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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Benarab, Achref Yin, Guisheng Rafique, Fahad Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798350324303

Ontology matching plays a crucial role in the realm of ontology engineering, specifically in ontology development and integration. It serves the purpose of resolving the issue of heterogeneous semantics between ontologies. The alignment of ontologies is a critical step in enabling interoperability of systems and data, which is particularly important in a world where data is increasingly numerous and diverse. This paper proposes a fully automatic ontology matching approach, which utilizes machine learning algorithms. By combining a neural embedding approach and a mapping neural network, this approach achieves a precise and effective mapping between ontologies. The experimental findings on the well-known ontology benchmark 'anatomy dataset' are shown to be quite superior to previous matching tools. © 2023 IEEE.

关键词： Embeddings

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Recent Advances of Transition Metal Basic Salts for Electrocatalytic Oxygen Evolution Reaction and Overall Water Electrolysis

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Nano-Micro Letters 2023年第4期15卷 238-260页

作者： Bingrong Guo Yani Ding Haohao Huo Xinxin Wen Xiaoqian Ren Ping Xu Siwei Li Institute of Industrial Catalysis School of Chemical Engineering and TechnologyXi’an Jiaotong UniversityXi’an 710049People’s Republic of China Institute of Carbon Neutral Energy Technology School of Energy Science and EngineeringHarbin Institute of TechnologyHarbin 150001People’s Republic of China MIIT Key Laboratory of Critical Materials Technology for New Energy Conversion and Storage School of Chemistry and Chemical EngineeringHarbin Institute of TechnologyHarbin 150001People’s Republic of China

Electrocatalytic oxygen evolution reaction(OER)has been recognized as the bottleneck of overall water splitting,which is a promising approach for sustainable production of H_(2).Transition metal(TM)hydroxides are the most conventional and classical non-noble metal-based electrocatalysts for OER,while TM basic salts[M^(2+)(OH)_(2-x)(A_(m^(-))_(x/m),A=CO_(3)^(2−),NO_(3)^(−),F^(−),Cl^(−)]consisting of OH−and another anion have drawn extensive research interest due to its higher catalytic activity in the past *** this review,we summarize the recent advances of TM basic salts and their application in OER and further overall water *** categorize TM basic salt-based OER pre-catalysts into four types(CO_(3)^(2−),NO_(3)^(−),F^(−),Cl^(−)according to the anion,which is a key factor for their outstanding performance towards *** highlight experimental and theoretical methods for understanding the structure evolution during OER and the effect of anion on catalytic *** develop bifunctional TM basic salts as catalyst for the practical electrolysis application,we also review the present strategies for enhancing its hydrogen evolution reaction activity and thereby improving its overall water splitting ***,we conclude this review with a summary and perspective about the remaining challenges and future opportunities of TM basic salts as catalysts for water electrolysis.

关键词： Transition metal basic salts Electrocatalytic Oxygen evolution reaction(OER) Overall water electrolysis

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Link Prediction Based on the Relational Path Inference of Triangular Structures 9th

Link Prediction Based on the Relational Path Inference of Tr...

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9th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Li, Xin Han, Qilong Li, Lijie Wang, Ye College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9789819959709

Link prediction is used to complete the knowledge graph. Convolutional neural network models are commonly used for link prediction tasks, but they only consider the direct relations between entity pairs, ignoring the semantic information contained in the relation paths. In addition, the embedding dimension of the relation is generally larger than that of the entity in the ConvR model, which blocks the progress of downstream tasks. If we reduce the embedding dimension of the relation, the performance will be greatly degraded. This paper proposes a convolutional model PITri-R-ConvR based on triangular structure relational inference. The model uses relational path inference to capture semantic information, while using a triangular structure to ensure the reliability and computational efficiency of relational inference. In addition, the decoder R-ConvR improves the initial embedding of the ConvR model, which solves the problems of the ConvR model and significantly improves the prediction performance. Finally, this paper conducts sufficient experiments in multiple datasets to verify the superiority of the model and the rationality of each module. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Enriching Semantic Features for Medical Report Generation 12th

Enriching Semantic Features for Medical Report Generation

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Luan, Qi Pan, Haiwei Zhang, Kejia Shi, Kun Jia, Xiteng College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9783031446955

