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29th International Meshing Roundtable, IMR 2021

作者： Zheng, Xiaopeng Shi, Xin Wang, Chao Zhu, Yiming Luo, Zhongxuan Lei, Na Dalian University of Technology Liaoning Dalian China State Key Laboratory of Intelligent Manufacturing System Technology Beijing China Beijing Advanced Innovation Center for Imaging Theory and Technology Beijing China Institute of Artificial Intelligence Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781733489010

Quadrilateral meshes generated for the industrial applications have to meet some special quality demands and it remains a tricky problem. We propose a novel method for high quality quad mesh generation based on the equivalence relation between quadrilateral meshes and meromorphic quartic differentials[1]. As long as more than one meromorphic quartic differential were obtained, the linear space extended by them is also available for us according to the meromorphic quartic differentials space theory. We can search the space for better quadrilateral mesh and the high quality mesh construction problem becomes a optimization problem. This paper gives the algorithm and carries out experiments under two different mesh quality constrains including mesh cell size constrain and feature lines constrain. The experimental results demonstrate the efficiency and efficacy of the algorithm. © 2021 held by the authors of the individual papers.

关键词： Mesh generation

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Meta Balanced Network for Fair Face Recognition

arXiv

引用

arXiv 2022年

作者： Wang, Mei Zhang, Yaobin Deng, Weihong The Pattern Recognition and Intelligent System Lab. School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Key Lab. of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

Although deep face recognition has achieved impressive progress in recent years, controversy has arisen regarding discrimination based on skin tone, questioning their deployment into real-world scenarios. In this paper, we aim to systematically and scientifically study this bias from both data and algorithm aspects. First, using the dermatologist approved Fitzpatrick Skin Type classification system and Individual Typology Angle, we contribute a benchmark called Identity Shades (IDS) database, which effectively quantifies the degree of the bias with respect to skin tone in existing face recognition algorithms and commercial APIs. Further, we provide two skin-tone aware training datasets, called BUPT-Globalface dataset and BUPT-Balancedface dataset, to remove bias in training data. Finally, to mitigate the algorithmic bias, we propose a novel meta-learning algorithm, called Meta Balanced Network (MBN), which learns adaptive margins in large margin loss such that the model optimized by this loss can perform fairly across people with different skin tones. To determine the margins, our method optimizes a meta skewness loss on a clean and unbiased meta set and utilizes backward-on-backward automatic differentiation to perform a second order gradient descent step on the current margins. Extensive experiments show that MBN successfully mitigates bias and learns more balanced performance for people with different skin tones in face recognition. The proposed datasets are available at http://***/RFW/***. © 2022, CC BY.

关键词： Face recognition

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Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving

arXiv

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arXiv 2024年

作者： Hu, Xuemin Chen, Pan Wen, Yijun Tang, Bo Chen, Long School of Artificial Intelligence Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Wuhan430062 China Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States State Key Laboratory of Multimodal Artificial Intelligence Systems State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China WAYTOUS Inc. Beijing100083 China Shenzhen518107 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an710049 China

Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving systems. Safe RL methods are developed to handle this issue by constraining the expected safety violation costs as a training objective, but the occurring probability of an unsafe state is still high, which is unacceptable in autonomous driving tasks. Moreover, these methods are difficult to achieve a balance between the cost and return expectations, which leads to learning performance degradation for the algorithms. In this paper, we propose a novel algorithm based on the long and short-term constraints (LSTC) for safe RL. The short-term constraint aims to enhance the shortterm state safety that the vehicle explores, while the long-term constraint enhances the overall safety of the vehicle throughout the decision-making process, both of which are jointly used to enhance the vehicle safety in the training process. In addition, we develop a safe RL method with dual-constraint optimization based on the Lagrange multiplier to optimize the training process for end-to-end autonomous driving. Comprehensive experiments were conducted on the MetaDrive simulator. Experimental results demonstrate that the proposed method achieves higher safety in continuous state and action tasks, and exhibits higher exploration performance in long-distance decision-making tasks compared with state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

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3D-CVQE: An Effective 3D-CNN Quality Enhancement for Compressed Video Using Limited Coding Information

3D-CVQE: An Effective 3D-CNN Quality Enhancement for Compres...

引用

Data Compression Conference (DCC)

作者： Xuan Sun Pengyu Liu Kebin Jia Congcong Wang Beijing University of Technology Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (纸本)9781665403337;9781665447850

How to obtain higher quality reconstructed video within limited coding resources is a research focus for video coding. First, the fluctuating quality, missing pixel and position fluctuation characteristics of the compressed video are found in this paper based on the block-based coding frameworks. Then, in order to reduce the degradation of compressed video quality caused by the above three characteristics, an non-aligned 3D-CNNs for compressed video quality enhancement called "3D-CVQE" is proposed, which can preserve and utilize limited input information in both temporal and spatial domains effectively. Finally, the experiments results validate the effectiveness and generalization ability of the proposed 3D-CVQE approach in the quality enhancement of compressed video.

关键词： Video coding Degradation Fluctuations Data compression Encoding Quality assessment Video recording

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Semantic segmentation of track image based on deep neural network

引用

The Journal of China Universities of Posts and Telecommunications 2020年第5期27卷 23-33页

作者： Wang Zhaoying Zhou Junhua Liao Zhonghua Zhai Xiang Zhang Lianping School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Complex Product Advanced Manufacturing Kngineering Research Center Beijing Simulation CenterBeijing 100854China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System EngineeringBeijing 100854China Alibaba Cloud Computing Beijing 100020China

In this paper,deep learning technology was utilited to solve the railway track recognition in intrusion detection *** railway track recognition can be viewed as semantic segmentation task which extends image processing to pixel level *** encoder-decoder architecture DeepLabv3+model was applied in this work due to its good performance in semantic segmentation *** images of the railway track collected from the video surveillance of the train cab were used as experiment dataset in this work,the following improvements were made to the *** first aspect deals with over-fitting problem due to the limited amount of training *** augmentation and transfer learning are applied consequently to rich the diversity of data and enhance model robustness during the training ***,different gradient descent methods are compared to obtain the optimal optimizer for training model *** third problem relates to data sample imbalance,cross entropy(CE)loss is replaced by focal loss(FL)to address the issue of serious imbalance between positive and negative *** of the improved DeepLabv3+model with above solutions is demonstrated by experiment results with different system parameters.

