The new technology of direct decomposition of H_(2)S into high value-added H_(2) and S,as an alternative to the Claus process in industry,is an ideal route that can not only deal with toxic and abundant H_(2)S waste g...The new technology of direct decomposition of H_(2)S into high value-added H_(2) and S,as an alternative to the Claus process in industry,is an ideal route that can not only deal with toxic and abundant H_(2)S waste gas but also recover clean energy H_(2),which has significant socio-economic and ecological advantages.However,the highly effective decomposition of H_(2)S at low temperatures is still a great challenge,because of the stringent thermodynamic equilibrium constraints(only 20% even at high temperature of 1010℃).Conventional microwave catalysts exhibit unsatisfactory performance at low temperatures(below 600℃).Herein,Mo_(2)C@CeO_(2) catalysts with a core-shell structure were successfully developed for robust microwave catalytic decomposition of H_(2)S at low temperatures.Two carbon precursors,para-phenylenediamine(Mo_(2)C-p)and meta-phenylenediamine(Mo_(2)C-m),were employed to tailor Mo_(2)C configurations.Remarkably,the H_(2)S conversion of Mo_(2)C-p@CeO_(2) catalyst at a low temperature of 550℃ is as high as 92.1%,which is much higher than the H_(2)S equilibrium conversion under the conventional thermal conditions(2.6% at 550℃).To our knowledge,this represents the most active catalyst for microwave catalytic decomposition of H_(2)S at low temperature of 550℃.Notably,Mo_(2)C-p demonstrated superior intrinsic activity(84%)compared to Mo_(2)C-m(6.4%),with XPS analysis revealing that its enhanced performance stems from a higher concentration of Mo_(2+)active sites.This work presents a substitute approach for the efficient utilization of H_(2)S waste gas and opens up a novel avenue for the rational design of microwave catalysts for microwave catalytic reaction at low-temperature.展开更多
Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network...Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network applications by optimized back-propagation (BP) neural network. Particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. And in order to increase the identification performance, wavelet packet decomposition (WPD) was used to extract several hidden features from the time-frequency information of network traffic. The experimental results show that the average classification accuracy of various network applications can reach 97%. Moreover, this approach optimized by BP neural network takes 50% of the training time compared with the traditional neural network.展开更多
Blast vibration analysis constitutes the foundation for studying the control of blasting vibration damage and provides the precondition of controlling blasting vibration. Based on the characteristics of short-time non...Blast vibration analysis constitutes the foundation for studying the control of blasting vibration damage and provides the precondition of controlling blasting vibration. Based on the characteristics of short-time nonstationary random signal, the laws of energy distribution are investigated for blasting vibration signals in different blasting conditions by means of the wavelet packet analysis technique. The characteristics of wavelet transform and wavelet packet analysis are introduced. Then, blasting vibration signals of different blasting conditions are analysed by the wavelet packet analysis technique using MATLAB; energy distribution for different frequency bands is obtained. It is concluded that the energy distribution of blasting vibration signals varies with maximum decking charge,millisecond delay time and distances between explosion and the measuring point. The results show that the wavelet packet analysis method is an effective means for studying blasting seismic effect in its entirety, especially for constituting velocity-frequency criteria.展开更多
By employing function one-direction S-rough sets and rough law generation method based on function S-rough sets, ^-f-decomposition law and ^-F-decomposition rough law are proposed, and the measurement of rough law var...By employing function one-direction S-rough sets and rough law generation method based on function S-rough sets, ^-f-decomposition law and ^-F-decomposition rough law are proposed, and the measurement of rough law variation in the process of rough law ^-F-decomposition is researched. The concepts of law energy and attdbute ^-f-interference degree are presented, which make the variation of rough law become measurable. ^-f-decomposition law energy characteristic theorem, ^-f- decomposition law energy inequality theorem, ^-F-decomposition rough law energy characteristic theorem, and ^-f-decomposition law energy mean value theorem are presented.展开更多
The kinetic parameters of the exothermic decomposition reaction of s-Tripicryaminotrinitrobenzene under linear temperature rise condition are studied by means of DSC. The results show that the empirical kinetic model ...The kinetic parameters of the exothermic decomposition reaction of s-Tripicryaminotrinitrobenzene under linear temperature rise condition are studied by means of DSC. The results show that the empirical kinetic model function in differential form, apparent activation energy and pre-exponential constant of the reaction are 225.4 kJ·mol-1 and 1 019.53 s-1, respectively. The critical temperature of thermal explosion of the compound is 267.36 ℃.展开更多
基金supported by the National Natural Science Foundation of China(22178295,21706225)Natural Science Foundation of Hunan Province(2025JJ50085)Hunan Collaborative Innovation Center of New Chemical Technologies for Environmental Benignity and Efficient Resource Utilization.
