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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Qiao, Tingting Chen, He Chai, Bingqian Liu, Wenchao Qi, Guijie Xie, Yizhuang Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Recent advancements in remote sensing object detection have progressed, but distinguishing objects from complex backgrounds remains difficult, especially for tiny objects. To address this, we introduce the Advanced Scale-Aware Attention (ASA) module, which retains key channel information while minimizing computational cost. This module reorganizes channels into the batch dimension and splits them into multiple sub-features. The ASA module contains three branches: the first encodes weights based on scale and channel relationships. The second and third branches use different convolution kernel sizes to capture features across various receptive fields. These branches collaborate to enhance pixel-level relationships, improving foreground-background contrast. On the AI-TOD dataset, our method improved AP50 by 8.6% over state-of-the-art approaches. © 2024 IEEE.

关键词： Object recognition

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Image Editing based on Diffusion Model for Remote Sensing Image Change Captioning 2

Image Editing based on Diffusion Model for Remote Sensing Im...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Cai, Miaoxin Chen, He Li, Can Gan, Shuyu Chen, Liang Zhuang, Yin Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Remote Sensing Image Change Captioning (RSICC) is a task that utilizes natural language to describe changes in remote sensing images of the same area captured at different times. However, the significant temporal intervals between multi-temporal images, the infrequency of observable changes, and the limitations on observation locations make it difficult to acquire and annotate a large and diverse dataset for analyzing change in multi-temporal images. The scarcity of labeled data hinders the training of RSICC models, leading to poor generalization. Compared to annotated registered bi-temporal image, single-temporal data is easier to obtain. Therefore, to tackle the issue of poor generalization of RSICC models under limited annotated sample conditions, a text-guided image pairs generation (TGIPG) method is proposed to create synthetic RSICC datasets from single-temporal data and randomly sampled text instructions via a diffusion-based controllable image editing model. This approach generates more valid pairs of multi-temporal samples to address the constraints of limited change information. Specifically, this method utilizes language instructions to introduce change information into the diffusion process, gradually transforming the pre-phase image into the post-phase image. Our experiments on the LEVIR-CC dataset show that synthetic data can significantly enhance the performance of any RSICC model, with a restricted number of training samples, by employing this plug-and-play TGIPG method. © 2024 IEEE.

关键词： Spatio-temporal data

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MDFNet: Multimodal Feature Decomposition and Fusion Network for Multimodal Remote Sensing Image Semantic Segmentation 2

MDFNet: Multimodal Feature Decomposition and Fusion Network ...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Wei, Tianyu Chen, He Wang, Jue Liu, Wenchao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

The semantic segmentation of multimodal remote sensing (RS) images, utilizing optical and synthetic aperture radar (SAR) data, has raised attention in recent studies. Advanced studies adaptively modeled and fused modality-share and modality-specific information beneficial for segmentation and achieved competitive performance. However, in challenging scenarios such as low image contrast or blurred textures in optical images and speckle noises or foreshortening in SAR images, modality-specific information may lost. This study proposed a multimodal feature decomposition and fusion network (MDFNet) designed for multimodal RS image semantic segmentation using optical and SAR images. By decomposing multimodal features into modality-share and modality-difference features and applying gradient descent on modality-difference features, modality-specific information beneficial for segmentation can be retained. Specifically, we designed a MDF decoder with MDF blocks. MDF block maps multimodal features into the same feature space and calculates the difference of multimodal features to obtain modality-share and modality-difference features respectively, then fuses these features. MDF decoder optimizes modality-difference features by gradient descent using adaptive modeling, thereby retaining modality-specific information that is beneficial for segmentation. Comprehensive experiments conducted on the DFC20 dataset demonstrated that the proposed MDFNet surpasses representative methods. © 2024 IEEE.

