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16th International Conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Hu, Jiarui Wang, Zhipeng Lu, Ping Yuan, Philip F. Zhou, Yanmin Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai201210 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China College of Architecture and Urban Planning Tongji University Shanghai201804 China

ISBN: (纸本)9789819964949

Tactile sensor plays an important role in the human-robot interaction by providing environmental information to robots. How to effectively use the haptic information of array sensors to encode and extract haptic features for object recognition is the focus of current research. Previous studies have provided methods to construct array sensors in order to acquire tactile information efficiently. New advances in event-driven array sensors could capture spatial spiking tactile data, which include rich spatial information. In this paper, we propose a Residualized Graph Self-attention Spiking Neural Network (Tacformer) for tactile object recognition. The proposed Tacformer pays attention to both the spatial properties and the temporal sensing values of each sensor point in event-driven array sensors. Experimental results on two tactile datasets show that the accuracy, precision, recall and F1-value of the method in this paper are improved. On the EvTouch-Objects dataset, Tacformer has improved the accuracy by 8.34% compared with the GNN-SNN based a baseline approach. On the EvTouch-Containers dataset, Tacformer also has increased the accuracy by 16.17%. The Tacformer could be applied to facilitate robot tasks like grasping and sorting. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Object recognition

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Gas and Droplet Dynamics for Filament-Assisted Free-Space Optical Communication through Clouds

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Ultrafast science 2024年第5期4卷 36-43页

作者： Haiyi Liu Jiawei Zheng Huanyu Li Jiayun Xue Pengfei Qi Xianzhu Liu Lie Lin Weiwei Liu Huilin Jiang Tianjin Key Laboratory of Micro-scale Optical Information Science and Technology Tianjin 300350China College of Opto-Electronic Engineering Changchun University of Science and TechnologyChangchun 130022China National and Local Joint Engineering Research Center of Space Optoelectronics Technology Changchun University of Science and TechnologyChangchun 130022China

Femtosecond laser filamentation has recently emerged as a promising technique to actively create a channel through clouds and fog,thereby providing a revolutionary opportunity to overcome the obstacle of fog-induced attenuation for free-space optical communication(FSOC)in ***,the underlying physics remains elusive,which is critical for optimizing time window and efficiency of guiding light in this *** this work,the time evolution of the filament-induced channel is investigated under various laser pulse energies and repetition *** combined diffusion model is built to reveal the contributions of gas molecules and aerosol droplets in competition of guiding and defocusing effect of the filament-induced *** related findings can deepen our understanding on the underlying physics of the air channel induced by the filament,provide insight into the optimizing time window and efficiency of guiding light,and potentially contribute to the improvement of filament-assisted FSOC.

关键词： diffusion model optimizing time window gas dynamics air channel femtosecond laser filamentation guiding light droplet dynamics

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VUV imaging of type-ⅠELM filamentary structures and their temporal characteristics on EAST

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Plasma science and technology 2024年第11期26卷 22-31页

作者： Rongjing DENG Tingfeng MING Bang LI Qiqi SHI Shanwei HOU Shuqi YANG Xiaoju LIU Shaocheng LIU Guoqiang LI Xiang GAO Yasuhiro SUZUKI Yunfeng LIANG Institute of Plasma Physics Hefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031People’s Republic of China University of Science and Technology of China Hefei 230026People’s Republic of China Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Optoelectronic EngineeringShenzhen UniversityShenzhen 518060People’s Republic of China Graduate School of Advanced Science and Engineering Hiroshima UniversityHigashi-Hiroshima 739-8527Japan Forschungszentrum Jülich GmbH Institut für Energie-und Klimaforschung-PlasmaphysikJülich D-52425Germany

In the H-mode experiments conducted on the Experimental Advanced Superconducting Tokamak(EAST),fluctuations induced by the so-called edge localized modes(ELMs)are captured by a high-speed vacuum ultraviolet(VUV)imaging *** field line-aligned filamentary structures are analyzed in this *** transport induced by ELM filaments in the scrape-off layer(SOL)under different discharge conditions is analyzed by comparing the VUV signals with the divertor probe *** is found that convective transport along open field lines towards the divertor target dominates the parallel ion particle transport mechanism during *** toroidal mode number of the filamentary structure derived from the VUV images increases with the electron density pedestal *** analysis of the toroidal distribution characteristics during ELM bursts reveals toroidal *** influence of resonance magnetic perturbation(RMP)on the ELM size is also analyzed using VUV imaging *** the phase difference of the coil changes periodically,the widths of the filaments change as ***,the temporal evolution of the ELMs on the VUV signals provides rise time and decay time for each single ELM event,and the results indicate a negative correlation trend between these two times.

