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Intermittent control for nonlinear hybrid stochastic delayed systems with measurement error via dynamic event-triggered mechanism

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ISA Transactions 2025年

作者： Zhao, Hairui Chen, Dongyan Hu, Jun Department of Applied Mathematics Harbin University of Science and Technology Harbin150080 China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology Harbin150080 China School of Automation Harbin University of Science and Technology Harbin150080 China

This paper deals with the problem of intermittent control based on dynamic event-triggered mechanism (DETM) for nonlinear hybrid stochastic delayed systems (NHSDSs) with measurement error. The DETM and intermittent control strategy are employed to alleviate the network burden in feedback and forward channels, respectively. After giving the existence and uniqueness theorem for the solution to NHSDSs, the sufficient condition to guarantee p-th moment exponential stability and almost surely exponential stability of NHSDSs is established by comparing with the constructed auxiliary system. On this basis, the intermittent control based on self-triggered mechanism is developed. In addition, it is also proved that neither the DETM nor the self-triggered mechanism can produce the Zeno phenomenon. Finally, a simulation example is given to verify the validity of the theoretical results. © 2025 Elsevier Ltd

关键词： Stabilization

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Outlier-Resistant Distributed Filtering for Multi-Rate Nonlinear systems under Amplify-and-Forward Relays

Outlier-Resistant Distributed Filtering for Multi-Rate Nonli...

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第42届中国控制会议

作者： Shuting Fan Jun Hu Chaoqing Jia Hairui Zhao Department of Applied Mathematics Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology School of Automation Harbin University of Science and Technology

In this paper, the outlier-resistant distributed filtering problem based on amplify-and-forward relays is studied for discrete time-varying nonlinear multi-rate systems with multiple measurement delays over sensor networks, where the augmenting method is utilized to transform the multi-rate system into a single rate system. An amplify-and-forward(AF) relay is set between the sensor and the filter to extend the transmission distance of the signal and ensure the communication transmission *** outlier-resistant distributed filter is constructed by introducing a saturation function to limit the innovations, then the upper bound on the filtering error covariance is obtained and the filter gain is designed to minimize such obtained upper bound. Finally,a numerical example is used to show the effectiveness of the outlier-resistant distributed filtering algorithm based on AF relays.

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Mitigating Over-Trust in Chat Companion Robot Interaction Using Augmented Reality 23

Mitigating Over-Trust in Chat Companion Robot Interaction Us...

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Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering

作者： Guobao Wang Changshun Zhao Ying Liu Di He Yuqing Wang Kaiyue Zhang Yanshan University/Jiangsu Xuzhou Construction Machinery Group Co. Ltd. China Officers College of PAP China National Key Laboratory for Complex Systems Simulation China University of Electronic Science and Technology of China China Jiangsu Province Tongshan Secondary Vocational School China

ISBN: (纸本)9798400708831

This study aims to explore the effects of different human-machine interfaces, system performance, and robot interaction modalities on over-trust in chat companion robot systems. We designed and implemented three types of interfaces: 2D Virtual Interface + Virtual Robot, 3D Augmented Reality Interface + AR-based Virtual Robot, and 3D Real World + NAO Physical Robot. We also manipulated the system performance (high or low) and the robot interaction modality (language, gesture, or both). We conducted a user study with 30 participants, who interacted with the chat companion robot under different conditions and rated their trust, distrust, and human-robot distance. We also measured the user experience in terms of usefulness and ease of use. The results showed that the type of interface significantly influenced the trust, distrust, and human-robot distance of the users, and that the 3D augmented reality interface was the most effective in enhancing trust and reducing distrust and distance. The performance of the system also affected the trust and distance of the users, and the gesture interface was the most sensitive to the performance variation. The modality of the interaction did not have a significant impact on the dependent variables. The user experience ratings also indicated that the 3D augmented reality interface was the most useful and easy to use. The implications and limitations of these findings are discussed in the paper.

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Forget less,count better:a domain-incremental self-distillation learning benchmark for lifelong crowd counting

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Frontiers of Information technology & Electronic Engineering 2023年第2期24卷 187-202页

作者： Jiaqi GAO Jingqi LI Hongming SHAN Yanyun QU James ZWANG Fei-Yue WANG Junping ZHANG Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Institute of Science and Technology for Brain-inspired Intelligence Fudan UniversityShanghai 200433China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai 201210China School of Information Science and Technology Xiamen UniversityXiamen 361005China College of Information Sciences and Technology the Pennsylvania State UniversityUniversity ParkPA 16802USA State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Crowd counting has important applications in public safety and pandemic control.A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain ***-the-shelf methods have some drawbacks when handling multiple domains:(1)the models will achieve limited performance(even drop dramatically)among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains,which is called catastrophic forgetting;(2)the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift;(3)it leads to linearly increasing storage overhead,either mixing all the data for training or simply training dozens of separate models for different domains when new ones are *** overcome these issues,we investigate a new crowd counting task in incremental domain training setting called lifelong crowd *** goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental ***,we propose a self-distillation learning framework as a benchmark(forget less,count better,or FLCB)for lifelong crowd counting,which helps the model leverage previous meaningful knowledge in a sustainable manner for better crowd counting to mitigate the forgetting when new data arrive.A new quantitative metric,normalized Backward Transfer(nBwT),is developed to evaluate the forgetting degree of the model in the lifelong learning *** experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

关键词： Crowd counting Knowledge distillation Lifelong learning

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Distributed Resilient State Estimation for Time-Varying Fractional-Order Discrete complex Networks with Encoding-Decoding Scheme

Distributed Resilient State Estimation for Time-Varying Frac...

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第42届中国控制会议

作者： Yu Chen Jun Hu Jiaxing Li Aozhan Zhou Hui Yu Department of Applied Mathematics Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology School of Automation Harbin University of Science and Technology

In this paper,the distributed state estimation method with resilient attenuation feature is proposed for time-varying fractional-order complex networks under encoding-decoding *** encoding-decoding-induced dynamic errors for distinct nodes are characterized by the truncated Gaussian *** order to compensate the effects induced by encodingdecoding scheme,the variances of encoding-decoding-induced dynamic errors are considered in process of designing the resilient distributed estimation *** particular,the upper bounds of updated estimation error covariances are derived ***,the upper bounds are minimized by constructing the gain matrices at each sampling ***,a sufficient condition is provided to guarantee the boundedness of estimation error dynamics in the mean-square ***,the validity of distributed resilient state estimation scheme is demonstrated by a simulation example.

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Practical framework for data-driven RANSmodeling with data augmentation

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Acta Mechanica Sinica 2021年第12期37卷 1748-1756页

作者： Xianwen Guo Zhenhua Xia Shiyi Chen State Key Laboratory for Turbulence and Complex Systems College of EngineeringPeking UniversityBeijing 100871China Department of Engineering Mechanics Zhejiang UniversityHangzhou 310027China Department of Mechanics and Aerospace Engineering Southern University of Science and TechnologyShenzhen 518055China

Inspired by the iterative procedure of computing mean fields with known Reynolds stresses(Guo et al.,Theor Appl Mech Lett,2021),we proposed a way to achieve data augmentation by utilizing the intermediate mean fields after proper *** also proposed modifications to the Tensor Basis Neural Network(Ling et al.,J Fluid Mech,2016)*** the modification of the learning targets and the inclusions of wall distance and logarithm of normalized eddy viscosity in the model inputs,the modified version of the model with augmented training datasets shows better performance on Reynolds stress predictions for two dimensional incompressible flow over periodic hills under different ***,better propagated mean velocity fields can be achieved,showing better agreements with the direct numerical simulations(DNS)results.

关键词： RANS closure Data augmentation Machine learning TBNN

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Comparative Effects of Doses of Nauclea latifolia and 17 Alpha Methyltestosterone on Masculinization and Growth Rates of Tilapia Orechromis niloticus Larvae in Senegal

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Open Journal of Applied sciences 2025年第1期15卷 1-12页

作者： Mouhamadou Amadou Ly Fafanding Sane Robane Faye Baboucar Sane Malick Aliou Sene Cheikh Tidiane Ba Laboratory of Biological Agronomic Food Sciences and Modeling of Complex Systems Faculty of Agronomic Sciences Aquaculture and Food Technologies Gaston Berger University of Saint-Louis Saint-Louis Senegal Laboratory of Parasitology-Helminthology Faculty of Science and Technology Cheikh Anta Diop University of Dakar Dakar Senegal

