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Infrared Dim Small Target Detection Algorithm Based on Adaptive Attention Transformer Network

Journal of Shanghai Jiaotong University (Science) 2025年 1-13页

作者： Ding, Leqi Xia, Yilin Wang, Biyun Cai, Yunze Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China National Key Laboratory of Air-based Information Perception and Fusion Henan Luoyang471009 China Shanghai Institute of Aerospace Control Technology Infrared Detection Technology R & D Center of China Aerospace Science and Technology Corporation Shanghai201109 China

This paper presents a novel algorithm for detecting infrared dim small targets in complex backgrounds by integrating both global and local feature information. The proposed method employs an adaptive attention transformer network which addresses challenges associated with computational complexity and information redundancy in traditional self-attention mechanisms. A global context aggregation module is designed to address the loss of small target features in high-level feature maps. This module compensates for spatial distribution information during the multi-scale feature fusion process, ensuring the preservation of target features. Comparison experiments were conducted on two datasets, NUAA-SIRST and IRSTD-1k. The proposed algorithm achieved 0.784 for mIoU and 0.878 for F-measure on NUAA-SIRST, 0.632 for mIoU and 0.807 for F-measure on IRST-1k, all of which are the highest values. The proposed algorithm shows a significant improvement over other existing detection algorithms, demonstrating its capability to accurately extract both local and global features of targets with high recall rates and low false alarm rates across various scenarios. The ablation study results highlight the significant improvements contributed by the key modules to the proposed algorithm. (Figure presented.) © Shanghai Jiao Tong University 2025.

关键词： A adaptive attention global context aggregation module infrared dim small target detection TP391.4 Transformer network

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Enhanced Tube-Based Event-Triggered Stochastic Model Predictive Control With Additive Uncertainties

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 596-605页

作者： Chenxi Gu Xinli Wang Kang Li Xiaohong Yin Shaoyuan Li Lei Wang the School of Control Science and Engineering Shandong University IEEE the School of Electronic and Electrical Engineering University of Leeds the Qingdao University of Science and Technology the Department of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China

This paper proposes an event-triggered stochastic model predictive control for discrete-time linear time-invariant(LTI) systems under additive stochastic disturbances. It first constructs a probabilistic invariant set and a probabilistic reachable set based on the priori knowledge of system *** with enhanced robust tubes, the chance constraints are then formulated into a deterministic form. To alleviate the online computational burden, a novel event-triggered stochastic model predictive control is developed, where the triggering condition is designed based on the past and future optimal trajectory tracking errors in order to achieve a good trade-off between system resource utilization and control performance. Two triggering parameters σ and γ are used to adjust the frequency of solving the optimization problem. The probabilistic feasibility and stability of the system under the event-triggered mechanism are also examined. Finally, numerical studies on the control of a heating, ventilation, and air conditioning(HVAC) system confirm the efficacy of the proposed control.

关键词： Event-triggered mechanism heating, ventilation, and air conditioning (HVAC) control probabilistic reachable set stochastic model predictive control

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Superatomic catalyst for efficient nitric oxide reduction using Al8O3 superatom-oxide framework

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Science China Chemistry 2025年第5期68卷 2096-2100页

作者： Kaidong Shen Jiuqi Yi Chang Xu Qiquan Luo Xiaojun Wu Jinlong Yang Department of Chemistry Anhui University School of Chemistry and Materials Sciences University of Science and Technology of China Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China Innovation Center of Chemistry for Energy Materials (iChEM) University of Science and Technology of China

For cluster catalysts,reducing their size to single atoms gives rise to precise and high-selective catalytic performance,at the cost of losing some ***,the entirety with atom-like electronic shells and fine-tunable properties as clusters,are promising candidates for cluster ***,we predicted a superatom-assembled two-dimension Al8O3superatom-oxide framework（SOF） using first principles calculation,where the Al8core comprises two 8-electron Al4superatoms and further linked by oxygen atoms in a graphene-like lattice,resulting in porous and stable *** Al8O3-SOF serves as an efficient superatomic catalyst for nitric oxide（NO） reduction reaction,where the Al4superatomic unit acts cohesively as the active site throughout the catalytic process and its superatomic P orbital plays an important role in activating NO ***,the catalytic activity of Al8O3-SOF increases when the two central Al atoms of the Al8core are replaced by Ga atoms,reducing the limiting potential to -0.48 V comparable to that of the reported Pt（100）.Our work proposes a novel series of superatomic catalysts and reveals the superatomic behavior in the catalytic process,providing references for the development of efficient heterogeneous catalysts.

