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Generative deep learning for probabilistic streamflow forecasting: conditional variational auto-encoder

JOURNAL OF HYDROLOGY 2024年 629卷

作者： Jahangir, Mohammad Sina Quilty, John Univ Waterloo Dept Civil & Environm Engn Waterloo ON Canada

Probabilistic hydrological forecasting has gained increasing importance in recent years, as it offers essential information for risk-based decision-making and flood management. Traditional hydrological models often produce deterministic forecasts, which do not account for the inherent uncertainties in hydrological systems. Although many previous studies have investigated using deep learning (DL) models for hydrological prediction, the development of probabilistic DL models (especially, generative models) has not yet been thoroughly examined for hydrological forecasting. The present study investigates the efficacy of a generative DL model, namely, conditional variational auto-encoder (CVAE). CVAE is applied for one-seven day(s) ahead probabilistic streamflow forecasting in 75 basins from the Canadian model parameter experiment (CANOPEX) database. The CVAE forecast model, which outputs forecasts in the form of a probability distribution, was benchmarked against two state-of-the-art quantile-based DL models: quantile-based encoder-decoder (ED) and quantile-based CVAE (QCVAE). The latter outputs forecasts for specific quantiles of a probability distribution (here, q = 0.05, 0.5, 0.95). More than 9000 models were developed based on different basins, input variable sets, and model structures. The models were evaluated in terms of point forecast accuracy and forecast reliability. The results indicate that the CVAE model generally outperforms the benchmark models in terms of reliability at a 90 % confidence level (median reliability of 92.49 % compared to 87.35 % and 84.59 % for ED and QCVAE, respectively). However, the quantile-based forecast models produce slightly more accurate point forecasts than the CVAE (median Kling-Gupta efficiency (KGE) of 0.88 compared to 0.90 for both ED and QCVAE). Notably, the CVAE model exhibits superior probabilistic forecasts in basins with poor point forecast accuracy, highlighting its usefulness over benchmark methods in difficult-to-fore

关键词： conditional variational auto-encoder encoder-Decoder Deep Learning Streamflow Forecasting LSTM

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Anomaly Score for Multiple Operating Modes of Rotating Machines by Using conditional variational auto-encoder 19

Anomaly Score for Multiple Operating Modes of Rotating Machi...

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19th IMEKO TC10 Conference on MACRO meets NANO in Measurement for Diagnostics, Optimization and Control

作者： Hiranaka, Yukio Tsujino, Koichi Katsumura, Hidenori Nakagawa, Masashi AiSpirits Inc. Tokyo Japan Hiranaka Technology Research Susono Japan Device & System Platform Development Center Co. Ltd. Kawasaki Japan

ISBN: (纸本)9781713884125

Human Experts may diagnose the state of a rotating machine by listening to its vibration sounds. variational auto-encoder (VAE) is an alternative method to realize the diagnosis by AI learning. Previous studies have shown that when the normal state can be assumed to be a single Gaussian distribution, anomaly scores can be calculated as deviations from the center of the distribution in the VAE latent space. However, when the normal state consists of different modes corresponding to operating conditions, the calculation may not be a simple task. As a way to solve this problem, the use of conditional variational auto-encoder (CVAE) which performs VAE learning including operating conditions, seems promising. In this study, we show verification results that the anomaly score based on the normalized Euclidian distance in the CVAE latent space can detect anomalous conditions using synthesized data and real acceleration measurement data with rotation speed changes. © 2023 19th IMEKO TC10 Conference "MACRO meets NANO in Measurement for Diagnostics, Optimization and Control".

