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International Conference on Applied Nonlinear Dynamics, Vibration and Control, ICANDVC 2024

作者： Lv, Zhenwei Zeng, Chunnian Ma, Haijun Wang, Weiping An, Shufan School of Automotive Engineering Wuhan University of Technology Wuhan430070 China Henan Fuwo Intelligent Equipment Company Limited Zhengzhou450000 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9789819633166

In this paper, the consensus problem of third-order heterogeneous platoon under self-triggered and event-triggered scheme is studied. Different and uncertain driveline time constants of vehicles are the main manifestation of the heterogeneity. Based on this heterogeneous platoon, the string stability under the nonlinear distance policy and constant time headway policy are studied. Then, an event-triggered strategy is introduced into the system to save limited communication and computing resources. Continuous communication between vehicles during detecting the event-triggering function is no need under the proposed event-triggered scheme. Furthermore, Taylor formula is used to design a self triggering scheme. Under the proposed self-triggered scheme, triggering interval is calculated directly by the information of last triggering-instant. Therefore, the Zeno behavior and the continuously detecting of event-triggering function can be avoided. Finally, the effectiveness of the theoretical results is verified by simulations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Event-triggered Heterogeneous platoon Self-triggered Spacing policy String stability

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MLSDET: Multi-LLM Statistical Deep Ensemble for Chinese AI-Generated Text Detection

MLSDET: Multi-LLM Statistical Deep Ensemble for Chinese AI-G...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Mao, Dianhui Zhang, Denghui Zhang, Ao Zhao, Zhihua School of Computer Science and Engineering Beijing Technology and Business University China School of Law China University of Political Science and Law Beijing China

ISBN: (纸本)9798350368741

With the rapid advancements in pre-trained large language models like ChatGPT, the surge of AI-generated text, particularly in Chinese, has presented significant challenges to existing detection systems due to its increasing realism and complexity. To address this, we introduce MLSDET: a groundbreaking Multi-LLM Statistical Deep Ensemble framework designed for high-precision detection of AI-generated Chinese text. MLSDET uniquely integrates a Mixture of Experts (MoE) architecture with a novel cross-entropy metric, setting a new benchmark for robustness and generalization. By employing a diverse ensemble of large language models (LLMs), including Qwen, Wenzhong-GPT2, and LLaMA, our approach extracts intricate features such as log-rank, entropy, log-likelihood, and the newly introduced LLMs-crossEntropy, accurately capturing both model consensus and the statistical distribution differences between AI-generated and human-authored text. Experimental results on the HC3-Chinese dataset show that MLSDET surpasses traditional zero-shot methods like CLTR by 15.94% in F1 score and competes effectively with existing methods, offering a scalable solution for real-world applications. © 2025 IEEE.

关键词： AI-generated text detection Mixture of Experts Statistical Features

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HASLB: Huge Page Allocation Strategy Optimized for Load-Balance in Parallel Computing Programs 20th

HASLB: Huge Page Allocation Strategy Optimized for Load-Bal...

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Yue, Lei Wu, Tao Shen, Yang Zhang, Jin Zhang, Wenzhe School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819628292

Equitable resource allocation is currently considered as a key enabler for achieving complete parallelism in parallel computing. Optimized memory management mechanisms are crucial for load balance in parallel tasks. In some cases, the performance of Linux’s huge page management mechanism deteriorates when handling parallel tasks due to the nonhomogeneous allocation of memory resources among parallel processes. This research introduces HASLB, a huge page allocation strategy optimized for parallel programs. HASLB evaluates the status of parallel processes based on the count of huge pages acquired by each process and the number of page faults it encountered. Following the results of the analysis, huge pages are allocated using a method conducive to load balance. The problems in the Linux transparent huge page mechanism are optimized through this strategy, which limits the greedy allocation mechanism in some situations. HASLB achieves a reasonable allocation of memory resources among parallel processes with low overhead, benefiting the load balance of parallel computing programs. In addition, contiguous memory would be protected because of limiting the greedy allocation of huge pages. The parallel benchmark experiments show a maximum performance improvement of 6.4% compared to Linux in the BFS benchmark. In the IS benchmark program, huge page consumption exhibits a 9% reduction. © IFIP International Federation for Information Processing 2025.