关键词： Semantic Segmentation

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SC-DVNet: A Dual-Branch Recognition Network for Synthetic Aperture Radar Images Based on Scattering Features 2

SC-DVNet: A Dual-Branch Recognition Network for Synthetic Ap...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Chen, Fan Shi, Hao He, Jingfei Yin, Yifei Meng, Fanyu Chen, Liang Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Ship classification using Synthetic Aperture Radar (SAR) images is a crucial component in marine monitoring. With the rise of deep neural networks (DNN), the abstract feature maps obtained by CNN-based methods have significantly improved classification performance. However, most current DNN-based methods are predominantly inherited from optical imagery, designed only for SAR intensity images, which may present challenges for further improving recognition performance. Therefore, this paper proposes using the point Scattering Center (SC) model for scattering feature extraction combined with graph convolutional networks for ship classification tasks. Comprehensive ablation experiments were carried out to demonstrate the efficacy of the proposed approach. Finally, experimental results on OpenSARShip1.0 dataset verify that the performance of the suggested approach notably outperforms DNN-driven techniques. © 2024 IEEE.

关键词： Deep neural networks

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Maximum ladle shell temperature prediction based on GABP neural network

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International Journal of Wireless and Mobile Computing 2023年第2期24卷 120-126页

作者： Sun, Ying Huang, Peng Tao, Bo Yun, Juntong Zhao, Guojun Liu, Xin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China

intelligent manufacturing is the main development trend of today's manufacturing industry, and talents are the first resource. Through the analysis of the current situation of cultivating talents in mechanical engineering in colleges and universities, it is found that most students of this major have difficulty in involving in knowledge of other fields outside their specialties, and their knowledge structure is relatively single. In response to the above problems, this paper proposes the training mode of multidisciplinary cross-integration of talents in mechanical engineering, which is analysed through the study of maximum temperature prediction of steel ladle shell. The BP neural network based on the improved genetic algorithm is trained on the experimental data samples to achieve the maximum temperature prediction of ladle shell under different thickness combinations of insulation layer, safety layer and working layer. By learning the knowledge of target prediction, students' overall development is promoted. © 2023 Inderscience Enterprises Ltd.

关键词： Forecasting

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Distributed Resilient Fusion Filtering for Nonlinear Systems with Random Sensor Delays:Optimized Algorithm Design and Boundedness Analysis

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Journal of Systems science & Complexity 2023年第4期36卷 1423-1442页

作者： HU Jun HU Zhibin DONG Hongli LIU Hongjian School of Automation Harbin University of Science and TechnologyHarbin 150080China Department of Mathematics Harbin University of Science and TechnologyHarbin 150080China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and TechnologyHarbin 150080China Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum UniversityDaqing 163318China School of Mathematics-Physics and Finance Anhui Polytechnic UniversityWuhu 241000China

This paper is concerned with the distributed resilient fusion filtering(DRFF)problem for a class of time-varying multi-sensor nonlinear stochastic systems(MNSSs)with random sensor delays(RSDs).The phenomenon of the RSDs is modeled by a set of random variables with certain statistical *** addition,the nonlinear function is handled via Taylor expansion in order to deal with the nonlinear fusion filtering *** aim of the addressed issue is to propose a DRFF scheme for MNSSs such that,for both RSDs and estimator gain perturbations,certain upper bounds of estimation error covariance(EEC)are given and locally minimized at every sample *** the light of the obtained local filters,a new DRFF algorithm is developed via the matrix-weighted fusion ***,a sufficient condition is presented,which can guarantee that the local upper bound of the EEC is ***,a numerical example is provided,which can show the usefulness of the developed DRFF approach.

