In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of ind...In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.展开更多
Large-scale and diverse businesses based on the cloud computing platform bring the heavy network traffic to cloud data centers.However,the unbalanced workload of cloud data center network easily leads to the network c...Large-scale and diverse businesses based on the cloud computing platform bring the heavy network traffic to cloud data centers.However,the unbalanced workload of cloud data center network easily leads to the network congestion,the low resource utilization rate,the long delay,the low reliability,and the low throughput.In order to improve the utilization efficiency and the quality of services(QoS)of cloud system,especially to solve the problem of network congestion,we propose MTSS,a multi-path traffic scheduling mechanism based on software defined networking(SDN).MTSS utilizes the data flow scheduling flexibility of SDN and the multi-path feature of the fat-tree structure to improve the traffic balance of the cloud data center network.A heuristic traffic balancing algorithm is presented for MTSS,which periodically monitors the network link and dynamically adjusts the traffic on the heavy link to achieve programmable data forwarding and load balancing.The experimental results show that MTSS outperforms equal-cost multi-path protocol(ECMP),by effectively reducing the packet loss rate and delay.In addition,MTSS improves the utilization efficiency,the reliability and the throughput rate of the cloud data center network.展开更多
An algorithm for direction angle of arrival(DOA) estimation and array calibration of signals from multiple mobile users in the CDMA systems and multi-path environment was presented . The main idea is that the algorith...An algorithm for direction angle of arrival(DOA) estimation and array calibration of signals from multiple mobile users in the CDMA systems and multi-path environment was presented . The main idea is that the algorithm employs code-matched filter and model of the inter-symbol interference and multiple-access interference exactly. The correlation matrices of the received signals before and after code-matched filtering were employed to eliminate the effect of the additive white Gaussian noise, and a new mathematical problem was created, a new maximum likelihood method based on the strong law of large number was derived for DOA estimation and array calibration. Computer simulation results prove that the algorithm is effective.展开更多
Back-propagation artificial neural network (BPANN) is used in ball backward spinning in order to form thin-walled tubular parts with longitudinal inner ribs. By selecting the process parameters which have a great infl...Back-propagation artificial neural network (BPANN) is used in ball backward spinning in order to form thin-walled tubular parts with longitudinal inner ribs. By selecting the process parameters which have a great influence on the height of inner ribs as well as fish scale on the surface of the spun part, a BPANN of 3-8-1 structure is established for predicting the height of inner rib and recognizing the fish scale defect. Experiments data have proved that the average relative error between the measured value and the predicted value of the height of inner rib is not more than 5%. It is evident that BPANN can not only predict the height of inner ribs of the spun part accurately, but recognize and prevent the occurrence of the quality defect of fish scale successfully, and combining BPANN with the ball backward spinning is essential to obtain the desired spun part.展开更多
A novel dual-band antenna is proposed for mitigating the multi-path interference in the global navigation satellite system（GNSS） applications. The radiation patches consist of a shortedannular-ring reduced-surface-w...A novel dual-band antenna is proposed for mitigating the multi-path interference in the global navigation satellite system（GNSS） applications. The radiation patches consist of a shortedannular-ring reduced-surface-wave（SAR-RSW） element and an inverted-shorted-annular-ring reduced-surface-wave（ISAR-RSW）element. One key feature of the design is the proximity-coupled probe feeds to increase impedance bandwidth. The other is the defected ground structure band rejection filters to suppress the interaction effect between the SAR-RSW and the ISAR-RSW elements. In addition, trans-directional couplers are used to obtain tight coupling. Measurement results indicate that the antenna has a larger than 10 d B return loss bandwidth and a less than 3 d B axial-ratio（AR） bandwidth in the range of（1.164 – 1.255） GHz and（1.552 – 1.610） GHz. The gain of the passive antenna in the whole operating band is more than 7 d Bi.展开更多
Damage assessment of the wing under blast wave is essential to the vulnerability reduction design of aircraft. This paper introduces a critical relative distance prediction method of aircraft wing damage based on the ...Damage assessment of the wing under blast wave is essential to the vulnerability reduction design of aircraft. This paper introduces a critical relative distance prediction method of aircraft wing damage based on the back-propagation artificial neural network(BP-ANN), which is trained by finite element simulation results. Moreover, the finite element method(FEM) for wing blast damage simulation has been validated by ground explosion tests and further used for damage mode determination and damage characteristics analysis. The analysis results indicate that the wing is more likely to be damaged when the root is struck from vertical directions than others for a small charge. With the increase of TNT equivalent charge, the main damage mode of the wing gradually changes from the local skin tearing to overall structural deformation and the overpressure threshold of wing damage decreases rapidly. Compared to the FEM-based damage assessment, the BP-ANN-based method can predict the wing damage under a random blast wave with an average relative error of 4.78%. The proposed method and conclusions can be used as a reference for damage assessment under blast wave and low-vulnerability design of aircraft structures.展开更多
An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accur...An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation(BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand(gross domestic product(GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand(population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.展开更多
基金Project(50734007) supported by the National Natural Science Foundation of China
文摘In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.
