Abstract A joint between two components can be seen as a means to transmit dynamic information from one side to the other. To identify the joint, a reverse process called decoupling can be applied. This is not as straightforward as the coupling, especially when the substructures have three-dimensional characteristics, or sensor mounting effects are significant, or the interface degrees-of-freedom (DoF) are inaccessible for response measurement and excitation. Acquiring frequency response functions (FRFs) at the interface DoF, therefore, becomes challenging. Consequently, one has to consider hybrid or expansion methods that can expand the observed dynamics on accessible DoF to inaccessible DoF. In this work, we attempt to identify the joint dynamics using the system equivalent model mixing (SEMM) decoupling method with a virtual point description of the interface. Measurements are made only at the internal DoF of the uncoupled substructures and also of the coupled structure assuming that the joint dynamics are observable in the assembled state. Expanding them to the interface DoF and performing coupling and decoupling operations iteratively, the joint is identified. The substructures under consideration are a disk and blade—an academic test geometry that has a total of 18 blades but only one blade-to-disk joint is considered in this investigation. The joint is a typical dove-tail assembly. The method is shown to identify the joint without any direct interface DoF measurement.

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Visible light positioning techniques employing received signal strength (RSS)–based fingerprints are becoming popular and ubiquitous. However, RSS is more susceptible to signal degradation and environmental changes resulting in location inaccuracies. To minimize these limitations, clustering in conjunction with linear regression is applied to RSS database made up of light intensity variations of light emitting diodes. Optimum cluster size is determined and trained clusters are exploited for location assessment by curtailing the difference between the database readings and cluster centroids. Regression is then applied on the clustered data, which partitions it further and helps refining the results. Simulation results of the proposed algorithm dictate a significant improvement in location estimation accuracy of up to 40 cm in an indoor environment with the dimensions of 5 m × 5 m × 4 m and exhibit superior performance than many state‐of‐the‐art RSS‐based methods.

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