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An Invariant Representation of Coupler Curves Using a variational autoencoder: Application to Path Synthesis of Four-Bar Mechanisms

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2024年第1期24卷 011008-011008页

作者： Nurizada, Anar Purwar, Anurag SUNY Stony Brook Dept Mech Engn Comp Aided Design & Innovat Lab Stony Brook NY 11794 USA

This paper focuses on the representation and synthesis of coupler curves of planar mechanisms using a deep neural network. While the path synthesis of planar mechanisms is not a new problem, the effective representation of coupler curves in the context of neural networks has not been fully explored. This study compares four commonly used features or representations of four-bar coupler curves: Fourier descriptors, wavelets, point coordinates, and images. The results demonstrate that these diverse representations can be unified using a generative AI framework called variational autoencoder (VAE). This study shows that a VAE can provide a standalone representation of a coupler curve, regardless of the input representation, and that the compact latent dimensions of the VAE can be used to describe coupler curves of four-bar linkages. Additionally, a new approach that utilizes a VAE in conjunction with a fully connected neural network to generate dimensional parameters of four-bar linkage mechanisms is proposed. This research presents a novel opportunity for the automated conceptual design of mechanisms for robots and machines.

关键词： planar four-bar linkage path synthesis Fourier descriptors wavelets variational autoencoder neural networks machine learning deep learning

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Stochastic-Expert variational autoencoder for Collaborative Filtering 22

Stochastic-Expert Variational Autoencoder for Collaborative ...

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31st ACM Web Conference (WWW)

作者： Cho, Yoon-Sik Oh, Min-hwan Chung Ang Univ Seoul South Korea Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781450390965

Motivated by the recent successes of deep generative models used for collaborative filtering, we propose a novel framework of VAE for collaborative filtering using multiple experts and stochastic expert selection, which allows the model to learn a richer and more complex latent representation of user preferences. In our method, individual experts are sampled stochastically at each user-item interaction which can effectively utilize the variability among multiple experts. While we propose this framework in the context of collaborative filtering, the proposed stochastic expert technique can be used to enhance VAEs in general beyond the application of collaborative filtering. Hence, this novel technique can be of independent interest. We comprehensively evaluate our proposed method, Stochastic-Expert variational autoencoder ( SE-VAE) on numerical experiments on the real-world benchmark datasets from MovieLens and Netflix and show that it consistently outperforms the existing state-of-the-art methods across all metrics. Our proposed stochastic expert framework is generic and adaptable to any VAE architecture. The experimental results show that the adaptations to various architectures provided performance gains over the existing methods.

关键词： Collaborative Filtering variational autoencoder Deep Generative Models Neural Networks Recommender Systems variational Inference

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ContrastVAE: Contrastive variational autoencoder for Sequential Recommendation 22

ContrastVAE: Contrastive Variational AutoEncoder for Sequent...

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31st ACM International Conference on Information and Knowledge Management (CIKM)

作者： Wang, Yu Zhang, Hengrui Liu, Zhiwei Yang, Liangwei Yu, Philip S. Univ Illinois Chicago IL 60607 USA Salesforce San Francisco CA USA

ISBN: (纸本)9781450392365

Aiming at exploiting the rich information in user behaviour sequences, sequential recommendation has been widely adopted in real-world recommender systems. However, current methods suffer from the following issues: 1) sparsity of user-item interactions, 2) uncertainty of sequential records, 3) long-tail items. In this paper, we propose to incorporate contrastive learning into the framework of variational autoencoders to address these challenges simultaneously. Firstly, we introduce ContrastELBO, a novel training objective that extends the conventional single-view ELBO to two-view case and theoretically builds a connection between VAE and contrastive learning from a two-view perspective. Then we propose Contrastive variational autoencoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation. We further introduce two simple yet effective augmentation strategies named model augmentation and variational augmentation to create a second view of a sequence and thus making contrastive learning possible. Experiments on four benchmark datasets demonstrate the effectiveness of ContrastVAE and the proposed augmentation methods. Codes are available at https://***/YuWang1024/ContrastVAE

关键词： Sequential Recommendation variational autoencoder Contrastive Learning

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Noise-robust Early Detection of Cooling Fan Deterioration with a variational autoencoder-based Method 9

Noise-robust Early Detection of Cooling Fan Deterioration wi...

