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Innovative Industrial Clusters (IIC), characterized by geographical aggregation and technological collaboration among technology enterprises and institutions, serve as pivotal drivers of regional economic competitiveness and technological advancements. Prior research on cluster identification, crucial for IIC analysis, has predominantly emphasized geographical dimensions while overlooking technological proximity. Addressing these limitations, this study introduces a comprehensive framework incorporating multiple indices and methods for accurately identifying IIC using patent data. To unearth latent technological insights within patent documents, Latent Dirichlet Allocation (LDA) is employed to generate topics from a collection of terms. Utilizing the applicants' names and addresses recorded in patents, an Application Programming Interface (API) map systems facilitates the extraction of geographic locations. Subsequently, a Multivariate Density-Based Spatial Clustering of Applications with Noise (MDBSCAN) algorithm, which accounts for both technological and spatial distances, is deployed to delineate IIC. Moreover, a bipartite network model based on patent geographic information collected from the patent is constructed to analyze the technological distribution on the geography and development mode of IIC. The utilization of the model and methodologies is demonstrated through a case study on the China flexible electronics industry (FEI). The findings reveal that the clusters identified via this novel approach are significantly correlated with both technological innovation and geographical factors. Moreover, the MDBSCAN algorithm demonstrates notable superiority over other algorithms in terms of computational precision and efficiency, as evidenced by the case analysis. [ABSTRACT FROM AUTHOR]

This work presents a generalized substructure-based topology optimization method for passive constrained layer damping (PCLD) structures. Here, the model of PCLD structure is obtained by the Kirchhoff–Love thin plate formulation, and the whole structure is assumed to be composed of substructures with different yet connected scales and artificial lattice geometry features. Each substructure is condensed into a super-element to obtain the associated density-related matrices under the different geometry feature parameters, and the surrogate model for the stiffness and mass matrix of PCLD substructures with different densities has been particularly built. Using cubic spline interpolation, the derivatives of super-element matrices to the associated densities can be evaluated efficiently and accurately. The modal loss factor is defined as objective functions and topology optimization for the PCLD structures is formulated based on the model for PCLD plates that are described by combining the condensed substructures. Numerical examples under two lattice patterns of substructures and their corresponding physical tests show that the correctness and superiority of this substructure-based topology optimization approach for PCLD plates are verified. [ABSTRACT FROM AUTHOR]

Patent application is one of the important ways to protect innovation achievements that have great commercial value for enterprises; it is the initial step for enterprises to set the business development track, as well as a powerful means to protect their core competitiveness. The emergence of a large amount of patent data makes the effective detection of patent data difficult, and patent infringement cases occur frequently. Manual measurement in patent detection is slow, costly, and subjective, and can only play an auxiliary role in measuring the validity of patents. Protecting the inventive achievements of patent holders and realizing more accurate and effective patent detection were the issues explored by academics. There are five main methods to measure patent similarity: clustering-based method, vector space model (VSM)-based method, subject–action–object (SAO) structure-based method, deep learning-based method, and patent structure-based method. To solve this problem, this paper proposes a calculation method to fuse the similarity of patent text and image. Firstly, the SAO structure extraction technique is used for the patent text to obtain the effective content of the text, and the SAO structure is compared for similarity; secondly, the patent image information is extracted and compared; finally, the patent similarity is obtained by fusing the two aspects of information. The feasibility and effectiveness of the scheme are proven by studying a large number of patent similarity cases in the field of mechanical structures. [ABSTRACT FROM AUTHOR]

Axiomatic design theory is widely used in new product development by providing design solutions through mapping between functional requirements and design parameters. However, the theory does not provide a method to help designer obtain and select design parameters. To this end, this paper introduces patent analysis to overcome the deficiency. Firstly, functional requirements are transformed into patent search terms, and design parameters are obtained from patents. Secondly, morphological matrix is used to represent the relationships between target function and multiple design parameters. Thirdly, design parameters with higher patent frequency are chose and combined into a new scheme. Finally, the scheme is evaluated by the independent axiom of Axiomatic Design theory. The methodology is demonstrated and validated with a case study of spa shower.