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IET International Radar Conference 2023, IRC 2023

作者： Cao, Yice Zhu, Dayi Wu, Zhenhua Chen, Jie Huang, Zhixiang Yang, Lixia Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University Hefei230601 China 38th Research Institute China Electronics Technology Group Corporation Hefei230088 China

ISBN: (纸本)9781839539954

Gathering reliable labeled samples for polarimetric synthetic aperture (PolSAR) image classification is laborious. Moreover, applying a trained classifier to new domains often leads to noticeable performance degradation due to domain disparities. Therefore, this paper proposes the novel complex-valued cross-domain (CD) few-shot learning classification (CCFSLC) method for PolSAR images to address these issues. Firstly, the transferrable knowledge learning module (TKLM) with a complex-valued feature encoder (CVFE) is trained using source data with sufficient labeled samples. Then, the deep few-shot learning module (DFSLM), constructed using the pre-trained CVFE, is trained by episodes in both source and target domains, with only minimal target labeled samples. Meanwhile, the adversarial domain adaptation module (ADAM) is employed to eliminate domain shift. The proposed CCFSLC mainly focuses on exploring discriminative information from raw PolSAR data, while reducing the domain gap to recognize novel categories in unseen domains with only a few annotated samples. Experiments on typical PolSAR datasets validate the effectiveness of the proposed method. © The Institution of Engineering & technology 2023.

关键词： Polarimeters

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

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7th International Conference on Educational technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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TIME-INDEPENDENT SPIKING NEURON VIA MEMBRANE POTENTIAL ESTIMATION FOR EFFICIENT SPIKING NEURAL NETWORKS

arXiv

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arXiv 2024年

作者： Chen, Hanqi Yu, Lixing Zhan, Shaojie Yao, Penghui Shao, Jiankun School of Information Science and Engineering Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan China State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing China

The computational inefficiency of spiking neural networks (SNNs) is primarily due to the sequential updates of membrane potential, which becomes more pronounced during extended encoding periods compared to artificial neural networks (ANNs). This highlights the need to parallelize SNN computations effectively to leverage available hardware parallelism. To address this, we propose Membrane Potential Estimation Parallel Spiking Neurons (MPE-PSN), a parallel computation method for spiking neurons that enhances computational efficiency by enabling parallel processing while preserving the intrinsic dynamic characteristics of SNNs. Our approach exhibits promise for enhancing computational efficiency, particularly under conditions of elevated neuron density. Empirical experiments demonstrate that our method achieves state-of-the-art (SOTA) accuracy and efficiency on neuromorphic datasets. Codes are available at https://***/chrazqee/MPE-PSN. © 2024, CC BY.

关键词： Neurons

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The chordata olfactory receptor database

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Protein & Cell 2025年第4期16卷 283-292页

作者： Wei Han Siyu Bao Jintao Liu Yiran Wu Liting Zeng Tao Zhang Ningmeng Chen Kai Yao Shunguo Fan Aiping Huang Yuanyuan Feng Guiquan Zhang Ruiyi Zhang Hongjin Zhu Tian Hua Zhijie Liu Lina Cao Xingxu Huang Suwen Zhao Human Institute ShanghaiTech UniversityShanghai 201210China Research Center for Life Sciences Computing Zhejiang LabHangzhou 311121China Department of Intelligent Edge Cloud China Telecom Cloud Technology Co.Ltd.Shanghai 200120China School of Life Science and Technology ShanghaiTech UniversityShanghai 201210China School of Information Science and Technology Shanghai Tech UniversityShanghai 201210China Shanghai Key Laboratory of High-Resolution Electron Microscopy Shanghai Tech UniversityShanghai 201210China Zhejiang Provincial Key Laboratory of Pancreatic Disease The First Affiliated Hospitaland Institute of Translational MedicineZhejiang University School of MedicineHangzhou 310029China Shanghai Clinical Research and Trial Center Shanghai 201210China

Introduction of database Olfaction is one of the oldest chemosensory systems in chordates,playing crucial roles in their foraging,predator evasion,social communication,mating and parental care(Guo et al.,2023;Li and Liberles,2015;Liberles,2014).The initial step of olfaction is the binding and activation of olfactory receptors(ORs)by odorants in a combinatorial way(Malnic et al.,1999).

