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Accuracy of generative deep learning model for macular anatomy prediction from optical coherence tomography images in macular hole surgery

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Kwon, Han Jo Heo, Jun Park, Su Hwan Park, Sung Who Byon, Iksoo Pusan Natl Univ Sch Med Biomed Res Inst Pusan Natl Univ HospDept Ophthalmol Gudeok Ro 179 Busan 49241 South Korea Pusan Natl Univ Yangsan Hosp Res Inst Convergence Biomed Sci & Technol Dept Ophthalmol Geumo Ro 20 Yangsan Si 50612 Gyeongsangnam D South Korea

This study aims to propose a generative deep learning model (GDLM) based on a variational autoencoder that predicts macular optical coherence tomography (OCT) images following full-thickness macular hole (FTMH) surgery and evaluate its clinical accuracy. Preoperative and 6-month postoperative swept-source OCT data were collected from 150 patients with successfully closed FTMH using 6 x 6 mm2 macular volume scan datasets. Randomly selected and augmented 120,000 training and 5000 validation pairs of OCT images were used to train the GDLM. We assessed the accuracy and F1 score of concordance for neurosensory retinal areas, performed Bland-Altman analysis of foveolar height (FH) and mean foveal thickness (MFT), and predicted postoperative external limiting membrane (ELM) and ellipsoid zone (EZ) restoration accuracy between artificial intelligence (AI)-OCT and ground truth (GT)-OCT images. Accuracy and F1 scores were 94.7% and 0.891, respectively. Average FH (228.2 vs. 233.4 mu m, P = 0.587) and MFT (271.4 vs. 273.3 mu m, P = 0.819) were similar between AI- and GT-OCT images, within 30.0% differences of 95% limits of agreement. ELM and EZ recovery prediction accuracy was 88.0% and 92.0%, respectively. The proposed GDLM accurately predicted macular OCT images following FTMH surgery, aiding patient and surgeon understanding of postoperative macular features.

关键词： Deep learning Generative deep learning model Macular hole Optical coherence tomography variational autoencoder

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A novel deep learning framework for rolling bearing fault diagnosis enhancement using VAE-augmented CNN model

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HELIYON 2024年第15期10卷 e35407页

作者： Wang, Yu Li, Dexiong Li, Lei Sun, Runde Wang, Shuqing Shijiazhuang Inst Railway Technol Dept Elect Engn Shijiazhuang 050041 Peoples R China

In the context of burgeoning industrial advancement, there is an increasing trend towards the integration of intelligence and precision in mechanical equipment. Central to the functionality of such equipment is the rolling bearing, whose operational integrity significantly impacts the overall performance of the machinery. This underscores the imperative for reliable fault diagnosis mechanisms in the continuous monitoring of rolling bearing conditions within industrial production environments. Vibration signals are primarily used for fault diagnosis in mechanical equipment because they provide comprehensive information about the equipment's condition. However, fault data often contain high noise levels, high-frequency variations, and irregularities, along with a significant amount of redundant information, like duplication, overlap, and unnecessary information during signal transmission. These characteristics present considerable challenges for effective fault feature extraction and diagnosis, reducing the accuracy and reliability of traditional fault detection methods. This research introduces an innovative fault diagnosis methodology for rolling bearings using deep convolutional neural networks (CNNs) enhanced with variational autoencoders (VAEs). This deep learning approach aims to precisely identify and classify faults by extracting detailed vibration signal features. The VAE enhances noise robustness, while the CNN improves signal data expressiveness, addressing issues like gradient vanishing and explosion. The model employs the reparameterization trick for unsupervised learning of latent features and further trains with the CNN. The system incorporates adaptive threshold methods, the "3/5" strategy, and Dropout methods. The diagnosis accuracy of the VAE-CNN model for different fault types at different rotational speeds typically reaches more than 90 %, and it achieves a generally acceptable diagnosis result. Meanwhile, the VAE-CNN augmented fault diagnosis mode

关键词： Rolling bearing Fault diagnosis variational autoencoder Deep learning Convolutional neural networks

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PLAYING TECHNIQUE CLASSIFICATION BASED ON DEEP COLLABORATIVE LEARNING OF variational AUTO-ENCODER AND GAUSSIAN PROCESS

PLAYING TECHNIQUE CLASSIFICATION BASED ON DEEP COLLABORATIVE...

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IEEE International Conference on Multimedia and Expo

作者： Sih-Huei Chen Yuan-Shan Lee Min-Che Hsieh Jia-Ching Wang Dept. of Computer Science and Information Engineering National Central University Taiwan

Modeling musical timbre is critical for various music information retrieval (MIR) tasks. This work addresses the task of classifying playing techniques, which involves extremely subtle variations of timbre among different categories. A deep collaborative learning framework is proposed to represent a music with greater discriminative power than previously achieved. Firstly, a novel variational autoencoder (VAE) is developed to eliminate the variation of acoustic features within a class. Secondly, a Gaussian process classifier is jointly learned to distinguish the variations of timbres between classes, which increases the discriminative power of the learned representations. We derive a new lower bound that guides a VAE-based representation. Experiments were conducted on a database of seven classes of guitar playing techniques. The experimental results demonstrated that the proposed method outperforms baselines in terms of the F1-score and accuracy.

