Combating Curse of Dimensionality in Resilient Monitoring Systems: Conditions for Lossless Decomposition

Abstract

Resilient monitoring systems (RMSs) are sensor networks that degrade gracefully under cyber-attacks on their sensors. The recently developed RMSs, while being effective in the attacked sensors identification and isolation, exhibited a drawback in their operation-an exponentially increasing assessment time as a function of the number of sensors in the network. To combat this curse of dimensionality, a decomposition approach has been proposed, which led to a dramatic reduction of the assessment time, irrespective of the sensor network dimensionality. However, information losses and, thus, reductions in the level of resiliency due to the decomposition, have not been investigated. This paper is intended to carry out such an investigation. Specifically, it derives conditions for lossless decomposition in terms of the Renyi-2 entropy. The development is based on the analysis of matrices, which characterize coupling of process variables and on a monotonicity property of the Dempster-Shafer combination rule on a class of functions, which arise within the RMS operation.

AI-based modeling and data-driven evaluation for smart manufacturing processes

“…Dimensionality reduction and feature selection/extraction methods, e.g. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Canonical Correlation Analysis (CCA), play a critical role in dealing with noise and redundant features and must be considered as a preprocessing stage of manufacturing data analysis, which leads to better insights and robust decisions [19]. Some previous manufacturing fault detection studies have focused on utilizing the mentioned techniques for extracting the most relevant features and classification.…”

Data-driven Predictive Analysis for Smart Manufacturing Processes Based on a Decomposition Approach

“…The key to leveraging manufacturing data lies in constant monitoring of processes, which can be associated with different issues, e.g., noisy signals. Dimensionality reduction and feature selection/extraction methods play a critical role in dealing with noise and redundant features and must be considered as a preprocessing stage of manufacturing data analysis, which leads to better insights and robust decisions [8]. Some previous manufacturing fault detection studies have focused on utilizing the mentioned techniques for extracting the most relevant features and classification.…”

Gene Targeting Differential Evolution: A Simple and Efficient Method for Large-Scale Optimization

“…Currently, many DE variants have also been widely studied for solving large-scale optimization problems (LSOPs) [26]. However, their performance is still restricted by the phenomenon of "curse of dimensionality" as the number of dimensions increases [27]- [31]. Since an LSOP often involves various characteristics of different variables, the evolution in different dimensions (i.e., variables) may become imbalanced.…”