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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Lingjun Zhou Hansi Ma Xiaomin Nie Yunchen Li Zhixue He Lei Wang Ke Li Fan Zhang Peng Cheng Laboratory Shenzhen China School of Electronic and Computer Engineering Peking University Shenzhen China Center of Material Science National University of Defense Technology Changsha China State Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Peking University Beijing China

We propose and experimentally demonstrate an ultra-compact and efficient power splitter on LNOI. The device is realized through the inverse design of a digital meta-structure and occupies a small footprint of only 4.8×4.8μm 2 . The measured results show that the insertion losses are below 2.5dB across the wavelength band of 1500–1560 nm, and the power imbalance between the two output ports is within 0.3 dB.

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Research on Observing Moving Marine Objects with Guided Multi-satellite Collaboration

Research on Observing Moving Marine Objects with Guided Mult...

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第34届中国控制与决策会议

作者： Lin Ma Jiahao Qin Ming Xu State Key Laboratory of Laser Propulsion & Application Space Engineering University Institute of Spacecraft Application System Engineering China Academy of Space Technology School of Astronautics Beihang University

Moving marine targets are attached increasingly great importance in the Earth observation missions,which gives to growth in the number of observation demands with higher data diversity *** cope with the challenge,multiple satellites of various types need to work collaboratively to observe moving marine objects,which is the main focus of this ***,the Constraint Satisfy Problem(CSP) model for multi-satellite collaboration is proposed,which maximizes the task completion rate and target discovery probability for electronic satellites and imaging satellites,***,the track prediction and dynamic decomposition of potential region can be obtained from Gaussian projection,based on which the paper proposed two optimization algorithms to solve the CSP model in stages.

关键词： Multi-satellite scheduling Guided-Collaboration Moving marine object Multi-Iayer coding Genetic algorithm robustness evaluation

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Towards Accurate Unified Anomaly Segmentation

Towards Accurate Unified Anomaly Segmentation

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wenxin Ma Qingsong Yao Xiang Zhang Zhelong Huang Zihang Jiang S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) USTC Suzhou Institute for Advance Research Suzhou Jiangsu China Stanford University Palo Alto California United State School of Medicine Shanghai University Shanghai China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Unsupervised anomaly detection (UAD) from images strives to model normal data distributions, creating discriminative representations to distinguish and precisely localize anomalies. Despite recent advancements in the efficient and unified one-for-all scheme, challenges persist in accurately segmenting anomalies for further monitoring. Moreover, this problem is obscured by the widely-used AUROC metric under imbalanced UAD settings. This motivates us to emphasize the significance of precise segmentation of anomaly pixels using pAP and DSC as metrics. To address the unsolved segmentation task, we introduce the Unified Anomaly Segmentation (UniAS). UniAS presents a multi-level hybrid pipeline that progressively enhances normal information from coarse to fine, incorporating a novel multi-granularity gated CNN (MGG-CNN) into Transformer layers to explicitly aggregate local details from different granularities. UniAS achieves state-of-the-art anomaly segmentation performance, attaining 65.12/59.33 and 40.06/32.50 in pAP/DSC on the MVTec-AD and VisA datasets, respectively, surpassing previous methods significantly. The codes are shared at https://***/Mwxinnn/UniAS.

关键词： Measurement Image segmentation Computer vision Codes Pipelines Logic gates Transformers Data models Monitoring Anomaly detection

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The Study on hydrothermal synthesis of ZrO2nanofibers under different conditions 3

The Study on hydrothermal synthesis of ZrO2nanofibers under ...

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3rd International Conference on Energy Resources and Sustainable Development, ICERSD 2020

作者： Xu, Lingna Cheng, Yifan Chen, Tianen Mao, Rong Han, Zhong College of Engineering and Technology Southwest University Chongqing400715 China State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing400044 China Hangzhou Longyuan Electric Power Company Ltd. Hangzhou311606 China

Nano-ZrO2 has a high atomic active surface and is used as catalyst carrier. The traditional method of preparing ZrO2 nanofibers is electrostatic spinning, which has the problems of high cost and high equipment requirements. In order to solve the shortcomings of traditional preparation methods and prepare excellent ZrO2 nanofibers, a series of ZrO2 nanofibers were prepared based on hydrothermal method by changing the types of reactants, the ratio of reactants to organic additives, solvents, reaction time and other relevant parameters, to explore the growth mechanism of ZrO2 nanofibers, providing a new idea for the preparation of ZrO2 nanofibers on the active surface of altiplano. © The Authors, published by EDP Sciences 2021.