关键词： superatomic catalyst cluster-assembling nitric oxide reduction first principles calculation

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Degree of cross-polarization of elliptical Gaussian partially coherent beams propagating in turbulent atmosphere

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Applied Optics 2025年第14期64卷 4040-4049页

作者： Han, Meimiao Ke, Xizheng School of Automation and Information Engineering Xi'an University of Technology Shaanxi Xi'an710048 China Shaanxi Key Laboratory of Intelligent Collaborative Network for Civil-Military Cooperation Shaanxi Xi'an710048 China

The cross-spectral density matrix for elliptical Gaussian partially coherent beams propagating along a slant path in non-Kolmogorov turbulence is derived based on the generalized Huygens-Fresnel principle and the quadratic approximation of the Rytov phase structure function. The spectral degree of cross-polarization (SDCP) between two distinct points of elliptical Gaussian partially coherent beams is analyzed for different source parameters, turbulence inner and outer scales, and zenith angles, and is compared with the classical degree of polarization (CDOP) when the two points coincide. The results show that the variations in the SDCP of an elliptical Gaussian partially coherent beam are more pronounced. As the relative distance between two points grows, the SDCP exhibits a "self-recovering" evolutionary trend with the off-axis distance initially declining, then rising, and ultimately stabilizing at a constant value. This stable value decreases with higher amplitude ratio and zenith angle but increases with longer coherence length and larger turbulence inner scale. Additionally, the impact of atmospheric turbulence on the SDCP becomes more significant over longer transmission distances. Finally, Monte Carlo simulations were used to validate the theoretical results. It is noted that the results have established a sound theoretical basis on the topic of improving the performance of free-space optical communication. © 2025 Optica Publishing Group.

关键词： Monte Carlo methods

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A Multiscale Convolution SAR Image Target Recognition Method Based on Complex-Valued Neural Networks

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 10657-10673页

作者： Hou, Guangyu Xin, Zhihui Liao, Guisheng Huang, Penghui Huang, Yuhao Zou, Rui Yunnan Normal University Yunnan Key Laboratory of Opto-Electronic Information Technology Kunming650500 China Xidian University National Laboratory of Radar Signal Processing Xi'an710071 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China

Recent advances in deep learning have driven significant success in synthetic aperture radar (SAR) automatic target recognition, particularly through convolutional neural network (CNN) based classification algorithms. However, SAR images possess distinctive physical scattering properties, owing to their unique imaging mechanism. Many deep learning algorithms rely solely on amplitude information, ignoring phase information, which may result in the loss of information in the original complex-valued SAR image and suboptimal performance. To tackle these problems, this article introduces a SAR target recognition approach based on complex-valued operations, designated as complex-valued residual mish activation and convolution block attention module (CBAM) net (CRMC-Net). The CRMC-Net effectively utilizes the amplitude and phase information in complex-valued SAR data. Specifically, first, the elements of CNN, including the input and output layers, the convolution layers, the activation functions, and the pooling layers, are extended to the complex-valued domain. Second, in order to further enhance the representation ability of the model, multiscale information of the target is extracted through different convolution kernel sizes. Finally, the network constructs many complex-valued operation blocks to enhance the robustness of the designed network, including the complex-valued residual block, complex-valued Mish activation function, and complex-valued CBAM. The experimental results obtained from the moving and stationary target capture and recognition dataset and OpenSARShip2.0 dataset demonstrate that the proposed network model outperforms the traditional real-valued models. It can further reduce the classification error and enhance performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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A Method for Segmenting Grain Particles in Hyperspectral Images

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IEEE Sensors Journal 2025年第10期25卷 17617-17630页

