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2024 International Conference on Automation Control, Algorithm, and intelligent Bionics, ACAIB 2024

作者： Ding, Jiali Lu, Junjie Zhang, Jun Li, Chang Huang, Zixin Wang, Lejun Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan China Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China Wuhan Textile University Hubei Key Laboratory of Digital Textile Equipment Wuhan China Chongqing University of Posts and Telecommunications School of Automation School of Industrial Internet Chongqing China

ISBN: (纸本)9781510682597

Humanoid robot has broad application prospects in services, entertainment, medical care, rescue, and other fields. Therefore, research on the stable walking problem of humanoid robot is particularly important. To solve this problem, this paper proposes a gait control method for humanoid robot according to reinforcement learning. We established a linkage model for humanoid robot and simplified it into a linear inverted pendulum model which was used for motion control of humanoid robot. Using DDPG network as an intelligent agent to train humanoid robot in gait control in a simulation environment. Due to the combination of DDPG and DQN structure, the stability and convergence of strategy network and critical network have been improved. The MATLAB simulation results show that under the action of the DDPG algorithm, the humanoid robot can walk smoothly along the predetermined trajectory through reinforcement learning. © 2024 SPIE.

关键词： Anthropomorphic robots

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Operando reconstruction towards stable CuI nanodots with favorable facets for selective CO_(2) electroreduction to C2H4

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Science China Chemistry 2023年第6期66卷 1834-1843页

作者： Wenjie Xue Hongxia Liu Xinqing Chen Xiaoju Yang Ruoou Yang Youwen Liu Minghao Li Xuan Yang Bao Yu Xia Bo You Key Laboratory of Material Chemistry for Energy Conversion and Storage Ministry of EducationHubei Key Laboratory of Material Chemistry and Service FailureSchool of Chemistry and Chemical EngineeringHuazhong University of Science and TechnologyWuhan 430074China School of Chemistry and Chemical Engineering Wuhan Textile UniversityWuhan 430200China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201210China State Key Laboratory of Materials Processing and Die&Mold Technology and School of Materials Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China

Cu-based electrocatalysts with favorable facets and Cu^(+)can boost CO_(2) reduction to valuable multicarbon ***,the inevitable Cu^(+)reduction and the phase evolution usually result in poor ***,we fabricate CuI nanodots with favorable(220)facets and a stable Cu^(+)state,accomplished by operando reconstruction of Cu(OH)_(2) under CO_(2)-and I--containing electrolytes for enhanced CO_(2)-to-C_(2)H_(4) *** X-ray absorption spectroscopy(XAS),in-situ Raman spectroscopy and thermodynamic potential analysis reveal the preferred formation of *** gas electroresponse and density functional theory(DFT)calculations reveal that CO_(2)-related species induce the exposure of the(220)plane of Cu ***,the small size of nanodots enables the adequate contact with I^(-),which guarantees the rapid formation of Cu I instead of the electroreduction to Cu^(0).As a result,the resulting catalysts exhibit a high C2H4 Faradaic efficiency of 72.4%at a large current density of 800 m A cm^(-2) and robust stability for 12 h in a flow *** in-situ ATR-SEIRS spectroscopic characterizations and DFT calculations indicate that the(220)facets and stable Cu^(+) in CuI nanodots synergistically facilitate CO_(2)/*CO adsorption and*CO dimerization.

关键词： CO_(2)reduction reaction cuprous iodide favorable facets halogen electrolyte reconstruction

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Enhancing Hindi Feature Representation Through Fusion of Dual-Script Word Embeddings 30

Enhancing Hindi Feature Representation Through Fusion of Dua...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wang, Lianxi Tian, Yujia Chen, Zhuowei School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou510006 China Guangzhou Key Laboratory of Multilingual Intelligent Processing Guangzhou510006 China

ISBN: (纸本)9782493814104

Pretrained language models excel in various natural language processing tasks but often neglect the integration of different scripts within a language, constraining their ability to capture richer semantic information, such as in Hindi. In this work, we present a dual-script enhanced feature representation method for Hindi. We combine single-script features from Devanagari and Romanized Hindi Roberta using concatenation, addition, cross-attention, and convolutional networks. The experiment results show that using a dual-script approach significantly improves model performance across various tasks. The addition fusion technique excels in sequence generation tasks, while for text classification, the CNN-based dual-script enhanced representation performs best with longer sentences, and the addition fusion technique is more effective for shorter sequences. Our approach shows significant advantages in multiple natural language processing tasks, providing a new perspective on feature representation for Hindi. Our code has been released on https://***/JohnnyChanV/Hindi-Fusion. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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Energy management of solid oxide fuel cell/lithium battery hybrid system

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CSEE Journal of Power and Energy Systems 2022年 1-14页

作者： Li, Dong Zhong, Xiao Bo Xu, Yuan Wu Wei, Chong Yang Zhao, Song Jiang, Jianhua Deng, Zhonghua Fu, Xiaowei Li, Xi School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Hubei Wuhan430074 China College of Computer Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan430081 China

