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arXiv 2021年

作者： Yao, Man Gao, Huanhuan Zhao, Guangshe Wang, Dingheng Lin, Yihan Yang, Zhaoxu Li, Guoqi School of Automation Science and Engineering Xi'an Jiaotong University Xi'an710000 China Department of Precision Instrument Center for Brain Inspired Computing Research Tsinghua University Beijing100084 China School of Aerospace Engineering Xi'an Jiaotong University Xi'an710000 China

How to effectively and efficiently deal with spatiotemporal event streams, where the events are generally sparse and non-uniform and have the µs temporal resolution, is of great value and has various real-life applications. Spiking neural network (SNN), as one of the brain-inspired event-triggered computing models, has the potential to extract effective spatio-temporal features from the event streams. However, when aggregating individual events into frames with a new higher temporal resolution, existing SNN models do not attach importance to that the serial frames have different signal-to-noise ratios since event streams are sparse and non-uniform. This situation interferes with the performance of existing SNNs. In this work, we propose a temporal-wise attention SNN (TA-SNN) model to learn frame-based representation for processing event streams. Concretely, we extend the attention concept to temporal-wise input to judge the significance of frames for the final decision at the training stage, and discard the irrelevant frames at the inference stage. We demonstrate that TA-SNN models improve the accuracy of event streams classification tasks. We also study the impact of multiple-scale temporal resolutions for frame-based representation. Our approach is tested on three different classification tasks: gesture recognition, image classification, and spoken digit recognition. We report the state-of-the-art results on these tasks, and get the essential improvement of accuracy (almost 19%) for gesture recognition with only 60 ms. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Hypervelocity impact induced shock acoustic emission waves for quantitative damage evaluation using in situ miniaturized piezoelectric sensor network

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Chinese Journal of Aeronautics 2019年第5期32卷 1059-1070页

作者： Menglong LIU Qiang WANG Qingming ZHANG Renrong LONG Fangsen CUI Zhongqing SU Institute of High Performance Computing A*STAR (Agency for Science Technology and Research) School of Automation Nanjing University of Posts and Telecommunications State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Institute of High Performance Computing (A*STAR Agency for Science Technology and Research) Department of Mechanical Engineering The Hong Kong Polytechnic University

Manmade debris and natural meteoroids, travelling in the Low Earth Orbit at a speed of several kilometers per second, pose a severe safety concern to the spacecraft in service through the HyperVelocity Impact(HVI). To address this issue, an investigation of shock Acoustic Emission(AE) waves induced by HVI to a downscaled two-layer Whipple shielding structure is performed,to realize a quantitative damage evaluation. Firstly a hybrid numerical model integrating smoothparticle hydrodynamics and finite element is built to obtain the wave response. The projectiles, with various impact velocities and directions, are modelled to impact the shielding structure with different thicknesses. Then experimental validation is carried out with built-in miniaturized piezoelectric sensors to in situ sense the HVI-induced AE waves. A quantitative agreement is obtained between numerical and experimental results, demonstrating the correctness of the hybrid model and facilitating the explanation of obtained AE signals in experiment. Based on the understanding of HVI-induced wave components, assessment of the damage severity, i.e., whether the outer shielding layer is perforated or not, is performed using the energy ratio between the regions of ‘‘high frequency" and ‘‘low frequency" in the acquired AE signals. Lastly, the direct-arrival fundamentalsymmetric wave mode is isolated from each sensing signal to be input into an enhanced delay-andsum algorithm, which visualizes HVI spots accurately and instantaneously with different sensor network configuration. All these works demonstrate the potential of quantitative, in situ, and real time HVI monitoring using miniaturized piezoelectric sensor network.

关键词： Acoustic Emission(AE) Damage detection Hypervelocity impact Piezoelectric sensor Whipple shielding assembly

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Distributed adaptive finite-time time-varying group formation tracking for high-order multi-agent systems with directed topologies 39

Distributed adaptive finite-time time-varying group formatio...

