We study the influence of ionized impurity scattering on the electron transport in resonant-phonon-assisted terahertz （THz） quantum-cascade lasers （QCLs）. We treat the ionized impurity scattering rates within the ...We study the influence of ionized impurity scattering on the electron transport in resonant-phonon-assisted terahertz （THz） quantum-cascade lasers （QCLs）. We treat the ionized impurity scattering rates within the single subband static screening approximation. We find that the ionized impurity scattering supplies an additional current channel across the device,and affects the electrondistribution in different subbands. We conclude that the ionized impurity scattering should be taken into account in the study of the transport properties of resonant-phonon-assisted THz QCLs.展开更多
By employing dynamic Monte Carlo simulations, we investigate a coil-to-toroid transition of self-attractive semiflexible polymers and the spatial distributions of nanoparticles in self- attractive semiflexible polymer...By employing dynamic Monte Carlo simulations, we investigate a coil-to-toroid transition of self-attractive semiflexible polymers and the spatial distributions of nanoparticles in self- attractive semiflexible polymer/nanoparticle composites. The conformation of self-attractive semiflexible polymers depends on bending energy and self-attractive interactions between monomers in polymer chains. A three-stage process of toroid formation for self-attractive semiflexible chains is shown： several isolated toroids, a loose toroid structure, and a compact toroid structure. Utilizing the compact toroid conformations of self-attractive semiflexible chains, we can control effectively the spatial distributions of nanoparticles in self-attractive semiflexible polymer nanocomposites, and an unconventional toroid structure of nanoparti- cles is observed.展开更多
Measurement uncertainty plays an important role in laser tracking measurement analyses. In the present work, the guides to the expression of uncertainty in measurement(GUM) uncertainty framework(GUF) and its supplemen...Measurement uncertainty plays an important role in laser tracking measurement analyses. In the present work, the guides to the expression of uncertainty in measurement(GUM) uncertainty framework(GUF) and its supplement, the Monte Carlo method, were used to estimate the uncertainty of task-specific laser tracker measurements. First, the sources of error in laser tracker measurement were analyzed in detail, including instruments, measuring network fusion, measurement strategies, measurement process factors(such as the operator), measurement environment, and task-specific data processing. Second, the GUM and Monte Carlo methods and their application to laser tracker measurement were presented. Finally, a case study involving the uncertainty estimation of a cylindricity measurement process using the GUF and Monte Carlo methods was illustrated. The expanded uncertainty results(at 95% confidence levels) obtained with the Monte Carlo method are 0.069 mm(least-squares criterion) and 0.062 mm(minimum zone criterion), respectively, while with the GUM uncertainty framework, none but the result of least-squares criterion can be got, which is 0.071 mm. Thus, the GUM uncertainty framework slightly underestimates the overall uncertainty by 10%. The results demonstrate that the two methods have different characteristics in task-specific uncertainty evaluations of laser tracker measurements. The results indicate that the Monte Carlo method is a practical tool for applying the principle of propagation of distributions and does not depend on the assumptions and limitations required by the law of propagation of uncertainties(GUF). These features of the Monte Carlo method reduce the risk of an unreliable measurement of uncertainty estimation, particularly in cases of complicated measurement models, without the need to evaluate partial derivatives. In addition, the impact of sampling strategy and evaluation method on the uncertainty of the measurement results can also be taken into account with Monte Carlo method, which plays a guiding role in measurement planning.展开更多
This paper addresses the issues of channel estimation in a Multiple-Input/Multiple-Output (MIMO) system. Markov Chain Monte Carlo (MCMC) method is employed to jointly estimate the Channel State Information (CSI) and t...This paper addresses the issues of channel estimation in a Multiple-Input/Multiple-Output (MIMO) system. Markov Chain Monte Carlo (MCMC) method is employed to jointly estimate the Channel State Information (CSI) and the transmitted signals. The deduced algorithms can work well under circumstances of low Signal-to-Noise Ratio (SNR). Simulation results are presented to demonstrate their effectiveness.展开更多
Based on genetic algorithms, a solution algorithm is presented for the bi-level decision making problem with continuous variables in the upper level in accordance with the bi-level decision making principle. The algor...Based on genetic algorithms, a solution algorithm is presented for the bi-level decision making problem with continuous variables in the upper level in accordance with the bi-level decision making principle. The algorithm is compared with Monte Carlo simulated annealing algorithm, and its feasibility and effectiveness are verified with two calculating examples.展开更多
