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IEEE International Conference on Big Data (Big Data)

作者： Kim, Jeeyung Seoul Natl Univ Datamining Lab Ind Engn Seoul South Korea

ISBN: (纸本)9781728108582

The factors impacting on what users purchase can be classified as an internal factor which is time-invariant user's unique taste and an external factor which is time-varying item characteristic. However, the current recommendation system has a limitation of making recommendations based only on the user's history without taking into account the item features trends of the time, which prevents precise recommendations. In this paper, the recommendation system that reflects inter-items trends of time-based bin is proposed. We focus on creating a hybrid recommender system that could effectively combine time-varying content data with the users history data. Specifically, we use conditional variational autoencoder (VAE) to add a time dynamic item features to user-item implicit feedback data. In this case, distributed representation of items in the specific period is used as a condition that is added to input and latent variable of VAE respectively. In detail, the distributed representation per time bin can be extracted using ISTM. By applying a condition into VAE, a hybrid recommendation system can be created to reflect the item time-varying features. The proposed model in this paper differs from current studies in that it reflects the changing characteristics inherent of the products and utilizes it for recommendation. The Movielens 1M data and Amazon women's clothing dataset are used for the evaluation of the proposed model.

关键词： recommender system conditional variational autoencoder long short term memory

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Radio Galaxy Morphology Simulation via Residual conditional variational autoencoder 15

Radio Galaxy Morphology Simulation via Residual Conditional ...

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15th International Conference on Computational Intelligence and Security (CIS)

作者： Ma, Zhixian Zhu, Jie Zhu, Yongkai Xu, Haiguang Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Astrophys Shanghai 200240 Peoples R China

ISBN: (纸本)9781728160924

We propose a radio galaxy morphology simulation approach by using a conditional variational autoencoder composed of residual convolutional blocks namely ResCVAE. It estimates the distribution of the existed morphology-labeled radio galaxy images by mapping them to a latent low-dimension space, and simulates new images according to the obtained distribution. From designed experiments we find that the pixelwise cross-entropy (PCE) loss, as a component of the optimization objective for the ResCVAE, trains the network parameters to obtain lower reconstruction loss and more accurate simulation performance than the mean squared error (MSE) loss function under the same training settings. In addition, the strategies of applying condition and residual convolutional blocks are also evaluated and compared to the other networks, which suggests a domination of our proposed approach.

关键词： image simulation pattern analysis conditional variational autoencoder residual

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A conditional variational autoencoder Algorithm for Reconstructing Defect Data of Magnetic Flux Leakage

A Conditional Variational Autoencoder Algorithm for Reconstr...

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第32届中国控制与决策会议

作者： Senxiang Lu Jing Wu Jianan Zhang School of Information Science and Engineering Northeastern University Liaoning Provincial Institute of Measurement

Magnetic flux leakage（MFL） testing is widely used in pipeline nondestructive *** acquisition quality of MFL signal is an important prerequisite for the accuracy of pipeline detection and ***,data loss often occurs in the process of detection,due to the interference of external environmental ***,it is important to reconstruct the missing *** paper analyzes four types of MFL data loss,and presents a conditional variational autoencoder algorithm to reconstruct defect data of MFL for these types of *** algorithm effectiveness is tested by comparing with traditional variational autoencoder *** results demonstrate that the proposed method can improve the accuracy of reconstructing defect data loss.

关键词： Magnetic Flux Leakage Data Loss conditional variational autoencoder Defect Data Reconstructing

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Molecular generative model based on conditional variational autoencoder for de novo molecular design

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JOURNAL OF CHEMINFORMATICS 2018年第1期10卷 1-9页

作者： Lim, Jaechang Ryu, Seongok Kim, Jin Woo Kim, Woo Youn Korea Adv Inst Sci & Technol Dept Chem 291 Daehak Ro Daejeon 34141 South Korea Korea Adv Inst Sci & Technol KI Artificial Intelligence 291 Daehak Ro Daejeon 34141 South Korea

We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.

关键词： Molecular design conditional variational autoencoder Deep learning

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conditional variational autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT

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SENSORS 2017年第9期17卷 1967页

作者： Lopez-Martin, Manuel Carro, Belen Sanchez-Esguevillas, Antonio Lloret, Jaime Univ Valladolid ETSIT Dpto TSyCeIT Paseo Belen 15 E-47011 Valladolid Spain Univ Politecn Valencia Inst Invest Gest Integrada Zonas Costeras Camino Vera S-N E-46022 Valencia Spain

The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host's network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery.

