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Julolidine functionalized benzimidazoline-doped fullerene derivatives for efficient and stable perovskite solar cells

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Interdisciplinary Materials 2024年第3期3卷 369-379页

作者： Yanqing Zhu Chenglong Li JiaHui Chen Yuxi Zhang Jianfeng Lu Min Hu Wangnan Li Fuzhi Huang Yi-Bing Cheng Hyesung Park Shengqiang Xiao State Key Laboratory of Silicate Materials for Architectures Wuhan University of TechnologyWuhanChina State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhanChina School of Electronic and Electrical Engineering Hubei Province Engineering Research Centre for Intelligent Micro-Nano Medical Equipment and Key TechnologiesWuhan Textile UniversityWuhanChina Hubei Key Laboratory of Low Dimensional Optoelectronic Material and Devices Hubei University of Arts and ScienceXiangyangHubeiChina KU-KIST Graduate School of Converging Science and Technology Korea UniversitySeoulRepublic of Korea Department of Integrative Energy Engineering Korea UniversitySeoulRepublic of Korea Hubei Key Laboratory of Fuel Cell WuhanChina

Fullerene derivatives are highly attractive materials in solar cells,organic thermoelectrics,and other ***,the intrinsic low electron mobility and electrical conductivity restrict their potential device performance,such as perovskite solar cells(PSCs).Herein,we successfully enhanced the electric properties and morphology of phenyl-C61-butyric acid methyl ester(PCBM)by n-doping it with a benzimidazoline derivative,9-(1,3-dimethyl-2,3-dihydro-1H-benzoimidazol-2-yl)-julolidine(JLBI-H)via a solution *** found the n-doping can not only improve the conductivity and optimize the band alignment but also enable the PCBM to have a constantly strong charge extraction ability in a wide temperature from 173 to 373 K,which guarantees a stable photovoltaic performance of the corresponding PSCs under a wide range of operating *** the JLBI-H-doped PCBM,we improved the efficiency from 17.9%to 19.8%,along with enhanced stability of the nonencapsulated devices following the aging protocol of ISOS-D-1.

关键词： charge extraction electron mobility n-doping PSCs wide operation temperature

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Research on Waxiang Dialect Recognition Based on Wav2vec2.0 Pre-Training and Transfer Learning

Research on Waxiang Dialect Recognition Based on Wav2vec2.0 ...

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2024 International Conference on Computing, Robotics and System Sciences, ICRSS 2024

作者： Deng, Mixuan Tang, Pengju Gao, Yanxia Li, Huixuan Zhen, Guodong Fu, Kaifeng Wu, Xiaotian Huaihua University Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua China

ISBN: (纸本)9798331511296

As a unique local dialect, Waxiang dialect carries rich regional culture and historical information. However, with the acceleration of modernization and the increase of population mobility, the inheritance and protection of Waxiang dialect are facing severe challenges. By constructing the 'Xuewatong' APP- Waxiang dialect digital protection platform, based on cloud storage technology, digital resource display, multimedia integration and other technologies, and providing learning resources through big data analysis of user needs, it can effectively promote the digital recording and preservation of Waxiang dialect language resources, and significantly improve the dissemination and educational effect of Waxiang dialect culture, showing the great potential of digital technology in dialect protection. © 2024 IEEE.

关键词： Linguistics

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4th International Conference on Computer Science and Blockchain, CCSB 2024

作者： Huang, Guimin Shi, Canjun School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350356502

Short textual similarity has been more and more widely used in the field of natural language processing. However, people tend to pay more attention to the semantic similarity of sentences and ignore the word order similarity in sentences. For this reason, we propose a short text similarity computation model that fuses semantics and word order. In terms of semantics, we propose a adjacency-aware similarity model based on deep convolutional neural networks, which effectively exploits the adjacency relationship in sentences and extracts text features from multiple perspectives for similarity prediction. In terms of word order, a word order model incorporating an external knowledge base is proposed, which uses an external knowledge base with pointwise mutual information to construct word order dense vectors to assess word order similarity. Ultimately, we propose a comprehensive assessment model for short text similarity that incorporates semantic and word order information. We conducted experiments on the public dataset MRPC, and the experimental results show that our model outperforms existing models in terms of performance. © 2024 IEEE.

关键词： Convolutional neural networks

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One-bit splitting deceptive jamming against SAR

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Defence technology（防务技术） 2022年第10期18卷 1760-1777页

作者： Bo Zhao Lei Huang Bing Li Shi-qi Liu Wei-min Bao Guangdong Key Laboratory of Intelligent Information Processing College of Information EngineeringShenzhen UniversityShenzhen518060China China Aerospace Science and Technology Corporation Beijing100048China

One-bit quantization is a promising technique due to its performance retention and complexity reduction in a deceptive jammer against synthetic aperture radar(SAR).In this paper,the 1-bit quantization technology is utilized to agilely generate split false targets in the SAR imagery once the radar signal is intercepted,which reduces the complexity of the jammer significantly with guaranteed focusing quality.A single-frequency threshold is used to decompose harmonics incurred by the 1-bit quantization,and its parameters are adjusted through different pulse repetition intervals to provide steerable *** this way,the SAR signal is split into coupled false scatterers during the 1-bit *** further deploying amplitude,time-delay,and Doppler frequency modulations on the 1-bit intercepted signal,the split false targets are *** proposed approach is compared with different deceptive jamming methods to show its validity in effectiveness and cost,and numerical experiments are also presented for verification.

