In this study,we improved the dispersibility of the stocks in the headbox of an inclined wire machine to produce a distinct paper,and analyzed some factors affecting paper formation in the production of multiply paper...In this study,we improved the dispersibility of the stocks in the headbox of an inclined wire machine to produce a distinct paper,and analyzed some factors affecting paper formation in the production of multiply paper.We used FLUENT6.3 to analyze the flow of the stocks in the headbox and select the structure of the diffusion part required for improving the dispersibility of fibers.Moreover,based on a simulation experiment,the optimal rational angle of the diffusion part(g)was found to be approximately 8°~10°,and it improved the paper formation in the case of usage of two plates.Using the equation for the formation of paper layers in the headbox of an inclined wire machine,we obtained a paper with the given basic weight by controlling the inclined angle of the wire(a),initial height of water(H),and concentration of the stocks.We considered the effect of a and H of the stocks in the headbox on the fiber distribution,and according to the results,a should be set as approximately 20°~30°and H should be maximally high.When producing multi-ply paper by a wire,the line pressure of the couch roll should be maintained at 1.8~2.0 kN/m to avoid the damage to the paper sheets.In addition,we found the optimal structure parameter of the dehydrated roll was as follows:hole ratio of approximately 30%of the dehydrated roll surface area,width of 1.5~2.0 mm,slot pitch of 5~6 mm,slot depth of 2~3 mm,and inclined angle of diffusion part(b)of 5°.展开更多
The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate ...The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.展开更多
文摘In this study,we improved the dispersibility of the stocks in the headbox of an inclined wire machine to produce a distinct paper,and analyzed some factors affecting paper formation in the production of multiply paper.We used FLUENT6.3 to analyze the flow of the stocks in the headbox and select the structure of the diffusion part required for improving the dispersibility of fibers.Moreover,based on a simulation experiment,the optimal rational angle of the diffusion part(g)was found to be approximately 8°~10°,and it improved the paper formation in the case of usage of two plates.Using the equation for the formation of paper layers in the headbox of an inclined wire machine,we obtained a paper with the given basic weight by controlling the inclined angle of the wire(a),initial height of water(H),and concentration of the stocks.We considered the effect of a and H of the stocks in the headbox on the fiber distribution,and according to the results,a should be set as approximately 20°~30°and H should be maximally high.When producing multi-ply paper by a wire,the line pressure of the couch roll should be maintained at 1.8~2.0 kN/m to avoid the damage to the paper sheets.In addition,we found the optimal structure parameter of the dehydrated roll was as follows:hole ratio of approximately 30%of the dehydrated roll surface area,width of 1.5~2.0 mm,slot pitch of 5~6 mm,slot depth of 2~3 mm,and inclined angle of diffusion part(b)of 5°.
基金financially supported by the National Natural Science Foundation of China,No.61263011,81000554Program in Sun Yat-sen University supported by Fundamental Research Funds for the Central Universities,No.11ykpy07+1 种基金Natural Science Foundation of Guangdong Province,No.S2011010005309Innovation Fund of Xinjiang Medical University,No.XJC201209
文摘The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index–approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.
