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Parametrische Modellierung und generatives tiefes Lernen für den Brückenentwurf

BAUTECHNIK 2024年第3期101卷 174-180页

作者： Kraus, Michael A. Kuhn, Sophia V. Hodel, Anna Bischof, Rafael Maissen, Alessandro Salamanca Mino, Luis Perez-Cruz, Fernando Swiss Fed Inst Technol Inst Baustat & Konstrukt Prof Massiv & Bruckenbau Stefano Franscini Platz 5 CH-8093 Zurich Switzerland Andreasturm 18th FloorAndreasstr 5 CH-8050 Zurich Switzerland

In Anbetracht der erheblichen Umweltauswirkungen des Bauwesens wird die Analyse und v. a. Optimierung der Nachhaltigkeit von Strukturen unter Beibehaltung des etablierten Zuverlassigkeitsniveaus immer wichtiger. Im Hochbausektor existieren erste Werkzeuge zur Lebenszyklusanalyse, diese sind jedoch nicht direkt ubertragbar auf Bruckentragwerke. Dieser Beitrag fasst die wesentlichen Ansatze und Ergebnisse von aktuellen Forschungsprojekten der Autoren an der ETH zusammen und erlautert insbesondere einen neuen Deep-Learning-basierten Ansatz zur Erkundung und Modellierung des Entwurfsraums parametrischer Bruckenmodelle und deren Leistungsbewertungen und veranschaulicht die Anwendung fur eine Mehrzieloptimierung von Stahlbetonrahmenbrucken. Zunachst werden Daten unter Verwendung eines parametrischen Bruckenmodells sowie der Ankoppelung von Analysesoftware synthetisch generiert und anschliessend bedingte variationelle autoencoder (CVAE) als Metamodell trainiert. Der CVAE dient im Rahmen des konzeptionellen Bruckenentwurfs als effizienter Co-Pilot sowohl fur die Vorwarts- als auch Ruckwartsanalyse. Die mit dem CVAE durchgefuhrte Sensitivitatsanalyse zeigt Beziehungen zwischen Entwurfsparametern und/oder Leistungskenngrossen sowie Optimierungspotenziale auf. Das hier vorgestellte integrierte Framework besitzt das Potenzial zur Realisierung einer effizienten Bruckenplanung unter insbesondere den Kriterien der Nachhaltigkeit und Tragsicherheit und kann problemlos auf andere parametrische Fragestellungen erweitert werden. Parametric modeling and generative deep learning for bridge designGiven the significant environmental impact of the construction industry, the analysis and, above all, optimization of the sustainability of structures while maintaining established levels of reliability are becoming increasingly important. While there are initial tools for life cycle analysis in the building sector, these are not directly transferable to bridge structures. This paper introduces

关键词： Bruckenbau kunstliche Intelligenz generativer Entwurf bedingte variationelle autoencoder bridge design artificial intelligence generative design conditional variational autoencoder

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EmoMusicTV: Emotion-Conditioned Symbolic Music Generation With Hierarchical Transformer VAE

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 1076-1088页

作者： Ji, Shulei Yang, Xinyu Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China

Emotion is one of the most crucial attributes of music. However, due to the scarcity of emotional music datasets, emotion-conditioned symbolic music generation using deep learning techniques has not been investigated in depth. In particular, no study explores conditional music generation with the guidance of emotion, and few studies adopt time-varying emotional conditions. To address these issues, first, we endow three public lead sheet datasets with fine-grained emotions by automatically computing the valence labels from the chord progressions. Second, we propose a novel and effective encoder-decoder architecture named EmoMusicTV to explore the impact of emotional conditions on multiple music generation tasks and to capture the rich variability of musical sequences. EmoMusicTV is a transformer-based variational autoencoder (VAE) that contains a hierarchical latent variable structure to model holistic properties of the music segments and short-term variations within bars. The piece-level and bar-level emotional labels are embedded in their corresponding latent spaces to guide music generation. Third, we pretrain EmoMusicTV with the lead sheet continuation task to further improve its performance on conditional melody or harmony generation. Experimental results demonstrate that EmoMusicTV outperforms previous methods on three tasks, i.e., melody harmonization, melody generation given harmony, and lead sheet generation. Ablation studies verify the significant roles of emotional conditions and hierarchical latent variable structure on conditional music generation. Human listening shows that the lead sheets generated by EmoMusicTV are closer to the ground truth (GT) and perform slightly worse than the GT in conveying emotional polarity.