关键词： Memory management

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A Digital Twin and Consensus Empowered Cooperative Control Framework for Platoon-Based Autonomous Driving

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1096-1111页

作者： Jiayu Cao Supeng Leng Kai Xiong Xiaosha Chen School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China College of Computer Science and Technology and also with College of Data Science Taiyuan University of TechnologyTaiyuan 030024China

Platoon-based autonomous driving is indispensable for traffic automation,but it confronts substantial constraints in rugged terrains with unreliable links and scarce communication *** paper proposes a novel hierarchical Digital Twin(DT)and consensus empowered cooperative control framework for safe driving in harsh ***,leveraging intra-platoon information exchange,one platoon-level DT is constructed on the leader and multiple vehicle-level DTs are distributed among platoon *** leader first makes critical platoon-driving decisions based on the platoon-level ***,considering the impact of unreliable links on the platoon-level DT accuracy and the consequent risk of unsafe decision-making,a distributed consensus scheme is proposed to negotiate critical decisions *** successful negotiation,vehicles proceed to execute critical decisions,relying on their vehicle-level ***,a Space-Air-Ground-Integrated-Network(SAGIN)enabled information exchange is utilized to update the platoon-level DT for subsequent safe decision-making in scenarios with unreliable links,no roadside units,and obstructed ***,based on this framework,an adaptive platooning scheme is designed to minimize total delay and ensure driving *** results indicate that our proposed scheme improves driving safety by 21.1％and reduces total delay by 24.2％in harsh areas compared with existing approaches.

关键词： platoon autonomous driving distributed consensus digital twin space-air-ground integrated network

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A Lightweight Fault Diagnosis Method Based on Knowledge Distillation Under Time-Varying Rotational Speeds

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IEEE Access 2025年 13卷 36374-36387页

作者： Yang, Xiwang Wang, Yarong Gao, Lele Luo, Jia Jing, Licheng Huang, Jinying Liu, Guangpu Yang, Chenfeng Shanxi University of Electronic Science and Technology School of Information and Communication Engineering Linfen041000 China North University of China School of Computer Science and Technology Taiyuan030051 China North University of China School of Mechanical and Electrical Engineering Taiyuan030051 China North University of China School of Energy and Power Engineering Taiyuan030051 China North University of China School of Mechanical Engineering Taiyuan030051 China

Intelligent fault diagnosis models for engineering must meet real-time requirements while satisfying high diagnosis rates. However, collecting a large amount of fault information of different working conditions in the engineering environment is difficult, and the lack of fault data is a problem that has been difficult to solve. This paper presents a lightweight fault diagnosis model for wind turbine gearboxes based on transfer learning and knowledge distillation. The model is designed to be lightweight and can be used under time-varying rotational speeds. First, TL-ResPConv based on transfer learning and partial convolution was designed as a teacher network model to train a lightweight student network model for fault diagnosis based on TL-ResPConv-KD. Then, the student model obtained through knowledge distillation training is used as the distilled fault diagnosis model. Finally, the performance of the different models is validated and compared on a laboratory simulated planetary gearbox dataset. The experiments prove that the proposed model outperforms other models in fault recognition, achieving a balance between lightweight and high diagnostic accuracy. This makes it a practical network with higher diagnostic accuracy for fault diagnosis under time-varying rotational speeds, meeting the demand for lightweight deployment of edge equipment. © 2013 IEEE.

关键词： Probability distributions

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Threshold Password-Hardening Updatable Oblivious Key Management

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4799-4814页

作者： Jiang, Changsong Xu, Chunxiang Liu, Zhen Dong, Xinfeng Zhang, Wenzheng University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Singapore Management University School of Computing and Information Systems Bras Basah 188065 Singapore Huzhou313001 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Science and Technology on Communication Security Laboratory Chengdu610041 China

We propose a threshold password-hardening updatable oblivious key management system dubbed TPH-UOKM for cloud storage. In TPH-UOKM, a group of key servers share a user-specific secret key for a user, and assist the user in producing her/his password-derived private key in a threshold and oblivious way, where the password is hardened to resist offline dictionary guessing attacks. Anyone can outsource data protected with the user's password-derived public key to the cloud server, and merely the user holding the correct password can recover the password-derived private key for data access. TPH-UOKM can accomplish decryption of N ciphertexts with the complexity O(1) of communication between a user and the key servers, which outperforms existing schemes. TPH-UOKM supports password update. The cloud server can update all protected data of a user with an update token to be accessible only with the new password, which resists password leakage. We present a two-level proactivization mechanism to periodically update user-specific secret key shares and the key servers to thwart perpetual compromise of them, where the renewal of user-specific secret key shares reduces computation and communication costs compared to existing approaches. Provable security and high efficiency of TPH-UOKM are demonstrated by comprehensive analyses and performance evaluations. © 2005-2012 IEEE.

