Identification of critical inspection samples among railroad wheels by similarity-based agglomerative clustering

Stratman, Brant; Mahadevan, Sankaran; Biswas, Gautam

doi:10.3233/ica-2011-0372

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…In the future, we plan to expand the proposed topological approach to clustering and classification of dynamic multilayer networks, as well as to derive theoretical stability guarantees for topological graph clustering. Another interesting direction is to combine the concept of similarity-based agglomerative clustering (SBAC) (86)(87)(88) with CPD by grouping points with less or more common shape feature in the population. We envision that such topological SBAC might be particularly useful in biomedical imaging, such as tumor detection.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Topological clustering of multilayer networks

Yuvaraj

Dey

Lyubchich

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Multilayer networks continue to gain significant attention in many areas of study, particularly due to their high utility in modeling interdependent systems such as critical infrastructures, human brain connectome, and socioenvironmental ecosystems. However, clustering of multilayer networks, especially using the information on higher-order interactions of the system entities, still remains in its infancy. In turn, higher-order connectivity is often the key in such multilayer network applications as developing optimal partitioning of critical infrastructures in order to isolate unhealthy system components under cyber-physical threats and simultaneous identification of multiple brain regions affected by trauma or mental illness. In this paper, we introduce the concepts of topological data analysis to studies of complex multilayer networks and propose a topological approach for network clustering. The key rationale is to group nodes based not on pairwise connectivity patterns or relationships between observations recorded at two individual nodes but based on how similar in shape their local neighborhoods are at various resolution scales. Since shapes of local node neighborhoods are quantified using a topological summary in terms of persistence diagrams, we refer to the approach as clustering using persistence diagrams (CPD). CPD systematically accounts for the important heterogeneous higher-order properties of node interactions within and in-between network layers and integrates information from the node neighbors. We illustrate the utility of CPD by applying it to an emerging problem of societal importance: vulnerability zoning of residential properties to weather- and climate-induced risks in the context of house insurance claim dynamics.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Topological clustering of multilayer networks

Yuvaraj

Dey

Lyubchich

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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“…Thus, instantaneous modal parameters of the structure based on its dynamic responses under a single severe loading event can be used to detect whether a structure is damaged and to detect the location of the damage. System identification of structures has been a subject of active research in recent years (Schoefs et al, 2011;Stratman et al, 2011;Marano et al, 2011;Kang et al, 2012;Hazra et al, 2012;Yan and Ren, 2012;Cho et al, 2012;Theodoridis et al, 2012). This research has application in damage detection of structures (Figueiredo et al, 2011;Osornio-Rios et al, 2012;Qiao et al, 2012;Xiang and Liang, 2012;Nishikawa et al, 2012;Raich and Liszkai, 2012), which can then be used for health monitoring of structures (Adeli and Saleh, 1997;Yuen and Katafygiotis, 2006;Park et al, 2007;Moaveni et al, 2009;Bocca et al, 2011;Cusson et al, 2011;Xia et al, 2011;Gangone et al, 2011;Bitaraf et al, 2012) currently a very important area of structural engineering research.…”

Section: Introductionmentioning

confidence: 99%

Identification of Instantaneous Modal Parameter of Time‐Varying Systems via a Wavelet‐Based Approach and Its Application

Liu

Huang

2013

Computer aided Civil Eng

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This work presents an efficient approach using time‐varying autoregressive with exogenous input (TVARX) model and a substructure technique to identify the instantaneous modal parameters of a linear time‐varying structure and its substructures. The identified instantaneous natural frequencies can be used to identify earthquake damage to a building, including the specific floors that are damaged. An appropriate TVARX model of the dynamic responses of a structure or substructure is established using a basis function expansion and regression approach combined with continuous wavelet transform. The effectiveness of the proposed approach is validated using numerically simulated earthquake responses of a five‐storey shear building with time‐varying stiffness and damping coefficients. In terms of accuracy in determining the instantaneous modal parameters of a structure from noisy responses, the proposed approach is superior to typical basis function expansion and regression approach. The proposed method is further applied to process the dynamic responses of an eight‐storey steel frame in shaking table tests to identify its instantaneous modal parameters and to locate the storeys whose columns yielded under a strong base excitation.

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