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New Results on Almost Periodic Dynamics of a Delayed Nicholson’s Blowflies Model with Feedback Control

IAENG International Journal of Applied Mathematics 2025年第4期55卷 842-848页

作者： Sun, Nan Duan, Lian Xie, Dong Department of Mathematics Anhui University of Science and Technology Huainan232001 China Anhui Province Engineering Laboratory for Big Data Analysis Early Warning Technology of Coal Mine Safety Department of Mathematics Anhui University of Science and Technology Huainan232001 China Department of Electronic and Information Engineering Bozhou University Bozhou236800 China

This paper considers a delayed almost periodic Nicholson’s blowflies model with feedback control. Through employing the almost periodic differential equations theory and Lyapunov functional approach, some principles are set up for ensuring the persistence, existence and global asymptotic stability of positive almost periodic solutions of the considered model. Furthermore, a numerical example is provided for validating the theoretical analysis. The achieved results are novel and serve as an addition to the existing ones. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lyapunov functions

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Graphene-supported isolated platinum atoms and platinum dimers for CO_(2) hydrogenation:Catalytic activity and selectivity variations

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Chinese Chemical Letters 2025年第4期36卷 241-245页

作者： Sanmei Wang Dengxin Yan Wenhua Zhang Liangbing Wang Research Institute of Interdisciplinary Sciences(RISE)and School of Materials Science&Engineering Dongguan University of TechnologyDongguan 523808China State Key Laboratory for Powder Metallurgy School of Materials Science and EngineeringCentral South UniversityChangsha 410083China Hefei National Laboratory for Physical Sciences at the Microscale and Synergetic Innovation Centre of Quantum Information&Quantum Physics University of Science and Technology of ChinaHefei 230026China Laboratory for Chemical Technology Ghent UniversityGhent 9052Belgium

Manipulating catalyst structures to control product selectivity while maintaining high activity presents a considerable challenge in CO_(2)*** density functional theory calculations and microkinetic analysis,we proposed that graphene-supported isolated Pt atoms(Pt1/graphene)and Pt_(2)dimers(Pt_(2)/graphene)exhibited distinct selectivity in CO_(2)***_(1)/graphene facilitated the conversion of CO_(2)into formic acid,whereas Pt_(2)/graphene favored methanol *** variation in product selectivity arose from the synergistic interaction of Pt_(2)dimers,which facilitated the migration of H atoms between two Pt atoms and promoted the transformation from*COOH intermediates to*C(OH)_(2)intermediates,altering the reaction pathways compared to isolated Pt ***,an analysis of the catalytic activities of three Pt_(1)/graphene and three Pt_(2)/graphene structures revealed that the turnover frequencies for formic acid generation on Pt_(1ii)/graphene and methanol generation on Pt_(2i)/graphene were as high as 744.48 h-1and 789.48 h^(-1),*** values rivaled or even surpassed those previously reported in the literature under identical *** study provides valuable insights into optimizing catalyst structures to achieve desired products in CO_(2)hydrogenation.

关键词： CO_(2)hydrogenation Graphene Pt single-atom Pt_(2)dimers DFT

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Detection of Perfect Stealthy Attacks on Cyber-Physical systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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Semantic-Aware Prompt Learning for Multimodal Sarcasm Detection

Semantic-Aware Prompt Learning for Multimodal Sarcasm Detect...

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

作者： Wang, Guangjin Wang, Bao Xu, Fuyong Zhu, Zhenfang Wang, Peipei Wang, Ru Liu, Peiyu School of Information Science and Engineering Shandong Normal University Jinan China School of Information Science and Electrical Engineering Shandong Jiao Tong University Jinan China Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal sarcasm detection aims to identify whether utterances express sarcastic intentions contrary to their literal meaning based on multimodal information. However, existing methods fail to explore the model's "ability to understand" the semantics expressed by sentences in the image context from semantic diversity perspectives. In this paper, we propose a multi-view semantic awareness method, which concretizes semantics from multiple perspectives to improve the model's ability to capture different semantic features. Specifically, two learnable prefixes are attached to the text representation respectively to construct semantic representations from both the literal meaning and sarcastic intention perspectives. Then, image-text information is further fused through cross-attention to guide the semantic representation of different perspectives in the image context. Finally, the semantics expressed by prefixes are strengthened through KL divergence, thereby encouraging the model to capture two distinctive semantic features. Experiments on benchmark datasets demonstrate the effectiveness of our method. © 2025 IEEE.

