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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Duan, Jingqi Wu, Yunjie Wang, Ziyu State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Science and Technology on Aircraft Control Laboratory Beijing100191 China Large Aircraft Advanced Training Center Beihang University Beijing100191 China

ISBN: (纸本)9789811966125

In this paper, a fault-tolerant control scheme based on prescribed performance control is proposed to solve the pitch angle attitude control problem of large civil aircraft when actuator faults and external disturbances exist simultaneously. Firstly, the longitudinal motion model of civil aircraft with actuator fault is established. Among them, actuator faults and external disturbances are regarded as lumped disturbances. Then, a performance envelope is constructed based on the hyperbolic cosecant function which can adjust the transient convergence rate and the steady convergence rate respectively. In order to achieve fast online estimation of lumped disturbances, an RBF neural network observer is constructed. The introduction of tracking differentiator solves the problems of initial state constraint of prescribed performance control and "differential-explosion" in back-stepping control. After proving the stability of the system from the perspective of theoretical analysis, the simulation of pitch angle control under the condition of abrupt actuator faults and external disturbance is completed based on Boeing 747 longitudinal motion model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Attitude control

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Vulnerability Detection Based on Adapter Tuning and Enhanced Feature Learning 36

Vulnerability Detection Based on Adapter Tuning and Enhanced...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Luo, Hui Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China PengCheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangdong Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangdong Guangzhou China

ISBN: (纸本)1891706594

Pre-trained code models have achieved promising results in the vulnerability detection field. The prevailing approach is to adapt these models with vulnerability datasets using inefficient full-model fine-tuning. These pre-trained models primarily capture the semantic features of code, while neglecting its structural characteristics. To address these issues, this paper proposes a vulnerability detection method with adapter tuning and enhanced feature learning. First, adapter modules are introduced to UniXcoder and tune only the parameters in the adapters to extract semantic features. This significantly reduces the number of training parameters and better adapts the model to downstream tasks. Then, structural features, including control and data flow information, are extracted from the Program Dependence Graph (PDG) to compensate for the limitations of pre-trained models that rely solely on semantic features. Finally, the semantic and structural features are integrated to train the detection model. The experimental results demonstrate that our proposed method outperforms state-of-the-art approaches and is highly efficient in terms of both training parameters and training data. © 2024 Knowledge systems Institute Graduate School. All rights reserved.

关键词： Data flow graphs

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Learning to see speckle in the weak laser field through multimode fiber

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Optoelectronics Letters 2025年

作者： JI Yunqi SONG Binbin LI Xueqing LI Yonghui The Engineering Research Center of Learning-Based Intelligent System (Ministry of Education)， the Key Laboratory of Computer Vision and Systems (Ministry of Education)， and the School of Computer Science and Engineering， Tianjin University of Technology

Multimode fibers (MMFs) have great potential for endoscopic imaging due to the high number of modes and a small core diameter. Deep learning based on neural networks has received increasing attention in the field of scattering image reconstruction. However， most studies focus on designing complex network architectures to improve reconstruction， but these network models struggle to reconstruct images in a weak laser field. In the paper， a lightweight generative adversarial network model combined with a histogram specification algorithm is designed to reconstruct speckles in the weak laser field through MMF. Experimental results show that the reconstruction results of our algorithm have better metrics. Moreover， the model demonstrates excellent cross-domain generalization ability with regards to the Fashion-MNIST dataset. It is worth mentioning that we found that the speckles after inactivation still retain the ability to be reconstructed， which enhances the robustness of the model

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A Joint Longitudinal and Lateral Guidance Scheme for Reentry Gliding Phase of Hypersonic Vehicles

A Joint Longitudinal and Lateral Guidance Scheme for Reentry...

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Wang, Ziyu Wu, Yunjie Duan, Jingqi State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Science and Technology on Aircraft Control Laboratory Beijing100191 China Large Aircraft Advanced Training Center Beihang University Beijing100191 China

ISBN: (纸本)9789811966125

A joint longitudinal and lateral guidance law design scheme is proposed for the reference trajectory tracking problem of the reentry gliding phase of hypersonic vehicles. With respect to the longitudinal and lateral motion model of the hypersonic vehicle, a longitudinal guidance law based on the Global Fast Terminal Sliding Mode (GFTSM) algorithm and a lateral guidance law based on the Extended State Observer (ESO) is designed respectively, which are combined to get the bank angle for the hypersonic vehicle and obtain the final guidance scheme. From theoretical analysis, it shows that the guidance scheme has the characteristics of global rapidity and finite-time convergence, and both longitudinal and lateral guidance law are robust to system uncertainties which are caused by certain parameter perturbation and external interference. The effectiveness and superiority of this joint guidance scheme compared to traditional guidance method are verified by simulation experiments using the specific gliding reentry model and reference trajectory of a hypersonic vehicle. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hypersonic vehicles

