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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Shen, Aijia Zhao, Liangbo Xu, Fanyun Wang, Guoqing Liu, Wenchao Shen, Zimeng Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (纸本)9798331515669

Remote sensing image target detection is one of the key technologies in the field of intelligent interpretation of remote sensing images, and it has significant application value in various areas, including military defense. When performing remote sensing image object detection on airborne and spaceborne platforms, the vast amount of remote sensing data processing and limited computational resources impose high real-time requirements on the object detection algorithms. This paper designs a lightweight object detection network model named YOLOv5-tiny, based on the existing deep learning network detection model YOLOv5s, and deploys it on the Jetson TX2 development board for training and testing. Experimental results show that the proposed YOLOv5-tiny model, when tested on the SAR-AIRcraft-1.0 with an input image size of 640x640, is 10 times smaller than YOLOv5s;it has a computational cost of 5.2 GFLOPs, which is 1/5 of YOLOv5s, and the processing time for a single image is reduced to half that of YOLOv5s, with only a 0.1% decrease in accuracy. © 2024 IEEE.

关键词： Object tracking

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Prototype-Guided Representation Distribution Optimization for Change Detection in High-Resolution Remote Sensing Imagery 2

Prototype-Guided Representation Distribution Optimization fo...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Wang, Guoqing Chen, He Wei, Xin Shi, Junqing Chen, Liang Liu, Wenchao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China

ISBN: (纸本)9798331515669

Change detection is the process of automatically extracting change areas from bi-temporal remote sensing images. The key of deep learning-based change detection methods is to extract features that represent changes/non-changes. Most of the existing methods only enhance the representation ability of the changed regions through the model architecture design, but lack further constraints in the representation space. This paper introduces a prototype-guided representation distribution optimization method. By comparing pixel features with prototypes, confusing pixels can be determined, and these pixels are adaptively weighted during learning, so that similar representations are closer and different representations are farther apart. In addition, the representation distribution is also applied as transferred knowledge to knowledge distillation for lightweight CD model. Experimental results on two public datasets show the effectiveness of the proposed method. © 2024 IEEE.

关键词： Change detection

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Exploring the Cation Regulation Mechanism for Interfacial Water Involved in the Hydrogen Evolution Reaction by In Situ Raman Spectroscopy

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Nano-Micro Letters 2024年第3期16卷 303-312页

作者： Xueqiu You Dongao Zhang Xia‑Guang Zhang Xiangyu Li Jing‑Hua Tian Yao‑Hui Wang Jian‑Feng Li School of Ocean Information Engineering Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent ProcessingJimei UniversityXiamen 361021People’s Republic of China State Key Laboratory of Physical Chemistry of Solid Surfaces MOE Key Laboratory of Spectrochemical Analysis and InstrumentationiChEMCollege of Chemistry and Chemical EngineeringCollege of MaterialsCollege of EnergyXiamen UniversityXiamen 361005People’s Republic of China Key Laboratory of Green Chemical Media and Reactions Ministry of EducationCollaborative Innovation Center of Henan Province for Green Manufacturing of Fine ChemicalsCollege of Chemistry and Chemical EngineeringHenan Normal UniversityXinxiang 453007People’s Republic of China Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province(IKKEM) Xiamen 361005People’s Republic of China

Interfacial water molecules are the most important participants in the hydrogen evolution reaction(HER).Hence,understanding the behavior and role that interfacial water plays will ultimately reveal the HER ***,investigating interfacial water is extremely challenging owing to the interference caused by bulk water molecules and complexity of the interfacial ***,the behaviors of interfacial water in different cationic electrolytes on Pd surfaces were investigated by the electrochemistry,in situ core-shell nanostructure enhanced Raman spectroscopy and theoretical simulation *** spectral evidence reveals a red shift in the frequency and a decrease in the intensity of interfacial water as the potential is shifted in the positively *** comparing the different cation electrolyte systems at a given potential,the frequency of the interfacial water peak increases in the specified order:Li+

关键词： In situ Raman Interfacial water Hydrogen evolution reaction Cations

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Research on Pedestrian Gender Detection Based on A Lightweight Model 36

Research on Pedestrian Gender Detection Based on A Lightweig...