We proposed an enhanced image binarization method.The proposed solution incorporates Monte-Carlo simulation into the local thresholding method to address the essential issues with respect to complex background,spatial...We proposed an enhanced image binarization method.The proposed solution incorporates Monte-Carlo simulation into the local thresholding method to address the essential issues with respect to complex background,spatially-changed illumination,and uncertainties of block size in traditional method.The proposed method first partitions the image into square blocks that reflect local characteristics of the image.After image partitioning,each block is binarized using Otsu’s thresholding method.To minimize the influence of the block size and the boundary effect,we incorporate Monte-Carlo simulation into the binarization algorithm.Iterative calculation with varying block sizes during Monte-Carlo simulation generates a probability map,which illustrates the probability of each pixel classified as foreground.By setting a probability threshold,and separating foreground and background of the source image,the final binary image can be obtained.The described method has been tested by benchmark tests.Results demonstrate that the proposed method performs well in dealing with the complex background and illumination condition.展开更多
In this paper, we revisit foundations of the applications of physical measurement and Lindenmayer system to the modeling of plants. The measurement is proposed to a formal procedure and measuring the mass of leaves on...In this paper, we revisit foundations of the applications of physical measurement and Lindenmayer system to the modeling of plants. The measurement is proposed to a formal procedure and measuring the mass of leaves on a tree, tailored to branching plant structures with Simpson＇ s rule and Monte Carlo Methods. L-system is possible to visualize mathematical models of biological structures and processes. The formalism is illustrated using theoretical branching systems, and applied to analyze total leaves number as well as total weight of them.展开更多
Monte Carlo simulation was applied to Assembly Success Bate (ASK) analyses. ASR of two peg-in-hole robot assemblies was used as an example by taking component parts' sizes, manufacturing tolerances and robot repea...Monte Carlo simulation was applied to Assembly Success Bate (ASK) analyses. ASR of two peg-in-hole robot assemblies was used as an example by taking component parts' sizes, manufacturing tolerances and robot repeatability into account. A statistic arithmetic expression was proposed and deduced in this paper, which offers an alternative method of estimating the accuracy of ASR, without having to repeat the simulations. This statistic method also helps to choose a suitable sample size, if error reduction is desired. Monte Carlo simulation results demonstrated the feasibility of the method.展开更多
Autonomous aerial refueling(AAR)has demonstrated significant benefits to aviation by extending the aircraft range and endurance.It is of significance to assess system safety for autonomous aerial refueling.In this pap...Autonomous aerial refueling(AAR)has demonstrated significant benefits to aviation by extending the aircraft range and endurance.It is of significance to assess system safety for autonomous aerial refueling.In this paper,the reachability analysis method is adopted to assess system safety.Due to system uncertainties,the aerial refueling system can be considered as a stochastic system.Thus,probabilistic reachability is considered.Since there is a close relationship between reachability probability and collision probability,the collision probability of the AAR system is analyzed by using reachability analysis techniques.Then,the collision probability is accessed by using the Monte-Carlo experiment method.Finally,simulations demonstrate the effectiveness of the proposed safety assessment method.展开更多
文摘We study the influence of ionized impurity scattering on the electron transport in resonant-phonon-assisted terahertz （THz） quantum-cascade lasers （QCLs）. We treat the ionized impurity scattering rates within the single subband static screening approximation. We find that the ionized impurity scattering supplies an additional current channel across the device,and affects the electrondistribution in different subbands. We conclude that the ionized impurity scattering should be taken into account in the study of the transport properties of resonant-phonon-assisted THz QCLs.
文摘By employing dynamic Monte Carlo simulations, we investigate a coil-to-toroid transition of self-attractive semiflexible polymers and the spatial distributions of nanoparticles in self- attractive semiflexible polymer/nanoparticle composites. The conformation of self-attractive semiflexible polymers depends on bending energy and self-attractive interactions between monomers in polymer chains. A three-stage process of toroid formation for self-attractive semiflexible chains is shown： several isolated toroids, a loose toroid structure, and a compact toroid structure. Utilizing the compact toroid conformations of self-attractive semiflexible chains, we can control effectively the spatial distributions of nanoparticles in self-attractive semiflexible polymer nanocomposites, and an unconventional toroid structure of nanoparti- cles is observed.
