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3D printing of self-powered triboelectric intelligent system with enhanced output performance for material identification via machine learning

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Chemical engineering Journal 2025年 515卷

作者： Huang, Bingyu Xu, Kailong Zhang, Xiaofei Zhang, Yongcheng Long, Yun-Ze Ge, Shuzhi Sam Wei, Xiangxia School of Automation Shandong Key Laboratory of Industrial Control Technology Qingdao University Qingdao266071 China College of Materials Science and Engineering Qingdao University Qingdao266071 China Collaborative Innovation Center for Nanomaterials & Devices College of Physics Qingdao University Qingdao266071 China College of Physics Qingdao University Qingdao266071 China Department of Electrical & Computer Engineering National University of Singapore 117576 Singapore

Triboelectric nanogenerators (TENGs) have attracted global attention for energy conversion from various types of mechanical energy into electricity. Traditional approaches for improving triboelectric conversion efficiency generally involved complicated preparation methods and strict structural design, which cannot fulfill the stringent requirements of material adaptability and application scenarios. In this work, a flexible TENG was developed based on zirconium-doped barium titanate (BaZrxTi1−xO3, BZT) particles in polydimethylsiloxane (PDMS) matrix. Subsequently, BZT/PDMS composites with patterned structures were fabricated by direct ink writing (DIW) based three-dimensional (3D) printing to significantly enhance the electrical performance. It was demonstrated that the TENG could deliver a maximum open-circuit voltage (VOC) of 350 V and a short-circuit current (ISC) of 4.9 µA with a maximum power density of 2.4 W m−2. Moreover, an effective utilization of the TENG was designed to detect the presence of humans, saving a huge amount of electrical energy. Finally, a TENG-based tactile sensor was integrated with machine learning to construct a material identification system, which was capable of recognizing different materials with a high accuracy of 98.54 %. The TENG-based tactile sensor with machine learning demonstrated a tremendous application in material perception, promoting the development of versatile sensors in the field of machine intelligence. © 2025 Elsevier B.V.

关键词： Liquid crystals

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Dynamic Event-Triggered Optimized Backstepping Containment control of a Class of Stochastic Nonlinear MASs Using Reinforcement Learning

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IEEE Internet of Things Journal 2025年

作者： Teng, Ao Wen, Guoguang Wang, Yidi Xu, Jiayang Meng, Yunhe Huang, Tingwen Beijing Jiaotong University School of Mathematics and Statistics Beijing 100044 China National University of Defense Technology College of Aerospace Science and Engineering Hunan Changsha 410073 China Sun Yat-sen University (Zhuhai Campus) School of Artificial Intelligence Zhuhai 519082 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen 518055 China

This paper focuses on the fully distributed dynamic event-triggered optimized backstepping (OB) containment control for a class of stochastic nonlinear high-order strict-feedback multiagent systems (MASs) based on reinforcement learning (RL) algorithm. Firstly, an OB control scheme is proposed using the critic-actor structure. In this scheme, neural networks are applied to approximate the value function and the controllers are derived from the gradients of simple positive functions. This approach simplifies the algorithm and effectively addresses two fundamental requirements commonly encountered in RL-based optimal control: known dynamics and persistent excitation. Furthermore, the static and dynamic event-triggered mechanisms are designed in order to reduce the control actions in the highest layer for nonlinear high-order stochastic MASs. Then, the distributed event-triggered control protocols based on RL are proposed for the strict-feedback nonlinear MASs with stochastic disturbances. Finally, simulation results are shown to demonstrate the effectiveness of the theoretical analysis. © 2014 IEEE.

关键词： containment control event-triggered mechanism Multiagent system (MAS) reinforcement learning (RL) stochastic system

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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Erratum to: Optimized design of a reinforced exhaust at low velocity (RELV) system for efficient viral aerosol removal in elevators (Building Simulation, (2025), 10.1007/s12273-025-1258-2)

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Building Simulation 2025年 1-1页

作者： Liu, Sumei Xu, Longhui Chen, Bingqian Deng, Zhipeng Du, Chengbo Li, Pin Zhao, Runmin Tianjin Key Laboratory of Indoor Air Environmental Quality Control School of Environmental Science and Engineering Tianjin University Tianjin300072 China School of Energy and Electrical Engineering Qinghai University Xining810016 China BYD Auto Industry Company Limited Shenzhen518119 China Department of Mechanical and Aerospace Engineering College of Engineering and Computer Science University of Central Florida OrlandoFL32816 United States School of Mechanical Engineering Purdue University West LafayetteIN47907 United States Shanghai Junyu Information Technology Co. Ltd Shanghai China