关键词： chordates chemosensory systems introduction database olfaction foraging olfaction binding activation olfactory olfactory receptors odorants

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Optical and Electrical Analysis of an Advanced 4T All-perovskite Tandem Solar Cell

Optical and Electrical Analysis of an Advanced 4T All-perovs...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Zhaosheng Xia Xingang Ren Shengyang Wei Zhixiang Huang Information Materials and Intelligent Sensing Laboratory of Anhui Province Institute of Physical Science and Information Technology Anhui University Hefei China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

The investigation of the optoelectronic characteristics of all-perovskite tandem solar cells holds pivotal significance in surpassing the Shockley-Queisser limit of single-junction perovskite solar cells. Initially, we established two distinct four-terminal tandem solar cells (4T TSCs) composed of different perovskite materials: FA 0.8 Cs 0.2 Pb(I 0.7 Br 0.3 ) 3 (E g = 1.75 eV) and MASnI 3 (E g = 1.3 eV). Through numerical simulations of the optoelectronic characteristics of 4T TSCs, we evaluated the performance of the TSCs at varying perovskite layer thicknesses. Following optimization, the power conversion efficiency (PCE) of the 4T TSCs increased from the initial 30.34% to 33.54%. Through an in-depth analysis of the cell and energy distribution in TSCs, it was observed that the light-coupling layer significantly influenced the PCE of the 4T TSCs. Upon optimizing the light-coupling layer, the PCE of the TSC increased from the initial 30.34% to 34.7%. This study provides crucial theoretical and experimental foundations for optimizing the performance of all-perovskite TSCs.

关键词： Photovoltaic cells Numerical simulation Perovskites Optical coupling Optical materials Power conversion Electromagnetics

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EDA: Enhanced Domain-Adversarial Training for Anatomical Landmark Detection 22

EDA: Enhanced Domain-Adversarial Training for Anatomical Lan...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Yang, Fan Zhou, S. Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Anhui Hefei 230026 China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research University of Science and Technology of China Jiangsu Suzhou 215123 China Key Laboratory of Precision and Intelligent Chemistry Ustc Anhui Hefei 230026 China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology Cas Beijing 100190 China

ISBN: (纸本)9798331520526

Manually annotating anatomical landmarks in medical images requires experienced clinicians and is a labor-intensive process. However, recent AI-assisted methods for landmark detection often rely on the training and test data originating from the same domain. This work introduces a novel unsupervised domain adaptation (UDA) framework aimed at anatomical landmark detection from a medical image, designed to bridge the gap between a source domain with labels and an unlabeled target domain. Specifically, we have developed a new Domain-Adversarial Network that incorporates skip connections to transfer and fuse high-resolution feature maps. Additionally, we proposed Dynamic Gaussian Learning, which allows the model to escape from local error regions. We carry out experiments on landmark detection for both head and chest, and the results demonstrate that our method achieves state-of-the-art performances in each experiment. © 2025 IEEE.

关键词： Anatomical Landmark Detection Unsupervised Domain Adaptation

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Consistency of Compositional Generalization Across Multiple Levels 39

Consistency of Compositional Generalization Across Multiple ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Chuanhao Li, Zhen Jing, Chenchen Fan, Xiaomeng Ye, Wenbo Wu, Yuwei Jia, Yunde Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China School of Computer Science Zhejiang University China

ISBN: (纸本)157735897X

Compositional generalization is the capability of a model to understand novel compositions composed of seen concepts. There are multiple levels of novel compositions including phrase-phrase level, phrase-word level, and word-word level. Existing methods achieve promising compositional generalization, but the consistency of compositional generalization across multiple levels of novel compositions remains unexplored. The consistency refers to that a model should generalize to a phrase-phrase level novel composition, and phrase-word/word-word level novel compositions that can be derived from it simultaneously. In this paper, we propose a meta-learning based framework, for achieving consistent compositional generalization across multiple levels. The basic idea is to progressively learn compositions from simple to complex for consistency. Specifically, we divide the original training set into multiple validation sets based on compositional complexity, and introduce multiple meta-weight-nets to generate sample weights for samples in different validation sets. To fit the validation sets in order of increasing compositional complexity, we optimize the parameters of each meta-weight-net independently and sequentially in a multilevel optimization manner. We build a GQA-CCG dataset to quantitatively evaluate the consistency. Experimental results on visual question answering and temporal video grounding, demonstrate the effectiveness of the proposed framework. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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An Improved PI Controller for Stiction Compensation of Control Valves in Process Industry

An Improved PI Controller for Stiction Compensation of Contr...

