The matrix crack evolution of cross-ply ceramic matrix composites under uniaxial tensile loading is investigated using the energy balance method.Under tensile loading,the cross-ply ceramic matrix composites have five ...The matrix crack evolution of cross-ply ceramic matrix composites under uniaxial tensile loading is investigated using the energy balance method.Under tensile loading,the cross-ply ceramic matrix composites have five damage modes.The cracking mode 3 contains transverse cracking,matrix cracking and fiber/matrix interface debonding.The cracking mode 5 only contains matrix cracking and fiber/matrix interface debonding.The cracking stress of modes 3 and 5 appearing between existing transverse cracks is determined.And the multiple matrix crack evolution of mode 3 is determined.The effects of ply thickness,fiber volume fraction,interface shear stress and interface debonding energy on the cracking stress and matrix crack evolution are analyzed.Results indicate that the cracking mode 3 is more likely to appear between transverse cracks for the SiC/CAS material.展开更多
The matrix metalloproteinases（MMPs） are a family of zinc-dependent endopeptidases originally characterized as secreted proteases responsible for degrading extracellular matrix proteins.Their canonical role in matrix...The matrix metalloproteinases（MMPs） are a family of zinc-dependent endopeptidases originally characterized as secreted proteases responsible for degrading extracellular matrix proteins.Their canonical role in matrix remodelling is of significant importance in neural development and regeneration,but emerging roles for MMPs,especially in signal transduction pathways,are also of obvious importance in a neural context.Misregulation of MMP activity is a hallmark of many neuropathologies,and members of every branch of the MMP family have been implicated in aspects of neural development and disease.However,while extraordinary research efforts have been made to elucidate the molecular mechanisms involving MMPs,methodological constraints and complexities of the research models have impeded progress.Here we discuss the current state of our understanding of the roles of MMPs in neural development using recent examples and advocate a phylogenetically diverse approach to MMP research as a means to both circumvent the challenges associated with specific model organisms,and to provide a broader evolutionary context from which to synthesize an understanding of the underlying biology.展开更多
Cloud computing, a recently emerged paradigm faces major challenges in achieving the privacy of migrated data, network security, etc. Too many cryptographic technologies are raised to solve these issues based on ident...Cloud computing, a recently emerged paradigm faces major challenges in achieving the privacy of migrated data, network security, etc. Too many cryptographic technologies are raised to solve these issues based on identity, attributes and prediction algorithms yet;these techniques are highly prone to attackers. This would raise a need of an effective encryption technique, which would ensure secure data migration. With this scenario, our proposed methodology Efficient Probabilistic Public Key Encryption(EPPKE) is optimized with Covariance Matrix Adaptation Evolution Strategies(CMA-ES). It ensures data integrity through the Luhn algorithm with BLAKE 2b encapsulation. This enables an optimized security to the data which is migrated through cloud. The proposed methodology is implemented in Open Stack with Java Language. It achieves better results by providing security compared to other existing techniques like RSA, IBA, ABE, PBE, etc.展开更多
To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First...To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First,this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge.Second,the evaluation indicators are fused with expert knowledge and the ER algorithm.According to the fusion results,a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established,and the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model parameters.This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion.Moreover,it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data.Finally,a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method.The research results showthat this method has strong applicability to the network security situation assessment of complex Industrial Internet systems.It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures,thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.展开更多
To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy(KE-VFS-CMA-ES) is prese...To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy(KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions(LF-NDS). Secondly, aiming to obtain the High-Fidelity(HF) sample points located in promising areas, the K-means clustering algorithm and the space-filling strategy are used to extract knowledge from the LF-NDS to the HF space. Finally,the KE-VFS model is established by means of the obtained HF and LF sample points. In the second part, a novel model management based on the Modified Hypervolume Improvement(MHVI) criterion and pre-screening strategy is proposed. In each generation of KE-VFS-CMA-ES, excessive candidate points are firstly generated and then calculated by the MHVI criterion to find out a few potential points, which will be evaluated by the HF model. Through the above two parts,the promising areas can be detected and the potential points can be screened out, which contributes to speeding up the optimization process twofold. Three classic benchmark functions and a time-consuming engineering case of the aerospace integrally stiffened shell are studied, and results illustrate the excellent efficiency, robustness and applicability of KE-VFS-CMA-ES compared with other four known multi-objective optimization algorithms.展开更多
