Nanolayer CrAlN/TiSiN coating designed for tribological applications

With the goal to produce a hard and tough coating intended for tribological applications, CrAlN/TiSiN nanolayer coating was prepared by alternative deposition of CrAlN and TiSiN layers. In the first part of the article, a detailed study of phase composition, microstructure, and layer structure of CrAlN/TiSiN coating is presented. In the second part, its mechanical properties, fracture and tribological behavior are compared to the nanocomposite TiSiN coating. An industrial magnetron sputtering unit was used for coating deposition. X-ray photoelectron spectroscopy, energy dispersive X-ray spectroscopy, X-ray diffraction, scanning electron microscopy, and transmission electron microscopy were used for compositional and microstructural analysis. Mechanical properties and fracture behavior were studied by instrumented indentation and focused ion beam techniques. Tribological properties were evaluated by ball-on-disk test in a linear reciprocal mode. A complex layer structure was found in the nanolayer coating. The TiSiN layers were epitaxially stabilized inside the coating which led to formation of dislocations at interfaces, to introduction of disturbances in the coating growth, and as a result, to development of fine-grained columnar microstructure. Indentation load required for the onset of fracture was twice lower for the nanolayer CrAlN/TiSiN, compared to the nanocomposite TiSiN coating. This agrees very well with their mechanical properties, with H³/E² being twice higher for the TiSiN coating. However, the nanolayer coating experienced less severe damage, which had a strong impact on tribological behavior. A magnitude of order lower wear rate and four times lower steady state friction coefficient were found for the nanolayer coating.     

Synthesis of new hyperbranched urethane-acrylates and their evaluation in UV-curable coatings

Three series of hyperbranched urethane-acrylates (HB-UA), based on aliphatic hyperbranched polyesters and polyethyleneglycol acrylate, were prepared and evaluated for use in UV-curable coatings. UV-curing kinetics were monitored by FT-IR spectroscopy. The thermal and mechanical properties of UV-cured HB-UA were investigated by means of differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA) and correlated to the mechanical properties of coatings (hardness and flexibility). These new HB-UA's are very reactive and did not show oxygen inhibition. Obtained coatings have good mechanical properties and solvent resistance.     

Sensor control for multi-object state-space estimation using random finite sets

The problem addressed in this paper is information theoretic sensor control for recursive Bayesian multi-object state-space estimation using random finite sets. The proposed algorithm is formulated in the framework of partially observed Markov decision processes where the reward function associated with different sensor actions is computed via the Rényi or alpha divergence between the multi-object prior and the multi-object posterior densities. The proposed algorithm in implemented via the sequential Monte Carlo method. The paper then presents a case study where the problem is to localise an unknown number of sources using a controllable moving sensor which provides range-only detections. Four sensor control reward functions are compared in the study and the proposed scheme is found to perform the best.     

Augmented AVI video file for road surveying

This paper presents a novel video augmentation approach: a video recording of a real entity augmented by structured data. This augmented video is a self-contained entity integrating values of perceptible and non-perceptible attributes of the same real entity. It simplifies data search, storage and management. Streaming, sharing, collaboration and synchronization are also simplified by the fact that the structured data (so-called augmented data) are integrated in the video file and therefore only one file needs to be manipulated. Our approach was applied to address the issues which road engineers experience while using the ROad Measurement and Data Acquisition System (ROMDAS). The ROMDAS system collects and analyses the road-condition state through video recordings and the data corresponding acquired by specific measuring devices. Currently, however, road engineers have to search the videos manually in order to find details of interest provided by the analysis of the data measured due to the separation of the video from the data. The ROMDAS system manages a large collection of road-condition data. It does not offer history management of video recordings of the same road captured at different time. We present this hybrid video augmentation system: the Augmented Video stream Framework (AVF). It allows creation, search, history management and playback of such augmented video files for effective road surveying based on the discrete data recorded in parallel to the video recordings. The AVF provides road engineers with effective and more comfortable perceptible and non-perceptible data search, storage and history management to be used for (collaborative) road inspection and maintenance.     

Explanation and reliability of prediction models: the case of breast cancer recurrence

In this paper, we describe the first practical application of two methods, which bridge the gap between the non-expert user and machine learning models. The first is a method for explaining classifiers’ predictions, which provides the user with additional information about the decision-making process of a classifier. The second is a reliability estimation methodology for regression predictions, which helps the users to decide to what extent to trust a particular prediction. Both methods are successfully applied to a novel breast cancer recurrence prediction data set and the results are evaluated by expert oncologists.     

