Tribotronics—Towards active tribology

A constant trend towards more compact mechanical systems with higher power densities and increased thermo-mechanical loads emphasises the importance of the development of new design approaches and novel tribological systems. Ignoring this may cause a significant slow down in technological and industrial development. Tribotronics or active tribology based on adaptive performance is thought of as being critical in the implementation of smart machine concepts. Recognition of the importance of tribotronics, or active control of system loss outputs, such as those through friction and wear will have significant beneficial economic consequences as a result of the associated accelerated rate of technological progress. These smart tribotronic systems can be embedded in a great variety of machines and mechanisms. If this integration is made at the design stage, products that are more flexible, efficient and reliable can be produced. The concept of tribotronics is presented and discussed in this paper. Some illustrative examples that show the feasibility of an “active” approach are given. In addition, various possibilities already reported in literature are discussed.     

Computing distance transformations in convex and non-convex domains

It is shown that the well known two-pass sequential local transformation algorithm for computing a distance transformation in rectangular domains may fail in some convex integer domains, but that a four-pass algorithm is sufficient in all two-dimensional convex domains. For non-convex domains the number of passes necessary is shown to be generally greater. Two propagation algorithms for computing the distance transformation are described and shown theoretically and experimentally to be computationally more efficient than the sequential local transformation algorithm in non-convex domains of complex shape. The relationship of the distance transformation in non-convex domains to some more general transformations is explored.     

Determination of the molecular weight distribution of cellulose on calibrated gel columns

CMC samples obtained by gel filtration on a preparative scale were used to calibrate an agarose column. The relation between molecular weight and the peak elution volume for monodisperse samples were calculated as well as the relation between band broadening and the peak elution volume. In the calculations of the molecular weight distribution curves of the studied CMC samples, consideration was given to the variable band broadening by proper transformations of the experimental data, thus making it possible to obtain numerical solutions free from oscillations. The method admits a rapid determination of the molecular weight distribution of cellulose and related materials.     

Antioxidative effects of polyamines

The antioxidative effects of spermine, spermidine and putrescine were determined by measurement of primary and secondary oxidation products of polyunsaturated fatty acids, using gas and liquid chromatography as well as spectrophotometric recordings. It was demonstrated that polyamines inhibit the oxidation of polyunsaturated fatty acids,α-tocopherol and carotenoid pigments. Both linear and nonlinear dose/response relationships have been observed. The efficiency of a given polyamine was correlated with the number of amine groups in the molecule. Spermine was, thus, more efficient than spermidine, which in turn had a higher efficiency than putrescine. The relative antioxidative effect was as follows: spermine (100.0), spermidine (61.0), putrescine (23.0), ethoxyquin (27.6), ascorbyl palmitate (18.3), octyl gallate (7.9), tert butylhydroquinone (6.3), butylated hydroxyanisole (3.6) andα-tocopherol (3.4).     

Note: The intermodulation lockin analyzer

Nonlinear systems can be probed by driving them with two or more pure tones while measuring the intermodulation products of the drive tones in the response. We describe a digital lockin analyzer which is designed explicitly for this purpose. The analyzer is implemented on a field-programmable gate array, providing speed in analysis, real-time feedback, and stability in operation. The use of the analyzer is demonstrated for intermodulation atomic force microscopy. A generalization of the intermodulation spectral technique to arbitrary drive waveforms is discussed.     

Grid resource brokering algorithms enabling advance reservations and resource selection based on performance predictions

We present algorithms, methods, and software for a Grid resource manager, that performs resource brokering and job scheduling in production Grids. This decentralized broker selects computational resources based on actual job requirements, job characteristics, and information provided by the resources, with the aim to minimize the total time to delivery for the individual application. The total time to delivery includes the time for program execution, batch queue waiting, and transfer of executable and input/output data to and from the resource. The main features of the resource broker include two alternative approaches to advance reservations, resource selection algorithms based on computer benchmark results and network performance predictions, and a basic adaptation facility. The broker is implemented as a built-in component of a job submission client for the NorduGrid/ARC middleware.     

Learning to Locate Informative Features for Visual Identification

Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, inter-object variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive “training” examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle object-specific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an on-line algorithm that takes one image from a known category and builds an efficient “same” versus “different” classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, category-specific characteristics are learned automatically in an off-line training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods.