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Chinese Intelligent Systems Conference, CISC 2019

作者： Guo, Chenyang Yu, Guizhen Zhang, Li Niu, Huan Zhou, Bin Wang, Zhangyu Li, Da School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beihang University Beijing100191 China

ISBN: (纸本)9789813296978

Pedestrians are one of the most important elements in traffic scenes. In autopilot system, pedestrians need to be accurately detected. We found the latest research that 3D light detection and ranging (LIDAR) sensors can provide more accurate pedestrian location information. In this paper, the AdaBoost algorithm is used to improve the accuracy of the support vector machine (SVM) and high-accuracy pedestrian detection based on real-time 3D point cloud data. The first step is to process 3D points to 2D grid, followed by using k-means clustering algorithm to extract candidate points of the pedestrian. Next, nine features are chosen to train the SVM, the AdaBoost iterative process is used to reduce the further error rate to meet the classification requirements. This method has achieved significant progress in our experiment, the average classification accuracy has been improved to 92.4% per scan. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Adaptive boosting

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Hybrid Resource Orchestration and Scheduling for Cyber-Physical-Human Systems

Hybrid Resource Orchestration and Scheduling for Cyber-Physi...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Jianyong Zhu Xu Wang Tianyu Wo Chunming Hu Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

ISBN: (纸本)9781728176499;9781728176505

Recently Cyber-Physical-Human Systems (CPHS) have been attracted much attention. Unlike existing computing paradigms such as Cloud computing and Edge computing, CPHS usually comprise many types of hybrid resources such as software and hardware in the cloud layer, network devices and edge servers in the network layer, as well as the physical devices and human participation. Such hybrid resource makes it challenging to efficient resource scheduling for CPHS. Previous studies on CPHS often focus on improving scheduling efficiency for specific application scenarios and thus are limited for other CPHS. In this work, we present a unified hybrid resource scheduling framework for CPHS, including a hybrid resource model for multiple types of resource providers and participants in CPHS, a hybrid resource orchestration tool that can describe our hybrid resource model as the resource requirements, and a scheduling mechanism to satisfy the resource requirements especially by considering the resource constraints and crosslayer scheduling in CPHS. We perform the case study of typical CPHS scenarios and show that our hybrid resource model and scheduling framework is effective, and provide a new view for improving the resource provision efficiency in CPHS.

关键词： Cloud computing Processor scheduling High performance computing Conferences Computational modeling Tools Software

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A Vision-Based Method for Vehicle Forward Collision Warning

A Vision-Based Method for Vehicle Forward Collision Warning

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Chinese Intelligent Systems Conference, CISC 2019

作者： Zhang, Yanfei Wang, Zhangyu Zhou, Bin Yu, Guizhen Hu, Chaowei Zhang, Li School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beihang University Beijing100191 China

ISBN: (纸本)9789813296978

Forward Collision Warning (FCW) system can automatically measure the distance between obstacles and provide early warning, which can effectively provide safety guarantee for vehicle travel and reduce the probability of traffic accidents. The vision-based approaches are always popular because vision sensors have the characteristics of low cost and rich image information. In this paper, a vision-based forward collision warning method is proposed. The method contains three main stages: (1) detect obstacle based on multi-feature fusion using convolution neural networks (CNN), (2) estimate the relative distance, relative velocity from vehicles and collision time, (3) design obstacles hazard level discrimination strategy for different road scenarios. The algorithm is tested on our own dataset and the experiment results showed that the method has good feasibility and robustness. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Hazards

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Intensity Enhancement Via Gan for Multimodal Facial Expression Recognition

Intensity Enhancement Via Gan for Multimodal Facial Expressi...

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IEEE International Conference on Image Processing

作者： Kangkang Zhu Yunhong Wang Hongyu Yang Di Huang Liming Chen Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Face expression recognition (FER) on low intensity is not well studied in the literature. This paper investigates this new problem and presents a novel Generative Adversarial Network (GAN) based multimodal approach to it. The method models the tasks of intensity enhancement and expression recognition jointly, ensuring that the synthesize faces not only present expression of high intensity, but also truly contribute to promoting the performance of FER. Extensive experiments are conducted on the BU-3DFE and BU-4DFE datasets. State-of-the-art FER performance clearly validates the effectiveness of the proposed method.