基金supported by the National Key Research and Development Program of China(2018YFB1003702)the National Natural Science Foundation of China(61472192)the Scientific and Technological Support Project(Society)of Jiangsu Province(BE2016776)
文摘Large-scale and diverse businesses based on the cloud computing platform bring the heavy network traffic to cloud data centers.However,the unbalanced workload of cloud data center network easily leads to the network congestion,the low resource utilization rate,the long delay,the low reliability,and the low throughput.In order to improve the utilization efficiency and the quality of services(QoS)of cloud system,especially to solve the problem of network congestion,we propose MTSS,a multi-path traffic scheduling mechanism based on software defined networking(SDN).MTSS utilizes the data flow scheduling flexibility of SDN and the multi-path feature of the fat-tree structure to improve the traffic balance of the cloud data center network.A heuristic traffic balancing algorithm is presented for MTSS,which periodically monitors the network link and dynamically adjusts the traffic on the heavy link to achieve programmable data forwarding and load balancing.The experimental results show that MTSS outperforms equal-cost multi-path protocol(ECMP),by effectively reducing the packet loss rate and delay.In addition,MTSS improves the utilization efficiency,the reliability and the throughput rate of the cloud data center network.
基金Project supported by the National Defense Pre-research Foundation
文摘An algorithm for direction angle of arrival(DOA) estimation and array calibration of signals from multiple mobile users in the CDMA systems and multi-path environment was presented . The main idea is that the algorithm employs code-matched filter and model of the inter-symbol interference and multiple-access interference exactly. The correlation matrices of the received signals before and after code-matched filtering were employed to eliminate the effect of the additive white Gaussian noise, and a new mathematical problem was created, a new maximum likelihood method based on the strong law of large number was derived for DOA estimation and array calibration. Computer simulation results prove that the algorithm is effective.
文摘Back-propagation artificial neural network (BPANN) is used in ball backward spinning in order to form thin-walled tubular parts with longitudinal inner ribs. By selecting the process parameters which have a great influence on the height of inner ribs as well as fish scale on the surface of the spun part, a BPANN of 3-8-1 structure is established for predicting the height of inner rib and recognizing the fish scale defect. Experiments data have proved that the average relative error between the measured value and the predicted value of the height of inner rib is not more than 5%. It is evident that BPANN can not only predict the height of inner ribs of the spun part accurately, but recognize and prevent the occurrence of the quality defect of fish scale successfully, and combining BPANN with the ball backward spinning is essential to obtain the desired spun part.
基金supported by the National Natural Science Foundation of China(61071044)the Traffic Applied Basic Research Project of the Ministry of Transport of China(2010-329-225-030)+2 种基金the Doctor Startup Foundation of Liaoning Province(20141103)the Scientific Research Project of the Department of Education of Liaoning Province(L2013196)the Fundamental Research Funds for the Central Universities(2014YB05)
文摘A novel dual-band antenna is proposed for mitigating the multi-path interference in the global navigation satellite system（GNSS） applications. The radiation patches consist of a shortedannular-ring reduced-surface-wave（SAR-RSW） element and an inverted-shorted-annular-ring reduced-surface-wave（ISAR-RSW）element. One key feature of the design is the proximity-coupled probe feeds to increase impedance bandwidth. The other is the defected ground structure band rejection filters to suppress the interaction effect between the SAR-RSW and the ISAR-RSW elements. In addition, trans-directional couplers are used to obtain tight coupling. Measurement results indicate that the antenna has a larger than 10 d B return loss bandwidth and a less than 3 d B axial-ratio（AR） bandwidth in the range of（1.164 – 1.255） GHz and（1.552 – 1.610） GHz. The gain of the passive antenna in the whole operating band is more than 7 d Bi.
基金supported by the Natural Science Foundation of Shaanxi Province (Grant No. 2020JQ-122)the Fund support of Science and Technology on Transient Impact Laboratory。
文摘Damage assessment of the wing under blast wave is essential to the vulnerability reduction design of aircraft. This paper introduces a critical relative distance prediction method of aircraft wing damage based on the back-propagation artificial neural network(BP-ANN), which is trained by finite element simulation results. Moreover, the finite element method(FEM) for wing blast damage simulation has been validated by ground explosion tests and further used for damage mode determination and damage characteristics analysis. The analysis results indicate that the wing is more likely to be damaged when the root is struck from vertical directions than others for a small charge. With the increase of TNT equivalent charge, the main damage mode of the wing gradually changes from the local skin tearing to overall structural deformation and the overpressure threshold of wing damage decreases rapidly. Compared to the FEM-based damage assessment, the BP-ANN-based method can predict the wing damage under a random blast wave with an average relative error of 4.78%. The proposed method and conclusions can be used as a reference for damage assessment under blast wave and low-vulnerability design of aircraft structures.
文摘An important problem in demand planning for energy consumption is developing an accurate energy forecasting model. In fact, it is not possible to allocate the energy resources in an optimal manner without having accurate demand value. A new energy forecasting model was proposed based on the back-propagation(BP) type neural network and imperialist competitive algorithm. The proposed method offers the advantage of local search ability of BP technique and global search ability of imperialist competitive algorithm. Two types of empirical data regarding the energy demand(gross domestic product(GDP), population, import, export and energy demand) in Turkey from 1979 to 2005 and electricity demand(population, GDP, total revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010 were investigated to demonstrate the applicability and merits of the present method. The performance of the proposed model is found to be better than that of conventional back-propagation neural network with low mean absolute error.
