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Flexible and highly-sensitive pressure sensor based on controllably oxidized MXene

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InfoMat 2022年第9期4卷 60-71页

作者： Yanan Ma Yongfa Cheng Jian Wang Si Fu Mengjun Zhou Yue Yang Baowen Li Xin Zhang Ce-Wen Nan State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center of Smart Materials and Devices&International School of Materials Science and EngineeringWuhan University of TechnologyWuhanChina School of Mathematics Physics and Optoelectronic EngineeringHubei University of Automotive TechnologyShiyanChina Center for Nanoscale Characterization&Devices(CNCD) Wuhan National Laboratory for Optoelectronics(WNLO)Huazhong University of Science and Technology(HUST)WuhanChina Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationInstitutes of Physical Science and Information TechnologyAnhui UniversityHefeiChina State Key Lab of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijingChina

Conductive Ti_(3)C_(2)T_(x)MXenes have been widely investigated for the construction of flexible and highly-sensitive pressure *** the inevitable oxidation of solution-processed MXene has been recognized,the effect of the irreversible oxidation of MXene on its electrical conductivity and sensing properties is yet to be ***,we construct a highly-sensitive and degradable piezoresistive pressure sensor by coating Ti_(3)C_(2)T_(x)MXene flakes with different degrees of in situ oxidation onto paper substrates using the dipping-drying *** situ oxidation can tune the intrinsic resistance and expand the interlayer distance of MXene *** partially oxidized MXene-based piezoresistive pressure sensor exhibits a high sensitivity of 28.43 kPa^(-1),which is greater than those of pristine MXene,over-oxidized MXene,and state-of-the-art paper-based pressure ***,these sensors exhibit a short response time of 98.3 ms,good durability over 5000 measurement cycles,and a low force detection limit of 0.8 ***,MXene-based sensing elements are easily degraded and environmentally *** MXene-based pressure sensor shows promise for practical applications in tracking body movements,sports coaching,remote health monitoring,and human–computer interactions.

关键词： adjustable sensitivity in situ oxidation interlayer distance MXene pressure sensor

Learning Comprehensive Representation via Selective Activation and Dual-Level Orthogonality for Pedestrian Attribute Recognition

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wu, Junyi Huang, Yan Gao, Min Niu, Yuzhen Chen, Yuzhong Wu, Qiang Zhao, Jianqiang Ministry of Education Engineering Research Center of Big Data Intelligence China Ai Research Center Sdic Intelligence Xiamen Information Co. Ltd Xiamen China Xiamen Meiya Pico Information Security Research Institute Co. Ltd Xiamen China Institute of Automation Beijing China Fuzhou University Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou China University of Technology Sydney School of Electrical and Data Engineering Ultimo Australia

Multi-label Pedestrian Attribute Recognition (PAR) involves identifying a series of semantic attributes in person images. Existing PAR solutions typically rely on CNN as the backbone network to extract pedestrian features. Unfortunately, CNNs process only one adjacent region at a time, resulting in the disappearance of long-range relations between different attribute-specific regions. To address this limitation, we adopt the Vision Transformer (ViT) instead of CNN as the backbone for PAR, aiming to build long-range relations and extract more robust features. However, PAR suffers from an inherent attribute imbalance issue, causing ViT to naturally focus more on attributes that appear frequently in the training set and ignore some pedestrian attributes that appear less. The native features extracted by ViT are not able to tolerate the imbalance attribute distribution issue. To tackle this issue, we propose a novel component and a dual-level loss: the Selective Feature Activation Method (SFAM), the Orthogonal Feature Activation Loss (OFALoss), and Orthogonal Weight Regularization Loss (OWRLoss). SFAM smartly suppresses the more informative attribute-specific features, thus compelling the PAR model to pay greater attention to attribute-specific regions that are often overlooked. The proposed OFALoss enforces an orthogonal constraint on the original feature extracted by ViT and the suppressed features from SFAM, promoting the comprehensiveness of feature representation in each attribute-specific region. Furthermore, OWRLoss is employed for decreasing correlations among entries of the last shared classification layer, which can alleviate the highly correlated of weight vectors caused by non-uniform distribution. This can prevent excessive mutual interference among different attributes during attribute recognition. Our model-agnostic approach is plug-and-play, requiring no additional training parameters in the training process. We conduct experiments on several benchmark P

关键词： Dual-Level Orthogonal Regularization Pedestrian Attribute Recognition Selective Feature Activation

Influence of Membrane Characteristics on Efficiency of Vacuum Membrane Distillation: a Lattice Boltzmann Study

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arXiv 2025年

作者： Zhang, Hongxuan Gong, Dian Zhou, Yiling Qin, Zhangrong Wen, Binghai Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China CAS Key Laboratory of Low-Carbon Conversion Science and Engineering Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China PetroChina Shanghai Advanced Materials Research Institute Co.Ltd Shanghai201210 China

