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IEEE International Conference on Industrial Technology (ICIT)

作者： Nanxin Huang Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

Driven by advancements in industrial production and artificial intelligence, the need for pose estimation of new ob-jects in areas like robotic manipulation and virtual reality is increasing. We introduce a zero-shot object pose estimation approach that identifies the poses of objects excluded from the training dataset, removing the requirement for re-modeling. The method is built around a multi-level features fusion framework de-signed to enhance generalization. First, a trainable feature extraction module filters and selects multi-level features extracted by the backbone network. Unlike traditional convolutional ker-nels, we incorporate a dynamic convolution kernel to enhance the feature extraction capability. Second, in the feature fusion module, we adopt a dynamic weight generation strategy to perform weighted fusion of multi-level features. This method enhances template matching by effectively describing similarities between unseen objects (those absent from the training set) and templates, leveraging robust and adaptive feature representations to narrow the gap with seen objects. Experimental results demonstrate that our approach achieves state-of-the-art performance on two popu-lar benchmark datasets, LineMod and LineMod-Occlusion, proves that our method has better generalization than previous models.

关键词： Training Solid modeling Convolution Service robots Pose estimation Zero shot learning Virtual reality Benchmark testing Feature extraction Robustness

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Intrinsic-feature-guided 3D Object Detection

arXiv

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arXiv 2025年

作者： Zhang, Wanjing Wang, Chenxing School of Automation Southeast University 2# Sipailou Xuanwu Nanjing210096 China Key laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

LiDAR-based 3D object detection is essential for autonomous driving systems. However, LiDAR point clouds may appear to have sparsity, uneven distribution, and incomplete structures, significantly limiting the detection performance. In road driving environments, target objects referring to vehicles, pedestrians and cyclists are well-suited for enhancing representation through the complete template guidance, considering their grid and topological structures. Therefore, this paper presents an intrinsic-feature-guided 3D object detection method based on a template-assisted feature enhancement module, which extracts intrinsic features from relatively generalized templates and provides rich structural information for foreground objects. Furthermore, a proposal-level contrastive learning mechanism is designed to enhance the feature differences between foreground and background objects. The proposed modules can act as plug-and-play components and improve the performance of multiple existing methods. Extensive experiments illustrate that the proposed method achieves the highly competitive detection results. Code will be available at https://***/zhangwanjingjj/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Object recognition

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Abnormalities of brain dynamics based on large-scale cortical network modeling in autism spectrum disorder

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Neural Networks 2025年 189卷 107561页

作者： Si, Youyou Zhang, Honghui Du, Lin Deng, Zichen School of Mathematics and Statistics Northwestern Polytechnical University Shaanxi Xi'an710072 China School of Aeronautics Northwestern Polytechnical University Shaanxi Xi'an710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Shaanxi Xi'an710072 China

Synaptic increase is a common phenomenon in the brain of autism spectrum disorder (ASD). However, the impact of increased synapses on the neurophysiological activity of ASD remains unclear. To address this, we propose a large-scale cortical network model based on empirical structural connectivity data using the Wendling model, which successfully simulates both pathological and physiological electroencephalography (EEG) signals. Building on this, the EEG functional network is constructed using the phase lag index, effectively characterizing the functional connectivity. Our modeling results indicate that EEG activity and functional network properties undergo significant changes by globally increasing synaptic coupling strength. Specifically, it leads to abnormal neural oscillations clinically reported in ASD, including the decreased dominant frequency, the decreased relative power in the α band and the increased relative power in the δ+θ band, particularly in the frontal lobe. At the same time, the clustering coefficient and global efficiency of the functional network decrease, while the characteristic path length increases, suggesting that the functional network of ASD is inefficient and poorly integrated. Additionally, we find insufficient functional connectivity across multiple brain regions in ASD, along with decreased wavelet coherence in the α band within the frontal lobe and between the frontal and temporal lobes. Considering that most of the synaptic increases in ASD are limited, brain regions are further randomly selected to increase the local synaptic coupling strength. The results show that disturbances in local brain regions can also facilitate the development of ASD. This study reveals the intrinsic link between synapse increase and abnormal brain activity in ASD, and inspires treatments related to synapse pruning. © 2025 Elsevier Ltd

关键词： Neurons

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FAU zeolites from coal/rice husk co-combustion ash (co-ash) for CO2 adsorption: Synthesis, characterization, adsorption and regeneration performance

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Separation and Purification Technology 2025年 369卷

作者： Xu, Qing Lu, Jianyi Zhou, Zhiyong Jin, Yujia Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control Department of Environmental Science and Engineering North China Electric Power University Baoding071003 China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and Engineering North China Electric Power University Beijing102206 China