关键词： intrusion detection variational methods conditional variational autoencoder feature recovery neural networks

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Design Space Exploration and Explanation via conditional variational autoencoders in Meta-Model-Based Conceptual Design of Pedestrian Bridges

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AUTOMATION IN CONSTRUCTION 2024年 163卷

作者： Balmer, Vera Kuhn, Sophia V. Bischof, Rafael Salamanca, Luis Kaufmann, Walter Perez-Cruz, Fernando Kraus, Michael A. Swiss Fed Inst Technol Inst Struct Engn Stefano Franscini Pl 5 CH-8093 Zurich Switzerland Swiss Fed Inst Technol Ctr Augmented Computat Design Architecture Engn & Wolfgang Pauli Str 27 CH-8093 Zurich Switzerland Swiss Data Sci Ctr SDSC Andreasstr 5 CH-8092 Zurich Switzerland Univ Str 6 CH-8006 Zurich Switzerland

Today, engineers rely on conventional iterative (often manual) techniques for conceptual design. Emerging parametric models facilitate design space exploration based on quantifiable performance metrics, yet remain time-consuming and computationally expensive, leaving room for improvement. This paper provides a design exploration and explanation framework to augment the designer via a conditional variational autoencoder (CVAE), which serves as a forward performance predictor as well as an inverse design generator conditioned on a set of performance requests. Hence, the CVAE overcomes the limitations of traditional iterative techniques by learning a differentiable mapping for a highly nonlinear design space, thus enabling sensitivity analysis. These methods allow for informing designers about (i) relations of the model between features and performances and (ii) structural improvements under user-defined objectives. The framework is tested on a case-study and proves its potential to serve as a future co-pilot for conceptual design studies of diverse civil structures and beyond.

关键词： Computational design Design space exploration Generative AI conditional variational autoencoder Explainable AI Pedestrian bridge

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conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data

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WIND ENERGY 2021年第10期24卷 1122-1139页

作者： Mylonas, Charilaos Abdallah, Imad Chatzi, Eleni Fed Inst Technol Zurich Chair Struct Mech & Monitoring Zurich Switzerland

Wind turbine fatigue estimation is based on time-consuming Monte Carlo simulations for various wind conditions, followed by cycle-counting procedures and the application of engineering damage models. The outputs of the fatigue simulations are large in volume and of high dimensionality, as they typically consist of estimates on finite-element computational meshes. The strain and stress tensor time series, which are the primary quantities of interest when considering the problem of fatigue estimation, are dictated by complex vibration characteristics due to the coupled effect of aerodynamics, structural dynamics, geometrically non-linear mechanics, and control. A variational Auto-Encoder (VAE) is trained in order to model the probability distribution of the accumulated fatigue on the root cross-section of a simulated wind turbine blade. The VAE is conditioned on historical data that correspond to coarse wind-field measurement statistics, such as mean hub-height wind speed, standard deviation of hub-height wind speed and shear exponent. In the absence of direct measurements of structural loads, the proposed technique finds applications in making long-term probabilistic deterioration predictions from historical Supervisory, Control, and Data Acquisition (SCADA) data, while capturing the inherent aleatoric uncertainty due to the incomplete information on strain time series of the wind turbine structure, when only SCADA data statistics are available.

关键词： blade root fatigue conditional variational autoencoder CVAE deep generative models high dimensional simulation outputs SCADA uncertainty quantification wind turbine blade

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Generalized zero-shot domain adaptation via coupled conditional variational autoencoders

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NEURAL NETWORKS 2023年 163卷 40-52页

作者： Wang, Qian Breckon, Toby P. Univ Durham Dept Comp Sci Durham England Univ Durham Dept Engn Durham England

