检索结果-内蒙古大学图书馆

Uncertainty-Guided Diffusion Model for Camouflaged Object Detection

IEEE Transactions on Multimedia 2025年

作者： Yang, Jinsheng Zhong, Bineng Liang, Qihua Mo, Zhiyi Zhang, Shengping Song, Shuxiang Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining & Security China Wuzhou University Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou543002 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China

Recently, diffusion models have significantly improved the performance of Camouflaged Object Detection (COD) by adding noise to a mask and iteratively denoising it to match the target distributions. Due to the direct extraction of features from noisy masks and the lack of conditional constraints on a prediction area, the diffusion model may deviate from a correct prediction range and produces mispredictions in regions with high uncertainty. To address this issue, we propose an uncertainty-guided diffusion model (UGDNet) for COD, which explicitly quantifies uncertainty and integrates it as an anchor condition into the diffusion models to provide an initialization of the diffusion regions. The core idea is first to utilize a probability representation and transformer to explicitly model uncertainty, aiming to identify areas where a model may generate overconfident mispredictions. Then, we use the uncertainty as an anchor condition to provide a reference prediction range for the diffusion model, guiding each step of the diffusion process. Furthermore, we use uncertainty to guide feature aggregation, prompting the model to pay extra attention to the semantic features of regions with high uncertainty to refine the segmentation results further. The experimental results indicate that our proposed UGDNet achieves higher accuracy than existing state-of-the-art models on five COD benchmarks, including COD10K, NC4K, CAMO, CHAMELEON, and CDS2K. © 1999-2012 IEEE.

关键词： Semantics

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Fabrication of lithium niobate metasurfaces via a combination of FIB and ICP-RIE

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Chinese Optics Letters 2022年第11期20卷 95-99页

作者： Chunyan Jin Wei Wu Lei Cao Bofeng Gao Jiaxin Chen Wei Cai Mengxin Ren Jingjun Xu Key Laboratory of Weak-Light Nonlinear Photonics Ministry of EducationSchool of Physics and TEDA Applied Physics InstituteNankai UniversityTianjin 300071China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin 300308China Collaborative Innovation Center of Extreme Optics Shanxi UniversityTaiyuan 030006China

Lithium niobate(LN)metasurfaces have emerged as a new platform for manipulating electromagnetic ***,we report a fabrication technique for LN nano-grating metasurfaces by combining focused ion beam(FIB)milling with inductively coupled plasma reactive ion etching(ICP-RIE).Steep sidewalls with angles larger than 80°are *** quasi-bound states in the continuum are observed from our *** measured transmission spectra show good agreement with the numerical simulations,confirming the high quality of the fabricated *** technique can be applied to fabricate the LN metasurfaces with sharp resonances for various applications in optical communications,on-chip photonics,laser physics,sensing,and so on.

关键词： lithium niobate metasurfaces fabrication FIB ICP-RIE

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Weapon Target Assignment for Surface-to-air Defense with an Improved Reinforcement Genetic Algorithm

Weapon Target Assignment for Surface-to-air Defense with an ...

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2021 China Automation Congress, CAC 2021

作者： Gong, Hua Liu, Dalong Zhang, Yong Xu, Ke School of Science Shenyang Ligong University Shenyang110159 China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China

ISBN: (纸本)9781665426473

Decision optimization in surface-to-air defense and coordinated weapon target assignment can improve the defense effect and the utilization rate of weapons. Considering the constraints of weapon capability, a multi-target optimization model is established to maximize the damage probability and minimize the weapon consumption cost. A dual population genetic algorithm improved by reinforcement learning is designed to solve this model. The chromosome exchange ratio is controlled by reinforcement learning to ensure population diversity. Experimental results verify the convergence speed and accuracy of the improved reinforcement genetic algorithm. © 2021 IEEE

关键词： Genetic algorithms

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Ground-to-air Defense Firepower Assignment Decision Based on Non-cooperative Game

Ground-to-air Defense Firepower Assignment Decision Based on...