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第43届中国控制会议

作者： Dong Yang Xun Lang Jiande Wu Yumin Lang Lei Xie Hongye Su Department of Electronic Engineering School of Information Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University State Key Laboratory of Industrial Control Technology Zhejiang University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the process industry, limit cycles are mostly induced by stiction-related nonlinearity in control valves, severely deteriorating control loop performance. Such problem highlights the importance of compensating for oscillations due to valve stiction. To this end, this work introduces an improved PI controller(IPIC) for stiction compensation, which retains the advantages of the traditional PI controller with fewer adjustment parameters. The enhancement involves nonlinear variation of the integral gain based on instantaneous error. Additionally, a differential evolution algorithm is utilized for controller *** to state-of-the-art methods in setpoint tracking, disturbance rejection, and robustness to parameter uncertainties, the proposed approach exhibits superior performance.

关键词： Stiction Limit Cycle Nonlinear PI controller Differential Evolution Algorithm

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FDAC: Federated Domain Adaptation via Dual Contrastive Learning

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Yi, Chang'an Chen, Haotian Zhang, Yifan Xu, Yonghui Zhou, Yan Cui, Lizhen Yu, Han Foshan University Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology School of Electronic and Information Engineering Foshan China Jinan China School of Computing Singapore Singapore College of Computing and Data Science Singapore Singapore

Federated domain adaptation (FDA) aims to transfer knowledge collaboratively from multiple source domains to related but different unlabelled target domains. The data of each domain are locally maintained, and various domain gaps exist among them, resulting in extreme challenges in simultaneously mitigating diverse distribution shifts and preserving discriminative knowledge without accessing the source data. Many existing works have failed to fully explore different source models to measure domain shifts and leverage semantic knowledge, resulting in skewed alignment and partial preservation of discriminative information. In this paper, we propose a novel approach named FDAC to address Federated Domain Adaptation by thoroughly investigating source models via dual Contrastive mechanisms. FDAC contrastively increases the data diversity to align features across domains in a fine-grained manner by manipulating the latent deep architecture and compensating for knowledge from each source domain;simultaneously, it contrastively utilizes the comprehensive semantic knowledge of different source domains to guide the adaptation process. Extensive experiments on several real datasets demonstrate that FDAC outperforms all comparative methods under most conditions. Furthermore, FDAC only needs approximately half of the communication rounds compared with the state-of-the-art methods, indicating that FDAC can significantly improve communication efficiency, which is another key factor in the federated setting. The source code is publicly available at https://***/ycarobot/FDAC. © 1991-2012 IEEE.

关键词： Contrastive learning Domain adaptation Federated learning Vision transformer

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RMSLRS: Real-Time Multi-terminal Sign Language Recognition System 13th

RMSLRS: Real-Time Multi-terminal Sign Language Recognition S...

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13th International Conference on Innovations in Bio-Inspired computing and Applications, IBICA 2022, and 12th World Congress on information and Communication Technologies, WICT 2022

作者： Zhao, Yilin Zhang, Biao Ma, Kun Business School of Jinan University University of Jinan Jinan250022 China School of Computer and Information Hefei University of Technology Hefei230009 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China

ISBN: (纸本)9783031274985

Sign language is commonly used by deaf or speech-impaired people to convey meaning. Sign language recognition (SLR) aims to help users learn and use sign language by recognizing the signs from given videos. Although sign language has gained social acceptance, few sign language recognition systems have been developed with educational purposes. Current SLR system has a single form of expression and lacks real-time interaction and feedback with an expensive cost. And videos captured in real scenes are complex, which has impact on the performance of models. To this end, this paper has proposed a novel real-time multi-terminal sign language recognition system (RMSLRS). Specifically, a lightweight sign language recognition model based on MediaPipe Holistic is proposed to perform sign language inference by sensing multi-dimensional information such as pose, facial expression, and hand tracking, achieving near real-time performance on mobile and desktop devices. Then, a novel pre-processing module is proposed to reduce the adverse effects of background noise in videos through the YOLO model and OpenCv. Furthermore, a novel technical architecture for front-end and back-end separation and multi-terminal deployment is designed, including WeChat applet, desktop application and website. Finally, this system has been deployed with gratifying success in practice. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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