With increasing water scarcity, membrane distillation technology has gained widespread attention as an innovative method for seawater desalination. However, existing studies often overlook the influence of membrane characteristics on mass transfer efficiency. This study, based on the lattice Boltzmann method, proposes a model for a novel Poly(tetraethynylpyrene) membrane material to reveal the influence of membrane characteristics on the performance of vacuum membrane distillation. The model considers the factors such as porosity, tortuosity, membrane thickness, pore size, membrane surface wettability and temperature difference on the permeate flux. The results show that the permeate flux increases linearly with the porosity and decreases exponentially with the tortuosity factor. There is an optimal membrane thickness range (2 m) beyond which the permeate flux decreases exponentially. In addition, the permeate flux increases exponentially with increasing temperature difference and pore size. Further analysis of the effect of membrane surface wettability shows that permeate flux increases with increasing hydrophobicity. Finally, the feed temperature and tortuosity factor have the largest effect on permeate flux, followed by membrane thickness and, subsequently, pore size. The model can be further extended to study other configurations of membrane distillation technologies. Copyright © 2025, The Authors. All rights reserved.

关键词： Wetting

ONE2SET + Large Language Model: Best Partners for keyphrase Generation

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arXiv 2024年

作者： Shao, Liangying Zhang, Liang Peng, Minlong Ma, Guoqi Yue, Hao Sun, Mingming Su, Jinsong School of Informatics Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Xiamen University Ministry of Culture and Tourism China Cognitive Computing Lab Baidu Research China Beijing Institute of Mathematical Sciences and Applications China

keyphrase generation (KPG) aims to automatically generate a collection of phrases representing the core concepts of a given document. The dominant paradigms in KPG include ONE2SEQ and ONE2SET. Recently, there has been increasing interest in applying large language models (LLMs) to KPG. Our preliminary experiments reveal that it is challenging for a single model to excel in both recall and precision. Further analysis shows that: 1) the ONE2SET paradigm owns the advantage of high recall, but suffers from improper assignments of supervision signals during training;2) LLMs are powerful in keyphrase selection, but existing selection methods often make redundant selections. Given these observations, we introduce a generate-then-select framework decomposing KPG into two steps, where we adopt a ONE2SET-based model as generator to produce candidates and then use an LLM as selector to select keyphrases from these candidates. Particularly, we make two important improvements on our generator and selector: 1) we design an Optimal Transport-based assignment strategy to address the above improper assignments;2) we model the keyphrase selection as a sequence labeling task to alleviate redundant selections. Experimental results on multiple benchmark datasets show that our framework significantly surpasses state-ofthe- art models, especially in absent keyphrase prediction. We release our code at https: //***/DeepLearnXMU/KPG-SetLLM. Copyright © 2024, The Authors. All rights reserved.

关键词： Modeling languages

Boosting Explore-Exploit Behavior for Navigating Vehicle by Maintaining Informative Topological Frontier

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IEEE Transactions on Intelligent Vehicles

IEEE Transactions on intelligent Vehicles 2024年 1-11页

作者： Liu, Qiming Cui, Xinru Liu, Zhe Wang, Hesheng Department of Automation Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

Target search in unknown environments places high demands not only on an autonomous vehicle's ability to perceive and interpret target cues, but also on its conscious of collecting these cues by active exploration. While existing navigation methods have successfully built target-driven policies by maintaining memory of explored areas, there has been a lack of focus on facilitating target-aware exploration-the informative frontier information at unexplored yet visible areas is often overlooked. In this paper, we introduce a novel topology-based memory structure, Frontier-enhanced Topological Memory (FTM), and a Hierarchical Topology Encoding and Extraction (HTEE) module, fostering the autonomous vehicle's awareness of both environmental exploration and target approach. Specifically, FTM innovatively incorporates informative ghost nodes on traditional topological map to represent unexplored yet visible regions. We leverage an online-trained implicit scene representation to estimate the positions and generate features of these ghost nodes. The HTEE then employs implicit graph convolutions and attention mechanisms to extract cognitive information from FTM, taking into account the hierarchical memory structure, target cues, and current state. Our design bolsters cognitive navigation decisions. The experiments in the high-fidelity environments, including performance tests, visualizations, and interpretability experiments, validate the effectiveness of our approach in enhancing the vehicle's exploratory behavior. The improved exploration awareness for target cue collection, in turn, enhances the success rate and path efficiency of target search. Furthermore, we demonstrate the adaptability of our algorithm in real-world physical environments. IEEE

关键词： Data mining

DFTR: Depth-supervised Fusion Transformer for Salient Object Detection

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arXiv 2022年

作者： Zhu, Heqin Sun, Xu Li, Yuexiang Ma, Kai Zhou, S. Kevin Zheng, Yefeng Key Lab of Intelligent Information Processing Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China Tencent Jarvis Lab Shenzhen China University of Science and Technology of China Suzhou215123 China