关键词： Nanofibers

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SimMAT: Exploring Transferability from Vision Foundation Models to Any Image Modality

arXiv

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arXiv 2024年

作者： Lei, Chenyang Chen, Liyi Cen, Jun Chen, Xiao Lei, Zhen Heide, Felix Liu, Ziwei Chen, Qifeng Zhang, Zhaoxiang Center for Artificial Intelligence and Robotics HKISI CAS Hong Kong Princeton University Department of Computer Science United States The Hong Kong Polytechnic University Hong Kong The Hong Kong University of Science and Technology Hong Kong State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA China Nanyang Technological University Singapore

Foundation models like ChatGPT and Sora that are trained on a huge scale of data have made a revolutionary social impact. However, it is extremely challenging for sensors in many different fields to collect similar scales of natural images to train strong foundation models. To this end, this work presents a simple and effective framework SimMAT to study an open problem: the transferability from vision foundation models trained on natural RGB images to other image modalities of different physical properties (e.g., polarization). SimMAT consists of a modality-agnostic transfer layer (MAT) and a pretrained foundation model. We apply SimMAT to a representative vision foundation model Segment Anything Model (SAM) to support any evaluated new image modality. Given the absence of relevant benchmarks, we construct a new benchmark to evaluate the transfer learning performance. Our experiments confirm the intriguing potential of transferring vision foundation models in enhancing other sensors’ performance. Specifically, SimMAT can improve the segmentation performance (mIoU) from 22.15% to 53.88% on average for evaluated modalities and consistently outperforms other baselines. We hope that SimMAT can raise awareness of cross-modal transfer learning and benefit various fields for better results with vision foundation models. © 2024, CC BY.

关键词： Image coding

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Real-time Terrain Recognition based on Transformer and Gait Prediction based on CNN-LSTM-Attention for Exosuit

Real-time Terrain Recognition based on Transformer and Gait ...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Fangliang Yang Chunjie Chen Zhuo Wang Yinsheng Xu Yao Liu Xinyu Wu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Guilin University of Technology Guilin China

For wearable soft exosuits, an imprecise control strategy can easily injure the wearer, while real-time terrain recognition and accurate gait phase estimation can effectively improve the control strategy of the exosuit and make the wearer comfortable and safe. In this paper, a real-time terrain recognition algorithm based on Transformer and a gait estimation algorithm based on CNN-LSTM-Attention are implemented. The Transformer-based recognition of different terrains improves the recognition accuracy to some extent, and the CNN-LSTM-Attention based feature extraction for temporal signals such as gait phase is also extremely noticeable. Experiments show that the Transformer-based algorithm achieves 99.64 % recognition accuracy in six different terrain environments. In the gait phase estimation experiment, CNN-LSTM-CBAM achieved the best performance with an evaluation index r2 of 0.9221. The aforementioned terrain recognition algorithms and gait phase estimation algorithms may have a positive impact on soft exosuit and dynamic prosthetics research.

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Phonon transport in freestanding SrTiO3 down to the monolayer limit

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Physical Review B 2023年第11期108卷 115435-115435页

作者： Qi Wang Chen Wang Cheng Chi Niuchang Ouyang Ruiqiang Guo Nuo Yang Yue Chen Thermal Science Research Center Shandong Institute of Advanced Technology Jinan Shandong Province 250103 China Department of Mechanical Engineering The University of Hong Kong Pokfulam Road Hong Kong SAR China Key Laboratory of Power Station Energy Transfer Conversion and System of Ministry of Education School of Energy Power and Mechanical Engineering North China Electric Power University Beijing 102206 China State Key Laboratory of Coal Combustion School of Energy and Power Engineering Huazhong University of Science and Technology Wuhan 430074 China

Perovskites down to the monolayer limit have emerged and have attracted increased interest due to their two-dimensional nature with potentially novel physical properties. Here, we investigate the phonon transport in the oxide perovskite SrTiO3 with thicknesses from the monolayer limit to 10 nm by constructing an accurate first-principles machine-learning potential and combining it with the Boltzmann transport equation and homogeneous nonequilibrium molecular dynamics simulations. Compared to its bulk counterpart, the phonon dispersion relation of monolayer SrTiO3 is insensitive to temperature, and the calculated in-plane thermal conductivity of monolayer SrTiO3 is much larger than that of bulk SrTiO3, which mainly results from the unique out-of-plane atomic vibrations in monolayer SrTiO3. The thermal conductivity of SrTiO3 thin film first decreases and then approaches the bulk value as thickness increases from the monolayer limit to 10 nm. The hardening of the out-of-plane acoustic phonon branch and the transition of low-frequency optical phonons can explain the observed trend in thermal conductivity transitions. Our study demonstrates that monolayer SrTiO3 has a higher thermal conductivity than its bulk counterpart with covalent bonds at the first-principles level of accuracy, and dimension reduction has a weak inhibition on phonon transport in freestanding atomically smooth SrTiO3 thin films, which furthers the understanding of phonon transport in two-dimensional perovskite thin films.