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2021 China Automation Congress, CAC 2021

作者： Gong, Hua Cui, Mengya Zhang, Yong Sun, Wenjuan School of Science Shenyang Ligong University Shenyang110159 China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China

ISBN: (纸本)9781665426473

The key problem of ground-to-air defense is how to make accurate and effective firepower assignment decision for attacking targets. A multi-objective optimization model of ground-to-air defense firepower assignment is established with the aim of maximizing the damage probability and minimizing the weapon consumption, and non-cooperative game theory is used to solve the problem. A non-cooperative game model is established by mapping the two objectives to players, and weapon assignment schemes to strategy profiles. An improved tabu genetic algorithm based on Nash equilibrium is designed to get the assignment scheme. Experimental results show that the proposed method has better convergence and stability than the improved genetic algorithm based on tabu search for multi-objective optimization model. © 2021 IEEE

关键词： Genetic algorithms

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MGTN: Multi-scale Graph Transformer Network for 3D Point Cloud Semantic Segmentation

MGTN: Multi-scale Graph Transformer Network for 3D Point Clo...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Da Ai Siyu Qin Zihe Nie Hui Yuan Ying Liu Xi’an Key Laboratory of Image Processing Technology and Applications for Public Security Xi’an University of Posts and Telecommunications Xi’an China School of Communication and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

The structural similarity of point clouds presents challenges in accurately recognizing and segmenting semantic information at the demarcation points of complex scenes or objects. In this study, we propose a multi-scale graph transformer network (MGTN) for 3D point cloud semantic segmentation. First, a multi-scale graph convolution (MSG-Conv) is devised to address the limitations faced by existing methods when extracting local and global features of point cloud data with varying densities simultaneously. Subsequently, we employ a graph-transformer (G-T) module to enhance edge details and spatial position information in the point cloud, thereby improving recognition accuracy for small objects and confusing elements such as columns and beams. Extensive testing on ShapeNet parts and S3DIS datasets was conducted to demonstrate the effectiveness of MGTN. Compared to the baseline network DGCNN, our proposed MGTN achieves substantial performance improvements, as evidenced by notable increases in mIoU of 1.5% and 18.5% on the ShapeNet parts and S3DIS datasets respectively. Additionally, MGTN outperforms the recent CFSA- Net by 2.3% and 3.4% on OA and mIoU respectively.

关键词： Point cloud compression Three-dimensional displays Visual communication Convolution Semantic segmentation Semantics Transformers Feature extraction Data mining Testing

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Link reliability prediction for long-range underwater acoustic communications between gliders

Link reliability prediction for long-range underwater acoust...

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2021 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2021

作者： Zhang, Lan Feng, W. Li, Jianlong Zhu, Huijie Science and Technology on Communication Information Security Control Laboratory Jiaxing314000 China Research Institution of China Electronics Technology Group Corporation Jiaxing341000 China Zhejiang University Ocean College Zhoushan316021 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China

ISBN: (纸本)9781665439442

Channel modeling and the prediction of the reliability of acoustic link is the key to a successful deployment of underwater acoustic networks (UAN). In this paper, we build a Bellhop-based simulation framework driven by the environmental data to assess and predict the quality of the long-range and low-frequency communication links between the unmanned platforms in the deep ocean. The environmental data is measured in the South China Sea Experiment 2020 (SCSEx20). Results of channel modeling and link reliability prediction are reported in terms of the estimated channel impulse response (CIR), signal-to-noise ratio (SNR), and bit rate error (BER). The analysis shows how the communication performance of the physical level is dominated by the environmental data, aiming at evaluating the communication performance, and correlating the variation of the environmental conditions to the reliability of the entire communication link. © 2021 IEEE.

关键词： Underwater acoustic communication

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Dynamic Threat Assessment of Air multi-target Based on DBN-TOPSIS Method

Dynamic Threat Assessment of Air multi-target Based on DBN-T...

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2021 China Automation Congress, CAC 2021

作者： Gong, Hua Yu, Xiaoye Zhang, Yong Liu, Fang School of Science Shenyang Ligong University Shenyang110159 China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin300308 China

