As a key component of injection molding,multi-cavity hot runner(MCHR)system faces the crucial problem of polymer melt filling imbalance among the cavities.The thermal imbalance in the system has been considered as the...As a key component of injection molding,multi-cavity hot runner(MCHR)system faces the crucial problem of polymer melt filling imbalance among the cavities.The thermal imbalance in the system has been considered as the leading cause.Hence,the solution may rest with the synchronization of those heating processes in MCHR system.This paper proposes a’Master-Slave’generalized predictive synchronization control(MS-GPSC)method with’Mr.Slowest’strategy for preheating stage of MCHR system.The core of the proposed method is choosing the heating process with slowest dynamics as the’Master’to track the setpoint,while the other heating processes are treated as‘Slaves’tracking the output of’Master’.This proposed method is shown to have the good ability of temperature synchronization.The corresponding analysis is conducted on parameters tuning and stability,simulations and experiments show the strategy is effective.展开更多
风电功率预测对电力系统的安全稳定运行具有重要意义。针对多风电场的超短期概率预测问题,提出了一种基于Bagging混合策略和核密度估计(kernel density estimation,KDE)的稀疏向量自回归预测方法。首先通过时间序列分解和余项自举,生成...风电功率预测对电力系统的安全稳定运行具有重要意义。针对多风电场的超短期概率预测问题,提出了一种基于Bagging混合策略和核密度估计(kernel density estimation,KDE)的稀疏向量自回归预测方法。首先通过时间序列分解和余项自举,生成若干自举时间序列。对于每个时间序列,采用向量自回归(vector autoregression,VAR)模型进行预测。针对传统模型在风场数量较多时容易出现的过拟合问题,采用稀疏向量自回归模型,筛选最有效的回归系数,得到稀疏系数矩阵。每个时间序列训练的预测模型分别产生点预测结果,对于多重点预测结果,使用KDE方法产生概率密度的预测结果。在真实风电集群数据上,验证所提多场站概率预测方法的有效性,采用分位数得分评估概率预测精度。相关实验结果表明,该方法可以有效提高概率预测精度。展开更多
In this study, we propose a new temperature compensation control strategy for a multi-cavity hot runner injection molding system, At first, the melt filling time of each cavity can be measured by installing temperatur...In this study, we propose a new temperature compensation control strategy for a multi-cavity hot runner injection molding system, At first, the melt filling time of each cavity can be measured by installing temperature sensors on the position around end filling area, and filling time difference between the various cavities can be calculated. Then the melt temperature of each hot nozzle can be adjusted automatically by a control strategy established based on the Fuzzy Theory and a program compiled with LABVIEW software. Temperature changes the melt mobility, so the adjustment of temperature can equalize the filling time of the melt in each cavity, which can reduced the mass deviation between each cavity and make product properties of each cavity consistent. The conclusion of the experiment is as follows： For this contact lens box of a four-cavity Hot Runner mold, by applying hot runner temperature compensation control system, time difference can be reduced from 0.05 s to 0.01 s at each cavity, and the mass Standard deviation of the four cavity can be improved from 0.006 to 0.002. The ratio of imbalance can be reduced from 20% to 4%. Hence, the hot runner temperature compensation control system has significant feasibility and high potential in improving melt flow balance of multi-cavity molding application.展开更多
The protection of the entanglement between two V-atoms(EBTVA)in a multi-cavity coupling system is studied.The whole system consists of two V-atoms.The two V-atoms are initially in the maximum entangled state and inter...The protection of the entanglement between two V-atoms(EBTVA)in a multi-cavity coupling system is studied.The whole system consists of two V-atoms.The two V-atoms are initially in the maximum entangled state and interacts locally with its own dissipative cavity which is coupled to the external cavities with high quality factor(ECWHQF).The results show that,when there is no ECWHQF,the EBTVA can be protected effectively in the case where the V-atom and the dissipative cavity are weak coupled in large detuning,while when there are different numbers n of ECWHQF coupled to two dissipative cavities,by adjusting the parameters of the number n of ECWHQF and the coupling strength k between cavities,the EBTVA can be protected perfectly and continuously.Our result provides an effective method for protecting entanglement resources of three-level system.展开更多
