Artificial Intelligence for Fault Detection of Automotive Electric Motors by Federico Soresini, Dario Barri, Ivan Cazzaniga, Federico Maria Ballo, Gianpiero Mastinu, Massimiliano Gobbi

“…Based on a review of Neural Networks, including Variational Autoencoders (VAEs), Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the performance of six AI architectures is compared: AE, VAE, 1D CNN AE, 1D CNN VAE, LSTM AE and LSTM VAE. …”
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Identification of multiple power quality disturbances in hybrid microgrid using deep stacked auto-encoder based bi-directional LSTM classifier by Ravi Kumar Jalli, Lipsa Priyadarshini, P.K. Dash, Ranjeeta Bisoi

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From Spectrum to Image: A Novel Deep Clustering Network for Lactose-Free Milk Adulteration Detection by Chong Zhang, Shankui Ding, Ying He

“…Experimental results indicate that our method is superior to K-means, Variational Autoencoder (VAE) clustering, and other approaches. …”
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Latent spectral-spatial diffusion model for single hyperspectral super-resolution by Yingsong Cheng, Yong Ma, Fan Fan, Jiayi Ma, Yuan Yao, Xiaoguang Mei

“…Subsequently, the diffusion model undergoes efficient optimization through a variant of the variational bound on the data likelihood. During the reverse transformation, LSDiff systematically converts Gaussian noise into SR images, conditioned on the low-resolution input. …”
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GLLR-HAD: Global-local low-rank integration for hyperspectral image anomaly detection by Yu Bai, Yanling Zhang, Lili Zhang, Tan Zhao

“…Specifically, global low-rank decomposition captures structural anomalies by decomposing the image into a basis matrix and a coefficient matrix, effectively enhancing the recognition of large-scale background variations. Complementarily, local low-rank modeling introduces an adaptive step-size partitioning strategy and a Local Feature Enhancement and Multi-Scale Low-Rank Fusion Module (LFEMLS) to flexibly extract regional details, to perfect the modeling of local variations, and to adapt to localized spectral variability. …”
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FOCC: A Synthetically Balanced Federated One-Class-Classification for Cyber Threat Intelligence in Software Defined Networking by Syed Hussain Ali Kazmi, Faizan Qamar, Rosilah Hassan, Kashif Nisar

“…Therefore, this study proposes a novel framework called Federated One Class Classification (FOCC), which contains parallel inference with threat-specific independent autoencoders as local model at each domain and empowered with Variational Auto Encoders (VAEs). …”
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Latent Space Representation of Human Movement: Assessing the Effects of Fatigue by Thomas Rousseau, Gentiane Venture, Vincent Hernandez

“…The latent space representation provides insights into motor control variations, revealing patterns that can be used to monitor fatigue levels and optimize training or rehabilitation programs.…”
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Real-Time Acoustic Holography for Target Pattern Acoustic Field Reconstruction With Iterative Unsupervised Learning by Minjie Ma, Xuewei Wang, Yang Li, Jia Wang

“…A unit transducer is integrated with an acoustic lens, utilizing lens thickness and acoustic velocity variations to produce accurate phase maps at specific frequencies. …”
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Input-output optics as a causal time series mapping: A generative machine learning solution by Abhijit Sen, Bikram Keshari Parida, Kurt Jacobs, Denys I. Bondar

“…We further find that a generative model, in particular a variational autoencoder, significantly outperforms traditional autoencoders at learning the complex response of many-body quantum systems. …”
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MESM: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models by Feng Wang, Jinming Chu, Liyan Shen, Shan Chang

“…MESM consists of three key modules, as follows: First, MESM extracts multimodal representations from protein sequence information, protein structure information, and point cloud features through Sequence Variational Autoencoder (SVAE), Variational Graph Autoencoder (VGAE), and PointNet Autoencoder (PAE). …”
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Visual Episodic Memory-based Exploration by Jack Vice, Natalie Ruiz-Sanchez, Pamela K Douglas, Gita Sukthankar

“…When guiding robotic exploration, our proposed method outperforms the Curiosity-driven Variational Autoencoder (CVAE) at finding dynamic anomalies.…”
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TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks by Mohammed Baz

