Bayesian Hypothesis Testing for Block Sparse Signal Recovery

This letter presents a novel Block Bayesian Hypothesis Testing Algorithm (Block-BHTA) for reconstructing block sparse signals with unknown block structures. The Block-BHTA comprises the detection and recovery of the supports, and the estimation of the amplitudes of the block sparse signal. The support detection and recovery is performed using a Bayesian hypothesis testing. Then, based on the detected and reconstructed supports, the nonzero amplitudes are estimated by linear MMSE. The effectiveness of Block-BHTA is demonstrated by numerical experiments.Comment: 5 pages, 2 figures. arXiv admin note: text overlap with arXiv:1412.231

Investigation of CO Tolerance in Proton Exchange Membrane Fuel Cells

The need for an efficient, non-polluting power source for vehicles in urban environments has resulted in increased attention to the option of fuel cell powered vehicles of high efficiency and low emissions. Of various fuel cell systems considered, the proton exchange membrane (PEM) fuel cell technology seems to be the most suitable one for the terrestrial transportation applications. This is thanks to its low temperature of operation (hence, fast cold start), and a combination of high power density and high energy conversion efficiency. Besides automobile and stationary applications (distributed power for homes, office buildings, and as back-up for critical applications such as hospitals and credit card centers), future consumer electronics also demands compact long-lasting sources of power, and fuel cell is a promising candidate in these applications. The goal of a cost effective and high performance fuel cell has resulted in very active multidisciplinary research. Although significant progress has been made on PEM fuel cells over the last twenty years, further progress in fuel cell research is still needed before the commercially viable fuel cell utilization in transportation, potable and stationary applications. A chief goal among others is the design of PEM fuel cells that can operate with impure hydrogen containing traces of CO, which has been the objective of this research. Standard Pt and PtRu anode catalyst has been studied systematically under practical fuel cell conditions, in an attempt to understand the mechanism and kinetics of H2/CO electrooxidation on these noble metal catalysts. In the study of Pt as anode catalyst, it was found that the fuel cell performance was strongly affected by the anode flow rate and cathode oxygen pressure. A CO electrooxidation kinetic model was developed taking into account the CO inventory in the anode, which can successfully simulate the experimental results. It was found that there is finite CO electrooxidation even on Pt anode with H2/CO as anode feed. Thus, anode overpotential and outlet CO concentration is a function of anode inlet flow rate at a constant current density. The on-line monitoring of CO concentration in PEM fuel cell anode exit has proved that the ~{!0~}ligand mechanism~{!1~} and ~{!0~}bifunctional mechanism~{!1~} coexist as the CO tolerance mechanisms for PtRu anode catalyst. For PtRu anode catalyst, sustained potential oscillations were observed when the fuel cell was operated at constant current density with H2/CO as anode feed. Temperature was found to be the key bifurcation parameter besides current density and the anode flow rate for the onset of potential oscillations. The anode kinetic model was extended further to unsteady state which can reasonably reproduce and adequately explain the oscillatory phenomenon. The potential oscillations are due to the coupling of anode electrooxidation of H2 and CO on PtRu alloy surface, on which OHad can be formed more facile, preferably on top of Ru atoms at lower overpotentials. One parameter bifurcation and local linear stability analysis have shown that the bifurcation experienced during the variation of fuel cell temperature is a Hopf bifurcation, which leads to stable potential oscillations when the fuel cell is set at constant current density. It was further found that a PEM fuel cell operated in an autonomous oscillatory state produces higher time-averaged cell voltage and power density as compared to the stable steady-state operation, which may be useful for developing an operational strategy for improved management of power output in PEM fuel cells with the presence of CO in anode feed. Finally, an Electrochemical Preferential Oxidation (ECPrOx) process is proposed to replace the conventional PrOx for cleaning CO from reformate gas, which can selectively oxidized CO electrochemically while generating supplemental electrical power without wasting hydrogen

Graph learning under spectral sparsity constraints

Graph inference plays an essential role in machine learning, pattern recognition, and classification. Signal processing based approaches in literature generally assume some variational property of the observed data on the graph. We make a case for inferring graphs on which the observed data has high variation. We propose a signal processing based inference model that allows for wideband frequency variation in the data and propose an algorithm for graph inference. The proposed inference algorithm consists of two steps: 1) learning orthogonal eigenvectors of a graph from the data; 2) recovering the adjacency matrix of the graph topology from the given graph eigenvectors. The first step is solved by an iterative algorithm with a closed-form solution. In the second step, the adjacency matrix is inferred from the eigenvectors by solving a convex optimization problem. Numerical results on synthetic data show the proposed inference algorithm can effectively capture the meaningful graph topology from observed data under the wideband assumption

An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring

An improved mixture of probabilistic principal component analysis (PPCA) has been introduced for nonlinear data-driven process monitoring in this paper. To realize this purpose, the technique of a mixture of probabilistic principal component analyzers is utilized to establish the model of the underlying nonlinear process with local PPCA models, where a novel composite monitoring statistic is proposed based on the integration of two monitoring statistics in modified PPCA-based fault detection approach. Besides, the weighted mean of the monitoring statistics aforementioned is utilized as a metrics to detect potential abnormalities. The virtues of the proposed algorithm are discussed in comparison with several unsupervised algorithms. Finally, Tennessee Eastman process and an autosuspension model are employed to demonstrate the effectiveness of the proposed scheme further

Preparation of Functionalized Graphene and Gold Nanocomposites – Self-assembly and Catalytic Properties

Nanocomposites and nanomaterials have been attracting more attention in various fields. Nanocomposites can be prepared with a variety of special physical, thermal, and other unique properties. They have better properties than conventional microscale composites and can be synthesized using simple and inexpensive techniques. A composite material consists of an assemblage of two materials of different natures completing and allowing us to obtain a material of which the set of performance characteristics is greater than that of the components taken separately. In our recent research, some functionalized nanocomposites and nanomaterials have been prepared and investigated. In addition, some of the analytical methods, theoretical treatments, and synthetic tools, which are being applied in the area of self-assembly and supramolecular chemistry, will be highlighted. In this chapter, we summarize our main research contributions in recent years in two sections: (1) preparation and catalytic properties of some functionalized graphene nanocomposites; (2) preparation and catalytic properties of some functionalized gold nanocomposites. These works not only provided important inspirations for developing graphene-hybridized materials but also opened new possibilities to improve the photocatalytic activity of photocatalyst