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39th Chinese Control Conference, CCC 2020

作者： Tian, Lei Hua, Yongzhao Dong, Xiwang Li, Qingdong Ren, Zhang Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Aerospace Engineering University of Bristol BristolBS8 1TR United Kingdom

ISBN: (纸本)9789881563903

This paper investigates finite-time time-varying group formation (TVGF) tracking control problems for high-order multi-agent systems (MASs) with directed topologies. Firstly, the agents are classified into three types, i.e., the virtual leader, the group leader and the follower. A virtual leader is used to control the macroscopic movement of MASs. Subgroups can be interacted and coordinated with each other by the communication among group leaders. Secondly, a novel finite-time group formation tracking protocol using only the neighboring interaction is constructed based on adaptive integral sliding mode control. Then sufficient conditions for MASs with unknown external disturbances and directed topologies to realize finite-time TVGF tracking are derived using Lyapunov theory. Finally, a numerical simulation example is performed to demonstrate the effectiveness of theoretical results. © 2020 Technical Committee on Control Theory, Chinese Association of automation.

关键词： Multi agent systems

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LLMs can generate robotic scripts from goal-oriented instructions in biological laboratory automation

arXiv

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arXiv 2023年

作者： Inagaki, Takashi Kato, Akari Takahashi, Koichi Ozaki, Haruka Kanda, Genki N. Laboratory Automation Supplier's Association Japan Department of Mechanical Systems Engineering Graduate School of Engineering Nagoya University Aichi Nagoya Japan Laboratory for Biologically Inspired Computing RIKEN Center for Biosystems Dynamics Research Hyogo Kobe Japan Graduate School of Media and Governance Keio University Fujisawa Japan Graduate School of Frontier Biosciences Osaka University Suita Japan Bioinformatics Laboratory Institute of Medicine University of Tsukuba Ibaraki Tsukuba Japan Center for Artificial Intelligence Research University of Tsukuba Ibaraki Tsukuba Japan

The use of laboratory automation by all researchers may substantially accelerate scientific activities by humans, including those in the life sciences. However, computer programs to operate robots should be written to implement laboratory automation, which requires technical knowledge and skills that may not be part of a researcher's training or expertise. In the last few years, there has been remarkable development in large language models (LLMs) such as GPT-4, which can generate computer codes based on natural language instructions. In this study, we used LLMs, including GPT-4, to generate scripts for robot operations in biological experiments based on ambiguous instructions. GPT-4 successfully generates scripts for OT-2, an automated liquid-handling robot, from simple instructions in natural language without specifying the robotic actions. Conventionally, translating the nuances of biological experiments into low-level robot actions requires researchers to understand both biology and robotics, imagine robot actions, and write robotic scripts. Our results showed that GPT-4 can connect the context of biological experiments with robot operation through simple prompts with expert-level contextual understanding and inherent knowledge. Replacing robot script programming, which is a tedious task for biological researchers, with natural-language LLM instructions that do not consider robot behavior significantly increases the number of researchers who can benefit from automating biological experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Robot programming

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Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection

arXiv

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arXiv 2023年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Liu, Zhiwei Wu, Jia Yu, Zhengtao Yu, Philip S. The Institute of Information Engineering Chinese Academy of Sciences the School of Cyber Security University of Chinese Academy of Sciences Beijing China The School of Cyber Science and Technology Beihang University Beijing1000191 China Salesforce AI Research CA94301 United States The School of Computing Macquarie University Sydney Australia The Faculty of Information Engineering and Automation Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China The Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

Real-world social events typically exhibit a severe class-imbalance distribution, which makes the trained detection model encounter a serious generalization challenge. Most studies solve this problem from the frequency perspective and emphasize the representation or classifier learning for tail classes. While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance. To this end, we propose a novel uncertainty-guided class imbalance learning framework - UCLSED, and its variant - UCL-ECSED, for imbalanced social event detection tasks. We aim to improve the overall model performance by enhancing model generalization to those uncertain classes. Considering performance degradation usually comes from misclassifying samples as their confusing neighboring classes, we focus on boundary learning in latent space and classifier learning with high-quality uncertainty estimation. First, we design a novel uncertainty-guided contrastive learning loss, namely UCL and its variant - UCL-EC, to manipulate distinguishable representation distribution for imbalanced data. During training, they force all classes, especially uncertain ones, to adaptively adjust a clear separable boundary in the feature space. Second, to obtain more robust and accurate class uncertainty, we combine the results of multi-view evidential classifiers via the Dempster-Shafer theory under the supervision of an additional calibration method. We conduct experiments on three severely imbalanced social event datasets including Events2012_100, Events2018_100, and CrisisLexT_7. Our model significantly improves social event representation and classification tasks in almost all classes, especially those uncertain ones. Copyright © 2023, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Large Language Models for Robotics: Opportunities, Challenges, and Perspectives