Medical reports play an important role in diagnosing a patient’s illness. However, writing medical reports is time-consuming and labor-intensive, and writing high-quality medical reports often requires extensive clinical experience, which can be a challenge for less experienced doctors. Based on this, the medical report generation tasks emerged. The model architecture used is basically Encoder-Decoder architecture. In this architecture, the extraction quality of visual features is particularly critical. Based on this, previous research has focused on how to better extract visual features, but most researchers have neglected the acquisition of textual features. In this paper, we focus on how to obtain better textual features and how to perform multi-modal feature fusion of visual and textual features more effectively. To this end, we propose two modules, the BM25 Multi-Source Text feature interaction module (BM25-MST) and the Multi-Modal Feature memory fusion module (Mul-MF),respectively. The aim of this BM25-MST module is to generate a multi-source text feature containing rich semantic information, and then use the Mul-MF module to perform full fusion of visual features and text features. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Diagnosis

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Superior stability of Li5Mg@Cu anodes for lithium metal batteries:Investigating the suppression effects of magnesium on lithium dendrite growth

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材料科学技术（英文版） 2025年第8期211卷 288-302页

作者： Ruijun Yao Zhuoyu Li Longke Bao Rui Deng Kai Zheng Yiming Hu Jiahui Li Hao Zhang Shaobo Tu Rongpei Shi Junwei Wu Changming Li Xingjun Liu College of Materials Science and Engineering Institute of Materials Genome and Big DataHarbin Institute of Technology(Shenzhen)Shenzhen 518055China Department of Materials Process Engineering Graduate School of EngineeringNagoya UniversityFuro-choChikusa-kuNagoya464-8603Japan School of Physics and Materials Science Nanchang University999 Xuefu RoadHonggutan DistrictNanchang 330031China College of Materials Science and Engineering Institute of Materials Genome and Big DataHarbin Institute of Technology(Shenzhen)Shenzhen 518055China 21C Innovation Laboratory(21C LAB) Contemporary Amperex Technology Ltd.Ningde 352100China Institute for Materials Science and Devices Suzhou University of Science and TechnologySuzhou 215011China

Li metal is widely recognized as the desired anode for next-generation energy storage,Li metal batteries,due to its highest theoretical capacity and lowest ***,it suffers from unstable elec-trochemical behaviors like dendrite growth and side reactions in practical ***,we report a highly stable anode with collector,Li5Mg@Cu,realized by the melting-rolling *** Li5Mg@Cu anode delivers ultrahigh cycle stability for 2000 and 1000 h at the current densities of 1 and 2 mA cm-2,respectively in symmetric ***,the Li5Mg@Cu|LFP cell exhibits a high-capacity retention of 91.8％for 1000 cycles and 78.8％for 2000 cycles at 1 ***,we investigate the suppression effects of Mg on the dendrite growth by studying the performance of LixMg@Cu electrodes with different Mg contents(2.0-16.7 at％).The exchange current density,surface energy,Li+diffusion coefficient,and chem-ical stability of LixMg@Cu concretely reveal this improving suppression effect when Mg content becomes *** addition,a Mg-rich phase with"hollow brick"morphology forming in the high Mg content LixMg@Cu guides the uniform deposition of *** study reveals the suppression effects of Mg on Li dendrites growth and offers a perspective for finding the optimal component of Li-Mg alloys.

关键词： Lithium dendrite Lithium metal anode Lithium-magnesium alloy Cycle performance Suppression effect Stability

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Local-Global Temporal Difference Learning for Satellite Video Super-Resolution

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IEEE Transactions on Circuits and Systems for Video technology 2024年第4期34卷 2789-2802页