关键词： variational autoencoder Gaussian process Collaborative learning Playing technique classification

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Disentangling Forced Trends in the North Atlantic Jet From Natural Variability Using Deep Learning

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JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 2024年第14期129卷

作者： Hermoso, Alejandro Schemm, Sebastian Swiss Fed Inst Technol Inst Atmospher & Climate Sci Zurich Switzerland

Regional weather variability and extremes over Europe are strongly linked to variations in the North Atlantic jet stream, especially during the winter season. Projections of the evolution of the North Atlantic jet are essential for estimating the regional impacts of climate change. Therefore, separating forced trends in the North Atlantic jet from its natural variability is an extremely relevant task. Here, a deep learning based method, the Latent Linear Adjustment autoencoder (LLAE), is used for this purpose on an ensemble of fully-coupled climate simulations. The LLAE is based on a variational autoencoder and an additional linear component. The model uses detrended temperature and geopotential to predict the component of the zonal wind associated with natural variability. The residual between this prediction and the original wind field is interpreted as the forced component of the jet. The method is first tested for the geostrophic wind for which the forced trend can be obtained analytically from the difference between geostrophic wind computed from detrended and full geopotential. Despite the large variability of the total trends, the LLAE is shown to be effective in extracting the forced component of the trend for each individual ensemble member in both geostrophic and full wind fields. The LLAE-derived forced trend shows an increase in the upper-level zonal wind speed along a southwest-northeast oriented band over the ocean and a jet extension toward Europe. These are common characteristics over different periods and show some similarities to the upper-level zonal wind speed trend obtained from the ERA5 reanalysis. The North Atlantic jet stream, which is a narrow and strong air current flowing from west to east, has a substantial influence on daily weather variability and temperature and precipitation extremes in Europe. It is therefore essential to investigate how the North Atlantic jet is being modified under climate change. Trends in the North Atlantic jet a

关键词： jet streams North Atlantic forced trends dynamical adjustment variational autoencoder

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Simpler rather than Challenging: Design of Non-Dyadic Human-Robot Collaboration to Mediate Human-Human Concurrent Tasks 23

Simpler rather than Challenging: Design of Non-Dyadic Human-...

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Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems

作者： Francesco Semeraro Jon Carberry Angelo Cangelosi The University of Manchester Manchester United Kingdom BAE Systems (Operations) Ltd. Warton United Kingdom

ISBN: (纸本)9781450394321

Human-robot interaction (HRI) is progressively addressing multi-party scenarios, where a robot interacts with more than one human user at the same time. Conversely, research in this area is still at an early stage for human-robot collaboration (HRC). The intervention of a robot in human collaboration could be helpful to handle mutual disturbances of workers operating at the same time on the same target object. Therefore, this work outlines design methodologies of non-dyadic human-robot collaborations to address concurrent human-human tasks in manufacturing applications. After this, preliminary results regarding a robotic agent's high-level understanding of such scenarios realised through a variational autoencoder trained by means of transfer learning are shown.

关键词： multi-party human-robot collaboration transfer learning deep learning concurrent tasks non-dyadic human-robot collaboration multi-user activity recognition variational autoencoder

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Data-driven fault diagnosis based on the integrated deep nonlinear dynamic system model

Data-driven fault diagnosis based on the integrated deep non...

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第43届中国控制会议

作者： Xiaochu Tang Na Tao Yi Zhang Yuan Li School of Automation Shenyang Aerospace University College of Information Engineering Shenyang University of Chemical Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

To ensure the safety and reliability of complex industrial processes are very ***,extracting multiple features of data effectively is a great significance to improve the accuracy of modeling for fault ***,uncertainty and nonlinearity are main characteristics of industrial process ***,it is challenging for modeling to extract multiple features of process data at the same *** this paper,an integrated nonlinear dynamic system model is proposed for fault diagnosis based on variational autoencoder-linear dynamic system(VAE-LDS).First,the deep learning algorithm variational autoencoder(VAE) is used to extract the nonlinear data feature and learn the potential representation of ***,the VAE model is embedded into the linear dynamic system(LDS) so that the dynamics and uncertainty underlying data can be extracted *** this way,A comprehensive model integrating multiple features can be ***,the proposed method is applied to the TE process for fault diagnosis comparing with other *** results show the proposed method has superior performance.

关键词： Data-driven variational autoencoder Linear Dynamic System Fault Detection Fault Diagnosis

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A Tensorflow Based Feature Learning Method Application in Fault Detecting of Tract Motor

A Tensorflow Based Feature Learning Method Application in Fa...

