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Pendulum-like oscillation controller for micro aerial vehicle with ducted fan based on LQR and PSO

Science China(Technological Sciences) 2013年第2期56卷 423-429页

作者： DUAN HaiBin SUN ChangHao State Key Laboratory of Virtual Reality Technology and Systems Beihang University Science and Technology on Aircraft Control Laboratory Beihang University

This paper describes a novel type of pendulum-like oscillation controller for micro air vehicle(MAV) hover and stare state in the presence of external disturbances,which is based on linear-quadratic regulator(LQR) and particle swarm optimization(PSO).A linear mathematical model of pendulum phenomenon based upon actual wind tunnel test data representing the hover mode is established,and a hybrid LQR and PSO approach is proposed to stabilize *** is applied to parameter optimization of the designed LQR controller.A series of comparative experiments are conducted,and the results have verified the feasibility,effectiveness and robustness of our proposed approach.

关键词： micro air vehicle (MAV) pendulum-like oscillation linear-quadratic regulator (LQR) particle swarm optimization (PSO)

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Learning Semantic Concepts from Noisy Media Collection for Automatic Image Annotation

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Chinese Journal of Electronics 2015年第4期24卷 790-794页

作者： TIAN Feng SHEN Xukun School of Computer and Information Technology Northeast Petroleum University State Key Laboratory of Virtual Reality Technology and Systems Beihang University

Along with the explosive growth of images, automatic image annotation has attracted great interest of various research communities. However, despite the great progress achieved in the past two decades, automatic annotation is still an important open problem in computer vision, and can hardly achieve satisfactory performance in real-world environment. In this paper, we address the problem of annotation when noise is interfering with the dataset. A semantic neighborhood learning model on noisy media collection is proposed. Missing labels are replenished, and semantic balanced neighborhood is construct. The model allows the integration of multiple label metric learning and local nonnegative sparse *** construct semantic consistent neighborhood for each sample, thus corresponding neighbors have higher global similarity, partial correlation, conceptual similarity along with semantic balance. Meanwhile, an iterative denoising method is also proposed. The method proposed makes a marked improvement as compared to the current state-ofthe-art.

关键词： Image annotation Noisy media collection Semantic neighborhood

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Joint self-supervised and reference-guided learning for depth inpainting

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Computational Visual Media 2022年第4期8卷 597-612页

作者： Heng Wu Kui Fu Yifan Zhao Haokun Song Jia Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Depth information can benefit various computer vision tasks on both images and ***,depth maps may suffer from invalid values in many pixels,and also large *** improve such data,we propose a joint self-supervised and reference-guided learning approach for depth *** the self-supervised learning strategy,we introduce an improved spatial convolutional sparse coding module in which total variation regularization is employed to enhance the structural information while preserving edge *** module alternately learns a convolutional dictionary and sparse coding from a corrupted depth ***,both the learned convolutional dictionary and sparse coding are convolved to yield an initial depth map,which is effectively smoothed using local contextual *** reference-guided learning part is inspired by the fact that adjacent pixels with close colors in the RGB image tend to have similar depth *** thus construct a hierarchical joint bilateral filter module using the corresponding color image to fill in large *** summary,our approach integrates a convolutional sparse coding module to preserve local contextual information and a hierarchical joint bilateral filter module for filling using specific adjacent *** results show that the proposed approach works well for both invalid value restoration and large hole inpainting.

关键词： depth inpainting self-supervised learning reference-guided learning

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Mitigating Surrogate Bias in Black-Box Pedestrian Attacks via Mask-Based Gradient Backpropagation

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Su, Binyi Li, Dongying Zhang, Hua Chen, Haiyong Li, Yong Zhou, Zhong Hebei University of Technology School of Artificial Intelligence and Data Science Tianjin300401 China China Xiongan Group Digital City Technology Co. Ltd China Chinese Academy of Sciences State Key Laboratory of Information Security Institute of Information Engineering Beijing100093 China Beihang University School of Instrumentation and Optoelectronic Engineering Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Zhongguancun Laboratory Beijing100190 China

