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cellsarts
(2023-01-31 23:31):
#paper Comparison of Different Packing Materials for the Biofiltration of Air Toxics DOI 10.1023/A:1021240500817 比较了四种不同的生物过滤器生物除臭滤塔的填料(两种多孔陶瓷、珍珠岩和开孔聚氨酯泡沫)去除废气中甲苯蒸汽的效果。重点是评估各填料在相对较短的气体保留时间(13.5秒和27秒)下的性能。反应器最初是作为生物滴滤器运行的，连续进料和滴入营养液。在观察到生物质生物滴滤床明显堵塞后，操作模式切换为仅定期供应矿物营养物质的生物过滤。这利于废气处理系统的稳定的运行，调查的过程持续超过6个月。牛骨瓷(CBP)是一种含有微量元素和宏量元素的陶瓷材料，其填充反应器表现出最高的性能。临界负荷(即发生95%去除率的负荷)为29克/m3/小时1，气体保留时间为13.5秒;66克/m3/小时，气体保留时间为27秒。在长期实验后，从反应器中取出填料并进行检查。将反应器分为顶部、中间和底部三个部分，以确定生物质是否存在空间分异。测定包括双染色技术，以计数总微生物和活微生物，并测定水分，蛋白质和干重含量。采用变性梯度凝胶电泳进行微生物群落分析。结果表明，大多数反应器都有相当比例的非活性生物质。相比之下，牛骨瓷CBP填料生物滤池的活性生物量密度明显较高，这可能是其去除甲苯性能较高的原因。分析表明，良好的物质条件和CBP缓慢释放的养分为工艺培养提供了较好的环境条件。
Abstract:
Modern CNNs are learning the weights of vast numbers of convolutional operators. In this paper, we raise the fundamental question if this is actually necessary. We show that even in …
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Modern CNNs are learning the weights of vast numbers of convolutional operators. In this paper, we raise the fundamental question if this is actually necessary. We show that even in the extreme case of only randomly initializing and never updating spatial filters, certain CNN architectures can be trained to surpass the accuracy of standard training. By reinterpreting the notion of pointwise (1×1) convolutions as an operator to learn linear combinations (LC) of frozen (random) spatial filters, we are able to analyze these effects and propose a generic LC convolution block that allows tuning of the linear combination rate. Empirically, we show that this approach not only allows us to reach high test accuracies on CIFAR and ImageNet but also has favorable properties regarding model robustness, generalization, sparsity, and the total number of necessary weights. Additionally, we propose a novel weight sharing mechanism, which allows sharing of a single weight tensor between all spatial convolution layers to massively reduce the number of weights.
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