关键词： KL divergence multi-view semantic representation multimodal sarcasm detection semantic representation enhancement

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Gait Sequence Upsampling using Diffusion Models for Single LiDAR Sensors

Gait Sequence Upsampling using Diffusion Models for Single L...

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2025 IEEE/SICE International Symposium on system Integration, SII 2025

作者： Ahn, Jeongho Nakashima, Kazuto Yoshino, Koki Iwashita, Yumi Kurazume, Ryo Kyushu University Graduate School of Information Science and Electrical Engineering Japan Kyushu University Faculty of Information Science and Electrical Engineering Japan California Institute of Technology Jet Propulsion Laboratory United States

ISBN: (纸本)9798331531614

Recently, 3D LiDAR has emerged as a promising technology in the field of gait-based person identification, serving as an alternative to traditional RGB cameras due to its robustness under varying lighting conditions and its ability to capture 3D geometric information. However, long capture distances or the use of low-cost LiDAR sensors often result in sparse human point clouds, leading to a significant decline in identification performance. To address these challenges, we propose a sparse-to-dense upsampling model for pedestrian point clouds in gait recognition using 3D LiDAR, named LidarGSU, which is designed to enhance the generalization capability of existing identification models. Our method utilizes diffusion probabilistic models (DPMs), which have shown high fidelity in generative tasks such as image completion. In this work, we leverage DPMs on sparse sequential pedestrian point clouds as conditional masks in a video-to-video translation approach, applied in an inpainting manner. We conducted extensive experiments on the SUSTeck1K dataset to evaluate the generative quality and recognition performance of the proposed method. Furthermore, we demonstrate the applicability of our upsampling model using a real-world dataset, captured with a low-resolution sensor across varying measurement distances. © 2025 IEEE.

关键词： Image enhancement

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Spatial and Temporal Distribution of Metal Elements in the Rivers of Northern Jiangsu Province

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Journal of Ocean University of China 2025年第1期24卷 157-168页

作者： YUAN Liping LIU Qian JIAN Huimin MI Tiezhu YANG Fuxia YAO Qingzhen Frontiers Science Center for Deep Ocean Multispheres and Earth System and Key Laboratory of Marine Chemistry Theory and TechnologyMinistry of EducationOcean University of ChinaQingdao 266100China Laboratory of Marine Ecology and Environmental Science Qingdao National Laboratory for Marine Science and TechnologyQingdao 266071China College of Environmental Science and Engineering Ocean University of ChinaQingdao 266100China Key Laboratory of Land and Sea Ecological Governance and Systematic Regulation of the Ministry of Ecology and Environment Shandong Academy for Environmental PlanningJinan 250101China

The concentrations of V,Cr,Mn,Fe,Co,Ni,Cu,Zn,As,Mo,Cd,and Pb were determined in Doulonggang River,Xinyanggang River,Huangshagang River,Sheyanghe River,Guangaizongqu River,and Linhonghe River at the North of Jiangsu Province in *** annual average concentrations of V,Cr,Mn,Fe,Co,Ni,Cu,Zn,As,Mo,Cd and Pb in the six rivers were 2.20,1.22,4.53,21.9,0.13,2.79,1.77,4.00,2.97,3.87,0.01,0.19μg L^(-1)*** highest concentration of most trace metals were found in Guangaizongqu River and Linhonghe River,and the lowest concentration were found in Xinyanggang River and Huangshagang *** principal component analysis(PCA)resulted of two factors together explained 91.2%of the variance with>1initial eigenvalue,indicating that both natural and anthropogenic activities were contributing factors as the source of metal abundance in rivers of northern Jiangsu *** first major component represented the influence of anthropogenic activities,including industry and agriculture,with a contribution rate of 54.1%,affected Cr,Fe,Cu,Zn,Mo,Cd and *** second(such as V,Mn,Co,Ni,As)was a mixed source,including the natural processes such as precipitation,erosion and weathering and anthropogenic action like industry and agriculture,with a contribution rate of 37.1%.Seasonal variations in trace metal concentrations were influenced by temperature,salinity,water discharge,and input of external *** highest concentrations were found in wet season and were strongly influenced by rainfall and seasonal industrial and agricultural activities.

