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2024 International Conference on Optical and Photonic Engineering, icOPEN 2024

作者： Tan, Ji Niu, Haipeng Luo, Suofan Liu, Jia He, Zhaoshui School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong University of Technology Key Laboratory for IoT Intelligent Information Processing and System Integration Ministry of Education Guangzhou510006 China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

The scattering media measurement for structured light techniques remains challenging due to the subsurface scattering that destroys fringe patterns and introduces phase error. Recently, deep learning has been successfully applied to fringe analysis. Unfortunately, collecting a large scale of labeled data is difficult, especially in scattering media measurement scenarios. Inspired by image style transfer, we proposed a high-precision scattering medium measurement method based on self-supervised fringe domain transformation. Specifically, by treating degraded fringe and ideal fringe as two pattern styles, a cycle generation strategy is developed to achieve fringe enhancement by translating between two fringe domains. Two generators and two discriminators are used to form cycle-consistency constraints for self-supervised learning with unpaired dataset. Additionally, we combine numerical and physical constraints to jointly optimize network parameters, effectively suppressing phase-shift errors and improving phase quality. Since the proposed approach eliminates the need for label data, it exhibits great potential for applying deep learning to scattering medium measurement. Experiments verify that our method can obtain high-quality 3D morphology of translucent objects. © 2025 SPIE.

关键词： Velocity measurement

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Extinction Chains Reveal Intermediate Phases Between the Safety and Collapse in Mutualistic Ecosystems

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Engineering 2024年第12期43卷 89-98页

作者： Guangwei Wang Xueming Liu Ying Xiao Ye Yuan Linqiang Pan Xiaohong Guan Jianxi Gao Hai-Tao Zhang The MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Digital Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Guangdong HUST Industrial Technology Research Institute Huazhong University of Science and TechnologyDongguan 523808China Guangdong Provincial Engineering Technology Center of Autonomous Unmanned Vessels Dongguan 523808China MOE Key Laboratory of Intelligent Networks and Network Security&State Key Laboratory of Manufacturing Systems Xi’an Jiaotong UniversityXi’an 710049China Tsinghua National Laboratory of Information Science and Technology Department of AutomationTsinghua UniversityBeijing 100084China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA

Ecosystems are undergoing unprecedented persistent deterioration due to unsustainable anthropogenic human activities,such as overfishing and deforestation,and the effects of such damage on ecological stability are *** recent advances in experimental and theoretical studies on regime shifts and tipping points,theoretical tools for understanding the extinction chain,which is the sequence of species extinctions resulting from overexploitation,are still lacking,especially for large-scale nonlinear networked *** this study,we developed a mathematical tool to predict regime shifts and extinction chains in ecosystems under multiple exploitation situations and verified it in 26 real-world mutualistic networks of various sizes and *** discovered five phases during the exploitation process:safe,partial extinction,bistable,tristable,and collapse,which enabled the optimal design of restoration strategies for degraded or collapsed *** validated our approach using a 20-year dataset from an eelgrass restoration ***,we also found a specific region in the diagram spanning exploitation rates and competition intensities,where exploiting more species helps increase *** computational tool provides insights into harvesting,fishing,exploitation,or deforestation plans while conserving or restoring the biodiversity of mutualistic ecosystems.

关键词： Complex system Network science Overexploitation Regime shift Metastability

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Prospective Role of Foundation Models in Advancing Autonomous Vehicles

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Research 2025年第1期7卷 667-691页

作者： Jianhua Wu Bingzhao Gao Jincheng Gao Jianhao Yu Hongqing Chu Qiankun Yu Xun Gong Yi Chang H Eric Tseng Hong Chen Jie Chen School of Automotive Studies Tongji UniversityShanghai 201804China Frontiers Science Center for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China SAIC Intelligent Technology Shanghai 201805China College of Artificial Intelligence Jilin UniversityChangchun 130012China Research and Advanced Engineering Ford Motor CompanyDearbornMI 48124USA College of Electronic and Information Engineering Tongji UniversityShanghai 201804China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai 201210China

With the development of artificial intelligence and breakthroughs in deep learning,large-scale foundation models(FMs),such as generative pre-trained transformer(GPT),Sora,etc.,have achieved remarkable results in many fields including natural language processing and computer *** application of FMs in autonomous driving holds considerable *** example,they can contribute to enhancing scene understanding and *** pre-training on rich linguistic and visual data,FMs can understand and interpret various elements in a driving scene,and provide cognitive reasoning to give linguistic and action instructions for driving decisions and ***,FMs can augment data based on the understanding of driving scenarios to provide feasible scenes of those rare occurrences in the long tail distribution that are unlikely to be encountered during routine driving and data *** enhancement can subsequently lead to improvement in the accuracy and reliability of autonomous driving *** testament to the potential of FMs'applications lies in world models,exemplified by the DREAMER series,which showcases the ability to comprehend physical laws and *** from massive data under the paradigm of self-supervised learning,world models can generate unseen yet plausible driving environments,facilitating the enhancement in the prediction of road users'behaviors and the off-line training of driving *** this paper,we synthesize the applications and future trends of FMs in autonomous *** utilizing the powerful capabilities of FMs,we strive to tackle the potential issues stemming from the long-tail distribution in autonomous driving,consequently advancing overall safety in this domain.

关键词： autonomous driving computer visionthe artificial intelligence natural language processing deep learninglarge scale autonomous vehicles understand interpr enhancing scene understanding reasoningby

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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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Wind farm combination forecasting model based on dynamic graph attention 8

Wind farm combination forecasting model based on dynamic gra...

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8th International Conference on Electronic technology and information Science, ICETIS 2023

作者： Liao, Xuechao Cheng, Yiqun School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China Key Laboratory of Intelligent Information Processing and Real-time Industrial Systems Hubei Wuhan430065 China

ISBN: (纸本)9781510666535

In recent years, wind power has become more and more important in the energy component. In order to improve the prediction accuracy of wind farms and help management and scheduling, a multi-site short-term wind power spatiotemporal combination forecasting model based on dynamic graph convolution and graph attention is proposed. Firstly, graph convolution is used to realize neighbor aggregation of temporal features between multiple sites, and the graph attention mechanism is used to enhance its ability to extract spatial features. At the same time, in view of the problem that the traditional model cannot deal with the real-time change of graph node correlation, the adjacency matrix is dynamically constructed according to the correlation coefficient and distance between nodes in the graph convolution process. Finally, the Gated Recurrent Unit is used to process the context information of dynamic graph convolution output to complete the prediction of wind power. The experimental results show that the proposed combined model is optimal in the aspects of prediction accuracy, stability and multi-step prediction performance. © 2023 SPIE.

关键词： Convolution

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Ultraviolet photodetectors based on ferroelectric depolarization field

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Journal of Energy Chemistry 2023年第2期77卷 487-498,I0013页

作者： Xiaoyu Zhou Qingqing Ke Silin Tang Jilong Luo Zihan Lu School of Microelectronics Science and Technology Sun Yat-sen UniversityZhuhai 519082GuangdongChina Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen UniversityZhuhai 519082GuangdongChina

Ultraviolet(UV)photodetectors are extensively adopted in the fields of the Internet of Things,optical communications and ***,with broadening the application scope of UV photodetectors,developing integrated devices with more functionalities rather than basic photo-detecting ability are highly required and have been triggered ever-growing interest in scientific and industrial *** thin films have become a potential candidate in the field of UV detection due to their wide bandgap and unique photovoltaic ***,ferroelectric thin films perform excellent dielectric,piezoelectric,pyroelectric,acousto-optic effects,etc.,which can satisfy the demand for the diversified development of UV *** this review,according to the different roles of ferroelectric thin films in the device,the UV photodetectors based on ferroelectric films are classified into ferroelectric depolarization field driven type,ferroelectric depolarization field and built-in electric field co-driven type,and ferroelectric field enhanced *** three types of ferroelectric UV photodetectors have great potential and are expected to promote the development of a new generation of UV detection *** the end of the paper,the advantages and challenges of three types of ferroelectric UV photodetectors are summarized,and the possible development direction in the future is proposed.