关键词： Training Image recognition Face recognition Conferences Generative adversarial networks Gallium nitride Task analysis

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Penetration trajectory optimization for the hypersonic gliding vehicle encountering two interceptors

arXiv

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arXiv 2022年

作者： Shen, Zhipeng Yu, Jianglong Dong, Xiwang Hua, Yongzhao Ren, Zhang School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

The penetration trajectory optimization problem for the hypersonic gliding vehicle (HGV) encountering two interceptors is investigated. The HGV penetration trajectory optimization problem considering the terminal target area is formulated as a nonconvex optimal control problem. The nonconvex optimal control problem is transformed into a second-order cone programming (SOCP) problem, which can be solved by state-of-the-art interior-point methods. In addition, a penetration strategy that only requires the initial line-of-sight angle information of the interceptors is proposed. The convergent trajectory obtained by the proposed method allows the HGV to evade two interceptors and reach the target area successfully. Furthermore, a successive SOCP method with a variable trust region is presented, which is critical to balancing the trade-off between time consumption and optimality. Finally, the effectiveness and performance of the proposed method are verified by numerical simulations. Copyright © 2022, The Authors. All rights reserved.

关键词： Numerical methods

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Structured probabilistic end-to-end learning from crowds 29

Structured probabilistic end-to-end learning from crowds

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Chen, Zhijun Wang, Huimin Sun, Hailong Chen, Pengpeng Han, Tao Liu, Xudong Yang, Jie SKLSDE Lab School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China Web Information Systems Delft University of Technology Netherlands

ISBN: (纸本)9780999241165

End-to-end learning from crowds has recently been introduced as an EM-free approach to training deep neural networks directly from noisy crowdsourced annotations. It models the relationship between true labels and annotations with a specific type of neural layer, termed as the crowd layer, which can be trained using pure backpropagation. Parameters of the crowd layer, however, can hardly be interpreted as annotator reliability, as compared with the more principled probabilistic approach. The lack of probabilistic interpretation further prevents extensions of the approach to account for important factors of annotation processes, e.g., instance difficulty. This paper presents SpeeLFC, a structured probabilistic model that incorporates the constraints of probability axioms for parameters of the crowd layer, which allows to explicitly model annotator reliability while benefiting from the end-to-end training of neural networks. Moreover, we propose SpeeLFC-D, which further takes into account instance difficulty. Extensive validation on real-world datasets shows that our methods improve the state-of-the-art. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Deep neural networks

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Vision-Based Vehicle Detection in Foggy Days by Convolutional Neural Network

Vision-Based Vehicle Detection in Foggy Days by Convolutiona...

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Chinese Intelligent Systems Conference, CISC 2019

作者： Yu, Guizhen Wang, Sifen Li, Mingxing Guo, Yaxin Wang, Zhangyu School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9789813296978

Vehicle detection plays an important role in advanced driving assisted system and autonomous driving system. However, the existing vehicle detection methods are not robust in harsh environments, especially in foggy environment. To solve this problem, a vision-based vehicle detection structure using convolutional neural network is presented to detect the vehicle in foggy days. In our vehicle detection structure, a pair of encoders and decoders is used to estimate atmospheric illumination and transmissivity, and to establish the defogging image firstly. And then, the vehicle detection is implemented by a proposed vehicle detection method which predict the left-top key point as well as the right- bottom key point of the vehicle, thus get the bounding box of the vehicle. To verify the effectiveness of the new method, a data set based on the video generated from PreScan simulation platform is set up. And the new vehicle detection method is tested in multiple scenarios such as left turn, right turn, uphill, downhill. Experimental results show that our vehicle detection structure can effectively detect vehicles in foggy days. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolution

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Modeling Taxi Customer Searching Behavior Using High-Resolution GPS data

Modeling Taxi Customer Searching Behavior Using High-Resolut...

