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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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The Optimally Designed variational autoencoder Networks for Clustering and Recovery of Incomplete Multimedia Data

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SENSORS 2019年第4期19卷 809-809页

作者： Yu, Xiulan Li, Hongyu Zhang, Zufan Gan, Chenquan Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China

Clustering analysis of massive data in wireless multimedia sensor networks (WMSN) has become a hot topic. However, most data clustering algorithms have difficulty in obtaining latent nonlinear correlations of data features, resulting in a low clustering accuracy. In addition, it is difficult to extract features from missing or corrupted data, so incomplete data are widely used in practical work. In this paper, the optimally designed variational autoencoder networks is proposed for extracting features of incomplete data and using high-order fuzzy c-means algorithm (HOFCM) to improve cluster performance of incomplete data. Specifically, the feature extraction model is improved by using variational autoencoder to learn the feature of incomplete data. To capture nonlinear correlations in different heterogeneous data patterns, tensor based fuzzy c-means algorithm is used to cluster low-dimensional features. The tensor distance is used as the distance measure to capture the unknown correlations of data as much as possible. Finally, in the case that the clustering results are obtained, the missing data can be restored by using the low-dimensional features. Experiments on real datasets show that the proposed algorithm not only can improve the clustering performance of incomplete data effectively, but also can fill in missing features and get better data reconstruction results.

关键词： feature learning incomplete multimedia data fuzzy c-means variational autoencoder

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Application of domain-adaptive convolutional variational autoencoder for stress-state prediction

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KNOWLEDGE-BASED SYSTEMS 2022年 248卷 1页

作者： Lee, Sang Min Park, Sang-Youn Choi, Byoung-Ho Korea Univ Coll Engn Sch Mech Engn Seoul South Korea

Applying data-driven methods such as deep learning in material mechanics is challenging because producing a sufficiently large, labeled dataset is costly resource-wise. This paper outlines a new approach to overcoming this difficulty by transferring knowledge from a source domain of finite-element-analysis data to a target domain of real-world test-specimen images so that a model capable of accurate and robust predictions in both domains may be constructed. To achieve this transfer of knowledge, discrepancy-based unsupervised domain adaptation is adopted into a convolutional variational autoencoder structure. To evaluate the proposed approach, a four-point bending experiment was conducted on 6061 aluminum alloy and 316 stainless steel to produce 550 unlabeled target-domain data images. The same bending situation was analyzed using the finite-element method implemented in the commercial software package ABAQUS to produce 6000 labeled, source-domain data images. The proposed domain-adaptive convolutional variational autoencoder was trained using the maximum mean discrepancy method on the target-and the source-domain data. The predictions using the domain-adapted convolutional variational autoencoder were relatively more accurate than those using the model trained only on the source domain. It is expected that the proposed approach can address the scarcity of labeled data in various applications of material mechanics and provide a base technology for the development of various data-driven approaches.(C) 2022 Elsevier B.V. All rights reserved.

关键词： Unsupervised domain adaptation Stress analysis Four-point bending variational autoencoder Deep learning Convolutional neural network

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Semi-supervised automatic seizure detection using personalized anomaly detecting variational autoencoder with behind-the-ear EEG

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022年 213卷 106542-106542页

作者： You, Sungmin Cho, Baek Hwan Shon, Young-Min Seo, Dae-Won Kim, In Young Hanyang Univ Dept Biomed Engn Seoul South Korea Sungkyunkwan Univ Med AI Res Ctr Samsung Med Ctr Sch Med Seoul South Korea Sungkyunkwan Univ Samsung Adv Inst Hlth Sci & Technol Dept Med Device Management & Res Seoul South Korea Sungkyunkwan Univ Samsung Med Ctr Dept Neurol Sch Med Seoul South Korea

