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Superelastic alloy based electrical interconnects for highly stretchable electronics

npj Flexible Electronics 2022年第1期6卷 91-98页

作者： Yangyong Zhao Weifan Zhou Yixiang Shi Xianqing Yang Yuanyuan Bai Lianhui Li Shuqi Wang Tie Li Simin Feng Ting Zhang i-lab Key Laboratory of Multifunctional Nanomaterials and Smart SystemsSuzhou Institute of Nano-Tech and Nano-Bionics(SINANO)Chinese Academy of Sciences(CAS)398 Ruoshui RoadSuzhou 215123P.R.China Gusu Laboratory for Materials Science 388 Ruoshui RoadSuzhou 215123P.R.China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031P.R.China

To achieve stretchable inorganic electronics,improving elastic stretchability of the electrical interconnects becomes a bottleneck needed to be ***,we propose a material of Ni-Ti superelastic alloy for the design and fabrication of deformable interconnects,whose intrinsic elastic property overcomes the low intrinsic elastic strain limit of conventional *** serpentine interconnect made by Ni-Ti alloy with an intrinsic elastic strain limit of~7.5%represents a much higher elastic stretchability than conventional Cu *** deformation behavior of the interconnect is systematically investigated through finite element analysis(FEA)simulations and *** results reveal that the interconnect exhibits an elastic stretchability up to 196%,and its resistance only changes by 0.4%with 100% ***,the potentials and challenges of other superelastic alloys as electrical interconnects are *** proposed superelastic alloys fundamentally boost the stretchable properties of electrical interconnects,which would open up opportunities for flexible and stretchable electronics.

关键词： alloy alloys deformation

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Neuro-Inspired Continual Anthropomorphic Grasping

SSRN

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SSRN 2023年

作者： Li, Wanyi Wei, Wei Wang, Peng Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences 999077 Hong Kong

Humans are able to learn continuously grasping objects dexterously throughout their lifetime under an endless variety of ever-changing scenarios. This ability is enabled partly by underlying biological neural mechanisms. Most current works of anthropomorphic robotic grasping learning lack the capability of continual learning. They utilize fixed large dataset to train grasp models and the trained grasp models are difficult to improve continually over time. By incorporating several of discovered biological neural mechanisms supporting continual lifelong learning, we propose a neuro-inspired continual anthropomorphic grasping (NICAG) approach. It consists of a continual learning framework of anthropomorphic grasping and a neuro-inspired continual learning algorithm. Compared with other methods, our NICAG approach not only achieves better continual learning capability with lower average loss and forgetting, but also gets higher success rate for grasping. It indicates that our approach performs better on alleviating forgetting and preserving grasp knowledge. The proposed system offers an approach for endowing anthropomorphic robotic hands with the ability to learn to grasp different objects continually and incrementally over time, and has great potential to make a profound impact on robots in households and factories. © 2023, The Authors. All rights reserved.

关键词： Large dataset

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Learning Cross-modality Information Bottleneck Representation for Heterogeneous Person Re-Identification

arXiv

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arXiv 2023年

作者： Shi, Haichao Luo, Mandi Zhang, Xiao-Yu He, Ran The Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Technology and Standards Research Institute China Academy of Information and Communications Technology Beijing100083 China The Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China

Visible-Infrared person re-identification (VI-ReID) is an important and challenging task in intelligent video surveillance. Existing methods mainly focus on learning a shared feature space to reduce the modality discrepancy between visible and infrared modalities, which still leave two problems underexplored: information redundancy and modality complementarity. To this end, properly eliminating the identity-irrelevant information as well as making up for the modality-specific information are critical and remains a challenging endeavor. To tackle the above problems, we present a novel mutual information and modality consensus network, namely CMInfoNet, to extract modality-invariant identity features with the most representative information and reduce the redundancies. The key insight of our method is to find an optimal representation to capture more identity-relevant information and compress the irrelevant parts by optimizing a mutual information bottleneck trade-off. Besides, we propose an automatically search strategy to find the most prominent parts that identify the pedestrians. To eliminate the cross- and intra-modality variations, we also devise a modality consensus module to align the visible and infrared modalities for task-specific guidance. Moreover, the global-local feature representations can also be acquired for key parts discrimination. Experimental results on four benchmarks, i.e., SYSU-MM01, RegDB, Occluded-DukeMTMC, Occluded-REID, Partial-REID and Partial_iLIDS dataset, have demonstrated the effectiveness of CMInfoNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Redundancy

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Biomimetic Flexible Neuro-Probe System for Early Warning with Force Feedback to Avoid Vascular Damage 22

Biomimetic Flexible Neuro-Probe System for Early Warning wit...

