical position so that we can capture the contextual information from each image during the clustering process. This has appeared to be particularly useful on images gathered from online photo-sharing applications such as Flickr. Hence, our STC-based approach is aimed at dealing with the challenges induced by capturing contextual information from Flickr images and extracting related events. We evaluate our algorithm using different annotated datasets mainly gathered from Flickr. As part of this evaluation we investigate the effects of using different parameters, such as time and space granularities, and compare these effects. In addition, we evaluate the performance of our algorithm with respect to mining events from image collections. Our experimental results clearly demonstrate the effectiveness of our STC-based algorithm in extracting and clustering events.     

An investigation on wear behavior of UHMWPE/carbide composites at elevated temperatures

Ultra-high molecular weight polyethylene (UHMWPE) is extensively used in frictional applications due to its advanced wear resistance. This advanced polymer is reinforced with hard particulate fillers for further developments against wear conditions. Since elevated temperatures prevail in the service conditions, wear behavior of UHMWPE composites is an important issue for the engineering applications. In the present work, UHMWPE-based composites including silicon carbide (SiC) fillers were fabricated in a compression molding chamber. In the specimen preparation stage, molding pressure, filler amount, and filler particle size were varied to investigate the influence of these variables. Upon deciding the optimum parameters from the wear tests conducted at room temperature, the wear experiments were repeated for the optimum specimen at elevated temperatures, such as 40 and 60°C. According to the results, the wear behavior of the SiC/UHMWPE composites is heavily changed by the effect of elevated temperature. Adhesive effect is pronounced at elevated temperatures while the wear characteristics possess the abrasive effect in the sliding path. In addition, the composites exhibit an accelerated material loss as temperature increases during the frictional system.     

Improvements of the structural,thermal, and mechanical properties of structural adhesive with functionalized boron nitride nanoparticles

Investigations on the production and development of nanoparticle-reinforced polymer materials have been attracted attention by researchers. Various nanoparticles have been used to improve the mechanical, chemical, thermal, and physical properties of polymer matrix composites. Boron compounds come to the fore to improve the mechanical and thermal properties of polymers. In this study, mechanical, thermal, and structural properties of structural adhesive have been examined by adding nano hexagonal boron nitride (h-BN) to epoxy matrix at different percentages (0.5, 1, 2, 3, 4, and 5%). For this purpose, nano h-BN particles were functionalized with 3-aminopropyltriethoxysilane (APTES) to disperse the h-BN nanoparticles homogeneously in epoxy matrix and to form a strong bond at the matrix interface. Two-component structural epoxy adhesive was modified by using functionalized h-BN nanoparticles. The structural and thermal properties of the modified adhesives were investigated by scanning electron microscopy and energy dispersion X-ray spectroscopy, Fourier transform infrared spectroscopy, differential scanning calorimetry, and thermogravimetric analysis techniques. Tensile test and dynamic mechanical analysis were performed to determine the mechanical properties of the adhesives. When the results obtained from analysis were examined, it was seen that the nano h-BN particles functionalized with APTES were homogeneously dispersed in the epoxy matrix and formed a strong bond. In addition that, it was concluded from the experimental results that the thermal and mechanical properties of adhesives were improved by adding functionalized nano h-BN particles into epoxy at different ratios.