Flow profile measurement in microchannel using the optical feedback interferometry sensing technique

The need to accurately measure flow profiles in microfluidic channels is well recognised. In this work, we present a new optical feedback interferometry (OFI) flow sensor that accurately measures local velocity in fluids and enables reconstruction of a velocity profile inside a microchannel. OFI is a self-aligned interferometric technique that uses the laser as both the transmitter and the receiver thus offering high sensitivity, fast response, and a simple and compact optical design. The system described here is based on a commercial semiconductor laser and has been designed to achieve a micrometer-range spatial resolution. The sensor performance was validated by reconstructing the velocity profile inside a circular cross-section flow-channel with 320  $\upmu $ m internal diameter, with a relative error smaller than 1.8 %. The local flow velocity is directly measured, thus avoiding the need for model based profile calculation and uncertainties inherent to this approach. The system was validated by successfully extracting the flow profiles in both Newtonian and shear-thinning liquids.     

Metal Alloys for Fusion‐Based Additive Manufacturing

Metal additive manufacturing (AM) is an innovative manufacturing technique, which builds parts incrementally layer by layer. Thus, metal AM has inherent advantages in part complexity, time, and waste saving. However, due to its complex thermal cycle and rapid solidification during processing, the alloys well suit and commercially used for metal AM today are limited. Therefore, it is important to understand the alloying strategy and current progress with materials performance to consider alloy development for metal AM. This review presents the current range of alloys available for metal AM, including titanium, steel, nickel, aluminum, less common alloys (including Mg alloys, metal matrix composites alloys, and low melting point alloys), and compositionally complex alloys (including bulk metallic glasses and high entropy alloys) with a focus on the relationship between compositions, processing, microstructures, and properties of each alloy system. In addition, some promising alloy systems for metal AM are highlighted. Approaches for designing and optimizing new materials for metal AM have been summarized.

     

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.     

Convex combinations of strict t-norms

Alsina, Frank, and Schweizer posed a question whether a non-trivial convex combination of triangular norms can ever be a triangular norm. In this paper we deal with the class of strict t-norms with well-defined first partial derivatives along their zero borders. We show that the answer to the question is negative for certain couples of such t-norms and we state out possible further research.     

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.     

Influence of Particle Size Distribution,Moisture Content,and Particle Shape on the Flow Properties of Bulk Solids

Abstract

There has been a great deal of research on characterising bulk solids. However, some of the characteristics, particularly the time‐dependent and random changes of properties, still remain insufficiently understood. The focus of this article is to study the influence of particle size, size distribution, moisture content, and particle shape on the unconfined yield strength of bulk solids. We present some interesting results about the influence of the particle size distribution on the unconfined yield strength of a bulk solid and a new method for determining the influence of particle shape on the unconfined yield strength of a bulk solid, which was made possible by the design and use of a novel attritor.     

Designing fast LTL model checking algorithms for many-core GPUs

Recent technological developments made various many-core hardware platforms widely accessible. These massively parallel architectures have been used to significantly accelerate many computation demanding tasks. In this paper, we show how the algorithms for LTL model checking can be redesigned in order to accelerate LTL model checking on many-core GPU platforms. Our detailed experimental evaluation demonstrates that using the NVIDIA CUDA technology results in a significant speedup of the verification process. Together with state space generation based on shared hash-table and DFS exploration, our CUDA accelerated model checker is the fastest among state-of-the-art shared memory model checking tools.     

A novel split-and-merge algorithm for hierarchical clustering of Gaussian mixture models

The paper presents a novel split-and-merge algorithm for hierarchical clustering of Gaussian mixture models, which tends to improve on the local optimal solution determined by the initial constellation. It is initialized by local optimal parameters obtained by using a baseline approach similar to k-means, and it tends to approach more closely to the global optimum of the target clustering function, by iteratively splitting and merging the clusters of Gaussian components obtained as the output of the baseline algorithm. The algorithm is further improved by introducing model selection in order to obtain the best possible trade-off between recognition accuracy and computational load in a Gaussian selection task applied within an actual recognition system. The proposed method is tested both on artificial data and in the framework of Gaussian selection performed within a real continuous speech recognition system, and in both cases an improvement over the baseline method has been observed.