WebbNils Müller and Fabian Schönfeld, May 7 th 2024. Following our previous tutorial on Slow Feature Analysis (SFA) we now talk about xSFA - an unsupervised learning algorithm and extension to the original SFA algorithm that utilizes the slow features generated by SFA to reconstruct the individual sources of a nonlinear mixture, a process also ... Webb12 juni 2024 · To address this challenge, a slow feature analysis (SFA)-based fault detection method is applied. The SFA-based method furnishes four monitoring charts …

Fault Detection in Managed Pressure Drilling Using Slow Feature …

Webb22 maj 2024 · More precisely, we propose a quantum version of Slow Feature Analysis (QSFA), a dimensionality reduction technique that maps the dataset in a lower dimensional space where we can apply a novel quantum classification procedure, the Quantum Frobenius Distance (QFD). Webb15 dec. 2024 · Recently, slow feature analysis (SFA) has been applied to manage the time-wise dynamics in the batch control process due to its superiority of extracting slowly-varying slow features ... In summary, the pseudo code of the KDSFA similarity factor for the fault diagnosis of the AHU system is illustrated in Table 2. ims552 individual assignment

Slow Feature Analysis: Unsupervised Learning of Invariances

WebbSlow Feature Analysis (SFA) Linear dimensionality reduction and feature extraction method to be trained on time-series data. The data is decorrelated by whitening and linearly projected into the most slowly changing subspace. Webb9 juni 2024 · Slow feature analysis is an unsupervised algorithm, ... whose code is made available, can be readily extended to linear and non-linear manifolds embedded in high-dimensional systems. View. WebbOne of them being Slow Feature Analysis (SFA), an algorithm that uses time-series data to learn latent features that contain important infor- mation about input [1]. Even though SFA has been around for almost two decades, the research on it is rel- atively scarce. lithium price usd per ton