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International Conference on Intelligent Transportation

作者： Zhen Guo Mengyan Hao Bin Yu School of Transportation Science and Engineering Beihang University Beijing PR China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing PR China

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

Occupancy of vacant taxis brings waste of resources and heavy traffic pressure to urban traffic network. To enable better efficiency of taxis and less traffic congestion, a logit-based model is developed to describe vacant taxi drivers' route choice behavior and decision-making mechanism when searching for next customer. The proposed model is based on a multinomial logit model (MNL), considering two novel influencing indicators including the path unreliability (PU) and expected rate of return (EROR). In order to simplify the model construction and reduce computational cost, we divide the research area into identical squares with a 0.5-km resolution. Then the customer searching movements are extracted from the high-resolution GPS data of more than 8000 taxis in Shanghai to validate the logit model. The model results show that the customer searching behavior of vacant taxi drivers is significantly influenced by PU and EROR. Moreover, the effect of these two indicators on customer searching behavior varies with the time of day.

关键词： Public transportation Global Positioning System Trajectory data mining Computed tomography Delays Vehicles

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Efficient Low-Resolution Face Recognition via Bridge Distillation

arXiv

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arXiv 2024年

作者： Ge, Shiming Zhao, Shengwei Li, Chenyu Zhang, Yu Li, Jia The Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China SenseTime Group Limited 100084 China The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China The Peng Cheng Laboratory Shenzhen518055 China

Face recognition in the wild is now advancing towards light-weight models, fast inference speed and resolution-adapted capability. In this paper, we propose a bridge distillation approach to turn a complex face model pretrained on private high-resolution faces into a light-weight one for low-resolution face recognition. In our approach, such a cross-dataset resolution-adapted knowledge transfer problem is solved via two-step distillation. In the first step, we conduct cross-dataset distillation to transfer the prior knowledge from private high-resolution faces to public high-resolution faces and generate compact and discriminative features. In the second step, the resolution-adapted distillation is conducted to further transfer the prior knowledge to synthetic low-resolution faces via multi-task learning. By learning low-resolution face representations and mimicking the adapted high-resolution knowledge, a light-weight student model can be constructed with high efficiency and promising accuracy in recognizing low-resolution faces. Experimental results show that the student model performs impressively in recognizing low-resolution faces with only 0.21M parameters and 0.057MB memory. Meanwhile, its speed reaches up to 14,705, 934 and 763 faces per second on GPU, CPU and mobile phone, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi-task learning

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Post-Quantum Blockchain over Lattice

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Computers, Materials & Continua 2020年第5期63卷 845-859页

作者： Xiao Zhang Faguo Wu Wang Yao Wenhua Wang Zhiming Zheng School of Mathematics and Systems Science Beihang Universityand Key Laboratory of MathematicsInformatics and Behavioral SemanticsMinistry of EducationBeijing100191China Peng Cheng Laboratory Shenzhen518055China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing100191China Aviation Industry Development Research Center of China BeijingChina

Blockchain is an emerging decentralized architecture and distributed computing paradigm underlying Bitcoin and other cryptocurrencies,and has recently attracted intensive attention from governments,financial institutions,high-tech enterprises,and the capital *** cryptographic security relies on asymmetric cryptography,such as ECC,***,with the surprising development of quantum technology,asymmetric cryptography schemes mentioned above would become ***,lattice-based cryptography scheme was proposed to be secure against attacks in the quantum *** 2018,with the aid of Bonsai Trees technology,Yin et al.[Yin,Wen,Li et al.(2018)]proposed a lattice-based authentication method which can extend a lattice space to multiple lattice spaces accompanied by the corresponding *** their scheme has theoretical significance,it is unpractical in actual situation due to extremely large key size and signature *** this paper,aiming at tackling the critical issue of transaction size,we propose a post quantum blockchain over *** using SampleMat and signature without trapdoor,we can reduce the key size and signature size of our transaction authentication approach by a significant *** of using a whole set of vectors as a basis,we can use only one vector and rotate it enough times to form a *** on the hardness assumption of Short Integer Solution(SIS),we demonstrate that the proposed anti-quantum transaction authentication scheme over lattice provides existential unforgeability against adaptive chosen-message attacks in the random *** compared to the Yin et al.[Yin,Wen,Li et al.(2018)]scheme,our scheme has better performance in terms of energy consumption,signature size and signing key *** the underlying lattice problem is intractable even for quantum computers,our scheme would work well in the quantum age.

关键词： Blockchain post quantum lattice random oracle

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