The research of fuel cell and lithium battery hybrid system has attracted more and more researchers because of its advantages of low emission. However, the lower efficiency of energy management has been a critical factor that obstruct the commercialization of hybrid system. In this work, based on finite element ideas, a solid oxide full cell node model is developed to accurately estimate the operating status of the fuel cell. As for lithium battery, previous energy management studies generally only focused on state of charge, however, state of health is also a key parameter for lithium battery. In this paper, considering the strong coupling between variables we use particle filter algorithm to jointly estimate the state of charge and state of health. To reasonably distribute and manage the energy in the hybrid system, a T-S fuzzy controller for the hybrid system is designed by controlling the state variables in the SOFC model and the state of lithium battery. Finally, our algorithm is verified by DSPACE simulation platform. The result shows that the hybrid system with the energy management strategy not only meets the real-time sharply changing demand but also ensures the hybrid system working efficiently and safely. © 2022 China Electric Power Research Institute. All rights reserved.

关键词： Hybrid systems

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Portrait Matting via Semantic and Detail Guidance 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Wang, Deming Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783031476341

ISBN: (纸本)9783031476334

Portrait matting is a challenging computer vision task that aims to estimate the per-pixel opacity of the foreground human regions. To produce high-quality alpha mattes, the majority of available methods employ a user-supplied trimap as an auxiliary input. However, obtaining trimap is difficult, which restricts the application of these methods. Recently, trimap-free approaches have been developed, but the alpha quality is significantly lower than that of trimap-based methods due to the lack of guidance. In this paper, we propose a portrait matting framework via semantic and detail guidance. We use the mask obtained from portrait segmentation as semantic guidance to improve semantic understanding of portrait matting, and we present a detail encoder block to capture the low-level feature around the subject as detail guidance, which helps to refine alpha details, such as hair. The proposed approach achieves remarkable results on both composition and real-world benchmarks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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VAGeo: View-specific Attention for Cross-View Object Geo-Localization

VAGeo: View-specific Attention for Cross-View Object Geo-Loc...

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

作者： Li, Zhongyang Yuan, Xin Liu, Wei Xu, Xin School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China

ISBN: (纸本)9798350368741

Cross-view object geo-localization (CVOGL) aims to locate an object of interest in a captured ground- or drone-view image within the satellite image. However, existing works treat ground-view and drone-view query images equivalently, overlooking their inherent viewpoint discrepancies and the spatial correlation between the query image and the satellite-view reference image. To this end, this paper proposes a novel View-specific Attention Geo-localization method (VAGeo) for accurate CVOGL. Specifically, VAGeo contains two key modules: view-specific positional encoding (VSPE) module and channel-spatial hybrid attention (CSHA) module. In object-level, according to the characteristics of different viewpoints of ground and drone query images, viewpoint-specific positional codings are designed to more accurately identify the click-point object of the query image in the VSPE module. In feature-level, a hybrid attention in the CSHA module is introduced by combining channel attention and spatial attention mechanisms simultaneously for learning discriminative features. Extensive experimental results demonstrate that the proposed VAGeo gains a significant performance improvement, i.e., improving acc@0.25/acc@0.5 on the CVOGL dataset from 45.43%/42.24% to 48.21%/45.22% for ground-view, and from 61.97%/57.66% to 66.19%/61.87% for drone-view. © 2025 IEEE.

关键词： Cross-view Object-level Localization Hybrid Attention Mechanism View-specific Positional Encoding

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Electronegativity-Induced Single-Ion Conducting Polymer Electrolyte for Solid-State Lithium Batteries

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Energy & Environmental Materials 2023年第4期6卷 40-48页

作者： Tianyi Hou Yumin Qian Dinggen Li Bo Xu Zhenyu Huang Xueting Liu Haonan Wang Bowen Jiang Henghui Xu Yunhui Huang State Key Laboratory of Materials Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement Ministry of EducationSchool of PhysicsBeijing Institute of TechnologyBeijing 100081China School of Energy and Power Engineering Huazhong University of Science and TechnologyWuhan 430074China Jiangsu Jitri-Hust Intelligent Equipment Technology Nanjing 210000China

The application of solid polymer electrolytes(SPEs)is severely impeded by the insufficient ionic conductivity and low Li^(+)transference numbers(t_(Li)^(+)).Here,we report an iodine-driven strategy to address both the two longstanding issues of SPEs *** lodine-containing groups introduced on polymer chains effectively attract Li^(+)ions,facilitate Li^(+)transport,and promote the dissociation of Li ***,iodine is also favorable to alleviate the strong O-Li^(+)coordination through a Lewis acidbase interaction,further improving the ionic conductivity and t_(Li)^(+).As a proof of concept,an iodinated single-ion conducting polymer electrolyte(IPE)demonstrates a high ionic conductivity of 0.93 mS cm^(-1)and a high t_(Li)^(+)of 0.86 at 25℃,which is among the best results ever reported for ***,symmetric Li/Li cells with IPE achieve a long-term stability over 2600 h through the in-situ formed LiF-rich *** a result,Li-S battery with IPE maintains a high capacity of 623.7 mAh g^(-1)over 300 cycles with an average Coulombic efficiency of 99%.When matched with intercalation cathode chemistries,Li/IPE/LiFePO_(4)and Li/IPE/LiNi_(0.8)Mn_(0.1)Co_(0.1)O_(2)solid-state batteries also deliver high-capacity retentions of 95%and 97%at 0.2 C after 120 cycles,respectively.

