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arXiv 2024年

作者： Zhong, Jiafeng Li, Bin Yi, Jiangyan Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China

The task of partially spoofed audio localization aims to accurately determine audio authenticity at a frame level. Although some works have achieved encouraging results, utilizing boundary information within a single model remains an unexplored research topic. In this work, we propose a novel method called Boundary-aware Attention Mechanism (BAM). Specifically, it consists of two core modules: Boundary Enhancement and Boundary Frame-wise Attention. The former assembles the intra-frame and inter-frame information to extract discriminative boundary features that are subsequently used for boundary position detection and authenticity decision, while the latter leverages boundary prediction results to explicitly control the feature interaction between frames, which achieves effective discrimination between real and fake frames. Experimental results on PartialSpoof database demonstrate our proposed method achieves the best performance. The code is available at https://***/media-sec-lab/BAM. Copyright © 2024, The Authors. All rights reserved.

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Retinal Vessel Segmentation Based on UNet and Edge Detection 12

Retinal Vessel Segmentation Based on UNet and Edge Detection

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12th International Conference on information Technology in Medicine and Education, ITME 2022

作者： Yan, Peixin Li, Zuoyong Hu, Rong Cao, Xinrong Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Colloge of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

Retinal vessel segmentation is important for the diagnosis of many diseases. The segmentation of microscopic vessels is one of the challenges because of the complex structure of retinal vessels, which makes it challenging to tell the vessels apart from the background. In order to improve the segmentation performance of retinal vessels, we use the edge detection method of HED to optimize the segmentation effect of UNet. Using Hybrid Dilated Convolution not only increases the perceptual field, but also avoids the loss of information continuity caused by the continuous use of dilated convolution. The Pyramid Pooling Module is used at the end of the encoder to compensate for the information loss caused by downsampling. Finally, the output of edge detection and image segmentation is combined as the final result. The model is evaluated in two mainstream datasets DRIVE and CHASE-DB1, and the experimental results show that the proposed model can improve the performance of retinal vessel segmentation. © 2022 IEEE.

关键词： Convolution

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Resonant Mode Recognition with Convolutional Neural Network 15

Resonant Mode Recognition with Convolutional Neural Network

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15th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2023

作者： Xiao, Yuqi Lu, Kai Leung, Kwok Wa Leung, Chi-Sing City University of Hong Kong Department of Electrical Engineering Hong Kong Sun Yat-sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou China City University of Hong Kong State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering Hong Kong CityU Shenzhen Research Institute Information and Communication Technology Centre Shenzhen China

ISBN: (纸本)9798350338874

This research proposes a simple approach to recognize resonate modes in rectangular dielectric resonator antennas (DRAs) with machine learning (ML) techniques. The proposed method utilizes a convolutional neural network (CNN) with a focal loss function to classify various modes present in the antenna. DRAs with different dimensions are simulated with ANSYS HFSS to obtain their electric fields at various resonant frequencies. The resulting dataset comprises 700 fields, manually labeled according to their corresponding resonant modes. After training the CNN model using this dataset, it is capable of accurately classifying 12 low-order resonant modes in rectangular DRAs. The performance of the model is evaluated using a test set, where it achieves a recognition accuracy of 97.8%. © 2023 IEEE.

关键词： Machine learning

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios Engineering

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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An image enhancement algorithm based on multi-scale Retinex theory to improve the images quality of sensors 12

An image enhancement algorithm based on multi-scale Retinex ...

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SPIE 12th International Symposium on Multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Li, Yiyao Chen, Zhong Sun, Wenyuan School of Journalism and Communication Central China Normal University Luoyu Road 152 Wuhan China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Luoyu Road 1037 Wuhan China National Key Laboratory of Science and Technology on Multi-spectral Information Processing Luoyu Road 1037 Wuhan China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Luoyu Road 1037 Wuhan China Central China Normal University Library Luoyu Road 152 Wuhan China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