关键词： Bearing Anomaly Detection Condition Based Maintenance conditional variational auto-encoder Industry Innovation Infrastructure

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Computational detection and interpretation of heart disease based on conditional variational auto-encoder and stacked ensemble-learning framework

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 88卷

作者： Abdellatif, Abdallah Mubarak, Hamza Abdellatef, Hamdan Kanesan, Jeevan Abdelltif, Yahya Chow, Chee-Onn Chuah, Joon Huang Gheni, Hassan Muwafaq Kendall, Graham Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia Griffith Univ Sch Engn & Built Environm Southport Qld 4222 Australia Lebanese Amer Univ Sch Engn Elect & Comp Engn Dept Byblos Lebanon Beirut Arab Univ Fac Med Riad El Solh 11-5020 Beirut 11072809 Lebanon Al Mustaqbal Univ Coll Comp Tech Engn Dept Hillah 51001 Iraq Univ Nottingham Sch Comp Sci Malaysia Campus Semenyih 43500 Selangor Malaysia Univ Nottingham Sch Comp Sci Nottingham NG7 2RD England

Worldwide, cardiovascular disease is the leading cause of death. Based on clinical data, a Machine Learning (ML) system can detect cardiac disease in its early stages, which enables a reduction in mortality rates. However, imbalanced and high dimensionality data have been a persistent challenge in ML, impeding accurate predictive data analysis in many real-world applications, such as the detection of cardiovascular disease. To address this, computational methods targeting heart disease detection have been developed. However, their performance is still inadequate. Hence, this study presents a new stack predictor for the heart disease model (termed SPFHD). SPFHD employs five common tree-based ensemble learning algorithms as base models for heart disease detection. In addition, the predictions from the base models are integrated using a support vector machine algorithm to enhance the accuracy of heart disease detection. A new conditional variational autoencoder (CVAE) based method is developed to overcome the imbalance issue, which performs better than the conventional balancing methods. Finally, the SPFHD model is tuned by Bayesian optimization. The results show that the proposed SPFHD model outperforms the state-of-art methods over four datasets achieving higher f1-score of 4.68 %, 4.55 %, 2 %, and 1 % for HD clinical, Z-Alizadeh Sani, Statlog, and Cleveland, respectively. Moreover, this new framework offers vital interpretations which assist in understanding model success by leveraging the powerful SHapley Additive explanation (SHAP) algorithm. This highlights the most significant attributes for detecting heart disease and overcoming the limitations of current 'Black-box' methods that cannot reveal causal relationships between features.

关键词： Heart disease conditional variational auto-encoder Stacking ensemble learning SHAP Tree ensemble Hyperparameter optimization

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DECVAE: Data augmentation via conditional variational auto-encoder with distribution enhancement for few-shot fault diagnosis of mechanical system

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第4期35卷 046104-046104页

作者： Liu, Yikun Fu, Song Lin, Lin Zhang, Sihao Suo, Shiwei Xi, Jianjun Harbin Inst Technol Sch Mechatron Engn Harbin Peoples R China

conditional variational autoencoder (CVAE) has the potential for few-sample fault diagnosis of mechanical systems. Nevertheless, the scarcity of faulty samples leads the augmented samples generated using CVAE suffer from limited diversity. To address the issue, a novel CVAE variant namely CVAE with distribution augmentation (DECVAE) is developed, to generate a set of high-quality augmented samples that are different but share very similar characteristics and categories with the corresponding real samples. First, DECVAE add a new sample distribution distance loss into the optimization objective of traditional CVAE. Amplifying this loss in training process can make the augmented samples cover a larger space, thereby improving diversity. Second, DECVAE introduces an auxiliary classifier into traditional CVAE to enhance the sensitivity to category information, keeping the augmented samples class invariance. Furthermore, to ensure that the information of edge-distributed samples can be fully learned and make augmented samples representative and authentic, a novel multi-model independent fine-tuning strategy is designed to train the DECVAE, which utilizes multiple independent models to fairly focus on all samples of the minority class during DECVAE training. Finally, the effectiveness of the developed DECVAE in few-shot fault diagnosis of mechanical systems is verified on a series of comparative experiments.

