The accurate and efficient prediction of explosive detonation properties has important engineering significance for weapon design.Traditional methods for predicting detonation performance include empirical formulas,eq...The accurate and efficient prediction of explosive detonation properties has important engineering significance for weapon design.Traditional methods for predicting detonation performance include empirical formulas,equations of state,and quantum chemical calculation methods.In recent years,with the development of computer performance and deep learning methods,researchers have begun to apply deep learning methods to the prediction of explosive detonation performance.The deep learning method has the advantage of simple and rapid prediction of explosive detonation properties.However,some problems remain in the study of detonation properties based on deep learning.For example,there are few studies on the prediction of mixed explosives,on the prediction of the parameters of the equation of state of explosives,and on the application of explosive properties to predict the formulation of explosives.Based on an artificial neural network model and a one-dimensional convolutional neural network model,three improved deep learning models were established in this work with the aim of solving these problems.The training data for these models,called the detonation parameters prediction model,JWL equation of state(EOS)prediction model,and inverse prediction model,was obtained through the KHT thermochemical code.After training,the model was tested for overfitting using the validation-set test.Through the model-accuracy test,the prediction accuracy of the model for real explosive formulations was tested by comparing the predicted value with the reference value.The results show that the model errors were within 10%and 3%for the prediction of detonation pressure and detonation velocity,respectively.The accuracy refers to the prediction of tested explosive formulations which consist of TNT,RDX and HMX.For the prediction of the equation of state for explosives,the correlation coefficient between the prediction and the reference curves was above 0.99.For the prediction of the inverse prediction model,the prediction error of the explosive equation was within 9%.This indicates that the models have utility in engineering.展开更多
张衡一号卫星在轨6年积累了海量观测数据,检测其中的闪电哨声波事件(Lightning Whistler,LW)对于分析空间物理环境规律具有重要意义.但现有基于时频图像的方法推理速度过慢,完成任务需约40年.为此,研究首次从音频事件检测的角度提出高...张衡一号卫星在轨6年积累了海量观测数据,检测其中的闪电哨声波事件(Lightning Whistler,LW)对于分析空间物理环境规律具有重要意义.但现有基于时频图像的方法推理速度过慢,完成任务需约40年.为此,研究首次从音频事件检测的角度提出高速的闪电哨声波检测模型WhisNet,将检测的时间成本从40年压缩至54天.方法为以4 s滑动窗截取波形,提取梅尔频谱特征,利用轻量级卷积循环神经网络(CRNN)提取音频事件特征,输出层预测LW事件起始时间和持续时长.基于2020年4月1-10日的感应磁力仪(SCM)数据实验显示,WhisNet检测性能与传统方法相当,但计算量和参数量减少99%,速度提升98%.进一步在2020年5月SCM数据上的应用结果与WGLC(全球闪电气候学和时间序列,WWLLN Global Lightning Climatology and time series)全球闪电密度趋势高度一致,验证了WhisNet在大规模卫星数据处理中的准确性与适用性.研究结果为挖掘其他海量地球空间事件提供了重要参考.展开更多
文摘The accurate and efficient prediction of explosive detonation properties has important engineering significance for weapon design.Traditional methods for predicting detonation performance include empirical formulas,equations of state,and quantum chemical calculation methods.In recent years,with the development of computer performance and deep learning methods,researchers have begun to apply deep learning methods to the prediction of explosive detonation performance.The deep learning method has the advantage of simple and rapid prediction of explosive detonation properties.However,some problems remain in the study of detonation properties based on deep learning.For example,there are few studies on the prediction of mixed explosives,on the prediction of the parameters of the equation of state of explosives,and on the application of explosive properties to predict the formulation of explosives.Based on an artificial neural network model and a one-dimensional convolutional neural network model,three improved deep learning models were established in this work with the aim of solving these problems.The training data for these models,called the detonation parameters prediction model,JWL equation of state(EOS)prediction model,and inverse prediction model,was obtained through the KHT thermochemical code.After training,the model was tested for overfitting using the validation-set test.Through the model-accuracy test,the prediction accuracy of the model for real explosive formulations was tested by comparing the predicted value with the reference value.The results show that the model errors were within 10%and 3%for the prediction of detonation pressure and detonation velocity,respectively.The accuracy refers to the prediction of tested explosive formulations which consist of TNT,RDX and HMX.For the prediction of the equation of state for explosives,the correlation coefficient between the prediction and the reference curves was above 0.99.For the prediction of the inverse prediction model,the prediction error of the explosive equation was within 9%.This indicates that the models have utility in engineering.
文摘张衡一号卫星在轨6年积累了海量观测数据,检测其中的闪电哨声波事件(Lightning Whistler,LW)对于分析空间物理环境规律具有重要意义.但现有基于时频图像的方法推理速度过慢,完成任务需约40年.为此,研究首次从音频事件检测的角度提出高速的闪电哨声波检测模型WhisNet,将检测的时间成本从40年压缩至54天.方法为以4 s滑动窗截取波形,提取梅尔频谱特征,利用轻量级卷积循环神经网络(CRNN)提取音频事件特征,输出层预测LW事件起始时间和持续时长.基于2020年4月1-10日的感应磁力仪(SCM)数据实验显示,WhisNet检测性能与传统方法相当,但计算量和参数量减少99%,速度提升98%.进一步在2020年5月SCM数据上的应用结果与WGLC(全球闪电气候学和时间序列,WWLLN Global Lightning Climatology and time series)全球闪电密度趋势高度一致,验证了WhisNet在大规模卫星数据处理中的准确性与适用性.研究结果为挖掘其他海量地球空间事件提供了重要参考.