Nowadays, image recognition plays a pivotal role in acquiring data via sensors. However, the adaptability of traditional algorithms is hindered by the unpredictable nature of open environments, varying sensor quality, and image dimensions. Challenges arise in adverse conditions like inclement weather, low light, and optical distortions. Retinex-based methods have emerged as a viable solution, effectively enhancing images plagued by shadows or poor lighting. Yet, issues surface when images possess saturated colors;the conventional multi-scale Retinex with color restoration risks color inversion. Moreover, during gain compensation, extreme histogram values occupy significant gray level space, obscuring vital image details. This study delves into these challenges and proposes an enhanced multi-scale Retinex algorithm. Our approach substitutes logarithmic functions with tansig functions, eliminating color inversion risks. Additionally, a novel gain compensation method, integrating histogram stretching with Gamma correction, refines image clarity. The algorithm's robustness is evidenced in diverse scenarios, including adverse weather, low light, underwater imaging, and non-uniform lighting. Experimental results validate our method's superiority, surpassing other Retinex-based techniques both qualitatively and quantitatively. This research contributes valuable insights into image enhancement methodologies, fostering advancements in sensor-based data gathering in Smart Spaces. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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Fire Detection Based on Improved Feature Fusion 13

Fire Detection Based on Improved Feature Fusion

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13th International Conference on information Technology in Medicine and Education, ITME 2023

作者： Cao, Xinrong Wu, Jincai Li, Zuoyong College of Computer and Control Engineering Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China School of Electronic Electrical Engineering and Physics Fujian University of Technology Fuzhou350118 China Sichuang Technology Co. Ltd. Fuzhou350100 China

ISBN: (纸本)9798350319156

Fire is a serious disaster that endangers life and property worldwide. Accurate and prompt fire detection can provide early warning information for firefighting efforts. Deep learning-based object detection algorithms have proven to be useful in the field of fire detection. However, fire detection is a particularly challenging task, due to the low-resolution of fire images and the presence of blurring and deformation in the targets. Existing fire detection methods are limited by poor detection accuracy and slow speed in complex fire scenarios. To address these challenges, we propose a fire detection algorithm based on improved YOLOv5s. Firstly, the attention module is embedded into the network structure. We introduce this attention module into the last layer of the backbone to enhance feature extraction ability. Secondly, we modify the Neck structure to fully fuse different feature maps. Through fusing extra low-level features from the backbone, it utilizes various receptive fields to enhance the expression of feature. Thirdly, SIoU is introduced to achieve faster convergence during the training. SIoU take into account the direction of the mismatch to redefine penalty indicators. The experimental results show that our proposed algorithm outperforms commonly used models. Even compared to specific fire detectors, our model exhibits certain advantages. © 2023 IEEE.

关键词： Fires

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Explicit Predictive control of Forward DC-DC Converter Based on an Input-Mapping Method

Explicit Predictive Control of Forward DC-DC Converter Based...

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第43届中国控制会议

作者： Xiang Gu Dewei Li Aoyun Ma Yaru Yu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the practical application of model predictive control in power electronics, both online computational burden and model accuracy are crucial. This paper presents an explicit model predictive control method based on input-mapping *** address the issue of parameter drift in the forward DC-DC converter system, the input-mapping method is introduced to compensate for model deviations using historical data. Firstly, the average model of the forward DC-DC converter system is introduced. For the control objective of voltage tracking, an explicit predictive control using the incremental model is ***, input-mapping method is integrated with explicit predictive control. During offline computation, analytical forms of the input-mapping combination coefficients and the optimal solution function of explicit model predictive control are *** computation of combination coefficients is conducted to obtain the optimal solution rapidly. The effectiveness of the proposed method is verified through simulations in MATLAB.

关键词： Forward DC-DC converter explicit model predictive control input-mapping method

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STMAD:A Simple Multimodal Anomaly Detection for Thyroid Medical information 17

STMAD:A Simple Multimodal Anomaly Detection for Thyroid Medi...

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17th International Congress on Image and Signal processing, BioMedical Engineering and Informatics, CISP-BMEI 2024

作者： Xu, Wentong Fu, Chao Yu, Jing Si, Caifeng Li, Zuoyong Li, Chaochao Fan, Haoyi School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China First Affiliated Hospital of Zhengzhou University Department of Ultrasound Henan Zhengzhou450052 China School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou350121 China

ISBN: (纸本)9798331507398

Anomaly detection has been widely explored by training an out-of-distribution detector with only normal data. Medical image analysis, as a common tool, faces the challenge of how to further enhance the detection performance for thyroid cancer, especially with the application of various deep learning methods in the thyroid-related field. In this paper, we validate the effectiveness of sonographic feature descriptions (textual information) for thyroid cancer diagnosis. Building on this, we propose a simple multimodal self-supervised framework that leverages both textual and image modalities for thyroid cancer anomaly detection. Results on a private dataset demonstrate the effectiveness and superiority of our proposed method compared to advanced methods. © 2024 IEEE.