基金Project(51318010402)supported by General Armament Department Pre-Research Program of China
文摘Measurement uncertainty plays an important role in laser tracking measurement analyses. In the present work, the guides to the expression of uncertainty in measurement(GUM) uncertainty framework(GUF) and its supplement, the Monte Carlo method, were used to estimate the uncertainty of task-specific laser tracker measurements. First, the sources of error in laser tracker measurement were analyzed in detail, including instruments, measuring network fusion, measurement strategies, measurement process factors(such as the operator), measurement environment, and task-specific data processing. Second, the GUM and Monte Carlo methods and their application to laser tracker measurement were presented. Finally, a case study involving the uncertainty estimation of a cylindricity measurement process using the GUF and Monte Carlo methods was illustrated. The expanded uncertainty results(at 95% confidence levels) obtained with the Monte Carlo method are 0.069 mm(least-squares criterion) and 0.062 mm(minimum zone criterion), respectively, while with the GUM uncertainty framework, none but the result of least-squares criterion can be got, which is 0.071 mm. Thus, the GUM uncertainty framework slightly underestimates the overall uncertainty by 10%. The results demonstrate that the two methods have different characteristics in task-specific uncertainty evaluations of laser tracker measurements. The results indicate that the Monte Carlo method is a practical tool for applying the principle of propagation of distributions and does not depend on the assumptions and limitations required by the law of propagation of uncertainties(GUF). These features of the Monte Carlo method reduce the risk of an unreliable measurement of uncertainty estimation, particularly in cases of complicated measurement models, without the need to evaluate partial derivatives. In addition, the impact of sampling strategy and evaluation method on the uncertainty of the measurement results can also be taken into account with Monte Carlo method, which plays a guiding role in measurement planning.
文摘This paper addresses the issues of channel estimation in a Multiple-Input/Multiple-Output (MIMO) system. Markov Chain Monte Carlo (MCMC) method is employed to jointly estimate the Channel State Information (CSI) and the transmitted signals. The deduced algorithms can work well under circumstances of low Signal-to-Noise Ratio (SNR). Simulation results are presented to demonstrate their effectiveness.
文摘Based on genetic algorithms, a solution algorithm is presented for the bi-level decision making problem with continuous variables in the upper level in accordance with the bi-level decision making principle. The algorithm is compared with Monte Carlo simulated annealing algorithm, and its feasibility and effectiveness are verified with two calculating examples.
基金Project(2018YFC1505401)supported by the National Key R&D Program of ChinaProject(41702310)supported by the National Natural Science Foundation of China+1 种基金Project(SKLGP2017K014)supported by the Foundation of State Key Laboratory of Geohazard Prevention and Geo-environment Protection,ChinaProject(2018JJ3644)supported by the Natural Science Foundation of Hunan Province,China
文摘We proposed an enhanced image binarization method.The proposed solution incorporates Monte-Carlo simulation into the local thresholding method to address the essential issues with respect to complex background,spatially-changed illumination,and uncertainties of block size in traditional method.The proposed method first partitions the image into square blocks that reflect local characteristics of the image.After image partitioning,each block is binarized using Otsu’s thresholding method.To minimize the influence of the block size and the boundary effect,we incorporate Monte-Carlo simulation into the binarization algorithm.Iterative calculation with varying block sizes during Monte-Carlo simulation generates a probability map,which illustrates the probability of each pixel classified as foreground.By setting a probability threshold,and separating foreground and background of the source image,the final binary image can be obtained.The described method has been tested by benchmark tests.Results demonstrate that the proposed method performs well in dealing with the complex background and illumination condition.
文摘In this paper, we revisit foundations of the applications of physical measurement and Lindenmayer system to the modeling of plants. The measurement is proposed to a formal procedure and measuring the mass of leaves on a tree, tailored to branching plant structures with Simpson＇ s rule and Monte Carlo Methods. L-system is possible to visualize mathematical models of biological structures and processes. The formalism is illustrated using theoretical branching systems, and applied to analyze total leaves number as well as total weight of them.
文摘Monte Carlo simulation was applied to Assembly Success Bate (ASK) analyses. ASR of two peg-in-hole robot assemblies was used as an example by taking component parts' sizes, manufacturing tolerances and robot repeatability into account. A statistic arithmetic expression was proposed and deduced in this paper, which offers an alternative method of estimating the accuracy of ASR, without having to repeat the simulations. This statistic method also helps to choose a suitable sample size, if error reduction is desired. Monte Carlo simulation results demonstrated the feasibility of the method.
基金This work was supported by the National Natural Science Foundation of China(No.61933010).
文摘Autonomous aerial refueling(AAR)has demonstrated significant benefits to aviation by extending the aircraft range and endurance.It is of significance to assess system safety for autonomous aerial refueling.In this paper,the reachability analysis method is adopted to assess system safety.Due to system uncertainties,the aerial refueling system can be considered as a stochastic system.Thus,probabilistic reachability is considered.Since there is a close relationship between reachability probability and collision probability,the collision probability of the AAR system is analyzed by using reachability analysis techniques.Then,the collision probability is accessed by using the Monte-Carlo experiment method.Finally,simulations demonstrate the effectiveness of the proposed safety assessment method.