Background and Objective: Epilepsy is one of the most common neurologic diseases worldwide, and 30% of the patients live with uncontrolled seizures. For the safety of patients with epilepsy, an automatic seizure detection algorithm for continuous seizure monitoring in daily life is important to reduce risks related to seizures, including sudden unexpected death. Previous researchers applied machine learning to detect seizures with EEG, but the epileptic EEG waveform contains subtle changes that are difficult to identify. Furthermore, the imbalance problem due to the small proportion of ictal events caused poor prediction performance in supervised learning approaches. This study aimed to present a personalized deep learning-based anomaly detection algorithm for seizure monitoring with behind-the-ear electroen-cephalogram (EEG) signals. Methods: We collected behind-the-ear EEG signals from 16 patients with epilepsy in the hospital and used them to develop and evaluate seizure detection algorithms. We modified the variational autoencoder network to learn the latent representation of normal EEG signals and performed seizure detection by measuring the anomalies in EEG signals using the trained network. To personalize the algorithm, we also proposed a method to calibrate the anomaly score for each patient by comparing the representations in the latent space. Results: Our proposed algorithm showed a sensitivity of 90.4% with a false alarm rate of 0.83 per hour without personal calibration. On the other hand, the one-class support vector machine only showed a sensitivity of 84.6% with a false alarm rate of 2.17 per hour. Furthermore, our proposed model with per-sonal calibration achieved 94.2% sensitivity with a false alarm rate of 0.29 while detecting 49 of 52 ictal events. Conclusions: We proposed a novel seizure detection algorithm with behind-the-ear EEG signals via semi-supervised learning of an anomaly detecting variational autoencoder and personalization method of an

关键词： Epilepsy Seizure detection Deep learning Anomaly detection Behind-the-ear EEG variational autoencoder

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Fragment-based deep molecular generation using hierarchical chemical graph representation and multi-resolution graph variational autoencoder

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MOLECULAR INFORMATICS 2023年第5期42卷 e2200215页

作者： Gao, Zhenxiang Wang, Xinyu Gaines, Blake Blumenfeld Shi, Xuetao Bi, Jinbo Song, Minghu Univ Connecticut Dept Comp Sci & Engn Storrs CT 06269 USA Case Western Reserve Univ Ctr Artificial Intelligence Drug Discovery Sch Med Cleveland OH 44106 USA Univ Connecticut Dept Biomed Engn Storrs CT 06269 USA Univ Connecticut Dept Biomed Engn Storrs CT 06269 USA

Graph generative models have recently emerged as an interesting approach to construct molecular structures atom-by-atom or fragment-by-fragment. In this study, we adopt the fragment-based strategy and decompose each input molecule into a set of small chemical fragments. In drug discovery, a few drug molecules are designed by replacing certain chemical substituents with their bioisosteres or alternative chemical moieties. This inspires us to group decomposed fragments into different fragment clusters according to their local structural environment around bond-breaking positions. In this way, an input structure can be transformed into an equivalent three-layer graph, in which individual atoms, decomposed fragments, or obtained fragment clusters act as graph nodes at each corresponding layer. We further implement a prototype model, named multi-resolution graph variational autoencoder (MRGVAE), to learn embeddings of constituted nodes at each layer in a fine-to-coarse order. Our decoder adopts a similar but conversely hierarchical structure. It first predicts the next possible fragment cluster, then samples an exact fragment structure out of the determined fragment cluster, and sequentially attaches it to the preceding chemical moiety. Our proposed approach demonstrates comparatively good performance in molecular evaluation metrics compared with several other graph-based molecular generative models. The introduction of the additional fragment cluster graph layer will hopefully increase the odds of assembling new chemical moieties absent in the original training set and enhance their structural diversity. We hope that our prototyping work will inspire more creative research to explore the possibility of incorporating different kinds of chemical domain knowledge into a similar multi-resolution neural network architecture.

