As a substantial amount of multivariate time series data is being produced by the complex systems in Smart Manufacturing, improved anomaly detection frameworks are needed to reduce the operational risks and the monitoring burden placed on the system operators. However, building such frameworks is challenging, as a sufficiently large amount of defective training data is often not available and frameworks are required to capture both the temporal and contextual dependencies across different time steps while being robust to noise. In this paper, we propose an unsupervised Attention-based Convolutional Long Short-Term Memory (ConvLSTM) Autoencoder with Dynamic Thresholding (ACLAE-DT) framework for anomaly detection and diagnosis in multivariate time series. The framework starts by pre-processing and enriching the data, before constructing feature images to characterize the system statuses across different time steps by capturing the inter-correlations between pairs of time series. Afterwards, the constructed feature images are fed into an attention-based ConvLSTM autoencoder, which aims to encode the constructed feature images and capture the temporal behavior, followed by decoding the compressed knowledge representation to reconstruct the feature images input. The reconstruction errors are then computed and subjected to a statistical-based, dynamic thresholding mechanism to detect and diagnose the anomalies. Evaluation results conducted on real-life manufacturing data demonstrate the performance strengths of the proposed approach over state-of-the-art methods under different experimental settings.
With substantial recent developments in aviation technologies, Unmanned Aerial Vehicles (UAVs) are becoming increasingly integrated in commercial and military operations internationally. Research into the applications of aircraft data is essential in improving safety, reducing operational costs, and developing the next frontier of aerial technology. Having an outlier detection system that can accurately identify anomalous behaviour in aircraft is crucial for these reasons. This paper proposes a system incorporating a Long Short-Term Memory (LSTM) Deep Learning Autoencoder based method with a n…
Many real-world monitoring and surveillance applications require non-trivial anomaly detection to be run in the streaming model. We consider an incremental-learning approach, wherein a deep-autoencoding (DAE) model of what is normal is trained and used to detect anomalies at the same time. In the detection of anomalies, we utilise a novel thresholding mechanism, based on value at risk (VaR). We compare the resulting convolutional neural network (CNN) against a number of subspace methods, and present results on changedetection net.
University of Tübingen（图宾根大学）；University of Hohenheim（霍恩海姆大学）
The novel DISTributed Artificial neural Network Architecture (DISTANA) is a generative, recurrent graph convolution neural network. It implements a grid or mesh of locally parameterizable laterally connected network modules. DISTANA is specifically designed to identify the causality behind spatially distributed, non-linear dynamical processes. We show that DISTANA is very well-suited to denoise data streams, given that re-occurring patterns are observed, significantly outperforming alternative approaches, such as temporal convolution networks and ConvLSTMs, on a complex spatial wave propagatio…
Iterative Encoding-Decoding VAEs Anomaly Detection in NOAA's DART Time Series: A Machine Learning Approach for Enhancing Data Integrity for NASA's GRACE-FO Verification and Validation
University of California at Los Angeles（加利福尼亚大学洛杉矶分校）；NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）
Low-Power, Neuromorphic, Acoustic Anomaly Detection for Persistent Machine Monitoring
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；AeroVironment, Inc（AeroVironment公司）；University of New Mexico COSMIAC Research Center（新墨西哥大学COSMIAC研究中心）；Air Force Research Laboratory（空军研究实验室）
Fast reconstruction-based ROI triggering via anomaly detection in the CYGNO optical TPC