Synthesis of zeolite from industrial wastes has become an important environmental protection strategy. This study explored the synthesis of nano-sized NaX and NaY zeolites from coal/rice husk co-combustion ash (co-ash) using alkali-melting-hydrothermal synthesis and seed guiding methods, followed by modification with Li+ and K+ exchange. NaY zeolite, synthesized at a SiO2/Al2O3 ratio of 5 and crystallization temperature of 100 °C, exhibited the most complete crystal structure and optimal CO2 adsorption capacity. The specific surface area (SSA) of NaX and NaY zeolites (583.0 m2/g and 790.1 m2/g, respectively) are comparable to those of commercial zeolites, emphasizing their structural parity, and demonstrating their potential as effective CO2 adsorbents. NaY demonstrated a CO2 adsorption capacity of 5.31 mmol/g at 298.15 K and 1 bar, competitive with other conventionally synthesized and commercial zeolites. Cation exchange had a minimal impact on CO2 adsorption of NaY, with LiX improving capacity by 11.65 % compared to NaX, while LiY showed only a 2.94 % increase. K+ exchange negatively affected both zeolites. Kinetic and thermodynamic analyses, using the proposed first-order (PFO) and Langmuir-Freundlich models, indicated that CO2 adsorption is a rapid physisorption process with thermal effects ranging from 22 to 41 kJ/mol. Both zeolites exhibited excellent CO2/N2 selectivity and regeneration stability, with the loss of adsorption capacity retaining only 10.57 % and 9.63 % for NaX and NaY after fifteen cycles. This highlights their potential as efficient CO2 adsorbents. © 2025 Elsevier B.V.

关键词： Coal combustion

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EK-Net++: Real-time scene text detection with expand kernel distance and Epoch Adaptive Weight

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Expert systems with Applications 2025年 267卷

作者： Zhu, Boyuan Chen, Xi Tang, Quan Chen, C.L. Philip Liu, Fagui Computer Science and Engineering South China University of Technology Guangzhou510006 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518066 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Recently, scene text detection has received significant attention due to its wide applications. Accurate detection in complex scenes of multiple scales, orientations, and curvature remains a challenge. Component-based methods have emerged as the dominant approach for offering a more flexible way to achieve superior performance. However, Component-based methods employ the Vatti clipping algorithm for multi-instance training to handle the difficulty of arbitrary-shaped text. Yet we identify several bias results from these approaches called the "shrinked kernel" leading to decreased accuracy. Additionally, almost all text detection methods fail to consider the characteristics of different datasets and directly assign fixed loss weights to multi-instances. To address these limitations, we propose a new approach named Expand Kernel Network (EK-Net++), which presents the expand kernel distance map to compensate for the previous deficiency and incorporates three-stage regression to complete integral instance detection. Furthermore, we design the Epoch Adaptive Weight algorithm which dynamically distinguishes the characteristics of different datasets. EK-Net++ not only achieves precise positioning of arbitrary-shaped text but also strikes a balance between performance and speed. Evaluation results show that EK-Net++ attains state-of-the-art or competitive performance compared to other advanced methods, e.g., F-measure of 86.72% at 35.42 FPS on ICDAR 2015, F-measure of 85.83% at 40.13 FPS on CTW1500. © 2024 Elsevier Ltd

关键词： Epoch Adaptive Weight Expand kernel distance Real-time Scene text detection

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Contextual Multi-Armed Bandit-Based Dynamic Cooperative Link Configuration for AUV in UWASNs With Energy Harvesting

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao066004 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics BIC-ESAT College of Engineering Beijing100871 China

This paper investigates the cooperative link configuration problem for Autonomous Underwater Vehicle (AUV) in Underwater Acoustic (UWA) sensor networks with Energy Harvesting (EH), which aims to maximize long-term cumulative capacity by jointly optimizing cooperation relay, AUV transmission power, and relay transmission power. Subject to unknown time-varying Channel State Information (CSI), unpredictable stochastic EH, and variable communication topology caused by AUV mobility, the proposed problem is an unknown dynamic combination decision optimization problem. To resist the change of the dynamic UWA communication topology with unknown time-varying CSI and EH, a novel collaborative contextual multi-armed bandit learning framework is proposed, which allows relay nodes to learn cooperatively and enriches the learning information of relays. Consequently, the proposed learning framework can resist the change of the dynamic UWA communication topology efficiently, thereby achieving the superior link configuration quickly. Besides, a learning-rate adjustment rule for the dynamic UWA communication topology is proposed to adaptively balance the exploration-exploitation, thereby avoiding missing the real superior joint relay-power configuration. Finally, simulation results show the significantly superiority of the proposed scheme. © 2015 IEEE.