作者： Xiao, Yi Yuan, Qiangqiang Jiang, Kui Jin, Xianyu He, Jiang Zhang, Liangpei Lin, Chia-Wen The School of Geodesy and Geomatics Wuhan University Wuhan430000 China The School of Computer Science and Technology Harbin Institute of Technology Harbin150000 China The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430000 China The Department of Electrical Engineering the Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Optical-flow-based and kernel-based approaches have been extensively explored for temporal compensation in satellite Video Super-Resolution (VSR). However, these techniques are less generalized in large-scale or complex scenarios, especially in satellite videos. In this paper, we propose to exploit the well-defined temporal difference for efficient and effective temporal compensation. To fully utilize the local and global temporal information within frames, we systematically modeled the short-term and long-term temporal discrepancies since we observe that these discrepancies offer distinct and mutually complementary properties. Specifically, we devise a Short-term Temporal Difference Module (S-TDM) to extract local motion representations from RGB difference maps between adjacent frames, which yields more clues for accurate texture representation. To explore the global dependency in the entire frame sequence, a Long-term Temporal Difference Module (L-TDM) is proposed, where the differences between forward and backward segments are incorporated and activated to guide the modulation of the temporal feature, leading to a holistic global compensation. Moreover, we further propose a Difference Compensation Unit (DCU) to enrich the interaction between the spatial distribution of the target frame and temporal compensated results, which helps maintain spatial consistency while refining the features to avoid misalignment. Rigorous objective and subjective evaluations conducted across five mainstream video satellites demonstrate that our method performs favorably against state-of-the-art approaches. Code will be available at https://***/ XY-boy/LGTD. © 2023 IEEE.

关键词： Three dimensional displays

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Near-field radiative heat transfer between nanoporous GaN films

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Chinese Physics B 2024年第4期33卷 109-120页

作者：韩晓政张纪红刘皓佗吴小虎冷惠文 School of Electromechanical and Automotive Engineering Yantai UniversityYantai 264005China Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of EducationHarbin University of Science and TechnologyHarbin 150080China Thermal Science Research Center Shandong Institute of Advanced TechnologyJinan 250100China

Photon tunneling effects give rise to surface waves,amplifying radiative heat transfer in the near-field *** research has highlighted that the introduction of nanopores into materials creates additional pathways for heat transfer,leading to a substantial enhancement of near-field radiative heat transfer(NFRHT).Being a direct bandgap semiconductor,GaN has high thermal conductivity and stable resistance at high temperatures,and holds significant potential for applications in optoelectronic ***,study of NFRHT between nanoporous GaN films is currently lacking,hence the physical mechanism for adding nanopores to GaN films remains to be discussed in the field of *** this work,we delve into the NFRHT of GaN nanoporous films in terms of gap distance,GaN film thickness and the vacuum filling *** results demonstrate a 27.2%increase in heat flux for a 10 nm gap when the nanoporous filling ratio is ***,the spectral heat flux exhibits redshift with increase in the vacuum filling *** be more precise,the peak of spectral heat flux moves fromω=1.31×10^(14)rad·s^(-1)toω=1.23×10^(14)rad·s^(-1)when the vacuum filling ratio changes from f=0.1 to f=0.5;this can be attributed to the excitation of surface phonon *** introduction of graphene into these configurations can highly enhance the NFRHT,and the spectral heat flux exhibits a blueshift with increase in the vacuum filling ratio,which can be explained by the excitation of surface plasmon *** findings offer theoretical insights that can guide the extensive utilization of porous structures in thermal control,management and thermal modulation.

关键词： near-field radiative heat transfer nanoporous GaN film surface phonon polaritons surface plasmon polaritons

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TTST: A Top-k Token Selective Transformer for Remote Sensing Image Super-Resolution

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IEEE Transactions on Image Processing 2024年 33卷 738-752页

作者： Xiao, Yi Yuan, Qiangqiang Jiang, Kui He, Jiang Lin, Chia-Wen Zhang, Liangpei Wuhan University School of Geodesy and Geomatics Wuhan430079 China Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu300 Taiwan Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China

Transformer-based method has demonstrated promising performance in image super-resolution tasks, due to its long-range and global aggregation capability. However, the existing Transformer brings two critical challenges for applying it in large-area earth observation scenes: (1) redundant token representation due to most irrelevant tokens;(2) single-scale representation which ignores scale correlation modeling of similar ground observation targets. To this end, this paper proposes to adaptively eliminate the interference of irreverent tokens for a more compact self-attention calculation. Specifically, we devise a Residual Token Selective Group (RTSG) to grasp the most crucial token by dynamically selecting the top-k keys in terms of score ranking for each query. For better feature aggregation, a Multi-scale Feed-forward Layer (MFL) is developed to generate an enriched representation of multi-scale feature mixtures during feed-forward process. Moreover, we also proposed a Global Context Attention (GCA) to fully explore the most informative components, thus introducing more inductive bias to the RTSG for an accurate reconstruction. In particular, multiple cascaded RTSGs form our final Top-k Token Selective Transformer (TTST) to achieve progressive representation. Extensive experiments on simulated and real-world remote sensing datasets demonstrate our TTST could perform favorably against state-of-the-art CNN-based and Transformer-based methods, both qualitatively and quantitatively. In brief, TTST outperforms the state-of-the-art approach (HAT-L) in terms of PSNR by 0.14 dB on average, but only accounts for 47.26% and 46.97% of its computational cost and parameters. The code and pre-trained TTST will be available at https://***/XY-boy/TTST for validation. © 1992-2012 IEEE.

