A CVAE-based generative model for generalized B1 inhomogeneity corrected chemical exchange saturation transfer MRI at 5 T by Ruifen Zhang, Qiyang Zhang, Yin Wu

Subjects: Get full text

Heartbeat detection and personal authentication using a 60 GHz Doppler sensor by Takuma Asano, Shintaro Izumi, Hiroshi Kawaguchi

Subjects: Get full text

Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data by Seung-Hwan Choi, Dawn An, Inho Lee, Suwoong Lee

“…By combining the spatial feature extraction capability of Graph Convolutional Networks (GCNs) with the latent temporal feature modeling of Variational Autoencoders (VAEs), our method can effectively detect abnormal signs in the data, particularly in the lead-up to system failures. …”
Get full text

Detecting Emerging DGA Malware in Federated Environments via Variational Autoencoder-Based Clustering and Resource-Aware Client Selection by Ma Viet Duc, Pham Minh Dang, Tran Thu Phuong, Truong Duc Truong, Vu Hai, Nguyen Huu Thanh

“…To address this, we present FedSAGE, a security-aware federated intrusion detection framework that combines Variational Autoencoder (VAE)-based latent representation learning with unsupervised clustering and resource-efficient client selection. …”
Get full text

Application of Artificial Intelligence Virtual Image Technology in Photography Art Creation Under Deep Learning by Qiong Yao

Subjects: Get full text

Multimodal Trajectory Prediction for Diverse Vehicle Types in Autonomous Driving with Heterogeneous Data and Physical Constraints by Maoning Ge, Kento Ohtani, Ming Ding, Yingjie Niu, Yuxiao Zhang, Kazuya Takeda

Subjects: Get full text

Vibration-Based Anomaly Detection in Industrial Machines: A Comparison of Autoencoders and Latent Spaces by Luca Radicioni, Francesco Morgan Bono, Simone Cinquemani

“…This study explores the application of unsupervised learning methods, particularly Convolutional Autoencoders (CAEs) and variational Autoencoders (VAEs), for anomaly detection (AD) in vibration signals. …”
Get full text

CoTD-VAE: Interpretable Disentanglement of Static, Trend, and Event Components in Complex Time Series for Medical Applications by Li Huang, Qingfeng Chen

“…To address this challenge, we propose CoTD-VAE, a novel variational autoencoder framework for interpretable component disentanglement. …”
Get full text

A Transformer–VAE Approach for Detecting Ship Trajectory Anomalies in Cross-Sea Bridge Areas by Jiawei Hou, Hongzhu Zhou, Manel Grifoll, Yusheng Zhou, Jiao Liu, Yun Ye, Pengjun Zheng

“…To address these limitations, this study proposes an unsupervised trajectory anomaly detection model combining a transformer architecture with a variational autoencoder (transformer–VAE). By training on large volumes of unlabeled normal trajectory data, the transformer–VAE employs a multi-head self-attention mechanism to model both local and global temporal relationships within the latent feature space. …”
Get full text

iVAE: an interpretable representation learning framework enhances clustering performance for single-cell data by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen

“…Abstract Background Variational autoencoders (VAEs) serve as essential components in large generative models for extracting latent representations and have gained widespread application in biological domains. …”
Get full text

Multimodal anomaly detection in complex environments using video and audio fusion by Yuanyuan Wang, Yijie Zhao, Yanhua Huo, Yiping Lu

Get full text

From Envelope Spectra to Bearing Remaining Useful Life: An Intelligent Vibration-Based Prediction Model with Quantified Uncertainty by Haobin Wen, Long Zhang, Jyoti K. Sinha

“…Unlike traditional variational autoencoders, the probabilistic regressor and latent generator are formulated to quantify uncertainty in RUL estimates and learn meaningful latent representations conditioned on specific RUL. …”
Get full text

Machine learning for experimental design of ultrafast electron diffraction by Mohammad Shaaban, Sami El-Borgi, Aravind Krishnamoorthy

“…By building on CNN’s ability to learn compressed representations of diffraction patterns that map to distinct material dynamics, we construct Convolutional Variational Autoencoder models to track structural phase transformation in a model material system through the time trajectory of UED images in the low-dimensional latent space. …”
Get full text

Markov-CVAELabeller: A Deep Learning Approach for the Labelling of Fault Data by Christian Velasco-Gallego, Nieves Cubo-Mateo

“…Markov-CVAELabeller comprises three main phases: (1) image encoding through the application of the first-order Markov chain, (2) latent space representation through the consideration of a convolutional variational autoencoder (CVAE), and (3) clustering analysis through the implementation of <i>k</i>-means. …”
Get full text

Source-Free Domain Adaptation Framework for Rotary Machine Fault Diagnosis by Hoejun Jeong, Seungha Kim, Donghyun Seo, Jangwoo Kwon

“…To address this, we propose a robust fault diagnosis framework incorporating three key components: (1) an order-frequency-based preprocessing method to normalize rotational variations, (2) a U-Net variational autoencoder (U-NetVAE) to enhance adaptation through reconstruction learning, and (3) a test-time training (TTT) strategy enabling unsupervised target domain adaptation without access to source data. …”
Get full text

Evaluation of Different Generative Models to Support the Validation of Advanced Driver Assistance Systems by Manasa Mariam Mammen, Zafer Kayatas, Dieter Bestle

“…Four different approaches are trained and compared: Variational Autoencoder enhanced with a convolutional neural network (VAE), a basic Generative Adversarial Network (GAN), Wasserstein GAN (WGAN), and Time-Series GAN (TimeGAN). …”
Get full text

BlindFG: Learning Contextual Fishing Trajectories for Unreported Fishing Gear Classification by Changha Lee, Chan-Hyun Youn

Get full text

Fault Diagnosis for Imbalanced Datasets Based on Deep Convolution Fuzzy System by Junwei Zhu, Linfang Zhu

“…To address the data imbalance issue in the process of collecting bearing fault data in industrial environments and to enhance the robustness and generalization ability of fault diagnosis, this paper proposes a bearing fault diagnosis method based on a Bidirectional Autoregressive Variational Autoencoder (BAVAE) and a Deep Convolutional Interval Type-2 Fuzzy System (DCIT2FS). …”
Get full text

A systematic review of deep learning chemical language models in recent era by Hector Flores-Hernandez, Emmanuel Martinez-Ledesma

“…Transformers, recurrent neural networks (RNNs), generative adversarial networks (GANs), Structured Space State Sequence (S4) models, and variational autoencoders (VAEs) are considered the main deep learning architectures used for molecule generation in the set of retrieved articles. …”
Get full text

A State-Supervised Model and Novel Anomaly Index for Gas Turbines Blade Fault Detection Under Multi-Operating Conditions by Yuan Xiao, Kun Feng, Dongyan Miao, Peng Zhang, Jiaxin Yang

“…First, a State-Supervised Variational Autoencoder (SS-VAE) model is introduced, which integrates the learning process of turbine operational states into the VAE bypass, enabling it to capture variations in vibration signal data across different operating conditions. …”
Get full text