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Smart tailoring of molecular catalysts:Mounting approach to oxygen reduction reaction

Journal of Materials Science & technology 2024年第1期168卷 169-184页

作者： Anuj Kumar Mohd Ubaidullah Guoxin Zhang Jasvinder Kaur Saira Ajmal Mudassir Hasan Krishna Kumar Yadav Hafiz M.Adeel Sharif Ram K.Gupta Ghulam Yasin Nano-Technology Research Laboratory Department of ChemistryGLA UniversityMathuraUttar Pradesh 281406India Department of Chemistry College of ScienceKing Saud UniversityRiyadh 11451Saudi Arabia Department of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdaoShandong 266590China Institute for Advanced Study Shenzhen UniversityShenzhenGuangdong 518060China Department of Chemistry Kansas Polymer Research CenterPittsburg State UniversityPittsburgKS 66762United States Faculty of Science and Technology Madhyanchal Professional UniversityRatibadBhopal 462044India Environmental and Atmospheric Sciences Research Group Scientific Research CenterAl-Ayen UniversityThi-QarNasiriyah 64001Iraq Department of Chemical Engineering King Khalid University Saudi Arabia Department of Chemistry School of SciencesIFTM UniversityMoradabadUttar Pradesh 244102India Research Center for Eco-Environmental Engineering Dongguan University of TechnologyDongguan 523808China School of Environment and Civil Engineering Dongguan University of TechnologyDongguanGuangdong 523808China

Efficient electrocatalytic rupture of energy-rich molecules(H_(2)and O_(2))is a green approach for gener-ating clean energy for modern *** this context,porphyry-type molecular electrocatalysts act intelligently toward oxygen reduction reaction(ORR),a fundamental process in fuel cells,due to their redox-rich chemistry,which involves core metal ions and macrocyclic *** concerned scientific community has tried many times to correlate the ORR intermediates with their formation kinetics and simplify the associated multi H+/e-stages during the ORR process,constructing several volcano plots be-tween catalytic Tafel data,turnover frequencies,and overpotentials for many *** the fact that many review articles on molecular electrocatalysts for ORR have been published,understanding the strategic implications and molecular catalyst intelligence towards homogenous ORR has been poorly *** review examined the relationships between volcano plots of current *** parameters and the Sabatier principle in order to explain the intelligence of molecular electrocatalysts and approaches for their creation,as well as the difficulties and potential prospects of molecular *** facts distinguish this review from previously published articles and will pique the scien-tific community’s interest in avoiding trial-and-error procedures for catalyst creation while also allowing for more exact evaluations of the molecular catalyst’s performance.

关键词： Electrocatalysis Molecular catalysts Oxygen reduction reaction Sabatier principle

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A Channel-Augmented Multi-Domain Graph Convolutional Network for Fatigue Driving Detection 4

A Channel-Augmented Multi-Domain Graph Convolutional Network...

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4th International Conference on Industrial automation, robotics and Control Engineering, IARCE 2024

作者： Sun, Mingyi Cui, Weigang Li, Yang Beihang University Department of Automation Science and Electrical Engineering Beijing China School of Engineering Medicine Beihang University Beijing China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Virtual Reality Technology and Systems Department of Automation Science and Electrical Engineering Beijing China

ISBN: (纸本)9798350380323

Electroencephalography (EEG) has emerged as a crucial cornerstone within the realm of brain-computer interface (BCI) applications, with its significance notably pronounced in the field of fatigue detection. However, the inherent limitations of EEG acquisition equipment during real driving scenarios have contributed to the constrained robustness of existing models. Moreover, most of recent methods failed to extract robust multi-domain features, leading to a suboptimal performance. To address these challenges, we propose a novel channel-augmented multi-domain graph convolutional network (CA-MDGCNet). Specifically, the initial EEG signals are enriched by incorporating supplementary virtual EEG channels, distinguished as learnable parameters within the network architecture. Then, differential entropy features are extracted from the augmented EEG signals. Following this, a multi-domain graph convolutional network is designed to encode high-level EEG features by means of convolutions in diverse paths, which is beneficial to integrating the characteristics extracted from multiple domains. Finally, the classification block derives the detection outcome from the refined feature maps. To substantiate the potency of the proposed method, the validation was conducted on the publicly accessible SEED-VIG. The proposed CA-MDGCNet not only demonstrates more promising performance compared to state-of-the-art approaches but also underscores the potential viability of our method for the realm of fatigue driving detection. © 2024 IEEE.