This paper studies transfer-based black-box pedestrian attacks in traffic scenes, where an attacker aims to deceive a target model by generating malicious examples using a surrogate model. The goal is to craft adversarial examples that can be misdetected by the target model, even though the attacker has limited knowledge about the target model’s architecture and parameters. However, the contribution of adversarial transferability to improved attack performance may be weakened due to possible differences in model architectures and training datasets between the surrogate model and the target model (referred to as "surrogate bias"). To mitigate this challenging issue, we propose a black-box attack method by developing an adversarial patch generator based on a novel mask-based gradient backpropagation (MGB) method. The core innovation of this paper lies in the proposed MGB method, which combines random mask augmentation and weighted gradient backpropagation to enhance adversarial transferability, along with a mask regularization technique that stabilizes the patch distribution, improving attack robustness. Innovatively, MGB leverages the random mask augmentation combined with weighted gradient backpropagation to craft the adversarial patch, encouraging the model to learn more transferability patterns of the crafted patch. Furthermore, to mitigate the challenging variance shift issue raised by random masks between the training and testing phases, we propose a mask regularization method to penalize large value updates in the patch, effectively stabilizing the learned distribution of the adversarial patch. Extensive experiments conducted on the classical benchmark demonstrate that our proposed attacking method has achieved superior attacking performance against multiple mainstream detectors and pedestrian datasets. © 1963-2012 IEEE.

关键词： Object detection

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Interactive two-sided refraction for dynamic object on GPU

Interactive two-sided refraction for dynamic object on GPU

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4th International Conference on Image and Graphics

作者： Li, Shuai Gao, Yujian Hao, Aimin Wang, Lili State Key Laboratory of Virtual Reality Technology China

ISBN: (纸本)9780769529295

This paper introduces an approach to interactively compute two-sided refraction on GPU and gains comparable realistic effects with ray tracing, while most current interactive techniques are restricted to single refraction. Our approach is also efficient for total internal reflection (TIR). When processing rigid movable object, we render the object's normal to a cube map first before entering the render loop. And for deformable object, the object's normal is rendered to a cube map at each frame. Then we compute the back interface refraction point by binary search in fragment shader. At last the refracted color is determined by indexing into a distant environment cube map using the second refracted ray as texture coordinate. Because our approach can fully exploit the capabilities of GPU, it achieves realistic refraction results and interactive frame rate even for fairly complex objects on common PCs.

关键词： two-sided refraction total internal reflection dynamic objects real-time rendering GPU

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Modeling Garment Seam from a Single Image

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Journal of Computer Science & technology 2018年第3期33卷 463-474页

作者： Chen-Xu Zhang Xiao-Wu Chen Hong-Yu Wu Bin Zhou State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing 100191 China

We propose an automatic garment seam modeling framework to create a garment model with the seam structure from a single image. In order to achieve this, a marked seam image database and parametric seam models have been set up. Given a real seam image, we first identify the type of the seam image based on our marked semn image database and the seam parameters are parsed automatically by our sewing thread estimation method. Second the seam initial model is generated through the pre-defined parametric seam models. A garment model with the seam structure is finally obtained based on the seam position infrmation which users have marked on the garment. Moreover. we verify the effectiveness of our method with numerous experiments.

关键词： image-based 3D modeling garment modeling garment seam

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Multi-granularity sequence generation for hierarchical image classification

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Computational Visual Media 2024年第2期10卷 243-260页

作者： Xinda Liu Lili Wang State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Peng Cheng Laboratory Shengzhen 518000China

Hierarchical multi-granularity image classification is a challenging task that aims to tag each given image with multiple granularity labels *** methods tend to overlook that different image regions contribute differently to label prediction at different granularities,and also insufficiently consider relationships between the hierarchical multi-granularity *** introduce a sequence-to-sequence mechanism to overcome these two problems and propose a multi-granularity sequence generation(MGSG)approach for the hierarchical multi-granularity image classification ***,we introduce a transformer architecture to encode the image into visual representation ***,we traverse the taxonomic tree and organize the multi-granularity labels into sequences,and vectorize them and add positional *** proposed multi-granularity sequence generation method builds a decoder that takes visual representation sequences and semantic label embedding as inputs,and outputs the predicted multi-granularity label *** decoder models dependencies and correlations between multi-granularity labels through a masked multi-head self-attention mechanism,and relates visual information to the semantic label information through a crossmodality attention *** this way,the proposed method preserves the relationships between labels at different granularity levels and takes into account the influence of different image regions on labels with different *** on six public benchmarks qualitatively and quantitatively demonstrate the advantages of the proposed *** project is available at https://***/liuxindazz/mgs.