关键词： data augmentation conditional variational auto-encoder deep learning few-shot fault diagnosis

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Diversity-Promoting Human Motion Interpolation via conditional variational auto-encoder

Diversity-Promoting Human Motion Interpolation via Condition...

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International Workshop on Advanced Imaging Technology (IWAIT)

作者： Gu, Chunzhi Zhao, Shuofeng Zhang, Chao Univ Fukui Fukui Japan Wenzhou Med Univ Wenzhou Peoples R China

ISBN: (数字)9781510653320

ISBN: (纸本)9781510653320;9781510653313

In this paper, we present a deep generative model based method to generate diverse human motion interpolation results. We resort to the conditional variational auto-encoder (CVAE) to learn human motion conditioned on a pair of given start and end motions, by leveraging the Recurrent Neural Network (RNN) structure for both the encoder and the decoder. Additionally, we introduce a regularization loss to further promote sample diversity. Once trained, our method is able to generate multiple plausible coherent motions by repetitively sampling from the learned latent space. Experiments on the publicly available dataset demonstrate the effectiveness of our method, in terms of sample plausibility and diversity.

关键词： Diverse human motion interpolation conditional variational auto-encoder

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Generating emotional response by conditional variational auto-encoder in open-domain dialogue system

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NEUROCOMPUTING 2021年 460卷 106-116页

作者： Liu, Mengjuan Bao, Xiaoming Liu, Jiang Zhao, Pei Shen, Yuchen Univ Elect Sci & Technol China Network & Data Secur Key Lab Sichuan Prov Chengdu 610054 Peoples R China

An important goal in open-domain dialogue research is to make chatbot generate emotional responses given a context. To achieve this, some researchers have attempted to introduce affective information into neural dialogue models. However, these neural dialogue models containing affective information still suf-fer from the problem of generating safe but meaningless responses, such as I don't know, which makes users lose interest in chatting quickly. Fortunately, the latest research has proven that conditional vari-ational auto-encoder (CVAE) can solve this problem and enhance the responses' diversity. In this paper, we combine affective knowledge into the CVAE-based model to generate diverse and affective responses. First, we use an affective lexicon to understand each word's emotion in the input sentences and feed the affective vector with its embedding vector together into the CVAE-based model. Next, we construct semantic and affective loss functions, enabling the model to simultaneously learn the response's seman-tic and affective distributions. Additionally, we formulate a ranking rule to help rank the candidate responses according to their syntax, semantics, and affection scores, thereby enhancing the emotion and relevance while retaining the response's diversity. Finally, we evaluate the proposed model on the DailyDialog dataset and Reddit dataset. The experimental results show that our model can generate more emotional, diverse, and context-relevant responses compared to the baselines. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Open-domain Dialogue Sequence-to-sequence conditional variational auto-encoder Affective lexicon

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Data Augmentation for Electricity Theft Detection Using conditional variational auto-encoder

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ENERGIES 2020年第17期13卷 4291-4291页

作者： Gong, Xuejiao Tang, Bo Zhu, Ruijin Liao, Wenlong Song, Like Tibet Agr & Anim Husb Univ Elect Engn Coll Nyingchi 860000 Peoples R China Tianjin Univ Key Lab Smart Grid Minist Educ Tianjin 300072 Peoples R China State Grid Jibei Elect Power Co Ltd Maintenance Branch Beijing 102488 Peoples R China

Due to the strong concealment of electricity theft and the limitation of inspection resources, the number of power theft samples mastered by the power department is insufficient, which limits the accuracy of power theft detection. Therefore, a data augmentation method for electricity theft detection based on the conditional variational auto-encoder (CVAE) is proposed. Firstly, the stealing power curves are mapped into low dimensional latent variables by using the encoder composed of convolutional layers, and the new stealing power curves are reconstructed by the decoder composed of deconvolutional layers. Then, five typical attack models are proposed, and the convolutional neural network is constructed as a classifier according to the data characteristics of stealing power curves. Finally, the effectiveness and adaptability of the proposed method is verified by a smart meters' data set from London. The simulation results show that the CVAE can take into account the shapes and distribution characteristics of samples at the same time, and the generated stealing power curves have the best effect on the performance improvement of the classifier than the traditional augmentation methods such as the random oversampling method, synthetic minority over-sampling technique, and conditional generative adversarial network. Moreover, it is suitable for different classifiers.