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36th Chinese control and Decision Conference, CCDC 2024

作者： Yuling, Hu Xinyi, Wang Weiguang, Zou Beijing University Of Civil Engineering And Architecture Institute Of Electrical And Information Engineering China Beijing University Of Civil Engineering And Architecture Beijing Key Laboratory Of Intelligent Processing For Building Big Data Beijing China

ISBN: (纸本)9798350387780

In recent years, a variety of emergency events have been occurring in buildings in countries. For there are some differences between male and female in movement ability and evacuation behavior, gender has a non-negligible impact on evacuation and decision-making of emergency events. Thus, it is of great practical significance to detect the gender of pedestrians at entrances or exits of buildings in real time to grasp the proportion of different gender people in buildings and to formulate emergency warning or evacuation strategies scientifically. In response to the requirement of rapid emergency response, this paper proposes a pedestrian gender detection method based on a lightweight object detection model YOLOX-Tiny. In addition, considering the inaccurate pedestrian gender detection caused by the change of pedestrian posture and camera angle, the attention mechanism CBAM is introduced to enhance the ability of filtering a large amount of redundant information, obtaining key information and acquiring pedestrian gender characteristic information. Through the experiments of self-made pedestrian gender datasets, the results show that the detection accuracy of the improved model is increased by 1.8%, and the detection speed reaches 251FPS, which can meet both speed and accuracy of pedestrian gender detection in emergencies. © 2024 IEEE.

关键词： Object detection

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Global information Edge Augmentation Network for Remote Sensing Image Continuous Super-Resolution 2

Global Information Edge Augmentation Network for Remote Sens...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Gao, Shize Lei, Yong Zhang, Jingya Wang, Guoqing Liu, Wenchao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China

ISBN: (纸本)9798331515669

The continuous-scale super-resolution (SR) method for remote sensing (RS) images has the potential to achieve flexible scale SR with a single network, representing a significant research area in the field of RS. However, the majority of prevailing methods learn features from localization, thereby neglecting the global semantic coherence of RS images. This results in unbalanced SR outcomes. Furthermore, the optimization strategy based on the multilayer perceptron (MLP) utilized in the implicit neural representation (INR) method results in blurred reconstructed image edges. To address these issues, a novel continuous-scale SR method for RS images, a global information edge augmentation network (GIEAN), is proposed. Initially, the global state space model (GSSM) aggregates the global information of the image from diverse perspectives and learns the contextual interactions at disparate locations of the image globally. Subsequently, the dual edge enhancement module (DEEM) learns the image body and edges independently through the main branch and edge branch, respectively, with the objective of enhancing the edge component of the SR results. Extensive experimental evaluation on the UCMerced dataset demonstrates the superiority of GIEAN over existing continuous-scale SR techniques. © 2024 IEEE.

关键词： Image enhancement

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Containment control Protocol Design for PDE Agents Subject to External Disturbances 42

Containment Control Protocol Design for PDE Agents Subject t...

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42nd Chinese control Conference, CCC 2023

作者： Chen, Yining School of Artificial Intelligence Tiangong University Tianjin300387 China Ministry of Education Key Laboratory of System Control and Information Processing Shanghai639798 China

ISBN: (纸本)9789887581543

This paper mainly solves the containment control issue for a network of infinite-dimensional agents described by wave equations. To eliminate influences caused by external disturbances, observers based on partial differential equations are proposed. A novel interaction protocol is then designed to reach containment control under a fixed directed communication topology. Different from the existing works, disturbances with unbounded derivatives are allowed in this paper. A proper Lyapunov function is constructed and the sufficient condition assuring containment control is obtained. Finally, the theoretical results are verified via numerical examples. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Lyapunov functions

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Building Segmentation from Remote Sensing Image via DWT Attention Networks 23

Building Segmentation from Remote Sensing Image via DWT Atte...

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5th International Conference on Pattern Recognition and intelligent Systems, PRIS 2023

作者： Wu, Yingbin Zhou, Mingquan Geng, Shengling Zhang, Dan College of Computer Qinghai Normal University Xining China College of Math and Information Yuncheng University Yuncheng China State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

ISBN: (纸本)9781450399968

The attention mechanism has been widely used and achieved good results in many visual tasks. But the calculations of attention mechanism in vision tasks consume huge spaces and times, which is the obvious disadvantage of this method. In order to alleviate this problem, we use the DWT(Discrete Wavelet Transform) method to reduce the complexity of attention calculation. DWT can transform an N-dimensional vector into two vectors, one is the low-frequency component of N/2 dimension and the other is high-frequency component of N/2 dimension too. We only use the low-frequency to calculate the attention matrixes, which can reduce the complexity of matrix multiplication, then the time and space consumption of the network is reduced significantly. We also find that the building segmentation in the remote sensing image is different from the other scene segmentation, that the sizes and numbers of different classes of the targets in the general scene images are obvious. Despite all this, our method is still applicable for the targets with large numbers and sizes in general scene images, but not for the targets with small sizes and numbers, and this view is also verified by the subsequent experiments on different datasets. We apply our method on three typical networks (Danet, Swin and Segmenter), and carry out comprehensive experiments on the Cityscape dataset and three building segmentation datasets (Inria Aerial Dataset, Massachusetts Buildings Dataset and Chinese Style Architecture Dataset). The experiments show that, our method is more suitable for building segmentation and can reduce the complexity of the model calculation in building segmentation, and the Mean IoU of segmentation results is not reduced clearly, some even improved. © 2023 ACM.