Multiphoton microscopy is the enabling tool for biomedical research,but the aberrations of biological tissues have limited its imaging performance.Adaptive optics(AO)has been developed to partially overcome aberration...Multiphoton microscopy is the enabling tool for biomedical research,but the aberrations of biological tissues have limited its imaging performance.Adaptive optics(AO)has been developed to partially overcome aberration to restore imaging performance.For indirect AO,algorithm is the key to its successful implementation.Here,based on the fact that indirect AO has an analogy to the black-box optimization problem,we successfully apply the covariance matrix adaptation evolution strategy(CMA-ES)used in the latter,to indirect AO in multiphoton microscopy(MPM).Compared with the traditional genetic algorithm(GA),our algorithm has a greater improvement in convergence speed and convergence accuracy,which provides the possibility of realizing real-time dynamic aberration correction for deep in vivo biological tissues.展开更多
In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail ...In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail to accurately characterize the complex influence of marine environments.To overcome these challenges,we propose an acoustic physics-informed intelligent path planning framework for underwater target search,integrating three core modules:The acoustic-physical modeling module adopts 3D ray-tracing theory and the active sonar equation to construct a physics-driven sonar detection model,explicitly accounting for environmental factors that influence sonar performance across heterogeneous spaces.The hybrid parallel computing module adopts a message passing interface(MPI)/open multi-processing(Open MP)hybrid strategy for large-scale acoustic simulations,combining computational domain decomposition and physics-intensive task acceleration.The search path optimization module adopts the covariance matrix adaptation evolution algorithm to solve continuous optimization problems of heading angles,which ensures maximum search coverage for targets.Largescale experiments conducted in the Pacific and Atlantic Oceans demonstrate the framework's effectiveness:(1)Precise capture of sonar detection range variations from 5.45 km to 50 km in heterogeneous marine environments.(2)Significant speedup of 453.43×for acoustic physics modeling through hybrid parallelization.(3)Notable improvements of 7.23%in detection coverage and 15.86%reduction in optimization time compared to the optimal baseline method.The framework provides a robust solution for underwater search missions in complex marine environments.展开更多
To improve the accuracy of the network security situation, a security situation automatic prediction model based on accumulative data preprocess and support vector machine （SVM） optimized by covariance matrix adapti...To improve the accuracy of the network security situation, a security situation automatic prediction model based on accumulative data preprocess and support vector machine （SVM） optimized by covariance matrix adaptive evolutionary strategy （CMA-ES） is proposed. The proposed model adopts SVM which has strong nonlinear ability. Also, the hyper parameters for SVM are optimized through the CMA-ES which owns good performance in finding optimization automatically. Considering the irregularity of network security situation values, we accumulate the original sequence, so that the internal rules of discrete data can be revealed and it is easy to model. Simulation experiments show that the proposed model has faster convergence-speed and higher prediction accuracy than other extant prediction models.展开更多
基金Supported by the Graduate Innovation Foundation of Jiangsu Province(CX08B-133Z)the Doctoral Innovation Foundation of Nanjing University of Aeronautics and Astronautics(BCXJ08-05)~~
文摘The matrix crack evolution of cross-ply ceramic matrix composites under uniaxial tensile loading is investigated using the energy balance method.Under tensile loading,the cross-ply ceramic matrix composites have five damage modes.The cracking mode 3 contains transverse cracking,matrix cracking and fiber/matrix interface debonding.The cracking mode 5 only contains matrix cracking and fiber/matrix interface debonding.The cracking stress of modes 3 and 5 appearing between existing transverse cracks is determined.And the multiple matrix crack evolution of mode 3 is determined.The effects of ply thickness,fiber volume fraction,interface shear stress and interface debonding energy on the cracking stress and matrix crack evolution are analyzed.Results indicate that the cracking mode 3 is more likely to appear between transverse cracks for the SiC/CAS material.
