Nonparametric identification of added masses in frequency domain: a numerical study

Abstract This paper presents a theoretical derivation and reports on a numerical verification of a model-free method for identification of added masses in truss structures. No parametric numerical model of the monitored structure is required, so that there is no need for initial model updating and fine tuning. This is a continuation and an improvement of a previous research that resulted in a time-domain identification method, which was tested to be accurate but very time-consuming. A general methodology is briefly introduced, including the inverse problem, and a numerical verification is reported. The aim of the numerical study is to test the accuracy of the proposed method and its sensitivity to various parameters (such as simulated measurement noise and decay rate of the exponential FFT window) in a numerically controlled environment. The verification uses a finite element model of the same real structure that was tested with the time-domain version of the approach. A natural further step is a lab verification based on experimental data

BrightBox - A rough set based technology for diagnosing mistakes of machine learning models

The paper presents a novel approach to investigating mistakes in machine learning model operations. The considered approach is the basis for BrightBox – a diagnostic technology that can be used for analyzing prediction models and identifying model- and data-related issues. The idea is to generate surrogate rough set-based models from data that approximate decisions made by monitored black-box models. Such approximators are used to compute neighborhoods of instances that undergo the diagnostic process — the neighborhoods consist of historical instances that were processed in a similar way by rough set-based models. The diagnostic process is then based on the analysis of mistakes registered in such neighborhoods. The experiments performed on real-world data sets confirm that such analysis can provide us with efficient and valid insights about the reasons for the poor performance of machine learning models.</p