Patch layout from feature graphs

The structuring of surface meshes is a labor intensive task in reverse engineering. For example, in CAD, scanned triangle meshes must be divided into characteristic/uniform patches to enable conversion into high-level spline surfaces. Typical industrial techniques, like rolling ball blends, are very labor intensive.We provide a novel, robust and quick algorithm for the automatic generation of a patch layout based on a topology consistent feature graph. The graph separates the surface along feature lines into functional and geometric building blocks. Our algorithm then thickens the edges of the feature graph and forms new regions with low varying curvature. Further, these new regions-so-called fillets and node patches-will have highly smooth boundary curves, making the algorithm an ideal preprocessor for a subsequent spline fitting algorithm.     

Method of classifier selection using the genetic approach

Abstract: The paper presents a novel machine learning algorithm used for training a compound classifier system that consists of a set of area classifiers. Area classifiers recognize objects derived from the respective competence area. Splitting feature space into areas and selecting area classifiers are two key processes of the algorithm; both take place simultaneously in the course of an optimization process aimed at maximizing the system performance. An evolutionary algorithm is used to find the optimal solution. A number of experiments have been carried out to evaluate system performance. The results prove that the proposed method outperforms each elementary classifier as well as simple voting.     

Model predictive control of a rotary cement kiln

A first principles model of a cement kiln is used to control and optimize the burning of clinker in the cement production process. The model considers heat transfer between a gas and a feed state via convection and radiation. Furthermore, it contains effects such as chemical reactions, feed transport, energy losses and energy input. A model predictive controller is used to stabilize a temperature profile along the rotary kiln, guarantee good combustion conditions and maximize production. Moving horizon estimation was used for online estimation of selected model parameters and unmeasured states. Results from the pilot site are presented.     

A learning-based colour image segmentation with extended and compact structural tensor feature representation

In this paper a novel Tensor-Based Image Segmentation Algorithm (TBISA) is presented, which is dedicated for segmentation of colour images. A purpose of TBISA is to distinguish specific objects based on their characteristics, i.e. shape, colour, texture, or a mixture of these features. All of those information are available in colour channel data. Nonetheless, performing image analysis on the pixel level using RGB values, does not allow to access information on texture which is hidden in relation between neighbouring pixels. Therefore, to take full advantage of all available information, we propose to incorporate the Structural Tensors as a feature extraction method. It forms enriched feature set which, apart from colour and intensity, conveys also information of texture. This set is next processed by different classification algorithms for image segmentation. Quality of TBISA is evaluated in a series of experiments carried on benchmark images. Obtained results prove that the proposed method allows accurate and fast image segmentation.     

QuadCover - Surface Parameterization using Branched Coverings

We introduce an algorithm for the automatic computation of global parameterizations on arbitrary simplicial 2-manifolds, whose parameter lines are guided by a given frame field, for example, by principal curvature frames. The parameter lines are globally continuous and allow a remeshing of the surface into quadrilaterals. The algorithm converts a given frame field into a single vector field on a branched covering of the 2-manifold and generates an integrable vector field by a Hodge decomposition on the covering space. Except for an optional smoothing and alignment of the initial frame field, the algorithm is fully automatic and generates high quality quadrilateral meshes.     

Yielding of Microstructured Geomaterial by Distinct Element Method Analysis

The purpose of this paper is to present macro- and micro-study on the yielding of microstructured geomaterials by numerical experiments. This target is achieved by carrying out 63 one-dimensional and biaxial compressions tests on an idealized bonded geomaterial with an extension of distinct element method, into which bond contact models proposed were implemented. Numerical results indicate that: (a) preconsolidated pressure appears to attribute to bond and looseness in the geomaterials, and an increase in void ratio leads to a decrease in yielding stress in one-dimensional tests; (b) an increase in bonding strength at interparticle contacts results in an increase in yielding stress and cohesion, and an internal friction angle that is smaller than the critical state value; (c) the observed first-yielding (initiation of bond breakage) is stress path dependent, and gross-yield (defined with respect to volumetric strain) of microstructured geomaterials is evidently related to bond breakage.     

Antibiotic susceptibility of Bifidobacterium thermophilum and Bifidobacterium pseudolongum isolates from animal sources

The widespread use of antimicrobial substances has led to resistant populations of microorganisms in several ecosystems. In animal husbandry, the application of antibiotics has contributed to resistance development in pathogenic and commensal bacteria. These strains or their resistance genes can be spread along several ecological routes, including the food chain. Antibiotic resistance is important in terms of the safety of industrial strains, such as probiotics for food and feed. Bifidobacterium thermophilum and Bifidobacterium pseudolongum are known to comprise the major part of the bifidobacterial microbiota in the gut and feces of cattle and pigs. In this study, the antimicrobial susceptibility in bifidobacterial isolates of these species was investigated. Isolates from the beef and pork production chain were identified and typed to strain level, and the antimicrobial susceptibility level was tested to a set of antibiotics. Isolates with low susceptibility levels were screened by PCR for already described resistance genes. Strains atypically resistant to clindamycin, erythromycin, and tetracycline were determined. The resistance genes tet(O), tet(W), and erm(X) were detected in the bifidobacterial species that were examined.     

Atomic RMI: A Distributed Transactional Memory Framework

This paper presents Atomic RMI, a distributed transactional memory framework that supports the control flow model of execution. Atomic RMI extends Java RMI with distributed transactions that can run on many Java virtual machines located on different network nodes. Our system employs SVA, a fully-pessimistic concurrency control algorithm that provides exclusive access to shared objects and supports rollback and fault tolerance. SVA is capable of achieving a relatively high level of parallelism by interweaving transactions that access the same objects and by making transactions that do not share objects independent of one another. It also allows any operations within transactions, including irrevocable ones, like system calls, and provides an unobtrusive API. Our evaluation shows that in most cases Atomic RMI performs better than fine grained mutual-exclusion and read/write locking mechanisms. Atomic RMI also performs better than an optimistic transactional memory in environments with high contention and a high ratio of write operations, while being competitive otherwise.