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A machine learning-based interaction force model for non-spherical and irregular particles in low Reynolds number incompressible flows

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POWDER TECHNOLOGY 2021年 392卷 632-638页

作者： Hwang, Soohwan Pan, Jianhua Fan, Liang-Shih Ohio State Univ William G Lowrie Dept Chem & Biomol Engn Columbus OH 43210 USA

In this study, interaction force of non-spherical particles in low Reynolds number gas-solid flow is investigated by neural network approaches. An artificial neural network (ANN) model is developed to correlate the non spherical particle shape and the flow conditions with the interaction force. To define the particle shape, spherical harmonic expansion is applied. Furthermore, variational autoencoder model is then used to extract latent geometric features. The latent vector is utilized as an input with the Reynolds number for the ANN. The interaction force data, which is used as output data of the ANN, is obtained by particle resolved direct numerical simulation for 5200 non-spherical particles. The proposed model enables unsupervised extraction for non-spherical particle shapes and accurate predictions on the interaction force without heavy computation. This study provides the model that can explain complicated shapes of particles and be applied to a large scale, computational fluid dynamics simulation. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Non-spherical particle Artificial neural network Interaction force Spherical harmonic method variational autoencoder Particle resolved direct numerical simulation

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Fabric Defect Detection Using Deep Convolution Neural Network

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AATCC JOURNAL OF RESEARCH 2021年第1_suppl期8卷 143-150页

作者： Fan, Junjun Wong, Wai Keung Wen, Jiajun Gao, Can Mo, Dongmei Lai, Zhihui Shenzhen Univ Shenzhen Peoples R China Hong Kong Polytech Univ Hong Kong Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Shenzhen Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen Peoples R China

Fabric defect detection plays an increasingly important role in the industrial automation application for fabric production, but how to detect defects rapidly and accurately is still challenging. In this study, we propose a powerful fabric defect detection method using a hybrid of convolutional neural network (CNN) and variational autoencoder (VAE). The convolutional layers are used for extracting fabric image pattern features and the variational autoencoder is used for modeling the latent characteristics and inferring a reconstruction. The defect positions can be detected by the differences between the original image and the reconstruction image. The proposed method is validated on public patterned fabric datasets. The experimental results demonstrate that the proposed model can achieve outstanding performance in both image level and pixel level defect detection.

关键词： Computer Vision Deep Convolutional Neural Network Fabric Defect Detection variational autoencoder

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From Drug Molecules to Thermoset Shape Memory Polymers: A Machine Learning Approach

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ACS APPLIED MATERIALS & INTERFACES 2021年第50期13卷 60508-60521页

作者： Yan, Cheng Feng, Xiaming Li, Guoqiang Louisiana State Univ Dept Mech & Ind Engn Baton Rouge LA 70803 USA

Ultraviolet (UV)-curable thermoset shape memory polymers (TSMPs) with high recovery stress but mild glass transition temperature (T-g) are highly desired for 3D/4D printing lightweight load-bearing structures and devices. However, a bottleneck is that high recovery stress usually means high T-g. For a few TSMPs with high recovery stress, their T-g values are close to the decomposition temperature, and thus, the shape memory effect cannot be triggered safely and effectively. While machine learning (ML) has served as a useful tool to discover new materials and drugs, the grand challenge of using ML to discover new TSMPs persists in the very limited data available. Here, we report an enhanced ML approach by combining the transfer learning-variational autoencoder with a weighted-vector combination method. By learning a large data set with drug molecules in a pretraining process, we were able to effectively map the TSMPs to a hidden space that is much closer to a Gaussian distribution. Through this approach, we created a large compositional space and were able to discover five new types of UV-curable TSMPs with desired properties, one of which was validated by the experiments. Our contribution includes (1) representing the features of TSMPs by drug molecules to overcome the barrier of a limited training data set and (2) developing a ML framework that is able to overcome the barrier of mapping the molar ratio information. It is shown that this approach can effectively learn TSMP features by utilizing the relatedness between the data-scarce (and biased) TSMP target and data-abundant drug source, and the result is much more accurate and more robust than the benchmark set by the support vector machine method using direct label encoding and Morgan encoding. Therefore, it is believed that this framework is a state-of-the-art study in the TSMP field. This study opens new opportunities for discovering not only new TSMPs but also other thermoset polymers.