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9th International Conference on Condition Monitoring and Diagnosis (CMD)

作者： Yanagihashi, Hiroyuki Sudo, Takashi Toshiba Co Ltd Corp Res & Dev Ctr Kawasaki Kanagawa Japan

ISBN: (纸本)9784886864314

Electrical equipment usually requires air cooling by fans, and it is important to detect early signs of fan deterioration. However, when using sensors to detect deterioration of fans in the real world, the sensor signal is often mixed with external noise. In this paper, we analyze the deterioration trends in the operating sounds of cooling fans actually used for 3 and 7 years in uninterruptible power supplies. Furthermore, we propose a deep learning-based method for noise-robust early detection of deterioration based on fan operating sounds called variational autoencoder-based deterioration estimation (VAE-DE). Existing anomaly detection methods based on autoencoders often react to external noise mixed into deterioration trend, which causes false positives. While another conventional method using variational autoencoders has the property of being robust against noise, it is not reactive to slight sound changes that accompany fan deterioration. If a deterioration trend is known, noise-robust early detection of deterioration can be achieved through learning using a loss function that includes a term for separation from normal sounds. Experiments investigating deterioration sounds in a fan showed changes in latent variables for early deterioration sounds and corresponding increases in the anomaly score. We also show that anomaly scores can be robustly estimated even in the presence of noise. The results reveal that the proposed method performs better than conventional methods.

关键词： deterioration estimation cooling fan anomaly detection neural network variational autoencoder

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Extensive framework based on novel convolutional and variational autoencoder based on maximization of mutual information for anomaly detection

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NEURAL COMPUTING & APPLICATIONS 2021年第20期33卷 13785-13807页

作者： Yu, Qien Kavitha, Muthusubash Kurita, Takio Hiroshima Univ Dept Informat Engn Higashihiroshima Hiroshima 7398521 Japan Hiroshima Univ Grad Sch Adv Sci & Engn Higashihiroshima Hiroshima 7398521 Japan Nagasaki Univ Sch Informat & DataSci Nagasaki 8528521 Japan

In present study, we proposed a general framework based on a convolutional kernel and a variational autoencoder (CVAE) for anomaly detection on both complex image and vector datasets. The main idea is to maximize mutual information (MMI) through regularizing key information as follows: (1) the features between original input and the representation of latent space, (2) that between the first convolutional layer output and the last convolutional layer input, (3) original input and output of the decoder to train the model. Therefore, the proposed CVAE is optimized by combining the representations learned across the three different objectives targeted at MMI on both local and global variables with the original training objective function of Kullback-Leibler divergence distributions. It allowed achieving the additional supervision power for the detection of image and vector data anomalies using convolutional and fully connected layers, respectively. Our proposal CVAE combined by regularizing multiple discriminator spaces to detect anomalies was introduced for the first time as far as we know. To evaluate the reliability of the proposed CVAE-MMI, it was compared with the convolutional autoencoder-based model using the original objective function. Furthermore, the performance of our network was compared over state-of-the-art approaches in distinguishing anomalies concerning both image and vector datasets. The proposed structure outperformed the state-of-the-arts with high and stable area under the curve values.

