International Journal of Computer and Applications

Adsorption of emerging contaminants from water and wastewater by modified biochar: A review.

A convolutional discriminative feature learning method is presented for induction motor fault diagnosis. The approach firstly utilizes back-propagation (BP)-based neural network to learn local filters capturing discriminative information. Then, a feed-forward convolutional pooling architecture is built to extract final features through these local filters. Due to the discriminative learning of BP-based neural network, the learned local filters can discover potential discriminative patterns. Also, the convolutional pooling architecture is able to derive invariant and robust features. Therefore, the proposed method can learn robust and discriminative representation from the raw sensory data of induction motors in an efficient and automatic way. Finally, the learned representations are fed into support vector machine classifier to identify six different fault conditions. Experiments performed on a machine fault simulator indicate that compared with the current state-of-the-art methods, the proposed method shows significant performance gains, and it is effective and efficient for induction motor fault diagnosis.

Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis

The 2007 discovery of quantized conductance in HgTe quantum wells delivered the field of topological insulators (TIs) its first experimental confirmation. While many three-dimensional TIs have since been identified, HgTe remains the only known two-dimensional system in this class. Difficulty fabricating HgTe quantum wells has, moreover, hampered their widespread use. With the goal of breaking this logjam we provide a blueprint for stabilizing a robust TI state in a more readily available two-dimensional material---graphene. Using symmetry arguments, density functional theory, and tight-binding simulations, we predict that graphene endowed with certain heavy adatoms realizes a TI with substantial band gap. For indium and thallium, our most promising adatom candidates, a modest 6% coverage produces an estimated gap near 80K and 240K, respectively, which should be detectable in transport or spectroscopic measurements. Engineering such a robust topological phase in graphene could pave the way for a new generation of devices for spintronics, ultra-low-dissipation electronics and quantum information processing.

Engineering a robust quantum spin Hall state in graphene via adatom deposition

This has been wonderful support for the University and I very much appreciate your role and others at JSC in giving us good advice and direction. As you can see from the table of contents, the program has covered a very broad range of topics. The 15 technical reports are those which were directly supported by the grant. Note also. that 13 M.Sc. and 11 Ph.D. students have also been directly involved in this effort. Finally, four staff members (Tesar, Tosunoglu, Hooper, and Freeman) have beer. involved and participated in the direction of the research.

University of Texas at Austin의 연구 현황

An energy balanced QoS based cluster head selection strategy for WSN

We report a highly sensitive and selective ammonia (NH3) gas sensor made from liquid exfoliated MoSe2 nanosheets. The powder obtained after exfoliation was used to make a two-terminal sensor on a q...

MoSe2 Crystalline Nanosheets for Room-Temperature Ammonia Sensing

This article demonstrates the use of a p-MoS2/n-WO3 heterojunctions based ultra sensitive and selective chemiresistive ammonia sensor that operates at 200◦ C. Surprisingly, the composite based sens...

Sandeep Sharma

Papers

An energy balanced QoS based cluster head selection strategy for WSN

MoSe2 Crystalline Nanosheets for Room-Temperature Ammonia Sensing

MoS2/WO3 Nanosheets for Detection of Ammonia

MoS2/MoO3 Nanocomposite for Selective NH3 Detection in a Humid Environment

Effect of tungsten doping on structural and optical properties of rutile TiO2 and band gap narrowing