关键词： Distributed resilient fusion filtering matrix-weighted fusion nonlinear time-varying systems random sensor delays

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LucidGrasp: Robotic Framework for Autonomous Manipulation of laboratory Equipment with Different Degrees of Transparency via 6D Pose Estimation

LucidGrasp: Robotic Framework for Autonomous Manipulation of...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Makarova, Maria Trinitatova, Daria Liu, Qian Tsetserukou, Dzmitry Intelligent Space Robotics Laboratory Moscow121205 Russia Dalian University of Technology Department of Computer Science and Technology China

ISBN: (纸本)9781665481090

Many modern robotic systems operate autonomously, however they often lack the ability to accurately analyze the environment and adapt to changing external conditions, while teleoperation systems often require special operator skills. In the field of laboratory automation, the number of automated processes is growing, however such systems are usually developed to perform specific tasks. In addition, many of the objects used in this field are transparent, making it difficult to analyze them using visual channels. The contributions of this work include the development of a robotic framework with autonomous mode for manipulating liquid-filled objects with different degrees of transparency in complex pose combinations. The conducted experiments demonstrated the robustness of the designed visual perception system to accurately estimate object poses for autonomous manipulation, and confirmed the performance of the algorithms in dexterous operations such as liquid dispensing. The proposed robotic framework can be applied for laboratory automation, since it allows solving the problem of performing non-trivial manipulation tasks with the analysis of object poses of varying degrees of transparency and liquid levels, requiring high accuracy and repeatability. © 2024 IEEE.

关键词： Remote control

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TransCGAN-based Parameter Extraction Framework for SAR Image Simulation 2

TransCGAN-based Parameter Extraction Framework for SAR Image...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Deng, Sidan He, Jingfei Mao, Yongfei Zhao, Liangbo Chen, Liang Shi, Hao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing Innovation Center Chongqing China 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 Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing Innovation Center Beijing China

ISBN: (纸本)9798331515669

Synthetic Aperture Radar (SAR) images have a wide range of applications due to their all-weather and all-day working conditions. However, SAR images with different scenarios and imaging conditions are insufficient or even rare, which is required in specific SAR image tasks. Fortunately, SAR image simulation technology can provide ample simulated images under these conditions at a low cost, addressing the scarcity of specific real SAR data. Accurate parameters are crucial for obtaining high-quality simulated images. However, it is time-consuming and labour-intensive to adjust parameters manually, and it often fails to achieve accurate simulation parameters. To tackle this problem, this paper proposes a TransCGAN-based SAR image simulation method that combines deep learning with traditional methods. By training TransCGAN, a conditional generative adversarial network (CGAN) integrated with Transformer architecture, a mapping between SAR images and simulation parameters is established. This enables the extraction of simulation parameters directly from the real SAR image, guided by the corresponding real SAR image. Ultimately, the parameters are subsequently converted into simulated SAR images via simulation software. Experimental results demonstrate that our TransCGAN-based method can effectively extract accurate simulation parameters from real SAR images, resulting in simulated images holding high similarity to real images. © 2024 IEEE.

关键词： Generative adversarial networks

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Relaxed Stability Criteria for Time-Delay Systems:A Novel Quadratic Function Convex Approximation Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 996-1006页

作者： Shenquan Wang Wenchengyu Ji Yulian Jiang Yanzheng Zhu Jian Sun College of Electrical and Electronic Engineering Changchun University of TechnologyChangchun 130012China the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China Key Laboratory of Complex Systems Intelligent Control and Decision Beijing Institute of TechnologyBeijing 100081China

This paper develops a quadratic function convex approximation approach to deal with the negative definite problem of the quadratic function induced by stability analysis of linear systems with time-varying *** introducing two adjustable parameters and two free variables,a novel convex function greater than or equal to the quadratic function is constructed,regardless of the sign of the coefficient in the quadratic *** developed lemma can also be degenerated into the existing quadratic function negative-determination(QFND)lemma and relaxed QFND lemma respectively,by setting two adjustable parameters and two free variables as some particular ***,for a linear system with time-varying delays,a relaxed stability criterion is established via our developed lemma,together with the quivalent reciprocal combination technique and the Bessel-Legendre *** a result,the conservatism can be reduced via the proposed approach in the context of constructing Lyapunov-Krasovskii functionals for the stability analysis of linear time-varying delay ***,the superiority of our results is illustrated through three numerical examples.

关键词： Equivalent reciprocal combination technique quadratic function convex approximation approach stability timevarying delay

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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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