文摘The matrix metalloproteinases（MMPs） are a family of zinc-dependent endopeptidases originally characterized as secreted proteases responsible for degrading extracellular matrix proteins.Their canonical role in matrix remodelling is of significant importance in neural development and regeneration,but emerging roles for MMPs,especially in signal transduction pathways,are also of obvious importance in a neural context.Misregulation of MMP activity is a hallmark of many neuropathologies,and members of every branch of the MMP family have been implicated in aspects of neural development and disease.However,while extraordinary research efforts have been made to elucidate the molecular mechanisms involving MMPs,methodological constraints and complexities of the research models have impeded progress.Here we discuss the current state of our understanding of the roles of MMPs in neural development using recent examples and advocate a phylogenetically diverse approach to MMP research as a means to both circumvent the challenges associated with specific model organisms,and to provide a broader evolutionary context from which to synthesize an understanding of the underlying biology.
文摘Cloud computing, a recently emerged paradigm faces major challenges in achieving the privacy of migrated data, network security, etc. Too many cryptographic technologies are raised to solve these issues based on identity, attributes and prediction algorithms yet;these techniques are highly prone to attackers. This would raise a need of an effective encryption technique, which would ensure secure data migration. With this scenario, our proposed methodology Efficient Probabilistic Public Key Encryption(EPPKE) is optimized with Covariance Matrix Adaptation Evolution Strategies(CMA-ES). It ensures data integrity through the Luhn algorithm with BLAKE 2b encapsulation. This enables an optimized security to the data which is migrated through cloud. The proposed methodology is implemented in Open Stack with Java Language. It achieves better results by providing security compared to other existing techniques like RSA, IBA, ABE, PBE, etc.
基金supported by the Provincial Universities Basic Business Expense Scientific Research Projects of Heilongjiang Province(No.2021-KYYWF-0179)the Science and Technology Project of Henan Province(No.212102310991)+2 种基金the Opening Project of Shanghai Key Laboratory of Integrated Administration Technologies for Information Security(No.AGK2015003)the Key Scientific Research Project of Henan Province(No.21A413001)the Postgraduate Innovation Project of Harbin Normal University(No.HSDSSCX2021-121).
文摘To address the problem of network security situation assessment in the Industrial Internet,this paper adopts the evidential reasoning(ER)algorithm and belief rule base(BRB)method to establish an assessment model.First,this paper analyzes the influencing factors of the Industrial Internet and selects evaluation indicators that contain not only quantitative data but also qualitative knowledge.Second,the evaluation indicators are fused with expert knowledge and the ER algorithm.According to the fusion results,a network security situation assessment model of the Industrial Internet based on the ER and BRB method is established,and the projection covariance matrix adaptive evolution strategy(P-CMA-ES)is used to optimize the model parameters.This method can not only utilize semiquantitative information effectively but also use more uncertain information and prevent the problem of combinatorial explosion.Moreover,it solves the problem of the uncertainty of expert knowledge and overcomes the problem of low modeling accuracy caused by insufficient data.Finally,a network security situation assessment case of the Industrial Internet is analyzed to verify the effectiveness and superiority of the method.The research results showthat this method has strong applicability to the network security situation assessment of complex Industrial Internet systems.It can accurately reflect the actual network security situation of Industrial Internet systems and provide safe and reliable suggestions for network administrators to take timely countermeasures,thereby improving the risk monitoring and emergency response capabilities of the Industrial Internet.
基金supported by the National Natural Science Foundation of China(Nos.11902065,11825202)the Fundamental Research Funds for the Central Universities,China(No.DUT21RC(3)013).