关键词： trace metal distribution rivers northern Jiangsu Province

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A Novel Method based on Hybridization of Generative Adversarial Imputation Nets and SDAE-Kriging for RUL Prediction of Lithium-ion Battery in Scenarios of Missing and Incomplete Data

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IEEE Transactions on Industry Applications 2025年第3期61卷 4590-4599页

作者： Li, Wei Li, Yongsheng Wang, Ningbo Garg, Akhil Gao, Liang Bose, Bibaswan Shankhwar, Kalpana Hefei University of Technology School of Mechanical Engineering Hefei230009 China Huazhong University of Science and Technology School of Civil and Hydraulic Engineering Wuhan430074 China Huazhong University of Science and Technology School of Energy and Power Engineering Wuhan430074 China Huazhong University of Science and Technology State Key Laboratory of Digital Manufacturing Equipment and Technology School of Mechanical Science and Engineering Wuhan430074 China Centre for Automotive Research and Tribology Indian Institute of Technology Hauz Khas Delhi110016 India Indraprastha Institute of Information Technology New Delhi Delhi India

Lithium-ion batteries (LIBs) have received enormous attention as the core components of Electric vehicles (EVs). An unavoidable issue is that battery performance will continue to degrade as materials age and cycle time increases. Accurately predicting the Remaining useful life (RUL) of LIBs is an important prerequisite to ensure the safe driving of EVs. However, the actual battery management system may encounter sensor or communication system failures, resulting in missing or incomplete data, which will result in inaccurate battery RUL predictions. This article presents a novel method based on hybridization of Generative adversarial imputation nets (GAIN) and Stacked denoised autoencoder with Kriging (SDAE-Kriging) for the prediction of RUL of LIBs in scenarios of missing and incomplete data. In the proposed method, the GAIN is leveraged to realize the filling of missing and incomplete data. The SDAE-Kriging is used to predict the RUL of LIBs with filled data. Different missing rates (10%, 20%, 30%, and 40%) are investigated to establish RUL prediction models. It is manifested that the GAIN has better data filling results on two datasets. After completing the data filling, the results show that SDAE-Kriging has high prediction accuracy (RMSE is less than 0.1) for both datasets, even when the missing rate is 40%. The proposed scheme can provide an effective solution for the RUL prediction of LIBs in the scenario of missing and incomplete data in practical industrial applications. © 1972-2012 IEEE.

关键词： Generative adversarial networks

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DVMTICANET: A Synthetic Multisource Interactive Adaptation Framework for Intelligent Fault Diagnosis Under Large Cross-Domain Discrepancies

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IEEE Sensors Journal 2025年第9期25卷 15625-15641页

作者： Jiang, Huiming Zhu, Haifeng Yuan, Jing Zhao, Qian Li, Yanbin Chen, Jin University of Shanghai for Science and Technology School of Mechanical Engineering Shanghai200093 China Shanghai Academy of Spaceflight Technology Shanghai201109 China Shanghai Jiao Tong University State Key Laboratory of Mechanical System and Vibration Shanghai200240 China