关键词： deep molecular generation fragment-based molecular generation graph-based deep generative model variational autoencoder

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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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Smart anomaly detection for Slocum underwater gliders with a variational autoencoder with long short-term memory networks

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APPLIED OCEAN RESEARCH 2022年 120卷 103030-103030页

作者： Bedja-Johnson, Zachary Wu, Peng Grande, Davide Anderlini, Enrico UCL Dept Mech Engn Gower St London WC1E 6BT England

Autonomous underwater vehicles (AUVs) are used extensively for monitoring the world's oceans, taking measurements of oceanographic characteristics along the water column. Presently, there is no holistic anomaly detection system in operation and AUVs require experienced pilots to monitor the progress of missions. This results in a large operational overhead and reduces the number of AUVs that can be deployed simultaneously. This article proposes an online anomaly detection system for underwater gliders based on a data-driven approach. A novel Long Short-Term Memory (LSTM) variational autoencoder (VAE) has been developed and trained using field data from four deployments with healthy glider behaviour and then tested against four deployments where faults are present. The system is able to detect wing loss with a high degree of accuracy on gliders unseen during the models training, highlighting the generality of the model to different platforms. Additionally, the VAE method outperforms model-based solution for the detection of biofouling, proving its generality to different types of anomalies. The proposed smart anomaly detection will contribute to increasing the capacity of AUVs and reducing the dependence on support vessels and experienced pilots.

关键词： Anomaly detection variational autoencoder Long short-term memory network Underwater glider Marine autonomous system

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Reconstruction and fast prediction of 3D heat and mass transfer based on a variational autoencoder

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2023年 149卷

作者： Liu, Gongyan Li, Runze Zhou, Xiaozhou Sun, Tianrui Zhang, Yufei Tsinghua Univ Sch Aerosp Engn Beijing Peoples R China Siemens Ltd Beijing 100084 Peoples R China

Reconstruction and fast prediction of heat and mass transfer are important for the improvement of data center operations and energy savings. In this study, an artificial neural network (ANN) and variational autoencoder (VAE) composite model is proposed for the reconstruction and prediction of 3D flowfields with high accuracy and efficiency. The VAE model is trained to extract features of the problem and to realize 3D physical field reconstruction. The ANN is employed to achieve the constructability of the extracted features. A dataset of steady temperature/velocity fields is acquired by computational fluid dynamics and heat transfer (CFD/HT) and fed to train the deep learning model. The proposed ANN-VAE model is experimentally proven to achieve promising field prediction accuracy with a significantly reduced computational cost. CFD/HT method is often incapable of occasions that require instant knowledge of heat and mass transfer. The proposed ANN-VAE method convert the time-consuming simulations into the training stage. Driven by large amount of simulation data, the trained ANN-VAE model is capable of field prediction. Compared to the CFD/HT method, the ANN-VAE method speeds up the physical field prediction by approximately 380,000 times, with mean accuracies of 97.3% for temperature field prediction and 97.9% for velocity field prediction, making it feasible for real-time prediction of heat and mass transfer.

关键词： Heat and mass transfer simulation variational autoencoder Artificial neural network Physical field prediction Data center

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Unsupervised feature learning for online voltage stability evaluation and monitoring based on variational autoencoder

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ELECTRIC POWER SYSTEMS RESEARCH 2020年第0期182卷 106253-000页

作者： Yang, Haosen Qiu, Robert C. Shi, Xin He, Xing Shanghai Jiao Tong Univ Res Ctr Big Data Engn Technol State Energy Smart Grid Resarch & Dev Ctr Dept Elect Engn Shanghai 200240 Peoples R China

With the increase of uncertain elements in power systems and extensive deployment of online monitoring devices, it is necessary to search a more real-time and robust voltage stability assessment method. This study, using PMU monitoring data, explores a novel data-driven approach for long-term voltage stability assessment based on variational autoencoder (VAE). Our method is capable of extracting the most representative features by an unsupervised data mining method in a probabilistic learning way. Different from most of familiar feature extraction methods, it regularizes latent features in an expected stochastic distribution. Furthermore, a statistical indicator by sampling latent features after variance reduction is proposed to assess long-term voltage stability. Our approach is tested in various simulated power systems with different load increment models. Other cases show the accuracy and speed of our approach for estimating voltage collapse point. These testing cases successfully demonstrate the accuracy and effectiveness of our approach.

关键词： Voltage stability variational autoencoder Unsupervised feature learning PMU monitoring data

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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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