“…From an architectural point of view, TVGeAN builds on the autoencoder approach complemented by sparse and variational learning units. …”
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Large-Scale Completion of Ionospheric TEC Maps Using Machine Learning Models With Constraints Conditions by Qingfeng Li, Hanxian Fang, Chao Xiao, Die Duan, Hongtao Huang, Ganming Ren

“…This study proposes a novel hybrid model combining a variational autoencoder (VAE) and a generative adversarial network (GAN) to improve the quality and diversity of generated TEC samples. …”
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Quantifying Uncertainties in the Quiet‐Time Ionosphere‐Thermosphere Using WAM‐IPE by Weijia Zhan, Alireza Doostan, Eric Sutton, Tzu‐Wei Fang

“…Ensemble simulations of the coupled Whole Atmosphere Model with Ionosphere Plasmasphere Electrodynamics (WAM‐IPE) driven by synthetic solar wind drivers generated through a multi‐channel variational autoencoder (MCVAE) model are obtained. …”
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Importance-Aware Resource Allocations for MIMO Semantic Communication by Yue Cao, Youlong Wu, Lixiang Lian, Meixia Tao

“…Traditional systems suffer from bit-level redundancy in 6G, while JSCC struggles with complex channel variations. Our solution decouples semantic processing from channel coding through a three-tier architecture: (1) Variational autoencoder (VAE)-based semantic encoder and decoder for source coding, (2) A communication-informed bottleneck attribution (CIBA) mechanism quantifying feature importance for learning tasks, and (3) An importance-aware resource allocation scheme aligning communication objectives with deep learning tasks. …”
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Out-of-distribution reject option method for dataset shift problem in early disease onset prediction by Taisei Tosaki, Eiichiro Uchino, Ryosuke Kojima, Yohei Mineharu, Yuji Okamoto, Mikio Arita, Nobuyuki Miyai, Yoshinori Tamada, Tatsuya Mikami, Koichi Murashita, Shigeyuki Nakaji, Yasushi Okuno

“…In the five OOD detection approaches (the variational autoencoder, neural network ensemble std, neural network ensemble epistemic, neural network energy, and neural network Gaussian mixture based energy measurement), the variational autoencoder method demonstrated notably higher stability and a greater improvement in AUROC. …”
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Analysis method combining improved AE algorithm and signal reconstruction in mechanical faults by Zhenhua Niu

“…Traditional diagnostic methods have shortcomings in accuracy and robustness.MethodsTherefore, the study integrates variational autoencoders with long short-term memory network models, enhances them using dropout methods, and proposes a hybrid diagnostic analysis model that combines improved autoencoder algorithms and signal reconstruction.ResultsThe experiment outcomes indicated that under the slow degradation mode of the bearing, the precision, recall, F1 score, and overall accuracy of the improved autoencoder model were 0.931, 0.933, 0.920, and 0.939, respectively, which were better than the pre-modified model. …”
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Anomaly Detection Based on Machine Learning for the CMS Electromagnetic Calorimeter Online Data Quality Monitoring by Harilal Abhirami, Park Kyungmin, Paulini Manfred

“…We introduce a novel method that maximizes the anomaly detection performance making use of the time-dependence of anomalies and the spatial variations in the detector response. The autoencoder-based system efficiently detects anomalies in real time and maintains a very low false discovery rate. …”
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Combining diffusion and transformer models for enhanced promoter synthesis and strength prediction in deep learning by Xin Lei, Xing Wang, Guanlin Chen, Ce Liang, Quhuan Li, Huaiguang Jiang, Wei Xiong

“…The experimental findings suggest that the synthetic promoters by the diffusion model not only share key biological features with their natural counterparts but also demonstrate greater similarity to natural promoters than those generated by a variational autoencoder. In predicting promoter strength, the transformer model demonstrated improved performance over the convolutional neural network. …”
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scPDA: denoising protein expression in droplet-based single-cell data by Ouyang Zhu, Jun Li

“…Here, we introduce scPDA, a probabilistic model that employs a variational autoencoder to achieve high computational efficiency. scPDA eliminates the use of empty droplets and shares information across proteins to increase denoising efficiency. …”
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