In target tracking, the measurements collected by sensors can be biased in some real scenarios, e.g., due to systematic error. To accurately estimate the target trajectory, it is essential that the measurement bias be...In target tracking, the measurements collected by sensors can be biased in some real scenarios, e.g., due to systematic error. To accurately estimate the target trajectory, it is essential that the measurement bias be identified in the first place. We investigate the iterative bias estimation process based on the expectation-maximization（EM）algorithm, for cases where sufficiently large numbers of measurements are at hand. With the assistance of extended Kalman filtering and smoothing, we derive two EM estimation processes to estimate the measurement bias which is formulated as a random variable in one state-space model and a constant value in another. More importantly,we theoretically derive the global convergence result of the EM-based measurement bias estimation and reveal the link between the two proposed EM estimation processes in the respective state-space models. It is found that the bias estimate in the second state-space model is more accurate and of less complexity. Furthermore, the EM-based iterative estimation converges faster in the second state-space model than in the first one. As a byproduct, the target trajectory can be simultaneously estimated with the measurement bias, after processing a batch of measurements.These results are confirmed by our simulations.展开更多
An integrated macro and micro multi-scale model for the three-dimensional microstructure simulation of Ni-based superalloy investment castings was developed, and applied to industrial castings to investigate grain evo...An integrated macro and micro multi-scale model for the three-dimensional microstructure simulation of Ni-based superalloy investment castings was developed, and applied to industrial castings to investigate grain evolution during solidification. A ray tracing method was used to deal with the complex heat radiation transfer. The rnicrostructure evolution was simulated based on the Modified Cellular Automaton method, which was coupled with three-dimensional nested macro and micro grids. Experi- ments for Ni-based superalloy turbine wheel investment casting were carried out, which showed a good correspondence with the simulated results. It is indicated that the proposed model is able to predict the microstructure of the casting precisely, which provides a tool for the optimizing process.展开更多
The non-Markov process exists widely in thermodymanic process,while it usually requires the packing of many transistors and memories with great system complexity in a traditional device structure to minic such functio...The non-Markov process exists widely in thermodymanic process,while it usually requires the packing of many transistors and memories with great system complexity in a traditional device structure to minic such functions.Two-dimensional(2D)material-based resistive random access memory(RRAM)devices have the potential for next-generation computing systems with much-reduced complexity.Here,we achieve a non-Markov chain in an individual RRAM device based on 2D mineral material mica with a vertical metal/mica/metal structure.We find that the potassium ions(K+)in 2D mica gradually move in the direction of the applied electric field,making the initially insulating mica conductive.The accumulation of K+is changed by an electric field,and the 2D-mica RRAM has both single and double memory windows,a high on/off ratio,decent stability,and repeatability.This is the first time a non-Markov chain process has been established in a single RRAM,in which the movement of K+is dependent on the stimulated voltage as well as their past states.This work not only uncovers an intrinsic inner ionic conductivity of 2D mica,but also opens the door for the production of such RRAM devices with numerous functions and applications.展开更多
基金supported by the National Natural Science Foundation of China(No.61601254)the KC Wong Magna Fund of Ningbo University,China
文摘In target tracking, the measurements collected by sensors can be biased in some real scenarios, e.g., due to systematic error. To accurately estimate the target trajectory, it is essential that the measurement bias be identified in the first place. We investigate the iterative bias estimation process based on the expectation-maximization（EM）algorithm, for cases where sufficiently large numbers of measurements are at hand. With the assistance of extended Kalman filtering and smoothing, we derive two EM estimation processes to estimate the measurement bias which is formulated as a random variable in one state-space model and a constant value in another. More importantly,we theoretically derive the global convergence result of the EM-based measurement bias estimation and reveal the link between the two proposed EM estimation processes in the respective state-space models. It is found that the bias estimate in the second state-space model is more accurate and of less complexity. Furthermore, the EM-based iterative estimation converges faster in the second state-space model than in the first one. As a byproduct, the target trajectory can be simultaneously estimated with the measurement bias, after processing a batch of measurements.These results are confirmed by our simulations.
基金financially supported by the National Basic Research Program of China (Grant Nos. 2005CB724105 and 2011CB706801)the National Natural Science Foundation of China (Grant No. 10477010)+1 种基金the National High Technology Research, Development Program of China (Grant No. 2007AA04Z141)the Important National Science & Technology Specific Projects (Grant No. 2009ZX04006-041-04)
文摘An integrated macro and micro multi-scale model for the three-dimensional microstructure simulation of Ni-based superalloy investment castings was developed, and applied to industrial castings to investigate grain evolution during solidification. A ray tracing method was used to deal with the complex heat radiation transfer. The rnicrostructure evolution was simulated based on the Modified Cellular Automaton method, which was coupled with three-dimensional nested macro and micro grids. Experi- ments for Ni-based superalloy turbine wheel investment casting were carried out, which showed a good correspondence with the simulated results. It is indicated that the proposed model is able to predict the microstructure of the casting precisely, which provides a tool for the optimizing process.
基金This work was supported by the National Natural Science Foundation of China(51920105002,51991340,51722206,and 51991343)Guangdong Innovative and Entrepreneurial Research Team Program(2017ZT07C341)+1 种基金the Bureau of Industry and Information Technology of Shenzhen for the“2017 Graphene Manufacturing Innovation Center Project”(201901171523)the Shenzhen Basic Research Program(JCYJ20200109144620815 and JCYJ20200109144616617).
文摘The non-Markov process exists widely in thermodymanic process,while it usually requires the packing of many transistors and memories with great system complexity in a traditional device structure to minic such functions.Two-dimensional(2D)material-based resistive random access memory(RRAM)devices have the potential for next-generation computing systems with much-reduced complexity.Here,we achieve a non-Markov chain in an individual RRAM device based on 2D mineral material mica with a vertical metal/mica/metal structure.We find that the potassium ions(K+)in 2D mica gradually move in the direction of the applied electric field,making the initially insulating mica conductive.The accumulation of K+is changed by an electric field,and the 2D-mica RRAM has both single and double memory windows,a high on/off ratio,decent stability,and repeatability.This is the first time a non-Markov chain process has been established in a single RRAM,in which the movement of K+is dependent on the stimulated voltage as well as their past states.This work not only uncovers an intrinsic inner ionic conductivity of 2D mica,but also opens the door for the production of such RRAM devices with numerous functions and applications.