关键词： railway track recognition convolutional neural networks semantic segmentation DeepLabv3+

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_{2}$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-Databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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An Improved Task Planning Method for Unmanned Swarms with Coupled Physical and Logical Constraints

An Improved Task Planning Method for Unmanned Swarms with Co...

引用

IEEE International Conference on Unmanned systems (ICUS)

作者： Xiubin Chen Lei Zhang Fang Xu Weiran Yao School of Astronautics Harbin Institude of Technology Harbin China Wuhan 2nd Ship Design and Research Institute Wuhan China Marine Military Intelligence Innovation Institute CSSC Systems Engineering Research Institute Beijing China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

Traditional task allocation methods for unmanned swarm systems ignore the effects of actual paths, resulting in estimation accuracy reduction. This paper formulates task planning problem by incorporating physical and logical constraints, and establishes an integrated framework of task allocation and path planning. Conflict-based search method is used to address path planning with physical constraints. A genetic algorithm is employed to solve multi-traveling salesman allocation problem. A bounded suboptimal optimization, a data dictionary, and an island model are introduced to accelerate the convergence speed of the genetic algorithm. The experiments verify that compared to the decoupled task planning methods, the proposed method improves task execution efficiency and remains adaptability to various complex spatial maps.

关键词： Dictionaries Search methods Estimation Path planning Data models Planning Resource management Optimization Genetic algorithms Convergence

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Memory-Adaptive Vision-and-Language Navigation

SSRN

引用

SSRN 2023年

作者： He, Keji Jing, Ya Huang, Yan Lu, Zhihe An, Dong Wang, Liang Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China ByteDance AI Lab Beijing China National University of Singapore Singapore School of Future Technology University of Chinese Academy of Sciences Beijing China

Vision-and-Language Navigation (VLN) requests an agent to navigate in 3D environments following given instructions, where history is critical for decisionmaking in dynamic navigation process. Particularly, a memory bank storing histories is widely used in existing methods to incorporate with multimodel representations in current scenes for better decision-making. However, by weighting each history with a simple scalar, those methods cannot purely utilize the informative cues that co-exist with detrimental contents in each history, thereby inevitably introducing noises into decision-making. To that end, we propose a novel Memory-Adaptive Model (MAM) that can dynamically restrain the detrimental contents in histories for retaining contents that benefit navigation only. Specifically, two key modules, Visual and Textual Adaptive Modules, are designed to restrain history noises based on scene-related vision and text, respectively. A Reliability Estimator Module is further introduced to refine above adaptation operations. Our experiments on the widely used RxR and R2R datasets show that MAM outperforms its baseline method by 4.0% / 2.5% and 2% / 1% on the validation unseen/test split, respectively, wrt the SR metric. © 2023, The Authors. All rights reserved.

关键词： Navigation

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Parallel Theory-Based Risk Assessment for Networked Manufacturing systems

Parallel Theory-Based Risk Assessment for Networked Manufact...

引用

Digital Twins and Parallel intelligence (DTPI), IEEE International Conference on

作者： Zhikun Tao Gang Xiong Shaoming Peng Zhen Shen Fan Yang Yunjun Han School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Rail Transportation Shandong Jiaotong University Jinan China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

The increasing applications of robots and other automated machines into intelligent manufacturing poses great requirement of timely risk evaluation and recognition, which is critical to the stable operation of a factory. However, only a little previous work is centered on this problem. In this paper, we present a close-loop manufacturing situation management framework base on parallel theory to address the challenge of risk recognition in real time. A graph illustrates the relationships between various factors in networked manufacturing, followed by the introduction of corresponding risk measurement indices. And then the fuzzy analytic hierarchy process (FAHP) method is applied to assess the risk of the manufacturing. The factor with the maximum risk is selected out with max-max algorithmic operators and is input into the real system for checking out. The innovation of this paper lie in the proposition of a real time risk analytic framework for operational streamlines base on parallel theory and the integration of network graph with FAHP method for risk evaluation. The numerical testing results show that the model is valid, and its corresponding evaluation method can quickly recognize the highest risk occurred in the manufacturing. The risk evaluation framework and method can be generalized to other networked and complex systems, e.g., transportation systems.

关键词： Technological innovation Conferences Transportation Real-time systems Production facilities Libraries Manufacturing Digital twins Robots Testing

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Design of a Novel Autonomous Docking system With a Supporting Robotic Fish

Design of a Novel Autonomous Docking System With a Supportin...

引用

IEEE International Conference on Real-time computing and Robotics (RCAR)

作者： Huijie Dong Chunyang Zhao Tiezhu Qiao Jian Wang Di Chen Yan Meng Junzhi Yu Ministry of Education Key Laboratory of Advanced Transducers and Intelligent Control System Taiyuan University of Technology Taiyuan China Institute of Automation Laboratory of Cognitive and Decision Intelligence for Complex System Chinese Academy of Sciences Beijing China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China College of Electronics and Information Engineering Guangdong Ocean University Zhanjiang China

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named ‘CourierFish’. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks.

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