[Objectives] This study was carried out to determine texture parameters for texture profile analysis （TPA）, and optimize the texture determination of ‘Yali’ by texture analyzer.[Methods] The traditional varieties ...[Objectives] This study was carried out to determine texture parameters for texture profile analysis （TPA）, and optimize the texture determination of ‘Yali’ by texture analyzer.[Methods] The traditional varieties of ‘Yali’ were taken as the materials, and texture parameters were determined at different compression rates and deformations at target.[Results] In the process of the TPA, the deformation at target had an extremely significant influence on 8 TPA texture parameters, namely, the hardness, cohesiveness, springiness, adhesiveness, gumminess, resilience, fracturability, and chewiness （ P ≤0.01）, while the compression rate had significant influence on the hardness and gumminess （ P ≤0.05）, had an extremely significant influence on fracturability （ P ≤0.01）, and had no significant influence on other 5 TPA parameters.[Conclusions] Taking the compression rate of 1 mm/s and 20% deformation at target as the experimental conditions for TPA could avoid the impact load of high speed on the tissue and objectively reflect the textural characteristics of ‘Yali’ pulp tissue.展开更多
A method for the optimal fiber input power determination is presented by employing the variation characteristics of signal to noise ratio(SNR) in spontaneous Brillouin-scattering-based sensing system. And a heterodyne...A method for the optimal fiber input power determination is presented by employing the variation characteristics of signal to noise ratio(SNR) in spontaneous Brillouin-scattering-based sensing system. And a heterodyne detection system is constructed for measuring the Brillouin scattering spectra with different fiber input powers. The Brillouin spectrum width and system SNR can be simultaneously measured from these spectra, and the optimal fiber input power can be obtained from such information. In the experiment, for 48.8-km-long standard single-mode fiber(SSMF), the optimal fiber input power values are all approximately 0 dBm obtained by the maximum SNR position for different local oscillator power values and average times.展开更多
Network intrusion poses a severe threat to the Internet.However,existing intrusion detection models cannot effectively distinguish different intrusions with high-degree feature overlap.In addition,efficient real-time ...Network intrusion poses a severe threat to the Internet.However,existing intrusion detection models cannot effectively distinguish different intrusions with high-degree feature overlap.In addition,efficient real-time detection is an urgent problem.To address the two above problems,we propose a Latent Dirichlet Allocation topic model-based framework for real-time network Intrusion Detection(LDA-ID),consisting of static and online LDA-ID.The problem of feature overlap is transformed into static LDA-ID topic number optimization and topic selection.Thus,the detection is based on the latent topic features.To achieve efficient real-time detection,we design an online computing mode for static LDA-ID,in which a parameter iteration method based on momentum is proposed to balance the contribution of prior knowledge and new information.Furthermore,we design two matching mechanisms to accommodate the static and online LDA-ID,respectively.Experimental results on the public NSL-KDD and UNSW-NB15 datasets show that our framework gets higher accuracy than the others.展开更多
The knowledge of transient temperature of the ladle wall is a key factor in optimizing energy consumption in steelmaking process.The transient temperature needs to be estimated.A nonlinear lumped parameter model was u...The knowledge of transient temperature of the ladle wall is a key factor in optimizing energy consumption in steelmaking process.The transient temperature needs to be estimated.A nonlinear lumped parameter model was used to model the thermal dynamics of the ladle.Then,the bounded Jacobian nonlinear observer was utilized to estimate the temperature.With this method,the estimation model became a closed-loop model and the observer gains were obtained by solving linear matrix inequalities and simply implemented to the system.Comparison between the simulation and recorded data at a participating steel plant in Thailand showed that the nonlinear observer accurately estimated the temperature of the ladle lining.This estimated temperature was very useful in determining suitable tapping temperature for energy conservation and steel quality.展开更多