A novel multi-cavity Helmholtz muffler is proposed. The multi-cavity Helmholtz muffler is composed of steel structures and silicone membranes. With suitable construction, the Helmholtz muffler can be designed to exhib...A novel multi-cavity Helmholtz muffler is proposed. The multi-cavity Helmholtz muffler is composed of steel structures and silicone membranes. With suitable construction, the Helmholtz muffler can be designed to exhibit negative mass density in low frequency, and the muffling frequency can be adjusted when we change the internal structure of the cavity,which will be very attractive for noise control. In this paper, we investigate the influence of the membranes and the cavities on noise reduction characteristics with theoretical calculations and simulations. The results show that the numbers of membranes and the volumes of the cavities can have a great effect on the position of the muffling frequency. The number of cavities can have a great effect on the width of the muffling frequency(reduce the noise by 10 dB). With different combinations of the membranes and cavities, we can get different muffling frequencies, which can meet different muffling demands in practical applications and is more flexible than the traditional Helmholtz cavity.展开更多
This study is subject to the finite element and abd uc tive network method application in the multi-cavity die. In order to select the optimal cooling system parameters to minimize the warp of a die-casting die, t he ...This study is subject to the finite element and abd uc tive network method application in the multi-cavity die. In order to select the optimal cooling system parameters to minimize the warp of a die-casting die, t he Taguchi’s method and the abductive network are used. These methods are appli ed to create an efficient model with functional nodes for the considered problem . Once the cooling system parameters are developed, this network can be used to predict the warp for the die-casting die accurately. A simulated annealing (SA) optimization algorithm with a performance index is then applied to the neur al network for searching the optimal cooling system parameters, and obtain rathe r satisfactory result as compared with the corresponding finite element veri fication.展开更多
This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine lear...This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial prediction.The bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and BA-DTR.It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as well.In addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive assessment.With this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also measured.They were then compiled as the dataset to develop the PPV predictive models.The results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR model.Of those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.展开更多
基金supported in part by National Natural Science Foundation of China(62203127)Basic and Applied Basic Research Project of Guangzhou City(2023A04J1712)+1 种基金The Foshan-HKUST Projects Program(FSUST19-FYTRI01)GDAS’Project of Science and Technology Development(2020GDASYL-20200202001).
文摘As a key component of injection molding,multi-cavity hot runner(MCHR)system faces the crucial problem of polymer melt filling imbalance among the cavities.The thermal imbalance in the system has been considered as the leading cause.Hence,the solution may rest with the synchronization of those heating processes in MCHR system.This paper proposes a’Master-Slave’generalized predictive synchronization control(MS-GPSC)method with’Mr.Slowest’strategy for preheating stage of MCHR system.The core of the proposed method is choosing the heating process with slowest dynamics as the’Master’to track the setpoint,while the other heating processes are treated as‘Slaves’tracking the output of’Master’.This proposed method is shown to have the good ability of temperature synchronization.The corresponding analysis is conducted on parameters tuning and stability,simulations and experiments show the strategy is effective.