关键词： Thermal properties Perovskites Thin films Boltzmann theory Density functional calculations Molecular dynamics

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Soft-Landing Dynamics of a Type of Four-Legged Space Lander

SSRN

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SSRN 2024年

作者： Shi, Tengfei Shi, Tengfei Yang, Yinping Zhang, Zhijuan Liu, Cuicui Liu, Caishan Ma, Daolin Applied Mechanics and Structure Safety Key Laboratory of Sichuan Province School of Mechanics and Aerospace Engineering Southwest Jiaotong University Chengdu610031 China State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China Institute of Dynamics and Control College of Mechanical Engineering and Applied Electronics Technology Beijing University of Technology Beijing100871 China China Academy of Space Technology Beijing100094 China School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of Marine Intelligent Equipment and System Ministry of Education Shanghai200240 China

Analyzing the landing dynamics response is essential for enhancing the success rate of surface explorations on Mars, the moon, or asteroids. This paper develops a comprehensive dynamics model for a space lander equipped with four sets of buffer legs, each consisting of a primary strut, two secondary struts, and a footpad, during its soft landing process. The degrees of freedom of the main body and the footpads are defined as generalized coordinates to describe the motion of the space lander. The compression force of the buffer struts is obtained by interpolating real experimental data, and the contact force between the footpad and the ground is calculated using Archimedes law for granular media. Then, the dynamical model for the lander in terms of minimal degrees of freedom can be constructed by applying the Lagrange equations of the second kind. The proposed dynamics model is validated by the experiments of a single-legged lander vertical touchdown and a four-legged lander free touchdown. Moreover, histories of buffer force, acceleration of the main body, and other key physical quantities are computed to investigate the landing process. This highly efficient and accurate dynamics model provides a method to perform extensive simulations to design and optimize a soft landing process. © 2024, The Authors. All rights reserved.

关键词： Landing

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Multi-scale Contrastive Learning for Gastroenteroscopy Classification

Multi-scale Contrastive Learning for Gastroenteroscopy Class...

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Annual IEEE Symposium on Computer-Based Medical systems

作者： Dan Li Xuechen Li Zhibin Peng Wenting Chen Linlin Shen Guangyao Wu Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology ShenZhen University Shenzhen China City University of Hong Kong Hong Kong SAR China Shenzhen Institute of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University General Hospital

In gastroenteroscopy image analysis, numerous CADs demonstrate that deep learning aids doctors' diagnosis. The shapes and sizes of the lesions are varied. And in the clinic, the dataset appears to be data imbalanced. However, existing methods directly classify by texture and ignore lesions with various shapes and sizes. To address the issue above, we propose a deep neural network, which consists of multi-scale feature extraction, contrastive feature learning and a multi-scale feature fusion module. We train the contrastive feature learning module and multi-scale feature fusion module simultaneously to alleviate the issue of data distribution differences. Thus, the proposed network can better identify various categories. Extensive experiments on the Hyper Kvasir dataset show that the proposed Hybrid-M2CL outperforms the benchmark proposed by the dataset with 5.0% Macro Precision, 3.3% Macro Recall, 3.4% Macro F1-score, 3.3% Micro Precision, 3.6% MCC. In addition, it outperforms the SOTA by 1.1% Macro F1-score, 2.6% MCC, and 2.0% B-ACC.

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Simple and Effective Frequency-Aware Image Restoration for Industrial Visual Anomaly Detection

SSRN

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SSRN 2024年

作者： Liu, Tongkun Li, Bing Du, Xiao Jiang, Bingke Geng, Leqi Wang, Feiyang Zhao, Zhuo State Key Laboratory for Manufacturing System Engineering Xi’an Jiaotong University No.99 Yanxiang Road Yanta District Shaanxi Xi’an710054 China International Joint Research Laboratory for Micro/Nano Manufacturing and Measurement Technologies Xi’an Jiaotong University No.99 Yanxiang Road Yanta District Shaanxi Xi’an710054 China Anhui Province Key Laboratory of Machine Vision Inspection Yangtze River Delta HIT Robot Technology Research Institute Wuhu24100 China

Image reconstruction-based anomaly detection models are widely explored in industrial visual inspection. However, existing models usually suffer from the trade-off between normal reconstruction fidelity and abnormal reconstruction distinguishability, which damages the performance. In this paper, we find that the above trade-off can be better mitigated by leveraging the distinct frequency biases between normal and abnormal reconstruction errors. To this end, we propose Frequency-aware Image Restoration (FAIR), a novel self-supervised image restoration task that restores images from their high-frequency components. It enables precise reconstruction of normal patterns while mitigating unfavorable generalization to anomalies. Using only a simple vanilla UNet, FAIR achieves convincing performance with high efficiency on various industrial visual inspection datasets. Code: https://***/liutongkun/FAIR. © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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