Adaptive content-based music retrieval system

This paper presents a tunable content-based music retrieval (CBMR) system suitable the for retrieval of music audio clips. The audio clips are represented as extracted feature vectors. The CBMR system is expert-tunable by altering the feature space. The feature space is tuned according to the expert-specified similarity criteria expressed in terms of clusters of similar audio clips. The main goal of tuning the feature space is to improve retrieval performance, since some features may have more impact on perceived similarity than others. The tuning process utilizes our genetic algorithm. The R-tree index for efficient retrieval of audio clips is based on the clustering of feature vectors. For each cluster a minimal bounding rectangle (MBR) is formed, thus providing objects for indexing. Inserting new nodes into the R-tree is efficiently performed because of the chosen Quadratic Split algorithm. Our CBMR system implements the point query and the n-nearest neighbors query with the O(logn) time complexity. Different objective functions based on cluster similarity and dissimilarity measures are used for the genetic algorithm. We have found that all of them have similar impact on the retrieval performance in terms of precision and recall. The paper includes experimental results in measuring retrieval performance, reporting significant improvement over the untuned feature space.     

Semantic integration of enterprise information systems using meta-metadata ontology

This paper proposes a non-domain-specific metadata ontology as a core component in a semantic model-based document management system (DMS), a potential contender towards the enterprise information systems of the next generation. What we developed is the core semantic component of an ontology-driven DMS, providing a robust semantic base for describing documents’ metadata. We also enabled semantic services such as automated semantic translation of metadata from one domain to another. The core semantic base consists of three semantic layers, each one serving a different view of documents’ metadata. The core semantic component’s base layer represents a non-domain-specific metadata ontology founded on ebRIM specification. The main purpose of this ontology is to serve as a meta-metadata ontology for other domain-specific metadata ontologies. The base semantic layer provides a generic metadata view. For the sake of enabling domain-specific views of documents’ metadata, we implemented two domain-specific metadata ontologies, semantically layered on top of ebRIM, serving domain-specific views of the metadata. In order to enable semantic translation of metadata from one domain to another, we established model-to-model mappings between these semantic layers by introducing SWRL rules. Having the semantic translation of metadata automated not only allows for effortless switching between different metadata views, but also opens the door for automating the process of documents long-term archiving. For the case study, we chose judicial domain as a promising ground for improving the efficiency of the judiciary by introducing the semantics in this field.     

Nelson Goodman’s Arguments Against Perspective: A Geometrical Analysis

Nelson Goodman’s highly influential book Languages of Art argued that that all visual experience is merely a cultural construct. In presenting his thesis Goodman relied on a series of geometrical arguments intended to show that the geometrical construction of perspective does not rely on the geometry of light rays. In this article I analyze the geometrical validity of Goodman’s arguments.     

Development of ultra–low highly active and durable hybrid compressive platinum lattice cathode catalysts for polymer electrolyte membrane fuel cells

A highly active and stable catalyst/support system is developed by using a two-step process. In the first step, activated carbon composite support (ACCS) is synthesized that retains its activity after accelerated stress test (AST). A 30% Pt/ACCS catalyst shows no loss of mass activity and power density after 5000 cycles at 1.0–1.5 V while the commercial Pt/C and Pt/290G catalysts show drastic mass activity losses (57.5% and 66.2%, respectively) and power density losses (88.7% and 84.0%, respectively). In the second step, Pt catalyst with a compressive Pt lattice (Pt¹) is synthesized through a USC-developed annealing procedure in which Co atoms previously embedded in the support diffuse into Pt. The 30% Pt¹/ACCS shows high initial power density (rated) of 0.174 g_Pt kW^?1 and high stability of 24 mV loss at 0.8 A cm^?2 with an electrochemical active surface area (ECSA) loss of 42% after 30,000 cycles (0.6–1.0 V). The support stability under 1.0–1.5 V potential cycling shows potential loss of 8 mV at 1.5 A cm^?2 and ECSA loss of 22% after 5000 cycles. Improved stability and activity of Pt*/ACCS catalyst are due to synergistic effect of catalytic activity and stability of ACCS and formation of compressive Pt lattice catalyst.