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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Zhou, Yu Yang, Huiran Wang, Xueying Yang, Heng Sun, Ke Zhou, Zhitao Sun, Liuyang Li, Meng Zhao, Jianlong Tao, Tiger H. Wei, Xiaoling State Key Laboratory Of Transducer Technology Shanghai Institute Of Microsystem And Information Technology Chinese Academy Of Sciences Shanghai China School Of Graduate Study University Of Chinese Academy Of Sciences Beijing China Center Of Materials Science And Optoelectronics Engineering University Of Chinese Academy Of Sciences Beijing China 2020 X-Lab Shanghai Institute Of Microsystem And Information Technology Chinese Academy Of Sciences Shanghai China Center For Excellence In Brain Science And Intelligence Technology Chinese Academy Of Sciences Shanghai200031 China Jiangxi Nanchang China Guangdong Institute Of Intelligence Science And Technology Zhuhai Guangdong Hengqin China Tianqiao And Chrissy Chen Institute For Translational Research Shanghai China

ISBN: (纸本)9784886864352

Flexible neuro-probes are considered to be able to form stable neural interface due to its mechanical compliance with surrounding tissue, thus allow for chronical recording. How to minimize acute injury during the probe implantation and avoid vasculature during insertions is a key challenge. Inspired by the natural mosquito mouthparts, we designed a biomimetic neuro-probe system that combines sensors and a flexible electrode array. The system can monitor the force feedback while implanting the electrode array with minimal trauma, thus avoiding additional damage to intracranial tissues such as vessels. At the same time, the adjustable implant system enables customized cross-brain implantation. The high signal-to-noise ratio sensor signal output and outstanding postoperative recording performance prove that the neuro-probe system has an anticipated application prospect in neuroscience and brain machine interface. © 2023 IEEJ.

关键词： bionic system minimally invasive mosquito-like neural probe tactile sensor

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Deep Audio-visual Learning:A Survey

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International Journal of automation and computing 2021年第3期18卷 351-376页

作者： Hao Zhu Man-Di Luo Rui Wang Ai-Hua Zheng Ran He Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and TechnologyAnhui UniversityHefei 230601China Center for Research on Intelligent Perception and Computing(CRIPAC)and National Laboratory of Pattern Recognition(NLPR) Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of the Chinese Academy of SciencesBeijing 100049China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China

Audio-visual learning,aimed at exploiting the relationship between audio and visual modalities,has drawn considerable attention since deep learning started to be used *** tend to leverage these two modalities to improve the performance of previously considered single-modality tasks or address new challenging *** this paper,we provide a comprehensive survey of recent audio-visual learning *** divide the current audio-visual learning tasks into four different subfields:audiovisual separation and localization,audio-visual correspondence learning,audio-visual generation,and audio-visual representation ***-of-the-art methods,as well as the remaining challenges of each subfield,are further ***,we summarize the commonly used datasets and challenges.

关键词： Deep audio-visual learning audio-visual separation and localization correspondence learning generative models representation learning

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Real-time Trajectory Planning and Tracking Control of Bionic Underwater Robot in Dynamic Environment

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Cyborg and Bionic Systems 2024年第1期5卷 448-460页

作者： Feng Ding Rui Wang Tiandong Zhang Gang Zheng Zhengxing Wu Shuo Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Artificial Intelligence University of Chinese Academy of SciencesBeijingChina Centrale Lille CRIStAL-Centre de Recherche en Informatique Signal et Automatique de LilleUniversity of Lille59000 LilleFrance Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghaiChina

In this article,we study the trajectory planning and tracking control of a bionic underwater robot under multiple dynamic *** first introduce the design of the bionic leopard cabinet underwater robot developed in our ***,we model the trajectory planning problem of the bionic underwater robot by combining its dynamics and physical ***,we conduct global trajectory planning for bionic underwater robots based on the temporal-spatial Bezier *** addition,based on the improved proximal policy optimization,local dynamic obstacle avoidance trajectory replanning is carried *** addition,we design the fuzzy proportional-integral-derivative controller for tracking control of the planned ***,the effectiveness of the real-time trajectory planning and tracking control method is verified by comparative simulation in dynamic environment and semiphysical simulation of *** them,the real-time trajectory planning method has advantages in trajectory length,trajectory smoothness,and planning *** error of trajectory tracking control method is controlled around 0.2 m.

关键词： real time trajectory planning tracking control bionic underwater robots global trajectory planning bionic leopard cabinet underwater robot dynamic environment trajectory planning bionic underwater robot

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A multi-modal global instance tracking benchmark (MGIT): better locating target in complex spatio-temporal and causal relationship 23

A multi-modal global instance tracking benchmark (MGIT): bet...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Shiyu Hu Dailing Zhang Meiqi Wu Xiaokun Feng Xuchen Li Xin Zhao Kaiqi Huang School of Artificial Intelligence University of Chinese Academy of Sciences and Institute of Automation Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences School of Computer Science Beijing University of Posts and Telecommunications School of Artificial Intelligence University of Chinese Academy of Sciences and Institute of Automation Chinese Academy of Sciences and Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences

Tracking an arbitrary moving target in a video sequence is the foundation for high-level tasks like video understanding. Although existing visual-based trackers have demonstrated good tracking capabilities in short video sequences, they always perform poorly in complex environments, as represented by the recently proposed global instance tracking task, which consists of longer videos with more complicated narrative content. Recently, several works have introduced natural language into object tracking, desiring to address the limitations of relying only on a single visual modality. However, these selected videos are still short sequences with uncomplicated spatio-temporal and causal relationships, and the provided semantic descriptions are too simple to characterize video content. To address these issues, we (1) first propose a new multi-modal global instance tracking benchmark named MGIT. It consists of 150 long video sequences with a total of 2.03 million frames, aiming to fully represent the complex spatio-temporal and causal relationships coupled in longer narrative content. (2) Each video sequence is annotated with three semantic grains (i.e., action, activity, and story) to model the progressive process of human cognition. We expect this multi-granular annotation strategy can provide a favorable environment for multi-modal object tracking research and long video understanding. (3) Besides, we execute comparative experiments on existing multi-modal object tracking benchmarks, which not only explore the impact of different annotation methods, but also validate that our annotation method is a feasible solution for coupling human understanding into semantic labels. (4) Additionally, we conduct detailed experimental analyses on MGIT, and hope the explored performance bottlenecks of existing algorithms can support further research in multi-modal object tracking. The proposed benchmark, experimental results, and toolkit will be released gradually on http://videocube.a

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Enhancing Vision-Language Tracking by Effectively Converting Textual Cues into Visual Cues

Enhancing Vision-Language Tracking by Effectively Converting...

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International Conference on Acoustics, Speech, and Signal Processing (IcasSP)

作者： Xiaokun Feng Dailing Zhang Shiyu Hu Xuchen Li Meiqi Wu Jing Zhang Xiaotang Chen Kaiqi Huang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Institute of Automation Chinese Academy of Sciences Beijing China School of Physical and Mathematical Sciences Nanyang Technological University Singapore School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Vision-Language Tracking (VLT) aims to localize a target in video sequences using a visual template and language description. While textual cues enhance tracking potential, current datasets typically contain much more image data than text, limiting the ability of VLT methods to align the two modalities effectively. To address this imbalance, we propose a novel plug-and-play method named CTVLT that leverages the strong text-image alignment capabilities of foundation grounding models. CTVLT converts textual cues into interpretable visual heatmaps, which are easier for trackers to process. Specifically, we design a textual cue mapping module that transforms textual cues into target distribution heatmaps, visually representing the location described by the text. Additionally, the heatmap guidance module fuses these heatmaps with the search image to guide tracking more effectively. Extensive experiments on mainstream benchmarks demonstrate the effectiveness of our approach, achieving state-of-the-art performance and validating the utility of our method for enhanced VLT.

关键词： Heating systems Visualization Target tracking Grounding Video sequences Training data Transforms Benchmark testing Numerical models Speech processing

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Learning Novel Skills from Language-Generated Demonstrations

arXiv

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arXiv 2024年

作者： Jin, Ao-Qun Xiang, Tian-Yu Zhou, Xiao-Hu Gui, Mei-Jiang Xie, Xiao-Liang Liu, Shi-Qi Wang, Shuang-Yi Cao, Yue Duan, Sheng-Bin Xie, Fu-Chao Hou, Zeng-Guang The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Hubei Normal University Huangshi435002 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Information Shanxi University of Finance and Economics Taiyuan030006 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa China

Current robot learning algorithms for acquiring novel skills often rely on demonstration datasets or environment interactions, resulting in high labor costs and potential safety risks. To address these challenges, this study proposes a skill-learning framework that enables robots to acquire novel skills from natural language instructions. The proposed pipeline leverages vision-language models to generate demonstration videos of novel skills, which are processed by an inverse dynamics model to extract actions from the unlabeled demonstrations. These actions are subsequently mapped to environmental contexts via imitation learning, enabling robots to learn new skills effectively. Experimental evaluations in the MetaWorld simulation environments demonstrate the pipeline’s capability to generate high-fidelity and reliable demonstrations. Using the generated demonstrations, various skill learning algorithms achieve an accomplishment rate three times the original on novel tasks. These results highlight a novel approach to robot learning, offering a foundation for the intuitive and intelligent acquisition of novel robotic skills. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Spiking Neural Networks with Consistent Mapping Relations Allow High-Accuracy Inference

SSRN

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SSRN 2023年

作者： Li, Yang He, Xiang Kong, Qingqun Zeng, Yi Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Future Technology University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Shanghai China

Spike-based neuromorphic hardware has demonstrated prospects in low energy consumption and efficient inference, especially on edge devices. However, the direct training of deep spiking neural networks is challenging, and conversion-based methods still require significant time delay due to unresolved conversion errors. We point out that the root cause for the conversion error lies in the inconsistency between the mapping relationship of the origin activation function and that of the spike neurons' total input and output. We propose a consistent IF (CIF) neuron model and wake-sleep conversion (WSC) to solve the influence of the upper and lower bounds of the stable point on the exact conversion and theoretically achieve a loss-free conversion. In addition, our method significantly reduces time delay and improves inference performance on large-scale classification and object detection tasks, providing a solution for achieving more efficient neuromorphic systems. © 2023, The Authors. All rights reserved.

关键词： Time delay

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