关键词： Brain computer interface

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The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1313-1316页

作者： Tianyu Shen Jinlin Sun Shihan Kong Yutong Wang Juanjuan Li Xuan Li Fei-Yue Wang College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real *** this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

关键词： Intelligence DAO TAO

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Unleashing the potential of morphotropic phase boundary based hybrid triboelectric–piezoelectric nanogenerator

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Journal of Materiomics 2024年第4期10卷 792-802页

作者： Sugato Hajra Aneeta Manjari Padhan Basanta Kumar Panigrahi Phakkhananan Pakawanit Zvonko Jagličić Naratip Vittayakorn Yogendra Kumar Mishra Sanghoon Lee Hoe Joon Kim Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and TechnologyDaegu42988Republic of Korea Department of Electrical Engineering Siksha O Anusandhan UniversityBhubaneswar751030India Synchrotron Research and Applications Division Synchrotron Light Research InstituteNakhon Ratchasima30000Thailand Institute of Mathematics Physics and Mechanics&Faculty of Civil and Geodetic EngineeringUniversity of LjubljanaJadranska191000LjubljanaSlovenia Advanced Materials Research Unit Department of ChemistrySchool of ScienceKing Mongkut's Institute of Technology LadkrabangBangkok10520Thailand Mads Clausen Institute NanoSYDUniversity of Southern DenmarkAlsion 26400SønderborgDenmark Robotics and Mechatronics Research Center Daegu Gyeongbuk Institute of Science and Technology(DGIST)Daegu42988Republic of Korea

Morphotropic phase boundary(MPB)-based ceramics are excellent for energy harvesting due to their enhanced physical properties at phase boundaries,broad operating temperature range,and ability to customize properties for efficient conversion of mechanical energy into electrical *** this work,Bi_(1–x)Na_(x)Fe_(1–x)Nb_(x)O_(3)(x=0.20,0.30,0.32 and 0.40,BNFNO abbreviation)based ceramics were synthesized using a solid-state route and blended with Polydimethylsiloxane(PDMS)to achieve flexible *** material characterization and energy harvesting were performed by designing a hybrid piezoelectric(PENG)-triboelectric(TENG)*** voltage and current of PENG,TENG,and hybrid bearing same device area(2 cm×2 cm)were recorded as 11 V/0.3μA;60 V/0.7μA;110 V/2.2μ*** strategies for enhancing the output performance of the hybrid device were evaluated,such as increased surface area(creating micro-roughness and porous morphology)and increasing electrode size and multi-layer hybrid device *** self-powered acceleration monitoring was demonstrated using the hybrid ***,the low-frequency-based wave energy is converted into electrical energy,confirming the usage of hybrid PENG-TENG devices as a base for battery-free sensors and blue energy harvesting.

关键词： Lead-free Acceleration Wave energy Morphotropic

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Research on Path Planning of Mobile Robots Based on Snow Ablation Optimizer

Research on Path Planning of Mobile Robots Based on Snow Abl...

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第43届中国控制会议

作者： Wenlong Chen Zile Wang Yuhang Yang Siqi Wang Heng Wang Yasar Ayaz School of Automation and Electrical Engineering University of Science and Technology Beijing National Center of Artificial Intelligence (NCAI) Department of Robotics and Artificial Intelligence National University of Sciences and Technology (NUST)

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Path planning is one of the key technologies for mobile robots. Metaheuristic algorithms hold significant promise in generating an effective and reliable path for mobile robots. However, most metaheuristic algorithms do not balance exploration and exploitation in the solution space, which may lead to premature convergence and cannot guarantee to find a satisfying *** ablation optimizer(SAO) is a novel metaheuristic technique based on the sublimation and melting phenomenon of snow,which can overcome the shortcomings of most other metaheuristic algorithms. This paper proposes a path planning method for mobile robots based on the SAO algorithm and undertakes a comparative study with the planning method based on particle swarm optimization(PSO) using simulation experiments. The results show that the proposed path planner is superior to the PSO-based path planner in both planning speed and path quality.

关键词： Path Planning Mobile Robots Snow Ablation Optimizer Particle Swarm Optimization

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention 26

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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26th International Conference on Industrial technology, ICIT 2025

作者： Bao, Chengao Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Tokyo Institute of Technology Yokohama226-8502 Japan Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798331521950

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method. © 2025 IEEE.

关键词： Modal analysis

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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A Multi-Head Convolution Network with Attention Consistency for Facial Expression Recognition 42

A Multi-Head Convolution Network with Attention Consistency ...