关键词： Remote sensing

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Biologically inspired anthraquinone redox centers and biomass graphene for renewable colloidal gels toward ultrahigh-performance flexible micro-supercapacitors

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Journal of Materials science & technology 2023年第20期151卷 178-189页

作者： Tiansheng Wang Shunyou Hu Yuanyuan Hu Dong Wu Hao Wu Jinxu Huang Hao Wang Weiwei Zhao Wen Yu Mi Wang Jie Xu Jiaheng Zhang School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China Key Laboratory of Micro-systems and Micro-structures Manufacturing of Ministry of Education Harbin Institute of TechnologyHarbin 150001China National Innovation Center for Advanced Medical Devices Harbin Institute of TechnologyShenzhen 457001China Research Centre of Printed Flexible Electronics Harbin Institute of TechnologyShenzhen 518055China Sauvage Laboratory for Smart Materials School of Materials Science and EngineeringHarbin Institute of Technology(Shenzhen)ShenzhenGuangdong 518055China Department of Chemistry University of IdahoMoscowIdaho 83844-2343USA

Biomass carbon and small redox biomolecules are attractive materials for green,sustainable energy storage devices owing to their environmentally friendly,low-cost,scalable,and novel ***,most devices manufactured using these materials have low specific capacitance,poor cycle stability,short lifetime,complexity,and low precision of device ***,we report the directed self-assembly of mononuclear anthraquinone(MAQ)derivatives and porous lignin-based graphene oxide(PLGO)into a renewable colloidal gel through noncovalent *** self-assembled gel electrode materials exhibited high capacitance(484.8 F g^(−1) at a current density of 1 A g^(−1))and could be further printed as flexible micro-supercapacitors(FMSCs)with arbitrary patterns and a relatively high resolution on specific *** FMSCs exhibited excellent areal capacitance(43.6 mF cm^(−2)),energy and power densities(6.1μWh cm^(−2) and 50μW cm^(−2),respectively),and cycle stability(>10,000 cycles).Furthermore,the printed FMSCs and integrated FMSC arrays exhibited remarkable flexibility while maintaining a stable *** proposed approach can be applied to other quinone biomolecules and biomass-based carbon *** study provides a basis for fabricating green and sustainable energy storage device architectures with high capacitance,long-term cycling,high scalability,and high precision.

关键词： Biomass carbon Mononuclear anthraquinone Noncovalent interactions Renewable colloidal gel Flexible micro-supercapacitors

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Multi-channel mantis search spatial attention convolutional neural network based hyperspectral image change detection

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Multimedia Tools and Applications 2025年 1-22页

作者： Divya, V.M. Department of Computer Science and Engineering East West Institute of Technology Visvesvaraya Technological University Belagavi560018 India

Hyperspectral imaging has transformed remote sensing by offering detailed spectral data for monitoring environmental changes, like urban development and terrestrial cover variations. However, existing change detection techniques often face challenges like underfitting and overfitting. In this research, a novel approach is introduced for hyperspectral image change detection using a Multi-Channel Mantis Search (MCMS). This innovative methodology leverages the power of a Multi-Channel Convolutional Neural Network (MCNN) to process each spectral band independently, facilitating the capture of unique features within each band. The input hyperspectral images are gathered from the Pavia university Dataset and the Indian Pines Dataset. This approach leads to significantly improved feature extraction, enhancing the discrimination of different materials in hyperspectral images. To further enhance the performance of the change detection model, the Mantis Search Algorithm (MSA) is integrated. The MSA is employed to optimize the model's hyperparameters, fine-tuning its architecture and training parameters to achieve superior results. The combination of MCNN and MSA improves hyperspectral image change detection accuracy, achieving 98.21% and 99.31% with the Indian Pines and Pavia university datasets correspondingly. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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