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3rd International Conference on Electronics, Electrical and Information Engineering, ICEEIE 2023

作者： Liu, Zhaojun Wu, Qiong Gao, Hanjun Du, Xueqin Yang, Shuai He, Kun Li, Xuefei Liu, Zhaoyu State Key Laboratory of Virtual Reality Technology and Systems Beijing Engineering Technological Research Center of High-efficient & Green CNC Machining Process and Equipment Beihang University Beijing100191 China Beijing Aerospace Propulsion Institute Beijing Beijing100048 China Jingdezhen Research Institute of Beihang University Jingdezhen333000 China

ISBN: (纸本)9781510671423

Based on iSIGHT optimization platform and ABAQUS, the frames of rocket liquid engine with multiple forms are optimized. Fast parametric modeling method, sensitivity analysis method and multi-objective genetic algorithm NCGA are combined to optimize the dimensions of frames. The frame modeling of different forms can be accomplished by using specified frame design parameters, thus greatly improving design efficiency. Finite element analysis and optimization were carried out on a single four-point pusher frame as an example. The influence of different section size parameters on the frame performance is analyzed. The optimized structure of frame reduces the amount of material used and increases the 1st order natural frequency within the constraints. © 2023 SPIE.

关键词： Finite element method

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Scene-independent Localization by Learning Residual Coordinate Map with Cascaded Localizers

Scene-independent Localization by Learning Residual Coordina...

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International Symposium on Mixed and Augmented reality (ISMAR)

作者： Junyi Wang Yue Qi State Key Laboratory of Virtual Reality Technology and Systems Beihang University School of Computer Science and Technology Shandong University Qingdao Research Institute of Beihang University

Visual localization plays an essential role in a variety of different fields. The indirect learning based method obtains an excellent performance, but it requests a training process in the target scene before the localization. To achieve deep scene-independent localization, we start by proposing the representation called residual coordinate map between a pair of images. Based on the structure, we put forward a network called SILocNet with the proposed residual coordinate map as the output. The network consists of feature extraction, multi-level feature fusion and transformer based coordinate decoder. Moreover, considering the dynamic scene, we introduce an additional segmentation branch that distinguishes fixed and dynamic parts to promote network perception. With SILocNet in place, a cascaded localizer design is presented for reducing the accumulative error. Meanwhile, the simple mathematical analysis behind the cascaded localizers is also provided. To verify how well our algorithm could perform, we conduct experiments on static 7 Scenes, ScanNet and dynamic TUM RGB-D. In particular, we train the network on ScanNet and test it on 7 Scenes and TUM RGB-D to demonstrate the generality performance. All experiments demonstrate superior performance to other existing methods. Additionally, the effects of the cascaded localizer design, feature fusion, transformer based coordinate decoder and segmentation loss are also discussed.

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Application of an Improved Residual Attention Neural Network in Mechanical Part Classification 9th

Application of an Improved Residual Attention Neural Network...

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9th International Symposium on Artificial Intelligence and robotics, ISAIR 2024

作者： Zhang, Tianrui Wang, Aizeng School of Mechanical Engineering and Automation Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

ISBN: (纸本)9789819629138

This paper introduces an improved neural network that integrates residual network and attention mechanisms, achieving a significant improvement of 99.50% accuracy in mechanical part classification tasks. The proposed network enhance accuracy, precision, recall, and F1-score compared to traditional CNNs and other advanced networks in handling complex backgrounds and multi-angle part images. This advancement shows potential in smart manufacturing, offering greater automation, minimizing human intervention. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart manufacturing

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Overall Structure Modeling and Modal Analysis of Liquid Rocket Engine Based on ABAQUS Secondary Development Platform 5

Overall Structure Modeling and Modal Analysis of Liquid Rock...

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5th International Conference on Decision Science and Management, ICDSM 2023

作者： Du, Xueqin Wu, Qiong Gao, Hanjun Liu, Zhaojun Yang, Shuai He, Kun Li, Xuefei Liu, Zhaoyu Beihang University Technological Research Center of High-Efficient & Green Cnc Machining Process and Equipment State Key Laboratory of Virtual Reality Technology and Systems Beijing Engineering Beijing China Jingdezhen Research Institute of Beihang University Jingdezhen China Jingdezhen Research Institute of Beihang University Jingdezhen China Beijing Aerospace Propulsion Institute Beijing China

ISBN: (纸本)9798350325010

With the development of computer technology, dynamic simulation technology becomes more and more important in the engine design and development cycle. The development and use of relevant special programs greatly shorten the engine development cycle. In this paper, the components and connection modes of liquid rocket engine are simplified reasonably and efficiently, and a high-precision equivalent model with the maximum deviation of 9.754% and the minimum deviation of 0.423% of the natural frequency obtained by modal analysis is established;The ABAQUS secondary development technology is used to build a modal analysis platform, so as to quickly and accurately realize modal calculation, which is of guiding significance to the designers of liquid rocket engines. © 2023 IEEE.

