To improve the computational efficiency of the reliability-based design optimization(RBDO) of flexible mechanism, particle swarm optimization-advanced extremum response surface method(PSO-AERSM) was proposed by integr...To improve the computational efficiency of the reliability-based design optimization(RBDO) of flexible mechanism, particle swarm optimization-advanced extremum response surface method(PSO-AERSM) was proposed by integrating particle swarm optimization(PSO) algorithm and advanced extremum response surface method(AERSM). Firstly, the AERSM was developed and its mathematical model was established based on artificial neural network, and the PSO algorithm was investigated. And then the RBDO model of flexible mechanism was presented based on AERSM and PSO. Finally, regarding cross-sectional area as design variable, the reliability optimization of flexible mechanism was implemented subject to reliability degree and uncertainties based on the proposed approach. The optimization results show that the cross-section sizes obviously reduce by 22.96 mm^2 while keeping reliability degree. Through the comparison of methods, it is demonstrated that the AERSM holds high computational efficiency while keeping computational precision for the RBDO of flexible mechanism, and PSO algorithm minimizes the response of the objective function. The efforts of this work provide a useful sight for the reliability optimization of flexible mechanism, and enrich and develop the reliability theory as well.展开更多
Timing jitter is one of the main factors that influence on the accuracy of time domain precision measurement. Timing jitter compensation is one of the problems people concern. Because of the flaws of median method, PD...Timing jitter is one of the main factors that influence on the accuracy of time domain precision measurement. Timing jitter compensation is one of the problems people concern. Because of the flaws of median method, PDF deconvolution method and synthetic method, we put forward a new method for timing jitter compensation, which is called advanced median method. The theory of the advanced median method based on probability and statistics is analyzed, and the process of the advanced median method is summarized in this paper. Simulation and experiment show that compared with other methods, the new method could compensate timing jitter effectively.展开更多
目前,高价铁物种(high-valent iron species,HVIS)在基于铁基催化材料诱导的高级氧化工艺(advanced oxidation processes,AOPs)处理各种污染水体方面展现出较好的应用前景.然而,已有的研究对AOPs中HVIS的产生、控制、识别的刻画还不够...目前,高价铁物种(high-valent iron species,HVIS)在基于铁基催化材料诱导的高级氧化工艺(advanced oxidation processes,AOPs)处理各种污染水体方面展现出较好的应用前景.然而,已有的研究对AOPs中HVIS的产生、控制、识别的刻画还不够精细、明确,且HVIS对消除污染物的贡献程度和反应机制也缺乏条理性分析和综合性总结,这对依靠高活性HVIS的AOPs在实际应用中保持高性能长效修复效果会产生一定影响和限制.因此,重点讨论了基于铁基催化材料下HVIS的产生、控制,获取其主控影响因素、HVIS的识别手段及其与典型污染物反应机制,以期为基于HVIS的AOPs在未来发展应用中提供理论支撑与参考.展开更多
Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanc...Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.展开更多
基金Projects(51275138,51475025)supported by the National Natural Science Foundation of ChinaProject(12531109)supported by the Science Foundation of Heilongjiang Provincial Department of Education,China+1 种基金Projects(XJ2015002,G-YZ90)supported by Hong Kong Scholars Program,ChinaProject(2015M580037)supported by Postdoctoral Science Foundation of China
文摘To improve the computational efficiency of the reliability-based design optimization(RBDO) of flexible mechanism, particle swarm optimization-advanced extremum response surface method(PSO-AERSM) was proposed by integrating particle swarm optimization(PSO) algorithm and advanced extremum response surface method(AERSM). Firstly, the AERSM was developed and its mathematical model was established based on artificial neural network, and the PSO algorithm was investigated. And then the RBDO model of flexible mechanism was presented based on AERSM and PSO. Finally, regarding cross-sectional area as design variable, the reliability optimization of flexible mechanism was implemented subject to reliability degree and uncertainties based on the proposed approach. The optimization results show that the cross-section sizes obviously reduce by 22.96 mm^2 while keeping reliability degree. Through the comparison of methods, it is demonstrated that the AERSM holds high computational efficiency while keeping computational precision for the RBDO of flexible mechanism, and PSO algorithm minimizes the response of the objective function. The efforts of this work provide a useful sight for the reliability optimization of flexible mechanism, and enrich and develop the reliability theory as well.
基金National Natural Science Foundation of China (50605065)
文摘Timing jitter is one of the main factors that influence on the accuracy of time domain precision measurement. Timing jitter compensation is one of the problems people concern. Because of the flaws of median method, PDF deconvolution method and synthetic method, we put forward a new method for timing jitter compensation, which is called advanced median method. The theory of the advanced median method based on probability and statistics is analyzed, and the process of the advanced median method is summarized in this paper. Simulation and experiment show that compared with other methods, the new method could compensate timing jitter effectively.
文摘目前,高价铁物种(high-valent iron species,HVIS)在基于铁基催化材料诱导的高级氧化工艺(advanced oxidation processes,AOPs)处理各种污染水体方面展现出较好的应用前景.然而,已有的研究对AOPs中HVIS的产生、控制、识别的刻画还不够精细、明确,且HVIS对消除污染物的贡献程度和反应机制也缺乏条理性分析和综合性总结,这对依靠高活性HVIS的AOPs在实际应用中保持高性能长效修复效果会产生一定影响和限制.因此,重点讨论了基于铁基催化材料下HVIS的产生、控制,获取其主控影响因素、HVIS的识别手段及其与典型污染物反应机制,以期为基于HVIS的AOPs在未来发展应用中提供理论支撑与参考.
基金Project(2012CB725403)supported by the National Basic Research Program of ChinaProjects(71210001,51338008)supported by the National Natural Science Foundation of ChinaProject supported by World Capital Cities Smooth Traffic Collaborative Innovation Center and Singapore National Research Foundation Under Its Campus for Research Excellence and Technology Enterprise(CREATE)Programme
文摘Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.
