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392 results on '"Yin, Guangqiang"'

2. NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID Data

Author

Yin, Kangning, Ding, Zhen, Dong, Zhihua, Chen, Dongsheng, Fu, Jie, Ji, Xinhui, Yin, Guangqiang, and Wang, Zhiguo

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Federated learning (FL), a privacy-preserving distributed machine learning, has been rapidly applied in wireless communication networks. FL enables Internet of Things (IoT) clients to obtain well-trained models while preventing privacy leakage. Person detection can be deployed on edge devices with limited computing power if combined with FL to process the video data directly at the edge. However, due to the different hardware and deployment scenarios of different cameras, the data collected by the camera present non-independent and identically distributed (non-IID), and the global model derived from FL aggregation is less effective. Meanwhile, existing research lacks public data set for real-world FL object detection, which is not conducive to studying the non-IID problem on IoT cameras. Therefore, we open source a non-IID IoT person detection (NIPD) data set, which is collected from five different cameras. To our knowledge, this is the first true device-based non-IID person detection data set. Based on this data set, we explain how to establish a FL experimental platform and provide a benchmark for non-IID person detection. NIPD is expected to promote the application of FL and the security of smart city., Comment: 8 pages, 5 figures, 3 tables, FL-IJCAI 23 conference

Published
2023

3. Object Detection Based on Priori Attribute Information Aggregation Method

Author

Li, Chao, Pan, Jinyang, Liang, Jie, Liu, Xueting, Liu, Chunqi, Yin, Guangqiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Zhang, Yonghong, editor, Qi, Lianyong, editor, Liu, Qi, editor, Yin, Guangqiang, editor, and Liu, Xiaodong, editor

Published
2024
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4. Cross-Modal Retrieval Based on Semantic Filtering and Adaptive Pooling

Author

Qiao, Nan, Mao, Junyi, Xie, Hao, Wang, Zhiguo, Yin, Guangqiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Zhang, Yonghong, editor, Qi, Lianyong, editor, Liu, Qi, editor, Yin, Guangqiang, editor, and Liu, Xiaodong, editor

Published
2024
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5. MGAN: Attempting a Multimodal Graph Attention Network for Remote Sensing Cross-Modal Text-Image Retrieval

Author

Wang, Zhiming, Dong, Zhihua, Yang, Xiaoyu, Wang, Zhiguo, Yin, Guangqiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Zhang, Yonghong, editor, Qi, Lianyong, editor, Liu, Qi, editor, Yin, Guangqiang, editor, and Liu, Xiaodong, editor

Published
2024
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15. A Multi-task Joint Framework for Real-time Person Search

Author

Li, Ye, Yin, Kangning, Liang, Jie, Wang, Chunyu, and Yin, Guangqiang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Person search generally involves three important parts: person detection, feature extraction and identity comparison. However, person search integrating detection, extraction and comparison has the following drawbacks. Firstly, the accuracy of detection will affect the accuracy of comparison. Secondly, it is difficult to achieve real-time in real-world applications. To solve these problems, we propose a Multi-task Joint Framework for real-time person search (MJF), which optimizes the person detection, feature extraction and identity comparison respectively. For the person detection module, we proposed the YOLOv5-GS model, which is trained with person dataset. It combines the advantages of the Ghostnet and the Squeeze-and-Excitation (SE) block, and improves the speed and accuracy. For the feature extraction module, we design the Model Adaptation Architecture (MAA), which could select different network according to the number of people. It could balance the relationship between accuracy and speed. For identity comparison, we propose a Three Dimension (3D) Pooled Table and a matching strategy to improve identification accuracy. On the condition of 1920*1080 resolution video and 500 IDs table, the identification rate (IR) and frames per second (FPS) achieved by our method could reach 93.6% and 25.7

Published
2020

19. Triplet Online Instance Matching Loss for Person Re-identification

Author

Li, Ye, Yin, Guangqiang, Liu, Chunhui, Yang, Xiaoyu, and Wang, Zhiguo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Mining the shared features of same identity in different scene, and the unique features of different identity in same scene, are most significant challenges in the field of person re-identification (ReID). Online Instance Matching (OIM) loss function and Triplet loss function are main methods for person ReID. Unfortunately, both of them have drawbacks. OIM loss treats all samples equally and puts no emphasis on hard samples. Triplet loss processes batch construction in a complicated and fussy way and converges slowly. For these problems, we propose a Triplet Online Instance Matching (TOIM) loss function, which lays emphasis on the hard samples and improves the accuracy of person ReID effectively. It combines the advantages of OIM loss and Triplet loss and simplifies the process of batch construction, which leads to a more rapid convergence. It can be trained on-line when handle the joint detection and identification task. To validate our loss function, we collect and annotate a large-scale benchmark dataset (UESTC-PR) based on images taken from surveillance cameras, which contains 499 identities and 60,437 images. We evaluated our proposed loss function on Duke, Marker-1501 and UESTC-PR using ResNet-50, and the result shows that our proposed loss function outperforms the baseline methods by a maximum of 21.7%, including Softmax loss, OIM loss and Triplet loss.