关键词： Cooperative communication

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Facile construction of N-doped TiO2 nanohybrids with enriched oxygen vacancies for enhanced photocatalytic green hydrogen production

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Ceramics International 2025年

作者： Tao, Zichen Zhang, Bin Chen, Wenxin Cao, Ruwen Liu, Jie Wang, Lidong Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control Department of Environmental Science and Engineering North China Electric Power University Baoding071003 China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and Engineering North China Electric Power University Beijing102206 China

A series of N-doped TiO2 nanohybrids (xN-TiO2) were successfully prepared via a facile solvothermal route by using oleylamine as N source, for enhancing photocatalytic hydrogen evolution reaction (HER). The doped-N atoms induced the generation of abundant oxygen vacancies (OVs). The resulted impurity and defect levels in xN-TiO2 contributed to their narrowed band gap, enhanced visible light response, and promoted separation and transfer of photogenerated carrier. Moreover, the N-doping optimized the distribution of OVs, and therefore affect the conduction band (CB) location of catalyst. Herein, the 3N-TiO2 catalyst with excellent photoelectric properties and appropriate CB location, showed the photocatalytic HER rate of 3984.71 μmol h−1 g−1 under the simulated sunlight irradiation, which was 5.5 and 29.0 times that of the undoped TiO2 and commercial P25, respectively. Meanwhile, the 3N-TiO2 catalyst exhibited the excellent stability and salt-resistance. This work provides a novel insight for the band alignment of photocatalysts by defects regulation, and opens the new possibility for synthesizing N-doped TiO2 catalyst by a more practical and safe method. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Hydrogen evolution reaction

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Design of a Constant Current Output High Frequency AC-DC Converter for Battery Charging Applications

Design of a Constant Current Output High Frequency AC-DC Con...

引用

IEEE International Conference on Industrial Technology (ICIT)

作者： Zihao Wei Xiaoxian Song Liangwei Jin Dingbang Huang Sheng Zhang Zhijian Fang School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China School of Automation China University of Geosciences Wuhan China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

How to realize the high power factor, high efficiency, miniaturization and high power density of AC-DC converter is the key problem of battery charging applications. In this paper, an isolated AC-DC converter is proposed. The input voltage is rectified by the first stage AC-DC conversion, and the output is stabilized by the series resonant converter at the secondary stage. A sinusoidal modulation method is adopted to realize the constant current output characteristic independent of load. Then through the output current control loop and the PFC control loop, the constant current output and input high power factor are realized. The design of high switching frequency and resonant frequency provides conditions for high power density and miniaturization of converter design. Finally, the simulation of rated power of 960W was built by Matlab-simulink, which verified the rationality of the converter design.

关键词： Reactive power Power system measurements Density measurement Switching frequency Modulation Resonant converters Stability analysis Batteries High frequency Voltage control

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Federated Services: A Smart Service Ecology with Federated Security for Aligned Data Supply and Scenario-Oriented Demands

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IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 925-936页

作者： Jia, Xiaofeng Li, Juanjuan Wang, Shouwen Qi, Hongwei Wang, Fei-Yue Qin, Rui Zhang, Min Liang, Xiaolong Beijing Big Data Centre Beijing100101 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Technology Company Ltd. Beijing100190 China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing100190 China Macau University of Science and Technology Faculty of Innovation Engineering Macao999078 China

This paper introduces federated services as a smart service ecology with federated security to align distributed data supply with diversified service demands spanning digital and societal contexts. It presents the comprehensive researches on the theoretical foundation and technical system of federated services, aiming at advancing our understanding and implementation of this novel service paradigm. First, a thorough examination of the characteristics of federated security within federated services is conducted. Then, a five-layer technical framework is formulated under a decentralized intelligent architecture, ensuring secure, agile, and adaptable service provision. On this basis, the operational mechanisms underlying data federation and service confederation is analyzed, with emphasis on the smart supply-demand matching model. Furthermore, a scenario-oriented taxonomy of federated services accompanied by illustrative examples is proposed. Our work offers actionable insights and roadmap for realizing and advancing federated services, contributing to the refinement and wider adoption of this transformative service paradigm in the digital era. © 2014 Chinese Association of Automation.

关键词： Decentralized autonomous organizations and operations decentralized physical infrastructure networks federated security federated services multimodal large language models smart contracts

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Adapting Human Mesh Recovery with Vision-Language Feedback

arXiv

引用

arXiv 2025年

作者： Xu, Chongyang Huang, Buzhen Zhang, Chengfang Feng, Ziliang Wang, Yangang College of Computer Science Sichuan University Chengdu610065 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education The School of Automation Southeast University Nanjing210096 China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou646000 China

Human mesh recovery can be approached using either regression-based or optimization-based methods. Regression models achieve high pose accuracy but struggle with model-to-image alignment due to the lack of explicit 2D-3D correspondences. In contrast, optimization-based methods align 3D models to 2D observations but are prone to local minima and depth ambiguity. In this work, we leverage large vision-language models (VLMs) to generate interactive body part descriptions, which serve as implicit constraints to enhance 3D perception and limit the optimization space. Specifically, we formulate monocular human mesh recovery as a distribution adaptation task by integrating both 2D observations and language descriptions. To bridge the gap between text and 3D pose signals, we first train a text encoder and a pose VQ-VAE, aligning texts to body poses in a shared latent space using contrastive learning. Subsequently, we employ a diffusion-based framework to refine the initial parameters guided by gradients derived from both 2D observations and text descriptions. Finally, the model can produce poses with accurate 3D perception and image consistency. Experimental results on multiple benchmarks validate its effectiveness. The code will be made publicly available. © 2025, CC BY.

关键词： Mesh generation

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