Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning m...Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines （SVMs） are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigen- matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.展开更多
To further investigate the one-dimensional(1D)rheological consolidation mechanism of double-layered soil,the fractional derivative Merchant model(FDMM)and the non-Darcian flow model with the non-Newtonian index are re...To further investigate the one-dimensional(1D)rheological consolidation mechanism of double-layered soil,the fractional derivative Merchant model(FDMM)and the non-Darcian flow model with the non-Newtonian index are respectively introduced to describe the deformation of viscoelastic soil and the flow of pore water in the process of consolidation.Accordingly,an 1D rheological consolidation equation of double-layered soil is obtained,and its numerical analysis is performed by the implicit finite difference method.In order to verify its validity,the numerical solutions by the present method for some simplified cases are compared with the results in the related literature.Then,the influence of the revelent parameters on the rheological consolidation of double-layered soil are investigated.Numerical results indicate that the parameters of non-Darcian flow and FDMM of the first soil layer greatly influence the consolidation rate of double-layered soil.As the decrease of relative compressibility or the increase of relative permeability between the lower soil and the upper soil,the dissipation rate of excess pore water pressure and the settlement rate of the ground will be accelerated.Increasing the relative thickness of soil layer with high permeability or low compressibility will also accelerate the consolidation rate of double-layered soil.展开更多
Mesoporous polyethylene glycol-resorcinol and formaldehyde（PEG-RF） carbon xerogels were prepared by a new polymer blend method in which PEG-RF mixed organic xerogels were synthesized by blending thermally unstable p...Mesoporous polyethylene glycol-resorcinol and formaldehyde（PEG-RF） carbon xerogels were prepared by a new polymer blend method in which PEG-RF mixed organic xerogels were synthesized by blending thermally unstable polyethylene glycol with organic monomers, resorcinol and formaldehyde and then subjected to pyrolization at 1 000 ℃. The influences of mass ratio of PEG to the theoretical yield of RF xerogel, m（PEG）/m（RF） and the （relative） molecular mass of PEG on the pore structure and electric double layer capacitance（EDLC） performance of PEG-RF carbon xerogels were investigated. The results show that PEG under different conditions leads to the difference of phase separation structure of the polymer blend and thus the change of pore structure of PEG-RF carbon xerogels. Specific surface area and capacity of PEG-RF carbon xerogels in 30% H2SO4 solution can reach （755 m2/g） and 150 F/g, respectively. Their surface can be fully utilized to form electric double layer. However, the pore structure differences of PEG-RF carbon xerogels result in their different EDLC performances. The distributed capacitance effect increases with decreasing the pore size of PEG-RF carbon xerogels.展开更多
针对包含复杂约束条件的约束多目标优化问题(CMOP),在确保算法满足严格约束的同时,有效平衡算法的收敛性与多样性是重大挑战。因此,提出一种双种群双阶段的进化算法(DPDSEA)。该算法引入2个独立进化种群:主种群和副种群,并分别利用可行...针对包含复杂约束条件的约束多目标优化问题(CMOP),在确保算法满足严格约束的同时,有效平衡算法的收敛性与多样性是重大挑战。因此,提出一种双种群双阶段的进化算法(DPDSEA)。该算法引入2个独立进化种群:主种群和副种群,并分别利用可行性规则和改进的epsilon约束处理方法进行更新。在第一阶段,主种群和副种群分别探索约束Pareto前沿(CPF)与无约束Pareto前沿(UPF),从而获取UPF和CPF的位置信息;在第二阶段,设计一种分类方法,根据UPF与CPF的位置对CMOP进行分类,从而对不同类型的CMOP执行特定的进化策略;此外,提出一种随机扰动策略,在副种群进化到CPF附近时,对它进行随机扰动以产生一些位于CPF上的个体,从而促进主种群在CPF上的收敛与分布。把所提算法与6个具有代表性的算法:CMOES(Constrained Multi-objective Optimization based on Even Search)、dp-ACS(dual-population evolutionary algorithm based on Adaptive Constraint Strength)、c-DPEA(DualPopulation based Evolutionary Algorithm for constrained multi-objective optimization)、CAEAD(Constrained Evolutionary Algorithm based on Alternative Evolution and Degeneration)、BiCo(evolutionary algorithm with Bidirectional Coevolution)和DDCMOEA(Dual-stage Dual-population Evolutionary Algorithm for Constrained Multiobjective Optimization)在LIRCMOP和DASCMOP两个测试集上进行实验比较。实验结果表明,DPDSEA在23个问题中取得了15个最优反转世代距离(IGD)值和12个最优超体积(HV)值,展现了DPDSEA在处理复杂CMOP时显著的性能优势。展开更多
基金supported by Research Grants Council of Hong Kong under Grant No.17301214HKU CERG Grants,Fundamental Research Funds for the Central Universities+2 种基金the Research Funds of Renmin University of ChinaHung Hing Ying Physical Research Grantthe Natural Science Foundation of China under Grant No.11271144
文摘Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines （SVMs） are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigen- matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.