关键词： hierarchical multi-granularity classification vision and text transformer sequence generation fine-grained image recognition cross-modality attenti

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Monocular Video Guided Garment Simulation

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Journal of Computer Science & technology 2015年第3期30卷 528-539页

作者：李发明陈小武周彬卢飞翔郭侃符强 State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing 100191 China

We present a prototype to generate a garment-shape sequence guided by a monocular video sequence. It is a combination of a physically-based simulation and a boundary-based modification. Given a garment in the video worn on a mannequin, the simulation generates a garment initial shape by exploiting the mannequin shapes estimated from the video. The modification then deforms the simulated 3D shape into such a shape that matches the garment 2D boundary extracted from the video. According to the matching correspondences between the vertices on the shape and the points on the boundary, the modification is implemented by attracting the matched vertices and their neighboring vertices. For best-matching correspondences and efficient performance, three criteria are introduced to select the candidate vertices for matching. Since modifying each garment shape independently may cause inter-frame oscillations, changes by the modification are also propagated from one frame to the next frame. As a result, the generated garment 3D shape sequence is stable and similar to the garment video sequence. We demonstrate the effectiveness of our prototype with a number of examples.

关键词： garment simulation monocular video shape correspondence

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From animal collective behaviors to swarm robotic cooperation

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National Science Review 2023年第5期10卷 82-99页

作者： Haibin Duan Mengzhen Huo Yanming Fan State Key Laboratory of Virtual Reality Technology and Systems School of Automation Science and Electrical EngineeringBeihang University Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng Laboratory AVIC Shenyang Aircraft Design and Research Institute

The collective behaviors of animals,from schooling fish to packing wolves and flocking birds,display plenty of fascinating phenomena that result from simple interaction rules among *** emergent intelligent properties of the animal collective behaviors,such as self-organization,robustness,adaptability and expansibility,have inspired the design of autonomous unmanned swarm *** article reviews several typical natural collective behaviors,introduces the origin and connotation of swarm intelligence,and gives the application case of animal collective *** this basis,the article focuses on the forefront of progress and bionic achievements of aerial,ground and marine robotics swarms,illustrating the mapping relationship from biological cooperative mechanisms to cooperative unmanned cluster ***,considering the significance of the coexisting-cooperative-cognitive human-machine system,the key technologies to be solved are given as the reference directions for the subsequent exploration.

关键词： collective behaviors swarm intelligence cooperative robotics swarm human-machine system

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A multi-strategy pigeon-inspired optimization approach to active disturbance rejection control parameters tuning for vertical take-off and landing fixed-wing UAV

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Chinese Journal of Aeronautics 2022年第7期35卷 19-30,I0001页

作者： Hangxuan HE Haibin DUAN State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China Peng Cheng Laboratory Shenzhen 518000China

In this *** Disturbance Rejection Control(ADRC)is utilized in the pitch control of a vertical take-off and landing fixed-wing Unmanned Aerial Vehicle(UAV)to address the problem of height fluctuation during the transition from hover to level *** the difficulty of parameter tuning of ADRC as well as the requirement of accuracy and rapidity of the controller,a Multi-Strategy Pigeon-Inspired Optimization(MSPIO)algorithm is *** Swarm Optimization(PSO),Genetic Algorithm(GA),the basic Pigeon-Inspired Optimization(PIO),and an improved PIO algorithm CMPIO are *** addition,the optimized ADRC control system is compared with the pure Proportional-Integral-Derivative(PID)control system and the non-optimized ADRC control *** effectiveness of the designed control strategy for forward transition is verified and the faster convergence speed and better exploitation ability of the proposed MSPIO algorithm are confirmed by simulation results.

关键词： Active Disturbance Rejection Control(ADRC) Pigeon-inspired optimization algorithm Transition mode Unmanned Aerial Vehicle(UAV) Vertical take-off and landing

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