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27th International Workshop on Smart Antennas (WSA)

作者： Baur, Michael Boeck, Benedikt Turan, Nurettin Utschick, Wolfgang Tech Univ Munich TUM Sch Computat Informat & Technol Munich Germany

ISBN: (纸本)9798350362008;9798350361995

This work utilizes a variational autoencoder for channel estimation and evaluates it on real-world measurements. The estimator is trained solely on noisy channel observations and parameterizes an approximation to the mean squared error-optimal estimator by learning observation-dependent conditional first and second moments. The proposed estimator significantly outperforms related state-of-the-art estimators on real-world measurements. We investigate the effect of pre-training with synthetic data and find that the proposed estimator exhibits comparable results to the related estimators if trained on synthetic data and evaluated on the measurement data. Furthermore, pre-training on synthetic data also helps to reduce the required measurement training dataset size.

关键词： Channel estimation measurement data deep neural network generative model variational autoencoder

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variational autoencoder for Anomaly Detection in Event Data in Online Process Mining 23

Variational Autoencoder for Anomaly Detection in Event Data ...

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23rd International Conference on Enterprise Information Systems (ICEIS)

作者： Krajsic, Philippe Franczyk, Bogdan Univ Leipzig Business Informat Syst Inst Grimmaische Str 12 Leipzig Germany Wroclaw Univ Econ Business Informat Syst Inst Ul Komandorska 118-120 Wroclaw Poland

ISBN: (纸本)9789897585098

The analysis of event data recorded by information systems is becoming increasingly relevant. An increasing data-centric analysis of processes by using process mining techniques has a direct impact on the management of business processes. To achieve a positive impact on business process management, a high quality data basis is important. This paper presents an approach for the application of variational autoencoder for the filtering of anomalous event data in an online process mining environment, which help to improve the results of process mining techniques and thus positively influence business process management. For anomaly detection in an unsupervised environment, mass-volume and excess-mass scores are used as metrics. The results are compared on the basis of established algorithms such as one-class support vector machine, isolation forest and local outlier factor. These insights are used to highlight the benefits of this approach for process mining and business process management.

关键词： Anomaly Detection Business Process Management Deep Generative Model Process Mining variational autoencoder

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variational autoencoder Inverse Mapper: An End-to-End Deep Learning Framework for Inverse Problems

Variational Autoencoder Inverse Mapper: An End-to-End Deep L...

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International Joint Conference on Neural Networks (IJCNN)

作者： Almaeen, Manal Alanazi, Yasir Sato, Nobuo Melnitchouk, W. Kuchera, Michelle P. Li, Yaohang Old Dominion Univ Dept Comp Sci Norfolk VA 23529 USA Jefferson Lab Theory Ctr Newport News VA 23606 USA Davidson Coll Dept Phys Davidson NC 28035 USA

ISBN: (纸本)9780738133669

Inverse problems - using measured observations to determine unknown parameters - are well motivated but challenging in many science and engineering problems. In this paper, we propose an end-to-end deep learning framework, the variational autoencoder Inverse Mapper (VAIM), as an autoencoder-based neural network architecture for inverse problems. The encoder and decoder neural networks approximate the forward and backward mapping, respectively, and a variational latent layer is incorporated into VAIM to learn the posterior parameter distributions with respect to given observables. We demonstrate the effectiveness of VAIM for several toy inverse problems, with both finite and infinite solutions, and for constructing the inverse function mapping quantum correlation functions to observables in a Quantum Chromodynamics analysis of nucleon structure.

关键词： inverse problems variational autoencoder end-to-end learning latent space analysis ill-posed

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variational autoencoder for Early Stress Detection in Smart Agriculture: A Pilot Study

Variational Autoencoder for Early Stress Detection in Smart ...

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IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

作者： Zancanaro, Alberto Cisotto, Giulia Tegegn, Dagmawi Delelegn Manzoni, Sara L. Reguzzoni, Ivan Lotti, Edoardo Zoppis, Italo Univ Padua Dept Informat Engn DEI Padua Italy Univ Milano Bicocca Dept Informat Syst & Commun DISCo Milan Italy SeleTech Engn Srl Milan Italy

ISBN: (数字)9781665469982

ISBN: (纸本)9781665469982

The digitalization of the agrifood market is increasingly demanding for new technologies to support its transition towards smart agriculture, a sustainable food industry, and efficient management of greenhouses and crop breeding. In this work, we aim to exploit two emerging and promising technologies with application to the early detection of stressful conditions in plants. Two high-resolution near-infrared spectrometers, spanning the range from 1350 nm to 2150 nm, were used to acquire the reflectance spectra from a pothos (Epipremnum aureum) in two different hydration conditions, i.e., normal and anomalous. Then, we trained a machine learning model, i.e., a beta-variational autoencoder (beta-VAE), to identify the anomalies in the hydration of the plant over three months of acquisition. We are able to show the feasibility of our proposed combination of near-infrared spectrometry and the beta-VAE to accurately identify anomalies, i.e., to detect stressful conditions in plants. This contributes to the recent and promising advancements in smart agriculture, by exploiting a new generation of high-resolution, portable, and non-destructive near-infrared sensing technology and powerful machine learning data analytics.

