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Second-Order Statistic Deviation to Model Anomalies in the Design of Unsupervised Detectors
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Rilevatori di anomalie basati su Autoencoder per segnali accelerometrici
No full textNei contesti di monitoraggio secondo condizione e di monitoraggio strutturale, i segnali vibrazionali e soprattutto quelli accelerometrici, sono il tipico output dei sistemi adottati in questi processi. Grazie allo sviluppo dei paradigmi come IoT e Big Data, è possibile avere a disposizione una quantità di segnali tale da permettere l’uso dei metodi data-driven nello studio dei loro comportamenti anomali. L’algoritmo che ormai si è affermato e che costituisce la base di questo approccio è chiamato Analisi delle Componenti Principali (PCA); esso definisce un metodo robusto nella rilevazione di anomalie, ma che comunque presenta delle limitazioni dovute alla sua natura lineare. Questo lavoro tesi, si fonda su un'altra possibile soluzione, data dalle reti neurali. In particolare, si studia la rete di Autoencoder, il cui principio di funzionamento può essere visto come una estensione non lineare della PCA e che quindi può rappresentare un metodo di rilevazione di anomalie potenzialmente più prestazionale.
Il flusso di lavoro inizia con l’analisi dei segnali propri di una particolare applicazione di monitoraggio strutturale, a partire dalle cui caratteristiche vengono generate anomalie compatibili con l’applicazione. Dopo l’allenamento dei modelli di Autoencoder, le loro prestazioni in termini di rilevazione di anomalie vengono confrontate con quelle della PCA.
Inoltre, si esaminano dei metodi alternativi di costruzione dei rilevatori, il cui funzionamento si basa sulla rilevazione di anomalie a partire dagli spazi latenti della PCA e degli Autoencoder: come il predittore dello score e il classificatore binario supervisionato. Questi ultimi approcci possono essere particolarmente interessanti, grazie alla riduzione della quantità di dati che sia necessario trasmettere, nel caso in cui la parte che effettua la compressione debba essere implementata direttamente sul nodo sensore del sistema di monitoraggio e il rilevatore venga realizzato sul cloud
Anomaly detection challenges in monitoring applications
Get PDFMonitoring physical systems has become pervasive, particularly in critical applications where ensuring operational integrity is paramount. A fundamental task in this context is identifying anomalous behaviors, commonly referred to as anomaly detection. Over the past years, significant attention has been devoted to anomaly detection, leading to the development of more accurate and robust algorithms capable of processing complex data. However, several fundamental challenges remain. Firstly, anomaly detection is inherently unsupervised, making it difficult to exploit prior knowledge about possible anomalies during the design phase. Secondly, despite advances in related fields, anomaly detection has not been thoroughly analyzed from an information-theoretic perspective. This lack of exploration complicates its integration with other well-established tasks, such as signal compression. This dissertation aims to address these challenges by presenting both practical and information-theoretic frameworks for anomaly detection. In the first part, we design a tool designed to mitigate the challenge of evaluating anomaly detection performance in the absence of real anomalies. To this end, we develop robust mathematical models that emulate possible anomalies in time series data and propose a procedure for generating synthetic anomalies. Furthermore, we establish a theoretical framework for performance assessment based on a novel concept of distinguishability. In the second part, we employ these assessment tools to study the interaction between compression and anomaly detection from an information-theoretic standpoint. We demonstrate that common lossy compression algorithms can compromise the effectiveness of anomaly detection performed on compressed data. We then study how these tasks can be jointly optimized and offer insights for developing practical systems that integrate both functions. In a similar spirit, we design an autoencoder-based compression scheme that not only minimizes distortion but also preserves information critical for anomaly detection
A General Framework for the Assessment of Detectors of Anomalies in Time Series
Get PDFAnomalies are rare events, and this affects the design flow of detectors that monitor systems that behave normally most of the time but whose failure may have serious consequences. This limitation is particularly evident in the detector performance evaluation: it requires an abundance of normal and anomalous data but realistically faces a scarcity of the latter. To address this, in this article, we develop a framework comprising a set of abstract anomalies modeling the effects real-world failures and disturbances have on sensor readings. In addition, we devise synthetic generation procedures for these anomalies. Given a dataset of normal tracks from the actual application, one may apply such procedures to produce anomalous-like time series for a comprehensive detector assessment. We show that this framework can anticipate the detector performing best with real-world anomalies in the context of human and structural health monitoring, also highlighting that, in these cases, the best detector is not the most complex
On the Universal Approximation Properties of Deep Neural Networks Using MAM Neurons
Get PDFAs neural networks are trained to perform tasks of increasing complexity, their size increases, which presents several challenges in their deployment on devices with limited resources. To cope with this, a recently proposed approach hinges on substituting the classical Multiply-and-ACcumulate (MAC) neurons in the hidden layers with other neurons called Multiply-And-Max/min (MAM) whose selective behavior helps identify important interconnections, thus allowing aggressive pruning of the others. Hybrid MAM&MAC structures promise a 10x or even 100x reduction in their memory footprint compared to what can be obtained by pruning MAC-only structures. However, a cornerstone of maintaining this promise is the assumption that MAC&MAM architectures have the same expressive power as MAC-only ones. To concretize such a cornerstone, we take here a step in the theoretical characterization of the capabilities of mixed MAM&MAC networks. We prove, with two theorems, that two hidden MAM layers followed by a MAC neuron with possibly a normalization stage is a universal approximator
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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