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第30届中国控制与决策会议

作者： Wang Huizhong Qiao Linhan He Keke Lanzhou University of Technology

In purpose of detecting the inner and outer ring faults of tractor motor,one Feature Learning method,Variationa autoencoder,which based on Tensorflow,was cited to process the motor vibration *** method firstly normalized all data sets Next,these data sets were input into the built variational autoencoder model to train the weights and biases as the feature learning is going ***,a Softmax Regression model is used for multi-faults *** final results showed that this method can be used fo finishing multi-faults detecting missions excellently,and for every metric,the results are betterthan traditional Back Propagation Neura Network,from 87.51% to 93.61%.Hence,this unsupervised feature learning method decreased lots of Machine learning model's dependency on feature *** would be a good guidance of actual projects.

关键词： Tensorflow variational autoencoder Softmax Regression Feature Learning Fault Detection

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Generate Xi’an Drum Music Based on Compressed Coding

Generate Xi’an Drum Music Based on Compressed Coding

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第40届中国控制会议

作者： Haochen Wang Jiabao Li Yuxuan Lin Wen Ru Jiang Wu Computer Science Vrije Universiteit Amsterdam & Universiteit van Amsterdam School of Qianxuesen Xi'an Jiaotong University School of Artificial Intelligence Xi'an Jiaotong University School of Electronic and Information Engineering Xi'an Jiaotong University School of Humanities and Social Science Xi'an Jiaotong University

Xi’an Drum Music is a traditional form of Chinese music and its notes are recorded by Chinese *** Xi’an Drum Music is composed and translated by the elder musicians,Xi’an Drum Music becomes difficult to be *** this article,we use sparse coding and compressed coding to transfer the Chinese Character recording to genre and lyrics of Xi’ an Drum *** on our dataset of Xi’an Drum Music,we set up a method to generate Xi’ an Drum Music similar to Huffman coding,a model named Xi’an Drum Music Generation via variational autoencoder（DMGVAE） and the accuracy of Xi’ an Drum Music generation increases to *** coding on Xi’an Drum Music shows a novel method to generate Xi’ an Drum Music by compressed format,making potential application for generating the sparse traditional Chinese Music such as Xi’an Drum Music.

关键词： Xi’an Drum Music Compressed Coding variational autoencoder Deep Learning

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VAE-SPACE: DEEP GENERATIVE MODEL OF VOICE FUNDAMENTAL FREQUENCY CONTOURS

VAE-SPACE: DEEP GENERATIVE MODEL OF VOICE FUNDAMENTAL FREQUE...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Kou Tanaka Hirokazu Kameoka Kazuho Morikawa NTT Communication Science Laboratories NTT Corporation Japan Graduate School of informatics Nagoya University Japan

ISBN: (纸本)9781538646595

Modeling the speech generation process can provide flexible and interpretable ways to generate intended synthetic speech. In this paper, we present a deep generative model of fundamental frequency (F_0) contours of normal speech and singing voices. The generative model we propose in this paper 1) is able to accurately decompose an F_0 contour into the sum of phrase and accent components of the Fujisaki model, a mathematical model describing the control mechanism of vocal fold vibration, without an iterative algorithm, and 2) can represent/generate F_0 contours of both normal speech and singing voices reasonably well.

关键词： Deep generative model voice F_0 contour singing voice variational autoencoder gated convolutional network contour Voice fundamental frequencies control mechanism Speech Iterative algorithms Vocal music Singing Vocal Cords Outlines

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COIL: Constrained optimization in learned latent space: learning representations for valid solutions 22

COIL: Constrained optimization in learned latent space: lear...

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Proceedings of the Genetic and Evolutionary Computation Conference Companion

作者： Peter J. Bentley Soo Ling Lim Adam Gaier Linh Tran UCL London UK Autodesk Research London UK

ISBN: (纸本)9781450392686

Constrained optimization problems can be difficult because their search spaces have properties not conducive to search, e.g., multimodality, discontinuities, or deception. To address such difficulties, considerable research has been performed on creating novel evolutionary algorithms or specialized genetic operators. However, if the representation that defined the search space could be altered such that it only permitted valid solutions that satisfied the constraints, the task of finding the optimal would be made more feasible without any need for specialized optimization algorithms. We propose Constrained Optimization in Latent Space (COIL), which uses a VAE to generate a learned latent representation from a dataset comprising samples from the valid region of the search space according to a constraint, thus enabling the optimizer to find the objective in the new space defined by the learned representation. Preliminary experiments show promise: compared to an identical GA using a standard representation that cannot meet the constraints or find fit solutions, COIL with its learned latent representation can perfectly satisfy different types of constraints while finding high-fitness solutions.

关键词： constrained optimization genetic algorithm variational autoencoder learning latent representations

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