关键词： machine learning variational autoencoder transfer learning shape memory polymer material discovery drug molecules 4D printing

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Prediction of the microstructure of cold-compacted Astaloy 85Mo with deep generative models

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COMPUTATIONAL MATERIALS SCIENCE 2024年 242卷

作者： Schenk, Oliver Becker, Marco Deng, Yuanbin Niemietz, Philipp Bergs, Thomas Broeckmann, Christoph Chair & Inst Mat Applicat Mech Engn Augustinerbach 4 D-52062 Aachen Germany Rhein Westfal TH Aachen Mfg Technol Inst MTI Campus Blvd 30 D-52074 Aachen Germany Fraunhofer Inst Prod Technol IPT Steinbachstr 17 D-52074 Aachen Germany

In powder metallurgy, iron -based powders are compacted to so-called green bodies, which undergo a subsequent sintering process. The microstructure of the green bodies affects the sintering process and the resulting properties of the produced component. Assessing the microstructure experimentally is time consuming and costly, while simulation approaches rely on simplifications that significantly affect relevant microstructural information. This work investigates the suitability of deep generative models to synthetically generate realistic micrographs of green bodies as an alternative to experiments and simulations. For that purpose experiments comparing Generative Adversarial Neural Networks and variational autoencoders were conducted. The two deep learning frameworks were used to train models which produce synthetic micrographs in dependence of two parameters, namely powder particle size and green body porosity. The data utilized to train and evaluate the models was acquired from compacted water -atomized Astaloy 85Mo via scanning electron microscopy. Besides visual inspection, the trained models were evaluated via quantitative metrics. Distributions of relevant microstructural parameters such as pore perimeter or minimum and maximum pore feret diameter of the synthetically generated micrographs were compared to those of experimentally acquired micrographs. The results indicate that Generative Adversarial Networks represent a promising approach to train models, which produce synthetic micrographs that capture relevant properties of green body micrographs in dependence of given parameters such as powder particle size. In contrast, the variational autoencoders trained for this study did not capture microstructural properties well.

关键词： Generative models Generative Adversarial Network variational autoencoder Micrograph Powder metallurgy Microstructure

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Monolith to Microservices: VAE-Based GNN Approach with Duplication Consideration 16

Monolith to Microservices: VAE-Based GNN Approach with Dupli...

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16th IEEE International Conference on Service-Oriented System Engineering (SOSE)

作者： Sooksatra, Korn Maharjan, Rokin Cerny, Tomas Baylor Univ Dept Comp Sci Waco TX 76798 USA

ISBN: (数字)9781665475341

ISBN: (纸本)9781665475341

With the rise of cloud computing, many applications have been implemented into microservices to fully utilize cloud computing for scalability and maintainability purposes. However, there are some traditional monolith applications that developers would like to partition into microservices. Unfortunately, it is difficult to find a solution when considering multiple factors (i.e., the strong dependency in each cluster and how often different microservices communicate with each other). Further, because we allow duplications of classes in multiple microservices to reduce the communications between them, the number of duplicated classes is also another important factor for maintainability. Therefore, we need to use machine learning algorithms to approximate a good solution due to the infeasibility of finding the optimal solution. We apply the variational autoencoder to extract features of classes and use the fuzzy c means to group the classes into microservices according to their extracted features. As a result, our approach outperforms the other baselines in some significant metrics. Also, when we allow duplication, we find that it is helpful in terms of reducing the overhead of communications between microservices.

关键词： Microservices Machine learning Clustering Graph neural networks variational autoencoder

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DISENTANGLED SEQUENTIAL autoencoder WITH LOCAL CONSISTENCY FOR INFECTIOUS KERATITIS DIAGNOSIS 29

DISENTANGLED SEQUENTIAL AUTOENCODER WITH LOCAL CONSISTENCY F...