Domain adaptation aims to exploit useful information from the source domain where annotated training data are easier to obtain to address a learning problem in the target domain where only limited or even no annotated data are available. In classification problems, domain adaptation has been studied under the assumption all classes are available in the target domain regardless of the annotations. However, a common situation where only a subset of classes in the target domain are available has not attracted much attention. In this paper, we formulate this particular domain adaptation problem within a generalized zero-shot learning framework by treating the labelled source -domain samples as semantic representations for zero-shot learning. For this novel problem, neither conventional domain adaptation approaches nor zero-shot learning algorithms directly apply. To solve this problem, we present a novel Coupled conditional variational autoencoder (CCVAE) which can generate synthetic target-domain image features for unseen classes from real images in the source domain. Extensive experiments have been conducted on three domain adaptation datasets including a bespoke X-ray security checkpoint dataset to simulate a real-world application in aviation security. The results demonstrate the effectiveness of our proposed approach both against established benchmarks and in terms of real-world applicability.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Generalized zero -shot learning Domain adaptation conditional variational autoencoder Generalized zero-shot domain adaptation

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BOOSTING SPEECH ENHANCEMENT WITH CLEAN SELF-SUPERVISED FEATURES VIA conditional variational autoencoderS 49

BOOSTING SPEECH ENHANCEMENT WITH CLEAN SELF-SUPERVISED FEATU...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lee, Yoonhyung Jung, Kyomin Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea Seoul Natl Univ Automat & Syst Res Inst Seoul South Korea

ISBN: (纸本)9798350344868;9798350344851

Recently, Self-Supervised Features (SSF) trained on extensive speech datasets have shown significant performance gains across various speech processing tasks. Nevertheless, their effectiveness in Speech Enhancement (SE) systems is often suboptimal due to insufficient optimization for noisy environments. To address this issue, we present a novel methodology that directly utilizes SSFs extracted from clean speech for enhancing SE models. Specifically, we leverage the clean SSFs for latent space modeling within the conditional variational autoencoder (CVAE) framework. Consequently, we enable our model to fully leverage the knowledge existing in the clean SSFs without the interference of noise. In experiments, our approach yields clear improvements over existing methods that use SSFs across six evaluation metrics. Furthermore, we provide comprehensive analyses to validate the effectiveness of 1) incorporating clean SSFs within the CVAE framework and 2) the training techniques used to achieve optimal performance from our approach in SE systems. Code and audio samples are available at https://***/YoonhyungLee94/SSFCVAE

关键词： Speech enhancement self-supervised features conditional variational autoencoder posterior collapse

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Machine learning-based mechanical performance prediction and design of lattice structures

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INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES 2025年 294卷

作者： Liu, Yifan Huang, Wei Wang, Zhiyong Zhang, Jie Liu, Jiayi Huazhong Univ Sci & Technol Sch Naval Architecture & Ocean Engn Wuhan 430074 Peoples R China Taiyuan Univ Technol Coll Aeronaut & Astronaut Taiyuan 030024 Peoples R China Natl Univ Singapore Dept Civil & Environm Engn 1 Engn Dr 2 Singapore 117576 Singapore Hubei Key Lab Naval Architecture & Ocean Engn Hydr Wuhan 430074 Peoples R China

A data-driven framework is proposed that integrates automated geometric modeling and high-throughput finite element simulations to enable both forward prediction and inverse design of lattice unit cells. By establishing an efficient workflow, a substantial numerical simulation database is generated encompassing diverse truss-based topologies, the fidelity of which is corroborated by 3D-printed prototypes and quasi-static compression tests. A specially designed network is trained to predict elastic modulus, demonstrating improved accuracy (MSE = 0.046, R > 0.994) and reduced overfitting compared to graph neural network (GNN). Building upon this predictive model, a conditional variational autoencoder (C-VAE) is introduced that learns a low-dimensional latent space simultaneously conditioned on topological features and mechanical performance. Subsequent dimensionality reduction and clustering analyses, utilizing principal component analysis (PCA) and K-means algorithms, elucidated intrinsic correlations between rod connectivity and structural stiffness. Ultimately, by coupling the C-VAE with the predictive model, a high-throughput inverse design strategy is realized, enabling the fabrication of unit cells that achieve the prescribed elastic modulus with remarkable fidelity (design error < 2 %), and with a computational design time on the order of 10 s. And the design speed is approximately 66 times faster than that of traditional topology optimization methods. This paradigm significantly accelerates the design of 3D-printed architected materials, offering a promising and data-efficient approach for exploring complex structural geometries.

关键词： Machine learning Inverse design Lattice unit cell 3D-printed architected materials Elastic modulus conditional variational autoencoder

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