Automated salient object detection (SOD) plays an increasingly crucial role in many computer vision applications. By reformulating the depth information as supervision rather than as input, depth-supervised convolutional neural networks (CNN) have achieved promising results on both RGB and RGB-D SOD scenarios with the merits of no requirements for extra depth networks and depth inputs in the inference stage. This paper, for the first time, seeks to expand the applicability of depth supervision to the Transformer architecture. Specifically, we develop a Depth-supervised Fusion TRansformer (DFTR), to further improve the accuracy of both RGB and RGB-D SOD. The proposed DFTR involves three primary features: 1) DFTR, to the best of our knowledge, is the first pure Transformer-based model for depth-supervised SOD;2) A multi-scale feature aggregation (MFA) module is proposed to fully exploit the multi-scale features encoded by the Swin Transformer in a coarse-to-fine manner;3) To enable bidirectional information flow across different streams of features, a novel multi-stage feature fusion (MFF) module is further integrated into our DFTR with the emphasis on salient regions at different network learning stages. We extensively evaluate the proposed DFTR on ten benchmarking datasets. Experimental results show that our DFTR consistently outperforms the existing state-of-the-art methods for both RGB and RGB-D SOD tasks. The code and model will be made publicly available. © 2022, CC BY-NC-SA.

关键词： Object detection

Balanced Contrastive Learning for Long-Tailed Visual Recognition

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arXiv 2022年

作者： Zhu, Jianggang Wang, Zheng Chen, Jingjing Chen, Yi-Ping Phoebe Jiang, Yu-Gang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Department of Computer Science and Information Technology La Trobe University Australia

Real-world data typically follow a long-tailed distribution, where a few majority categories occupy most of the data while most minority categories contain a limited number of samples. Classification models minimizing cross-entropy struggle to represent and classify the tail classes. Although the problem of learning unbiased classifiers has been well studied, methods for representing imbalanced data are under-explored. In this paper, we focus on representation learning for imbalanced data. Recently, supervised contrastive learning has shown promising performance on balanced data recently. However, through our theoretical analysis, we find that for long-tailed data, it fails to form a regular simplex which is an ideal geometric configuration for representation learning. To correct the optimization behavior of SCL and further improve the performance of long-tailed visual recognition, we propose a novel loss for balanced contrastive learning (BCL). Compared with SCL, we have two improvements in BCL: class-averaging, which balances the gradient contribution of negative classes;class-complement, which allows all classes to appear in every mini-batch. The proposed balanced contrastive learning (BCL) method satisfies the condition of forming a regular simplex and assists the optimization of cross-entropy. Equipped with BCL, the proposed two-branch framework can obtain a stronger feature representation and achieve competitive performance on long-tailed benchmark datasets such as CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, and iNaturalist2018. Our code is available at this URL. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

OmniViD: A Generative Framework for Universal Video Understanding

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arXiv 2024年

作者： Wang, Junke Chen, Dongdong Luo, Chong He, Bo Yuan, Lu Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Microsoft Cloud + AI Microsoft Research Asia China University of Maryland College Park United States

The core of video understanding tasks, such as recognition, captioning, and tracking, is to automatically detect objects or actions in a video and analyze their temporal evolution. Despite sharing a common goal, different tasks often rely on distinct model architectures and annotation formats. In contrast, natural language processing benefits from a unified output space, i.e., text sequences, which simplifies the training of powerful foundational language models, such as GPT-3, with extensive training corpora. Inspired by this, we seek to unify the output space of video understanding tasks by using languages as labels and additionally introducing time and box tokens. In this way, a variety of video tasks could be formulated as video-grounded token generation. This enables us to address various types of video tasks, including classification (such as action recognition), captioning (covering clip captioning, video question answering, and dense video captioning), and localization tasks (such as visual object tracking) within a fully shared encoder-decoder architecture, following a generative framework. Through comprehensive experiments, we demonstrate such a simple and straightforward idea is quite effective and can achieve state-of-the-art or competitive results on seven video benchmarks, providing a novel perspective for more universal video understanding. Code is available at https://***/wangjk666/OmniVid. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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Hand and gesture

Virtual Reality & intelligent Hardware 2021年第3期3卷 I0001-I0002页

作者： Xilin CHEN Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China

Hands play an important role in our daily *** use our hands for manipulation in working,emphasis in speaking,communication in non-verbal environment,*** gesture not only uses for simple commands in traffic control,but also extends as a kind of language-sign *** the areas of VR/AR and HCI,understanding hand and its action can greatly improve user *** covers a broad topics related to hands,including hand detection,tracking,hand pose estimation,gesture recognition,and sign language *** papers are collected in this *** cover different topics related to hand and gesture.

关键词： verbal language. hands