Aiming at the problem that ensemble empirical mode decomposition(EEMD)method can not completely neutralize the added noise in the decomposition process,which leads to poor reconstruction of decomposition results and l...Aiming at the problem that ensemble empirical mode decomposition(EEMD)method can not completely neutralize the added noise in the decomposition process,which leads to poor reconstruction of decomposition results and low accuracy of traffic flow prediction,a traffic flow prediction model based on modified ensemble empirical mode decomposition(MEEMD),double-layer bidirectional long-short term memory(DBiLSTM)and attention mechanism is proposed.Firstly,the intrinsic mode functions(IMFs)and residual components(Res)are obtained by using MEEMD algorithm to decompose the original traffic data and separate the noise in the data.Secondly,the IMFs and Res are put into the DBiLSTM network for training.Finally,the attention mechanism is used to enhance the extraction of data features,then the obtained results are reconstructed and added.The experimental results show that in different scenarios,the MEEMD-DBiLSTM-attention(MEEMD-DBA)model can reduce the data reconstruction error effectively and improve the accuracy of the short-term traffic flow prediction.展开更多
为了有效解决阳城煤矿千米深部采掘工作面的高温热害问题,建设经济高效的井下集中制冷降温系统,结合煤矿自有电厂冷却水系统和井筒富余的排水管路系统,设计出井下集中制冷降温系统,并归纳总结了采掘工作面冷负荷和管路系统水力计算方法...为了有效解决阳城煤矿千米深部采掘工作面的高温热害问题,建设经济高效的井下集中制冷降温系统,结合煤矿自有电厂冷却水系统和井筒富余的排水管路系统,设计出井下集中制冷降温系统,并归纳总结了采掘工作面冷负荷和管路系统水力计算方法,简化了冷却水工艺和复杂的计算过程。工程应用结果表明:设计的矿用制冷装置单台制冷量可达到3 600 k W,能够满足深部采区"一面三头"的降温需求。通过对采掘工作面降温前后的风流参数进行观测,表明降温后的风流温湿度指标均优于预定指标,工作面作业环境得到明显改善。展开更多
基金Supported by the National Natural Science Foundation of China(No.62162040,61966023)the Higher Educational Innovation Foundation Project of Gansu Province of China(No.2021A-028)the Science and Technology Plan of Gansu Province(No.21ZD4GA028).
文摘Aiming at the problem that ensemble empirical mode decomposition(EEMD)method can not completely neutralize the added noise in the decomposition process,which leads to poor reconstruction of decomposition results and low accuracy of traffic flow prediction,a traffic flow prediction model based on modified ensemble empirical mode decomposition(MEEMD),double-layer bidirectional long-short term memory(DBiLSTM)and attention mechanism is proposed.Firstly,the intrinsic mode functions(IMFs)and residual components(Res)are obtained by using MEEMD algorithm to decompose the original traffic data and separate the noise in the data.Secondly,the IMFs and Res are put into the DBiLSTM network for training.Finally,the attention mechanism is used to enhance the extraction of data features,then the obtained results are reconstructed and added.The experimental results show that in different scenarios,the MEEMD-DBiLSTM-attention(MEEMD-DBA)model can reduce the data reconstruction error effectively and improve the accuracy of the short-term traffic flow prediction.
文摘为了有效解决阳城煤矿千米深部采掘工作面的高温热害问题,建设经济高效的井下集中制冷降温系统,结合煤矿自有电厂冷却水系统和井筒富余的排水管路系统,设计出井下集中制冷降温系统,并归纳总结了采掘工作面冷负荷和管路系统水力计算方法,简化了冷却水工艺和复杂的计算过程。工程应用结果表明:设计的矿用制冷装置单台制冷量可达到3 600 k W,能够满足深部采区"一面三头"的降温需求。通过对采掘工作面降温前后的风流参数进行观测,表明降温后的风流温湿度指标均优于预定指标,工作面作业环境得到明显改善。
