A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solvin...A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.展开更多
AA5083 friction stir welds were produced using systematic experimental design, the process forces and heat input with varying parameters were studied. Helpful empirical models were developed in designing friction stir...AA5083 friction stir welds were produced using systematic experimental design, the process forces and heat input with varying parameters were studied. Helpful empirical models were developed in designing friction stir welding （FSW） tools and FSW welders. These models may be further helpful for making process parameter choice for this sort of alloy, defining welding program and control of process parameters by using computer numerical control friction stir welding welders. The results show that tool rotational speed, welding speed and tool shoulder diameter are most significant parameters affecting axial force and heat input, while longitudinal force is significantly affected by welding speed and probe diameter.展开更多
Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input ...Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.展开更多
This work presents an anticipatory terminal iterative learning control scheme for a class of batch proc- esses, where only the final system output is measurable and the control input is constant in each operations. Th...This work presents an anticipatory terminal iterative learning control scheme for a class of batch proc- esses, where only the final system output is measurable and the control input is constant in each operations. The propgsed approach works well with input constraints provided that the desired control input with respect to the desired trajectory is within the samratiorl bound. The tracking error convergence is established with rigorous mathe- matical analysis. Simulation results .are provided to showthe effectiveness, of the proposed approach.展开更多
Cavity beam position monitor(BPM) is widely used in a precise electron beam position measurement. Based on high performance oscilloscope-embedded EPICS input/output controller,we developed an on-line cavity BPM signal...Cavity beam position monitor(BPM) is widely used in a precise electron beam position measurement. Based on high performance oscilloscope-embedded EPICS input/output controller,we developed an on-line cavity BPM signal processing system for fast data acquisition solution when designing a cavity BPM.Also,methods for extracting the position information from cavity pickup signals and calibration algorithm are included in this solution.展开更多
The in-process changes of weld nugget growth during the Resistance Spot Welding were investigated based on the resistance of input electrical impedance. To compute the time varying resistance of input electrical imped...The in-process changes of weld nugget growth during the Resistance Spot Welding were investigated based on the resistance of input electrical impedance. To compute the time varying resistance of input electrical impedance, the welding voltage and current signals are measured simultaneously and then converted into complex-valued signals by using Hilbert transform. Comparing with the dynamic contact resistance as reported in literature, it showed that the time varying resistance of input electrical impedance can be accurately correlated with the physical changes of weld nugget growth. Therefore, it can be used to characterize the in-process changes of weld nugget growth. Several new findings were reported based on the investigation of spot welds under no weld, with and without weld expulsion conditions.展开更多
Input theory as a theoretical foundation in language teaching plays an important role in SLA.Though a wealth of re⁃search has been done by linguists to demonstrate the importance of language input in SLA,little has be...Input theory as a theoretical foundation in language teaching plays an important role in SLA.Though a wealth of re⁃search has been done by linguists to demonstrate the importance of language input in SLA,little has been written about the type and amount of language input for successful SLA,especially its processing model while acquiring a second language.This paper first discusses the Krashen’s input hypothesis in language learning,and then an introduction to Chaudron’s processing model of in⁃put is made.In the final part,the author explains the acquisition process based on word acquisition and grammar acquisition and concludes that in the process of acquiring a second language,the language learners reconstruct a new cognitive model by taking in consistent comprehensible language input.展开更多
Globally a large number of process-based models have been assessed for quantification of agricultural greenhouse gas (GHG) emissions. Modelling approaches minimize the presence of spatial variability of biogeochemical...Globally a large number of process-based models have been assessed for quantification of agricultural greenhouse gas (GHG) emissions. Modelling approaches minimize the presence of spatial variability of biogeochemical processes, leading to improved estimates of GHGs as well as identifying mitigation and policy options. The comparative performance of the three dynamic models (e.g., DNDC v9.4, DailyDayCent and ECOSSE v5+) with minimum numbers of common input parameters was evaluated against measured variables. Simulations were performed on conventionally-tilled spring barley crops receiving N fertilizer at 135 - 159 kg·N·ha<sup>-</sup><sup>1</sup>·yr<sup>-</sup><sup>1</sup> and crop residues at 3 t·ha<sup>-</sup><sup>1</sup>·yr<sup>-</sup><sup>1</sup>. For surface soil nitrate (0 - 10 cm), the ECOSSE and DNDC simulated values showed significant correlations with measured values (R<sup>2</sup> = 0.31 - 0.55, p 0.05). Only the ECOSSE-simulated N<sub>2</sub>O fluxes showed a significant relationship (R<sup>2</sup> = 0.33, p 0.05) with values measured from fertilized fields, but not with unfertilized ones. The DNDC and DailyDayCent models significantly underestimated seasonal/annual N<sub>2</sub>O fluxes compared to ECOSSE, with emission factors (EFs), based on an 8-year average, were 0.09%, 0.31% and 0.52%, respectively. Predictions of ecosystem respiration by both DailyDayCent and DNDC showed reasonable agreement with Eddy Covariance data (R<sup>2</sup> = 0.34 - 0.41, p 0.05). Compared to the measured value (3624 kg·C·ha<sup>-</sup><sup>1</sup>·yr<sup>-</sup><sup>1</sup>), the ECOSSE underestimated annual heterotrophic respiration by 7% but this was smaller than the DNDC (50%) and DailyDayCent (24%) estimates. All models simulated CH<sub>4</sub> uptake we展开更多
基金Supported by the National Natural Science Foundation of China (No.60374037, No.60574036), the Program for New Century Excellent Talents in University of China (NCET), and the Specialized Research Fund for the Doctoral Program of Higher Edu-cation of China (No.20050055013).
文摘A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.
