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Fe-SSZ-13分子筛的制备及其SCR催化性能和机理的研究进展
No full textFe-SSZ-13分子筛对于氮氧化物(NO_x)具有优异的选择性催化还原活性,高N_2选择性和水热稳定性,以及抵抗H_2O和SO_2中毒能力,可潜在应用于重型柴油车尾气的脱硝。本文首先综述了Fe-SSZ-13的分子筛合成方法,指出了各合成方法的优缺点;其次,总结讨论了目前Fe-SSZ-13分子筛对于氮氧化物催化机理的研究,针对目前Fe-SSZ-13分子筛普遍存在催化活性不高与催化温度局限的问题,介绍了铁形态优化、分子筛与催化剂杂化协同作用及复合型分子筛合成等优化调控方法;最后,提出开发低成本、高效率和环保的Fe-SSZ-13分子筛合成方法及其催化机理的探索是今后的研究重点
生物基酚类合成树脂的研究进展
No full text石油资源的日益枯竭与保护环境的要求促使人们不断探索石油基工业化学品的可持续替代物。可持续化学品主要从天然植物或农业废物获取。由于在制备、使用和后处理过程中具有生物降解性和低毒性,这些资源可以合成环境友好的新材料。在日常使用的聚合物中,酚醛树脂、环氧树脂、苯并噁嗪、聚氨酯在电子行业、建筑、防火材料、粘合剂、涂料领域也拥有广泛的用途。本文综述了通过腰果酚、木质素衍生物、香豆素、植物油等农业和工业废弃物中天然酚类替代石油基苯酚及其衍生物来制备上述树脂的研究状况,以及未来的发展方向
Control Strategy of Rope Driven Upper Exoskeleton Robot Based on Screw Method
No full textWith the advance of the deep aging process or society, the spread of upper limb motor dysfunction has become a serious social problem, and the development of excellent performance of upper limb rehabilitation robot has become a major social demand of the country.This thesis will take the 7-DOF rope driven upper exoskeleton rehabilitation robot as the research object.According to (he mechanical structure of the 7-DOF upper exoskeleton prototype, the kinematics and dynamics model were established, the feasible motion space of the upper exoskeleton was obtained, and the relationship between the joint pose and the rope length, and (he joint moment and the tension on the rope was calculated. According to the actual needs, the closed-loop position control and force control frame of the motor are built.The upper limb exoskeleton robot is controlled based on the spin method
Cross-Domain Depth Estimation Network for 3D Vessel Reconstruction in OCT Angiography
No full textOptical Coherence Tomography Angiography (OCTA) has been widely used by ophthalmologists for decision-making due to its superiority in providing caplillary details. Many of the OCTA imaging devices used in clinic provide high-quality 2D en face representations, while their 3D data quality are largely limited by low signal-to-noise ratio and strong projection artifacts, which restrict the performance of depth-resolved 3D analysis. In this paper, we propose a novel 2D-to-3D vessel reconstruction framework based on the 2D en face OCTA images. This framework takes advantage of the detailed 2D OCTA depth map for prediction and thus does not rely on any 3D volumetric data. Based on the data with available vessel depth labels, we first introduce a network with structure constraint blocks to estimate the depth map of blood vessels in other cross-domain en face OCTA data with unavailable labels. Afterwards, a depth adversarial adaptation module is proposed for better unsupervised cross-domain training, since images captured using different devices may suffer from varying image contrast and noise levels. Finally, vessels are reconstructed in 3D space by utilizing the estimated depth map and 2D vascular information. Experimental results demonstrate the effectiveness of our method and its potential to guide subsequent vascular analysis in 3D domain
Semi-Supervised GANs with Complementary Generator Pair for Retinopathy Screening
No full textSeveral typical types of retinopathy are major causes of blindness. However, early detection of retinopathy is quite not easy since few symptoms are observable in the early stage, attributing to the development of non-mydriatic retinal cameras, these cameras produce high-resolution retinal fundus images that provide the possibility of Computer-Aided-Diagnosis (CAD) via deep learning to assist diagnosing retinopathy. Deep learning algorithms usually rely on a large number of labeled images that are expensive and time-consuming to obtain in the medical imaging area. Moreover, the random distribution of various lesions that often vary greatly in size also brings significant challenges to learn discriminative information from high-resolution fundus images. In this paper, we present generative adversarial networks simultaneously equipped with a good generator and a bad generator (GBGANs) to make up for the incomplete data distribution given limited fundus images. To improve the generative feasibility of the generator, we introduce a pre-trained feature extractor to acquire condensed features for each fundus image in advance. Experimental results on integrated three public iChallenge datasets show that the proposed GBGANs could fully utilize the available fundus images to identify retinopathy with little label cost
