This article deals with two important issues in digital filter implementation： roundoff noise and limit cycles. A novel class of robust state-space realizations, called normal realizations, is derived and characteriz...This article deals with two important issues in digital filter implementation： roundoff noise and limit cycles. A novel class of robust state-space realizations, called normal realizations, is derived and characterized. It is seen that these realizations are free of limit cycles. Another interesting property of the normal realizations is that they yield a minimal error propagation gain. The optimal realization problem, defined as to find those normal realizations that minimize roundoff noise gain, is formulated and solved analytically. A design example is presented to demonstrate the behavior of the optimal normal realizations and to compare them with several well-known digital filter realizations in terms of minimizing the roundoff noise and the error propagation.展开更多
In order to study the failure process of an anchorage structure and the evolution law of the body＇s defor- mation field, anchor push-out tests were carried out based on digital speckle correlation methods （DSCM）. T...In order to study the failure process of an anchorage structure and the evolution law of the body＇s defor- mation field, anchor push-out tests were carried out based on digital speckle correlation methods （DSCM）. The stress distribution of the anchorage interface was investigated using the particle flow numerical simulation method. The results indicate that there are three stages in the deformation and fail- ure process of an anchorage structure： elastic bonding stage, a de-bonding stage and a failure stage. The stress distribution in the interface controls the stability of the structure. In the elastic bonding stage, the shear stress peak point of the interface is close to the loading end, and the displacement field gradually develops into a ＂V＂ shape, in the de-bonding stage, there is a shear stress plateau in the center of the anchorage section, and shear strain localization begins to form in the deformation field. In the failure stage, the bonding of the interface fails rapidly and the shear stress peak point moves to the anchorage free end. The anchorage structure moves integrally along the macro-cracl~ The de-bonding stage is a research focus in the deformation and failure process of an anchorage structure, and plays an important guiding role in roadway support design and prediction of the stability of the surrounding rock.展开更多
In the digital economy era,many manufacturing enterprises are leveraging digital service enterprises to enhance their digital innovation processes.This paper introduces the concept of“digital innovation network embed...In the digital economy era,many manufacturing enterprises are leveraging digital service enterprises to enhance their digital innovation processes.This paper introduces the concept of“digital innovation network embeddedness”to describe this trend.Unlike traditional strategic resources,which are constrained by high-value,relatively static,restricted flow,and exclusivity,digital resources demonstrate superior fluidity,non-rivalrous access,and high value-driven interdependencies.To bridge this theoretical gap,this study examines the distinctive attributes of digital resources through the dual lenses of resource orchestration theory and dynamic capability theory.Specifically,it proposes big data capability as a mediating mechanism and organizational structure flexibility as a critical moderating factor.Developing this integrated research framework aims to elucidate the underlying mechanisms through which digital innovation network embeddedness dynamically shapes new product development(NPD)performance.Ultimately,this study seeks to advance theoretical understanding and provide actionable insights for digitally empowered manufacturing enterprises to enhance their NPD outcomes.The framework is tested using data from 559 manufacturing enterprises located in South China.There are three findings.(1)An inverted U-shaped relationship exists between digital innovation network structure and NPD performance,and between relationship embeddedness and NPD performance,respectively.(2)Big data capability mediates the relationship between moderate levels of digital innovation network embeddedness and NPD performance.However,at high levels of digital innovation network embeddedness,big data capability does not significantly mediate the relationship between digital innovation network structure/relationship embeddedness and NPD performance,respectively.(3)Organizational structure flexibility positively moderates the relationship between digital innovation network relationship embeddedness,big data capability,and NPD performance.Moreover,while mediated moderation occurred,the direct moderation effect on digital innovation network embeddedness is nonsignificant.The conclusions of this study provide insights for manufacturing enterprises seeking to enhance NPD performance within the context of digital innovation network embeddedness.展开更多
Inverse problem-solving methods have found applications in various fields,such as structural mechanics,acoustics,and non-destructive testing.However,accurately solving inverse problems becomes challenging when observe...Inverse problem-solving methods have found applications in various fields,such as structural mechanics,acoustics,and non-destructive testing.However,accurately solving inverse problems becomes challenging when observed data are incomplete.Fortunately,advancements in computer science have paved the way for data-based methods,enabling the discovery of nonlinear relationships within diverse data sets.In this paper,a step-by-step completion method of displacement information is introduced and a data-driven approach for predicting structural parameters is proposed.The accuracy of the proposed approach is 23.83%higher than that of the Genetic Algorithm,demonstrating the outstanding accuracy and efficiency of the data-driven approach.This work establishes a framework for solving mechanical inverse problems by leveraging a data-based method,and proposes a promising avenue for extending the application of the data-driven approach to structural health monitoring.展开更多
基金the National Nature Science Foundation of China (60774021)
文摘This article deals with two important issues in digital filter implementation： roundoff noise and limit cycles. A novel class of robust state-space realizations, called normal realizations, is derived and characterized. It is seen that these realizations are free of limit cycles. Another interesting property of the normal realizations is that they yield a minimal error propagation gain. The optimal realization problem, defined as to find those normal realizations that minimize roundoff noise gain, is formulated and solved analytically. A design example is presented to demonstrate the behavior of the optimal normal realizations and to compare them with several well-known digital filter realizations in terms of minimizing the roundoff noise and the error propagation.
