530 research outputs found
Effect of boron promotion on the performance of Pt/Al2O3 catalysts during propane dehydrogenation
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Investigation of Infrared Absorption of Carbon Dioxide in Metal–Organic Framework Material
Due to the advantages of the high surface area and high porosity, Metal-Organic Framework (MOF) materials are widely used in gas storage. As a typical MOF material, Zeolitic Imidazolate Framework-8 (Zif-8) has high selectivity and adsorption capability of carbon dioxide (CO2). In this thesis, we investigated the infrared absorption of CO2 at the 2.0 m window using a lab-built optical spectroscopic system consisting of a broadband super-continuum light source, a monochromator, and a high-gain IR photodetector. In order to reduce the common-mode noise from the light source, we wrote a LabVIEW program to synchronize the data collected from the sampling and reference channel to minimize the effect of intensity fluctuation. Based on our experimental results, we obtained the vibrational-band IR absorption spectra of CO2 in ZIF-8, which is similar to that from the gas cell. However, the IR enhancement factor is abnormally large and cannot be solely attributed to the CO2 adsorption concentration by MOF. In parallel, we tested a MOF coated multi-mode fiber sensor to study the property of CO2/N2 selectivity of MOF, showing a non-linear relationship between the IR absorption and CO2 concentration. Therefore, we conclude that MOF has an extraordinary capability to enhance the IR gas sensitivity, but requires further investigation to understand the physics mechanism
A Distributed Polling Service-Based Medium Access Control Protocol: Prototyping and Experimental Validation
Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection
Co-saliency detection aims to discover the common and salient foregrounds
from a group of relevant images. For this task, we present a novel adaptive
graph convolutional network with attention graph clustering (GCAGC). Three
major contributions have been made, and are experimentally shown to have
substantial practical merits. First, we propose a graph convolutional network
design to extract information cues to characterize the intra- and interimage
correspondence. Second, we develop an attention graph clustering algorithm to
discriminate the common objects from all the salient foreground objects in an
unsupervised fashion. Third, we present a unified framework with
encoder-decoder structure to jointly train and optimize the graph convolutional
network, attention graph cluster, and co-saliency detection decoder in an
end-to-end manner. We evaluate our proposed GCAGC method on three cosaliency
detection benchmark datasets (iCoseg, Cosal2015 and COCO-SEG). Our GCAGC method
obtains significant improvements over the state-of-the-arts on most of them.Comment: CVPR202
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