Published
2020

20. Text-Image Cross-modal Retrieval Based on Transformer

Author

YANG Xiaoyu, LI Chao, CHEN Shunyao, LI Haoliang, YIN Guangqiang

Subjects
transformer, cross-modal retrieval, hierarchical feature extraction, feature aggregation, feature share, Computer software, QA76.75-76.765, Technology (General), T1-995
Abstract

With the growth of Internet multimedia data,text image retrieval has become a research hotspot.In image and text retrieval,the mutual attention mechanism is used to achieve better image-text matching results by interacting image and text features.However,this method cannot obtain image features and text features separately,and requires interaction of image and text features in the later stage of large-scale retrieval,which consumes a lot of time and is not able to achieve fast retrieval and ma-tching.However,the cross-modal image text feature learning based on Transformer has achieved good results and has received more and more attention from researchers.This paper designs a novel Transformer-based text image retrieval network structure(HAS-Net),which mainly has the following improvements:a hierarchical Transformer coding structure is designed to better utilize the underlying grammatical information and high-level semantic information;the traditional global feature aggregation method is improved,and the self-attention mechanism is used to design a new feature aggregation method;by sharing the Transformer coding layer,image features and text features are mapped to a common feature coding space.Finally,experiments are conducted on the MS-COCO and Flickr30k datasets,the cross-modal retrieval performance has been improved,and it is in a leading position among similar algorithms.It is proved that the designed network structure is effective.

Published
2023
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24. Water-Soluble 3D Covalent Organic Framework that Displays an Enhanced Enrichment Effect of Photosensitizers and Catalysts for the Reduction of Protons to H2

Author

Gao, Zhong-Zheng, Wang, Ze-Kun, Wei, Lei, Yin, Guangqiang, Tian, Jia, Liu, Chuan-Zhi, Wang, Hui, Zhang, Dan-Wei, Zhang, Yue-Biao, Li, Xiaopeng, Liu, Yi, and Li, Zhan-Ting

Subjects
Macromolecular and Materials Chemistry, Chemical Sciences, Affordable and Clean Energy, covalent organic framework, supramolecular organic framework, three-dimensional polymer, visible light photocatalysis, hydrogen generation, Engineering, Nanoscience & Nanotechnology, Chemical sciences, Physical sciences
Abstract

Covalent organic frameworks (COFs) are emerging porous polymers that have 2D or 3D long-range ordering. Currently available COFs are typically insoluble or decompose upon dissolution, which remarkably restricts their practical implementations. For 3D COFs, the achievement of noninterpenetration, which maximizes their porosity-derived applications, also remains a challenge synthetically. Here, we report the synthesis of the first highly water-soluble 3D COF (sCOF-101) from irreversible polymerization of a preorganized supramolecular organic framework through cucurbit[8]uril (CB[8])-controlled [2 + 2] photodimerization. Synchrotron X-ray scattering and diffraction analyses confirm that sCOF-101 exhibits porosity periodicity, with a channel diameter of 2.3 nm, in both water and the solid state and retains the periodicity under both strongly acidic and basic conditions. As an ordered 3D polymer, sCOF-101 can enrich [Ru(bpy)3]2+ photosensitizers and redox-active polyoxometalates in water, which leads to remarkable increase of their photocatalytic activity for proton reduction to produce H2.

Published
2020

25. Application of a new risk identification method in the development of civil aircraft high lift system

Author

AN Gang, LIU Renjing, and YIN Guangqiang

Subjects
safety analysis, risk identification, risk assessment, system function hazard analysis, fault tree analysis, Motor vehicles. Aeronautics. Astronautics, TL1-4050
Abstract

High lift system is one of the key systems to ensure aircraft flight safety, so its safety and reliability are highly required.In the development of high lift system, the application of less mature technology will increase the development risk.Combined with engineering data, on the basis of airworthiness safety design and analysis methods, a development technology is established.Taking a civil aircraft high lift system as an example, the risk identification method identifies the technical risks affecting its safety, and gives the technical improvement method and risk control strategy.The results show that there are risks in the non-commanded down of the flap and the non-commanded retract of the flap in a high lift system of a civil aircraft.By reducing the failure probability of the control channel and the monitor channel module of the flap electronic control unit, the failure probability of the non-command down/retract of the flap can be reduced from 1.656×10-9/fh to 9.424×10-10/fh, which meets the system safety requirements and avoids risks.