作者： Yang, Zhen Niu, Yuhang He, Tingting Jiang, Huawei Zhao, Like Ministry of Education Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Henan University of Technology Zhengzhou450001 China Ministry of Education Zhengzhou450001 China Advanced Laser Technology Laboratory of Anhui Province Hefei230000 China

Hyperspectral imaging, known for its high spectral resolution and nondestructive detection characteristics, has been widely applied in grain quality evaluation. Grain quality evaluation utilizing near-infrared hyperspectral images often uses individual particles as the evaluation unit and thus relies on the segmentation of particles. However, widely applicable methods for the segmentation of grain particles remain inadequate because of the challenge of segmenting adhesive particles. Therefore, this study introduces a new method designed to significantly improve the accuracy of segmenting grain particles. This method considers the shape characteristics of grains to correct the oversegmentation in watershed segmentation caused by adhesive particles. The feasibility of the method was tested by applying it to three grains: corn, wheat, and peanuts. Results demonstrated that the new method performed significantly better than watershed segmentation, with accuracies of no less than 90% for the three grains. The new method has the potential to support various studies related to grain quality evaluation, such as mold identification and moisture detection of individual particles. © 2001-2012 IEEE.

关键词： Image segmentation

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Spatial-temporal distribution and emission of urban scale air pollutants in Hefei based on Mobile-DOAS

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Journal of Environmental Sciences 2025年第5期151卷 238-251页

作者： Zhidong Zhang Pinhua Xie Ang Li Min Qin Jin Xu Zhaokun Hu Xin Tian Feng Hu Yinsheng Lv Jiangyi Zheng Youtao Li School of Environmental Science and Optoelectronic Technology University of Science and Technology of ChinaHefei 230026China Key Laboratory of Environmental Optics and Technology Anhui Institute of Optics and Fine MechanicsHefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China University of Chinese Academy of Sciences Beijing 100049China Information Materials and Intelligent Sensing Laboratory of Anhui Province Institutes of Physical Science and Information TechnologyAnhui UniversityHefei 230601China

As a significant city in the Yangtze River Delta regions,Hefei has experienced rapid changes in the sources of air pollution due to its high-speed economic development and urban ***,there has been limited research in recent years on the spatial-temporal distribution and emission of its atmospheric *** address this,this study conducted mobile observations of urban roads using the Mobile-DOAS instrument from June 2021 to May *** monitoring results exhibit a favourable consistent with TROPOMI satellite data and ground monitoring station ***,there were pronounced seasonal variations in air ***,high concentration of HCHO and NO_(2)were closely associated with traffic congestion on roadways,while heightened SO_(2)levels were attributed to winter heating and industrial *** study also revealed that with the implementation of road policies,the average vehicle speed increased by 95.4%,while the NO concentration decreased by 54.4%.In the estimation of urban NO_(x)emission flux,it was observed that in temporal terms,compared with inventory data,the emissions calculated viamobile measurements exhibitedmore distinct seasonal patterns,with the highest emission rate of 349 g/sec in winter and the lowest of 142 g/sec in *** spatial terms,the significant difference in emissions between the inner and outer ring roads also suggests the presence of the city’s primary NO_(x)emission sources in the area between these two *** study offers data support for formulating the next phase of air pollution control measures in urban areas.

关键词： Mobile-DOAS HCHO NO_(2) SO_(2) Spatial-temporal distribution NOx emission

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Image reconstruction based on Graph structure for MRI-guided diffuse optical tomography 17

Image reconstruction based on Graph structure for MRI-guided...