关键词： UV photodetector Ferroelectric Thin film Depolarization field Built-in electric field

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A Differentiable Framework for Image-Based SAR Target Reconstruction with 3D Gaussian Splatting 2

A Differentiable Framework for Image-Based SAR Target Recons...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Liu, Zinan Niu, Shengren Qiu, Xiaolan Peng, Lingxiao Zhong, Lihua Ding, Chibiao Chinese Academy of Sciences The National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Beijing China Chinese Academy of Sciences Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Aerospace Information Research Institute Suzhou China Chinese Academy of Sciences Key Laboratory of Technology in Geospatial Information Processing and Application Systems Aerospace Information Research Institute Beijing China

ISBN: (纸本)9798331515669

Image-based synthetic aperture radar (SAR) target three-dimensional (3D) reconstruction is an important application for extracting target information from high-resolution two-dimensional (2D) SAR images. However, due to the complex mechanism of SAR imaging, developing a data-driven, fully automated 3D reconstruction framework with explicit physical constraints is very difficult. Thus, this paper proposes a differentiable framework for image-based SAR target reconstruction with 3D Gaussian Splatting, which can automatically realize the 3D target reconstruction from multi-view SAR images. Specifically, the proposed differentiable framework accumulates and generates simulated SAR images according to the SAR imaging geometry and projection during the forward simulation stage. By calculating the loss between the simulated and ground truth SAR images and backpropagating, the spatial positions of the 3D Gaussians are continuously adjusted until convergence, generating the 3D point cloud model of the target. The experiment is conducted on the constructed T72 SAR image dataset and the reconstruction results demonstrate the effectiveness of the proposed framework. © 2024 IEEE.

关键词： 3D reconstruction

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Low power consumption reduced state and transition MLSE in optical interconnects

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Science China(information Sciences) 2023年第10期66卷 325-326页

作者： Weihao NI Fan LI Wei WANG Zhibin LUO Xingwen YI Yi CAI Zhaohui LI School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Jiangsu Engineering Research Center of Novel Optical Fiber Technology and Communication Network Suzhou Key Laboratory of Advanced Optical Communication Network Technology Soochow University

Intensity modulation/direct detection(IM/DD) optical fiber communication system is an appropriate candidate for optical interconnect applications due to its low cost and low power consumption characteristics [1]. 4-level pulse amplitude modulation(PAM-4) is widely discussed in optical interconnects due to its simpler transceiver configuration and implementation [2].

关键词： Optical interconnects

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Predicting Protein-Ligand Binding Affinity with Multi-Scale Structural Features

Predicting Protein-Ligand Binding Affinity with Multi-Scale ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Han Zhao, Jingtong Wang, Shengkun He, Zhiquan Ouyang, Xike Gao, Ting Northeast Normal University School of Information Science and Technology Changchun China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)9798350337488

Predicting protein-ligand binding affinity is important in areas such as drug discovery, gene regulation and signal transduction. The DTA(Drug-Target Affinity) method based on protein structure can not only effectively compensates for the lack of binding information, but also more in line with real biological processes. Although the structure-based DTA methods have achieved good performance, the existing methods still have the problem of only considering single-scale structural features and ignoring multi-scale structural features. In order to solve this problem, we propose the MSSDTA (Multi-Scale Structural Representation Drug-Target Affinity Prediction), which extracts multi-scale protein features by integrating the surface node features and structural node features of proteins. At the same time, the drug representation network is used to fuse the 2D molecular structure characteristics and chemical characteristics of the drug to effectively distinguish the drug molecules with similar planar structures. Finally, the affinity prediction network is used to generate protein-ligand binding affinity scores. We verify the performance of this model on the PDBbind v.2019 dataset. The experimental results show that the proposed method achieves excellent performance. © 2023 IEEE.

关键词： Affinity Prediction Network Drug Representation Network Multi-Scale Protein Features Protein-Ligand Binding Affinity

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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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