One of the main obstacles to the development of aquaculture in Senegal is the availability of fry and juveniles in sufficient quantities. The objective of our study is to evaluate the effects of treatment with the hormone 17 alpha methyl testosterone (17-&-MT) and different doses of Nauclea latifolia root powder on the masculinization rates of tilapia Oreochromis niloticus larvae in Senegal. A total of 450 larvae of Oreochromis niloticus, with an average size of 0.012 g, were placed in 15 aquariums, each containing 30 individuals and fed one of the 5 diets in triplicate. These individuals were fed either a diet containing a plant extract at a dose of 180 mg (T180), 200 mg (T200) and 250 mg (T250) per kg of food, or a diet containing 60 mg/kg of the hormone 17-&-MT (TMT), or a diet without either the plant extract or the hormone (T0). Individuals were fed for 30 days with the treatment diets, and then with the industrial feed for the rest of the experiment. At the end of the study, the results of the masculinization rate were: 80.49%, 73.13%, 69.23%, 66.67% and 45.95% for T250;T200, TMT, T180 and T0 respectively. In summary, we can say that the 250 mg/kg dose provides better masculinization, followed by 200mg/kg and then 17 α MT. For the plant, the increase in the percentage of males is dose-dependent. These results also translate into better growth of individuals in this order. However, the relatively lower survival rates observed in batches treated with Nauclea latifolia extract indicate that these treatments could have a deleterious effect on the survival of Oreochromis niloticus larvae. These results suggest the use of N. latifolia extract at a dose of 250 mg/kg feed for better production of male O. niloticus larvae without compromising their growth.

关键词： Nauclea latifolia Masculinization Rate Growth Performance Oreochromis niloticus

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm 29

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning

Large Language Models are Few-Shot Summarizers: Multi-Intent...

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International Conference on Software Engineering (ICSE)

作者： Mingyang Geng Shangwen Wang Dezun Dong Haotian Wang Ge Li Zhi Jin Xiaoguang Mao Xiangke Liao College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems Key Lab of High Confidence Software Technology Peking University Beijing China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a long time, a bottleneck for existing approaches is that given a code snippet, they can only generate one comment while developers usually need to know information from diverse perspectives such as what is the functionality of this code snippet and how to use it. To tackle this limitation, this study empirically investigates the feasibility of utilizing large language models (LLMs) to generate comments that can fulfill developers' diverse intents. Our intuition is based on the facts that (1) the code and its pairwise comment are used during the pre-training process of LLMs to build the semantic connection between the natural language and programming language, and (2) comments in the real-world projects, which are collected for the pre-training, usually contain different developers' intents. We thus postulate that the LLMs can already understand the code from different perspectives after the pre-training. Indeed, experiments on two large-scale datasets demonstrate the rationale of our insights: by adopting the in-context learning paradigm and giving adequate prompts to the LLM (e.g., providing it with ten or more examples), the LLM can significantly outperform a state-of-the-art supervised learning approach on generating comments with multiple intents. Results also show that customized strategies for constructing the prompts and post-processing strategies for reranking the results can both boost the LLM's performances, which shed light on future research directions for using LLMs to achieve comment generation.

关键词： Computer languages Codes Supervised learning Natural languages Semantics Task analysis Software engineering

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Multi-step Temporal Modeling for UAV Tracking

arXiv

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arXiv 2024年

作者： Yuan, Xiaoying Xu, Tingfa Liu, Xincong Wang, Ying Qin, Haolin Fang, Yuqiang Li, Jianan Beijing Institute of Technology Beijing100081 China The Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University Beijing101416 China The Key Laboratory of Photoelectronic Imaging Technology and System Ministry of Education of China Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401135 China

In the realm of unmanned aerial vehicle (UAV) tracking, Siamese-based approaches have gained traction due to their optimal balance between efficiency and precision. However, UAV scenarios often present challenges such as insufficient sampling resolution, fast motion and small objects with limited feature information. As a result, temporal context in UAV tracking tasks plays a pivotal role in target location, overshadowing the target’s precise features. In this paper, we introduce MT-Track, a streamlined and efficient multi-step temporal modeling framework designed to harness the temporal context from historical frames for enhanced UAV tracking. This temporal integration occurs in two steps: correlation map generation and correlation map refinement. Specifically, we unveil a unique temporal correlation module that dynamically assesses the interplay between the template and search region features. This module leverages temporal information to refresh the template feature, yielding a more precise correlation map. Subsequently, we propose a mutual transformer module to refine the correlation maps of historical and current frames by modeling the temporal knowledge in the tracking sequence. This method significantly trims computational demands compared to the raw transformer. The compact yet potent nature of our tracking framework ensures commendable tracking outcomes, particularly in extended tracking scenarios. Comprehensive tests across four renowned UAV benchmarks substantiate the superior efficacy of our approach, delivering real-time performance at 84.7 FPS on a single GPU. Real-world test on the NVIDIA AGX hardware platform achieves a speed exceeding 30 FPS, validating the practicality of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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