检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Lu, Liping He, Zhican Chu, Duanfeng Wang, Rukang Peng, Saiqian Zhou, Pan School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China Intelligent Transportation Systems Research Center Wuhan University of Technology Wuhan430070 China Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

This paper proposes a novel method for multi-lane convoy formation control that uses large language models (LLMs) to tackle coordination challenges in dynamic highway environments. Each connected and autonomous vehicle in the convoy uses a knowledge-driven approach to make real-time adaptive decisions based on various scenarios. Our method enables vehicles to dynamically perform tasks, including obstacle avoidance, convoy joining/leaving, and escort formation switching, all while maintaining the overall convoy structure. We design a Interlaced formation control strategy based on locally dynamic distributed graphs, ensuring the convoy remains stable and flexible. We conduct extensive experiments in the SUMO simulation platform across multiple traffic scenarios, and the results demonstrate that the proposed method is effective, robust, and adaptable to dynamic environments. The code is available at: https://***/chuduanfeng/ConvoyLLM . Copyright © 2025, The Authors. All rights reserved.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Mesh2Brep: B-rep Reconstruction via Robust Primitive Fitting and Intersection-aware Constraints

引用

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

作者： Shen, Zeyu Zhao, Mingyang Yan, Dong-Ming Wang, Wencheng Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China The University of Chinese Academy of Sciences Beijing100049 China CAIR Hong Kong Institute of Science & Innovation Chinese Academy of Sciences China MAIS Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

In boundary representation (B-rep) reconstruction for computer aided design (CAD) applications, it is still challenging with existing methods to distinguish primitives in the smoothly blended regions reasonably. Thus, intensive manual post-processing is always required for correcting the primitives and their neighboring relationships to obtain a valid B-rep solid, seriously preventing the efficiency. In this paper, we address these challenges by presenting two novel techniques. The first is to robustly extract primitives by iteratively estimating the probability distribution of the noise to eliminate outliers. The second is to present intersection-aware constraints, like tangency and collinearity constraints, to correctly obtain intersections between primitives, which have not been explored in existing methods to our knowledge. Therefore, we can effectively extract primitives, especially those blended smoothly, and obtain high-quality relationships between them. As a result, a valid B-rep model can be constructed without a lot of manual post-processing on topology correction, while not with existing methods. As a benefit, with our constructed B-rep models, their corresponding meshes can be intuitively and conveniently edited, which is quite useful in CAD applications. Experimental results show that our proposed B-rep construction method outperforms both classical and recent learning-based methods in terms of reconstruction efficiency and accuracy. © 2025 IEEE.

关键词： Mesh generation

来源：评论

学校读者我要写书评

暂无评论

Air Pollution Monitoring By Integrating Local and Global Information in Self-Adaptive Multiscale Transform Domain

引用

IEEE Transactions on Multimedia 2025年

作者： Gu, Ke Liu, Yuchen Liu, Hongyan Liu, Bo Qiao, Junfei Lin, Weisi Zhang, Wenjun Beijing University of Technology School of Information Science and Technology Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing100124 China Massey University School of Mathematical and Computational Sciences New Zealand Nanyang Technological University College of Computing and Data Science Singapore Shanghai Jiao Tong University Institute of Image Communication and Information Processing Shanghai China

As the deadliest form of pollution, air pollution had a prolonged severe damage to the human health and life safety of nearly 99% of the world's population. Facing to the problem that billions of tons of pollutants discharged into the air per year by industrial activities, this paper proposed a novel image-based air pollution monitor (IAPM) by incorporating local and global information in the self-adaptive multiscale transform domain, so as to achieve the timely and effective leakage detection of typical air pollutants from a single image. To be specific, this paper first developed a screen-shaped module according to two significant findings in visual neuroscience, which include the high sensitivity of human eyes to horizontal and vertical stimuli and the center-surround inhibition, by designing and fusing the square module, horizontal strip module and vertical strip module parallelly for simulating the behaviour of human eyes to extract local features. Second, the learnable weights and proportional mapping were applied to incorporate the screen-shaped module and lightweight vision transformer as backbone, towards more richly exploiting and fusing local and global information just as the way a brain perceives external stimuli. Third, a new self-adaptive multiscale transform domain method was devised based on two motivations from the visual characteristics of multiscale perception and the brain characteristics of self-adaptive domain transform to modify the backbone by using the operations of pooling and pointwise convolution. Extensive experiments implemented on the datasets of carbon particulate matters and ethylene leakage confirmed the superior monitoring performance of the proposed IAPM model beyond the state-of-the-art (SOTA) peers by an accuracy gain of about 4%. Furthermore, the proposed IAPM model only required 0.089 GFLOPs and 0.15 million model parameters, remarkably outperforming SOTA competitors in computational efficiency and storage resources. © 1999-2

