Mining more discriminative temporal features to enrich temporal context representation is considered the key to fine-grained action recog-nition.Previous action recognition methods utilize a fixed spatiotemporal windo...Mining more discriminative temporal features to enrich temporal context representation is considered the key to fine-grained action recog-nition.Previous action recognition methods utilize a fixed spatiotemporal window to learn local video representation.However,these methods failed to capture complex motion patterns due to their limited receptive field.To solve the above problems,this paper proposes a lightweight Temporal Pyramid Excitation(TPE)module to capture the short,medium,and long-term temporal context.In this method,Temporal Pyramid(TP)module can effectively expand the temporal receptive field of the network by using the multi-temporal kernel decomposition without significantly increasing the computational cost.In addition,the Multi Excitation module can emphasize temporal importance to enhance the temporal feature representation learning.TPE can be integrated into ResNet50,and building a compact video learning framework-TPENet.Extensive validation experiments on several challenging benchmark(Something-Something V1,Something-Something V2,UCF-101,and HMDB51)datasets demonstrate that our method achieves a preferable balance between computation and accuracy.展开更多
The wastewater effluent from Radix aconiti processing, an important step in the production processes of traditional Chinese medicine（TCM）, is a type of toxic wastewater and difficult to treat. Plasma oxidation metho...The wastewater effluent from Radix aconiti processing, an important step in the production processes of traditional Chinese medicine（TCM）, is a type of toxic wastewater and difficult to treat. Plasma oxidation methods have emerged as feasible techniques for effective decomposition of toxic organic pollutants. This study examined the performance of a plasma reactor operated in a dielectric barrier discharge（DBD） to degrade the effluent from R. aconiti processing. The effects of treatment time, discharge voltage, initial pH value and the feeding gas for the reactor on the degradation of this TCM wastewater were investigated. A bacterium bioluminescence assay was adopted in this study to test the toxicity of the TCM wastewater after non-thermal plasma treatment. The degradation ratio of the main toxic component was 87.77% after 60 min treatment with oxygen used as feed gas and it was 99.59% when the initial p H value was 8.0. High discharge voltage and alkaline solution environment were beneficial for improving the degradation ratio. The treatment process was found to be capable of reducing the toxicity of the wastewater to a low level or even render it non-toxic. These experimental results suggested that the DBD plasma method may be a competitive technology for primary decomposition of biologically undegradable toxic organic pollutants in TCM wastewater.展开更多
基金supported by the research team of Xi’an Traffic Engineering Institute and the Young and middle-aged fund project of Xi’an Traffic Engineering Institute (2022KY-02).
文摘Mining more discriminative temporal features to enrich temporal context representation is considered the key to fine-grained action recog-nition.Previous action recognition methods utilize a fixed spatiotemporal window to learn local video representation.However,these methods failed to capture complex motion patterns due to their limited receptive field.To solve the above problems,this paper proposes a lightweight Temporal Pyramid Excitation(TPE)module to capture the short,medium,and long-term temporal context.In this method,Temporal Pyramid(TP)module can effectively expand the temporal receptive field of the network by using the multi-temporal kernel decomposition without significantly increasing the computational cost.In addition,the Multi Excitation module can emphasize temporal importance to enhance the temporal feature representation learning.TPE can be integrated into ResNet50,and building a compact video learning framework-TPENet.Extensive validation experiments on several challenging benchmark(Something-Something V1,Something-Something V2,UCF-101,and HMDB51)datasets demonstrate that our method achieves a preferable balance between computation and accuracy.
基金supported by the National Natural Science Foundation of China (No. 11075041)
文摘The wastewater effluent from Radix aconiti processing, an important step in the production processes of traditional Chinese medicine（TCM）, is a type of toxic wastewater and difficult to treat. Plasma oxidation methods have emerged as feasible techniques for effective decomposition of toxic organic pollutants. This study examined the performance of a plasma reactor operated in a dielectric barrier discharge（DBD） to degrade the effluent from R. aconiti processing. The effects of treatment time, discharge voltage, initial pH value and the feeding gas for the reactor on the degradation of this TCM wastewater were investigated. A bacterium bioluminescence assay was adopted in this study to test the toxicity of the TCM wastewater after non-thermal plasma treatment. The degradation ratio of the main toxic component was 87.77% after 60 min treatment with oxygen used as feed gas and it was 99.59% when the initial p H value was 8.0. High discharge voltage and alkaline solution environment were beneficial for improving the degradation ratio. The treatment process was found to be capable of reducing the toxicity of the wastewater to a low level or even render it non-toxic. These experimental results suggested that the DBD plasma method may be a competitive technology for primary decomposition of biologically undegradable toxic organic pollutants in TCM wastewater.