关键词： variational autoencoder Mutual information Anomaly detection Regularizer

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Semi-Supervised Recurrent variational autoencoder Approach for Visual Diagnosis of Atrial Fibrillation

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IEEE ACCESS 2021年 9卷 40227-40239页

作者： Costa, Nahuel Sanchez, Luciano Couso, Ines Univ Oviedo Comp Sci Dept Gijon 33202 Spain Univ Oviedo Stat Dept Gijon 33202 Spain

In this work we propose a semi-supervised framework to visually assess the progression of time series. To this end, we present a recurrent version of the VAE to exploit the generative properties that lead it to learn in an unsupervised way a continuous compressed representation of the data. We introduce a classifier in the VAE training process to control the regulation of the latent space, allowing the network to learn latent variables that set the basis for creating an explainable evaluation of the data. We use the proposed framework to address the diagnosis of Atrial Fibrillation (AF) first validating it with simulated data with known properties and subsequently testing it with intracardiac data obtained from pacemakers and defibrillator systems.

关键词： Time series analysis Diseases Heart beat Electrocardiography Data models Pacemakers Task analysis Graphical Analysis heart disease recurrent neural networks time series variational autoencoder

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variational autoencoder-enhanced deep neural network-based detection for MIMO systems

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2023年 6卷

作者： Omondi, Gevira Olwal, Thomas O. Department of Electrical Engineering/F'SATI Tshwane University of Technology Pretoria South Africa School of Engineering University of Nairobi Nairobi Kenya

Lately, there has been a substantial surge of interest in artificial intelligence (AI) as a promising technology to tremendously elevate the efficiency of multiple-input multiple-output (MIMO) detection within wireless communication networks. AI-aided methodologies like deep neural networks (DNNs) have empowered MIMO receivers to understand intricate channel dynamics and interference contexts, ultimately leading to noteworthy enhancements in throughput and the performance of error rates. However, the techniques in existing literature face challenges in effectively reconciling accuracy and efficiency across a spectrum of channel conditions. Therefore, this study presents a state-of-the-art DNN-centric architecture for MIMO signal detection called variational autoencoder-enhanced DNN-based detection (VAE-DNN-Det), which harnesses the power of variational autoencoders (VAEs) to efficiently capture underlying data distributions, thereby enhancing the DNN's ability to adapt to complex channel scenarios and achieve near-optimal performance. Simulations are carried out to contrast the bit error rate (BER) performance of the proposed scheme with traditional methods, where the proposed VAE-DNN-Det method attains a signal-to-noise ratio gain of nearly 1 dB compared to the traditional detection methods. © 2023 The Author(s)

关键词： Artificial intelligence Deep learning Deep neural network Emerging wireless communication MIMO detection variational autoencoder

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Fault diagnosis of power equipment based on variational autoencoder and semi-supervised learning

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第20期36卷 e8204-e8204页

作者： Ye, Bo Li, Feng Zhang, Linghao Chang, Zhengwei Wang, Bin Zhang, Xiaoyu Bodanbai, Sayina State Grid Xinjiang Elect Power Res Inst Urumqi Peoples R China State Grid Sichuan Elect Power Co Elect Power Sci Res Inst Chengdu Peoples R China State Grid Sichuan Elect Power Co Chengdu Peoples R China

The issue of fault diagnosis in power equipment is receiving increasing attention from scholars. Due to the important role played by bearings in power equipment, bearing faults have become the main factor causing the shutdown of wind turbines units. Therefore, this paper takes bearing equipment as an example for research. In order to solve the problem of insufficient and unbalanced fault sample data of wind turbines bearings, a fault diagnosis (FD) method based on variational autoencoder and semi-supervised learning is proposed in this paper. Firstly, based on Label Propagation-random forests (LP-RFs) and a small number of labeled fault samples, a semi-supervised learning algorithm is proposed to label the original data samples. Secondly, a small number of training samples are preprocessed by the variational autoencoder to reduce the imbalance of the fault samples. Then, the RFs-based method is adopted to train the processed fault samples to obtain a mature FD classifier. Finally, the proposed method is applied to FD for bearings, and the results show that the proposed method can realize bearings fault diagnosis (BFD). And meanwhile, the proposed method can also be applied for fault diagnosis in power transmission and transformation systems.