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

DiffDual-AD: Diffusion-Based Dual-Stage Adversarial Defense Framework in Remote Sensing with Denoiser Constraint

引用

IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Lu, Zihao Sun, Hao Lei, Lin Xu, Yanjie Sun, Yuli Kuang, Gangyao National University of Defense Technology State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System the College of Electronic Science and Technology Changsha410073 China National University of Defense Technology College of Aerospace Science and Engineering Changsha410073 China

Deep neural networks (DNNs), though highly effective in various Earth observation tasks with remote sensing images (RSIs), are vulnerable to adversarial attacks, threatening their reliability. Each maliciously attacked RSI potentially contains unique and critical information, but current defenses lack a unified framework for rapidly detecting adversarial RSIs and accurately restoring them to their natural state. To bridge this research gap, we propose a diffusion-based dual-stage adversarial defense (DiffDual-AD) framework. In the first stage, we propose a novel adversarial detection method based on the score expectation (AD-SE), which is integrated into the forward process of diffusion model with an improved denoiser constrained for adversarial defense. During the reverse process, the second stage introduces the distance and label-guided adversarial purification (DL-GAP) to restore adversarial RSIs to natural ones. With the help of two proposed guidelines and the specific denoiser, the DL-GAP effectively smooths out the adversarial perturbations from the detected adversarial RSIs while preserving semantic information and local key features. Finally, DL-GAP yields nonadversarial purified RSIs based on the results of AD-SE. Both stages are integrated within the diffusion models, complementing each other to form a pipelined operational mode. Extensive experiments across three RSI scene classification datasets have proven its efficacy in resisting adversarial RSIs in both whitebox and black-box scenarios, achieving an average adversarial detection accuracy of 87.02% with AD-SE and an average final classification accuracy of 81.50% with the entire DiffDual-AD. The performances of our proposed AD-SE and DL-GAP have surpassed other advanced adversarial detection and purification methods when applied to RSIs. Therefore, DiffDual-AD provides a unified and universal solution to preserve the utility and security of processing the adversarial RSIs, which advances the adversari

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A wolf in sheep's clothing: practical black-box adversarial attacks for evading learning-based windows malware detection in the wild 24

A wolf in sheep's clothing: practical black-box adversarial ...

引用

Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Xiang Ling Zhiyu Wu Bin Wang Wei Deng Jingzheng Wu Shouling Ji Tianyue Luo Yanjun Wu Intelligent Software Research Center Institute of Software Chinese Academy of Sciences and Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Zhejiang University Zhejiang Key Laboratory of Artificial Intelligence of Things (AIoT) Network and Data Security and Hangzhou Research Institute Xidian University Intelligent Software Research Center Institute of Software Chinese Academy of Sciences

ISBN: (纸本)9781939133441

Given the remarkable achievements of existing learning-based malware detection in both academia and industry, this paper presents MalGuise, a practical black-box adversarial attack framework that evaluates the security risks of existing learning-based Windows malware detection systems under the black-box setting. MalGuise first employs a novel semantics-preserving transformation of call-based redividing to concurrently manipulate both nodes and edges of malware's control-flow graph, making it less noticeable. By employing a Monte-Carlo-tree-search-based optimization, MalGuise then searches for an optimized sequence of call-based redividing transformations to apply to the input Windows malware for evasions. Finally, it reconstructs the adversarial malware file based on the optimized transformation sequence while adhering to Windows executable format constraints, thereby maintaining the same semantics as the original. MalGuise is systematically evaluated against three state-of-the-art learning-based Windows malware detection systems under the black-box setting. Evaluation results demonstrate that MalGuise achieves a remarkably high attack success rate, mostly exceeding 95%, with over 91% of the generated adversarial malware files maintaining the same semantics. Furthermore, MalGuise achieves up to a 74.97% attack success rate against five anti-virus products, highlighting potential tangible security concerns to real-world users.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Grey Wolf Optimizer with Ranking-Based Mutation Operator for IIR Model Identification

