Convolutional autoencoder anomaly detection and classification based on distribution PMU measurements

Ehsani, Narges; Aminifar, Farrokh; Mohsenian‐Rad, Hamed

doi:10.1049/gtd2.12424

Cited by 13 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After collecting the data, a convolutional-based encoder-decoder network to determine where anomalies exist in the workpiece was trained. The ability to detect anomalies in time series by applying convolutions was successfully demonstrated by multiple publications [1,[7][8][9]]. An undercomplete autoencoder is a feedforward deep neural network that tries to reconstruct its input 𝒙 ϵ ℝ 𝑝 and consists of two parts being an encoder and a decoder [10].…”

Section: Methodsmentioning

confidence: 99%

Multivariate Time Series Dataset of Milling 16MnCr5 for Anomaly Detection

Schlagenhauf¹,

Wolf²,

Puchta³

2022

Preprint

View full text Add to dashboard Cite

Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself did not get the same attention by researchers. That is why in this article, the authors present a pub-licly available multivariate time series dataset which was recorded during milling of 16MnCr5. Due to artificially introduced, though realistic anomalies in the workpiece the dataset can be ap-plied for anomaly detection. By using a convolutional autoencoder as a first model good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learn-ing. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics like anomaly detection and transfer learning.

show abstract

Section: Methodsmentioning

confidence: 99%

Multivariate Time Series Dataset of Milling 16MnCr5 for Anomaly Detection

Schlagenhauf¹,

Wolf²,

Puchta³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…After convergence, the optimized intercept and weights Ŵ0 , Ŵ1 , and Ŵ2 obtained from linear regression are applied to Equation (10), enabling the calculation of the final rating ( R).…”

Section: Hybrid Model (Caers-cf)mentioning

confidence: 99%

“…Beyond recreating the original data in the decoding phase, studies suggest that autoencoders can generate new outputs from the compressed input data. For instance, when detecting anomalies, a CAE is trained to reconstruct regular instances precisely but intentionally struggles with anomalies [10].…”

Section: Introductionmentioning

confidence: 99%

CAERS-CF: Enhancing Convolutional Autoencoder Recommendations through Collaborative Filtering

Ghadami,

Tran

2024

Preprint

View full text Add to dashboard Cite

Recommendation systems are crucial in boosting companies' revenues by implementing various strategies to engage customers and encourage them to invest in products or services. Businesses constantly desire to enhance these systems through different approaches. One effective method involves using hybrid recommendation systems, known for their ability to create high-performance models. We introduce a hybrid recommendation system that leverages two types of recommendation systems: first, a novel deep learning-based recommendation system that utilizes users' and items' content data, and second, a traditional recommendation system that employs users' past behaviour data. We introduce a novel deep learning-based recommendation system called Convolutional Autoencoder Recommendation System (CAERS). It uses a Convolutional Autoencoder (CAE) to capture high-order meaningful relationships between users' and items' content information and decode them to predict ratings. Subsequently, we design a traditional model-based collaborative filtering recommendation system (CF) that leverages users' past behaviour data, utilizing Singular Value Decomposition (SVD). Finally, in the last step, we combine the two method's predictions with linear regression. We determine the optimal weight for each prediction generated by collaborative filtering and the deep learning-based recommendation system. Our main objective is to introduce a hybrid model called CAERS-CF that leverages the strengths of the two mentioned approaches. For experimental purposes, we utilize two movie datasets to showcase the performance of CAERS-CF. Our model outperforms each constituent model individually and other state-of-the-art deep learning or hybrid models. Specifically, the hybrid CAERS-CF model exhibits improvements across various metrics, highlighting the effectiveness of our approach in enhancing recommendation systems.

show abstract

“…Also, a study by Manar Ahmed Hamza (2023) [148], mentioned that a Hybrid Denoising Autoencoder was specifically used for noise reduction in digital mammograms. • Anomaly Detection: Autoencoders have been successfully used in anomaly detection tasks, such as detecting fraud in large-scale accounting data [149] and anomaly detection in distribution systems [150]. Although it's not directly related to breast cancer classification, it shows the capability of autoencoders in anomaly detection.…”

Section: A Advantagesmentioning

confidence: 99%

Breast Cancer Classification Utilizing Deep Learning Techniques on Medical Images: A Comprehensive Review

Marwah K. Muttair

2024

jes

View full text Add to dashboard Cite

Breast cancer is a major public health concern, affecting millions of women worldwide. Good patient outcomes and a successful course of therapy depend on a prompt and accurate diagnosis. The application of deep learning algorithms to the breast cancer classification problem has yielded some encouraging results; this might open the way for more rapid and accurate diagnostics. This article examines the many deep learning (DL) approaches taken to date for BC classification tasks, outlining each one's strengths, weaknesses, and current challenges. This study discusses several DL algorithms like convolutional neural networks, multi-layer neural networks, and autoencoders that analyze histopathological pictures, mammograms, and other forms of medical imaging. Furthermore, we investigate the interpretability & explainability features of DL models as they pertain to BC diagnoses, drawing attention to the need for reliable decision-making tools for medical practitioners. Throughout the review, we identify challenges and potential biases in current research. Finally, we provide an outlook on the future directions of DL in BC classification, focusing on promising research areas. By highlighting the achievements and gaps in the existing literature, this review aims to inspire further advancements in DL-based BC diagnosis, eventually resulting in better healthcare and reliable diagnosis.

show abstract

Convolutional autoencoder anomaly detection and classification based on distribution PMU measurements

Cited by 13 publications

References 26 publications

Multivariate Time Series Dataset of Milling 16MnCr5 for Anomaly Detection

Multivariate Time Series Dataset of Milling 16MnCr5 for Anomaly Detection

CAERS-CF: Enhancing Convolutional Autoencoder Recommendations through Collaborative Filtering

Breast Cancer Classification Utilizing Deep Learning Techniques on Medical Images: A Comprehensive Review

Contact Info

Product

Resources

About