Multisource transfer fault diagnosis is currently a hot topic in the field of intelligent fault diagnosis (IFD). However, in practical scenarios, large cross-domain discrepancies usually exist among datasets of different working conditions, which brings more difficulties and challenges to multisource transfer fault diagnosis. To solve the tough issue, a synthetic multisource interactive adaptation framework is proposed in this article. First, in order to acquire satisfying multisource transfer fault diagnostic performances under large cross-domain discrepancies, the entire framework is established based on strategies of domain shared level adaptation, domain specific level adaptation, and personalized diagnostic knowledge learning. Second, a distinctive domain specific transferability metric is designed and utilized to induce these three strategies to better facilitate the positive transfer of the entire framework. Third, the deep convolutional variational autoencoder (DCVAE) is selected as the shared feature extractor, which can help preliminarily narrow the large cross-domain discrepancies due to its strong feature distribution regularization ability. Eventually, the experiments based on two different bearing datasets are conducted to verify the superiority and effectiveness of the proposed method, especially when the cross-domain discrepancies are quite large. © 2001-2012 IEEE.

关键词： Fault detection

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Power Allocation Based on Federated Multiagent Deep Reinforcement Learning for NOMA Maritime Networks

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IEEE Internet of Things Journal 2025年第9期12卷 12869-12884页

作者： He, Jiayi Liu, Zhiyong Zhang, Yakai Jin, Zihao Zhang, Qinyu School of Information Science and Engineering Shandong Weihai264209 China Shandong Provincial Key Laboratory of Marine Electronic Information and Intelligent Unmanned Systems Key Laboratory of Cross-Domain Synergy and Comprehensive Support for Unmanned Marine Systems of the Ministry of Industry and Information Technology Shandong Weihai264209 China Communication Engineering Research Center Guangdong Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China

The absence of established communication infrastructures at sea presents a significant hurdle for the development of the Internet of Things (IoT) in maritime environments. In this article, we propose a novel solution: a nonorthogonal multiple access (NOMA) maritime network employing power allocation facilitated by federated multiagent deep reinforcement learning (DRL). Conventional single-agent DRL algorithms encounter challenges, such as dimensionality explosion in real-world scenarios. We address this issue by extending these algorithms to incorporate multiple agents. Furthermore, to mitigate risks associated with centralized training, such as data leakage and network attacks, we adopt a federated learning (FL) framework for distributed training across multiple agents. By uploading only select parameters during training and keeping data locally, FL not only enhances algorithm convergence speed but also bolsters data privacy. Specifically, we introduce the federated multiagent deep Q-network (FLMADQN) algorithm tailored for power allocation in NOMA maritime networks. Our algorithm aims to maximize system throughput, optimize data transfer rates, and expedite convergence. Through extensive computer simulations, we validate the efficacy of our proposed approach. Results demonstrate that the FLMADQN algorithm significantly outperforms the traditional DRL algorithm, DQN, in NOMA maritime environments, improving average system throughput by 20.12%, peak system throughput by 45.10%, and system spectral efficiency by 48.33%. Moreover, FLMADQN exhibits a twofold increase in convergence speed compared to MADQN without FL and is 2.5 times faster than DQN. Our findings underscore the potential of federated multiagent DRL in advancing communication systems for maritime IoT applications. © 2014 IEEE.

关键词： Federated learning

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Normalized LMS adaptive filter algorithm: principles and verilog HDL implementation 4

Normalized LMS adaptive filter algorithm: principles and ver...

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4th International Conference on Electronic information and Communication engineering, EICE 2025

作者： Wang, Haoxu Li, Bingjie Liu, Ming Yang, Dezhen Chen, Zhijie Liu, Xu Wan, Peiyuan Beijing Embedded System Key Lab School of Information Science and Technology Beijing University of Technology Beijing China National Key Laboratory of Infrared Detection Technologies Beijing China

This article offers a detailed exploration of the Normalized Least Mean Squares (NLMS) algorithm, highlighting its enhanced adaptive filtering capabilities and robust performance in managing input signal correlations. It delves into the foundational principles of the NLMS algorithm and outlines the essential modules required for its implementation in Verilog HDL. The implementation and simulation of the NLMS adaptive filter are studied. The simulation outcomes indicate that the convergence rate of the algorithm is superior to that of the conventional LMS algorithm. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Adaptive filtering

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