基金financially supported by the National Key Basic Research Program of China (No.2010CB226805)the National Natural Science Foundation of China (Nos.51474136 and 51474013)+1 种基金the Opening Project Fund of State Key Laboratory of Mining Disaster Prevention and Control Co-founded by Shandong Province and the Ministry of Science and Technology (No.MDPC2013KF06)the Research Award Fund for the Excellent Youth of Shandong University of Science and Technology (No.2011KYJQ106)
文摘In order to study the failure process of an anchorage structure and the evolution law of the body＇s defor- mation field, anchor push-out tests were carried out based on digital speckle correlation methods （DSCM）. The stress distribution of the anchorage interface was investigated using the particle flow numerical simulation method. The results indicate that there are three stages in the deformation and fail- ure process of an anchorage structure： elastic bonding stage, a de-bonding stage and a failure stage. The stress distribution in the interface controls the stability of the structure. In the elastic bonding stage, the shear stress peak point of the interface is close to the loading end, and the displacement field gradually develops into a ＂V＂ shape, in the de-bonding stage, there is a shear stress plateau in the center of the anchorage section, and shear strain localization begins to form in the deformation field. In the failure stage, the bonding of the interface fails rapidly and the shear stress peak point moves to the anchorage free end. The anchorage structure moves integrally along the macro-cracl~ The de-bonding stage is a research focus in the deformation and failure process of an anchorage structure, and plays an important guiding role in roadway support design and prediction of the stability of the surrounding rock.
基金supported by the Post-Funded Project of the National Social Science Fund of China(No.23FGLB088)the General Project of the National Natural Science Foundation of China(No.71974059)+2 种基金the Ministry of Education in China Liberal Arts and Social Sciences Foundation(No.23YJA630124)the Development of Philosophy and Social Sciences in Guangzhou in 2021(No.2021GZYB12)the Guangdong Basic and Applied Basic Research Foundation(No.2024A1515030110).
文摘In the digital economy era,many manufacturing enterprises are leveraging digital service enterprises to enhance their digital innovation processes.This paper introduces the concept of“digital innovation network embeddedness”to describe this trend.Unlike traditional strategic resources,which are constrained by high-value,relatively static,restricted flow,and exclusivity,digital resources demonstrate superior fluidity,non-rivalrous access,and high value-driven interdependencies.To bridge this theoretical gap,this study examines the distinctive attributes of digital resources through the dual lenses of resource orchestration theory and dynamic capability theory.Specifically,it proposes big data capability as a mediating mechanism and organizational structure flexibility as a critical moderating factor.Developing this integrated research framework aims to elucidate the underlying mechanisms through which digital innovation network embeddedness dynamically shapes new product development(NPD)performance.Ultimately,this study seeks to advance theoretical understanding and provide actionable insights for digitally empowered manufacturing enterprises to enhance their NPD outcomes.The framework is tested using data from 559 manufacturing enterprises located in South China.There are three findings.(1)An inverted U-shaped relationship exists between digital innovation network structure and NPD performance,and between relationship embeddedness and NPD performance,respectively.(2)Big data capability mediates the relationship between moderate levels of digital innovation network embeddedness and NPD performance.However,at high levels of digital innovation network embeddedness,big data capability does not significantly mediate the relationship between digital innovation network structure/relationship embeddedness and NPD performance,respectively.(3)Organizational structure flexibility positively moderates the relationship between digital innovation network relationship embeddedness,big data capability,and NPD performance.Moreover,while mediated moderation occurred,the direct moderation effect on digital innovation network embeddedness is nonsignificant.The conclusions of this study provide insights for manufacturing enterprises seeking to enhance NPD performance within the context of digital innovation network embeddedness.
基金supported by the National Natural Science Foundation of China(Grant Nos.11991030,11991031,and 11972205).
文摘Inverse problem-solving methods have found applications in various fields,such as structural mechanics,acoustics,and non-destructive testing.However,accurately solving inverse problems becomes challenging when observed data are incomplete.Fortunately,advancements in computer science have paved the way for data-based methods,enabling the discovery of nonlinear relationships within diverse data sets.In this paper,a step-by-step completion method of displacement information is introduced and a data-driven approach for predicting structural parameters is proposed.The accuracy of the proposed approach is 23.83%higher than that of the Genetic Algorithm,demonstrating the outstanding accuracy and efficiency of the data-driven approach.This work establishes a framework for solving mechanical inverse problems by leveraging a data-based method,and proposes a promising avenue for extending the application of the data-driven approach to structural health monitoring.