引用

Chinese Journal of Electronics 2018年第5期27卷 1071-1079页

作者： ZHANG Sen ZHOU Yongquan College of Information Science and Engineering Guangxi University for Nationalities School Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence

A variant of Grey wolf optimizer(GWO),called grey wolf optimizer with Ranking-based mutation operator(RGWO) is applied to the Infinite impulse response(IIR) system identification problem. RGWO makes GWO faster and more robust. In RGWO, the rankingbased mutation operator is integrated into the GWO to accelerate the convergence speed, and thus enhance the performance. The simulation results over several models are presented and statistically validated. Compared to other robust evolutionary algorithms, RGWO performs significantly better in terms of the quality, speed, and the stability of the final solutions.

关键词： system identification IIR system Grey wolf optimizer Ranking-based mutation operator

来源：评论

学校读者我要写书评

暂无评论

Remote State Estimation for complex Networks Under Probabilistic Bit Flips: A Transmission Power Allocation Scheme

引用

IEEE Transactions on Automatic Control 2025年

作者： Song, Jiahao Wang, Zidong Liu, Qinyuan He, Xiao Tsinghua University Department of Automation Beijing100084 China Brunel University London Department of Computer Science UxbridgeUB8 3PH United Kingdom Tongji University Department of Computer Science and Technology Shanghai201804 China Ministry of Education Tongji University Key Laboratory of Embedded System and Service Computing Shanghai200092 China Shanghai Artificial Intelligence Laboratory Shanghai China

In this paper, the problem of remote state estimation is investigated for a class of complex networks with noisy wireless communication channels. The employment of the binary encoding scheme allows for the description of the influence of channel noises as probabilistic bit flips, where the probability of these bit flips is influenced by several factors with the signal-to-noise ratio (SNR) being a crucial one. In engineering practice, the method of adjusting the transmission power for each node in a complex network is commonly applied to modify the SNR so as to reduce the data distortion caused by bit flips. Furthermore, due to restricted communication resources, the total available transmission power is often limited particularly in large-scale systems such as complex networks. Consequently, the allocation of transmission power for all nodes under a total transmission power constraint becomes a concern. Utilizing the ultimately bounded filtering method, we devise a transmission-power-dependent state estimator, by which the combined effect of probabilistic bit flips and transmission power allocation on estimation performance is analyzed. Moreover, the task of co-designing the transmission power allocation scheme and estimator gains is modeled as an optimization problem, which is then addressed through a two-step optimization strategy. The existence of a unique optimal transmission power allocation scheme is also proven. Finally, numerical simulation examples are provided to demonstrate the effectiveness of the proposed co-design approach. © 1963-2012 IEEE.

关键词： Large scale systems

来源：评论

学校读者我要写书评

暂无评论

Spatio-Temporal reconstruction to fill spatial gaps in global satellite vegetation optical depth products

引用

Remote Sensing of Environment 2025年 325卷

作者： Wang, Hui Wei, Zushuai Miao, Linguang Tian, Feng Zhao, Tianjie Hu, Lu Meng, Lingkui School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China School of Artificial Intelligence Jianghan University Wuhan430056 China State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100101 China International Institute for Earth System Science Nanjing University Nanjing210023 China

Vegetation Optical Depth (VOD) serves as a crucial tool for monitoring vegetation characteristics and plays a vital role in terrestrial ecosystems. The vegetation optical depth dataset from AMSR-E/2 using multi-channel collaborative algorithm (MCCA-AMSR VOD) possesses a longer time series spanning from 2002 to 2022. However, due to the limitations of satellite orbital scanning gap and retrieval algorithms, existing VOD datasets do not achieve complete coverage in global land. To address the need for a VOD dataset with high spatio-temporal coverage across different bands, we reconstruct the MCCA-AMSR VOD dataset using the 3D partial convolutional neural network. Three evaluation methods are employed to further assessthe reconstructed VOD dataset. The reconstructed VOD achieves a higher average spatial coverage (95.01 %) compared with the original dataset (72.55 %). The correlation coefficients (R) between original and reconstructed VOD dataset for all five simulated missing regions exceeded 0.99, indicating that the reconstructed VOD has a very high correlation with the original dataset at the regional scale. Furthermore, comparison with optical vegetation indices indicates that the reconstructed VOD can also capture vegetation water contentwell. Therefore, reconstructed VOD can provide valuable support for research on drought and vegetation monitoring. © 2025 Elsevier Inc.