关键词： Information management

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Sparsity Exploitation via Joint Receive Processing and Transmit Beamforming Design for MIMO-OFDM ISAC Systems

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IEEE Transactions on communications 2025年第5期73卷 3593-3607页

作者： Xiao, Zichao Liu, Rang Li, Ming Wang, Wei Liu, Qian Dalian University of Technology School of Information and Communication Engineering Dalian116024 China University of California at Irvine Center for Pervasive Communications and Computing IrvineCA92697 United States Dalian University of Technology School of Computer Science and Technology Dalian116024 China

Integrated sensing and communication (ISAC) is widely recognized as a pivotal enabling technique for the advancement of future wireless networks. This paper aims to efficiently exploit the inherent sparsity of echo signals for the multi-input-multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) based ISAC system. A novel joint receive echo processing and transmit beamforming design is presented to achieve this goal. Specifically, we first propose a compressive sensing (CS)-assisted estimation approach to facilitate ISAC receive echo processing, which can not only enable accurate recovery of target information, but also allow a substantial reduction in the number of sensing subcarriers to be sampled and processed. Then, based on the proposed CS-assisted processing method, the associated transmit beamforming design is formulated with the objective of maximizing the sum-rate of multiuser communications while satisfying the transmit power budget and ensuring the received signal-to-noise ratio (SNR) for the designated sensing subcarriers. In order to address the formulated non-convex problem involving high-dimensional variables, an effective iterative algorithm employing majorization minimization (MM), fractional programming (FP), and the nonlinear equality alternative direction method of multipliers (neADMM) with closed-form solutions has been developed. Finally, extensive numerical simulations are conducted to verify the effectiveness of the proposed algorithm and the superior performance of the introduced sparsity exploitation strategy. © 1972-2012 IEEE.

关键词： Orthogonal frequency division multiplexing

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Cross-modality geometry-guided historical momentum learning for coupled noisy visible-infrared re-identification

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Multimedia Systems 2025年第3期31卷 1-11页

作者： Li, Yongxi Tang, Wenzhong Xiong, Lvhong Wang, Shuai Wang, Haoming Zhu, Xi School of Computer Science and Engineering Beihang University Beijing100191 China School of Electronic Information Engineering Beihang University Beijing100191 China Information Department Army Academy of Armored Force Changchun130000 China Key Laboratory of Aerospace Mobile Communication Ministry of Industry and Information Technology Beijing100191 China State Key Laboratory of CNS/ATM Beijing100191 China

Visible-Infrared Person Re-Identification (VIReID) is an identity retrieval task between visible and infrared modalities. Existing research aims to capture shared semantic features, which can retrieve the same identity under different modalities. Although the previous methods have achieved excellent performance, they still face critical limitations: (1) Coupled Noisy Problem: Due to the complexity of cross-modality annotation and the variability in crowdsourced labeling reliability, the presence of coupled noisy labels resulting from mismatched visible-infrared pairs is unavoidable. (2) Lack of historical relevance: Current approaches to noisy label learning predominantly focus on identifying potentially noisy samples based on features from the current training step, overlooking the rich semantic information in earlier training stages. These two limitations restrict the adaptability of such methods to scenarios with coupled noisy samples. We propose a Cross-Modality Geometry-Guided Historical Momentum Learning to address these issues. Firstly, we employ a Geometry-Guided Noisy Correspondence Detector to estimate confidence weights from each sample pair. Secondly, we design a historical momentum triplet loss function, which leverages features of early training steps to capture the training trajectory, addressing the historical smoothing issue in the training process. Finally, we utilize these high-confidence samples to learn the discriminative features through an iterative framework. Extensive experiments on the SYSU-MM01 and RegDB datasets demonstrate the effectiveness of our method against the state-of-the-art VIReID methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Geometry

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Introduction to the Special Issue on Deep Learning for Robust Human Body Language Understanding

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ACM Transactions on Multimedia Computing, communications and Applications 2025年第4期21卷 1-7页

作者： Guo, Dan McDaniel, Troy Wang, Shuhui Wang, Meng School of Computer and Information Hefei University of Technology Hefei China Department of Computer Science and Engineering Arizona State University TempeAZ United States Chinese Academy of Sciences Beijing China

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Air quality and dust level monitoring systems in hospitals using IoT

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Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

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