关键词： EAST tokamak VUV imaging filamentary structure temporal characteristics RMP

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Scalable matrix ring data center interconnect based on FSO technology

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Optics Express 2025年第12期33卷 25016-25038页

作者： Qian Kong Jianning Su Xiaowei Lu Liming Liu Haijun Yuan School of Electronic and Information Engineering University of Electronic Science and Technology of China Zhongshan Institute Zhongshan 528402 China College of Physics and Optoelectronic Engineering Shenzhen University SZU China Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province Shenzhen Key Lab of Micro-Nano Photonic Information Technology State Key Laboratory of Radio Frequency Heterogeneous Integration China

To address escalating data center traffic and scalability issues, this paper introduces a novel hybrid data center network (DCN) architecture using free-space optics (FSO). The proposed three-layer matrix ring topology employs wired intra-layer links and wireless FSO inter-layer connections, reducing cabling and end-to-end delay. An optimized FSO link allocation strategy based on priority principles further enhances performance. Simulations demonstrate significant delay reduction as FSO links increase (up to 35.7%). Comparative analysis against leaf-spine validates the matrix ring’s superiority, showing lower average delays under high loads and markedly better, more predictable delay distributions under bursts of traffic. This work offers a promising approach for flexible, efficient, and scalable future DCNs.

关键词： Heterodyne detection Machine learning Millimeter waves Multicore fibers Ring networks Visible light communications

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A Detection Method Toward Yellow Dragon Disease Spreads Using Spark and Deep Learning 3

A Detection Method Toward Yellow Dragon Disease Spreads Usin...

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3rd International Conference on High Performance Computing and Communication engineering, HPCCE 2023

作者： Xie, Wu Su, Zhenzhao Zhang, Huimin Kang, Ping Qin, Kun Fan, Yong Zhao, Quanyou Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541214 China Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Lingchuan Science and Technology Information Institute Guilin541299 China Guilin Hivision Technology Co. Ltd. Guilin541004 China School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541214 China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin541004 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

The detection methods of yellow dragon disease spread via wood lice transmission networks like social networks are very important for diverse citrus trees and farmers. Although current methods have some detection accuracy or low cost, the detection processes are relatively troublesome and the detection cycles are long, making it be a difficult problem to apply them in large-scale orange farms as practical scenarios to detect citrus yellow dragon disease in a timely manner. A new method toward detecting citrus yellow dragon disease spread utilizing Spark and deep learning is proposed for this problem. By obtaining citrus field video stream data through high-definition cameras, and transferring the stream data to the Spark cluster through Kafka like intelligent agents, it is practicable to use the structured streaming component under the Spark framework via big data ecosphere to process video or image stream data transmitted via monitoring. We construct a citrus yellow dragon disease detection model via YOLOv7, and use self-made citrus yellow dragon disease images as training and testing data sets. The preliminary experimental results show that the new methods achieved an accuracy of 83.14%. To reduce the occurrence of missed and false detections, the shallow detection heads are added to the feature fusion networks for improvement, extracting and fusing shallow network information to try to improve the detection effects of yellow dragon disease. Then replace the convolution operation in the ELAN (Effective Long-range Aggregation Network) module with deep separable convolution to reduce the number of model parameters. The preliminary experimental results show that compared to the original YOLOv7 model, our improved citrus yellow dragon disease detection model with YOLOv7 has an accuracy improvement of 2.43%, maintaining a higher detection accuracy with lower time than before. © 2024 SPIE.

关键词： Deep learning

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On the Robustness of Editing Large Language Models

On the Robustness of Editing Large Language Models

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Ma, Xinbei Ju, Tianjie Qiu, Jiyang Zhang, Zhuosheng Zhao, Hai Liu, Lifeng Wang, Yulong School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Baichuan Intelligent Technology China