文摘The new technology of direct decomposition of H_(2)S into high value-added H_(2) and S,as an alternative to the Claus process in industry,is an ideal route that can not only deal with toxic and abundant H_(2)S waste gas but also recover clean energy H_(2),which has significant socio-economic and ecological advantages.However,the highly effective decomposition of H_(2)S at low temperatures is still a great challenge,because of the stringent thermodynamic equilibrium constraints(only 20% even at high temperature of 1010℃).Conventional microwave catalysts exhibit unsatisfactory performance at low temperatures(below 600℃).Herein,Mo_(2)C@CeO_(2) catalysts with a core-shell structure were successfully developed for robust microwave catalytic decomposition of H_(2)S at low temperatures.Two carbon precursors,para-phenylenediamine(Mo_(2)C-p)and meta-phenylenediamine(Mo_(2)C-m),were employed to tailor Mo_(2)C configurations.Remarkably,the H_(2)S conversion of Mo_(2)C-p@CeO_(2) catalyst at a low temperature of 550℃ is as high as 92.1%,which is much higher than the H_(2)S equilibrium conversion under the conventional thermal conditions(2.6% at 550℃).To our knowledge,this represents the most active catalyst for microwave catalytic decomposition of H_(2)S at low temperature of 550℃.Notably,Mo_(2)C-p demonstrated superior intrinsic activity(84%)compared to Mo_(2)C-m(6.4%),with XPS analysis revealing that its enhanced performance stems from a higher concentration of Mo_(2+)active sites.This work presents a substitute approach for the efficient utilization of H_(2)S waste gas and opens up a novel avenue for the rational design of microwave catalysts for microwave catalytic reaction at low-temperature.
基金Project(2007CB311106) supported by National Key Basic Research Program of ChinaProject(NEUL20090101) supported by the Foundation of National Information Control Laboratory of China
文摘Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network applications by optimized back-propagation (BP) neural network. Particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. And in order to increase the identification performance, wavelet packet decomposition (WPD) was used to extract several hidden features from the time-frequency information of network traffic. The experimental results show that the average classification accuracy of various network applications can reach 97%. Moreover, this approach optimized by BP neural network takes 50% of the training time compared with the traditional neural network.
基金Project(50490272) supported by the National Natural Science Foundation of China project(2004036430) supported bythe Postdoctoral Science Foundation of China
文摘Blast vibration analysis constitutes the foundation for studying the control of blasting vibration damage and provides the precondition of controlling blasting vibration. Based on the characteristics of short-time nonstationary random signal, the laws of energy distribution are investigated for blasting vibration signals in different blasting conditions by means of the wavelet packet analysis technique. The characteristics of wavelet transform and wavelet packet analysis are introduced. Then, blasting vibration signals of different blasting conditions are analysed by the wavelet packet analysis technique using MATLAB; energy distribution for different frequency bands is obtained. It is concluded that the energy distribution of blasting vibration signals varies with maximum decking charge,millisecond delay time and distances between explosion and the measuring point. The results show that the wavelet packet analysis method is an effective means for studying blasting seismic effect in its entirety, especially for constituting velocity-frequency criteria.
基金supported by the Natural Science Foundation of Shandong Province (Y2007H02)
文摘By employing function one-direction S-rough sets and rough law generation method based on function S-rough sets, ^-f-decomposition law and ^-F-decomposition rough law are proposed, and the measurement of rough law variation in the process of rough law ^-F-decomposition is researched. The concepts of law energy and attdbute ^-f-interference degree are presented, which make the variation of rough law become measurable. ^-f-decomposition law energy characteristic theorem, ^-f- decomposition law energy inequality theorem, ^-F-decomposition rough law energy characteristic theorem, and ^-f-decomposition law energy mean value theorem are presented.
基金National Nature Science Foundation of China (20573098)the Science and Technology Foundation Defense Key Laboratory of Pro-pellant and Explosive Combustion of China(514550101)
文摘The kinetic parameters of the exothermic decomposition reaction of s-Tripicryaminotrinitrobenzene under linear temperature rise condition are studied by means of DSC. The results show that the empirical kinetic model function in differential form, apparent activation energy and pre-exponential constant of the reaction are 225.4 kJ·mol-1 and 1 019.53 s-1, respectively. The critical temperature of thermal explosion of the compound is 267.36 ℃.