关键词： Symbolic music generation lead sheets emotional condition conditional variational autoencoder transformer

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Multi-Label Zero-Shot Learning With Adversarial and variational Techniques

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IEEE ACCESS 2024年 12卷 94990-95006页

作者： Gull, Muqaddas Arif, Omar Natl Univ Sci & Technol NUST Sch Elect Engn & Comp Sci Islamabad 44000 Pakistan Amer Univ Sharjah Dept Comp Sci & Engn Sharjah U Arab Emirates

Multi-label zero-shot learning expands upon the traditional single-label zero-shot learning paradigm by addressing the challenge of accurately classifying images containing multiple unseen classes, which are not part of the training data. Current techniques rely on attention mechanisms to tackle the complexities of multi-label zero-shot learning (ZSL) and generalized zero-shot learning (GZSL). However, the generation of features, especially within the context of a generative approach, remains an unexplored area. In this paper, we propose a generative approach that leverages the capabilities of conditional variational autoencoder (CVAE) and conditional Generative Adversarial Network (CGAN) to enhance the quality of generative data for both multi-label ZSL and GZSL. Additionally, we introduce a novel "Regressor" as a supplementary tool to improve the reconstruction of visual features. This Regressor operates in conjunction with a "cycle-consistency loss" to ensure that the generated features preserve the key qualities of the original features even after undergoing transformations. To gauge the efficacy of our proposed approach, we conducted comprehensive experiments on two widely recognized benchmark datasets: NUS-WIDE and MS COCO. Our evaluation spanned both multi-label ZSL and GZSL scenarios. Notably, our approach yielded significant enhancements in mean Average Precision (mAP) for both datasets. Specifically, we observed a 0.2% increase in performance on the NUS-WIDE dataset and a notable 2.6% improvement on the MS COCO dataset in the context of Multi-label ZSL. The results clearly demonstrate that our generative approach outperforms existing methods on these widely-recognized datasets.

关键词： Semantics Visualization Generative adversarial networks Task analysis Data models Zero-shot learning Training Encoders conditional variational autoencoder conditional generative adversarial network generalized zero-shot learning regressor zero-shot learning

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Deep learning-based surrogate models for spatial field solution reconstruction and uncertainty quantification in Structural Health Monitoring applications

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COMPUTERS & STRUCTURES 2024年 301卷

作者： Silionis, Nicholas E. Liangou, Theodora Anyfantis, Konstantinos N. Natl Tech Univ Athens Sch Naval Architecture & Marine Engn Ship Hull Struct Hlth Monitoring S H SHM Grp Heroon Polytech Ave Athens 15780 Greece

In recent years, increasingly complex computational models are being built to describe physical systems which has led to increased use of surrogate models to reduce computational cost. In problems related to Structural Health Monitoring (SHM), models capable of handling both high -dimensional data and quantifying uncertainty are required. In this work, our goal is to propose a conditional deep generative model as a surrogate aimed at such applications and high -dimensional stochastic structural simulations in general. To that end, a conditional variational autoencoder (CVAE) utilizing convolutional neural networks (CNNs) is employed to obtain reconstructions of spatially ordered structural response quantities for structural elements that are subjected to stochastic loading. Two numerical examples, inspired by potential SHM applications, are utilized to demonstrate the performance of the surrogate. The model is able to achieve high reconstruction accuracy compared to the reference Finite Element (FE) solutions, while at the same time successfully encoding the load uncertainty.

关键词： Surrogate modeling conditional variational autoencoder Structural Health Monitoring Probabilistic machine learning Deep generative models

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A DDoS Identification Method for Unbalanced Data CVWGG

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Computers, Materials & Continua 2024年第12期81卷 3825-3851页

作者： Haizhen Wang Na Jia Yang He Pan Tan College of Computer and Control Engineering Qiqihar UniversityQiqihar161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar161006China