ISBN: (纸本)9781665426473

In the traditional air target threat assessment, only quantitative threat degree is considered, and the target threat range assessment is missing. Aiming at the problem, a comprehensive dynamic threat assessment method is designed in this paper. This comprehensive method considers two terms of the target threat: threat degree and threat range. In terms of threat degree assessment, a dynamic Bayesian network (DBN) and a technique for order preference by similarity to an ideal solution (TOPSIS) are fused. The multi-target fusion assessment method DBN-TOPSIS is proposed. This method maps the probability of multi-target threat level obtained by dynamic Bayesian network to TOPSIS evaluation matrix. This strategy solves the problem of multi-target threat ranking. In terms of threat range assessment, a grey prediction threat range assessment method GM(1,1) is obtained. This method solves the problem of small sample threat range assessment. The experimental results show that the multi-target fusion threat assessment method is reasonable, and the target threat range can directly reflect the change of target threat degree in airspace. © 2021 IEEE

关键词： Bayesian networks

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Modulation recognition of composite signal based on ResNet and frequency domain graph 3

Modulation recognition of composite signal based on ResNet a...

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2021 3rd International Conference on Artificial Intelligence and Computer science, AICS 2021

作者： Wang, Lijun Han, Yu Ge, Hongfang Zhao, Yongkuan Shen, Lei Science and Technology on Communication Information Security Control Laboratory Jiaxing China The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing China School of Communication Engineering Hangzhou Dianzi University Hangzhou China

Aiming at the problem of composite signal recognition in measurement and control system, a modulation recognition method of composite signal is proposed based on clipped internal modulation signal frequency image and residual network. Residual network has the advantages of fast convergence and high recognition accuracy, which is used to realize the recognition of different modulation modes and different bandwidth signals. The experimental results show that, compared with the traditional decision tree algorithm and the constellation based CNN recognition algorithm, the recognition performance of the proposed method is improved by more than 3dB for the variable bandwidth composite modulation signal set (BPSK-FM and QPSK-FM with internal modulation bandwidth of 128kHz, BPSK-FM and QPSK-FM with internal modulation bandwidth of 2kHz and 2FSK-FM with internal modulation bandwidth of 32kHz and 2kHz). © Journal of Physics: Conference Series 2021.

关键词： Quadrature phase shift keying

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Design Compact YOLO based Network for Small Target Detection on Infrared Image

Design Compact YOLO based Network for Small Target Detection...

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Chinese Automation Congress (CAC)

作者： Shuang Liu Zhicheng Liu Yuhai Li Wancheng Liu Chenyang Ge Longjun Liu Institute of Artificial Intelligence and Robotics (IAIR) Xi’an Jiaotong University Xi’an China Science and Technology on Electro-Optical Information Security Control Laboratory Tianjin China

most YOLO object detection neural networks prefer to focus on traditional RGB image, but previous studies rarely consider special YOLO network with compact architecture for infrared image. In this paper, we analyze original YOLO network architecture, and we propose a compact YOLO based network by using different blocks from previous work for small target detection on infrared image. We use small target detection layer, GhostConv convolution, Focus structure, Focal EIOU Loss and soft NMS modules to improve original YOLO network structure, which improves the accuracy and speed of target detection for infrared image. The experimental results show that the accuracy of target detection of original model can be effectively improved by adding the small target detection layer, the Focus structure and the BI-FPN structure. By replacing the GhostConv convolution, the calculation speed is significantly improved. At last, after the model is quantized and deployed with NCNN framework, the object detection speed can reached to 35ms per infrared image.

关键词： Automation Convolution Neural networks Object detection Network architecture

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Modulation recognition of composite modulation signal based on two-fold digital receiver and goodness of fit test

Modulation recognition of composite modulation signal based ...

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2021 IEEE International Conference on Electronic technology, Communication and information, ICETCI 2021

作者： Lijun, Wang Yu, Han Pan, Zhou Hongfang, Ge Lei, Shen Science and Technology on Communication Information Security Control Laboratory Jiaxing China The 36th Research Institute of China Electronics Technology Group Corporation Jiaxing China School of Communication Engineering Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781665432511

Aiming at the modulation recognition problem of mixed signal set {BPSK, QPSK, BPSK-FM, QPSK-FM} with unknown parameters, this paper proposes a modulation recognition algorithm based on two-fold PLL (phase locked loop) and goodness of fit test. The PLL is used to extract the features of the composite modulation signal, and the goodness of fit test is used to measure the distance of the phase cumulative distribution of the demodulation information. Theoretical analysis and simulation results show that the performance of the recognition method based on two-fold PLL and goodness of fit test is better than the widely used modulation recognition method based on constellation. ©2021 IEEE.

关键词： Phase locked loops

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