关键词： Optical depth

来源：评论

学校读者我要写书评

暂无评论

Evolutionary neural architecture search for automatically designing CNN-GRU-Attention neural networks for turntable servo systems

引用

Expert systems with Applications 2025年 283卷

作者： Xie, Cheng Xue, Bing Yang, Mingyi Zhang, Mengjie Xu, Zhigang Wang, Junyi Chen, Songlin Liu, Yang Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang110169 China Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand University of Chinese Academy of Sciences Beijing101408 China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin Institute of Technology Heilongjiang Harbin150001 China

Turntable servo systems are important experimental devices utilized in the semi-physical simulation and testing of aircraft. Building a model for turntable servo systems, which can accurately predict their operating states or behaviors, is important for the development and debugging of the whole servo control system. However, servo systems usually have complex nonlinear characteristics and external disturbances, which brings challenges to the accurate modeling of them. Therefore, this paper develops a modeling method for turntable servo systems by using CNN-GRU-Attention hybrid neural network models. The CNN-GRU-Attention model is used for compensating the nonlinear terms of the known dynamics model, thus effectively improving the modeling accuracy. Considering the complex architectures of the above hybrid model, an evolutionary neural architecture search (ENAS) algorithm is proposed accordingly, which can automatically design the architecture of the CNN-GRU-Attention models. During the designing process, a variable-length encoding strategy is proposed to represent the possible architectures, and novel crossover and mutation operators are proposed accordingly for the evolution of individuals. In addition, the real-time constraints are considered in the design of the search space, so that all the searched models can meet the real-time requirements. The experimental results show the CNN-GRU-Attention models obtained by the proposed ENAS algorithm achieve superior prediction performance over the peer competitors in most tasks. The effectiveness of the proposed method is further verified by analyzing the convergence and the search results. © 2025

关键词： Synchros

来源：评论

学校读者我要写书评

暂无评论

Transfer learning of multicellular organization via single-cell and spatial transcriptomics

引用

PLoS Computational Biology 2025年第4 April期21卷 e1012991页

作者： Tan, Yecheng Wang, Ai Wang, Zezhou Lin, Wei Yan, Yan Nie, Qing Shi, Jifan Research Institute of Intelligent Complex Systems Fudan University Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Department of Cardiology Zhongshan Hospital Fudan University Shanghai China Shanghai Center for Mathematical Sciences Fudan University Shanghai China State Key Laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science Fudan University Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China Department of Mathematics University of California Irvine IrvineCA United States

Biological tissues exhibit complex gene expression and multicellular patterns that are valuable to dissect. Single-cell RNA sequencing (scRNA-seq) provides full coverages of genes, but lacks spatial information, whereas spatial transcriptomics (ST) measures spatial locations of individual or group of cells, with more restrictions on gene information. Here we show a transfer learning method named iSORT to decipher spatial organization of cells by integrating scRNA-seq and ST data. iSORT trains a neural network that maps gene expressions to spatial locations. iSORT can find spatial patterns at single-cell scale, identify spatial-organizing genes (SOGs) that drive the patterning, and infer pseudo-growth trajectories using a concept of SpaRNA velocity. Benchmarking on a range of biological systems, such as human cortex, mouse embryo, mouse brain, Drosophila embryo, and human developmental heart, demonstrates iSORT’s accuracy and practicality in reconstructing multicellular organization. We further conducted scRNA-seq and ST sequencing from normal and atherosclerotic arteries, and the functional enrichment analysis shows that SOGs found by iSORT are strongly associated with vascular structural anomalies. © 2025 Tan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Mammals

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：