关键词： power theft detection data augmentation conditional variational auto-encoder convolutional neural network deep learning

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conditional variational auto-encoder for Text-Driven Expressive AudioVisual Speech Synthesis 20

Conditional Variational Auto-Encoder for Text-Driven Express...

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Interspeech Conference

作者： Dahmani, Sara Colotte, Vincent Girard, Valerian Ouni, Slim Uniyersite Lorraine CNRS Inria LORIA F-54000 Nancy France

In recent years, the performance of speech synthesis systems has been improved thanks to deep learning-based models, but generating expressive audiovisual speech is still an open issue. The variational auto-encoders (VAE)s are recently proposed to learn latent representations of data. In this paper, we present a system for expressive text-to-audiovisual speech synthesis that learns a latent embedding space of emotions using a conditional generative model based on the variational auto-encoder framework. When conditioned on textual input, the VAE is able to learn an embedded representation that captures emotion characteristics from the signal, while being invariant to the phonetic content of the utterances. We applied this method in an unsupervised manner to generate duration, acoustic and visual features of speech. This conditional variational auto-encoder (CVAE) has been used to blend emotions together. This model was able to generate nuances of a given emotion or to generate new emotions that do not exist in our database. We conducted three perceptive experiments to evaluate our findings.

关键词： Expressive audiovisual speech synthesis conditional variational auto-encoder Expressive talking avatar emotion facial expression deep bidirectional long short-term memory (DBLSTM)

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Application of conditional variational auto-encoder to Magnetic Circuit Design with Magnetic Field Computation 22

Application of Conditional Variational Auto-Encoder to Magne...

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22nd International Conference on the Computation of Electromagnetic Fields (COMPUMAG)

作者： Kawamata, Ryota Wakao, Shinji Murata, Noboru Waseda Univ Dept Elect Engn & Biosci Shinjuku Ku Tokyo 1698555 Japan

ISBN: (纸本)9781728155920

In the design optimization of electric machine, we ordinarily derive the objective physical quantities, e.g., the shape of the investigated model as design variables, by using numerical method such as the finite element method with the analysis conditions. In recent years, the representation learning using Deep Learning much attracts attention because it can acquire the features of data as a distributed representation and reproduce corresponding data. In this paper, utilizing machine learning technology, we propose an application of conditional variational auto-encoder (CVAE) to reproduce the more adequate shape of magnetic materials, i.e., design variables, corresponding to the intended magnetic energy, i.e., objective function values.

关键词： Design optimization representation learning conditional variational auto-encoder magnetic circuit

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Optical Flow Prediction in auto Driving from Single Image via conditional variational auto-encoder

Optical Flow Prediction in Auto Driving from Single Image vi...

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International Conference on Unmanned Systems and Artificial Intelligence

作者： Jianyu Yan Lu Xu Kuizhi Mei Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China

ISBN: (数字)9781728158594

ISBN: (纸本)9781728158600

In auto-driving tasks, visual prediction is very meaningful and difficult, because the motion of the predicted target has many possible outputs. It is very effective to predict the motion of the target by optical flow, but the output of the previous optical flow models is fixed. In this paper, we proposed an optical flow prediction model via conditional variational auto-encoder. In experiment, the model can effectively predict a variety of possible optical motions in the real world. A number of experimental results show that our models outperform all prior state-of-the-art on the test of a recent optical flow prediction competition.

关键词： conditional variational auto-encoder Optical Flow Prediction auto-Driving (key words)

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