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Generation of tunable superchiral spot in metal-insulator-metal waveguide

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Chinese Optics Letters 2023年第1期21卷 125-130页

作者：庄涛胡海峰詹其文 School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Zhangjiang Laboratory Shanghai 201204China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China

The chiral feature of an optical field can be evaluated by the parameter of g-factor enhancement,which is helpful to enhance chiroptic signals from a chiral *** this work,the superchiral spot has been theoretically proposed in metal-insulator-metal *** g-factor enhancement of the superchiral spot can be enhanced by 67-fold more than that of circularly polarized light,and the spot is confined in the deep wavelength scale along each spatial ***,the position of the superchiral spot can be tuned by manipulating the incident *** tunable superchiral spot may find applications in chiral imaging and sensing.

关键词： circular dichroism superchiral spot radially polarized beam metal-insulator-metal waveguide

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Probability distribution based resource management for multitenant cloud clusters

Probability distribution based resource management for multi...

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作者： Zhou, Jun Feng, Dan Wang, Fang Future Network Research Center Purple Mountain Laboratories Nanjing China School of Computer Huazhong University of Science and Technology Wuhan China Key Laboratory of Information Storage System Wuhan National Laboratory for Optoelectronics Wuhan China

Accurate resource allocation and colocating jobs are effective ways to increase resource utilization and reduce costs in modern datacenters. The main challenges are fluctuations in resource consumption and interference among colocated jobs. Therefore, we propose a resource management schema based on the probability distributions of resource consumption and completion time for multitenant cloud clusters. First, we found that the characteristics of the task can be well described by the probability distribution of resource consumption and completion time, and the probability distribution function can be obtained by Gaussian fitting. Second, we propose a probability distribution based resource allocation (PDRA) strategy for batch jobs. Third, we design a tail latency aware allocation (TLAA) strategy to use transient resources efficiently while ensuring tail latency requirements. Finally, we design a cost-effective resource revocation (CERR) strategy to revoke transient resources with minimal eviction costs. Experimental results demonstrate the efficiency of our resource management. Our resource allocation strategies (PDRA and TLAA) can effectively improve resource utilization and reduce job completion time. CERR can reduce the impact of resource revocation on batch jobs and perform better than existing resource revocation strategies. © 2021 John Wiley & Sons Ltd.

关键词： Resource allocation

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Semantics-Aware Avatar Locomotion Adaption for Indoor Cross-Scene AR Telepresence

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IEEE Transactions on Visualization and computer Graphics 2025年 PP卷 PP页

作者： Li, Yi-Jun Yang, Hao-Zhong Shu, Wen-Tong Wang, Miao Tsinghua University Department of Computer Science and Technology Beijing100084 China Beihang University China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Zhongguancun Laboratory Beijing100094 China

Geographically dispersed users often rely on virtual avatars as intermediaries to facilitate interactive communication and collaboration. However, existing methods for augmented reality (AR) telepresence applications exhibit limitations, including restricted movement within confined sub-areas, lack of smooth transitions, and the necessity for manually establishing object mapping between dissimilar environments. We present a novel interactive AR framework for virtual avatar locomotion adaption while preserving semantic coherence across dissimilar indoor scenes. Initially, we conduct a preliminary user study to identify key attributes influencing preferred avatar movement. These attributes are quantified as features, and a dataset of user annotations on avatar movements is created. Based on the user interaction and scene configurations, we employ a deep reinforcement learning neural network to guide the avatar to the ideal position while maximizing semantic coherence. We validate our proposed framework through simulations and user studies by implementing an AR-based 3D telepresence prototype, demonstrating the efficacy of our framework in conveying user intentions across dissimilar environments, enabling natural and immersive 3D telepresence interactions. © 1995-2012 IEEE.

关键词： Reinforcement learning

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Geo-Enhanced High-Order Feature Interaction Network for Cloud API QoS Prediction 19th

Geo-Enhanced High-Order Feature Interaction Network for Clou...