关键词： Synthetic aperture radar(SAR) 1-Bit quantization Multi-false-target Single-frequency threshold

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Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification

Knowledge-Data Fusion Based Source-Free Semi-Supervised Doma...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Peng, Ruimin An, Jiayu Wu, Dongrui School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control Wuhan430074 China

ISBN: (纸本)9781665410205

Electroencephalogram (EEG)-based seizure sub-type classification enhances clinical diagnosis efficiency. Source-free semi-supervised domain adaptation (SF-SSDA), which transfers a pre-trained model to a new dataset with no source data and limited labeled target data, can be used for privacy-preserving seizure subtype classification. This paper considers two challenges in SF-SSDA for EEG-based seizure subtype classification: 1) How to effectively fuse both raw EEG data and expert knowledge in classifier design? 2) How to align the source and target domain distributions for SF-SSDA? We propose a Knowledge-Data Fusion based SF-SSDA approach, KDF-MutuaISHOT, for EEG-based seizure subtype classification. In source model training, KDF uses Jensen-Shannon Diver-gence to facilitate mutual learning between a feature-driven Decision Tree-based model and a data-driven Transformer-based model. To adapt KDF to a new target dataset, an SF-SSDA algorithm, MutualSHOT, is developed, which features a consistency-based pseudo-label selection strategy. Experiments on the public TUSZ and CHSZ datasets demonstrated that KDF-MutualSHOT outperformed other supervised and source-free domain adaptation approaches in cross-subject seizure subtype classification. © 2024 IEEE.

关键词： Self-supervised learning

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Spontaneous Recovery in Directed Dynamical Networks

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Engineering 2024年第6期37卷 208-214页

作者： Xueming Liu Xian Yan H.Eugene Stanley School of Artificial Intelligence and Automation&The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems&the Key Laboratory of Image Processing Huazhong University of Science and TechnologyWuhan 430074China Center for Polymer Studies Department of PhysicsBoston UniversityBostonMA 02215USA

Complex networked systems,which range from biological systems in the natural world to infrastructure systems in the human-made world,can exhibit spontaneous recovery after a failure;for example,a brain may spontaneously return to normal after a seizure,and traffic flow can become smooth again after a *** studies on the spontaneous recovery of dynamical networks have been limited to undirected ***,most real-world networks are *** fill this gap,we build a model in which nodes may alternately fail and recover,and we develop a theoretical tool to analyze the recovery properties of directed dynamical *** find that the tool can accurately predict the final fraction of active nodes,and the prediction accuracy decreases as the fraction of bidirectional links in the network increases,which emphasizes the importance of directionality in network *** to different initial states,directed dynamical networks may show alternative stable states under the same control parameter,exhibiting hysteresis *** addition,for networks with finite sizes,the fraction of active nodes may jump back and forth between high and low states,mimicking repetitive failure-recovery *** findings could help clarify the system recovery mechanism and enable better design of networked systems with high resilience.

关键词： Network resilience Directed dynamical networks Spontaneous recovery

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Alamouti code based on block repetition in FBMC/OQAM systems

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Digital Communications and Networks 2022年第1期8卷 58-63页

作者： Dejin Kong Xing Zheng Yukang Tian Tao Jiang Yue Zhang School of Electronic and Electrical Engineering Wuhan Textile UniversityWuhanChina State Key Laboratory of New Textile Materials and Advanced Processing Technologies Wuhan Textile UniversityWuhanChina Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhanChina Department of Engineering University of LeicesterLeicesterLEI 7RHUK

In this paper,we address the Alamouti code in Filter-Bank Multicarrier with Offset Quadrature Amplitude Modulation(FBMC/OQAM)and propose a novel block-wise Alamouti code,in which a repeated block is designed to eliminate the imaginary interference among FBMC/OQAM *** proposed approach exhibits a higher spectral efficiency than the conventional block-wise Alamouti scheme,as only one column of zero symbols is used as a guard interval instead of ***,compared to the conventional scheme,the bit error ratio is improved in the proposed block-wise Alamouti,as the assumption on channel invariance at two adjacent sub-carriers is not *** results indicate that,with a significantly reduced overhead,the proposed Alamouti scheme achieves better performance over the conventional block-wise Alamouti scheme.

关键词： Alamouti Filter-bank multicarrier Block repetition Imaginary interference

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A Convolutional Knowledge Tracking Model with Forgetting Behavior and Graph Embedding 3

A Convolutional Knowledge Tracking Model with Forgetting Beh...