关键词： Modal analysis

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Automatic Medical Report Generation Based on Detector Attention Module and GPT-Based Word LSTM

Automatic Medical Report Generation Based on Detector Attent...

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Data-driven Optimization of Complex systems (DOCS), International Conference on

作者： Yunchao Gu Junfeng Sun Xinliang Wang Renyu Li Zhong Zhou State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Research Unit of Virtual Body and Virtual Surgery Technologies Chinese Academy of Medical Sciences Beijing China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

The writing of medical reports is extremely time-consuming for professional doctors. Despite the introduction of numerous deep learning-driven models for automated medical report generation, there remains considerable room for enhancing their performance. One obstacle is that general automatic medical report generation models have failed to consider the location information of the lesions. The other is that these models are limited by the small diagnostic report corpus that fails to improve the fluency of diagnostic reports. Therefore, we propose a novel automatic report generation model to address the aforementioned two challenges. We first propose Detector Attention Module to fuse the coarse-grained visual features and fine-grained location features, which improves the performance of automatic medical report generation. Meanwhile, we employ the Generative Pre-Trained Transformer (GPT) model, which can be pre-trained on unsupervised massive diagnostic reports, to extract contextual semantic information, aiming to enhance the fluency of diagnostic reports. On the open-source IU X-ray dataset, our model has shown an average improvement of 0.6% across six evaluation metrics compared to the current state-of-the-art (SOTA). This indicates that our model has the capacity to produce more precise and comprehensive diagnostic reports.

关键词： Measurement Visualization Semantics Detectors Medical services Writing Feature extraction Transformers Medical diagnostic imaging Optimization

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SG-NeRF: Semantic-guided Point-based Neural Radiance Fields

SG-NeRF: Semantic-guided Point-based Neural Radiance Fields

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yansong Qu Yuze Wang Yue Qi State Key Laboratory of Virtual Reality Technology and Systems Beihang University Qingdao Research Institute of Beihang University Qingdao China Peng Cheng Laboratory Shenzhen China

Neural Radiance Fields (NeRF) can successfully reconstruct room-scale scenes and achieve photo-realistic novel view synthesis results with densely captured input images. However, capturing hundreds of high-quality images in a single room is extremely laborious. We tackle this problem by greatly reducing the number of images input to NeRF while maintaining high-quality rendering results in a room-scale scene. In this paper, we propose semantic-guided point-based NeRF (SG-NeRF), which is capable of reconstructing the radiance field of a room-scale scene with tens of images. To this end, we leverage sparse 3D point clouds with neural features to be the geometry constraints of NeRF optimization and semantic prediction of both 2D images and 3D point clouds to guide the neighboring neural points searching at the ray marching procedure. With the semantic guidance, the sampled query points are capable of searching for neighboring neural points, which are structurally related to the query points accurately in a large area since of the unevenly distributed sparse point clouds. Extensive experimental results demonstrate that our approach outperforms previous state-of-the-art methods.

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A Comparative Study of Extended Kalman Filtering and Unscented Kalman Filtering on Lie Group for Stewart Platform state Estimation 6

A Comparative Study of Extended Kalman Filtering and Unscent...

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6th International Conference on Control and robotics Engineering, ICCRE 2021

作者： Xie, Binhai Dai, Shuling Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Beijing100191 China

ISBN: (纸本)9780738126128

For Stewart platform, high-quality kinematic motion signal plays an vital role in assessing flight training fidelity and providing feedback for trajectory following. In addition to relying on numerically solving forward kinematic problem from measurement of six leg displacement sensors to obtain kinematic motion, some researchers began to employ sensor fusion scheme through deploying inertial measurement unit (IMU) on upper moving platform. In this paper, we will construct Extended Kalman Filtering (EKF) and Unscented Kalman Filtering (UKF) on Lie group to address this fusion problem. This fusion problem is slightly different from Simultaneous Localization and Mapping (SLAM) or Visual Inertial Odometry (VIO) in that six linear displacement sensors are tightly coupled with IMU sensors while those sensors in SLAM or VIO problem still provides partial measurement of motion state. Numerical simulation experiment shows that both Lie group-based EKF (EKF-LG) and UKF (UKF-LG) which satisfy group affine property behave better than conventional Kalman filtering in consistency and accuracy. © 2021 IEEE.