arXiv

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arXiv 2024年

作者： Wang, Jiaqi Wu, Zihao Li, Yiwei Jiang, Hanqi Shu, Peng Shi, Enze Hu, Huawen Ma, Chong Liu, Yiheng Wang, Xuhui Yao, Yincheng Liu, Xuan Zhao, Huaqin Liu, Zhengliang Dai, Haixing Zhao, Lin Ge, Bao Li, Xiang Liu, Tianming Zhang, Shu The School of Computer Science Northwestern Polytechnical University Xi'An710072 China The School of Automation Northwestern Polytechnical University Xi'An710072 China The School of Computing The University of Georgia Athens30602 United States The College of Engineering University of Georgia Athens30602 United States The School of Physics and Information Technology Shaanxi Normal University Xi'An710119 China The School of Computer Science Shaanxi Normal University Xi'An710119 China The Department of Radiology Massachusetts General Hospital Harvard Medical School Boston02115 United States

Large language models (LLMs) have undergone significant expansion and have been increasingly integrated across various domains. Notably, in the realm of robot task planning, LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions. However, for embodied tasks, where robots interact with complex environments, text-only LLMs often face challenges due to a lack of compatibility with robotic visual perception. This study provides a comprehensive overview of the emerging integration of LLMs and multimodal LLMs into various robotic tasks. Additionally, we propose a framework that utilizes multimodal GPT-4V to enhance embodied task planning through the combination of natural language instructions and robot visual perceptions. Our results, based on diverse datasets, indicate that GPT-4V effectively enhances robot performance in embodied tasks. This extensive survey and evaluation of LLMs and multimodal LLMs across a variety of robotic tasks enriches the understanding of LLM-centric embodied intelligence and provides forward-looking insights toward bridging the gap in Human-Robot-Environment interaction. Copyright © 2024, The Authors. All rights reserved.

关键词： Robot programming

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Time Delay Estimation with Fixed-time SMC Scheme for Nonlinear Dynamics

Time Delay Estimation with Fixed-time SMC Scheme for Nonline...

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International Conference on Control, automation and Diagnosis (ICCAD)

作者： Saim Ahmed Ahmad Taher Azar Arezki Fekik Haoping Wang Chakib Ben Njima College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Department of Electrical Engineering University Akli Mohand Oulhadj-Bouria Rue Drissi Yahia Bouira Algeria CNRS LS2N UMR 6004 Nantes Université École Centrale Nantes Nantes France School of Automation Nanjing University of Science and Technology China University of Sousse Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Time delay estimation using fixed-time terminal sliding mode control (TDEFxSMC) for unknown nonlinear systems with uncertainty and external disturbances is presented in this study. Firstly, the notion of fixed-time terminal sliding mode control (FxSMC) is outlined and thoroughly examined. Using the FxSMC scheme, nonsingular fixed-time control, non-chatter control inputs, and high performance tracking positions have been achieved. To estimate the unknown dynamics, proposed method incorporates a TDE scheme. The Lyapunov theorem is applied to find whether the proposed system can achieve its stability in a fixed-time. The effectiveness of the suggested method is evaluated and demonstrated by presenting the results of the proposed method implemented to the nonlinear robot dynamics.

关键词： Uncertainty automation Delay effects Estimation 2-DOF Stability analysis Nonlinear dynamical systems

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Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video

Real-time Multi-person Eyeblink Detection in the Wild for Un...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wenzheng Zeng Yang Xiao Sicheng Wei Jinfang Gan Xintao Zhang Zhiguo Cao Zhiwen Fang Joey Tianyi Zhou Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Biomedical Engineering Southern Medical University Guangzhou China Department of Rehabilitation Medicine Zhujiang Hospital Southern Medical University Guangzhou China Centre for Frontier AI Research Agency for Science Technology and Research (A*STAR) Singapore Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore

Real-time eyeblink detection in the wild can widely serve for fatigue detection, face anti-spoofing, emotion analysis, etc. The existing research efforts generally focus on single-person cases towards trimmed video. However, multi-person scenario within untrimmed videos is also important for practical applications, which has not been well concerned yet. To address this, we shed light on this research field for the first time with essential contributions on dataset, theory, and practices. In particular, a large-scale dataset termed MPEblink that involves 686 untrimmed videos with 8748 eyeblink events is proposed under multi-person conditions. The samples are captured from uncon-strainedfilms to reveal “in the wild“ characteristics. Meanwhile, a real-time multi-person eyeblink detection method is also proposed. Being different from the existing counter-parts, our proposition runs in a one-stage spatio-temporal way with end-to-end learning capacity. Specifically, it simultaneously addresses the sub-tasks of face detection, face tracking, and human instance-level eyeblink detection. This paradigm holds 2 main advantages: (1) eyeblink features can be facilitated via the face's global context (e.g., head pose and illumination condition) with joint optimization and interaction, and (2) addressing these sub-tasks in parallel instead of sequential manner can save time remarkably to meet the real-time running requirement. Experiments on MPEblink verify the essential challenges of real-time multi-person eyeblink detection in the wild for untrimmed video. Our method also outperforms existing approaches by large margins and with a high inference speed.

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Analyzing Nobel Prize Literature with Large Language Models

arXiv

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arXiv 2024年

作者： Yang, Zhenyuan Liu, Zhengliang Zhang, Jing Lu, Cen Tai, Jiaxin Zhong, Tianyang Li, Yiwei Zhao, Siyan Yao, Teng Liu, Qing Yang, Jinlin Liu, Qixin Li, Zhaowei Wang, Kexin Ma, Longjun Zhu, Dajiang Ren, Yudan Ge, Bao Zhang, Wei Qiang, Ning Zhang, Tuo Liu, Tianming Guanghua School of Management Peking University Beijing China University of Georgia AthensGA United States Computer Science and Engineering The University of Texas at Arlington ArlingtonTX United States College of Computing Georgia Institute of Technology Atlanta United States Department of communication engineering Shaanxi Normal University Xi’an China School of Automation Northwestern Polytechnical University Xi’an China Xi’an University Xi’an China School of Physics and Information Technology Shaanxi Normal University Xi’an China School of Information Science and Technology Northwest University Xi’an China School of Computer and Cyber Sciences Augusta University AugustaGA United States

literary elements such as thematic analysis, intertextuality, cultural and historical contexts, linguistic and structural innovations, and character development. Given the Nobel Prize’s prestige and its emphasis on cultural, historical, and linguistic richness, applying LLMs to these works provides a deeper understanding of both human and AI approaches to interpretation. The study uses qualitative and quantitative evaluations of coherence, creativity, and fidelity to the text, revealing the strengths and limitations of AI in tasks typically reserved for human expertise. While LLMs demonstrate strong analytical capabilities, particularly in structured tasks, they often fall short in emotional nuance and coherence, areas where human interpretation excels. This research underscores the potential for human-AI collaboration in the humanities, opening new opportunities in literary studies and beyond. Copyright © 2024, The Authors. All rights reserved.

关键词： Linguistics

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Joint Embedding of Structural and Functional Brain Networks with Graph Neural Networks for Mental Illness Diagnosis

arXiv

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arXiv 2021年

作者： Zhu, Yanqiao Cui, Hejie He, Lifang Sun, Lichao Yang, Carl Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Computer Science Emory University United States Department of Computer Science and Engineering Lehigh University United States

Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis. Recently, Graph Neural Networks (GNNs) have become a de facto model for analyzing graph-structured data. However, how to employ GNNs to extract effective representations from brain networks in multiple modalities remains rarely explored. Moreover, as brain networks provide no initial node features, how to design informative node attributes and leverage edge weights for GNNs to learn is left unsolved. To this end, we develop a novel multiview GNN for multimodal brain networks. In particular, we treat each modality as a view for brain networks and employ contrastive learning for multimodal fusion. Then, we propose a GNN model which takes advantage of the message passing scheme by propagating messages based on degree statistics and brain region connectivities. Extensive experiments on two real-world disease datasets (HIV and Bipolar) demonstrate the effectiveness of our proposed method over state-of-the-art baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Graph neural networks

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