关键词： variational autoencoder deep learning anomaly detection near-infrared spectrometry smart agriculture

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variational autoencoder Assisted Neural Network Likelihood RSRP Prediction Model 33

Variational Autoencoder Assisted Neural Network Likelihood R...

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33rd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Li, Peizheng Wang, Xiaoyang Piechocki, Robert Kapoor, Shipra Doufexi, Angela Parekh, Arjun Univ Bristol Bristol Avon England BT Appl Res Bristol Avon England

ISBN: (数字)9781665480536

ISBN: (纸本)9781665480536

Measuring customer experience on mobile data is of utmost importance for global mobile operators. The reference signal received power (RSRP) is one of the important indicators for current mobile network management, evaluation and monitoring. Radio data gathered through the minimization of drive test (MDT), a 3GPP standard technique, is commonly used for radio network analysis. Collecting MDT data in different geographical areas is inefficient and constrained by the terrain conditions and user presence, hence is not an adequate technique for dynamic radio environments. In this paper, we study a generative model for RSRP prediction, exploiting MDT data and a digital twin (DT), and propose a data-driven, two-tier neural network (NN) model. In the first tier, environmental information related to user equipment (DE), base stations (BS) and network key performance indicators (KPI) are extracted through a variational autoencoder (VAE). The second tier is designed as a likelihood model. Here, the environmental features and real MDT data features are adopted, formulating an integrated training process. On validation, our proposed model that uses real- world data demonstrates an accuracy improvement of about 20% or more compared with the empirical model and about 10% when compared with a fully connected prediction network.

关键词： RSRP variational autoencoder likelihood model neural network

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variational autoencoder based Latent Factor Decoding of Multichannel EEG for Emotion Recognition

Variational Autoencoder based Latent Factor Decoding of Mult...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Li, Xiang Zhao, Zhigang Song, Dawei Zhang, Yazhou Niu, Chunyang Zhang, Junwei Huo, Jidong Li, Jing Qilu Univ Technol Shandong Acad Sci Natl Supercomp Ctr Jinan Key Lab Med Artificial IntelligenceShandong Comp Jinan Peoples R China Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Zhengzhou Univ Light Ind Softwarc Engn Coll Zhengzhou Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin Peoples R China Jiuquan Satellite Launch Ctr Alxa League Peoples R China

ISBN: (纸本)9781728118673

Robust cross-subject emotion recognition based on multichannel EEG has always been a hard work. In this work, we hypothesize there exists default brain variables across subjects in emotional processes. Hence, the states of the latent variables that related to emotional processing must contribute to building robust recognition models. We propose to utilize variational autoencoder (VAE) to determine the latent factors from the multi-channel EEG. Through sequence modeling method, we examine the emotion recognition performance based on the learnt latent factors. The performance of the proposed methodology is verified on two public datasets (DEAP and SEED), and compared with traditional matrix factorization based (ICA) and autoencoder based (AE) approaches. Experimental results demonstrate that neural network is suitable for unsupervised EEG modeling and our proposed emotion recognition framework achieves the state-of-the-art performance. As far as we know, it is the first work that introduces VAE into multichannel EEG decoding for emotion recognition.

关键词： Affective Computing Latent Factor Decoding Emotion Recognition EEG variational autoencoder

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Multi-rate Dynamic variational autoencoder for Fault Detection in Nonlinear Industrial Processes

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Ying, Ze Chang, Yuqing Yang, Jinsha Wang, Fuli He, Yuchen Northeastern University College of Information Science and Engineering The State Key Laboratory of Synthetical Automation for Process Industries Shenyang 110819 China Education and Sports Bureau of Zhuji City Zhejiang Province China China Jiliang University College of Mechanical and Electrical Engineering Hangzhou 310018 China

The variational autoencoder (VAE) has proven highly effective in monitoring nonlinear stochastic processes, primarily under the assumption of complete and temporally independent data. However, real-world industrial data often exhibit additional complexities, such as multiple sampling rates and dynamic behavior, which complicate the development of efficient latent variable models for nonlinear systems. To address these challenges, this study proposes a novel multi-rate dynamic variational autoencoder (MDVAE) model, specifically designed for dynamic industrial fault detection with incomplete dataset. In MDVAE, both the encoder and decoder are adapted to a multi-rate structure, enabling the latent variables to capture nonlinear correlations across varying sampling rates. Additionally, a first-order Markov chain is applied to the latent variables to represent the dynamic behavior of multi-rate nonlinear systems. The effectiveness of MDVAE is demonstrated using the Tennessee Eastman and coal-fired power generation processes. Experimental results show that the proposed model outperforms comparable approaches in addressing the coexisting challenges of multi-rate sampling and nonlinear dynamics. © 2025 IEEE.