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H observer-based control for fuzzy Hadamard fractional-order systems

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Computational and Applied Mathematics 2025年第4期44卷 1-14页

作者： Rhaima, Mohamed Gassara, Hamdi Mchiri, Lassaad El Hajjaji, Ahmed Ben Makhlouf, Abdellatif Department of Statistics and Operations Research College of Science King Saud University Riyadh Saudi Arabia Laboratory of Sciences Techniques of Automatic Control and Computer Engineering National School of Engineering of Sfax University of Sfax Sfax Tunisia Panthéon-Assas University Paris II Paris France Modeling Information and Systems Laboratory University of Picardie Jules Verne UFR of Sciences Amiens France Department of Mathematics Faculty of Sciences Sfax University Sfax Tunisia

While the $$H_\infty $$ observer-based control has found widespread application in the literature for integer-order systems, researchers have shown less interest in addressing the same issue within the fractional-order framework. In this context, this study delves into the application of $$H_\infty $$ observer-based control for the Hadamard fractional-order system (HFOS) described by the Takagi–Sugeno fuzzy models (TSFM). Using Lyapunov approach and by employing a matrix decoupling technique, LMI based conditions ensuring the existence of an observer and controller, are proposed. To minimize the impact of disturbances on the controlled output, $$H_\infty $$ optimization technique is used. The validity of our approach is substantiated through an example, underscoring the robustness and reliability of our proposed findings.

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HierDAMap: Towards Universal Domain Adaptive BEV Mapping via Hierarchical Perspective Priors

arXiv

引用

arXiv 2025年

作者： Li, Siyu Cao, Yihong Shi, Hao Zang, Yongsheng He, Xuan Yang, Kailun Li, Zhiyong School of Robotics the National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Hunan Vanguard Group Corporation Limited Changsha410100 China

The exploration of Bird’s-Eye View (BEV) mapping technology has driven significant innovation in visual perception technology for autonomous driving. BEV mapping models need to be applied to the unlabeled real world, making the study of unsupervised domain adaptation models an essential path. However, research on unsupervised domain adaptation for BEV mapping remains limited and cannot perfectly accommodate all BEV mapping tasks. To address this gap, this paper proposes HierDAMap, a universal and holistic BEV domain adaptation framework with hierarchical perspective priors. Unlike existing research that solely focuses on image-level learning using prior knowledge, this paper explores the guiding role of perspective prior knowledge across three distinct levels: global, sparse, and instance levels. With these priors, HierDA consists of three essential components, including Semantic-Guided Pseudo Supervision (SGPS), Dynamic-Aware Coherence Learning (DACL), and Cross-Domain Frustum Mixing (CDFM). SGPS constrains the cross-domain consistency of perspective feature distribution through pseudo labels generated by vision foundation models in 2D space. To mitigate feature distribution discrepancies caused by spatial variations, DACL employs uncertainty-aware predicted depth as an intermediary to derive dynamic BEV labels from perspective pseudo-labels, thereby constraining the coarse BEV features derived from corresponding perspective features. CDFM, on the other hand, leverages perspective masks of view frustum to mix multi-view perspective images from both domains, which guides cross-domain view transformation and encoding learning through mixed BEV labels. Furthermore, this paper introduces intra-domain feature exchange data augmentation to enhance the efficiency of domain adaptation learning. The proposed method is verified on multiple BEV mapping tasks, such as BEV semantic segmentation, high-definition semantic, and vectorized mapping. It demonstrates competitive perfo

关键词： Birds

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Recent Advances in Image Dehazing

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 410-436页

作者： Wencheng Wang Xiaohui Yuan College of Information and Control Engineering Weifang University IEEE Department of Computer Science and Engineering University of North Texas

Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles,which reduces the contrast,changes the color,and makes the object features difficult to identify by human vision and by some outdoor computer vision *** image dehazing is an important issue and has been widely researched in the field of computer *** role of image dehazing is to remove the influence of weather factors in order to improve the visual effects of the image and provide benefit to *** paper reviews the main techniques of image dehazing that have been developed over the past ***,we innovatively divide a number of approaches into three categories:image enhancement based methods,image fusion based methods and image restoration based *** methods are analyzed and corresponding sub-categories are introduced according to principles and *** quality evaluation methods are then described,sorted and discussed in ***,research progress is summarized and future research directions are suggested.

