Beijing University of Technology

About: Beijing University of Technology is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Microstructure & Laser. The organization has 31929 authors who have published 31987 publications receiving 352112 citations. The organization is also known as: Běijīng Gōngyè Dàxué & Beijing Polytechnic University.

Strontium-Doped Lanthanum Cobaltite and Manganite: Highly Active Catalysts for Toluene Complete Oxidation

Abstract: La1−xSrxMO3−δ (M = Co, Mn; x = 0, 0.4) catalysts have been fabricated via a strategy of citric acid complexing and hydrothermal treatment. The oxidation of toluene was used as a probe reaction for ...

Quality Evaluation of Image Dehazing Methods Using Synthetic Hazy Images

Abstract: To enhance the visibility and usability of images captured in hazy conditions, many image dehazing algorithms (DHAs) have been proposed. With so many image DHAs, there is a need to evaluate and compare these DHAs. Due to the lack of the reference haze-free images, DHAs are generally evaluated qualitatively using real hazy images. But it is possible to perform quantitative evaluation using synthetic hazy images since the reference haze-free images are available and full-reference (FR) image quality assessment (IQA) measures can be utilized. In this paper, we follow this strategy and study DHA evaluation using synthetic hazy images systematically. We first build a synthetic haze removing quality (SHRQ) database. It consists of two subsets: regular and aerial image subsets, which include 360 and 240 dehazed images created from 45 and 30 synthetic hazy images using 8 DHAs, respectively. Since aerial imaging is an important application area of dehazing, we create an aerial image subset specifically. We then carry out subjective quality evaluation study on these two subsets. We observe that taking DHA evaluation as an exact FR IQA process is questionable, and the state-of-the-art FR IQA measures are not effective for DHA evaluation. Thus, we propose a DHA quality evaluation method by integrating some dehazing-relevant features, including image structure recovering, color rendition, and over-enhancement of low-contrast areas. The proposed method works for both types of images, but we further improve it for aerial images by incorporating its specific characteristics. Experimental results on two subsets of the SHRQ database validate the effectiveness of the proposed measures.

Peering into water splitting mechanism of g-C3N4-carbon dots metal-free photocatalyst

Abstract: Photocatalytic water splitting is considered as one of the promising ways to provide clean fuels. Extensive efforts have been made in the past to develop various inorganic and organic materials systems as photocatalysts for water splitting by using visible light. Among these photocatalysts, it was recently demonstrated that incorporation of carbon dots (CDs) into graphitic carbon nitride (g-C3N4) results in new metal-free composites (g-C3N4-C) with excellent stability and impressive performance for photocatalytic water splitting. However, fundamental questions still remain to be addressed such as how added CDs influence the photocatalytic reaction through the bandgap tunability, charge transfer route and efficiency, as well as the specific function of CDs in the photocatalytic process. Understanding the chemical and physical behaviors of added CDs for the control of g-C3N4-C architecture is a critical need for its emergence from fundamental design of more efficient photocatalysts to practical applications. In this article, we report new materials with well-controlled architecture allowing for fine-tuning band gaps for broader visible light absorption and controlled understanding of the photocatalytic process. The well-defined model materials allow us to address the fundamental question regarding chemically bonding of CDs and how the chemical bonded CDs promote charge separation and transfer for highly efficient generation of H2.

Nonconcave penalized m-estimation with a diverging number of parameters

Abstract: M-estimation is a widely used technique for robust statistical inference. In this paper, we investigate the asymptotic properties of a nonconcave penalized M-estimator in sparse, high-dimensional, linear regression models. Compared with classic M-estimation, the nonconcave penalized M-estimation method can perform parameter estimation and variable selection simultaneously. The proposed method is resistant to heavy-tailed errors or outliers in the response. We show that, un- der certain appropriate conditions, the nonconcave penalized M-estimator has the so-called "Oracle Property"; it is able to select variables consistently, and the esti- mators of nonzero coefficients have the same asymptotic distribution as they would if the zero coefficients were known in advance. We obtain consistency and asymp- totic normality of the estimators when the dimension pn of the predictors satisfies the conditions pn log n/n → 0 and p 2/n → 0, respectively, where n is the sample size. Based on the idea of sure independence screening (SIS) and rank correla- tion, a robust rank SIS (RSIS) is introduced to deal with ultra-high dimensional data. Simulation studies were carried out to assess the performance of the proposed method for finite-sample cases, and a dataset was analyzed for illustration.