A high-aspect-ratio microchannel heat exchanger based on multi-tool milling process was developed. Several slotting cutters were stacked together for simultaneously machining several high-aspect-ratio microchannels wi...A high-aspect-ratio microchannel heat exchanger based on multi-tool milling process was developed. Several slotting cutters were stacked together for simultaneously machining several high-aspect-ratio microchannels with manifold structures. On the basis of multi-tool milling process, the structural design of the manifold side height, microchannel length, width, number, and interval were analyzed. The heat transfer performances of high-aspect-ratio microchannel heat exchangers with two different manifolds were investigated by experiments, and the influencing factors were analyzed. The results indicate that the magnitude of heat transfer area per unit volume dominates the heat transfer performances of plate-type micro heat exchanger, while the velocity distribution between microchannels has little effects on the heat transfer performances.展开更多
Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep...Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.展开更多
基金Projects(50675070 50805052) supported by the National Nature Science Foundation of China+1 种基金Projects(07118064 8451064101000320) supported by the Natural Science Foundation of Guangdong Province
文摘A high-aspect-ratio microchannel heat exchanger based on multi-tool milling process was developed. Several slotting cutters were stacked together for simultaneously machining several high-aspect-ratio microchannels with manifold structures. On the basis of multi-tool milling process, the structural design of the manifold side height, microchannel length, width, number, and interval were analyzed. The heat transfer performances of high-aspect-ratio microchannel heat exchangers with two different manifolds were investigated by experiments, and the influencing factors were analyzed. The results indicate that the magnitude of heat transfer area per unit volume dominates the heat transfer performances of plate-type micro heat exchanger, while the velocity distribution between microchannels has little effects on the heat transfer performances.
文摘Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