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42nd Chinese Control Conference, CCC 2023

作者： Liu, Wenkang Sun, Mingyi Li, Yang Beihang University The Department of Automation Science and Electrical Engineering Beijing100191 China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Peking University Advanced Institute of Information Technology Hangzhou310000 China

ISBN: (纸本)9789887581543

In recent years, the demand for facial expression recognition applications has increased rapidly, and its research has received extensive attention from researchers. However, the current recognition methods based on deep learning ignore the idea of multiple head attention and semantic consistency, resulting in the model only paying attention to the local area of the feature map. In addition, the model's inconsistent attention to the images before and after the flip, resulting the poor robustness, poor interpretation, and other shortcomings in the model. To address the above problems, we propose an Affinity Separation Loss (ASLoss), which improves the separability of samples through clustering. Moreover, a Separate Multi-head Attention block (SMA), and a Zonal Loss (ZLoss) are also designed to decentralize the model's attention. Experimental results demonstrate that our proposed MACNet method achieves competitive recognition performance on two public datasets RAF-DB and FERPlus. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Face recognition

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A Multi-DOF Robot System Based on LightGBM-Driven EEG Decoding Model for BCI Human-Machine Interaction 42

A Multi-DOF Robot System Based on LightGBM-Driven EEG Decodi...

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42nd Chinese Control Conference, CCC 2023

作者： Yan, Weidong Xu, Zhaoliang Li, Yang Beihang University The Department of Automation Science and Electrical Engineering Beijing100191 China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Advanced Institute of Information Technology Peking University Hangzhou310000 China

ISBN: (纸本)9789887581543

In this study, a multi-degree-of-freedom (Multi-DOF) robot (MDR) system based on a LightGBM-driven electroencephalogram (EEG) decoding model is designed and developed to assist subjects with hand motor dysfunction in their daily activities and neurorehabilitation. The system mainly consists of a motor imagery electroencephalogram (MI-EEG) evoking layer, an intention decoding layer, and an interaction executive layer. The MI-EEG evoking layer initially displays a virtual reality (VR) motion imagination scenario, instructing the subjects to imagine a real hand gripping movement, simultaneously collecting the electrical EEG signals and preprocessing the EEG signals. Secondly, in the intention decoding layer, a network combining temporal-spectral feature fusion and LightGBM (TSFF-LightGBM) for MI-BCI classification is used to more effectively boost brain decoding accuracy and decrease decoding time. Finally, in the interaction executive layer, the Multi-DOF wearable robot is developed to offer hand grasp motion kinesthetic feedback and visual feedback synced with MI. The following are the key benefits of the proposed MDR system: (1) We propose a new lightweight network structure more suitable for brain computer interface (BCI) interaction systems, achieving more accurate decoding and shorter identification time in different data sets, which helps to improve the practicability of the system and promote the practical clinical application of BCI rehabilitation technology. (2) We integrate BCI, VR, a wearable Multi-DOF robot, motion kinesthetic feedback, and visual feedback to improve human-machine interaction. Compared to the most recent investigations, the average accuracy of the MDR system on the publicly accessible datasets BCI IV 2a and HGD reached 75.89% and 93.53%, respectively. © 2023 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Brain computer interface

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A Fully Test-Time Adaptation Method for Motor Imagery

SSRN

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SSRN 2024年

作者： Peng, Yiheng Luo, Jingjing Wang, Hongbo Chen, Li Guo, Shijie Guo, Yuzhu Li, Yang The Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Engineering Research Center of AI and Robotics Ministry of Education Shanghai200043 China Department of Automation Sciences and Electrical Engineering Beihang University Beijing100191 China School of Automation Science and Electrical Engineering China

Transfer learning offers a promising solution to the annotation and calibration challenges associated with motor imagery (MI) electroencephalogram (EEG) decoding during neural-feedback rehabilitation. In this study, we address the challenge of online unsupervised cross-subject adaptation in MI-EEG classification, where target samples arrive sequentially, and we should adapt the model online with limited target data. We focus on the testing stage and propose a MI Fully Test-Time Adaptation (MI-FTTA) method that essentially enables online training using limited and unlabeled target data. MI-FTTA effectively alleviates the influence of noisy pseudo labels with teacher-student mutual learning and time-constrained sample selection. Through recalculating normalization statistics with limited accessible source and target information and prototype-based cross-domain contrastive learning, MI-FTTA adaptively enhances the representational capability of models. It thoroughly harnesses the limited online target samples to enhance adaptation performance. Experimental evaluations are conducted using three classification datasets , including two-class (BCI-IV2b), three-class (High-Gamma), and four-class (BCI-IV2a) scenarios. MI-FTTA demonstrates its superiority by surpassing direct source model deployment with an average performance gain of 4.11\% across different scenarios. Notably, our proposed approach also achieves a significant improvement over 12 different online test-time adaptation algorithms at three different backbones, thus affirming its *** work represents early endeavor in source-free fully test-time adaptation for cross-subject MI-EEG decoding. © 2024, The Authors. All rights reserved.

关键词： Transfer learning

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