Synthesis of MoSi2-TiB2 Nanocomposite by Mechanical Alloying through Two Methods and Comparison of Their Properties for Use in Thermal Spraying Process

this study, MoSi2-TiB2 nanocomposites with 10 and 20 wt.% of TiB2 were synthesized by mechanical alloying through two different methods. In the first method, elemental powders of molybdenum, silicon, titanium and boron were milled together for 60 hours. In the second method, MoSi2 was made by 30-hours milling of Mo and Si. Then, commercial TiB2 was added to the matrix and milling was continued for another 30 hours. Heat treatment was carried out on the resultant specimens at 1000˚C for 60 min. The effect of mechanical alloying on grain size and lattice strain was investigated by Williamson-Hall method using XRD patterns. The mechanical properties of the samples were determined by hardness test. It was found that TiB2 added to MoSi2 increased hardness considerably. Agglomeration process was carried out on the powders to be used in thermal spray process. The morphology and microstructure of the milled powders before and after agglomeration process were studied by SEM. The sphericity and particle size distribution of agglomerated particles were evaluated using Clemex software. The results showed that the nanocomposite powder produced by the first method had a higher quality for thermal spray process due to its higher hardness compared to the second one. It also had adequate particles sphericity

Kernel Learning: Automatic Selection of Optimal Kernels

Kernel methods are widely used to address a variety of learning tasks including classification, regression, ranking, clustering, and dimensionality reduction. The appropriate choice of a kernel is often left to the user. But, poor selections may lead to sub-optimal performance. Furthermore, searching for an appropriate kernel manually may be a time-consuming and imperfect art. Instead, the kernel selection process can be included as part of the overall learning problem. In this way, better performance guarantees can be given and the kernel selection process can be made automatic. In this workshop, we will be concerned with using sampled data to select or learn a kernel function or kernel matrix appropriate for the specific task at hand. We will discuss several scenarios, including classification, regression, and ranking, where the use of kernels is ubiquitous, and different settings including inductive, transductive, or semi-supervised learning. We also invite discussions on the closely related fields of features selection and extraction, and are interested in exploring further the connection with these topics. The goal is to cover all questions related to the problem of learning kernels: different problem formulations, the computational efficiency and accuracy of the algorithms that address these problems and their different strengths and weaknesses, and the theoretical guarantees provided. What is the computational complexity? Does it work in practice? The formulation of some other learning problems, e.g. multi-task learning problems, is often very similar. These problems and their solutions will also be discussed in this workshop

Balancing traffic load in wireless networks with curveball routing

We address the problem of balancing the traffic load in multi-hop wireless networks. We consider a point-to-point communicating network with a uniform distribution of source-sink pairs. When routing along shortest paths, the nodes that are centrally located forward a disproportionate amount of traffic. This translates into increased congestion and energy consumption. However, the maximum load can be decreased if the packets follow curved paths. We show that the optimum such routing scheme can be expressed in terms of geometric optics and computed by linear programming. We then propose a practical solution, which we call Curveball Routing that achieves results not much worse than the optimum. We evaluate our solution at three levels of fidelity: a Java high-level simulator, the ns2 simulator, and the Intel Mirage Sensor Network Testbed. Simulation results using the high-level simulator show that our solution successfully avoids the crowded center of the network, and reduces the maximum load by up to 40%. At the same time, the increase of the expected path length is small, i.e., only 8 % on average. Simulation results using the ns2 simulator show that our solution can increase throughput on moderately loaded networks by up to 15%, while testbed results show a reduction in peak message load by up to 25%. Our prototype suggests that our solution is easily deployable