China, as a whole, is about to meet the Millennium Development Goals for reducing the maternal mortality ratio (MMR) and infant mortality rate (IMR), but the disparities between rural area and urban area still exists....China, as a whole, is about to meet the Millennium Development Goals for reducing the maternal mortality ratio (MMR) and infant mortality rate (IMR), but the disparities between rural area and urban area still exists. This study estimated the potential effectiveness of expanding coverage with high impact interventions using the Lives Saved Tool (LiST). It was found that gestational hypertension, antepartum and postpartum hemorrhage, preterm birth, neonatal asphyxia, and neonatal childhood pneumonia and diarrhea are still the major killers of mothers and children in rural area in China. It was estimated that 30% of deaths among 0-59 month old children and 25% of maternal deaths in 2008 could be prevented in 2015 if primary health care intervention coverage expanded to a feasible level. The LiST death cause framework, compared to data from the Maternal and Child Mortality Surveillance System, represents 60%-80% of neonatal deaths, 40%-50% of deaths in 1-59 month old children and 40%-60% of maternal deaths in rural areas of western China.展开更多
Multi-photon polymerization is a well-established,yet actively developing,additive manufacturing technique for 3D printing on the micro/nanoscale.Like all additive manufacturing techniques,determining the process para...Multi-photon polymerization is a well-established,yet actively developing,additive manufacturing technique for 3D printing on the micro/nanoscale.Like all additive manufacturing techniques,determining the process parameters necessary to achieve dimensional accuracy for a structure 3D printed using this method is not always straightforward and can require time-consuming experimentation.In this work,an active machine learning based framework is presented for determining optimal process parameters for the recently developed,high-speed,layer-by-layer continuous projection 3D printing process.The proposed active learning framework uses Bayesian optimization to inform optimal experimentation in order to adaptively collect the most informative data for effective training of a Gaussian-process-regression-based machine learning model.This model then serves as a surrogate for the manufacturing process:predicting optimal process parameters for achieving a target geometry,e.g,the 2D geometry of each printed layer.Three representative 2D shapes at three different scales are used as test cases.In each case,the active learning framework improves the geometric accuracy,with drastic reductions of the errors to within the measurement accuracy in just four iterations of the Bayesian optimization using only a few hundred of total training data.The case studies indicate that the active learning framework developed in this work can be broadly applied to other additive manufacturing processes to increase accuracy with significantly reduced experimental data collection effortforoptimization.展开更多
基金Supported by Special Project of Hebei Provincial Department of Finance(F18R1908)Special Fund for Basic Scientific Research Project in Hebei Academy of Agriculture and Forestry Sciences(A2015020103)The Earmarked Fund for China Agriculture Research System(CARS-28)
文摘[Objectives] This study was carried out to determine texture parameters for texture profile analysis （TPA）, and optimize the texture determination of ‘Yali’ by texture analyzer.[Methods] The traditional varieties of ‘Yali’ were taken as the materials, and texture parameters were determined at different compression rates and deformations at target.[Results] In the process of the TPA, the deformation at target had an extremely significant influence on 8 TPA texture parameters, namely, the hardness, cohesiveness, springiness, adhesiveness, gumminess, resilience, fracturability, and chewiness （ P ≤0.01）, while the compression rate had significant influence on the hardness and gumminess （ P ≤0.05）, had an extremely significant influence on fracturability （ P ≤0.01）, and had no significant influence on other 5 TPA parameters.[Conclusions] Taking the compression rate of 1 mm/s and 20% deformation at target as the experimental conditions for TPA could avoid the impact load of high speed on the tissue and objectively reflect the textural characteristics of ‘Yali’ pulp tissue.
基金supported by the National Natural Science Foundation of China(No.61377088)the Natural Science Foundation of Hebei Province of China(Nos.E2015502053 and F2014502098)
文摘A method for the optimal fiber input power determination is presented by employing the variation characteristics of signal to noise ratio(SNR) in spontaneous Brillouin-scattering-based sensing system. And a heterodyne detection system is constructed for measuring the Brillouin scattering spectra with different fiber input powers. The Brillouin spectrum width and system SNR can be simultaneously measured from these spectra, and the optimal fiber input power can be obtained from such information. In the experiment, for 48.8-km-long standard single-mode fiber(SSMF), the optimal fiber input power values are all approximately 0 dBm obtained by the maximum SNR position for different local oscillator power values and average times.