As the popularity and dependence on the Internet increase,DDoS(distributed denial of service)attacks seriously threaten network *** accurately distinguishing between different types of DDoS attacks,targeted defense strategies can be formulated,significantly improving network protection *** attacks usually manifest as an abnormal increase in network traffic,and their diverse types of attacks,along with a severe data imbalance,make it difficult for traditional classification methods to effectively identify a small number of attack *** solve this problem,this paper proposes a DDoS recognition method CVWGG(conditional variational autoencoder-Wasserstein Generative Adversarial Network-gradient penalty-Gated Recurrent Unit)for unbalanced data,which generates less noisy data and high data quality compared with existing *** mainly includes unbalanced data processing for CVWG,feature extraction,and *** uses the CVAE(conditional variational autoencoder)to improve the WGAN(Wasserstein Generative Adversarial Network)and introduces a GP(gradient penalty)term to design the loss function to generate balanced data,which enhances the learning ability and stability of the ***,the GRU(Gated Recurrent Units)are used to capture the temporal features and patterns of the ***,the logsoftmax function is used to differentiate DDoS attack *** PyCharm and Python 3.10 for programming and evaluating performance with metrics such as accuracy and precision,the results show that the method achieved accuracy rates of 96.0%and 97.3%on two datasets,***,comparison and ablation experiment results demonstrate that CVWGG effectively mitigates the imbalance between DDoS attack categories,significantly improves the classification accuracy of different types of attacks and provides a valuable reference for network security defense.

关键词： conditional variational autoencoder generating adversarial networks DDoS attack

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A deep learning-based pipeline for developing multi-rib shape generative model with populational percentiles or anthropometrics as predictors

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COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 2024年 115卷 102388-102388页

作者： Huang, Yuan Holcombe, Sven A. Wang, Stewart C. Tang, Jisi Univ Michigan Res Investigator Int Ctr Automot Med ICAM Ann Arbor MI 48109 USA Univ Michigan Res Scientist Int Ctr Automot Med ICAM Ann Arbor MI USA Univ Michigan Surg & Director Int Ctr Automot Med ICAM Ann Arbor MI USA Chongqing Univ Bioengn Coll Key Lab Biorheol Sci & Technol Chongqing 400044 Peoples R China

Rib cross-sectional shapes (characterized by the outer contour and cortical bone thickness) affect the rib mechanical response under impact loading, thereby influence the rib injury pattern and risk. A statistical description of the rib shapes or their correlations to anthropometrics is a prerequisite to the development of numerical human body models representing target demographics. variational autoencoders (VAE) as anatomical shape generators remain to be explored in terms of utilizing the latent vectors to control or interpret the representativeness of the generated results. In this paper, we propose a pipeline for developing a multi -rib cross-sectional shape generative model from CT images, which consists of the achievement of rib cross-sectional shape data from CT images using an anatomical indexing system and regular grids, and a unified framework to fit shape distributions and associate shapes to anthropometrics for different rib categories. Specifically, we collected CT images including 3193 ribs, surface regular grid is generated for each rib based on anatomical coordinates, the rib crosssectional shapes are characterized by nodal coordinates and cortical bone thickness. The tensor structure of shape data based on regular grids enable the implementation of CNNs in the conditional variational autoencoder (CVAE). The CVAE is trained against an auxiliary classifier to decouple the low -dimensional representations of the inter- and intra- variations and fit each intra-variation by a Gaussian distribution simultaneously. Random tree regressors are further leveraged to associate each continuous intra-class space with the corresponding anthropometrics of the subjects, i.e., age, height and weight. As a result, with the rib class labels and the latent vectors sampled from Gaussian distributions or predicted from anthropometrics as the inputs, the decoder can generate valid rib cross-sectional shapes of given class labels (male/female, 2nd to 11th ribs) for arbitra

关键词： Rib cross-sectional shape Generative model conditional variational autoencoder Adversarial training Distribution model Correlation model

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Vehicle Trajectory Prediction With Multimodal and Dynamics-Aware Interaction Neural Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第12期73卷 18059-18072页

作者： Chang, Yawen Wang, Xudong Shanghai Jiao Tong Univ UM SJTU Joint Inst Shanghai 200240 Peoples R China

Trajectory prediction is essential for improving the reliability and efficiency of vehicle-to-everything (V2X) communications, since link failures and frequent handovers can be alleviated by foreseeing positions of vehicles. Yet, accurate prediction is difficult due to complex interaction and multimodal motions. Deep learning methods are dedicated to addressing these challenging issues and hence surpass traditional physics-based models dramatically. However, current methods mainly model interaction based on spatial relationship while overlooking dynamics-aware interaction, which hinders comprehensive perception. When dealing with multimodality, stochastic latent space without proper goals as regularization is generated to enable one-to-many mapping between historical and future trajectories, which restrains inference accuracy. To tackle these problems, a multimodal and dynamics-aware interaction neural network, MODA, is proposed for vehicle trajectory prediction. To handle dynamics-aware interaction, a graph attention network is conducted. By taking each vehicle as a node and interaction as an edge, self-attention mechanism adaptively calculates edge strength between nodes, which acts as dynamics importance between vehicles for interactive aggregation. To facilitate multimodality, conditional variational autoencoder is incorporated, where supervisory information regularizes the latent space of a recognition network in the training phase for goal orientation. The proposed model is evaluated on two real-world highway datasets: NGSIM I-80 and US-101. The experiments demonstrate that MODA outperforms the state-of-the-art methods by a considerable margin.