关键词： Lie groups

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Perform Like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference 29

Perform Like an Engine: A Closed-Loop Neural-Symbolic Learni...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Niu, Guanglin Li, Bo Zhang, Yongfei Pu, Shiliang Institute of Artificial Intelligence Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Beijing Key Laboratory of Digital Media Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Hikvision Research Institute Hangzhou China

Knowledge graph (KG) inference aims to address the natural incompleteness of KGs, including rule learning-based and KG embedding (KGE) models. However, the rule learning-based models suffer from low efficiency and generalization while KGE models lack interpretability. To address these challenges, we propose a novel and effective closed-loop neural-symbolic learning framework EngineKG via incorporating our developed KGE and rule learning modules. KGE module exploits symbolic rules and paths to enhance the semantic association between entities and relations for improving KG embeddings and interpretability. A novel rule pruning mechanism is proposed in the rule learning module by leveraging paths as initial candidate rules and employing KG embeddings together with concepts for extracting more high-quality rules. Experimental results on four real-world datasets show that our model outperforms the relevant baselines on link prediction tasks, demonstrating the superiority of our KG inference model in a neural-symbolic learning fashion. The source code and datasets of this paper are available at https://***/ngl567/EngineKG. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Embeddings

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Pixel-Level Domain Adaptation: A New Perspective for Enhancing Weakly Supervised Semantic Segmentation

arXiv

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arXiv 2024年

作者： Du, Ye Fu, Zehua Liu, Qingjie The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Hangzhou Innovation Institute Beihang University Hangzhou310051 China

Recent attention has been devoted to the pursuit of learning semantic segmentation models exclusively from image tags, a paradigm known as image-level Weakly Supervised Semantic Segmentation (WSSS). Existing attempts adopt the Class Activation Maps (CAMs) as priors to mine object regions yet observe the imbalanced activation issue, where only the most discriminative object parts are located. In this paper, we argue that the distribution discrepancy between the discriminative and the non-discriminative parts of objects prevents the model from producing complete and precise pseudo masks as ground truths. For this purpose, we propose a Pixel-Level Domain Adaptation (PLDA) method to encourage the model in learning pixel-wise domain-invariant features. Specifically, a multi-head domain classifier trained adversarially with the feature extraction is introduced to promote the emergence of pixel features that are invariant with respect to the shift between the source (i.e., the discriminative object parts) and the target (i.e., the non-discriminative object parts) domains. In addition, we come up with a Confident Pseudo-Supervision strategy to guarantee the discriminative ability of each pixel for the segmentation task, which serves as a complement to the intra-image domain adversarial training. Our method is conceptually simple, intuitive and can be easily integrated into existing WSSS methods. Taking several strong baseline models as instances, we experimentally demonstrate the effectiveness of our approach under a wide range of settings. © 2024, CC BY.

关键词： Semantic Segmentation

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GoHD: Gaze-oriented and Highly Disentangled Portrait Animation with Rhythmic Poses and Realistic Expressions

arXiv

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arXiv 2024年

作者： Zhou, Ziqi Quan, Weize Shi, Hailin Li, Wei Wang, Lili Yan, Dong-Ming MAIS Institute of Automation Chinese Academy of Sciences China University of Chinese Academy of Sciences China NIO Banma State Key Laboratory of Virtual Reality Technology and Systems Beihang University China

Audio-driven talking head generation necessitates seamless integration of audio and visual data amidst the challenges posed by diverse input portraits and intricate correlations between audio and facial motions. In response, we propose a robust framework GoHD designed to produce highly realistic, expressive, and controllable portrait videos from any reference identity with any motion. GoHD innovates with three key modules: Firstly, an animation module utilizing latent navigation is introduced to improve the generalization ability across unseen input styles. This module achieves high disentanglement of motion and identity, and it also incorporates gaze orientation to rectify unnatural eye movements that were previously overlooked. Secondly, a conformer-structured conditional diffusion model is designed to guarantee head poses that are aware of prosody. Thirdly, to estimate lip-synchronized and realistic expressions from the input audio within limited training data, a two-stage training strategy is devised to decouple frequent and frame-wise lip motion distillation from the generation of other more temporally dependent but less audio-related motions, e.g., blinks and frowns. Extensive experiments validate GoHD’s advanced generalization capabilities, demonstrating its effectiveness in generating realistic talking face results on arbitrary subjects. Our implementation is available at https://***/Jia1018/GoHD. Copyright © 2024, The Authors. All rights reserved.

关键词： Eye movements

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