基金Project(51578511)supported by the National Natural Science Foundation of China。
文摘To further investigate the one-dimensional(1D)rheological consolidation mechanism of double-layered soil,the fractional derivative Merchant model(FDMM)and the non-Darcian flow model with the non-Newtonian index are respectively introduced to describe the deformation of viscoelastic soil and the flow of pore water in the process of consolidation.Accordingly,an 1D rheological consolidation equation of double-layered soil is obtained,and its numerical analysis is performed by the implicit finite difference method.In order to verify its validity,the numerical solutions by the present method for some simplified cases are compared with the results in the related literature.Then,the influence of the revelent parameters on the rheological consolidation of double-layered soil are investigated.Numerical results indicate that the parameters of non-Darcian flow and FDMM of the first soil layer greatly influence the consolidation rate of double-layered soil.As the decrease of relative compressibility or the increase of relative permeability between the lower soil and the upper soil,the dissipation rate of excess pore water pressure and the settlement rate of the ground will be accelerated.Increasing the relative thickness of soil layer with high permeability or low compressibility will also accelerate the consolidation rate of double-layered soil.
文摘Mesoporous polyethylene glycol-resorcinol and formaldehyde（PEG-RF） carbon xerogels were prepared by a new polymer blend method in which PEG-RF mixed organic xerogels were synthesized by blending thermally unstable polyethylene glycol with organic monomers, resorcinol and formaldehyde and then subjected to pyrolization at 1 000 ℃. The influences of mass ratio of PEG to the theoretical yield of RF xerogel, m（PEG）/m（RF） and the （relative） molecular mass of PEG on the pore structure and electric double layer capacitance（EDLC） performance of PEG-RF carbon xerogels were investigated. The results show that PEG under different conditions leads to the difference of phase separation structure of the polymer blend and thus the change of pore structure of PEG-RF carbon xerogels. Specific surface area and capacity of PEG-RF carbon xerogels in 30% H2SO4 solution can reach （755 m2/g） and 150 F/g, respectively. Their surface can be fully utilized to form electric double layer. However, the pore structure differences of PEG-RF carbon xerogels result in their different EDLC performances. The distributed capacitance effect increases with decreasing the pore size of PEG-RF carbon xerogels.
文摘针对包含复杂约束条件的约束多目标优化问题(CMOP),在确保算法满足严格约束的同时,有效平衡算法的收敛性与多样性是重大挑战。因此,提出一种双种群双阶段的进化算法(DPDSEA)。该算法引入2个独立进化种群:主种群和副种群,并分别利用可行性规则和改进的epsilon约束处理方法进行更新。在第一阶段,主种群和副种群分别探索约束Pareto前沿(CPF)与无约束Pareto前沿(UPF),从而获取UPF和CPF的位置信息;在第二阶段,设计一种分类方法,根据UPF与CPF的位置对CMOP进行分类,从而对不同类型的CMOP执行特定的进化策略;此外,提出一种随机扰动策略,在副种群进化到CPF附近时,对它进行随机扰动以产生一些位于CPF上的个体,从而促进主种群在CPF上的收敛与分布。把所提算法与6个具有代表性的算法:CMOES(Constrained Multi-objective Optimization based on Even Search)、dp-ACS(dual-population evolutionary algorithm based on Adaptive Constraint Strength)、c-DPEA(DualPopulation based Evolutionary Algorithm for constrained multi-objective optimization)、CAEAD(Constrained Evolutionary Algorithm based on Alternative Evolution and Degeneration)、BiCo(evolutionary algorithm with Bidirectional Coevolution)和DDCMOEA(Dual-stage Dual-population Evolutionary Algorithm for Constrained Multiobjective Optimization)在LIRCMOP和DASCMOP两个测试集上进行实验比较。实验结果表明,DPDSEA在23个问题中取得了15个最优反转世代距离(IGD)值和12个最优超体积(HV)值,展现了DPDSEA在处理复杂CMOP时显著的性能优势。