关键词： Fault detection Latent variable model Multiple sampling rates Nonlinear dynamic system variational autoencoder

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variational autoencoder Based Fault Detection and Location Method for Power Distribution Network 8

Variational Autoencoder Based Fault Detection and Location M...

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8th International Conference on Condition Monitoring and Diagnosis (CMD)

作者： Wang, Xuhong Cui, Ping Du, Ying Yang, Yupu Shanghai Jiao Tong Univ Dept Automat Minist Educ Key Lab Syst Control & Informat Proc Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn State Energy Smart Grid R&D Ctr Shanghai Peoples R China

ISBN: (纸本)9781728159317

With the rapid development of the world economy and the continuous improvement of people's living standards, users put forward higher requirements for the quality and reliability of the power system. As a representative of deep learning, variational autoencoders play an important role in processing high-dimensional big data. In this paper, a novel variational autoencoder based distribution network abnormal monitoring and positioning method is proposed. Through an in-depth study of the variational autoencoder, we utilize the real-time dynamic distribution network status information provided by the wide-area measurement system, combined with the abilities of feature extracting and data reconstruction. Finally, the IEEE-33 simulation model was built in Matlab software and the experimental results were used to verify the correctness of this method. The results show that this method can quickly and accurately achieve the fault detection and location for the distribution network with strong robustness.

关键词： Anomaly detetion Distribution systems Fault location variational autoencoder

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variational autoencoder Leveraged MMSE Channel Estimation 56

Variational Autoencoder Leveraged MMSE Channel Estimation

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56th Asilomar Conference on Signals, Systems, and Computers

作者： Baur, Michael Fesl, Benedikt Koller, Michael Utschick, Wolfgang Tech Univ Munich Sch Computat Informat & Technol Munich Germany

ISBN: (纸本)9781665459068

We propose to utilize a variational autoencoder (VAE) for data-driven channel estimation. The underlying true and unknown channel distribution is modeled by the VAE as a conditional Gaussian distribution in a novel way, parameterized by the respective first and second order conditional moments. As a result, it can be observed that the linear minimum mean square error (LMMSE) estimator in its variant conditioned on the latent sample of the VAE approximates an optimal MSE estimator. Furthermore, we argue how a VAE-based channel estimator can approximate the MMSE channel estimator. We propose three variants of VAE estimators that differ in the data used during training and estimation. First, we show that given perfectly known channel state information at the input of the VAE during estimation, which is impractical, we obtain an estimator that can serve as a benchmark result for an estimation scenario. We then propose practically feasible approaches, where perfectly known channel state information is only necessary in the training phase or is not needed at all. Simulation results on 3GPP and QuaDRiGa channel data attest a small performance loss of the practical approaches and the superiority of our VAE approaches in comparison to other related channel estimation methods.

关键词： Channel estimation deep learning variational autoencoder MMSE estimator machine learning

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variational autoencoder Inspired by Brain's Convergence-Divergence Zones for Autonomous Driving Application 20th

Variational Autoencoder Inspired by Brain's Convergence-Dive...

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20th International Conference on Image Analysis and Processing (ICIAP)

作者： Plebe, Alice Da Lio, Mauro Univ Trento Dept Informat Engn & Comp Sci Trento Italy Univ Trento Dept Ind Engn Trento Italy

ISBN: (纸本)9783030306427;9783030306410

In the last decades, the research in autonomous vehicles has greatly improved thanks to the success of artificial neural models. Yet, self-driving cars are far from reaching human performances. It is our opinion that would be wise to reflect on why the human brain is so effective in learning tasks as complex as the one of driving, and to try to take inspiration for designing new artificial driving agents. For this aim, we consider two relevant and related neurocognitive theories: the Convergence-divergence Zones (CDZs) mechanism of mental simulation, and the predicting brain theory. Then, we propose an implementation of a semi-supervised variational autoencoder for visual perception, with an architecture that best approximates those two neurocognitive theories.

关键词： Mental imagery Deep learning Autonomous driving variational autoencoder Free energy

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