Published
2023
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28. Network Intrusion Detection Based on Hybrid Neural Network

Author

He, Guofeng, Lu, Qing, Yin, Guangqiang, Xiong, Hu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Lei, editor, Segal, Michael, editor, Chen, Jenhui, editor, and Qiu, Tie, editor

Published
2022
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31. Link Quality Aware Channel Allocation for Multichannel Body Sensor Networks

Author

Gao, Weifeng, Zhao, Zhiwei, Min, Geyong, Cao, Yue, Duan, Hancong, Liu, Lu, Long, Yimiao, and Yin, Guangqiang

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Cryptography and Security
Abstract

Body Sensor Network (BSN) is a typical Internet-of-Things (IoT) application for personalized health care. It consists of economically powered, wireless and implanted medical monitoring sensor nodes, which are designed to continually collect the medical information of the target patients. Multichannel is often used in BSNs to reduce the spectrum competition of the tremendous sensor nodes and the problem of channel assignment has attracted much research attention. The health sensing data in BSNs is often required to be delivered to a sink node (or server) before a certain deadline for real time monitoring or health emergency alarm. Therefore, deadline is of significant importance for multichannel allocation and scheduling. The existing works, though designed to meet the deadline, often overlook the impact of the unreliable wireless links. As a result, the health sensing data can still be overdue because of the scheduled lossy links. Besides, potential collisions in the schedules also incur considerable delay in delivering the sensing data. In this paper, we propose a novel deadline- driven Link quality Aware Channel Assignment scheme (LACA), where link quality, deadlines and collisions are jointly considered. LACA prioritizes links with urgent deadlines and heavy collisions. Besides, LACA allows the exploition of the spare slots for retransmissions on lossy links, which can further reduce the retransmission delay. Extensive simulation experiments show that compared to the existing approaches, LACA can better utilize the wireless spectrum and achieve higher packet delivery ratio before the deadline., Comment: PMC

Published
2017

33. Person Re-identification Algorithm Based on Spatial Attention Network

Author

Hou, Shaoqi, Liu, Chunhui, Yin, Kangning, Yin, Guangqiang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Zhe, editor, Wu, Fan, editor, and Das, Sajal K., editor

Published
2021
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34. DAFV: A Unified and Real-Time Framework of Joint Detection and Attributes Recognition for Fast Vehicles

Author

Chang, Yifan, Li, Chao, Li, Zhiqiang, Wang, Zhiguo, Yin, Guangqiang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Zhe, editor, Wu, Fan, editor, and Das, Sajal K., editor

Published
2021
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35. SAPN: Spatial Attention Pyramid Network for Cross-Domain Person Re-Identification

Author

Jia, Zhaoqian, Wang, Wenchao, Hou, Shaoqi, Li, Ye, Yin, Guangqiang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Zhe, editor, Wu, Fan, editor, and Das, Sajal K., editor

Published
2021
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38. A Real-Time Vehicle Logo Detection Method Based on Improved YOLOv2

Author

Yin, Kangning, Hou, Shaoqi, Li, Ye, Li, Chao, Yin, Guangqiang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yu, Dongxiao, editor, Dressler, Falko, editor, and Yu, Jiguo, editor

Published
2020
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41. Spatiotemporal Feature Extraction for Pedestrian Re-identification

Author

Li, Ye, Yin, Guangqiang, Hou, Shaoqi, Cui, Jianhai, Huang, Zicheng, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Biagioni, Edoardo S., editor, Zheng, Yao, editor, and Cheng, Siyao, editor

Published
2019
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49. Precisely Coordination‐Modulated Ultralong Organic Phosphorescence Enables Biomimetic Fluorescence‐Afterglow Dual‐Modal Information Encryption.

Author

Yin, Guangqiang, Huo, Guifei, Qi, Min, Liu, Depeng, Li, Longqiang, Zhou, Jiayin, Le, Xiaoxia, Wang, Yu, and Chen, Tao

Subjects
*OPTICAL materials, *OPTICAL properties, *PHOSPHORESCENCE spectroscopy, *PHOSPHORESCENCE, *ENERGY transfer, *LUMINESCENCE, *FLUORESCENCE
Abstract

Information leakage and counterfeiting are serious worldwide issues that tremendously impact legitimate businesses and human life. Inspired by the Noctiluca scintillans, a new fluorescence‐afterglow dual‐modal information encryption enabled by precisely coordination‐modulated ultralong organic phosphorescence (UOP) is presented. Strikingly, the optical properties including fluorescence, lifetime and intensity of UOP can be precisely modulated on‐demand through energy transfer by lanthanide (LnIII) coordination, which enables information camouflage by similar LnIII luminescence to provide misleading information along with data decryption in the form of mutual afterglow. Moreover, the important data can be encrypted in a spatial‐time‐resolved way by programmatically coding information with afterglow gradients, yielding greatly improved security for verifying the authenticity. This study provides a new avenue to precisely modulate the optical properties of UOP materials and broadens the scope of optical materials for innovative information encryption and anticounterfeiting applications. [ABSTRACT FROM AUTHOR]

Published
2024
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