文摘To accelerate the multi-objective optimization for expensive engineering cases, a Knowledge-Extraction-based Variable-Fidelity Surrogate-assisted Covariance Matrix Adaptation Evolution Strategy(KE-VFS-CMA-ES) is presented. In the first part, the KE-VFS model is established. Firstly, the optimization is performed using the low-fidelity surrogate model to obtain the Low-Fidelity Non-Dominated Solutions(LF-NDS). Secondly, aiming to obtain the High-Fidelity(HF) sample points located in promising areas, the K-means clustering algorithm and the space-filling strategy are used to extract knowledge from the LF-NDS to the HF space. Finally,the KE-VFS model is established by means of the obtained HF and LF sample points. In the second part, a novel model management based on the Modified Hypervolume Improvement(MHVI) criterion and pre-screening strategy is proposed. In each generation of KE-VFS-CMA-ES, excessive candidate points are firstly generated and then calculated by the MHVI criterion to find out a few potential points, which will be evaluated by the HF model. Through the above two parts,the promising areas can be detected and the potential points can be screened out, which contributes to speeding up the optimization process twofold. Three classic benchmark functions and a time-consuming engineering case of the aerospace integrally stiffened shell are studied, and results illustrate the excellent efficiency, robustness and applicability of KE-VFS-CMA-ES compared with other four known multi-objective optimization algorithms.
基金supported by the National Natural Science Foundation of China(Nos.62075135 and 61975126)the Science,Technology and Innovation Commission of Shenzhen Municipality(Nos.JCYJ20190808174819083 and JCYJ20190808175201640)。
文摘Multiphoton microscopy is the enabling tool for biomedical research,but the aberrations of biological tissues have limited its imaging performance.Adaptive optics(AO)has been developed to partially overcome aberration to restore imaging performance.For indirect AO,algorithm is the key to its successful implementation.Here,based on the fact that indirect AO has an analogy to the black-box optimization problem,we successfully apply the covariance matrix adaptation evolution strategy(CMA-ES)used in the latter,to indirect AO in multiphoton microscopy(MPM).Compared with the traditional genetic algorithm(GA),our algorithm has a greater improvement in convergence speed and convergence accuracy,which provides the possibility of realizing real-time dynamic aberration correction for deep in vivo biological tissues.
基金supported by Natural Science Foundation of Hu'nan Province(2024JJ5409)。
文摘In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail to accurately characterize the complex influence of marine environments.To overcome these challenges,we propose an acoustic physics-informed intelligent path planning framework for underwater target search,integrating three core modules:The acoustic-physical modeling module adopts 3D ray-tracing theory and the active sonar equation to construct a physics-driven sonar detection model,explicitly accounting for environmental factors that influence sonar performance across heterogeneous spaces.The hybrid parallel computing module adopts a message passing interface(MPI)/open multi-processing(Open MP)hybrid strategy for large-scale acoustic simulations,combining computational domain decomposition and physics-intensive task acceleration.The search path optimization module adopts the covariance matrix adaptation evolution algorithm to solve continuous optimization problems of heading angles,which ensures maximum search coverage for targets.Largescale experiments conducted in the Pacific and Atlantic Oceans demonstrate the framework's effectiveness:(1)Precise capture of sonar detection range variations from 5.45 km to 50 km in heterogeneous marine environments.(2)Significant speedup of 453.43×for acoustic physics modeling through hybrid parallelization.(3)Notable improvements of 7.23%in detection coverage and 15.86%reduction in optimization time compared to the optimal baseline method.The framework provides a robust solution for underwater search missions in complex marine environments.
基金supported by the National Natural Science Foundation of China (61403109,61202458)the Specialized Research Fund for the Doctoral Program of Higher Education of China (20112303120007)the Specialized Research Fund for Scientific and Technological Innovation Talents of Harbin (2016RAQXJ036)
文摘To improve the accuracy of the network security situation, a security situation automatic prediction model based on accumulative data preprocess and support vector machine （SVM） optimized by covariance matrix adaptive evolutionary strategy （CMA-ES） is proposed. The proposed model adopts SVM which has strong nonlinear ability. Also, the hyper parameters for SVM are optimized through the CMA-ES which owns good performance in finding optimization automatically. Considering the irregularity of network security situation values, we accumulate the original sequence, so that the internal rules of discrete data can be revealed and it is easy to model. Simulation experiments show that the proposed model has faster convergence-speed and higher prediction accuracy than other extant prediction models.