关键词： Remote sensing

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Model-based deep learning for fiber bundle infrared image restoration

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Defence Technology（防务技术） 2023年第9期27卷 38-45页

作者： Bo-wen Wang Le Li Hai-bo Yang Jia-xin Chen Yu-hai Li Qian Chen Chao Zuo Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense Nanjing University of Science and TechnologyNanjingJiangsu Province210094China Smart Computational Imaging(SCI)Laboratory Nanjing University of Science and TechnologyNanjingJiangsu Province210094China Science and Technology on Electro-Optical Information Security Control Lab Tianjin300308China

As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual *** to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio(SNR)imaging in fiber bundles,the iterative super-resolution reconstruction network based on a physical model is *** the constraint of solving the two subproblems of data fidelity and prior regularization term alternately,the network can efficiently“regenerate”the lost spatial resolution with deep *** building and calibrating a dual-path imaging system,the real-world dataset where paired low-resolution(LR)-high-resolution(HR)images on the same scene can be generated *** results on both the United States Air Force(USAF)resolution target and complex target objects demonstrate that the algorithm can restore high-contrast images without pixilated *** the basis of super-resolution reconstruction,compound eye image composition based on fiber bundle is also embedded in this paper for the actual imaging *** proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band,effectively promoting the engineering application of thermal radiation detection.

关键词： Fiber bundle Deep learning Infrared imaging Image restoration

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Pinning control-Based Reconstructibility Analysis and State Estimation for Boolean Networks

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Journal of Systems Science & Complexity 2025年

作者： YANG Junqi WANG Shangkun CHEN Yantao LI Zhiqiang School of Electrical Engineering and Automation Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment Henan Polytechnic University School of Mathematics and Information Science Henan University of Economics and Law

In this paper, the issues of both reconstructibility analysis and state estimation are investigated for locally reconstructible Boolean networks(BNs), where the pinning control is employed to design a special control sequence. First, this paper will focus on dealing with state unreconstructible cycle based on state set-estimation theory, and the concept of set-to-set reachability is proposed. Second, the locally reconstructible BNs are transformed to Boolean control networks(BCNs) by pinning control technique, where a logical matrix is designed by a special algorithm such that all states in the unreconstructible initial state set can evolve into the reconstructible state set, simultaneously. Third,a sufficient condition for the existence of a free Boolean control sequence is obtained, which guarantees that a locally reconstructible BN can be transformed into a reconstructible one. Besides, an optimization control algorithm is proposed to generate pinning control sequence and further optimize the reconstructibility of BNs. A kind of pinning control-based state estimation method is also ***, an example is provided to show the feasibility of the proposed methods.

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A Novel FL-AoI-Based control and Event-Triggered Strategy Integrating Network Characteristics for ICPS 49

A Novel FL-AoI-Based Control and Event-Triggered Strategy In...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Lu, Xuanzhao Xu, Qimin Zhang, Jinglong Lin, Meihan Chen, Cailian Shanghai Jiao Tong University Department of Automation Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China China

ISBN: (纸本)9798350331820

The joint design of control and transmission has been demonstrated to be a successful technique for enhancing the performance of industrial cyber-physical systems (ICPS). In the majority of existing works, the control cost and the transmission cost are defined independently, followed by a weighted total calculation. This approach suffers from a dimension consistency issue, leading the results to diverge from the system's actual optimal performance. Hence, it is necessary to consider the overall information of the loop and characterize the coupling relationship between control and transmission to construct the overall system performance function. This paper proposes a full loop age of information (FL-AoI) based control and transmission joint design architecture for multi-subsystem ICPS integrating multi-hop network. The state delay, input delay, and event trigger are all taken into consideration by FL-AoI to more fully portray the freshness of the information. We provide a novel control performance based on FL-AoI where the network characteristics are incorporated into the control cost, which could tackle the dimensionality mismatch brought by the form of weighted summation of the control and transmission cost. We also provide the FL-AoI-based strategy for the controller and event-triggering mechanism and derive the cost's boundary. The evaluation results demonstrate that, in comparison to the conventional joint design strategy, our solution increases the stability of the control system while decreasing the network burden. © 2023 IEEE.

关键词： Cyber Physical System

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