A metal ions (Ag, Bi, V, Mo) modified sol–gel method was used to prepare a mesoporous Ag0.01Bi0.85V0.54Mo0.45O4 catalytic membrane which was used in the selective oxidation of propane to acrolein. By optimizing the preparation parameters, a thin and perfect catalytically active membrane was successfully prepared. SEM results showed that the membrane thickness is 5 μm. XRD results revealed that Ag0.01Bi0.85V0.54Mo0.45O4 with a Scheelite structure, which is catalytically active for the selective oxidation of propane to acrolein, was formed in the catalytic membrane only when AgBiVMoO concentrations were higher than 40%. Catalytic reaction results demonstrated that the selective oxidation of propane could be controlled to a certain degree, such as to acrolein, in the catalytic membrane reactor (CMR) compared to the fixed bed reactor (FBR). For example, a selectivity of 54.85% for acrolein in the liquid phase was obtained in the CMR, while only 8.31% was achieved in the FBR. 相似文献
Logos are specially designed marks that identify goods, services, and organizations using distinguished characters, graphs, signals, and colors. Identifying logos can facilitate scene understanding, intelligent navigation, and object recognition. Although numerous logo recognition methods have been proposed for printed logos, a few methods have been specifically designed for logos in photos. Furthermore, most recognition methods use codebook-based approaches for the logos in photos. A codebook-based method is concerned with the generation of visual words for all the logo models. When new logos are added, the codebook reconstruction is required if effectiveness is a crucial factor. Moreover, logo detection in natural scenes is difficult because of perspective tilt and non-rigid deformation. Therefore, this study develops an extendable, but discriminating, model-based logo detection method. The proposed logo detection method is based on a support vector machine (SVM) using edge-based histograms of oriented gradient (HOGE) as features through multi-scale sliding window scanning. Thereafter, anti-distortion affine scale invariant feature transform (ASIFT) is used for logo verification with constraints on the ASIFT matching pairs and neighbors. The experimental results using the public Flickr-Logo database confirm that the proposed method has a higher retrieval and precision accuracy compared to existing model-based methods.