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IEEE International Conference on Image Processing (ICIP)

作者： Si, Yuxuan Fang, Zhengqing Kuang, Kun Huang, Zhengxing Yao, Yu-Feng Wu, Fei Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China Zhejiang Univ Sir Run Run Shaw Hosp Sch Med Hangzhou 310027 Peoples R China Key Lab Corneal Dis Res Zhejiang Prov Hangzhou 310027 Peoples R China Zhejiang Univ Shanghai Inst Adv Study Shanghai 201203 Peoples R China Shanghai AI Lab Shanghai 201203 Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Infectious keratitis is a major cause of visual impairment and a common blinding eye disease. Deep learning based prior researches mainly regard infectious keratitis diagnosis as a classification task on the slit-lamp images of single-visit. However, in real clinical applications, it is critical to analyze the lesion evolution characteristics represented by time-varying features over multiple-visits. To bridge this gap, in this paper, we focus on the problem with sequential clinical images of patients, and propose a novel disentangled sequential auto-encoder (DSLC-VAE) algorithm to separate the time-varying pathological features from the time-invariant ones for infectious keratitis diagnosis. Specifically, a inference model is exploited to generate time series of the shape and appearance of corneal lesions to represent keratitis progression, which are combined with location-related features to identify keratitis pathogen. Moreover, we construct a local consistent regularizer with a self-supervised task to enhance the consistency of the time-varying features across different infectious keratitis. Extensive experiments on real world dataset demonstrate superiority of our DSLC-VAE on both representation disentanglement and diagnosis accuracy.

关键词： Disentangled Representation Learning Sequential Learning variational autoencoder Local Consistency Constraint Infectious Keratitis

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Advanced temporal deep learning framework for enhanced predictive modeling in industrial treatment systems

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RESULTS IN ENGINEERING 2025年 25卷

作者： Ramya, S. Srinath, S. Tuppad, Pushpa JSS Sci & Technol Univ Dept Comp Sci & Engn Mysuru India JSS Sci & Technol Univ Dept Environm Engn Mysuru India

This research introduces an innovative hybrid modeling framework tailored for interval prediction, aimed at forecasting water quality parameters in industrial sewage treatment plants (STPs). It tackles key challenges in the field, including limited data availability, detecting anomalies, and selecting relevant features with precision. By addressing these critical gaps, the study advances predictive analytics for wastewater treatment systems. The main goal was to create a scalable and resilient model that consistently provides accurate forecasts for essential water quality indicators. To accomplish this, variational autoencoders (VAEs) were employed to generate synthetic datasets that mimic real-world patterns, improving data availability and enhancing the model's generalization capabilities. autoencoder paired with a Self-Organizing Map (SOM) was leveraged for anomaly detection and efficient feature selection. The study evaluated advanced architectures, including a Temporal Convolutional Network (TCN), TCN integrated with bidirectional Long Short-Term Memory (BiLSTM), and refined TCN_BiLSTM models featuring preand post-soft attention layers. The final model incorporated Multi- Head Attention mechanisms in both preand post-processing stages (TCN_BiLSTM_MultiHead_Attention), delivering a substantial performance improvement. The TCN_BiLSTM_MultiHead_Attention model proved to be the top performer, delivering state-of-the-art results with R2 scores of 0.9732, 0.9567, and 0.9638 for the training, validation, and test datasets, respectively. On the test set, it achieved an impressive MSE of 0.0008 and an MAE of 0.0198. These results underscore the model's exceptional accuracy in predicting key parameters, including BOD, COD, and AmmoniaNitrogen. The results highlight the significant potential of hybrid deep learning frameworks in capturing temporal patterns and complex dynamics within STP data. By integrating temporal pattern recognition, long-term dependency modeling, and

关键词： Treatment plant variational autoencoder Self-organizing map Temporal convolutional network Bidirectional long short-term memory Gated recurrent units Attention mechanism

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Missing Data Imputation on IoT Sensor Networks: Implications for on-Site Sensor Calibration

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IEEE SENSORS JOURNAL 2021年第20期21卷 22833-22845页

作者： Okafor, Nwamaka U. Delaney, Declan T. Univ Coll Dublin Sch Elect & Elect Engn Dublin D04 V1W8 4 Ireland