ISBN: (纸本)9798891761643

Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates. Model editing enables the manipulation of specific knowledge memories and the behavior of language generation without retraining. However, the robustness of model editing remains an open question. This work seeks to understand the strengths and limitations of editing methods, facilitating practical applications of communicative AI. We focus on three key research questions. RQ1: Can edited LLMs behave consistently resembling communicative AI in realistic situations? RQ2: To what extent does the rephrasing of prompts lead LLMs to deviate from the edited knowledge memory? RQ3: Which knowledge features are correlated with the performance and robustness of editing? Our empirical studies uncover a substantial disparity between existing editing methods and the practical application of LLMs. On rephrased prompts that are flexible but common in realistic applications, the performance of editing experiences a significant decline. Further analysis shows that more popular knowledge is memorized better, easier to recall, and more challenging to edit effectively. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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Intra-Guided Dual-Energy Material Decomposition Method for Dual-Source CBCT systems 46

Intra-Guided Dual-Energy Material Decomposition Method for D...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Zhong, Xinyun Lyu, Tianling Wu, Zhan Gao, Wei Xi, Yan Zhao, Wei Zhu, Wentao Chen, Yang Zhejiang Lab Hangzhou China First-Imaging Tech. Shanghai China Beihang University Beijing China Southeast University School of Computer Science and Engineering Laboratory of Image Science and Technology Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Nanjing China

ISBN: (纸本)9798350371499

Dual-energy CBCT imaging plays a crucial role in advanced imaging applications due to its ability to quantify material components. Although there are multiple established systems for dual-energy imaging, they often come with high deployment costs. This study investigates the feasibility of dual-energy material decomposition on a less expensive dual-source CBCT system. Due to the specific geometry of dual-source CBCT, cone-beam (CB) artifacts seriously degrade the material decomposition quality when the direct inversion method is adopted. In this paper, a novel dual-source intra-guided material decomposition framework is proposed to suppress CB artifacts in the material maps. Firstly, a dual-source two-pass algorithm is developed for artifact pre-correction during image reconstruction. Then, the structure of small cone-angle regions from one X-ray source is utilized to guide cone beam artifact removal in large cone-angle regions from another source. Finally, direct matrix inverse material decomposition is applied to the filtered image. Extensive experiments have been conducted on the authentic human phantom and demonstrate that the proposed framework achieves excellent performance in cone beam artifact reduction compared to other methods, and significantly enhances the quality of the material maps. © 2024 IEEE.

关键词： Image reconstruction

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Machine Learning-Assisted Single-Event Transient Model of 12nm FinFETs for Circuit-Level Simulation 15

Machine Learning-Assisted Single-Event Transient Model of 12...

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15th IEEE International Conference on ASIC, ASICON 2023

作者： Lin, Jianwen Cai, Linlin Chen, Yutao Zhang, Haoyu Chen, Wangyong Sun Yat-sen University School of Microelectronics Science and Technology Guangzhou510275 China Sun Yat-sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou510275 China

ISBN: (纸本)9798350312980

As the geometries of transistors continue to scale down, single-event transient (SET) are becoming a major source of soft errors in circuit design. This paper proposes a machine learning method for modeling SET in circuit-level simulations with the high efficiency, since traditional models such as the double exponential model are not coupled with circuit responses. The proposed artificial neural network (ANN) model shows a great prediction accuracy for single-event transient currents in 12nm FinFET technology under different time, linear energy transfer (LET), bias voltage, and strike positions. The trained model can be implemented into SPICE with Verilog-A for circuit-level simulation, and its validity in a 7-stage inverter chain and a 6T SRAM are demonstrated to provide the rapid guidance for radiation reliability. © 2023 IEEE.

关键词： FinFET machine learning neural network Single-event transient

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SeeMe: An intelligent edge server selection method for location-aware business task computing over IIoT

SeeMe: An intelligent edge server selection method for locat...

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作者： Dou, Wanchun Liu, Bowen Duan, Jirun Dai, Fei Qi, Lianyong Xu, Xiaolong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China School of Computer Science Qufu Normal University Qufu China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

In the past few years, latency-sensitive task computing over the industrial internet of things (IIoT) has played a key role in an increasing number of intelligent applications, such as intelligent self-driving vehicles and unmanned aircraft systems. The edge computing paradigm provides a basic functional infrastructure for across-domain business task computing on distributed edge servers. With this observation, a trade-off between the mobile devices and the fixed edge servers is needed to run moving task computing in a low-latency way. Given this challenge, an intelligent server selection method, named SeeMe, is proposed in this paper. Technically speaking, this method aims at minimizing the communication capacity and the transferring capacity in a multiobjective optimization way to find a low-latency edge server. The experiments and comparison analysis verify the availability of our method. © 2022 John Wiley & Sons Ltd.

关键词： Economic and social effects

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