基金supported by the National Natural Science Foundation of China(Grant No.U1636208,No.61862008,No.61902013)the Beihang Youth Top Talent Support Program(Grant No.YWF-21-BJJ-1039)。
文摘Network intrusion poses a severe threat to the Internet.However,existing intrusion detection models cannot effectively distinguish different intrusions with high-degree feature overlap.In addition,efficient real-time detection is an urgent problem.To address the two above problems,we propose a Latent Dirichlet Allocation topic model-based framework for real-time network Intrusion Detection(LDA-ID),consisting of static and online LDA-ID.The problem of feature overlap is transformed into static LDA-ID topic number optimization and topic selection.Thus,the detection is based on the latent topic features.To achieve efficient real-time detection,we design an online computing mode for static LDA-ID,in which a parameter iteration method based on momentum is proposed to balance the contribution of prior knowledge and new information.Furthermore,we design two matching mechanisms to accommodate the static and online LDA-ID,respectively.Experimental results on the public NSL-KDD and UNSW-NB15 datasets show that our framework gets higher accuracy than the others.
基金Item Sponsored by Ratchadaphiseksomphot Endowment Fund Part of Research Grant for New Scholar CU Researcher's Project of Thailand(RGN_2559_022_03_21)
文摘The knowledge of transient temperature of the ladle wall is a key factor in optimizing energy consumption in steelmaking process.The transient temperature needs to be estimated.A nonlinear lumped parameter model was used to model the thermal dynamics of the ladle.Then,the bounded Jacobian nonlinear observer was utilized to estimate the temperature.With this method,the estimation model became a closed-loop model and the observer gains were obtained by solving linear matrix inequalities and simply implemented to the system.Comparison between the simulation and recorded data at a participating steel plant in Thailand showed that the nonlinear observer accurately estimated the temperature of the ladle lining.This estimated temperature was very useful in determining suitable tapping temperature for energy conservation and steel quality.
基金supported jointly by WHO(CHN-12-MCN-005007)UNICEF(YH702H&N)Chinese Post-doctoral Foundation(2012M510295)
文摘China, as a whole, is about to meet the Millennium Development Goals for reducing the maternal mortality ratio (MMR) and infant mortality rate (IMR), but the disparities between rural area and urban area still exists. This study estimated the potential effectiveness of expanding coverage with high impact interventions using the Lives Saved Tool (LiST). It was found that gestational hypertension, antepartum and postpartum hemorrhage, preterm birth, neonatal asphyxia, and neonatal childhood pneumonia and diarrhea are still the major killers of mothers and children in rural area in China. It was estimated that 30% of deaths among 0-59 month old children and 25% of maternal deaths in 2008 could be prevented in 2015 if primary health care intervention coverage expanded to a feasible level. The LiST death cause framework, compared to data from the Maternal and Child Mortality Surveillance System, represents 60%-80% of neonatal deaths, 40%-50% of deaths in 1-59 month old children and 40%-60% of maternal deaths in rural areas of western China.
基金supported by the National Science Foundation(NSF)through grant numbers CMMI-2135585 and CMMI-2229143J.EJ.acknowledges the National Science Foundation for support under the Graduate Research Fellowship Program(GRFP)under grant number DGE-1842166.
文摘Multi-photon polymerization is a well-established,yet actively developing,additive manufacturing technique for 3D printing on the micro/nanoscale.Like all additive manufacturing techniques,determining the process parameters necessary to achieve dimensional accuracy for a structure 3D printed using this method is not always straightforward and can require time-consuming experimentation.In this work,an active machine learning based framework is presented for determining optimal process parameters for the recently developed,high-speed,layer-by-layer continuous projection 3D printing process.The proposed active learning framework uses Bayesian optimization to inform optimal experimentation in order to adaptively collect the most informative data for effective training of a Gaussian-process-regression-based machine learning model.This model then serves as a surrogate for the manufacturing process:predicting optimal process parameters for achieving a target geometry,e.g,the 2D geometry of each printed layer.Three representative 2D shapes at three different scales are used as test cases.In each case,the active learning framework improves the geometric accuracy,with drastic reductions of the errors to within the measurement accuracy in just four iterations of the Bayesian optimization using only a few hundred of total training data.The case studies indicate that the active learning framework developed in this work can be broadly applied to other additive manufacturing processes to increase accuracy with significantly reduced experimental data collection effortforoptimization.