文摘AA5083 friction stir welds were produced using systematic experimental design, the process forces and heat input with varying parameters were studied. Helpful empirical models were developed in designing friction stir welding （FSW） tools and FSW welders. These models may be further helpful for making process parameter choice for this sort of alloy, defining welding program and control of process parameters by using computer numerical control friction stir welding welders. The results show that tool rotational speed, welding speed and tool shoulder diameter are most significant parameters affecting axial force and heat input, while longitudinal force is significantly affected by welding speed and probe diameter.
基金Supported by Beijing Municipal Education Commission (No.xk100100435) and the Key Research Project of Science andTechnology from Sinopec (No.E03007).
文摘Many applications of principal component analysis (PCA) can be found in dimensionality reduction. But linear PCA method is not well suitable for nonlinear chemical processes. A new PCA method based on im-proved input training neural network (IT-NN) is proposed for the nonlinear system modelling in this paper. Mo-mentum factor and adaptive learning rate are introduced into learning algorithm to improve the training speed of IT-NN. Contrasting to the auto-associative neural network (ANN), IT-NN has less hidden layers and higher training speed. The effectiveness is illustrated through a comparison of IT-NN with linear PCA and ANN with experiments. Moreover, the IT-NN is combined with RBF neural network (RBF-NN) to model the yields of ethylene and propyl-ene in the naphtha pyrolysis system. From the illustrative example and practical application, IT-NN combined with RBF-NN is an effective method of nonlinear chemical process modelling.
基金Supported by the National Natural Science Foundation of China （60974040, 61120106009）, the Research Award Foundation for the Excellent Youth Scientists of Shandong Province of China （BS2011DX010）, and the High School Science ＆ Technol- ogy Fund Planning Project of Shandong Province of China （J 10LG32）.
文摘This work presents an anticipatory terminal iterative learning control scheme for a class of batch proc- esses, where only the final system output is measurable and the control input is constant in each operations. The propgsed approach works well with input constraints provided that the desired control input with respect to the desired trajectory is within the samratiorl bound. The tracking error convergence is established with rigorous mathe- matical analysis. Simulation results .are provided to showthe effectiveness, of the proposed approach.
基金Supported by National Natural Science Foundation(Grant No.11075198)
文摘Cavity beam position monitor(BPM) is widely used in a precise electron beam position measurement. Based on high performance oscilloscope-embedded EPICS input/output controller,we developed an on-line cavity BPM signal processing system for fast data acquisition solution when designing a cavity BPM.Also,methods for extracting the position information from cavity pickup signals and calibration algorithm are included in this solution.
文摘The in-process changes of weld nugget growth during the Resistance Spot Welding were investigated based on the resistance of input electrical impedance. To compute the time varying resistance of input electrical impedance, the welding voltage and current signals are measured simultaneously and then converted into complex-valued signals by using Hilbert transform. Comparing with the dynamic contact resistance as reported in literature, it showed that the time varying resistance of input electrical impedance can be accurately correlated with the physical changes of weld nugget growth. Therefore, it can be used to characterize the in-process changes of weld nugget growth. Several new findings were reported based on the investigation of spot welds under no weld, with and without weld expulsion conditions.
文摘Input theory as a theoretical foundation in language teaching plays an important role in SLA.Though a wealth of re⁃search has been done by linguists to demonstrate the importance of language input in SLA,little has been written about the type and amount of language input for successful SLA,especially its processing model while acquiring a second language.This paper first discusses the Krashen’s input hypothesis in language learning,and then an introduction to Chaudron’s processing model of in⁃put is made.In the final part,the author explains the acquisition process based on word acquisition and grammar acquisition and concludes that in the process of acquiring a second language,the language learners reconstruct a new cognitive model by taking in consistent comprehensible language input.
文摘Globally a large number of process-based models have been assessed for quantification of agricultural greenhouse gas (GHG) emissions. Modelling approaches minimize the presence of spatial variability of biogeochemical processes, leading to improved estimates of GHGs as well as identifying mitigation and policy options. The comparative performance of the three dynamic models (e.g., DNDC v9.4, DailyDayCent and ECOSSE v5+) with minimum numbers of common input parameters was evaluated against measured variables. Simulations were performed on conventionally-tilled spring barley crops receiving N fertilizer at 135 - 159 kg·N·ha<sup>-</sup><sup>1</sup>·yr<sup>-</sup><sup>1</sup> and crop residues at 3 t·ha<sup>-</sup><sup>1</sup>·yr<sup>-</sup><sup>1</sup>. For surface soil nitrate (0 - 10 cm), the ECOSSE and DNDC simulated values showed significant correlations with measured values (R<sup>2</sup> = 0.31 - 0.55, p 0.05). Only the ECOSSE-simulated N<sub>2</sub>O fluxes showed a significant relationship (R<sup>2</sup> = 0.33, p 0.05) with values measured from fertilized fields, but not with unfertilized ones. The DNDC and DailyDayCent models significantly underestimated seasonal/annual N<sub>2</sub>O fluxes compared to ECOSSE, with emission factors (EFs), based on an 8-year average, were 0.09%, 0.31% and 0.52%, respectively. Predictions of ecosystem respiration by both DailyDayCent and DNDC showed reasonable agreement with Eddy Covariance data (R<sup>2</sup> = 0.34 - 0.41, p 0.05). Compared to the measured value (3624 kg·C·ha<sup>-</sup><sup>1</sup>·yr<sup>-</sup><sup>1</sup>), the ECOSSE underestimated annual heterotrophic respiration by 7% but this was smaller than the DNDC (50%) and DailyDayCent (24%) estimates. All models simulated CH<sub>4</sub> uptake we