关键词： bearings fault fault diagnosis semi-supervised learning variational autoencoder

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TransVAE-DTA: Transformer and variational autoencoder network for drug-target binding affinity prediction

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 244卷 108003-108003页

作者： Zhou, Changjian Li, Zhongzheng Song, Jia Xiang, Wensheng Northeast Agr Univ Sch Life Sci Harbin Peoples R China Northeast Agr Univ Dept Data & Comp Harbin Peoples R China Northeast Agr Univ Sch Engn Harbin Peoples R China Northeast Agr Univ Sch Plant Protect Harbin Peoples R China Chinese Acad Agr Sci State Key Lab Biol Plant Dis & Insect Pests Inst Plant Protect Beijing Peoples R China

Background and objective: Recent studies have emphasized the significance of computational in silico drug-target binding affinity (DTA) prediction in the field of drug discovery and drug repurposing. However, existing DTA prediction approaches suffer from two major deficiencies that impede their progress. Firstly, while most methods primarily focus on the feature representations of drug-target binding affinity pairs, they fail to consider the longdistance relationships of proteins. Furthermore, many deep learning-based DTA predictors simply model the interaction of drug-target pairs through concatenation, which hampers the ability to enhance prediction performance. Methods: To address these issues, this study proposes a novel framework named TransVAE-DTA, which combines the transformer and variational autoencoder (VAE). Inspired by the early success of VAEs, we aim to further investigate the feasibility of VAEs for drug structure encoding, while utilizing the transformer architecture for target feature representation. Additionally, an adaptive attention pooling (AAP) module is designed to fuse the drug and target encoded features. Notably, TransVAE-DTA is proven to maximize the lower bound of the joint likelihood of drug, target, and their DTAs. Results: Experimental results demonstrate the superiority of TransVAE-DTA in drug-target binding affinity prediction assignments on two public Davis and KIBA datasets. Conclusions: In this research, the developed TransVAE-DTA opens a new avenue for engineering drug-target interactions.

关键词： Drug-target binding affinity prediction Transformer variational autoencoder Drug discovery

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Data augmentation for Gram-stain images based on Vector Quantized variational autoencoder

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NEUROCOMPUTING 2024年 600卷

作者： Shwetha, V Prasad, Keerthana Mukhopadhyay, Chiranjay Banerjee, Barnini Manipal Acad Higher Educ Manipal Inst Technol Dept Elect & Elect Engn Manipal 576104 Karnataka India Manipal Acad Higher Educ Manipal Sch Informat Sci Manipal 576104 Karnataka India Manipal Acad Higher Educ Kasturba Med Coll Dept Microbiol Manipal 576104 Karnataka India

Availability of large-scale datasets plays a significant role in segmentation and classification tasks using deep learning. However, domains such as healthcare inherently suffer from unavailability and inaccessibility of data. This leads to challenges in deploying CNN-based models for computer-aided diagnosis. This challenge extends to Gram-stain image analysis for detecting bacterial infections, which is a crucial task. The lack of datasets containing Gram-stained direct and culture smear images exacerbates the significant challenges in deep learning tasks. In this regard, we investigate a novel application of the variational autoencoder. Specifically, the Vector Quantized variational autoencoder model is trained to generate the Gram-stain images. Incorporating a novel loss function, where the quality loss ( L qu ) is derived by integrating the Loss SSIM , L 1 , and L 2 losses with the VQ-VAE loss ( Loss vq ) for proposed approach for Gram-stained direct and culture smear images. This modification facilitates the creation of images closely resembling the original input, leading to notable SSIM scores of 0.92 for Gram-stained culture images and 0.88 for Gram-stained direct smear images. The current study compares the proposed method with state-of-the-art machine learning based and CNN based transformations. This work also demonstrates the classification process with and without image augmentation. It shows that the area under the curve in the case of augmentation is higher by an average of 20%.

关键词： Bacteria detection Generative Adversarial network variational autoencoder VQ-VAE Augmentation Transformation Semantic segmentation CNN based classification

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