Mixing index is an important parameter to understand and assess the mixing state in various mixers including ribbon mixers,the typical food processing devices.Many mixing indices based on either sample variance method...Mixing index is an important parameter to understand and assess the mixing state in various mixers including ribbon mixers,the typical food processing devices.Many mixing indices based on either sample variance methods or non-sample variance methods have been proposed and used in the past,however,they were not well compared in the literature to evaluate their accuracy of assessing the final mixing state.In this study,discrete element method(DEM)modelling is used to investigate and compare the accuracy of these mixing indices for mixing of uniform particles in a horizontal cylindrical ribbon mixer.The sample variance methods for mixing indices are first compared both at particle-and macro-scale levels.In addition,non-sample variance methods,namely entropy and non-sampling indices are compared against the results from the sample variance methods.The simulation results indicate that,among the indices considered in this study,Lacey index shows the most accurate results.The Lacey index is regarded to be the most suitable mixing index to evaluate the steady-state mixing state of the ribbon mixer in the real-time(or without stopping the impeller)at both the particle-and macro-scale levels.The study is useful for the selection of a proper mixing index for a specific mixture in a given mixer.展开更多
By virtue of an increase in spectral efficiency by reducing the transmitted pilot tones, the compressed sensing （CS） has been widely applied to pilot-aided sparse channel estimation in orthogonal frequency division ...By virtue of an increase in spectral efficiency by reducing the transmitted pilot tones, the compressed sensing （CS） has been widely applied to pilot-aided sparse channel estimation in orthogonal frequency division multiplexing （OFDM） systems. The researches usually assume that the channel is strictly sparse and formulate the channel estimation as a standard compressed sensing problem. However, such strictly sparse assumption does not hold true in non-sample-spaced multiple channels. The authors in this article proposed a new method of compressed sensing based channel estimation in which an over-complete dictionary with a finer delay grid is applied to construct a sparse representation of the non-sample-spaced multipath channels. With the proposed, the channel estimation was formulated as the model-based CS problem and a modified model-based compressed sampling matching pursuit （CoSaMP） algorithm was applied to reconstruct the discrete-time channel impulse response （CIR）. Simulation indicates that the new method proposed here outperforms the traditional standard CS-based methods in terms of mean square error （MSE） and bit error rate （BER）.展开更多
基金This work is financially supported by the Australian Research Council(DP180101232).
文摘Mixing index is an important parameter to understand and assess the mixing state in various mixers including ribbon mixers,the typical food processing devices.Many mixing indices based on either sample variance methods or non-sample variance methods have been proposed and used in the past,however,they were not well compared in the literature to evaluate their accuracy of assessing the final mixing state.In this study,discrete element method(DEM)modelling is used to investigate and compare the accuracy of these mixing indices for mixing of uniform particles in a horizontal cylindrical ribbon mixer.The sample variance methods for mixing indices are first compared both at particle-and macro-scale levels.In addition,non-sample variance methods,namely entropy and non-sampling indices are compared against the results from the sample variance methods.The simulation results indicate that,among the indices considered in this study,Lacey index shows the most accurate results.The Lacey index is regarded to be the most suitable mixing index to evaluate the steady-state mixing state of the ribbon mixer in the real-time(or without stopping the impeller)at both the particle-and macro-scale levels.The study is useful for the selection of a proper mixing index for a specific mixture in a given mixer.
基金supported by the National Science and Technology Major Project (2012ZX03001039-002)
文摘By virtue of an increase in spectral efficiency by reducing the transmitted pilot tones, the compressed sensing （CS） has been widely applied to pilot-aided sparse channel estimation in orthogonal frequency division multiplexing （OFDM） systems. The researches usually assume that the channel is strictly sparse and formulate the channel estimation as a standard compressed sensing problem. However, such strictly sparse assumption does not hold true in non-sample-spaced multiple channels. The authors in this article proposed a new method of compressed sensing based channel estimation in which an over-complete dictionary with a finer delay grid is applied to construct a sparse representation of the non-sample-spaced multipath channels. With the proposed, the channel estimation was formulated as the model-based CS problem and a modified model-based compressed sampling matching pursuit （CoSaMP） algorithm was applied to reconstruct the discrete-time channel impulse response （CIR）. Simulation indicates that the new method proposed here outperforms the traditional standard CS-based methods in terms of mean square error （MSE） and bit error rate （BER）.