关键词： Self-supervised learning

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DYNAMIC STIFFNESS ANALYSIS OF ELECTRO-HYDRAULIC SERVO-controlLED HYDRAULIC MOTOR

DYNAMIC STIFFNESS ANALYSIS OF ELECTRO-HYDRAULIC SERVO-CONTRO...

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2024 CSAA/IET International Conference on Aircraft Utility systems, AUS 2024

作者： Chen, Taiqi Wu, Shuai Wei, Renlei Li, Chuandong Qin, Mingyuan Chu, Bing Yang, Yuliang Luo, Jianing School of Automation Science and Electrical Engineering Beihang University Beijing100191 China National Key Laboratory of Aircraft Integrated Flight Control Beihang University Beijing100191 China Key Laboratory of Advanced Aircraft Systems Beihang University Ministry of Industry and Information Technology Beijing100191 China AVIC Shenyang Aircraft Design and Research Institute Shenyang110087 China

Electro-hydraulic servo control systems find widespread applications in aerospace, engineering machinery, and robotics, where they are integral to maintaining the performance and safety standards of aircraft. This study focuses on examining the dynamic stiffness properties of hydraulic motors in these systems and analyzes how different parameters affect dynamic stiffness. Dynamic stiffness describes an actuator's rigidity and response characteristics in motion control scenarios, reflecting the stability and performance of aircraft actuators under complex dynamic operational conditions more accurately than static stiffness. The study employs modular modeling to establish a mathematical model for dynamic stiffness, revealing that the servo valve inherent frequency, damping coefficient, pipeline volume, and hydraulic oil bulk modulus exhibit low sensitivity. Proportional gain, servo valve gain, and servo valve flow coefficient show consistent sensitivity curves, indicating a strong correlation. Conversely, the leakage coefficient and hydraulic motor displacement sensitivity negatively correlate with frequency, while hydraulic motor inertia, damping coefficient, and gearbox reduction ratio positively correlate with frequency. These insights enhance the understanding of parameter impacts, aiding in the design and optimization of high-power-to-weight ratio hydraulic systems for aerospace applications. © The Institution of Engineering & Technology 2024.

关键词： Sensitivity analysis

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A Differentiable Framework for Image-Based SAR Target Reconstruction with 3D Gaussian Splatting

A Differentiable Framework for Image-Based SAR Target Recons...

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Zinan Liu Shengren Niu Xiaolan Qiu Lingxiao Peng Lihua Zhong Chibiao Ding The National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Aerospace Information Research Institute Chinese Academy of Sciences Suzhou China Key Laboratory of Technology in Geospatial Information Processing and Application Systems Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Image-based synthetic aperture radar (SAR) target three-dimensional (3D) reconstruction is an important application for extracting target information from high-resolution two-dimensional (2D) SAR images. However, due to the complex mechanism of SAR imaging, developing a data-driven, fully automated 3D reconstruction framework with explicit physical constraints is very difficult. Thus, this paper proposes a differentiable framework for image-based SAR target reconstruction with 3D Gaussian Splatting, which can automatically realize the 3D target reconstruction from multi-view SAR images. Specifically, the proposed differentiable framework accumulates and generates simulated SAR images according to the SAR imaging geometry and projection during the forward simulation stage. By calculating the loss between the simulated and ground truth SAR images and backpropagating, the spatial positions of the 3D Gaussians are continuously adjusted until convergence, generating the 3D point cloud model of the target. The experiment is conducted on the constructed T72 SAR image dataset and the reconstruction results demonstrate the effectiveness of the proposed framework.

关键词： Point cloud compression Geometry Solid modeling Three-dimensional displays Two-dimensional displays Data processing Radar polarimetry Robustness Image reconstruction Synthetic aperture radar

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