引用

17th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Zou, Peiwen Wei, Chengpu Hu, Ting Li, Zhe Sun, Zhonghua Jia, Kebin Feng, Jinchao Beijing Key Laboratory of Computational Intelligence and Intelligent System School of Information Science and Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

Diffuse optical tomography (DOT) is a non-invasive, label-free imaging technique widely used in applications such as breast cancer diagnosis and brain imaging. It allows for the quantitative measurement of tissue functional parameters, including the concentrations of oxyhemoglobin, deoxyhemoglobin, and water. However, the quality of reconstructed images is poor due to light scattering. To address this challenge, one effective strategy is to incorporate anatomical information from high-resolution imaging to guide DOT reconstruction. In this study, a new approach (GRI) is developed to leverage MRI images based on graph structure for DOT reconstruction. And its feasibility and effectiveness were evaluated with numerical simulations. The results indicate that GRI significantly improves the structural similarity index (SSIM) of reconstructed total hemoglobin (HbT) images, outperforming conventional Tikhonov regularization and direct regularization imaging (DRI) method by more than 63% and 82%, respectively. These findings are further supported by experiments using real patient data, which demonstrate GRI's potential in breast cancer diagnosis. © 2025 SPIE.

关键词： Optical tomography

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Pixel-Level Multidirectional Image Sharpness Linear Assessment for Optical Image Stabilizer Performance Monitoring

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IEEE Sensors Journal 2025年第9期25卷 15204-15215页

作者： Zhou, Yuhang Zhang, Zhe Yang, Bokang Ma, Jie Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology China-EU Institute for Clean and Renewable Energy Wuhan430074 China

Linear quantification of blur intensity in different directions of an image and blur-type classification are crucial for the detection of anomalies and optimization calibration of widely used optical image stabilizer (OIS). However, previous image quality assessment methods mainly focused on isotropic blur, emphasizing the correlation with subjective ratings, making it difficult to provide multidirectional linear sharpness assessment. Moreover, they invariably offer a single evaluation score regardless of the kind of fuzziness. In this article, we propose a pixel-level multidirectional image sharpness (PMIS) linear assessment method that, to our knowledge, is the first to provide the capability to linearly quantify blur across multiple directions and distinguish blur types in an end-to-end manner while assessing the degree of image blur. We introduce a novel method for extracting edge information to characterize image blur, significantly enhancing correlation with the human visual system (HVS) in blur perception by filtering out high-frequency edge noise. Uniform edge block selection and data postprocessing are introduced to adapt to HVS characteristics and enhance robustness. Using blur results from four different directions and simply setting thresholds, we are able to achieve an 84.5% classification accuracy in distinguishing between defocus blur, motion blur, and clear images. In addition, we creatively make a motion blur image quality (MBIQ) database, accurately representing motion blur through the concrete physical quantity of rotational speed. Experimental results confirm that PMIS achieves significant improvements over the previous methods especially on databases with anisotropic motion blur. © 2025 IEEE.

关键词： information filtering

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Fuzzy Terminal Sliding-Mode Control With Adaptive Switching Gain

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IEEE Transactions on Fuzzy Systems 2025年第5期33卷 1499-1509页

作者： Feng, Cheng-Cheng Sun, Hao-Yuan Han, Hong-Gui Du, Sheng-Li Beijing University of Technology School of Information Science and Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Laboratory for Smart Environmental Protection Engineering Research Center of Digital Community Ministry of Education Beijing Artificial Intelligence Institute Beijing100124 China

For complex dynamic systems, it is a great challenge to design an appropriate control strategy to achieve stable tracking control. To solve this problem, a fuzzy terminal sliding-mode control method with adaptive switching gain (FTSC-ASG) is designed to improve the control performance in this article. First, an adaptive interval type-2 fuzzy prediction model (AIT2-FPM) is designed to approximate the complex nonlinearity and parameter uncertainty. Specifically, the state prediction error evaluated by predictor is utilized to design the weight adaptive law to improve the accuracy of the AIT2-FPM. Second, a terminal sliding-mode controller is utilized to obtain control input of the system in a finite time. To suppress the chattering phenomenon, the switching gain is designed based on the output of AIT2-FPM and the changes of control input information can be utilized to evaluate the degree of chattering. Furthermore, a parameter adaptive strategy is designed to adjust the parameter of equivalent control law. Third, FTSC-ASG can achieve fast convergence of tracking error, and the finite time stability of FTSC-ASG is proven in detail. Finally, the simulation studies in the third-order nonlinear system and the inverted pendulum systems are given to evaluate the effectiveness of FTSC-ASG. © 1993-2012 IEEE.

关键词： Sliding mode control

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