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19th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Ma, Xinhong Zeng, Yehong Qi, Mingyang Si, Yali Chen, Zhen School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Changshu Institute of Technology Jiangsu Changshu215500 China

ISBN: (纸本)9789819623754

In the era of open cloud, cloud API is an important component and key enabling technology to achieve efficient data transmission, artificial intelligence algorithm empowerment, and software development cost reduction and efficiency. With the proliferation of cloud API with similar functionality, quality of service (QoS) becomes essential for differentiating cloud API performance, and QoS-driven prediction for cloud API has emerged as a critical topic for high quality cloud API selection. However, existing methods often inadequately utilize contextual information in resource-constrained scenario, limiting the improvement of cloud API prediction performance. We in this paper propose a geo-enhanced high-order feature interaction network (GHFIN) for cloud API QoS prediction. Firstly, a correlation analysis was conducted on real-world QoS dataset to explore the relationship between QoS data and geospatial context. Then, we design a geo-enhanced strategy that effectively utilizes the hierarchical relationships of geospatial entities (country, province, city, and district) through an offline real-time coordinate conversion module. On this basis, a geo-enhanced cloud API QoS prediction network GHFIN was designed, which aims to learn the high-order feature interactions through layer-wise feature stacking. Experimental results on two real-world QoS datasets illustrate the effectiveness of GHFIN compared to the classic and state-of-the-art methods, and our extensive analysis shows how the geospatial context and high-order feature interaction positively impact the performance of cloud API QoS prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Quality of service

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Automatic skin lesion classification using a novel densely connected convolutional network integrated with an attention module

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 8943-8956页

作者： Shan, Pufang Chen, Jialei Fu, Chong Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science The University of Auckland Auckland New Zealand

Automated skin lesion classification in dermoscopy images remains challenging due to the existence of artefacts and intrinsic cutaneous features, diversity of lesion morphology, insufficiency of training data, and class imbalance problem. To address these challenges, we propose a new densely connected convolutional network termed AttDenseNet-121, which is obtained by integrating the convolutional block attention module (CBAM) into DenseNet-121. CBAM is a simple yet efficient attention module, which is further improved by adding a novel pooling layer (multiscale-pooling) to effectively enhance its attention capability. The optimized CBAM strengthens the representation power of DenseNet-121 by emphasizing meaningful features and suppressing unnecessary ones, thus significantly enhancing the classification performance. Besides, to handle the imbalanced dataset, we employ an improved focal loss function rather than the traditional cross-entropy loss function to train AttDenseNet-121. The improved focal loss is calculated from a different perspective compared with the original focal loss. It makes the distribution of positive samples and negative samples in each batch the same as that in the original data, successfully mitigating the negative influence of class imbalance on this multi-class classification task. We conduct extensive experiments on the public benchmark dataset (HAM10000) and the results indicate that our proposed method achieves superior performance compared with that of the baselines and state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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Transforming Vision Transformer: Towards Efficient Multi-Task Asynchronous Learning

arXiv

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arXiv 2025年

作者： Zhong, Hanwen Chen, Jiaxin Zhang, Yutong Huang, Di Wang, Yunhong State Key Laboratory of Virtual Reality Technology and Systems Beihang University China School of Computer Science and Engineering Beihang University Beijing China

Multi-Task Learning (MTL) for Vision Transformer aims at enhancing the model capability by tackling multiple tasks simultaneously. Most recent works have predominantly focused on designing Mixture-of-Experts (MoE) structures and integrating Low-Rank Adaptation (LoRA) to efficiently perform multi-task learning. However, their rigid combination hampers both the optimization of MoE and the effectiveness of reparameterization of LoRA, leading to sub-optimal performance and low inference speed. In this work, we propose a novel approach dubbed Efficient Multi-Task Learning (EMTAL) by transforming a pre-trained Vision Transformer into an efficient multi-task learner during training, and reparameterizing the learned structure for efficient inference. Specifically, we firstly develop the MoEfied LoRA structure, which decomposes the pre-trained Transformer into a low-rank MoE structure and employ LoRA to fine-tune the parameters. Subsequently, we take into account the intrinsic asynchronous nature of multi-task learning and devise a learning Quality Retaining (QR) optimization mechanism, by leveraging the historical high-quality class logits to prevent a well-trained task from performance degradation. Finally, we design a router fading strategy to integrate the learned parameters into the original Transformer, archiving efficient inference. Extensive experiments on public benchmarks demonstrate the superiority of our method, compared to the state-of-the-art multi-task learning approaches. The project page is available at https://***/Yewen1486/EMTAL. Copyright © 2025, The Authors. All rights reserved.

关键词： Multi-task learning

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