关键词： Atmospheric scattering model image dehazing image enhancement quality assessment

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Dynamic convolution with complementary attention for image classification

Dynamic convolution with complementary attention for image c...

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2024 International Conference on computer Application and Information Security, ICCAIS 2024

作者： Zheng, Chusan Liu, Jingming Fan, Haoru Li, Yafeng Li, Jian Li, Ning Fan, Pan Sun, Jieqi School of Mathematics and Information Sciences Baoji University of Arts and Science Baoji 721013 China Cigarette Factory Shaanxi Tobacco Industry Co. Ltd. Baoji 721013 China Chengdu University of Technology Oxford Brookes College Chengdu 610051 China School of Computer Baoji University of Arts and Science Baoji 721016 China School of Electrical and Control Engineering School of Mathematics and Data Science Shaanxi University of Science and Technology Xi’an 710016 China

ISBN: (纸本)9781510689312

Convolution is the fundamental unit of operation in convolutional neural networks. However, traditional static convolution is subject to two significant limitations: a weak ability to effectively integrate global and local features and a high computational overhead. This paper introduces an innovative and efficient approach, the Complementary Attention Dynamic Convolution (CADC), to tackle the limitations of traditional static convolution in convolutional neural networks. CADC comprises a three-part architecture: two branches of attention and a fusion module. The dynamic convolution kernels are generated by integrating the results of the two branches of attention, which are weighted and fused. CADC is correlated with the input samples, efficiently integrates global and local features, and generates convolution kernels specific to the spatial location and channel domain. Extensive experiments have been conducted on popular benchmark datasets, and the experimental results demonstrate that the CADC method exhibits excellent performance in terms of the number of model parameters, computational complexity, and classification accuracy. © The Authors.

关键词： Attention mechanism convolution neural network dynamic kernel image classification

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GLOBAL PATH PLANNING OF CLIMBING ROBOT FOR WELD QUALITY INSPECTION IN LARGE SCALE STORAGE TANKS

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International Journal of Robotics and Automation 2025年第3期40卷 242-253页

作者： Wang, Ruifeng Gu, Shichao Liu, Xiaoguang Zhang, Hao Xu, Zhihao Pan, Jia Zhou, Xuefeng Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangdong Key Laboratory of Modern Control Technology GuangDong Guangzhou510070 China School of Rail Transportation Wuyi University GuangDong Jiangmen529020 China School of Mechanical Automotive Engineering South China University of Technology Guangdong Guangzhou510006 China Department of Computer Science University of Hong Kong 999077 Hong Kong

Climbing robots are considered efficient solutions for conducting quality inspections of welds on large storage tanks. Nevertheless, planning the global shortest path along spatially complex cross distributed welds poses a significant challenge for climbing robots. This paper introduces a novel graph-based global shortest climbing path planning method, facilitating swift inspect all welds by the robot. The three-dimensional (3D) weld lines undergo conversion into a two-dimensional (2D) weighted connected graph using a 3D model equivalence mapping approach. The dimensionality-reduced connectivity map is required to be an Euler graph;otherwise, the adoption of the Floyd and KM algorithms is necessary to construct an undirected Euler graph with the minimum total weight. Subsequently, an Euler circuit is searched in the Euler graph, considering the presence of plus edges, through an improved Fleury algorithm to obtain the shortest global path for the climbing robot. The paper also introduces the idea of Euler graph segmentation to address the challenge of Euler circuits in large or symmetric models. Simulations are conducted to validate the effectiveness and performance of the proposed planners. The results demonstrate that our method effectively reduces the length global path and enhances inspection efficiency. © 2025 Acta Press. All rights reserved.

关键词： Motion planning

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Solitary Density Waves for Improved Traffic Flow Model with Variable Brake Distances

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Communications in Theoretical Physics 2012年第2期57卷 301-307页

作者：朱文兴于瑞玲 School of Control Science and Engineering Shandong University Department of Computer Science and Technology Dezhou University

Traffic flow model is improved by introducing variable brake distances with varying *** of the traffic flow on a gradient is analyzed and the neutral stability condition is *** KdV(Korteweg-de Vries)equation is derived the use of nonlinear analysis and soliton solution is obtained in the meta-stable *** density waves are reproduced in the numerical *** is found that as uniform headway is less than the safety distance solitary wave exhibits upward form,otherwise it exhibits downward *** general the numerical results are in good agreement with the analytical results.

关键词： gradient highway solitary waves variable brake distances

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