The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective ...The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization task.This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection problems.The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation operator.The Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected features.The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy.As feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid function.To evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem.Moreover,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.展开更多
Objective:To observe the changing of biomechanical features during the degradation course of poly-D,L-lactic acid (PDLLA) rods in vivo and in vitro and to evaluate its value as an internal fixation material. Methods:P...Objective:To observe the changing of biomechanical features during the degradation course of poly-D,L-lactic acid (PDLLA) rods in vivo and in vitro and to evaluate its value as an internal fixation material. Methods:PDLLA rods were emerged into PBS simultaneous body fluid with constant temperature of 37 C and the rods were embedded into muscle tissue of 20 rabbits for degradation in vitro and in vivo . The rods were taken out in 2, 4, 6. 8 and 12 weeks. Biomechanical features of bending, shearing and axial compression strength, rigidity and elastic modulus were observed during the degradation course. Statistical method was used to test the changes of biomechanical parameters. Results: (1) There was similar changes of bending, compressive, shearing strength and bending, compressive and shearing rigidity of the PDLLA rods between in vivo and in vitro. (2)Bending, compressive, shearing strength decreased 33%, 18% and 43% respectively within the first stage of the degradation, and after 6 weeks of degradation, they decreased slowly. (3)Elastic modulus, bending, compressive and shearing rigidity.decreased sharply during the 6 weeks of degradation, with a drop of 22% , 39% and 30% respectively, and after 8 weeks, they decreased slowly. Even after 12 weeks of degradation, the strength of the rods was still higher than that of sponge bone. Conclusion: During the degradation of the material, the strength and rigidity of PDLLA rods can meet the need of fracture fixation of cancellous bones.展开更多
核函数主元分析KPCA(kernel princ ipal component analysis)能够提取机械故障信号的非线性特征,可以应用于机械故障状态识别。但是KPCA是一种无监督的特征提取方法,不能利用故障信号中的类别信息。本文介绍了一种核最优K-L变换,它可以...核函数主元分析KPCA(kernel princ ipal component analysis)能够提取机械故障信号的非线性特征,可以应用于机械故障状态识别。但是KPCA是一种无监督的特征提取方法,不能利用故障信号中的类别信息。本文介绍了一种核最优K-L变换,它可以充分利用类别信息,它能够提取类平均向量和方差向量中的判别信息,使提取的特征分类效果更好。在齿轮故障诊断实验中,采用核最优K-L变换提取故障信号的非线性特征,实验结果表明核最优K-L变换相比KPCA故障识别结果更为理想。展开更多
基金supported by Universiti Teknologi PETRONAS,under the Yayasan Universiti Teknologi PETRONAS (YUTP)Fundamental Research Grant Scheme (YUTPFRG/015LC0-274)support by Researchers Supporting Project Number (RSP-2023/309),King Saud University,Riyadh,Saudi Arabia.
文摘The process of selecting features or reducing dimensionality can be viewed as a multi-objective minimization problem in which both the number of features and error rate must be minimized.While it is a multi-objective problem,current methods tend to treat feature selection as a single-objective optimization task.This paper presents enhanced multi-objective grey wolf optimizer with Lévy flight and mutation phase(LMuMOGWO)for tackling feature selection problems.The proposed approach integrates two effective operators into the existing Multi-objective Grey Wolf optimizer(MOGWO):a Lévy flight and a mutation operator.The Lévy flight,a type of random walk with jump size determined by the Lévy distribution,enhances the global search capability of MOGWO,with the objective of maximizing classification accuracy while minimizing the number of selected features.The mutation operator is integrated to add more informative features that can assist in enhancing classification accuracy.As feature selection is a binary problem,the continuous search space is converted into a binary space using the sigmoid function.To evaluate the classification performance of the selected feature subset,the proposed approach employs a wrapper-based Artificial Neural Network(ANN).The effectiveness of the LMuMOGWO is validated on 12 conventional UCI benchmark datasets and compared with two existing variants of MOGWO,BMOGWO-S(based sigmoid),BMOGWO-V(based tanh)as well as Non-dominated Sorting Genetic Algorithm II(NSGA-II)and Multi-objective Particle Swarm Optimization(BMOPSO).The results demonstrate that the proposed LMuMOGWO approach is capable of successfully evolving and improving a set of randomly generated solutions for a given optimization problem.Moreover,the proposed approach outperforms existing approaches in most cases in terms of classification error rate,feature reduction,and computational cost.
文摘Objective:To observe the changing of biomechanical features during the degradation course of poly-D,L-lactic acid (PDLLA) rods in vivo and in vitro and to evaluate its value as an internal fixation material. Methods:PDLLA rods were emerged into PBS simultaneous body fluid with constant temperature of 37 C and the rods were embedded into muscle tissue of 20 rabbits for degradation in vitro and in vivo . The rods were taken out in 2, 4, 6. 8 and 12 weeks. Biomechanical features of bending, shearing and axial compression strength, rigidity and elastic modulus were observed during the degradation course. Statistical method was used to test the changes of biomechanical parameters. Results: (1) There was similar changes of bending, compressive, shearing strength and bending, compressive and shearing rigidity of the PDLLA rods between in vivo and in vitro. (2)Bending, compressive, shearing strength decreased 33%, 18% and 43% respectively within the first stage of the degradation, and after 6 weeks of degradation, they decreased slowly. (3)Elastic modulus, bending, compressive and shearing rigidity.decreased sharply during the 6 weeks of degradation, with a drop of 22% , 39% and 30% respectively, and after 8 weeks, they decreased slowly. Even after 12 weeks of degradation, the strength of the rods was still higher than that of sponge bone. Conclusion: During the degradation of the material, the strength and rigidity of PDLLA rods can meet the need of fracture fixation of cancellous bones.
文摘核函数主元分析KPCA(kernel princ ipal component analysis)能够提取机械故障信号的非线性特征,可以应用于机械故障状态识别。但是KPCA是一种无监督的特征提取方法,不能利用故障信号中的类别信息。本文介绍了一种核最优K-L变换,它可以充分利用类别信息,它能够提取类平均向量和方差向量中的判别信息,使提取的特征分类效果更好。在齿轮故障诊断实验中,采用核最优K-L变换提取故障信号的非线性特征,实验结果表明核最优K-L变换相比KPCA故障识别结果更为理想。
