Synthesis, Analysis, and Testing of BiOBr-Bi 2

In photocatalysis, the recombination of electron-hole pairs is generally regarded as one of its most serious drawbacks. The synthesis of various composites with heterojunction structures has increasingly shed light on preventing this recombination. In this work, a BiOBr-Bi2WO6 photocatalytic heterojunction semiconductor was synthesized by the facile hydrothermal method and applied in the photocatalytic degradation process. It was determined that both reaction time and temperature significantly affected the crystal structure and morphologies of the photocatalysts. BiOBr (50 at%)-Bi2WO6 composites were prepared under optimum synthesis conditions (120°C for 6 h) and by theoretically analyzing the DRS results, it was determined that they possessed the suitable band gap (2.61 eV) to be stimulated by visible-light irradiation. The photocatalytic activities of the as-prepared photocatalysts were evaluated by the degradation of Rhodamine B (RhB) under visible-light irradiation. The experimental conditions, including initial concentration, pH, and catalyst dosage, were explored and the photocatalysts in this system were proven stable enough to be reused for several runs. Moreover, the interpreted mechanism of the heterojunction enhancement effect proved that the synthesis of a heterojunction structure provided an effective method to decrease the recombination rate of the electron-hole pairs, thereby improving the photocatalytic activity

Convergence analysis on the alternating direction method of multipliers for the cosparse optimization problem

From a dual perspective of the sparse representation model, Nam et al. proposed the cosparse analysis model. In this paper, we aim to investigate the convergence of the alternating direction method of multipliers (ADMM) for the cosparse optimization problem. First, we examine the variational inequality representation of the cosparse optimization problem by introducing auxiliary variables. Second, ADMM is used to solve cosparse optimization problem. Finally, by utilizing a tight frame with a uniform row norm and building upon lemmas and the strict contraction theorem, we establish a worst-case $\mathcal{O}(1/t)$ convergence rate in the ergodic sense.Comment: 15 pag

Dynamic Phase Diagram of Catalytic Surface of Hexagonal Boron Nitride under Conditions of Oxidative Dehydrogenation of Propane.

Partially oxidized surfaces of hexagonal boron nitride (hBN) and several metal borides are unexpectedly excellent catalysts for oxidative dehydrogenation of alkanes to olefins, but the nature of the active site(s) on these B-containing interfaces remains elusive. We characterize the surface of the partially oxidized B-rich hBN surface under reaction conditions from first principles. The interface has thermal access to multiple different stoichiometries and multiple structures of each stoichiometry. The size of the thermal ensemble is composition-dependent. The phase diagram of the interface constructed on the basis of the statistical ensembles of many accessible states is very different from the one based on global minima. Phase boundaries shift and blur, and phases consist of several stoichiometries and structures. The BO layer transiently exposes the reactive -B═O motifs in the metastable states. The fluxionality and structural diversity emerging under reaction conditions must be taken into account in theoretically descriptions of the catalytic interface

Approaches to Feature Identification and Feature Selection for Binary and Multi-Class Classification

University of Minnesota Ph.D. dissertation. 2007. Major: Electrical Engineering. Advisor: Keshab Parhi. 1 computer file (PDF); 182 pages.In this dissertation, we address issues of (a) feature identification and extraction, and (b) feature selection. Nowadays, datasets are getting larger and larger, especially due to the growth of the internet data and bio-informatics. Thus, applying feature extraction and selection to reduce the dimensionality of the data size is crucial to data mining. Our first objective is to identify discriminative patterns in time series datasets. Using auto-regressive modeling, we show that, if two bands are selected appropriately, then the ratio of band power is amplified for one of the two states. We introduce a novel frequency-domain power ratio (FDPR) test to determine how these two bands should be selected. The FDPR computes the ratio of the two model filter transfer functions where the model filters are estimated using different parts of the time-series that correspond to two different states. The ratio implicitly cancels the effect of change of variance of the white noise that is input to the model. Thus, even in a highly non-stationary environment, the ratio feature is able to correctly identify a change of state. Synthesized data and application examples from seizure prediction are used to prove validity of the proposed approach. We also illustrate that combining the spectral power ratios features with absolute spectral powers and relative spectral powers as a feature set and then carefully selecting a small number features from a few electrodes can achieve a good detection and prediction performances on short-term datasets and long-term fragmented datasets collected from subjects with epilepsy. Our second objective is to develop efficient feature selection methods for binary classification (MUSE) and multi-class classification (M3U) that effectively select important features to achieve a good classification performance. We propose a novel incremental feature selection method based on minimum uncertainty and feature sample elimination (referred as MUSE) for binary classification. The proposed approach differs from prior mRMR approach in how the redundancy of the current feature with previously selected features is reduced. In the proposed approach, the feature samples are divided into a pre-specified number of bins; this step is referred to as feature quantization. A novel uncertainty score for each feature is computed by summing the conditional entropies of the bins, and the feature with the lowest uncertainty score is selected. For each bin, its impurity is computed by taking the minimum of the probability of Class 1 and of Class 2. The feature samples corresponding to the bins with impurities below a threshold are discarded and are not used for selection of the subsequent features. The significance of the MUSE feature selection method is demonstrated using the two datasets: arrhythmia and hand digit recognition (Gisette), and datasets for seizure prediction from five dogs and two humans. It is shown that the proposed method outperforms the prior mRMR feature selection method for most cases. We further extends the MUSE algorithm for multi-class classification problems. We propose a novel multiclass feature selection algorithm based on weighted conditional entropy, also referred to as uncertainty. The goal of the proposed algorithm is to select a feature subset such that, for each feature sample, there exists a feature that has a low uncertainty score in the selected feature subset. Features are first quantized into different bins. The proposed feature selection method first computes an uncertainty vector from weighted conditional entropy. Lower the uncertainty score for a class, better is the separability of the samples in that class. Next, an iterative feature selection method selects a feature in each iteration by (1) computing the minimum uncertainty score for each feature sample for all possible feature subset candidates, (2) computing the average minimum uncertainty score across all feature samples, and (3) selecting the feature that achieves the minimum of the mean of the minimum uncertainty score. The experimental results show that the proposed algorithm outperforms mRMR and achieves lower misclassification rates using various types of publicly available datasets. In most cases, the number of features necessary for a specified misclassification error is less than that required by traditional methods

Synthesis and Optimization of Visible Light Active BiVO 4

Monoclinic BiVO4 powders were synthesized via a novel route using potassium metavanadate (KVO3) prepared by calcination of K2CO3 and V2O5 as a starting material and followed by hydrothermal treatment and were investigated for the degradation of Rhodamine B (RhB) under visible light irradiation. The synthesized BiVO4 particles were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and UV-Visible (UV-Vis) light diffuse reflectance spectrophotometry. The synthesis produced pure monoclinic BiVO4 particles with multimorphological features containing flower-like, flake-ball, flake, cuboid-like, and plate-like shapes and exhibited strong absorption in the visible light range. The BiVO4 prepared via KVO3 possessed excellent photocatalytic activity for the degradation of RhB under visible light. The performance of this catalyst was found to be superior to other BiVO4 photocatalysts prepared via ammonium metavanadate (NH4VO3) using coprecipitation, combustion, and calcination methods reported in literature, respectively