LIBPhys, Department of Physics, University of Coimbra（科英布拉大学物理系LIBPhys部门）；Dipartimento di Matematica e Fisica, Università Roma Tre（罗马三大学数学与物理系）；INFN Sezione di Roma Tre（罗马三大学国家核物理研究所）；Gran Sasso Science Institute（格兰萨索科学研究所）；INFN Laboratori Nazionali del Gran Sasso（格兰萨索国家实验室）；INFN Laboratori Nazionali di Frascati（弗拉斯卡蒂国家实验室）；ENEA Centro Ricerche Frascati（弗拉斯卡蒂能源研究所）；Universidade Federal de Juiz de Fora, Faculdade de Engenharia（贝洛奥预托联邦大学工程学院）；Dipartimento di Fisica, Sapienza Università di Roma（罗马萨皮恩扎大学物理系）；INFN Sezione di Roma（罗马国家核物理研究所）；Universidade Estadual de Campinas (UNICAMP)（坎皮纳斯州立大学）；Dipartimento di Ingegneria Chimica, Materiali e Ambiente, Sapienza Università di Roma（罗马萨皮恩扎大学化学工程与材料环境系）；Department of Physics and Astronomy, University of Sheffield（谢菲尔德大学物理与天文学系）
IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer Study
organization= Department of Physics \& Astronomy, University of British Columbia , city= Vancouver , state= BC , country= Canada；organization= Department of Integrative Oncology, BC Cancer Research Institute , city= Vancouver , state= BC , country= Canada；organization= Department of Radiology, University of British Columbia , city= Vancouver , state= BC , country= Canada
A Foundation Model for Wearable Movement Data in Mental Health Research
Dartmouth College（达特茅斯学院）；National Institute of Diabetes and Digestive and Kidney Diseases（国家糖尿病、消化系统疾病和肾病研究所）；National Institutes of Health（美国国立卫生研究院）；Department of Computer Science at Dartmouth College（达特茅斯学院计算机科学系）
MVTec Software GmbH（MVTec软件有限公司）；University of Amsterdam（阿姆斯特丹大学）
Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data. Most commonly, this task is performed by thresholding a pixel-wise reconstruction error based on an $\ell^p$ distance. This procedure, however, leads to large residuals whenever the reconstruction encompasses slight localization inaccuracies around edges. It also fails to reveal defective regions that have been visually altered when intensity values stay roughly consistent. We show that these problems prevent these approaches from being applied to complex real-world scenarios and that…
This paper presents a fractional one-dimensional convolutional neural network (CNN) autoencoder for denoising the Electroencephalogram (EEG) signals which often get contaminated with noise during the recording process, mostly due to muscle artifacts (MA), introduced by the movement of muscles. The existing EEG denoising methods make use of decomposition, thresholding and filtering techniques. In the proposed approach, EEG signals are first transformed to orthogonal domain using Tchebichef moments before feeding to the proposed architecture. A new hyper-parameter ($α$) is introduced which refer…
Hybrid Autoencoder-Based Framework for Early Fault Detection in Wind Turbines
Department of Computer Science and Engineering, Alliance University（计算机科学与工程系，联盟大学）；College of Engineering and IT, University of Dubai（工程与信息技术学院，迪拜大学）；Shiv Nadar University（Shiv Nadar大学）
Amazon Web Services（亚马逊网络服务公司）；University of Southern California（南加州大学）；Los Angeles, CA, USA（美国加利福尼亚州洛杉矶）
Estimation of uncertainty in deep learning models is of vital importance, especially in medical imaging, where reliance on inference without taking into account uncertainty could lead to misdiagnosis. Recently, the probabilistic Variational AutoEncoder (VAE) has become a popular model for anomaly detection in applications such as lesion detection in medical images. The VAE is a generative graphical model that is used to learn the data distribution from samples and then generate new samples from this distribution. By training on normal samples, the VAE can be used to detect inputs that deviate …
Diagnosising Helicobacter pylori using AutoEncoders and Limited Annotations through Anomalous Staining Patterns in IHC Whole Slide Images
Universitat Autònoma de Barcelona（巴塞罗那自治大学）；Computer Vision Center（计算机视觉中心）；Hospital Universitari General de Catalunya-Grupo QuironSalud（加泰罗尼亚综合大学医院-奎荣萨尔集团）；Institut de Recerca contra la Leucèmia Josep Carreras (IJC)（何塞普·卡雷拉斯白血病研究所）
University of Southern California（南加利福尼亚大学）；Amazon Web Services（亚马逊云科技）
Despite impressive state-of-the-art performance on a wide variety of machine learning tasks, deep learning methods can produce over-confident predictions, particularly with limited training data. Therefore, quantifying uncertainty is particularly important in critical applications such as lesion detection and clinical diagnosis, where a realistic assessment of uncertainty is essential in determining surgical margins, disease status and appropriate treatment. In this work, we propose a novel approach that uses quantile regression for quantifying aleatoric uncertainty in both supervised and unsu…