关键词： Trajectory Long short term memory Vehicle dynamics Vehicle-to-everything Training Decoding Vehicular ad hoc networks Trajectory prediction dynamics-aware interaction graph attention network multimodality conditional variational autoencoder

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Solving two-stage stochastic integer programs via representation learning

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NEURAL NETWORKS 2025年 188卷 107446页

作者： Wu, Yaoxin Cao, Zhiguang Song, Wen Zhang, Yingqian Eindhoven Univ Technol Dept Ind Engn & Innovat Sci NL-5600 MB Eindhoven Netherlands Singapore Management Univ Singapore Sch Comp & Informat Syst Singapore 178902 Singapore Shandong Univ Inst Marine Sci & Technol Qingdao 266237 Peoples R China

Solving stochastic integer programs (SIPs) is extremely intractable due to the high computational complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) for scenario representation learning. A graph convolutional network (GCN) based VAE embeds scenarios into a low-dimensional latent space, conditioned on the deterministic context of each instance. With the latent representations of stochastic scenarios, we perform two auxiliary tasks: objective prediction and scenario contrast, which predict scenario objective values and the similarities between them, respectively. These tasks further integrate objective information into the representations through gradient backpropagation. Experiments show that the learned scenario representations can help reduce scenarios in SIPs, facilitating high-quality solutions in a short computational time. This superiority generalizes well to instances of larger sizes, more scenarios, and various distributions.

关键词： Stochastic integer programs conditional variational autoencoder Semi-supervised learning Contrastive learning

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A Specialized variational autoencoder for Cost-Efficient Pedestrian Trajectory Prediction

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2025年

作者： Li, Dongchen Lin, Zhimao Hu, Jinglu Waseda Univ Grad Sch Informat Prod & Syst 2-7 Hibikino Kitakyushu Fukuoka 8080135 Japan

The prediction of pedestrian trajectories represents a crucial and widely discussed topic in the field of AI-driven traffic scenarios. The prediction of pedestrian trajectories is constrained by two factors. First, pedestrians do not have the same traffic rule constraints as vehicles. Second, the computational power of in-vehicle systems is limited. This renders the application of traditional methods challenging. Previous methods have been observed to utilize redundant information, which can result in feature imbalance and the potential for model overfitting. In light of these limitations, we propose a lightweight conditional variational autoencoder model with post-process (L-CVAE-P) for pedestrian prediction scenarios. The L-CVAE-P focuses on the efficient interaction of multidimensional features to achieve a comprehensive enhancement of the model for real-world use. The model is tested on two public datasets and achieved state-of-the-art performance, while maintaining efficiency. The experimental results demonstrate that our work has developed and optimized a pedestrian trajectory prediction model for practical applications. (c) 2025 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： pedestrian trajectory conditional variational autoencoder temporal sequence prediction scenarios interaction multi-dimensional feature balance

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Few-shot learning with representative global prototype

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NEURAL NETWORKS 2024年 180卷 106600页

作者： Liu, Yukun Shi, Daming Lin, Hexiu Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

Few-shot learning is often challenged by low generalization performance due to the model is mostly learned with the base classes only. To mitigate the above issues, a few-shot learning method with representative global prototype is proposed in this paper. Specifically, to enhance generalization to novel class, we propose a strategy for jointly training base and novel classes. This process produces prototypes characterizing the class information called representative global prototypes. Additionally, to avoid the problem of data imbalance and prototype bias caused by newly added categories of sparse samples, a novel sample synthesis method is proposed for augmenting more representative samples of novel class. Finally, representative samples and non-representative samples with high uncertainty are selected to enhance the representational and discriminative abilities of the global prototype. Intensive experiments have been conducted on two popular benchmark datasets, and the experimental results show that this method significantly improves the classification ability of few-shot learning tasks and achieves state-of-the-art performance.

关键词： Few-shot learning Representative global prototype conditional variational autoencoder Sample synthesis

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