为了探究蔬菜渣和蔬菜汁混合后对其厌氧发酵产沼气潜力的影响,并为混合蔬菜渣汁厌氧发酵工程的应用提供合理性的分析与建议。采用批量式的发酵方式,选取白菜渣汁混合物为反应原料,在高温(55±1)℃下,底物质量分数为3%、白菜渣粒径为...为了探究蔬菜渣和蔬菜汁混合后对其厌氧发酵产沼气潜力的影响,并为混合蔬菜渣汁厌氧发酵工程的应用提供合理性的分析与建议。采用批量式的发酵方式,选取白菜渣汁混合物为反应原料,在高温(55±1)℃下,底物质量分数为3%、白菜渣粒径为0.85 mm,其中白菜渣加水的实验组3(白菜渣68.34 g,水331.66 mL接种物的量为120 m L)TS降解率、VS降解率、第2个日产气高峰、最高CH4含量和累积总产气量分别为56.13%、61.06%、525 m L、66.19%和3390 m L,发酵前后pH值分别为7.2和7.6均在产甲烷菌的正常pH范围,结果均优于其他实验组的对应指标。利用100 L的发酵罐对其进行连续搅拌厌氧发酵产沼气的小试放大实验并分析生物群落结构,通过高通量测序发现在纲水平上,古菌主要以甲烷杆菌纲、甲烷微菌纲为主;在属水平上,嗜热弯曲甲烷热杆菌属是反应器中最主要的产甲烷菌(占总菌量的55%~81%),甲烷八叠球菌属是第二主导的产甲烷菌属(占总菌量的16%~42%)。在连续搅拌过程中,甲烷主要由甲烷八叠球菌属利用CO_(2)和H2通过还原CO_(2)产甲烷途径和乙酸产甲烷途径产生,即甲烷八叠球菌属对连续搅拌实验组的甲烷产生起主导作用。展开更多
Abstract： In order to improve the recognition accuracy of key stroke authentication, a methodology based on feature extraction of keystroke sequence is presented in this paper. Firstly, the data of the users＇ keystr...Abstract： In order to improve the recognition accuracy of key stroke authentication, a methodology based on feature extraction of keystroke sequence is presented in this paper. Firstly, the data of the users＇ keystroke feature information that has too much deviation with the mean deviation is filtered out. Secondly, the probability of each input key is calculated and 10 values which do not have the best features are selected. Thirdly, they are weighed and a score evaluating the extent to which the user could be authenticated successfully is calculated. The benefit of using a third-party data set is more objective and comparable. At last,展开更多
The task of multimodal sentiment classification aims to associate multimodal information, such as images and texts with appropriate sentiment polarities. There are various levels that can affect human sentiment in vis...The task of multimodal sentiment classification aims to associate multimodal information, such as images and texts with appropriate sentiment polarities. There are various levels that can affect human sentiment in visual and textual modalities. However, most existing methods treat various levels of features independently without having effective method for feature fusion. In this paper, we propose a multi-level fusion classification(MFC) model to predict the sentiment polarity based on the fusing features from different levels by exploiting the dependency among them. The proposed architecture leverages convolutional neural networks(CNNs) with multiple layers to extract levels of features in image and text modalities. Considering the dependencies within the low-level and high-level features, a bi-directional(Bi) recurrent neural network(RNN) is adopted to integrate the learned features from different layers in CNNs. In addition, a conflict detection module is incorporated to address the conflict between modalities. Experiments on the Flickr dataset demonstrate that the MFC method achieves comparable performance compared with strong baseline methods.展开更多
文摘为了探究蔬菜渣和蔬菜汁混合后对其厌氧发酵产沼气潜力的影响,并为混合蔬菜渣汁厌氧发酵工程的应用提供合理性的分析与建议。采用批量式的发酵方式,选取白菜渣汁混合物为反应原料,在高温(55±1)℃下,底物质量分数为3%、白菜渣粒径为0.85 mm,其中白菜渣加水的实验组3(白菜渣68.34 g,水331.66 mL接种物的量为120 m L)TS降解率、VS降解率、第2个日产气高峰、最高CH4含量和累积总产气量分别为56.13%、61.06%、525 m L、66.19%和3390 m L,发酵前后pH值分别为7.2和7.6均在产甲烷菌的正常pH范围,结果均优于其他实验组的对应指标。利用100 L的发酵罐对其进行连续搅拌厌氧发酵产沼气的小试放大实验并分析生物群落结构,通过高通量测序发现在纲水平上,古菌主要以甲烷杆菌纲、甲烷微菌纲为主;在属水平上,嗜热弯曲甲烷热杆菌属是反应器中最主要的产甲烷菌(占总菌量的55%~81%),甲烷八叠球菌属是第二主导的产甲烷菌属(占总菌量的16%~42%)。在连续搅拌过程中,甲烷主要由甲烷八叠球菌属利用CO_(2)和H2通过还原CO_(2)产甲烷途径和乙酸产甲烷途径产生,即甲烷八叠球菌属对连续搅拌实验组的甲烷产生起主导作用。
基金This paper has been performed in the Project "Key Technology Research of Eavesdropping Detection in the Quantum Security Communication" supported by the National Natural Science Foundation of China
文摘Abstract： In order to improve the recognition accuracy of key stroke authentication, a methodology based on feature extraction of keystroke sequence is presented in this paper. Firstly, the data of the users＇ keystroke feature information that has too much deviation with the mean deviation is filtered out. Secondly, the probability of each input key is calculated and 10 values which do not have the best features are selected. Thirdly, they are weighed and a score evaluating the extent to which the user could be authenticated successfully is calculated. The benefit of using a third-party data set is more objective and comparable. At last,
基金supported in part by the National Key Research and Development(R&D)Program of China(2018YFB1403003)。
文摘The task of multimodal sentiment classification aims to associate multimodal information, such as images and texts with appropriate sentiment polarities. There are various levels that can affect human sentiment in visual and textual modalities. However, most existing methods treat various levels of features independently without having effective method for feature fusion. In this paper, we propose a multi-level fusion classification(MFC) model to predict the sentiment polarity based on the fusing features from different levels by exploiting the dependency among them. The proposed architecture leverages convolutional neural networks(CNNs) with multiple layers to extract levels of features in image and text modalities. Considering the dependencies within the low-level and high-level features, a bi-directional(Bi) recurrent neural network(RNN) is adopted to integrate the learned features from different layers in CNNs. In addition, a conflict detection module is incorporated to address the conflict between modalities. Experiments on the Flickr dataset demonstrate that the MFC method achieves comparable performance compared with strong baseline methods.