IoT sensors are becoming increasingly important supplement to traditional monitoring systems, particularly for in-situ based monitoring. Data collected using IoT sensors are often plagued with missing values occurring as a result of sensor faults, network failures, drifts and other operational issues. Missing data can have substantial impact on in-field sensor calibration methods. The goal of this research is to achieve effective calibration of sensors in the context of such missing data. To this end, two objectives are presented in this paper. 1) Identify and examine effective imputation strategy for missing data in IoT sensors. 2) Determine sensor calibration performance using calibration techniques on data set with imputed values. Specifically, this paper examines the performance of variational autoencoder (VAE), Neural Network with Random Weights (NNRW), Multiple Imputation by Chain Equations (MICE), Random Forest-based Imputation (missForest) and K-Nearest Neighbour (KNN) for imputation of missing values on IoT sensors. Furthermore, the performance of sensor calibration via different supervised algorithms trained on the imputed dataset were evaluated. The analysis showed VAE technique to outperform the other methods in imputing the missing values at different proportions of missingness on two real-world datasets. Experimental results also showed improved calibration performance with imputed dataset.

关键词： Sensors Calibration Artificial neural networks Mice Task analysis Sensor phenomena and characterization Measurement uncertainty Calibration imputation Internet of Things (IoT) missing data neural network regression sensors variational autoencoder XGBoost

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Deep CNN transferred from VAE and GAN for classifying irritating noise in automobile

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NEUROCOMPUTING 2021年 452卷 395-403页

作者： Kim, Jin-Young Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul 03722 South Korea

Noise from automobiles, such as buzzing, squeaking, and rattling (BSR) noises, is a key factor in automobile quality assessment. It is necessary to classify these noises for appropriate handling and prevention. Although many researchers have conducted studies to classify noise, they suffer from several problems: difficulty in extracting appropriate features, insufficient data to train a classifier, and weak robustness to surrounding noise. This paper proposes a method called latent semantic controlling generative adversarial networks (LSC-GAN) to solve these problems. To capture the features of data, a variational autoencoder (VAE), an autoencoder with approximate inference in a latent Gaussian model, learns the data representation by projecting them into the latent space according to their features and reconstructing the projected data. Because the generator and the discriminator of the LSC-GAN are trained simultaneously, the capacity to extract the characteristics of the data is improved and a knowledge space of classifiable data is also expanded with insufficient data. While data are generated by the generator, the encoder projects them back to the latent space according to their characteristics to advance the ability to extract features. Finally, the encoder is trained to the classifier, which is trained to classify BSR noises. The proposed classifier outperforms other models and achieves an accuracy of 96.68%. We confirm using a confusion matrix that the proposed model classifies the types of insufficient class better than other models. Our proposed model classifies data with accuracy of 94.68%, even if the data contains surrounding noise, which means it is more robust to BSR with surrounding noise than other models. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Deep learning Transfer learning variational autoencoder Generative adversarial network BSR noise

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Structural relational inference actor-critic for multi-agent reinforcement learning

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NEUROCOMPUTING 2021年 459卷 383-394页

作者： Zhang, Xianjie Liu, Yu Xu, Xiujuan Huang, Qiong Mao, Hangyu Carie, Anil Dalian Univ Technol Sch Software Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116620 Peoples R China Okinawa Inst Sci & Technol Grad Univ 1919-1 Tancha Onna Okinawa 9040495 Japan Huawei Noahs Ark Lab 3 Xinxi Rd Beijing 100085 Peoples R China VIT AP Sch Comp Sci Amaravathi 522237 India

Multi-agent reinforcement learning (MARL) is essential for a wide range of high-dimensional scenarios and complicated tasks with multiple agents. Many attempts have been made for agents with prior domain knowledge and predefined structure. However, the interaction relationship between agents in a multi-agent system (MAS) in general is usually unknown, and previous methods could not tackle dynamical activities in an ever-changing environment. Here we propose a multi-agent Actor-Critic algo-rithm called Structural Relational Inference Actor-Critic (SRI-AC), which is based on the framework of centralized training and decentralized execution. SRI-AC utilizes the latent codes in variational autoen-coder (VAE) to represent interactions between paired agents, and the reconstruction error is based on Graph Neural Network (GNN). With this framework, we test whether the reinforcement learning learners could form an interpretable structure while achieving better performance in both cooperative and com-petitive scenarios. The results indicate that SRI-AC could be applied to complex dynamic environments to find an interpretable structure while obtaining better performance compared to baseline algorithms. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Multi-agent systems Deep reinforcement learning variational autoencoder Actor-critic Graph neural network

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