关键词： iodine lithium polymer electrolytes single-ion conducting solid-state batteries

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Surrogate-Assisted Multiobjective Gene Selection for Cell Classification From Large-Scale Single-Cell RNA Sequencing Data

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IEEE Transactions on Evolutionary Computation 2025年第3期29卷 601-615页

作者： Lin, Jianqing He, Cheng Jiang, Hanjing Huang, Yabing Jin, Yaochu Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Wuhan430074 China Renmin Hospital of Wuhan University Department of Pathology Wuhan430060 China Westlake University Trustworth and General Artificial Intelligence Laboratory School of Engineering Hangzhou310030 China

Accurate cell classification is crucial but expensive for large-scale single-cell RNA sequencing (scRNA-seq) analysis. Gene selection (GS) emerges as a pivotal technique in identifying gene subsets of scRNA-seq for classification accuracy improvement and gene scale reduction. Nevertheless, the rising scale of scRNA-seq data presents challenges to existing GS methods regarding performance and computational time. Thus, we propose a surrogate-assisted evolutionary algorithm for multiobjective GS to address these deficiencies. An innovative two-phase initialization method is proposed to select sparse solutions to provide preliminary insights into gene contributions. Then, a binary competitive swarm optimizer is proposed for effective global search, where a local search method is embedded to eliminate irrelevant genes for efficiency consideration. Additionally, a surrogate model is adopted to forecast classification accuracy efficiently and substitutes part of the computationally expensive classification process. Experiments are conducted on eight large-scale scRNA-seq datasets with more than 20 000 genes. The effectiveness of the proposed GS method for scRNA-seq cell classification compared with eight state-of-the-art methods is validated. Gene expression analysis results of selected genes further validated the significance of the genes selected by the proposed method in the classification of scRNA-seq data. © 1997-2012 IEEE.

关键词： Gene expression

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intelligent Innovation Dataset on Scientific Research Outcomes and Patents

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Journal of Social Computing 2025年第1期6卷 63-73页

作者： Wu, Xinran Zou, Hui Xing, Yidan Qu, Jingjing Guo, Weiwei Fu, Xiaoming School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Shanghai University Department of Library and Information Science Shanghai200444 China Big Data Technology Co. Ltd. Shanghai200082 China Shanghai Artificial Intelligence Laboratory Shanghai200082 China Fudan University Department of Management Shanghai200433 China Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Institute of Computer Science University of Göttingen Göttingen37073 Germany

Various stakeholders, such as researchers, government agencies, businesses, and research laboratories require a large volume of reliable scientific research outcomes including research articles and patent data to support their work. These data are crucial for a variety of application, such as advancing scientific research, conducting business evaluations, and undertaking policy analysis. However, collecting such data is often a time-consuming and laborious task. Consequently, many users turn to using openly accessible data for their research. However, these existing open dataset releases typically suffer from lack of relationship between different data sources and a limited temporal coverage. To address this issue, we present a new open dataset, the intelligent Innovation Dataset (IIDS), which comprises six interrelated datasets spanning nearly 120 years, encompassing paper information, paper citation relationships, patent details, patent legal statuses, and funding information. The extensive contextual and extensive temporal coverage of the IIDS dataset will provide researchers and practitioners and policy maker with comprehensive data support, enabling them to conduct indepth scientific research and comprehensive data analyses. © 2020 Tsinghua University Press.

关键词： Decentralized finance

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Ranking the Spreading Influence of Nodes in Complex Networks by Combining Local Average Weight and Average Normalized Link Entropy 4

Ranking the Spreading Influence of Nodes in Complex Networks...

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4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Fan, Kaiyu Yang, Fan Tu, Dexin Tang, Xinlai Mao, Xiuyong Guangxi University of Science and Technology School of Computer Science and Technology Liuzhou545006 China Guangxi University of Science and Technology Key Laboratory of Intelligent Information Processing and Graph Processing Liuzhou545006 China

ISBN: (纸本)9798350343618

Ranking the spreading influence of nodes is of great importance for controlling the spreading process on networks. Although a few measures are proposed for ranking nodes in weighted networks, it is still a challenge in accurately and efficiently ranking the spreading influence of nodes in weighted networks. In this paper, by utilizing both of the local and global topological structure information, a novel method is proposed. First, we define a local average weight (LAW) to describe the local topological structure information of nodes. Second, partly based on the link diversity, we define an average normalized link entropy (ANLE) to describe the global topological structure information of nodes. Further, by combining the LAW and ANLE, we propose a novel ranking measure termed as LAW-ANLE (LA) centrality. The proposed LA is verified by extensive experiments on a series of networks, and the results show that the LA is feasible and effective in accurately ranking the spreading influence of nodes. © 2023 IEEE.

关键词： Complex networks

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