文摘风电功率预测对电力系统的安全稳定运行具有重要意义。针对多风电场的超短期概率预测问题,提出了一种基于Bagging混合策略和核密度估计(kernel density estimation,KDE)的稀疏向量自回归预测方法。首先通过时间序列分解和余项自举,生成若干自举时间序列。对于每个时间序列,采用向量自回归(vector autoregression,VAR)模型进行预测。针对传统模型在风场数量较多时容易出现的过拟合问题,采用稀疏向量自回归模型,筛选最有效的回归系数,得到稀疏系数矩阵。每个时间序列训练的预测模型分别产生点预测结果,对于多重点预测结果,使用KDE方法产生概率密度的预测结果。在真实风电集群数据上,验证所提多场站概率预测方法的有效性,采用分位数得分评估概率预测精度。相关实验结果表明,该方法可以有效提高概率预测精度。
文摘In this study, we propose a new temperature compensation control strategy for a multi-cavity hot runner injection molding system, At first, the melt filling time of each cavity can be measured by installing temperature sensors on the position around end filling area, and filling time difference between the various cavities can be calculated. Then the melt temperature of each hot nozzle can be adjusted automatically by a control strategy established based on the Fuzzy Theory and a program compiled with LABVIEW software. Temperature changes the melt mobility, so the adjustment of temperature can equalize the filling time of the melt in each cavity, which can reduced the mass deviation between each cavity and make product properties of each cavity consistent. The conclusion of the experiment is as follows： For this contact lens box of a four-cavity Hot Runner mold, by applying hot runner temperature compensation control system, time difference can be reduced from 0.05 s to 0.01 s at each cavity, and the mass Standard deviation of the four cavity can be improved from 0.006 to 0.002. The ratio of imbalance can be reduced from 20% to 4%. Hence, the hot runner temperature compensation control system has significant feasibility and high potential in improving melt flow balance of multi-cavity molding application.
基金the National Natural Science Foundation of China(Grant Nos.12064012 and 11374096).
文摘The protection of the entanglement between two V-atoms(EBTVA)in a multi-cavity coupling system is studied.The whole system consists of two V-atoms.The two V-atoms are initially in the maximum entangled state and interacts locally with its own dissipative cavity which is coupled to the external cavities with high quality factor(ECWHQF).The results show that,when there is no ECWHQF,the EBTVA can be protected effectively in the case where the V-atom and the dissipative cavity are weak coupled in large detuning,while when there are different numbers n of ECWHQF coupled to two dissipative cavities,by adjusting the parameters of the number n of ECWHQF and the coupling strength k between cavities,the EBTVA can be protected perfectly and continuously.Our result provides an effective method for protecting entanglement resources of three-level system.
基金Project supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.KYCX18 0249)
文摘A novel multi-cavity Helmholtz muffler is proposed. The multi-cavity Helmholtz muffler is composed of steel structures and silicone membranes. With suitable construction, the Helmholtz muffler can be designed to exhibit negative mass density in low frequency, and the muffling frequency can be adjusted when we change the internal structure of the cavity,which will be very attractive for noise control. In this paper, we investigate the influence of the membranes and the cavities on noise reduction characteristics with theoretical calculations and simulations. The results show that the numbers of membranes and the volumes of the cavities can have a great effect on the position of the muffling frequency. The number of cavities can have a great effect on the width of the muffling frequency(reduce the noise by 10 dB). With different combinations of the membranes and cavities, we can get different muffling frequencies, which can meet different muffling demands in practical applications and is more flexible than the traditional Helmholtz cavity.
文摘This study is subject to the finite element and abd uc tive network method application in the multi-cavity die. In order to select the optimal cooling system parameters to minimize the warp of a die-casting die, t he Taguchi’s method and the abductive network are used. These methods are appli ed to create an efficient model with functional nodes for the considered problem . Once the cooling system parameters are developed, this network can be used to predict the warp for the die-casting die accurately. A simulated annealing (SA) optimization algorithm with a performance index is then applied to the neur al network for searching the optimal cooling system parameters, and obtain rathe r satisfactory result as compared with the corresponding finite element veri fication.
基金funded by Vietnam National Foundation for Science and Tech-nology Development(NAFOSTED)under Grant No.105.99-2019.309.
文摘This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial prediction.The bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and BA-DTR.It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as well.In addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive assessment.With this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also measured.They were then compiled as the dataset to develop the PPV predictive models.The results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR model.Of those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.