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3rd International Conference on Electronics and Information technology, EIT 2024

作者： Huang, Guimin Huang, Zhiqing Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350369151

With the prevalence of various intelligent educational systems, it is imperative to uncover learners' proficiency in mastering knowledge points through their prior practice circumstances. Knowledge tracking is an extremely beneficial instrument in this regard. Among them, Convolutional Knowledge Tracking (CKT) has demonstrated excellent performance in numerous KT tasks, while it does not take into account the phenomenon of forgetting and the interconnection between knowledge points. In order to tackle these problems, we propose a model called FGE-CKT, which integrates forgetting behavior and graph embedding with CKT. Our model addresses the drawbacks of forgetting characteristics by modelling three key factors affecting students' forgetting behaviors through a fully connected neural net: repeated knowledge learning times, adjacent learning interval and interval between visits to repeat knowledge. At the same time, convolutional neural networks are used to simultaneously extract and incorporate connections between knowledge points into the model's input portion. It was confirmed by experiments on two well-established datasets that FGE-CKT achieved significant improvements in AUC performance. © 2024 IEEE.

关键词： Graph embeddings

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Self-powered sensor based on compressible ionic gel electrolyte for simultaneous determination of temperature and pressure

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InfoMat 2024年第7期6卷 62-75页

作者： Junjie Zou Yanan Ma Chenxu Liu Yimei Xie Xingyao Dai Xinhui Li Shuxuan Li Shaohui Peng Yang Yue Shuo Wang Ce-Wen Nan Xin Zhang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center of Smart Materials and Devices&International School of Materials Science and EngineeringWuhan University of TechnologyWuhanthe People's Republic of China Hubei Key Laboratory of Energy Storage and Power Battery School of MathematicsPhysics and Optoelectronic EngineeringHubei University of Automotive TechnologyShiyanthe People's Republic of China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationInstitutes of Physical Science and Information TechnologyAnhui UniversityHefeithe People's Republic of China State Key Lab of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijingthe People's Republic of China

The simultaneous detection of multiple stimuli,such as pressure and temperature,has long been a persistent challenge for developing electronic skin(eskin)to emulate the functionality of human ***,the demand for integrated power supply units is an additional pressing concern to achieve its lightweightness and ***,we propose a self-powered dual temperature–pressure(SPDM)sensor,which utilizes a compressible ionic gel electrolyte driven by the potential difference between MXene and Al *** SPDM sensor exhibits a rapid and timely response to changes in pressure-induced deformation,while exhibiting a slow and hysteretic response to temperature *** distinct response characteristics enable the differentiation of current signals generated by different stimuli through machine learning,resulting in an impressive accuracy rate of 99.1%.Furthermore,the developed SPDM sensor exhibits a wide pressure detection range of 0–800 kPa and a broad temperature detection range of 5–75C,encompassing the environmental conditions encountered in daily human *** dual-mode coupled strategy by machine learning provides an effective approach for temperature and pressure detection and discrimination,showcasing its potential applications in wearable electronics,intelligent robots,human–machine interactions,and so on.

关键词： e-skin gel electrolyte machine learning self-powered dual-mode sensor

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Boosting Oxygen Evolution Reaction Performance on NiFe‑Based Catalysts Through d‑Orbital Hybridization

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Nano-Micro Letters 2025年第1期17卷 281-292页

作者： Xing Wang Wei Pi Sheng Hu Haifeng Bao Na Yao Wei Luo State Key Laboratory of New Textile Materials and Advanced Processing Technology Key Laboratory of New Textile Materials and Applications of Hubei ProvinceSchool of Materials Science and EngineeringWuhan Textile UniversityWuhan 430200People’s Republic of China College of Chemistry and Molecular Sciences Wuhan UniversityWuhan 430072People’s Republic of China

Anion-exchange membrane water electrolyzers(AEMWEs)for green hydrogen production have received intensive attention due to their feasibility of using earth-abundant NiFe-based *** introducing a third metal into NiFe-based catalysts to construct asymmetrical M-NiFe units,the d-orbital and electronic structures can be adjusted,which is an important strategy to achieve sufficient oxygen evolution reaction(OER)performance in ***,the ternary NiFeM(M:La,Mo)catalysts featured with distinct M-NiFe units and varying d-orbitals are reported in this *** and theoretical calculation results reveal that the doping of La leads to optimized hybridization between d orbital in NiFeM and 2p in oxygen,resulting in enhanced adsorption strength of oxygen intermediates,and reduced rate-determining step energy barrier,which is responsible for the enhanced OER *** critically,the obtained NiFeLa catalyst only requires 1.58 V to reach 1 A cm^(−2) in an anion exchange membrane electrolyzer and demonstrates excellent long-term stability of up to 600 h.

关键词： NiFe-based catalysts d-orbital coupling Oxygen evolution reaction Anion exchange membrane electrolyzer

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