Metallic Nanoparticles in Heterogeneous Catalysis

Catalysis Letters - Heterogeneous catalysis is a chemical process achieved at solid–gas or solid–liquid interfaces. Many factors including the particle size, shape and metal-support...     

Investigation on the cutting quality characteristics of abrasive water jet machining of AA6061-B4C-hBN hybrid metal matrix composites

Aluminum metal matrix composites (AMMCs) explicitly show better physical and mechanical properties as compared to aluminum alloys and results in a more preferred material for a wide range of applications. The addition of reinforcements embargo AMMCs employment to industry requirements by increasing order of machining complexity. However, it can be machined with a high order of surface integrity by nonconventional approaches like abrasive water jet machining. Hybrid aluminum alloy composites were reinforced by B₄C (5–15?vol%) and solid lubricant hBN (15?vol%) particles and fabricated using a liquid metallurgy route. This research article deals with the experimental investigation on the effect of process parameters such as mesh size, abrasive flow rate, water pressure and work traverse speed of abrasive water jet machining on hybrid AA6061-B₄C-hBN composites. Water jet pressure and traverse speed have been proved to be the most significant parameters which influenced the responses like kerf taper angle and surface roughness. Increase in reinforcement particles affects both the kerf taper angle and surface roughness. SEM images of the machined surface show that cutting wear mechanism was largely operating in material removal.     

DanVideo: an MPEG-7 authoring and retrieval system for dance videos

A well-annotated dance media is an essential part of a nation’s identity, transcending cultural and language barriers. Many dance video archives suffer from problems concerning authoring and access, because of the complex spatio-temporal relationships that exist between the dancers in terms of movements of their body parts and the emotions expressed by them in a dance. This paper presents a system named DanVideo for semi-automatic authoring and access to dance archives. DanVideo provides methods of annotation and authoring and retrieval tools for choreographers, dancers, and students. We demonstrate how dance media can be semantically annotated and how this information can be used for the retrieval of the dance video semantics. In particular, DanVideo offers an MPEG-7 based semi-automatic authoring tool that takes dance video annotations generated by dance experts and produces MPEG-7 metadata. DanVideo also has a search engine that takes users’ queries and retrieves dance semantics from metadata arranged using tree-embedding technique and based on spatial, temporal and spatio-temporal features of dancers. The search engine also leverages a domain-specific ontology to process knowledge-based queries. We have assessed the dance-video queries and semantic annotations in terms of precision, recall, and fidelity.     

Performance analysis of allocation policies for interGrid resource provisioning

Several Grids have been established and used for varying science applications during the last years. Most of these Grids, however, work in isolation and with different utilisation levels. Previous work has introduced an architecture and a mechanism to enable resource sharing amongst Grids. It has demonstrated that there can be benefits for a Grid to offload requests or provide spare resources to another Grid. In this work, we address the problem of resource provisioning to Grid applications in multiple-Grid environments. The provisioning is carried out based on availability information obtained from queueing-based resource management systems deployed at the provider sites which are the participants of the Grids. We evaluate the performance of different allocation policies. In contrast to existing work on load sharing across Grids, the policies described here take into account the local load of resource providers, imprecise availability information and the compensation of providers for the resources offered to the Grid. In addition, we evaluate these policies along with a mechanism that allows resource sharing amongst Grids. Experimental results obtained through simulation show that the mechanism and policies are effective in redirecting requests thus improving the applications’ average weighted response time.     

An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the non-linearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.     

Design of fractal patch antenna for size and radar cross-section reduction

This research study presents a novel design of star-shaped fractal patch antenna for miniaturisation and backscattering radar cross-section (RCS) reduction. The proposed fractal antenna gives 50% size reduction compared with a conventional circular microstrip patch (CCMP) antenna. The antenna is studied experimentally for return loss behaviour using vector network analyser R&S ZVA40. It can be useful for wireless application in 0.85-4 GHz frequency band. Further, the study focuses on backscattering RCS (both monostatic and bistatic) reduction by the proposed antenna compared with the CCMP antenna. It is found that increase in number of fractal iterations included in the conventional patch to design fractal antenna geometry reduces backscattering RCS at multiband compared to the conventional patch antenna. This reduction in backscattering RCS by the antenna is observed at multiband. The antenna can be tuned for low backscattering by variation in the substrate dielectric constant and thickness and the superstrate dielectric constant and thickness. For maximum RCS reduction by the antenna, optimisation of substrate thickness becomes necessary. The study also deals with effect of frequency and aspect angle variation on backscattering RCS reduction.     

Study and Analysis of the Effect of Harmonics on the Hot Spot Temperature of a Distribution Transformer Using Finite-Volume Method

Transformers are critical components in power systems and their failure can cause long interruption of power supply. The condition of a transformer can be monitored by performing thermal analysis. The use of non-linear devices, such as rectifiers and converters, draws harmonic currents that increase losses in transformers, thereby increasing their operating temperature. In this article, a new numerical approach is presented for determining the rise in hot spot temperature in a 5-kVA, 400/400-V dry-type three-phase transformer laboratory prototype. The key novelty is that the additional winding eddy current loss due to non-linear loads is considered in the numerical modeling. The winding eddy current loss corresponding to harmonic distortion is estimated by conducting experiments and calculations. Numerical simulations are carried out for a wide range of non-linear loads using a commercial computational fluid dynamics package, FLUENT 6.3. The proposed numerical methodology is validated by performing experiments on the transformer for possible non-linear loads and comparing the measured hot spot temperature with the simulated values. Correlation equations for rise in hot spot temperature as a function of total harmonic distortion are presented, which can be used for estimating the life of transformers when connected to different types of loads.